JP7774607B2 - Water leak detection device, system, and method - Google Patents

Description

The present invention relates to an apparatus, system, and method for detecting underground water leaks in water pipes using a ground-penetrating radar device.

As underground water pipes deteriorate, it is becoming increasingly important to conduct maintenance work to detect and repair underground leaks.

Patent Document 1 cites a method for detecting leaks in water pipes by detecting the sound of water leakage. However, manually determining the sound of water leakage using, for example, a listening rod requires skill. Also, electromagnetic wave exploration using ground penetrating radar (GPR) can be used to identify the buried location of water pipes. However, Patent Document 1 states that while exploration using ground penetrating radar can identify buried objects and cavities, it cannot detect water leaks.

Patent document 2 describes an underground exploration method that determines the underground situation by evaluating the occurrence rate of reflected wave signal strength values received by underground radar.

Non-Patent Document 1 states that when a steel pipe buried underground is surveyed using ground-penetrating radar, the reflection position of the steel pipe becomes deeper as the groundwater level rises, making it possible to determine the volumetric water content and groundwater depth.

Japanese Patent Application Publication No. 9-196798 JP 2018-40615 A

Masaru Sakata, Toshimitsu Nozu, Keiji Tokutomi, Shingo Ogurabe, Toru Mino, "Estimation of volumetric water content and groundwater depth using ground-penetrating radar," Journal of the Society of Irrigation, Rural and Rural Engineering, Vol. 78, No. 4, 2010

However, when underground leaking water is mixed with the surrounding soil, it is difficult to detect differences in the intensity of reflected wave signals, and Patent Document 2 does not take into consideration the use of underground radar to detect underground leak points.

Furthermore, the method described in Non-Patent Document 1 has the problem that it is not possible to estimate the volumetric water content or groundwater depth in underground pipes because the reflection position cannot be determined for buried pipes made of materials and with diameters that do not show a strong reflection by ground-penetrating radar.

To know the underground conditions, a method of detecting them using a ground-penetrating radar is usually used. However, as described in Patent Document 1, there is a problem that even if a ground-penetrating radar is used, it is not possible to directly detect water leaks.
Here, the underground radar image obtained using the underground radar will be described in detail below.

Figure 1 shows an example of an image of the survey data obtained by underground radar surveying. An example of a radar device is one that a user manually pushes along the ground to perform a sweep survey. The radar device moves along the sweep direction while emitting electromagnetic waves into the ground. The reflected wave signal data is acquired by continuously receiving reflected waves while emitting electromagnetic waves.

This radar device is not limited to radar devices that are manually pushed by the user to perform sweeping surveys, and any radar device that can move along the ground can be used. For example, it could be a radar device mounted on a vehicle or a radar device that can move automatically along the ground. Note that while it is assumed that the radar device will be used to survey areas near buried water pipes, the extension direction of the buried pipes and the sweep direction above ground do not have to be parallel. It is also possible to acquire data in areas where there are no underground water pipes.

Figure 1 is a schematic diagram of a radar image 10 of a buried water pipe with a small diameter and made of a material that does not easily reflect radar, such as resin. The horizontal axis 11 represents the sweep direction of the underground radar. The vertical axis 12 represents the reflection time, which is the time it takes for electromagnetic waves to be emitted into the ground and reflected and received. The reflection time is longer for reflected waves from objects deeper underground, so the vertical axis 12 corresponds to the direction of depth underground.

The radar image 10 is an image that shows the received signal at each measurement location and reflection time, corresponding to the pixel brightness. Here, the stronger the received signal, the greater the brightness, which is depicted as white in Figure 1. On the other hand, the weaker the received signal, the less brightness, which is depicted as black in Figure 1. The greater the black-and-white contrast, the stronger the reflection. Incidentally, ground surface reflection 13 is reflection from the ground surface.

The hollow dotted rectangle 14 represents the buried location of the water pipe. Because the diameter of the water pipe is small and it is made of resin, no reflected image can be seen at the location of the hollow dotted rectangle 14. As a result, it is not possible to estimate the volumetric water content or groundwater depth underground. Furthermore, even if a ground-penetrating radar is used at a location where there is a water leak, the results obtained are similar to radar image 10, so the abnormality does not appear directly and cannot be detected.

The object of the present invention is to provide technology that uses underground radar to detect underground water leaks with higher accuracy.

A preferred aspect of the present invention is a water leak detection device characterized by having an attenuation evaluation processing unit that receives exploration data as input and outputs attenuation evaluation data, and a water leak location detection processing unit that has at least a horizontal comparison processing unit that outputs horizontal comparison data from the attenuation evaluation data output from the attenuation evaluation processing unit and outputs water leak location detection data. The exploration data is data that associates a received wave signal that is the result of irradiating electromagnetic waves into the ground and receiving a reflected wave, location information regarding the location at which the received wave signal is received, and the reflection time from when the electromagnetic waves are irradiated until the reflected wave is received. The attenuation evaluation data is data that associates multiple attenuation evaluation values, the location information, and the reflection time, and the attenuation evaluation value is a value calculated by defining an arbitrary range within the exploration data and evaluating the attenuation of the reflected wave from the received wave signal by determining the difference, variance, or standard deviation between the maximum and minimum values of the received wave intensity within that range. The horizontal comparison data is data in which multiple horizontal comparison values are associated with the position information and the reflection time information, and the horizontal comparison values are values calculated by extracting and normalizing the attenuation evaluation data whose reflection times are close within an arbitrary standard. The leak location detection data is the horizontal comparison data or data obtained by processing the horizontal comparison data.

Another preferred aspect of the present invention involves the steps of: evaluating the attenuation of reflected waves from received wave signals of exploration data in which a received wave signal obtained by irradiating electromagnetic waves underground and receiving a reflected wave, positional information regarding the position at which the received wave signal was received, and the reflection time from when the electromagnetic wave was irradiated until when the reflected wave was received, are associated with each other; and generating attenuation evaluation data in which a plurality of attenuation evaluation values, the positional information, and the reflection time information are associated with each other; and extracting and normalizing the attenuation evaluation data in which the reflection time is close to the reference value, and generating horizontal comparison data in which a plurality of horizontal comparison values are associated with the positional information and the reflection time information. a step of dividing the horizontal comparison data into multiple areas, calculating area evaluation values based on the horizontal comparison values calculated for each area, and generating area evaluation data in which the multiple area evaluation values, the location information, and the reflection time information are respectively associated; a step of calculating a water leakage score value based on the water leakage location detection data, and generating water leakage score data in which the multiple water leakage score values are respectively associated with the location information; and a step of generating score determination data in which information determining the presence or absence of underground leakage at each point indicated by the location information is respectively associated with the location information based on the water leakage score data.

Even if it is not possible to directly determine whether a water leak has occurred from the underground radar image obtained using the underground radar, it is possible to detect the location of the underground leak by evaluating the attenuation of the received signal. Furthermore, even if it is not possible to determine the location of the reflection by the buried pipe, it is possible to detect the location of the water leak from the location of the largest signal attenuation. Furthermore, because underground water leaks can be determined automatically, it is possible to detect the location of an underground water leak without the need for advanced knowledge or expertise in analyzing underground exploration data. Therefore, it is possible to provide technology that detects underground water leaks with higher accuracy.

