JP7623172B2 - System, method, and program - Google Patents

Description

This disclosure relates to systems, etc.

There is a technology in which sensors and other devices are placed in each location to obtain various information about each location. For example, Patent Document 1 describes a technology in which sensors are placed in each sales floor of a store, and the attention level, which is an index showing the degree to which people in a target area are paying attention to that area, is calculated based on the information obtained from the sensors. Patent Document 1 also describes a technology in which the attention level of a sales floor that does not have a sensor or has a broken sensor is predicted using the past attention levels of sales floors on other floors of the same store, or sales floors in other stores.

International Publication No. 2011/162119

As in Patent Document 1, when information in an area where there is no sensor or where the sensor has failed is supplemented with information from other areas, there is a problem that the accuracy of the supplementation may be low depending on the information from the other areas.

One example of the objective of this disclosure is to improve completion accuracy by using information from more appropriate areas.

The system according to one aspect of the present disclosure includes: a means for identifying an area of the same type as a predetermined area that is closer to the predetermined area than other areas; a means for determining whether a value of an area attribute of the predetermined area is similar to a value of the area attribute of the identified area; and, if they are similar, a means for complementing the value of the position attribute of a predetermined position included in the predetermined area based on the value of the position attribute of each position included in the identified area; and, if they are not similar, the means for identifying identifies an area of the same type that is next closest to the area whose distance from the predetermined area is identified.

A method according to one aspect of the present disclosure identifies, among areas of the same type as a predetermined area, an area that is closer to the predetermined area than other areas, determines whether the value of the area attribute for the predetermined area is similar to the value of the area attribute for the identified area, and if they are similar, complements the value of the position attribute for a predetermined position included in the predetermined area based on the value of the position attribute for each position included in the identified area, and if they are not similar, identifies, among the areas of the same type, the area next closest to the area whose distance from the predetermined area is identified.

In one aspect of the present disclosure, a program causes a computer to execute a process of identifying an area of the same type as a predetermined area that is closer to the predetermined area than other areas, a process of determining whether the value of the area attribute of the predetermined area is similar to the value of the area attribute of the identified area, and a process of complementing the value of the position attribute of a predetermined position included in the predetermined area based on the value of the position attribute of each position included in the identified area if they are similar, and in the identification process, if they are not similar, a process of identifying an area of the same type that is next closest to the area whose distance from the predetermined area is identified.

According to this disclosure, the accuracy of completion is improved by using information from more appropriate areas.

1 is a block diagram showing a configuration example of a system according to a first embodiment; 4 is a flowchart showing an operation example of the system according to the first embodiment; FIG. 11 is a block diagram showing a configuration example of an information processing device included in a system according to a second embodiment. 11A and 11B are explanatory diagrams showing examples of area attributes by area type, position attributes of complement targets, and areas to be identified. FIG. 13 is an explanatory diagram showing a first complement example. FIG. 11 is an explanatory diagram showing a second complement example; FIG. 11 is an explanatory diagram showing a third complement example; FIG. 11 is an explanatory diagram showing a fourth complement example; FIG. 13 is an explanatory diagram showing a fifth complement example. 13 is a flowchart showing an operation example of an information processing device included in the system according to the second embodiment; FIG. 2 is an explanatory diagram illustrating an example of a hardware configuration of a computer device.

Below, embodiments of the system, information processing device, method, program, and recording medium for recording the program according to the present disclosure will be described in detail with reference to the drawings. However, the drawings show the configuration of the embodiment of the present disclosure in a simplified manner. Furthermore, the embodiment of the present disclosure described below is an example, and can be modified as appropriate within the scope that maintains the same essence.

Here, we will briefly explain digital twins. Digital twins are a technology that collects information in the real world and reproduces the real world in a virtual space based on the collected information. More specifically, digital twins are a solution that collects information acquired in real time, reproduces situations that may actually occur in a virtual space such as a computer using technologies such as AI (Artificial Intelligence), executes simulations, and predicts what will happen in the real world. Time-series information is acquired through sensors installed in various locations and mobile objects equipped with sensors. When simulating in virtual space, a dynamic map is created by overlaying the acquired time-series information and future prediction information on the map information, and the actual situation is reproduced as a twin in the virtual space. In order to express the events (World) of the real world on the dynamic map, the data used in the simulation is accumulated in layers classified by purpose.

In the system described in this embodiment, for example, data including attribute values of each position attribute related to the position at each position of a device that emits information in real space is acquired. In the system, multiple attribute values of the same position attribute at different positions may be managed as a layer. In other words, a layer is a group of information consisting of multiple attribute values of a certain position attribute. Note that in the following description, the attribute value of a position attribute is also referred to as the value of the position attribute.

(Embodiment 1)
FIG. 1 is a block diagram showing a configuration example of a system according to a first embodiment. The system 1 complements the value of a position attribute related to a predetermined position included in a predetermined area by using the value of a position attribute related to each position included in another area. The predetermined area is not particularly limited. For example, the predetermined area may be outdoors or indoors. Here, the predetermined area is called a target area. The predetermined position is not particularly limited. The predetermined position is, for example, a position in the target area where there is no sensor, a position where a sensor has failed, a specified position, a central position, or the like. There may be a plurality of predetermined positions. The system 1 includes an identifying unit 101, a determining unit 102, and a complementing unit 103.

The identification unit 101 identifies areas that are close to the target area among areas of the same type as the target area. The target area is not particularly limited. Furthermore, the type of each area is not particularly limited. The types of each area are, for example, conference rooms, hospital rooms, stores, data centers, etc. The types may be divided into more detailed categories. For example, in the case of stores, the types of stores may be divided into more detailed categories such as restaurants, clothing stores, etc.

