JP7628757B2 - Lightning strike prediction system and power transmission accident prediction system using said system - Google Patents

Description

The present invention relates to a lightning strike prediction system and a power transmission accident prediction system using this system.

For example, a stable supply of electricity is necessary in production facilities such as semiconductor factories, where stable operation is required. However, natural phenomena such as lightning strikes can damage power lines and other power equipment, and power problems such as blackouts and voltage drops (momentary voltage drops) inevitably occur.

Here, Patent Document 1 describes a system for predicting the occurrence of lightning strikes and issuing a warning to the facility. That is, in the system described in this patent document, a lightning strike detection device is installed at the construction work site of a structure such as a tower or at a location in the vicinity of the construction work site, and an electrostatic field detection device is installed at the construction work site of the structure or at a location nearby the construction work site. The lightning strike detection device detects the occurrence of a lightning strike, and the electrostatic field detection device detects the strength of the electrostatic field between the cloud and the ground. Based on the levels of the detection signals output from both detection devices, the warning output device determines the rank of the warning information, and the ranked warning information is output from the warning output device. In addition, at this time, the detection level of the electrostatic field detection device is given priority when outputting the warning information.

JP 2013-250211 A

In the above-mentioned factories, the various installed equipment is highly electrically controlled and electrically networked, so measures against lightning damage (lightning damage countermeasures) are important not only for direct damage such as damage to equipment caused by lightning strikes, but also for indirect damage caused by power outages and voltage drops. Semiconductor factories are particularly susceptible to the above-mentioned effects, so there is a demand for more accurate lightning strike predictions than ever before. In the system described in Patent Document 1, lightning detection devices and electrostatic field detection devices are installed in fixed locations, so in order to accurately predict lightning strikes over a wide area, a corresponding number of detection devices must be installed, resulting in increased costs.

In light of the above, the technical problem to be solved in this specification is to realize highly accurate lightning predictions at low cost.

The above problem is solved by the lightning strike occurrence prediction system of the present invention. That is, this prediction system is characterized in that it comprises a plurality of electric field strength detection devices provided in a plurality of vehicles and capable of detecting the electric field strength in the space in which each vehicle is located, a vehicle position information acquisition device capable of acquiring the position information of each vehicle, and a data processing device that accumulates the position information and electric field strength as position data and electric field strength data, respectively, and executes a predetermined process, and that the data processing device is configured to execute a process of predicting the occurrence of a lightning strike based on the accumulated position data and electric field strength data.

As described above, according to the lightning strike prediction system of the present invention, the vehicle position information acquisition device acquires the position information of multiple vehicles, detects the electric field strength of the space in which these vehicles are located, and stores them in the data processing device as position data and electric field strength data, respectively. The data processing device then executes a process of predicting the occurrence of a lightning strike based on the accumulated position data and electric field strength data. Therefore, according to the present invention, a large amount of electric field strength data can be acquired over a very wide range in a state linked to the position data. Therefore, compared to conventional lightning strike warning technologies, lightning strike predictions can be made with much higher reliability. In addition, these position data and electric field strength data can be easily collected (stored), for example, by using connected vehicle services, which have become increasingly popular in recent years, so it is possible to build the above-mentioned prediction system without incurring a significant increase in cost.

In addition, in the lightning prediction system according to the present invention, the data processing device may be configured to be able to predict the occurrence of lightning strikes based on the location data, the electric field strength data, and data relating to meteorological information for the area in which the location data was obtained. Note that the meteorological information referred to here includes information generally recognized as meteorological information, such as temperature, humidity, amount of precipitation (amount of snowfall), area of precipitation, wind speed, wind volume, and air pressure, as well as information relating to lightning strikes that have occurred in the past.

In this way, by using, in addition to the location data and electric field strength data, data on meteorological information for the area from which the location data was obtained as data for prediction, it becomes possible to make more accurate lightning predictions. Note that the above-mentioned meteorological information may be information obtained from a known meteorological information provider, or may be information obtained by a sensor or the like attached to each vehicle.

In addition, in the lightning prediction system according to the present invention, the data processing device may be configured to be able to predict at least one of the area where lightning will occur, the time of occurrence, and the intensity.

If it is possible to predict the area or time when lightning will strike, it will be possible to identify facilities that require prior action. In addition, by predicting the strength of the lightning strike, it will be possible to predict to some extent the impact that the lightning will have on the facilities, enabling more appropriate decisions to be made, including whether or not prior action is required.

The lightning strike prediction system described above can also be provided as a power transmission accident prediction system using the lightning strike prediction system. In that case, the data processing device may be configured to predict the occurrence of a power transmission accident due to a lightning strike based on the position data, the electric field strength data, and data related to the position information of the power transmission equipment.

In this way, by predicting the occurrence of lightning strikes using data on the location information of the power transmission equipment in addition to the location data and electric field strength data, it is possible to accurately predict the possibility of lightning striking the power transmission equipment or its surroundings. This makes it possible to predict the occurrence of power transmission accidents due to lightning strikes with a higher probability.

In addition, in the power transmission accident prediction system according to the present invention, the data processing device may be configured to predict the occurrence of a power outage or voltage sag based on data related to the location information of the power transmission line as the power transmission facility.