FIG. 1 is an image diagram showing an example of a radar image acquired by a ground-penetrating radar. 1 is a diagram showing the overall configuration of a water leakage detection device in a first embodiment. 2 is a diagram showing the configuration of a water leakage point detection processing unit of the water leakage detection device according to the first embodiment; FIG. FIG. 1 is a schematic diagram showing the correspondence when a strong received signal is represented as pixel brightness in a radar image. FIG. 1 is a schematic diagram showing the correspondence when a weak received signal is represented as pixel brightness in a radar image. FIG. 2 is a schematic diagram showing pixel arrangement of the survey data. FIG. 10 is a schematic diagram showing an arrangement of pixels of attenuation evaluation data. FIG. 1 is a flow diagram showing a method for converting exploration data into attenuation assessment data. 1 is a schematic diagram showing a group of horizontal pixels with the same reflection time; FIG. 10 is an image diagram showing an image output of horizontal comparison data. 1 is a diagram illustrating a computer system that is an example of a hardware configuration that can realize a water leakage detection device according to a first embodiment. FIG. 10 is a diagram showing the configuration of a water leakage point detection processing unit of a water leakage detection device according to a second embodiment. FIG. 10 is an image diagram showing the results of illustrating water leakage point detection data consisting of area evaluation values. FIG. 10 is a diagram illustrating a computer system as an example of a hardware configuration that can realize a water leakage detection device according to a second embodiment. FIG. 10 is a flow chart showing the overall configuration of a water leakage detection device according to a third embodiment. FIG. 10 is a diagram showing the configuration of a water leakage determination processing unit of a water leakage detection device according to a third embodiment. FIG. 10 is an image diagram showing an outline of a method for calculating water leakage score data from area evaluation data. FIG. 10 is a graph plotting the determined water leakage scores. FIG. 11 is a diagram illustrating a computer system as an example of a hardware configuration that can realize a water leakage detection device according to a third embodiment. FIG. 10 is a flow chart showing the configuration of a water leakage determination processing unit of a water leakage detection device according to a fourth embodiment. FIG. 10 is a diagram illustrating a computer system as an example of a hardware configuration that can realize a water leakage detection device according to a fourth embodiment. FIG. 10 is a flow chart showing the overall configuration of a water leakage detection device according to a fifth embodiment. FIG. 10 is a schematic diagram showing an arrangement of areas in area evaluation data. FIG. 10 is a diagram illustrating a computer system as an example of a hardware configuration that can realize a water leakage detection device according to a fifth embodiment. FIG. 10 is a flow diagram showing the overall configuration of a water leakage detection system according to a sixth embodiment. FIG. 13 is a flow chart showing the overall configuration of a water leakage detection method according to a seventh embodiment.

An embodiment of the present invention will now be described with reference to the drawings. In all drawings used to explain the embodiment, identical components will generally be given the same reference numerals, and repeated explanations will be omitted where appropriate. It goes without saying that in the following embodiment, the components (including element steps, etc.) are not necessarily essential unless otherwise specified or considered to be clearly essential in principle.

It goes without saying that when we say "consisting of A," "made of A," "having A," or "including A," it does not exclude other elements, unless it is specifically stated that only that element is included. Similarly, in the following examples, when referring to the shape, positional relationship, etc. of components, etc., it is intended to include anything that is substantially similar or resembling that shape, etc., unless it is specifically stated or is clearly considered otherwise in principle. Before explaining the examples, we will provide an overview of the data obtained by ground-penetrating radar.

One example of an embodiment is a water leak detection device characterized by having an attenuation evaluation processing unit that inputs exploration data that associates position information regarding the position at which a reflected wave returning from irradiating electromagnetic waves underground is received, the reflection time from irradiating the electromagnetic waves until the reflected wave is received, and information on the reflected wave intensity, defines an arbitrary range within the exploration data, calculates an attenuation evaluation value by determining one or more indices of the difference between the maximum and minimum values of the received wave intensity within that range, variance, or standard deviation, and outputs attenuation evaluation data that associates multiple attenuation evaluation values, the position information, and the reflection time, and a water leak location detection processing unit that extracts at least attenuation evaluation data output from the attenuation evaluation processing unit that have similar reflection times within an arbitrary standard, converts the extracted attenuation evaluation data into normalized relative values using the maximum and minimum values obtained from the extracted attenuation evaluation data, and outputs water leak location detection data.

Fig. 2 is a diagram showing the overall configuration of a water leakage detection device 201 according to this embodiment. Fig. 3 is a diagram showing the configuration of a water leakage point detection processing unit S2501, which is an example of the water leakage point detection processing unit S25 in Fig. 2.
First, the processing of the water leak detection device shown in FIG. 2 will be described.

The water leak detection device 201 receives the exploration data 22 as input and outputs the leak location detection data 2601. The exploration data 22 is data that correlates location information related to the location where the reflected wave returned from irradiating the ground with electromagnetic waves is received, the reflection time from irradiating the electromagnetic waves to receiving the reflected wave, and information on the reflected wave intensity. The water leak detection device 201 has an attenuation evaluation processing unit S23 and a leak location detection processing unit S25, and receives the exploration data 22 as input and passes it to the attenuation evaluation processing unit S23. The attenuation evaluation processing unit S23 evaluates the attenuation of the received signal from the exploration data 22, converts it into attenuation evaluation data 24, and passes it to the leak location detection processing unit S25. The attenuation evaluation data 24 is data that defines an arbitrary range within the exploration data and calculates an attenuation evaluation value by determining one or more indices of the difference between the maximum and minimum values of the received wave intensity within that range, the variance, and the standard deviation. The attenuation evaluation data 24 correlates multiple attenuation evaluation values with the location information and the reflection time. The leak point detection processing unit S25 receives the attenuation evaluation data 24 as input and outputs leak point detection data 2601. The leak point detection processing unit S25 extracts at least the attenuation evaluation data with reflection times that are close within an arbitrary standard, converts multiple horizontal comparison values converted into relative values normalized using the maximum and minimum values obtained from the extracted attenuation evaluation data, and converts them into horizontal comparison data in which the position information and reflection time information are associated with each other, and outputs the leak point detection data 2601.

Next, we will describe the configuration of the leak location detection processing unit S2501 shown in Figure 3.

The leak point detection processing unit S2501 has a horizontal comparison processing unit S251 that compares the attenuation evaluation data 24 horizontally and converts it into horizontal comparison data. The horizontal comparison data is data in which multiple horizontal comparison values are converted into relative values normalized using the maximum and minimum values obtained from the extracted attenuation evaluation data, and the position information and reflection time information are associated with each other. The leak point detection processing unit S2501 is an example of the leak point detection processing unit S25 in Figure 2, and here S2501 is assumed to be the same as S251. Therefore, the horizontal comparison data output by the horizontal comparison processing unit S251 shown in Figure 3 becomes the leak point detection data 2601, which corresponds to the output of the water leak detection device 201 shown in Figure 2.

The water leak detection device 201 shown in Figure 2 receives underground radar survey data 22 and outputs leak location detection data 2601, highlighting underground leak locations for easier identification. The following describes each processing unit shown in Figure 2 in detail. The survey data 22 corresponds to measurement data acquired by the radar device. This survey data 22 also includes survey data extracted from data previously measured using an array-type underground radar device along an arbitrary line. The survey data 22 is data that associates location information regarding the location at which electromagnetic waves are emitted into the ground and the reflected waves are received, the reflection time from the emission of the electromagnetic waves to the reception of the reflected waves, and information on the intensity of the reflected waves. Note that the location information does not only include absolute locations such as coordinates in GPS (Global Positioning System) information, but also relative locations based on a certain location. For example, information such as the sweep distance and sweep direction from the point where the survey began is also included as location information. The radar image 10 shown in Figure 1 above is an example of survey data 22 displayed as an image.

A method for converting the exploration data 22 into the attenuation evaluation data 24 in the attenuation evaluation processing unit S23 will be described with reference to FIGS. 4A and 4B.
4A and 4B are diagrams showing the correspondence between pixel brightness and received signals in a radar image 10. The horizontal axis 31 represents the magnitude of the received signal, and the vertical axis 39 represents the reflection time from when the electromagnetic wave is emitted until it is received.

Received signal 33A shown in Figure 4A and received signal 33B shown in Figure 4B are signal waveforms obtained by irradiating electromagnetic waves underground and receiving reflected waves. Received signal 33A is a schematic representation of a strong reflected wave, i.e., a reflected wave with little attenuation. Received signal 33B is a schematic representation of a weak reflected wave, i.e., a reflected wave with large attenuation. Image pixel 34A and image pixel 34B are schematic diagrams showing received signals 33A and 33B as pixels of an underground radar image, with corresponding brightness. Here, areas with strong received signal strength have high brightness and are represented in white, while areas with weak received signal strength have low brightness and are represented in black. Received signal 33B has a smaller amplitude than received signal 33A. In this case, the brightness difference between each pixel is smaller for image pixel 34B than for image pixel 34A.