The method of calculating the distance between the target area and an area of the same type as the target area is not particularly limited. For example, the distance may be the distance between a certain position included in the target area and another position included in an area of the same type as the target area. The certain position and the other position are not particularly limited. For example, at least one of the certain position and the other position may be the center position of the target area. At least one of the certain position and the other position may be a randomly selected position.

The determination unit 102 determines whether the value of the area attribute related to the target area is similar to the value of the area attribute of the specified area. The area attribute is, for example, an attribute related to the type of each area. The area attribute may differ depending on the type. For example, when the type of area is a conference room, the area attribute may be the area size including each position, the number of chairs in the area, the arrangement of desks in the area, etc. The value of the area attribute related to the target area is a specific numerical value such as the area size, the number of chairs, the number of desks, etc., or a value indicating the arrangement direction or number of desks. Taking the area size as an example, the determination unit 102 may determine whether the area size of the specified area is within a predetermined range of the area size of the target area. The predetermined range is not particularly limited. If the area size of the specified area is within a predetermined range of the area size of the target area, the determination unit 102 determines that the area size of the target area is similar to the area size of the specified area.

If the determination unit 102 determines that the two locations are similar, the complement unit 103 complements the value of the location attribute of a specific location included in the target area based on the value of the location attribute of each location included in the specified area. The location attribute is not particularly limited. For example, the location attribute may be an attribute that represents the environment at each location. Examples of attributes that represent the environment include temperature, humidity, sound volume, and dirtiness. The value of the location attribute is a measurement value of a sensor that detects temperature, humidity, or sound volume, or a calculated value of the dirtiness level in the target area. The sensor may be a fixed sensor, or may be a sensor that is possessed by a mobile object. Note that, if there are multiple values of the same location attribute related to the same location at the same time or time period, the statistical value of those values may be used. The statistical value may be, for example, an average value, a median value, or a mode value. If the temperature values at a certain location around 10 o'clock are measured as 20°C, 21°C, and 22°C, the temperature value at a certain location around 10 o'clock is 21°C if the average value or median value is used.

The method of complementation is not particularly limited. For example, the complementing unit 103 may set the value of the position attribute related to a position included in the identified area as the value of the position attribute related to a predetermined position. When a value of the position attribute is associated with each of the multiple positions included in the identified area, the complementing unit 103 identifies a position from the multiple positions included in the identified area that has a similar positional relationship with the predetermined position in the target area. Then, the complementing unit 103 may set the value of the position attribute related to the identified position as the value of the position attribute related to the predetermined position. Furthermore, the complementing unit 103 may set, for example, a statistical value of the values of the position attribute related to each position included in the identified area as the value of the position attribute related to the predetermined position. The statistical value is, for example, the average value, the median, the mode, etc.

Furthermore, when the value of the region attribute for the target region and the value of the region attribute for the identified region are not similar, the identification unit 101 identifies, from among regions of the same type as the target region, the region that is the next closest in distance from the target region to the identified region. This allows the identification unit 101 to identify candidate regions in order of closest distance.

Fig. 2 is a flowchart showing an example of an operation of the system 1 according to the first embodiment. The identification unit 101 identifies an area that is close to an area of the same type as the type of the target area (step S101). The determination unit 102 determines whether the value of the area attribute related to the target area is similar to the value of the area attribute related to the identified area (step S102).

If it is determined that the regions are not similar (step S102: No), the identification unit 101 returns to step S101. As a result, the identification unit 101 returns to step S101 and identifies the next closest region from among regions of the same type.

On the other hand, if it is determined that the regions are similar (step S102: Yes), the complementing unit 103 complements the value of the position attribute for a specific position included in the target region based on the value of the position attribute for each position included in the region determined to be similar (step S103). After step S103, the system 1 ends the operation of the flow.

Next, the effects of the first embodiment will be described. The system 1 identifies areas that are close to the target area from among areas of the same type as the target area. The system 1 then complements the value of the position attribute of a specific position included in the target area based on the value of the position attribute of each position included in the identified area. For example, the closer an area is to the target area, the more likely it is that the area is in an environment similar to that of the target area. For this reason, the system 1 can complement more accurately by using the value of the position attribute of each position included in an area closer to the target area. Therefore, the system 1 can use the value of the position attribute of each position included in an appropriate area, improving the accuracy of the complement.

The first embodiment is not limited to the above-described example, and various modifications are possible. For example, each functional unit shown in FIG. 1 may be realized by one device (information processing device). Also, for example, each functional unit shown in FIG. 1 may be realized by multiple devices.

(Embodiment 2)
Next, the second embodiment will be described in detail with reference to the drawings. In the second embodiment, a more specific complement example will be described in detail. Below, the description of the contents that overlap with the above description will be omitted to the extent that the description of the second embodiment is not unclear.

FIG. 3 is a block diagram showing an example of a configuration of an information processing device included in the system according to the second embodiment. The system 2 includes, for example, an information processing device 20. The information processing device 20 may be a moving body such as a robot or a drone, a PC (Personal Computer), a smartphone, a tablet-type device, a server, or the like, but is not limited thereto. The information processing device 20 is connected to other devices via a communication network 21. Although not shown, the system 2 may have a simulation device in a digital twin. The information processing device 20 may be a simulation device. The system 2 may also have a moving body that uses the complemented value. The moving body may be, for example, a robot, a drone, or the like, but is not limited thereto. In the case of a robot, the robot may be, for example, a service robot that performs tasks such as guidance and cleaning. For example, the robot may perform assigned tasks based on the value of a position attribute related to each position included in the target area. For example, a cleaning robot may perform cleaning according to the degree of dirt at each position included in the target area. The information processing device 20 may be a robot as described above. The information processing device 20 may be a parent robot among multiple robots. System 2 may also have other devices that use the values of location attributes related to each location included in the target area. For example, a device that manages air conditioning may set the temperature of the air conditioning based on the temperature values at each location included in the target area.