When the attenuation of the received signal is large, the amplitude becomes small, so attenuation can be evaluated by calculating the difference in brightness between pixels in a local area, as shown in image pixel 34A and image pixel 34B. Because the brightness of each pixel represents the magnitude of the received signal, attenuation can also be evaluated by calculating the difference in the measured value of the received wave signal represented by each pixel instead of brightness. Hereinafter, the brightness and measured value (intensity) of the received wave signal corresponding to each pixel will be referred to as "pixel value," and the collection of pixel values will be referred to as "pixel data." The exploration data 22 is pixel data, and the attenuation evaluation processing unit S23 uses the local pixel data to evaluate attenuation and calculate an attenuation evaluation value.

The attenuation evaluation processing unit S23 is described below. The attenuation evaluation processing unit S23 converts the exploration data 22 into attenuation evaluation data 24. The attenuation evaluation data 24 is data that has the same pixel arrangement as the exploration data 22.

Figure 5 is a schematic representation of a pixel group 41, which is a number of image pixels of the exploration data 22. The x-axis represents the sweep direction of the ground-penetrating radar, and corresponds to position information relating to the position where the electromagnetic waves are emitted into the ground and the reflected waves are received. The y-axis corresponds to the reflection time from when the electromagnetic waves are emitted until they are received. Each pixel has position information on the x-axis and information on the received wave intensity corresponding to the reflection time on the y-axis. The x-axis and y-axis in Figures 6 and 8 are similar to those described above.

Figure 6 shows a schematic representation of the attenuation evaluation data 24, with pixel group 51 indicating the arrangement of data contained in the attenuation evaluation data 24 corresponding to pixel group 41 in Figure 5. Here, we will explain the flow for calculating the attenuation evaluation value at the position of pixel 52 in the attenuation evaluation data 24 from the exploration data 22.

Figure 7 shows the flow by which the attenuation evaluation processing unit S23 calculates the attenuation evaluation value. The attenuation evaluation value calculated in Figure 7 becomes part of the attenuation evaluation data 24. In Figure 7, the calculation range specification processing unit S61 extracts calculation range data 62, which is the data used for calculation, from the exploration data 22 and passes it to the attenuation difference calculation processing unit S63. The attenuation difference calculation processing unit S63 converts the calculation range data 62 into a attenuation difference value 64 and passes it to the attenuation evaluation data creation processing unit S65. The attenuation evaluation data creation processing unit S65 uses the attenuation difference value 64 to convert into a attenuation evaluation value and create part of the attenuation evaluation data 24.

The calculation range definition processing unit S61 selects pixel 52 shown in Figure 6 for which the attenuation difference value 64 is to be calculated, and defines a thick-line frame 43 centered on pixel 42 as shown in Figure 5. Pixel 42 in the exploration data 22 is the pixel located at the same position as pixel 52. The thick-line frame 43 indicates the calculation range data 62 used to calculate the attenuation difference value 64 corresponding to pixel 52.

The vertical and horizontal widths of the bold frame 43 are expected to range from one pixel to several hundred pixels and can be set arbitrarily. For example, the range to be set could be determined to correspond to the wavelength of the electromagnetic waves used to acquire the data. Then, the calculation range data 62 within the bold frame 43 is extracted.

The attenuation difference calculation processing unit S63 quantifies the magnitude of change by calculating the difference between the maximum and minimum values of signal strength within the calculation range data 62. Instead of the attenuation difference calculation processing unit S63, a value reflecting attenuation can also be calculated using a processing unit that calculates at least one of variance, standard deviation, etc. for the calculation range data 62. Alternatively, the value reflecting attenuation can be calculated by integrating multiple calculation results.

The attenuation evaluation data creation processing unit S65 stores the attenuation difference value 64 as a value corresponding to the position of pixel 52 in the attenuation evaluation data 24.

The flow shown in Figure 7 describes how to calculate the value of pixel 52 of the attenuation evaluation data 24, but the attenuation evaluation data 24 is generated by performing the same operation on all pixels in pixel group 41.

The attenuation evaluation processing unit S23 has the effect of being able to find underground water leak points from the characteristics of the extracted attenuation evaluation data 24. In other words, it identifies locations where signal attenuation is large, rather than the signal strength itself. Because water has the effect of attenuating electromagnetic signals, the location of a water leak can be detected by detecting locations that show attenuation characteristics from the attenuation evaluation data 24.

The horizontal comparison processing unit S251 included in the water leakage point detection processing unit S2501 shown in FIG. 3 normalizes the attenuation evaluation data 24 in the horizontal direction where the reflection time is the same.
FIG. 8 schematically illustrates the attenuation evaluation data 24, consisting of the attenuation evaluation values obtained by the processing flow of FIG. 7 . A pixel group 71 indicates the arrangement of data contained in the attenuation evaluation data 24 within a predetermined range. In FIG. 8 , a bold-line frame 72 indicates pixels in the horizontal direction with the same reflection time. The attenuation evaluation values within this bold-line frame 72 are normalized, with the maximum value as the upper limit and the minimum value as the lower limit. For example, normalization within a range of 0 to 10 is assumed. This process is performed sequentially for each row of the pixel group 71, resulting in the horizontal comparison data, which associates multiple horizontal comparison values, each normalized for the same reflection time, with the position information and the reflection time information. The normalization range may be set to an area with the same geological features, or, for example, every 15 meters, depending on the geological features and distance. Furthermore, in addition to using the maximum and minimum values in each horizontal direction as the basis, a method of normalizing the data range, for example, approximately 3σ away from the average value, based on the standard deviation σ calculated from the attenuation evaluation data 24 in each horizontal direction, is also conceivable. Furthermore, although the bold frame 72 shown in Figure 8 is a range of only one pixel on the y-axis, it is also possible for the vertical width of the bold frame to be several pixels. In that case, the bold frame 72 indicates pixels in the horizontal direction with similar reflection times, and can be processed in the same way. The vertical width of the bold frame 72 can be set when extracting the attenuation evaluation data with similar reflection times within an arbitrary standard.

Figure 9 is a schematic diagram showing a horizontal comparison data image 80, which is an image of the horizontal comparison data output by the horizontal comparison processing unit S251. The horizontal comparison data image 80 is an image in which each horizontal comparison value of the horizontal comparison data corresponds to the pixel brightness at each measurement position and reflection time. The horizontal axis 11 and vertical axis 12 correspond to the horizontal axis 11 and vertical axis 12 in Figure 1. Ground surface reflection 83 corresponds to the ground surface reflection 13 in Figure 1.

The black areas 81 are areas where the attenuation evaluation data 24 shows unique values in the horizontal direction at the same reflection time, and correspond to underground leaks. The attenuation evaluation processing unit S23 performs a calculation to compare brightness differences, for example, so it can distinguish between areas with low attenuation and areas with high attenuation, as shown in Figures 4A and 4B. When calculating the brightness difference, the difference is smaller for image pixel 34B (Figure 4B) with high attenuation, so areas where attenuation occurs have a low attenuation evaluation value and are displayed with low brightness (black). However, since attenuation is smaller closer to the ground surface and greater farther from the ground surface, performing a calculation to normalize in the horizontal direction highlights and emphasizes attenuated areas, as shown in Figure 9. The horizontal comparison processing unit S251 converts the attenuation evaluation data 24 into the horizontal comparison data, which has the effect of making it easier to find areas of underground leaks. The attenuation of the reflected wave from the received wave signal is evaluated by calculating the difference, variance, or standard deviation between the maximum and minimum values of the received wave intensity within the range indicated by the thick-line frame 43 within the exploration data, and the attenuation evaluation data 24 consisting of the calculated multiple attenuation evaluation values is compared horizontally to highlight the difference between areas with and without leaks.