In FIG. 3, the information processing device 20 has an identification unit 201, a determination unit 202, a completion unit 203, a prediction unit 204, and a storage unit 205. The information processing device 20 is an example in which a prediction unit 204 and a storage unit 205 are newly added to the functional units of the system 2 described in the first embodiment. The identification unit 201, the determination unit 202, and the completion unit 203 each have the functions of the identification unit 101, the determination unit 102, and the completion unit 103 described in the first embodiment as their basic configurations.

The storage unit 205 stores, for example, the processing results of each unit of the information processing device 20. Examples of the storage unit 205 include a read only memory (ROM), a random access memory (RAM), a semiconductor memory, a hard disk drive (HDD), and a solid state drive (SSD). The storage unit 205 may be a combination of these.

The storage unit 205 may store the type of area for each area. Furthermore, the storage unit 205 may store the location of each area. Note that this information may be stored not only in the storage unit 205 but also in a storage device accessible by the information processing device 20.

The storage unit 205 may also store the value of each position attribute in association with each position included in each region. The storage unit 205 may also store the value of each position attribute in association with each position included in each region in chronological order.

The identification unit 201 identifies areas that are closer to the target area than other areas among areas of the same type as the target area. The types of areas used in the detailed explanation of the second embodiment will be described later with reference to FIG. 4.

The identification unit 201 may also identify areas that are the same type as the target area and are close to the target area from areas that are included in the same building or facility as the target area. The identification unit 201 also identifies areas that are the same type as the target area and are close to the target area from areas that are included in the same building or facility as the target area. The identification unit 201 identifies areas that are the same type as the target area and are close to the target area from areas that are included in a building within a predetermined range of the building that includes the target area.

The identification unit 201 may also identify areas that are the same type as the target area and have a short distance from the target area from areas that have a high predetermined priority. Priority may be determined by area type. Priority by type will be described later with reference to FIG. 4.

Next, the determination unit 202 determines whether the value of the area attribute for the target area is similar to the value of the area attribute for the identified area. In this way, the determination unit 202 determines whether the target area is similar to the identified area. The area attribute is an attribute of the area related to the type of area. There may be multiple area attributes. The area attribute may be a dynamically changing attribute or a static attribute. An example of area attributes according to the type of area will be described later with reference to FIG. 4. The storage unit 205 may store the value of the area attribute corresponding to the type of area for each area.

For example, the determination unit 202 may determine whether the similarity between the value of the region attribute related to the target region and the value of the region attribute related to the identified region satisfies a predetermined condition. More specifically, for example, the determination unit 202 may determine that the regions are similar if the similarity is equal to or greater than a predetermined similarity. The method of calculating the similarity is not particularly limited. Also, for example, when there are multiple region attributes, the determination unit 202 may calculate the similarity for each region attribute and determine the similarity based on the average value of the calculated similarities. The determination unit 202 may determine that the two regions are similar if the average value of the similarities is equal to or greater than a predetermined similarity.

Alternatively, for example, the determination unit 202 may determine the similarity based on whether or not the value of the area attribute related to the specified area is included within a predetermined range from the value of the area attribute related to the target area. The predetermined range may be specified by a user, etc., or may be a fixed value. For example, when the area attribute is the area width, the determination unit 202 may determine the similarity of the values of the two area attributes based on whether or not the width of the specified area is included within a predetermined range from the width of the target area. The determination unit 202 may determine that the two widths are similar if the width of the specified area is included within a predetermined range from the width of the target area.

The determination unit 202 may determine whether the value of the area attribute for the target area matches the value of the area attribute for the identified area. For example, if the area attribute is floor material, the determination unit 202 may determine whether the floor material of the target area is the same as the floor material of the identified area.

If the determination unit 202 determines that the regions are not similar, the identification unit 201 identifies the region of the same type as the target region that is the next closest in distance from the target region to the region that has already been identified.

Figure 4 is an explanatory diagram showing examples of area attributes by area type, location attributes to be complemented, and areas to be identified. In Figure 4, the types of areas are conference rooms, hospital rooms, stores, data centers, etc.

The area attribute is an attribute of the area related to the type of area. The position attribute of the completion target is an attribute that represents the environment of each position included in the area. The position attribute of the completion target may be specified by the user. For example, a reception unit (not shown) may receive the specification of the position attribute of the completion target. In addition, at least one of the areas to be searched and the priority of the areas to be searched may be determined when identifying areas close to the target area, depending on the type of area.

First, a conference room will be taken as an example. In FIG. 4, for example, in the case of a conference room, the area attributes include the size of the conference room, the number of chairs in the conference room, the number of desks in the conference room, the arrangement of the desks in the conference room, and at least one of the materials of the floor and walls of the conference room. For example, the material of the floor may be carpet or wood. For example, the material of the walls may be concrete or wood. The area attributes may be at least one of these, or may be specified from these. For example, the position attributes to be complemented are temperature and volume. The areas to be searched are areas on the same floor, areas in the same building, and areas in neighboring buildings. Neighboring buildings are, for example, buildings within a predetermined range from the target area. Buildings are also referred to as buildings hereinafter. In FIG. 4, the priority used by the identification unit 201 when identifying is higher as the numerical value becomes smaller. For example, the priority of an area on the same floor is the highest, followed by an area in the same building, and finally the priority of an area in a neighboring building is the lowest.

For example, when the type of the target area is a conference room, the identification unit 201 identifies the area on the same floor that is closest to the target area among the areas whose type is a conference room. The determination unit 202 then determines whether the value of the area attribute of the target area is similar to the value of the area attribute of the identified area based on at least one of the size of the conference room, the number of chairs in the conference room, the number of desks in the conference room, the arrangement of the desks in the conference room, and the material of the walls and floor of the conference room. If it is determined that they are not similar, the identification unit 201 identifies the area on the same floor that is next closest to the target area among the areas whose type is a conference room after the area already identified. For example, if there is no area that is next closest, the identification unit 201 identifies the area that is closest to the target area among the areas in the same building that is closest to the target area among the areas whose type is a conference room. In this way, the identification unit 201 can identify areas sequentially based on priority.

Next, a hospital room will be taken as an example. In FIG. 4, for example, in the case of a hospital room, the area attributes are the number of patients in the room, the number of vacant beds in the room, the orientation of the windows in the room, etc. The area attributes may be at least one of these, or may be specified from among these. For example, the position attributes to be complemented are temperature and humidity. Areas to be searched are areas on the same floor, areas in other wards, and areas in hospitals of the same chain. Also, for example, areas on the same floor have the highest priority, followed by areas in other wards, and finally areas in hospitals of the same chain have the lowest priority.

Next, a store will be taken as an example for explanation. In FIG. 4, for example, in the case of a store, the area attributes are the size of the store, the material of the walls inside the store, the items (products) for sale in the store, etc. The area attributes may be at least one of these, or may be specified from among these. For example, the position attribute to be complemented is temperature. Areas to be searched are areas on the same floor, areas in the same facility, and areas of nearby stores outside the facility. Also, when identified by the identification unit 201, for example, areas on the same floor have the highest priority, followed by areas in the same facility, and finally areas of nearby stores outside the facility have the lowest priority.

Next, a data center will be taken as an example for explanation. In FIG. 4, for example, in the case of a data center, the area attributes are the size of the data center, the number of racks in the data center, the material of at least one of the floors and walls in the data sensor, the location of the air vents in the data center, etc. The area attributes may be at least one of these, or may be specified from among these. For example, the position attributes to be complemented are temperature, humidity, and volume. Areas to be searched are areas on the same floor and areas in the same facility. Also, when identified by the identification unit 201, for example, areas on the same floor have a high priority, and areas in the same facility have a low priority.

Returning to the description of FIG. 3, if the determination unit 202 determines that the two regions are similar, the complement unit 203 complements the value of the position attribute of a predetermined position included in the target region based on the value of the position attribute of each position included in the identified region. The predetermined position is not particularly limited as described in the first embodiment. Here, the predetermined position is called the target position. As described above, the position attribute is an attribute that represents the environment at each position. The position attribute may be predetermined according to the type of the target region, as shown in FIG. 4. To explain using the example of FIG. 4, in the case of a conference room, the complement unit 203 complements the temperature value of the target position based on the temperature value of each position included in the identified region. And, in the case of a conference room, the complement unit 203 complements the humidity value of the target position based on the humidity value of each position included in the identified region. Alternatively, the position attribute may be specified.

The complementing method is not particularly limited. For example, the complementing unit 203 may set the value of the position attribute related to a position included in the identified area as the value of the position attribute related to the target position. Alternatively, the complementing unit 203 may identify a position that has a similar positional relationship with the target position in the target area from multiple positions included in the identified area. Then, the complementing unit 203 may set the value of the position attribute related to the identified position as the value of the position attribute related to the target position.

Figure 5 is an explanatory diagram showing complement example 1. In Figure 5, there is no sensor in conference room X on the first floor of S building. Meanwhile, there is one sensor at position PN1 in conference room Y on the second floor of N building. Here, we take as an example a case where the value of the area attribute of conference room Y is similar to the value of the area attribute of conference room X. For example, the complement unit 203 sets the temperature value associated with position PN1 included in conference room Y to the temperature value associated with position PS1 included in conference room X. As a result, the temperature value associated with position PS1 included in conference room X in Figure 5 is complemented.

Returning to the description of FIG. 3, the complementing unit 203 may, for example, set a statistical value of the values of the position attribute related to each position included in the identified area as the value of the position attribute related to the target position. The statistical value may be, for example, the average value, the median value, the mode value, etc.

Figure 6 is an explanatory diagram showing a second complement example. In Figure 6, there is no sensor in conference room X on the first floor of the S building. Meanwhile, conference room Y on the second floor of the N building has a total of three sensors, located at positions PN1, PN2, and PN3. In Figure 6, the value of the area attribute of conference room Y is similar to the value of the area attribute of conference room X. For example, the complement unit 203 sets the statistical value of the temperature values associated with positions PN1, PN2, and PN3 included in conference room Y to the temperature value associated with position PS1 included in conference room X. As a result, the temperature value associated with position PS1 included in conference room X in Figure 6 is complemented.

Returning to the explanation of FIG. 3, the complementing unit 203, for example, identifies the correspondence between each of the multiple positions included in the target area and each of the multiple positions included in the specified area. For example, the complementing unit 203 may identify the correspondence based on the positions of windows, doors, and desks. For example, taking the position of a door as an example, the complementing unit 203 identifies the correspondence based on the similarity between the distance from the position of the door in the target area to each position included in the target area and the distance from the position of the door in the specified area to each position included in the specified area. Then, the complementing unit 203 calculates the difference between the value of the position attribute related to each position other than the target position included in the target area and the value of the position attribute related to each position corresponding to each of these positions. The complementing unit 203 calculates the value of the position attribute related to the target position by adding or subtracting a statistical value of the difference from the value of the position attribute related to the position corresponding to the target position.