Signals from shallow underground have little attenuation, so the reflected wave intensity is large, and as shown in Figure 4A, the difference in reflected wave intensity appears large, resulting in a large absolute value for the attenuation evaluation value. On the other hand, signals from deep underground have little reflected wave intensity because they propagate over long distances, so the difference in reflected wave intensity appears small, resulting in a small absolute value for the attenuation evaluation value, as shown in Figure 4B. By normalizing and evaluating in the horizontal direction, the influence of the depth dependency of the attenuation evaluation value can be mitigated. Therefore, underground water leakage locations that could not be displayed in the radar image 10 (Figure 1) can be identified in the horizontal comparison data image 80.

Therefore, by outputting the horizontal comparison data as leak location detection data 2601, the water leak detection device 201 has the effect of identifying underground leak locations that cannot be identified using the input exploration data, using a horizontal comparison data image 80 that emphasizes the difference between locations with and without leaks.

Figure 10 shows a computer system PPA00, which is an example of a hardware configuration capable of realizing the water leak detection device 201 according to this embodiment. In the computer system PPA00, various programs stored in memory resource PPA04 are loaded by processor PPP01 to perform various other processes, including data generation, transmission, and reception. Processor PPP01 then executes the processes corresponding to the programs. Note that computer system PPA00 is a computer such as a personal computer, tablet terminal, smartphone, server computer, blade server, or cloud server, and is a system that includes at least one of these computers. In other words, computer system PPA00 also encompasses systems that include, for example, a cloud server and a display computer (e.g., a tablet terminal or smartphone). Another example of computer system PPA00 is a controller that controls or manages some kind of device, including processor PPP01 and memory resource PPA04.

Specifically, as shown in FIG. 10, computer system PPA00 has one or more processors PPP01, one or more memory resources PPA04, one or more UI (User Interface) devices PPP02, and one or more NI (Network Interface) devices PPP03. Note that computer system PPA00 may also include components other than these. Furthermore, processor PPP01, UI device PPP02, NI device PPP03, and memory resource PPA04 are interconnected via bus PPP15.

The processor PPP01 is an arithmetic device that reads various programs stored in the memory resource PPA04 and executes the processing corresponding to each program. An example of such a program is an OS (Operating System). Examples of the processor PPP01 include a microprocessor, CPU (Central Processing Unit), GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), quantum processor, or other semiconductor devices capable of performing calculations.

Memory resource PPA04 is a storage device that stores the attenuation evaluation program PPP05, horizontal comparison program PPP06, search data PPP10, attenuation evaluation data PPP11, and horizontal comparison data PPP12. Examples of such memory resources include non-volatile memory and/or volatile memory. An example of volatile memory is RAM (Random Access Memory). Examples of non-volatile memory include rewritable storage media such as flash memory, hard disks, solid-state drives (SSDs), and read-only memory (ROMs), as well as USB (trademark) (Universal Serial Bus) memory, memory cards, and hard disks. RAMs such as magnetoresistive RAM (MRAM), phase-change RAM (PRAM), and resistive RAM (ReRAM) may also be considered non-volatile memory. Processor PPP01 may also provide a service for distributing the various programs stored in memory resource PPA04 to other computers.

UI device PPP02 is an input device that inputs user (or operator) instructions into computer system PPA00, and an output device that outputs information generated by computer system PPA00. Input devices include, for example, keyboards, touch panels, pointing devices such as mice, and audio input devices such as microphones. Unless otherwise specified below, input and output of information between computer system PPA00 and the user is performed via UI device PPP02. UI device PPP02 may be only an input device or only an output device. Output devices include display devices such as LCD displays, projectors that project information onto a screen, AR (virtual reality) glasses, printers that print on paper, smartphones, and smartwatches.

The NI device PPP03 is a communications device that communicates information with external devices. The NI device PPP03 communicates information with an underground radar device PPP17 and underground data PPP18, which stores survey data, via a predetermined communications network PPP16, such as the Internet or a local area network (LAN). The underground data PPP18 includes survey data previously measured by underground radar, and is a data storage environment stored on a server that can be accessed online or in the cloud.

Unless otherwise specified below, information communication between the computer system PPA00 (or processor PPP01) and external devices such as the underground radar device PPP17 is performed via the NI device PPP03.

This computer system PPA00 executes the processes of this embodiment by running various programs PPP05 and PPP06.

Exploration data PPP10 is acquired from the underground radar device PPP17 and underground data PPP18 via the NI device PPP03 and bus PPP15.

The search data PPP10 can be input directly by the user from the UI device PPP02, or the data can be uploaded and imported into the memory resource PPA04 via the bus PPP15.

Search data PPP10 corresponds to search data 22 and indicates the state in which search data 22 is stored in memory resource PPA04.

The attenuation evaluation program PPP05 executes the processing of the attenuation evaluation processing unit S23. Similarly, the horizontal comparison program PPP06 executes the processing of the horizontal comparison processing unit S251. Furthermore, the attenuation evaluation data PPP11 is the attenuation evaluation data 24 stored in the memory resource PPA04, and the horizontal comparison data PPP12 is the horizontal comparison data output by the horizontal comparison program PPP06 stored in the memory resource PPA04. The water leak detection device 201 outputs the horizontal comparison data PPP12 generated by the computer system PPA00 to the user via the UI device PPP02 as water leak location detection data 2601.

The UI device PPP02 can display, in diagram form and as images, not only the search data PPP10 imported by the computer system PPA00, but also various data PPP11 and PPP12 generated by executing various programs PPP05 and PPP06.

Therefore, the computer system shown in Figure 10 has the advantage of being able to automatically execute the processing of the water leak detection device 201.

As an alternative to outputting to the user using the UI device PPP02 described above, the data required for output to the user may be sent to an external processor system via the NI device PPP03. Examples of such data include the data to be output itself, data for generating output data in a separate processor system, a program describing the process for outputting to the user in the external processor system, or web data.

Instead of receiving input or operations from the user using the UI device PPP02 described above, data indicating user input or operations may be received from an external processor system via the NI device PPP03. From another perspective, the meaning of outputting data to the user may include not only the computer system PPA00 itself doing the output, but also having another entity other than the computer system PPA00 output the data (using it). Furthermore, the meaning of receiving input or operations from the user may include not only direct output or reception to the user by the UI device PPP02 of the computer system PPA00, but also indirect reception by the computer system PPA00.

The effects described in this specification are merely examples and are not limiting, and other effects may be present. This embodiment is not limited to the above-described embodiment and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to embodiments that include all of the components described. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

Furthermore, the above-mentioned configurations, functions, processing units, processing means, etc. may be implemented in part or in whole in hardware, for example by designing them as integrated circuits. Furthermore, the above-mentioned configurations, functions, etc. may be implemented in software by a processor interpreting and executing a program that implements each function. Information such as the program, judgment table, and files that implement each function can be stored in memory, a storage device such as an HDD or SSD, or a recording medium such as an IC (Integrated Circuit) card, SD (Secure Digital) card, or DVD (Digital Versatile Disc). Furthermore, the control lines and information lines shown are those considered necessary for explanation, and do not necessarily represent all control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected.

In Example 2, a water leak detection device 202 will be described in which the water leak location detection processing unit S25 of the water leak detection device 201 of Example 1 has been replaced with the water leak location detection processing unit S2502 shown in FIG. 11. The water leak detection device 202 has a structure in which the water leak location detection processing unit S25 of the water leak detection device 201 shown in FIG. 2 has been replaced with the water leak location detection processing unit S2502 shown in FIG. 11. In Example 1 (FIG. 3), FIG. 11 shows that the water leak location detection processing unit S2502 newly includes an area evaluation processing unit S252. In this example, the horizontal comparison processing unit S251 passes the horizontal comparison data 25 to the area evaluation processing unit S252. The area evaluation processing unit S252 divides the horizontal comparison data 25 into multiple areas, calculates an area evaluation value for each area based on the horizontal comparison value, and generates area evaluation data consisting of the area evaluation values. The area evaluation data is data in which multiple area evaluation values are associated with the position information and the reflection time information. The leak point detection processing unit S2502 outputs the area evaluation data as leak point detection data 2602.

The leak point detection processing unit S2502 receives the attenuation evaluation data 24 as input and outputs the leak point detection data 2602 calculated by the area evaluation processing unit S252. The leak detection device 202 receives the exploration data 22 as input and outputs the leak point detection data 2602.