Figure 7 is an explanatory diagram showing complement example 3. In Figure 7, in conference room X on the first floor of the S building, there are a total of two sensors, located at positions PS1 and PS4. Meanwhile, in conference room Y on the second floor of the N building, there are a total of four sensors, located at positions PN1 to PN4. In Figure 7, the value of the area attribute of conference room Y is similar to the value of the area attribute of conference room X. In Figure 7, the complement unit 203 complements the temperature values related to two positions (PS3 and PS4) in conference room X on the first floor of the S building.

The complementing unit 203 then identifies, for example, the correspondence between each of the multiple positions included in conference room X and each of the multiple positions included in conference room Y. For example, the complementing unit 203 may identify the correspondence based on the distance from the window position, the distance from the door position, the arrangement of desks, and the like. In FIG. 7, for example, position PS1 corresponds to position PN1, position PS2 corresponds to position PN2, position PS3 corresponds to position PN3, and position PS4 corresponds to position PN4.

Here, an example of complementing temperature values will be given. The complementing unit 203 subtracts the temperature value associated with position PS1, which corresponds to position PN1, from the temperature value associated with position PN1. The complementing unit 203 also calculates a value obtained by subtracting the temperature value associated with position PS2 from the temperature value associated with position PN2. The complementing unit 203 calculates the average of these subtracted values. The complementing unit 203 determines the value obtained by subtracting the average value from the temperature value associated with position PN3 as the temperature value associated with position PS3, which corresponds to position PN3. The complementing unit 203 determines the value obtained by subtracting the average value from the temperature value associated with position PN4 as the temperature value associated with position PS4, which corresponds to position PN4. In this way, the temperature value associated with position PS3 and the temperature value associated with position PS4 are complemented.

In the case of a hospital room, the complementing unit 203 may determine the positional correspondence between areas based on the arrangement of beds, etc. In the case of a data center, the complementing unit 203 may determine the positional correspondence between areas based on the positions of racks, the arrangement of racks, etc.

The complementing unit 203 may also complement the value of the electric field strength using radio wave propagation characteristics, etc.

Figure 8 is an explanatory diagram showing supplement example 4. In Figure 8, the target position is position PS1. As an example of the position attribute related to target position PS1, electric field strength is taken as an example. In conference room X on the first floor of S building, router 2001 is installed, but there is no terminal device. Meanwhile, in conference room Y on the second floor of N building, router 2002 is installed, and terminal device 2003 is present. Note that terminal device 2003 is not particularly limited as long as it is a device capable of receiving radio waves from router 2002, such as a PC, smartphone, or tablet device.

The complementing unit 203 obtains radio wave propagation characteristics (radio wave propagation model) for the router 2002 based on the transmission power of the router 2002, the value of the electric field strength when the terminal device 2003 communicates with the router 2002, and the distance from the position of the router 2002 to the position of the terminal device 2003. For example, the radio wave propagation model may be a function that uses the transmission power and distance as parameters and returns the value of the electric field strength. The complementing unit 203 then complements the value of the electric field strength for the target position PS1 based on the calculated radio wave propagation characteristics, using the transmission power of the router 2001 and the distance from the position of the router 2001 to the target position PS1 as parameters.

In addition, for example, the performance (e.g., transmission power) of router 2001 may be the same as that of router 2002, and the positional relationship between router 2002 and terminal device 2003 may be the same as the positional relationship between router 2001 and target position PS1. In such a case, complementation unit 203 may set the value of the electric field strength at terminal device 2003 to the value of the electric field strength at target position PS1. As a result, in FIG. 8, the value of the electric field strength related to position PS1 included in conference room X is complemented.

In the above example, an example of complementing the value of the position attribute related to the target position has been described. The complementing unit 203 may complement the value of the future position attribute instead of the current value of the position attribute. In addition, the prediction unit 204 may predict the value of the position attribute related to the same position at a different time from the value of the position attribute related to each position included in the target area. For example, the prediction unit 204 may predict the value of the position attribute related to the same position tomorrow from the value of the current position attribute related to each position included in the target area. At this time, the prediction unit 204 may further predict the value of the position attribute related to the same position tomorrow using other information. Examples of other information include schedule information for the target area and weather information around the target area. For example, the prediction unit 204 may predict the value of the position attribute related to the same position tomorrow from the value of the current position attribute related to each position included in the target area using current weather information and tomorrow's weather forecast information. For example, if the current weather and tomorrow's weather are the same, the prediction unit 204 may set the current temperature value related to each position included in the target area as the temperature value related to the same position tomorrow. The prediction unit 204 may also predict future values of location attributes related to each location from the time-series values of the location attributes at the same location. For example, the prediction unit 204 may predict the temperature value at each location tomorrow from the temperature values at each location for the past two days. In this way, the prediction method used by the prediction unit 204 is not particularly limited.

Then, the completion unit 203 may complete the value of the position attribute related to the target position based on the value of the position attribute related to each position in the identified area and the predicted value of the position attribute related to each position included in the target area. A specific example will be described with reference to FIG. 9.

Figure 9 is an explanatory diagram showing supplementary example 5. In Figure 9, in conference room X on the first floor of S building, there are two sensors in total, located at positions PS1 and PS4. On the other hand, in conference room Y on the second floor of N building, there are four sensors in total, located at positions PN1 to PN4.

Here, an example will be described in which the prediction unit 204 and the complementation unit 203 calculate tomorrow's temperature values for each of positions PS1 to PS4 included in conference room X on the first floor of the S building. First, the prediction unit 204 calculates tomorrow's temperature value for position PS1 from the current temperature value for position PS1. The prediction unit 204 calculates tomorrow's temperature value for position PS2 from the current temperature value for position PS2. For example, if the current weather and the weather forecast for tomorrow are the same, the prediction unit 204 sets the current temperature value for position PS1 to tomorrow's temperature value for position PS1. Then, the prediction unit 204 sets the current temperature value for position PS2 to tomorrow's temperature value for position PS2. In this way, the temperature value for position PS1 and the temperature value for position PS2 are predicted.

As described in FIG. 7, the complementing unit 203 determines the correspondence between each position included in the target region and each position included in the identified region. In FIG. 9, for example, position PS1 corresponds to position PN1, position PS2 corresponds to position PN2, position PS3 corresponds to position PN3, and position PS4 corresponds to position PN4.

As described in FIG. 7, the complement unit 203 subtracts tomorrow's temperature value (predicted value) for position PS1 corresponding to position PN1 from the current temperature value for position PN1. The complement unit 203 also calculates a value obtained by subtracting tomorrow's temperature value (predicted value) for position PS2 from the current temperature value for position PN2. The complement unit 203 calculates the average of these subtracted values. The complement unit 203 determines the value obtained by subtracting the average value from the current temperature value for position PN3 as tomorrow's temperature value for position PS3 corresponding to position PN3. The complement unit 203 determines the value obtained by subtracting the average value from the temperature value for position PN4 as tomorrow's temperature value for position PS4 corresponding to position PN4. In this way, the temperature value for position PS3 and the temperature value for position PS4 are complemented.

Fig. 10 is a flowchart showing an example of an operation of the information processing device 20 included in the system 2 according to the second embodiment. The identification unit 201 acquires the type of the target area (step S201). For example, if the storage unit 205 stores information on the type of area by area, the identification unit 201 reads out the information on the type of the target area from the storage unit 205.

Then, for example, the identification unit 201 identifies areas of the same type (step S202). In step S202, for example, the identification unit 201 searches the storage unit 205 for areas of the same type as the type of the target area. Also, in step S202, the identification unit 201 searches the storage unit 205 for areas that are within a predetermined distance from the target area and are of the same type as the type of the target area.

The identification unit 201 arranges the regions of the same type in order of proximity to the target region (step S203). In step S203, the identification unit 201 may arrange the regions of the same type in order of priority and proximity. An example of the priority is as described in FIG. 4.

The identification unit 201 determines whether or not there is an unselected area among the arranged areas (step S204). If there is an unselected area (step S204: Yes), the identification unit 201 selects areas in order of proximity (step S205). The determination unit 202 determines whether or not the value of the area attribute of the selected area is similar to the value of the area attribute of the target area (step S206). If it is determined that they are not similar (step S206: No), the completion unit 203 returns to step S204.

On the other hand, if it is determined that the selected regions are similar (step S206: Yes), the complementing unit 203 determines whether or not there is a value of the position attribute to be complemented for each position included in the selected region (step S207). That is, in step S207, the complementing unit 203 determines whether or not each position is associated with a value of the position attribute to be complemented. Here, the position attribute to be complemented may be predetermined as shown in FIG. 4. The position attribute to be complemented may also be specified by the user. If there is no such attribute (step S207: No), the complementing unit 203 returns to step S204. If there is such an attribute (step S207: Yes), the complementing unit 203 complements the value of the position attribute for the target position with the value of the position attribute for each position included in the selected region (step S208). After step S208, the information processing device 20 ends the operation of the flow.

If there is no unselected area (step S204: No), the information processing device 20 ends the operation of the flow. Note that the information processing device 20 may end the operation by outputting an error.

Next, the effects of the second embodiment will be described. The information processing device 20 may determine whether the similarity between the value of the region attribute related to the target region and the value of the region attribute related to the identified region satisfies a predetermined condition. This allows the information processing device 20 to relatively determine whether they are similar.

In addition, for example, the items arranged (desks, beds, etc.) differ depending on the type of room, such as a conference room or a hospital room. For this reason, the area attribute is an attribute related to each type of area. This allows the information processing device 20 to identify areas similar to the target area based on the value of the area attribute related to the type of area. Therefore, the information processing device 20 can identify areas that are more similar to the target area, thereby improving the accuracy of completion.

In addition, the closer an area is to the target area, the more similar the environment is. Therefore, if the position attribute of the area to be completed is an attribute that represents the environment of each position, the information processing device 20 can improve the accuracy of completion.

The information processing device 20 identifies an area that is close to the target area from areas that are the same type as the target area and are included in the same building or facility as the target area. This allows the information processing device 20 to identify an area that is closer to the target area from areas that are close to the target area and are estimated to have a similar environment. As described above, the closer the area, the more likely the environment is similar, so the information processing device 20 can complement the value of the location attribute related to the target position with greater accuracy.

The information processing device 20 identifies an area that is close to the target area from areas that are the same type as the target area and are included on the same floor of the same building as the target area. This allows the information processing device 20 to identify an area that is closer to the target area from areas that are close to the target area and are estimated to have a similar environment. As described above, the closer the area, the more likely the environment is similar, so the information processing device 20 can more accurately complement the value of the location attribute related to the target position.

The information processing device 20 identifies an area that is close to the target area from areas that are the same type as the target area and are included in buildings within a predetermined range from the building that contains the target area. This allows the information processing device 20 to identify an area that is close to the target area from areas that are estimated to be close. As described above, the closer the area, the more likely the environment is similar, so the information processing device 20 can complement the value of the location attribute related to the target position with greater accuracy.

The second embodiment is not limited to the above-described example, and various modifications are possible. Each functional unit of the information processing device 20 may be realized by multiple devices.

This concludes the explanation of each embodiment.