The area evaluation value is calculated as the average value of the horizontal comparison data contained within each area. In addition to the average value, it is also possible to use statistical values such as variance, standard deviation, and the difference between the maximum and minimum values. The size of the area can be set arbitrarily and is based on the wavelength of the electromagnetic waves to be irradiated and the resolution of the leak range to be determined. The set of divided area evaluation values becomes the leak location detection data 2602.

Figure 12 is a schematic diagram of an area evaluation data image 101, which is an image of the area evaluation data 28. The area evaluation data image 101 is an example of an image of the area evaluation data 28 in five levels. The square frame 102 indicates the area size. The darker the area evaluation data image 101, the smaller the area evaluation value, meaning a greater possibility of underground water leakage.

The area evaluation processing unit S252 divides the horizontal comparison data 25 into multiple areas, which has the effect of being able to evaluate the overall presence or absence of water leakage in the observation target. The water leak detection device 202 has the effect of taking the exploration data as input and outputting water leak location detection data 2602 that can macroscopically evaluate areas with a high probability of water leakage.

Figure 13 shows a computer system PPB00, which is an example of a hardware configuration capable of realizing the water leak detection device 202 according to this embodiment. The memory resource PPB04 is a storage device that stores an attenuation evaluation program PPP05, a horizontal comparison program PPP06, an area evaluation program PPP07, exploration data PPP10, attenuation evaluation data PPP11, horizontal comparison data PPP12, and area evaluation data PPP13.

The computer system PPB00 shown in Figure 13 has a configuration in which the memory resource PPA04 of the computer system PPA00 in Figure 10 has been replaced with the memory resource PPB04. The computer system PPB00 executes various programs PPP05 to PPP07 to perform the processing of the water leak detection device 202.

The area evaluation program PPP07 performs processing in the area evaluation processing unit S252 using the horizontal comparison data PPP12. The area evaluation data PPP13 is the leak location detection data 2602 output by the area evaluation program PPP07 and stored in the memory resource PPB04. The water leak detection device 202 outputs the area evaluation data PPP13 generated by the computer system PPB00 to the user via the UI device PPP02.

The UI device PPP02 can display, in diagram form and as images, not only the survey data PPP10 imported by the computer system PPB00, but also various data PPP11-PPP13 generated by executing various programs PPP05-PPP07. Therefore, the computer system PPB00 shown in Figure 13 has the advantage of being able to automatically execute the processing of the water leak detection device 202.

Fig. 14 is a diagram showing the overall configuration of the water leakage detection device 203 in Example 3. Fig. 15 is a diagram showing the configuration of a water leakage determination processing unit S2701, which is an example of the water leakage determination processing unit S27 in Fig. 14.
First, the processing of the water leakage detection device in FIG. 14 will be described.

The water leakage detection device 203 in Fig. 14 is different from the water leakage detection device 201 in Example 1 (Fig. 2) in that a water leakage determination processing unit S27 is newly added. The water leakage determination processing unit S27 performs processing to generate water leakage score data in which a plurality of water leakage score values corresponding to each point are associated with the position information, based on at least the water leakage point detection data 26, and outputs water leakage determination data 2801. Note that the water leakage point detection processing unit S25 corresponds to either the water leakage point detection processing unit S2501 in Example 1 (Fig. 3) or the water leakage point detection processing unit S2502 in Example 2 (Fig. 11). Furthermore, the water leakage point detection data 26 corresponds to either the water leakage point detection data 2601 in Example 1 (Fig. 3) or the water leakage point detection data 2602 in Example 2 (Fig. 11).
Next, the configuration of the water leakage determination processing unit S2701 shown in FIG. 15 will be described.

The water leak determination processing unit S2701 has a water leak score processing unit S271 that takes water leak location detection data 26 as input, calculates a water leak score value at each location, and calculates water leak score data consisting of multiple water leak score values. The water leak score data is data that associates multiple water leak score values corresponding to each location indicated by the location information with the location information, based on the water leak location detection data. The water leak determination processing unit S2701 is an example of the water leak determination processing unit S27 in FIG. 14, and here, S2701 is assumed to be the same as S27. Therefore, the water leak score data that is the output of the water leak score processing unit S271 shown in FIG. 15 becomes water leak determination data 2801, and corresponds to the output of the water leak detection device 203 shown in FIG. 14.

The water leakage score data output by the water leakage score processing unit S271 is a collection of water leakage score values calculated at each data acquisition location, and the calculation method is described below.

Figure 16 is an example of a water leak location detection data image 126, which is an image of the water leak location detection data 26. Here, the water leak location detection data 26 is water leak location detection data 2602 calculated based on Example 2. Using Figure 16, we will explain an outline of a method for calculating water leak score data from the water leak location detection data 26.

Empty rectangular boxes 121 to 124 represent a vertical row of areas divided by the area evaluation processing unit S252, and the area evaluation value contained within each box is used to calculate the leakage score at the data acquisition position directly above.

The leakage score at each point is calculated by calculating the average of the area evaluation values for the vertical column directly below. In addition to the average, it can also be calculated by calculating statistical values such as variance, standard deviation, and the difference between the maximum and minimum values.

In addition, it is possible to set regulations regarding depth, such as using the area evaluation value from the ground surface to a certain depth for the vertical column of area evaluation values used to calculate the leak score.

Figure 17 is a plot of the water leakage score data calculated using the above method. The vertical axis 130 indicates the water leakage score, and the horizontal axis 11 indicates the sweep direction. Water leakage score plot points 131 to 134 are plotted points for the water leakage scores calculated using the evaluation values of the areas within the hollow rectangular frames 121 to 124, and the same is true for the other plot points. For example, underground water leakage point 135 has a high water leakage score, indicating a high possibility of underground water leakage.

The water leak score processing unit S271 has the effect of being able to quantitatively evaluate the possibility of underground water leaks directly beneath each point. Therefore, the water leak determination processing unit S2701 also has the effect of outputting the results of a quantitative evaluation of the possibility of water leaks for the water leak point detection data. Locations with a high possibility of underground water leaks are easier to detect because the water leak score shows a unique value compared to other locations.

Figure 18 shows a computer system PPC00, which is an example of a hardware configuration capable of realizing the water leak detection device 203 of this embodiment. The memory resource PPC04 is a storage device that stores an attenuation evaluation program PPP05, a horizontal comparison program PPP06, an area evaluation program PPP07, a water leak score calculation program PPP08, exploration data PPP10, attenuation evaluation data PPP11, horizontal comparison data PPP12, area evaluation data PPP13, and water leak score data PPP14.

The computer system PPC00 shown in Figure 18 has a configuration in which the memory resource PPB04 of the computer system PPB00 in Figure 13 has been replaced with the memory resource PPC04. The computer system PPC00 executes various programs PPP05 to PPP08 to perform the processing of the water leak detection device 203 shown in Example 3 (Figure 14).

The water leakage score calculation program PPP08 performs processing in the water leakage score processing unit S271 using the area evaluation data PPP13. The water leakage score data PPP14 is the water leakage score data output by the water leakage score calculation program PPP08 and stored in the memory resource PPC04.

The UI device PPP02 can display, in diagram form and as images, not only the search data PPP10 imported by the computer system PPC00, but also various data PPP11-PPP14 generated by executing various programs PPP05-PPP08.

Therefore, the computer system shown in Figure 18 has the advantage of being able to automatically execute the processing of the water leak detection device 203 shown in Example 3.