(Computer device)
Next, an example of a hardware configuration in which each device, such as the information processing device 20 described in the second embodiment, is realized by a computer device will be described. Fig. 11 is an explanatory diagram showing an example of a hardware configuration of a computer device. For example, a part or all of the devices, such as the information processing device 20 described in the second embodiment, can be realized by using any combination of a computer device 30 and a program as shown in Fig. 11.

The computer device 30 has, for example, a processor 301, a ROM 302, a RAM 303, a storage device 304, a communication interface 305, and an input/output interface 306. Each component is connected to each other via a bus 307.

The processor 301 controls the entire computer device 30. Examples of the processor 301 include a CPU (Central Processing Unit) and a DSP (Digital Signal Processor). The computer device 30 has a ROM 302, a RAM 303, and a storage device 304 as a storage unit. Examples of the storage device 304 include a semiconductor memory such as a flash memory, a HDD, and an SSD. For example, the storage device 304 stores programs such as an OS (Operating System) program, application programs, and programs according to each embodiment. Alternatively, the ROM 302 stores programs such as application programs and programs according to each embodiment. The RAM 303 is used as a work area for the processor 301.

The processor 301 also loads programs stored in the storage device 304, ROM 302, etc. Then, the processor 301 executes each process coded in the program. The processor 301 may also download various programs via the communication network 31. The processor 301 also functions as a part or the whole of the computer device 30. The processor 301 may also execute processes or instructions in the illustrated flowchart based on the program.

The communication interface 305 is connected to a communication network 31, such as a LAN (Local Area Network) or a WAN (Wide Area Network), via a wireless or wired communication line. This allows the computer device 30 to be connected to external devices and external computers via the communication network 31. The communication interface 305 serves as an interface between the communication network 31 and the inside of the computer device 30. The communication interface 305 also controls the input and output of data from the external devices and external computers.

The input/output interface 306 is connected to at least one of an input device, an output device, and an input/output device. The connection method may be wireless or wired. Examples of the input device include a keyboard, a mouse, and a microphone. Examples of the output device include a display device, a lighting device, and a speaker that outputs sound. Examples of the input/output device include a touch panel display. The input device, output device, and input/output device may be built into the computer device or may be external.

The hardware configuration of the computer device 30 shown in FIG. 11 is an example. The computer device 30 may not have some of the components shown in FIG. 11. The computer device 30 may have components other than those shown in FIG. 11. The computer device 30 may have devices not shown. For example, the computer device 30 may have a camera as an imaging device. The computer device 30 may also have various sensors. For example, the computer device 30 may have a drive device. The processor 301 may read programs and data from a recording medium attached to the drive device or the like into the RAM 303. Examples of recording media include optical disks, flexible disks, magnetic optical disks, and USB (Universal Serial Bus) memories.

This concludes the explanation of the hardware configuration example for each device. There are also various variations in the method of implementing each device. For example, the system may be implemented by any combination of different computers and programs for each component. Also, multiple components in each device may be implemented by any combination of a single computer and program.

In addition, some or all of the components of each device may be realized by circuits for specific applications. In addition, some or all of the system may be realized by general-purpose circuits including a processor such as an FPGA (Field Programmable Gate Array). In addition, some or all of the system may be realized by a combination of circuits for specific applications and general-purpose circuits. In addition, these circuits may be a single integrated circuit. Or, these circuits may be divided into multiple integrated circuits. And the multiple integrated circuits may be configured by being connected via a bus or the like.

In addition, when some or all of the components of each device are realized by multiple computers, circuits, etc., the multiple computers, circuits, etc. may be centralized or distributed.

The method described in each embodiment is realized by execution by a computer device. Also, the method is realized by execution by a computer device of a program prepared in advance. The program described in each embodiment is recorded on a computer-readable recording medium such as an HDD, SSD, flexible disk, optical disk, flexible disk, magneto-optical disk, or USB memory. Then, the program is executed by being read from the recording medium by the computer. Also, the program may be distributed via the communication network 31.

The components of the system in each embodiment described above may be realized in hardware, such as computer device 30 shown in FIG. 11. Alternatively, each component may be realized by a computer device or firmware based on program control.

Although the present disclosure has been described above with reference to each embodiment, the present disclosure is not limited to the above-mentioned embodiment. The configuration and details of each disclosure may include embodiments to which various modifications that a person skilled in the art may understand within the scope of the present disclosure are applied. The present disclosure may include embodiments in which the matters described in this specification are appropriately combined or replaced as necessary. For example, matters described using a specific embodiment may also be applied to other embodiments to the extent that no contradiction occurs. For example, although multiple operations are described in order in the form of a flowchart, the order of description does not limit the order in which the multiple operations are performed. Therefore, when implementing each embodiment, the order of the multiple operations may be changed to the extent that the content is not impaired.

A part or all of the above-described embodiments can be described as follows. However, a part or all of the above-described embodiments is not limited to the following.

(Appendix 1)
A specifying means for specifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area;
a determination means for determining whether or not a value of an area attribute related to the predetermined area is similar to a value of the area attribute related to the identified area;
a complementing means for complementing a value of a position attribute related to a predetermined position included in the specified area based on a value of a position attribute related to each position included in the specified area when the values are similar;
Equipped with
When the regions are not similar, the specifying means specifies, from among the regions of the same type, a region next closest to the region whose distance from the predetermined region has been specified.
system.

(Appendix 2)
The determining means determines whether or not a similarity between a value of the region attribute related to the predetermined region and a value of the region attribute related to the identified region satisfies a predetermined condition;
the complementing means complements a value of the position attribute related to the predetermined position based on a value of the position attribute related to each position included in the identified area when the similarity satisfies the predetermined condition;
When the similarity does not satisfy the predetermined condition, the specifying means specifies, from among the regions of the same type, a region next closest to the region whose distance from the predetermined region has been specified.
2. The system of claim 1.