In Example 4, a water leak detection device 204 will be described in which the water leak determination processing unit S27 of the water leak detection device 203 of Example 3 shown in Figure 14 has been replaced with the water leak determination processing unit S2702 shown in Figure 19. The water leak detection device 204 has a configuration in which the water leak determination processing unit S27 of the water leak detection device 203 shown in Figure 14 has been replaced with the water leak determination processing unit S2702 shown in Figure 19. In Figure 19, compared to Example 3 (Figure 15), a score determination processing unit S272 is newly added to the water leak determination processing unit S2702. The water leak score processing unit S271 passes the output water leak score data 27 to the score determination processing unit S272. The score determination processing unit S272 determines the possibility of water leaking at the location corresponding to the data acquisition position from the water leak score data 27, detects locations of underground water leaks, and outputs the data as water leak determination data 2802. The water leak determination processing unit S2702 receives the water leak location detection data 26 as input, and the score determination processing unit S272 generates score determination data. The score determination data is data in which information determining the presence or absence of underground water leakage at each point indicated by the location information is associated with the location information. The water leak determination processing unit S2702 outputs the score determination data as water leak determination data 2802.

Example 4 has the advantage of being able to automatically determine the possibility of water leakage at each data acquisition location by inputting underground radar exploration data.

The score determination processing unit S272 determines the presence or absence of underground leakage from the leakage score data at each data acquisition location based on the amount of change when compared with a threshold or when compared with nearby locations, and generates score determination data. For example, a method can be applied in which a certain threshold is set and a leakage possibility is determined when that threshold is exceeded. By setting several thresholds and determining which threshold is exceeded, the possibility of leakage at each location can be evaluated on several levels. It is also possible to evaluate the leakage score value at the data acquisition location by comparing it with the leakage score value at surrounding locations. For example, the average leakage score values around the data acquisition location are calculated, and a score ratio, which is the ratio of the leakage score value to that average, is calculated. If the score ratio exceeds a certain value, it can be determined that there is a leakage possibility. It is also possible to evaluate the leakage possibility on several levels by setting several evaluation criteria for the score ratio and determining which criteria the score ratio exceeds.

The score determination processing unit S272 has the effect of being able to indicate the possibility of underground leakage based on the leakage score data. This allows the water leak detection device 204 to evaluate the attenuation of reflected waves from the exploration data 22 and output leakage determination data indicating the possibility of underground leakage, thereby making it possible to determine underground leakage. Furthermore, because underground leakage can be determined automatically, the person performing the measurement does not need to have advanced knowledge or expertise to analyze underground exploration data.

Figure 20 shows a computer system PPD00, which is an example of a hardware configuration capable of realizing the water leak detection device 204 according to this embodiment. The memory resource PPD04 is a storage device that stores an attenuation evaluation program PPP05, a horizontal comparison program PPP06, an area evaluation program PPP07, a water leak score calculation program PPP08, a score determination program PPP09, exploration data PPP10, attenuation evaluation data PPP11, horizontal comparison data PPP12, area evaluation data PPP13, water leak score data PPP14, and score determination data PPP19.

The computer system PPD00 shown in Figure 20 has a configuration in which the memory resource PPD04 of the computer system PPC00 in Figure 18 has been replaced with the memory resource PPD04 described above. The computer system PPD00 executes various programs PPP05 to PPP09 to perform the processing of the water leak detection device 204.

The score determination program PPP09 performs processing in the score determination processing unit S272 using the water leakage score data PPP14. The score determination data PPP19 is the score determination data output by the score determination program PPP09 and stored in the memory resource PPD04.

The UI device PPP02 can display, in diagram form and as images, not only the search data PPP10 imported by the computer system PPD00, but also various data PPP11-PPP14 and PPP19 generated by executing various programs PPP05-PPP09.

Therefore, the computer system shown in Figure 20 has the advantage of being able to automatically execute the processing of the water leak detection device 204.

In Example 5, a water leak detection device 202A will be described in which the water leak location detection processing unit S25 of the water leak detection device 201 in Example 1 (Figure 2) has been replaced with a water leak location detection processing unit S2502A shown in Figure 21.

Figure 21 shows the water leak location detection processing unit S2502A of the water leak detection device 202A. Figure 21 compares Example 1 (Figure 3) with a new area evaluation processing unit S252A. In this example, the horizontal comparison processing unit S251 passes horizontal comparison data 25 to the area evaluation processing unit S252A. In addition, location information 36 regarding underground water leakage is provided as input to the area evaluation processing unit S252A. The area evaluation processing unit S252A receives location information 36 regarding underground water leakage as input, sets the area evaluation value of the area identified by the location information regarding underground water leakage as a reference value, calculates secondary evaluation values which are ratios of other area evaluation values to the reference value, replaces the secondary evaluation value with the area evaluation value, and outputs area evaluation data in which information on the multiple replaced area evaluation values is associated with the location information and the reflection time information. The leak point detection processing unit S2502A receives attenuation evaluation data 24 and position information 36 related to underground leakage as input, and outputs leak point detection data 2602A calculated by the area evaluation processing unit S252A. The leak detection device 202A receives exploration data 22 and position information 36 related to underground leakage as input, and outputs leak point detection data 2602A.

Location information 36 regarding underground leaks corresponds to information about the location of an underground leak that has already been identified, as well as information about locations that are known not to contain underground leaks. For example, if it is known from a buried water pipe installation diagram that there is no buried water pipe near a certain measurement point, then there is no underground leak at that point. Therefore, the location information about that measurement point can be included in location information 36 regarding underground leaks as information about a location where there is no underground leak.

A method for converting the horizontal comparison data into leak location detection data 2602A using underground leak location information 36 is described below with reference to Figure 22. Note that the horizontal comparison data is assumed to have been calculated as described in Example 1.

Figure 22 shows multiple area groups 19-20 in the area evaluation data 28. A rectangular box 102 indicates one area. First, as in Example 1, the area evaluation processing unit S252A divides the horizontal comparison data into multiple areas and calculates area evaluation values. The calculated area evaluation value is called the primary evaluation value. Next, a reference area located directly below a point without water leakage is determined using the underground leakage location information 36. Here, the dotted box 1921 indicates the reference area. Next, a method for calculating a secondary evaluation value using the primary evaluation value contained in the reference area will be described. Here, a method for calculating a secondary evaluation value corresponding to the area in rectangular box 19212 will be described. First, a reference area is identified that is located at the same position on the vertical axis 12 as the area in rectangular box 19212 and is within the reference area. Here, the area in rectangular box 19211 corresponds to the reference area of rectangular box 19212. Next, the ratio of the primary evaluation value of the area in rectangular box 19212 to the primary evaluation value of the reference area is calculated, and this is called the secondary evaluation value. In this manner, the secondary evaluation values are calculated for other areas as well, and then the area evaluation values are replaced with the secondary evaluation values. The area evaluation processing unit S252A outputs area evaluation data in which the multiple area evaluation values replaced in this manner are associated with the location information and the reflection time information as leak location detection data 2602A.

By inputting location information 36 related to underground leaks, the area evaluation processing unit S252A can use underground exploration data from locations where there are definitely no leaks or where there are leaks as a reference point to compare with other locations. Therefore, if location information related to underground leaks exists in advance, the location where underground leaks are occurring can be accurately identified by comparing with the reference point.

The water leakage score processing unit S271 of Example 3 (Figure 15) can also be adapted to input location information 36 related to underground leakage and calculate water leakage score data based on underground exploration data for locations where there is or is certainly no water leakage point. In this case, the water leakage score processing unit S271 of Figure 14 calculates the water leakage score data from the water leakage point detection data 26 and the location information related to underground leakage. Below, we will describe a method for converting water leakage point detection data into the water leakage score data using the location information related to underground leakage. It is assumed that the water leakage point detection data is calculated as described in Example 2.

As shown in Example 3 (Figure 16) in Figure 16, the water leakage score value calculated from the area evaluation values for one vertical column, as indicated by the hollow rectangular boxes 121 to 124, is the primary score value. Here, the primary score value at locations where it is known that there is no water leakage is the reference score value, and the ratio of the primary score value to the reference score value at each measurement point is the secondary score value. It is also possible that the water leakage score value is replaced with the secondary score value, and S271 outputs water leakage score data consisting of this replaced water leakage score value. By using underground exploration data from locations where there is or is definitely no water leakage point as the reference, the water leakage score processing unit S271 has the effect of outputting water leakage score data that accurately and quantitatively evaluates the possibility of water leakage from the water leakage point detection data.

Figure 23 shows computer system QQA00, which is an example of a hardware configuration capable of realizing the water leak detection device 202A of this embodiment. Location information QQQ01 regarding underground water leakage is acquired via NI device PPP03 and bus PPP15. NI device PPP03 communicates with underground radar device PPP17 and underground data PPP18, which stores exploration data, via communication network PPP16. In addition to the exploration data, underground data PPP18 already contains location information regarding underground water leakage. In addition, location information regarding underground water leakage can also be acquired via bus PPP15 by a user inputting and uploading data to UI device PPP02. Location information QQQ01 regarding underground water leakage is location information 36 regarding underground water leakage stored in memory resource QQA02. The memory resource QQA02 is a storage device that stores the attenuation evaluation program PPP05, horizontal comparison program PPP06, area evaluation program QQQ07, exploration data PPP10, attenuation evaluation data PPP11, horizontal comparison data PPP12, area evaluation data QQQ13, and location information QQQ01 regarding underground leakage.

The computer system QQA00 shown in Figure 23 has a configuration in which the memory resource PPA04 of the computer system PPA00 in Figure 10 has been replaced with the memory resource QQA02. The computer system QQA00 executes the various programs PPP05, PPP06, and QQQ07 to perform the processing of the water leak detection device 202A.

The area evaluation program QQQ07 performs processing in the area evaluation processing unit S252A using the underground leakage position information QQQ01 and horizontal comparison data PPP12. The area evaluation data QQQ13 includes the area evaluation data 28 output by the area evaluation program QQQ07 and stored in the memory resource QQA02.

As described above, known information regarding the presence or absence of water leakage can be associated with location information and used as location information QQQ01 regarding underground water leakage. The processor PPP01 calculates an area evaluation value using the location information QQQ01 regarding underground water leakage, and obtains area evaluation data QQQ13.

The UI device PPP02 can display, in diagram form and as images, the various data PPP11, PPP12, and QQQ13 generated by the computer system QQA00 executing the various programs PPP05, PPP06, and QQQ07, in addition to the search data PPP10 imported by the computer system QQA00.

Therefore, the computer system shown in Figure 23 has the advantage of being able to automatically execute the processing of the water leak detection device 202A.

Figure 24 is a diagram showing the overall configuration of a water leak detection system 2000 in Example 6. The water leak detection system 2000 receives exploration data 22 as input and is composed of a water leak detection device 205, a display information extraction unit S29, and a display unit S31. The water leak detection device receives exploration data 22 as input and outputs leak location detection data 26 and leak determination data 2802. The display information extraction unit S29 generates display data 30 from the leak location detection data 26 and the leak determination data 2802, and the display unit S31 displays the display data 30.

The water leak detection device 205 is characterized by outputting new water leak location detection data 26 to the water leak detection device 203 of Example 3 (Figure 14). The water leak detection device 205 receives the exploration data 22 as input and outputs water leak location detection data 26 and water leak determination data 2802. Note that the water leak determination processing unit S27 shown in Figure 24 corresponds to either the water leak determination processing unit S2701 of Example 3 (Figure 15) or the water leak determination processing unit S2702 of Example 4 (Figure 19).

The display information extraction unit S29 receives the water leak determination data 2802 and the water leak location detection data 26 as input and outputs display data 30.

The display information extraction unit S29 extracts leak point detection data corresponding to points determined to have underground leaks in the leak determination data 2802 from the leak point detection data 26. Then, information about the points determined to have underground leaks and the extracted leak point detection data are output as display data 30. Note that it is also possible to similarly extract leak point detection data corresponding to points determined to have no underground leaks in the leak determination data 2802, and output information about the points determined to have no underground leaks and the extracted leak point detection data as display data 30.

The display information extraction unit S29 extracts and outputs the corresponding water leak detection data for locations where the water leak determination processing unit S27 has determined that there is an underground leak or that there is a high possibility of an underground leak, thereby showing the basis for the determination.

In the water leak detection system, the display data 30 output by the display information extraction unit S29 is displayed on the display unit S31. The display unit S31 can display coordinate information or map location information for points determined to have a water leak on a display or web server. It can also simultaneously display an area evaluation data image like the one shown in Figure 12 corresponding to the points determined to have a water leak.

By inputting the exploration data 22, the water leak detection system 2000 can display the locations where underground leaks have been determined, as well as presenting an area evaluation data image of the corresponding locations. This has the effect of providing the user with a basis for knowing that there is a possibility of a leak. In addition, by displaying an area evaluation data image such as that shown in Figure 16, it also has the effect of making it easier for the user to determine at what depth underground leaks are occurring.

In Example 7, Figure 25 is a flow diagram showing the steps of a water leak detection method for outputting the water leak determination data from the exploration data. This corresponds to the processing steps from inputting the exploration data 22 to outputting the water leak determination data 2802 when the water leak detection device 204 in Example 4 is used.

First, in the attenuation evaluation processing step S023, the attenuation of the reflected wave is evaluated from the exploration data, which associates a received wave signal that is a reflected wave returned from irradiating electromagnetic waves underground, positional information regarding the position at which the received wave signal was received, and the reflection time from when the electromagnetic waves were emitted until the reflected wave is received, and attenuation evaluation data is generated, which associates multiple attenuation evaluation values with the positional information and information on the reflection time. As described in Example 1 (Figure 2), the attenuation evaluation processing step S023 corresponds to the processing step in which the attenuation evaluation processing unit S23 converts the exploration data 22 into attenuation evaluation data 24.

In the horizontal comparison processing step S0251, the attenuation evaluation data with reflection times that are close to each other based on an arbitrary standard is extracted and normalized to generate horizontal comparison data in which multiple horizontal comparison values, the position information, and the reflection time information are associated with each other. The horizontal comparison processing step S0251 corresponds to the processing step in which the horizontal comparison processing unit S251 converts the attenuation evaluation data 24 into horizontal comparison data, as described in Example 1 (Figure 3).

In area evaluation processing step S0252, the horizontal comparison data is divided into multiple areas, an area evaluation value is calculated based on the horizontal comparison value calculated for each area, and area evaluation data in which the multiple area evaluation values, the position information, and the reflection time information are respectively associated is generated as water leak location detection data. Area evaluation processing step S0252 corresponds to the processing step in Example 2 (Figure 11) in which the area evaluation processing unit S252 inputs horizontal comparison data to calculate area evaluation data and outputs water leak location detection data 2602.

In the water leakage score processing step S0271, a water leakage score value at the point identified by the location information is calculated based on the water leakage location detection data, and water leakage score data is generated in which multiple water leakage score values are associated with the location information. The water leakage score processing step S0271 corresponds to the processing step in which the water leakage score processing unit S271 described in Example 3 (Figure 15) inputs water leakage location detection data 26 and outputs water leakage score data.

In the score determination processing step S0272, the possibility of water leakage at the point identified by the location information is determined based on the water leakage score data, and score determination data in which information determining the presence or absence of underground water leakage at each point indicated by the location information is associated with the location information is generated as the water leakage determination data. The score determination processing step S0272 corresponds to the processing step in which the score determination processing unit S272 described in Example 4 (Figure 19) determines the possibility of water leakage from the water leakage score data, detects locations of underground water leakage, and outputs water leakage determination data 2802.

As described above, the flow of the water leak detection method shown in Figure 25, which outputs the water leak determination data from the exploration data, has the effect of converting the exploration data into water leak determination data and indicating the possibility of underground water leakage. Furthermore, even if you do not have advanced knowledge or expertise in analyzing underground exploration data, you can determine underground water leakage by performing each step shown in Figure 25 in order.

The leak detection method shown in Example 7 above is capable of detecting leaks from plastic water pipes and water pipes with a diameter of approximately 50 mm or less.

Plastic water pipes include polyethylene pipes, hardened polyvinyl chloride pipes, impact-resistant hardened polyvinyl chloride pipes, heat-resistant hardened polyvinyl chloride pipes, and Elmex pipes. Buried pipes made of these materials usually do not reflect the electromagnetic waves emitted by ground-penetrating radar well, making them difficult to detect. Similarly, water pipes with a diameter of 50 mm or less, regardless of the material they are made of, are often below the resolution limit of ground-penetrating radar and are therefore difficult to detect.