(Appendix 3)
The region attributes are attributes associated with each region type.
3. The system of claim 1 or 2.

(Appendix 4)
The location attribute is an attribute that represents the environment of each location.
4. The system of claim 1.

(Appendix 5)
The identification means identifies an area that is close to the predetermined area from among areas of the same type that are included in the same building or facility as the predetermined area.
5. The system of claim 1.

(Appendix 6)
the specifying means specifies an area close to the predetermined area from among areas of the same type that are included in the same floor of the same building as the predetermined area, and
5. The system of claim 1.

(Appendix 7)
The identification means identifies an area close to the predetermined area from areas of the same type that are included in buildings within a predetermined range from the building including the predetermined area.
5. The system of claim 1.

(Appendix 8)
A specifying means for specifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area;
a determination means for determining whether or not a value of an area attribute related to the predetermined area is similar to a value of the area attribute related to the identified area;
a complementing means for complementing a value of a position attribute related to a predetermined position included in the specified area based on a value of a position attribute related to each position included in the specified area when the values are similar;
Equipped with
When the regions are not similar, the specifying means specifies, from among the regions of the same type, a region next closest to the region whose distance from the predetermined region has been specified.
Information processing device.

(Appendix 9)
Identifying an area that is closer to the predetermined area than other areas among areas of the same type as the predetermined area;
determining whether a value of a region attribute related to the predetermined region is similar to a value of the region attribute related to the identified region;
If the values are similar, the value of the position attribute related to the predetermined position included in the specified area is complemented based on the value of the position attribute related to each position included in the identified area.
If the regions are not similar, a region of the same type that is next closest to the region whose distance from the predetermined region is specified is specified.
method.

(Appendix 10)
On the computer,
A process of identifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area;
A process of determining whether a value of a region attribute related to the predetermined region is similar to a value of the region attribute related to the identified region;
If the two regions are similar, a process of complementing the value of the position attribute related to a predetermined position included in the specified region based on the value of the position attribute related to each position included in the specified region; and
Run the command,
In the process of identifying, if the regions are not similar, an area that is next closest to the region whose distance from the predetermined region is identified is identified from among the regions of the same type.
program.

(Appendix 11)
On the computer,
A process of identifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area;
A process of determining whether a value of a region attribute related to the predetermined region is similar to a value of the region attribute related to the identified region;
If the two regions are similar, a process of complementing the value of the position attribute related to a predetermined position included in the specified region based on the value of the position attribute related to each position included in the specified region; and
Run the command,
In the process of identifying, if the regions are not similar, an area that is next closest to the region whose distance from the predetermined region is identified is identified from among the regions of the same type.
A non-transitory recording medium readable by the computer, on which a program is recorded.

REFERENCE SIGNS LIST 1 System 2 System 20 Information processing device 21 Communication network 30 Computer device 31 Communication network 101 Identification unit 102 Determination unit 103 Complement unit 201 Identification unit 202 Determination unit 203 Complement unit 204 Prediction unit 205 Storage unit 301 Processor 302 ROM
303 RAM
304 Storage device 305 Communication interface 306 Output interface 307 Bus

Claims (8)

A specifying means for specifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area;
a determination means for determining whether or not a value of an area attribute related to the predetermined area is similar to a value of the area attribute related to the identified area;
a complementing means for complementing a value of a position attribute related to a predetermined position included in the specified area based on a value of a position attribute related to each position included in the specified area when the values are similar;
Equipped with
When the regions are not similar, the specifying means specifies , among the regions of the same type, a region next closest to the region whose distance from the predetermined region is specified,
The region attributes are attributes associated with each region type.
system. The determining means determines whether or not a similarity between a value of the region attribute related to the predetermined region and a value of the region attribute related to the identified region satisfies a predetermined condition;
the complementing means complements a value of the position attribute related to the predetermined position based on a value of the position attribute related to each position included in the identified area when the similarity satisfies the predetermined condition;
When the similarity does not satisfy the predetermined condition, the specifying means specifies, from among the regions of the same type, a region next closest to the region whose distance from the predetermined region has been specified.
The system of claim 1 . The location attribute is an attribute that represents the environment of each location.
3. A system according to claim 1 or 2 . The identification means identifies an area that is close to the predetermined area from among areas of the same type that are included in the same building or facility as the predetermined area.
A system according to any one of claims 1 to 3 . the specifying means specifies an area close to the predetermined area from among areas of the same type that are included in the same floor of the same building as the predetermined area, and
A system according to any one of claims 1 to 3 . The identification means identifies an area close to the predetermined area from areas of the same type that are included in buildings within a predetermined range from the building including the predetermined area.
A system according to any one of claims 1 to 3 . Identifying an area that is closer to the predetermined area than other areas among areas of the same type as the predetermined area;
determining whether a value of a region attribute related to the predetermined region is similar to a value of the region attribute related to the identified region;
If the values are similar, the value of the position attribute related to the predetermined position included in the specified area is complemented based on the value of the position attribute related to each position included in the identified area.
If the regions are not similar, a region of the same type that is next closest to the region whose distance from the predetermined region is specified is specified ;
The region attributes are attributes associated with each region type.
method. On the computer,
A process of identifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area;
A process of determining whether a value of a region attribute related to the predetermined region is similar to a value of the region attribute related to the identified region;
If the two regions are similar, a process of complementing the value of the position attribute related to a predetermined position included in the specified region based on the value of the position attribute related to each position included in the specified region; and
Run the command,
In the process of identifying, if the regions are not similar, an area of the same type is identified that is next closest to the region whose distance from the predetermined region is identified;
The region attributes are attributes associated with each region type.
program.

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