Because the dielectric constants of the soil and pipe materials are different, electromagnetic waves are reflected at their boundary. If the dielectric constant of the soil is ε _s and the dielectric constant of the pipe is ε _p , and the magnetic permeabilities are equal, the reflection coefficient Γ when electromagnetic waves are incident perpendicularly from the soil onto the pipe is expressed as (Equation 1).

The magnitude of the reflection coefficient Γ represents the ratio of the amplitude of the reflected wave to the amplitude of the incident wave, and is smaller when the pipe is plastic than when it is metal, so plastic pipes make it difficult for reflected images to appear in ground-penetrating radar images.

Non-patent document 1 reports that as the moisture content of the ground increases at the leak point, the propagation speed of electromagnetic waves slows down, and the time it takes for the electromagnetic waves to be reflected from the buried pipe increases, causing the reflected image of the buried pipe to appear deeper in the radar image.

However, if the buried pipe is made of plastic, the reflection of the electromagnetic waves is weak, so the reflected image of the buried pipe may not be visible in the radar image. Also, if the diameter of the buried pipe is smaller than the wavelength of the electromagnetic waves, the reflected image of the buried pipe may not appear in the radar image. In such cases, since the reflected image of the buried pipe cannot be identified, it is not possible to determine from the radar image that the time between the emission of the electromagnetic waves and their reflection has increased, making it impossible to determine whether there is an underground leak.

The water leak detection method shown in Example 7 above utilizes the fact that reflected signals near the leak point are attenuated by water, and detects the leak location by evaluating this attenuation. Therefore, even if the reflected image of the buried pipe cannot be confirmed, it is possible to evaluate the possibility of a water leak by evaluating the attenuation of the reflected signal.

Leak detection device 201, exploration data 22, attenuation evaluation processing unit S23, attenuation evaluation data 24, leak location detection processing unit S2501, horizontal comparison processing S251, leak location detection data 2601

Claims (11)

an attenuation evaluation processing unit that receives as input exploration data in which location information relating to a position at which an electromagnetic wave is irradiated underground and a reflected wave is received, a reflection time from the emission of the electromagnetic wave until the reflected wave is received, and information on the intensity of the reflected wave are all associated with each other, defines an arbitrary range within the exploration data, calculates an attenuation evaluation value by obtaining one or more indices of the difference between the maximum and minimum values of the reflected wave intensity within that range, variance, and standard deviation, and outputs attenuation evaluation data in which a plurality of attenuation evaluation values, the location information, and the reflection time are all associated with each other;
a leakage point detection processing unit that extracts at least the attenuation evaluation data output from the attenuation evaluation processing unit, each of which has a reflection time that is close to the other within an arbitrary standard, and converts the extracted attenuation evaluation data into a normalized relative value using the maximum and minimum values obtained from the extracted attenuation evaluation data, thereby outputting leakage point detection data;
A water leak detection device comprising: The water leak detection device according to claim 1,
The water leakage detection device has a horizontal comparison processing unit that extracts attenuation evaluation data having reflection times that are close to each other within an arbitrary standard for the attenuation evaluation data, and outputs horizontal comparison data in which a plurality of horizontal comparison values converted into relative values normalized using the maximum and minimum values obtained from the extracted attenuation evaluation data, the position information, and the reflection time information are respectively associated with each other, and the horizontal comparison data is the water leakage detection data. The water leakage detection device according to claim 2,
The water leakage detection device further includes an area evaluation processing unit that divides the horizontal comparison data into a plurality of areas, calculates an area evaluation value based on the horizontal comparison value calculated for each area, and outputs area evaluation data in which the plurality of area evaluation values, the position information, and the reflection time information are respectively associated with each other, and the area evaluation data is the water leakage location detection data. The water leakage detection device according to claim 3,
The water leak detection device further comprises a water leak determination processing unit that outputs water leak determination data through a process of calculating a plurality of water leak score values corresponding to each point indicated by the position information based on the water leak location detection data. The water leakage detection device according to claim 4,
The water leakage detection device is characterized in that the water leakage determination processing unit has a water leakage score processing unit that outputs water leakage score data in which a plurality of water leakage score values corresponding to each point indicated by the location information and the location information are respectively associated with each other based on the water leakage point detection data, and the water leakage determination data is the water leakage score data. The water leakage detection device according to claim 5,
The water leakage determination processing unit further includes a score determination processing unit that outputs score determination data in which information determining the presence or absence of underground leakage at each point indicated by the location information is associated with the location information based on the water leakage score data, and the score determination data is the water leakage determination data. The water leakage detection device according to claim 6,
A water leak detection device that determines whether or not there is an underground leak at each point indicated by the location information by performing at least one of comparing a water leak score value with a threshold value and comparing the water leak score value with surrounding water leak score values. The water leak detection device according to claim 1,
The area evaluation unit of the leak point detection processing unit further includes an area evaluation processing unit that receives location information regarding underground water leakage as input, sets the area evaluation value of the area specified by the location information regarding underground water leakage as a reference value, calculates a secondary evaluation value which is the ratio of other area evaluation values to the reference value, replaces the secondary evaluation value with the area evaluation value, and outputs area evaluation data in which the replaced multiple area evaluation values, the location information, and the reflection time are respectively associated with each other, and wherein the area evaluation data is the leak point detection data. A water leak detection system having a water leak detection device, a display information extraction unit, and a display unit,
an attenuation evaluation processing unit that receives as input exploration data in which a reflected wave signal obtained by irradiating an electromagnetic wave underground and receiving a reflected wave that has returned, position information relating to the position at which the reflected wave signal was received, and a reflection time from when the electromagnetic wave was irradiated until when the reflected wave was received are all associated with each other, and outputs attenuation evaluation data in which a plurality of attenuation evaluation values calculated by evaluating the attenuation of the reflected wave from the reflected wave signal of the exploration data are all associated with the position information and information on the reflection time;
a horizontal comparison processing unit that extracts, from the attenuation evaluation data output from the attenuation evaluation processing unit, attenuation evaluation data having reflection times that are close within an arbitrary standard, normalizes and calculates a plurality of horizontal comparison values, and outputs horizontal comparison data in which the position information and the reflection time information are associated with each other, and outputs water leakage point detection data using the horizontal comparison data;
a water leakage score processing unit that calculates a water leakage score value at a point specified by the position information based on the water leakage point detection data, and outputs water leakage score data in which a plurality of water leakage score values and the position information are respectively associated, and outputs water leakage determination data using the water leakage score data;
and
the display information extraction unit outputs display data obtained by extracting water leakage point detection data and water leakage determination data corresponding to the point identified by the location information;
The water leak detection system is characterized in that the display unit displays the display data. a step of evaluating the attenuation of the reflected wave from the reflected wave signal of the exploration data in which a reflected wave signal is received after irradiating an electromagnetic wave into the ground, position information relating to a position where the reflected wave signal is received, and a reflection time from when the electromagnetic wave is irradiated until when the reflected wave is received, are respectively associated with each other, and generating attenuation evaluation data in which a plurality of attenuation evaluation values are respectively associated with the position information and information on the reflection time;
generating horizontal comparison data in which a plurality of horizontal comparison values calculated by extracting and normalizing the attenuation evaluation data having reflection times that are close to each other within a reference range are associated with the position information and the reflection time information;
dividing the horizontal comparison data into a plurality of areas, calculating an area evaluation value based on the horizontal comparison value calculated for each of the areas, and generating area evaluation data in which the plurality of area evaluation values, the position information, and the reflection time information are associated with each other;
calculating a water leakage score value based on the water leakage location detection data, and generating water leakage score data in which a plurality of water leakage score values are associated with the location information;
generating score determination data in which information determining the presence or absence of underground water leakage at each point indicated by the location information based on the water leakage score data is associated with the location information;
A water leakage detection method comprising: The water leakage detection method according to claim 10,
A water leak detection method characterized in that the object of water leak detection is water leak from a plastic pipe or a water pipe with a diameter of 50 mm or less.