In this way, by predicting the occurrence of lightning strikes using data on the location information of power transmission lines as power transmission equipment, it is possible to predict with a high degree of accuracy which power transmission line or its surroundings is most likely to be struck by lightning. This makes it possible to identify in advance the facilities that will experience a power outage or voltage drop when lightning strikes a power transmission line, making it easy to take preventative measures.

As described above, the lightning strike prediction system of the present invention makes it possible to predict lightning strikes with a high probability at low cost.

1 is a conceptual diagram of a lightning occurrence prediction system according to a first embodiment of the present invention. FIG. 2 is a side view of a vehicle for explaining the configuration of the electric field intensity detection device shown in FIG. 1 . 2 is a flowchart according to an example of a prediction method using the prediction system shown in FIG. 1 . FIG. 11 is a conceptual diagram of a power transmission accident occurrence prediction system according to a second embodiment of the present invention. 5 is a flowchart according to an example of a prediction method using the prediction system shown in FIG. 4 .

The following describes the contents of the lightning strike prediction system according to the first embodiment of the present invention with reference to the drawings.

Figure 1 shows a conceptual diagram of a lightning strike prediction system 10 according to this embodiment. This prediction system 10 comprises a field intensity detection device 12 installed in multiple vehicles 11, a vehicle position information acquisition device 13, and a data processing device 14. Each component will be described in detail below.

The electric field strength detection device 12 is provided in all vehicles 11 from which electric field strength data, which will be described later, is to be acquired. In this embodiment, as shown in FIG. 2, the electric field strength detection device 12 includes a field effect transistor (FET) 15, an antenna 16 connected to a gate electrode 15a of the field effect transistor 15, a bias power supply 17 capable of applying a predetermined voltage V1 between the source electrode 15b and the drain electrode 15c of the field effect transistor 15, a measuring device 18 for measuring the magnitude of the current I flowing between the source electrode 15b and the drain electrode 15c, and an electric field strength calculation unit 19.

As the field effect transistor (FET) 15, as described below, any type of field effect transistor (junction FET, MOSFET, etc.) can be used as long as the ease of flow of the current I (current amount) changes depending on the magnitude of the voltage V2 applied to the gate electrode 15a, in other words, as long as the value of the current I can be converted into the voltage V2 and thus into the state of the electric field (e.g., electric field strength) that can be received by the antenna 16. In this embodiment, a MOSFET is used as the field effect transistor 15.

The antenna 16 is configured to receive an electric field near the ground surface where the vehicle 11 is located, and to apply a voltage V2 to the gate electrode 15a according to the state of the received electric field. For example, in this embodiment, the antenna 16 is configured so that the magnitude of the voltage V2 applied to the gate electrode 15a is proportional to the strength of the received electric field (electric field intensity).

The antenna 16 configured as described above is formed from the body metal part of the vehicle 11. In this embodiment, as shown in FIG. 2, the antenna 16 is formed from the door outer 11a of the vehicle 11. That is, by electrically connecting the gate electrode 15a of the field effect transistor 15 to the door outer 11a, which is the metal exterior plate of the vehicle 11, by a conductor or the like, the door outer 11a can function as the main body of the antenna 16.

In this case, the connection point of the gate electrode 15a to the door outer 11a is in principle arbitrary, but when considering the vertical bias of electric charges in the electric field near the ground surface, it is better to connect the gate electrode 15a to one side in the vertical direction of the door outer 11a. In this embodiment, as shown in Figure 2, the gate electrode 15a is connected to the lower part of the door outer 11a.

Any power source can be used as the bias power source 17. For example, it is preferable from a cost perspective to use an existing power source installed in the vehicle 11, such as a power source for the ECU of the vehicle 11.

The measuring device 18 is, for example, an ammeter, and is provided at a position where it can measure the magnitude (current value) of the current I flowing between the source electrode 15b and the drain electrode 15c of the field effect transistor 15. This measuring device 18 transmits information about the measured current value to the electric field intensity calculation unit 19. The measuring device 18 measures the current value at regular time intervals, for example. Of course, it may also measure each time a command is received from the data processing device 14.

The electric field strength calculation unit 19 is configured to be able to calculate the state of the electric field (here, the electric field strength) around the vehicle 11, more precisely around the antenna 16, based on information relating to the current value measured by the measurement device 18. The electric field strength calculation unit 19 also transmits information relating to the calculated electric field strength to a data communication device 20 provided in the vehicle 11. In this case, the electric field strength calculation unit 19 may transmit information relating to all of the calculated electric field strengths to the data communication device 20, or may transmit only information relating to electric field strengths equal to or greater than a preset threshold to the data communication device 20.

The data communication device 20 is configured to be able to transmit at least the electric field strength data, data relating to the position information of the vehicle 11, and data relating to the acquisition time of each piece of data to the data processing device 14. Of course, weather-related data other than the above, that is, data relating to the weather in the area where the vehicle position data was acquired, may also be transmitted, and is configured to be able to transmit information on phenomena generally recognized as weather information, such as temperature, humidity, amount of precipitation (amount of snowfall), area of rainfall, wind speed, wind volume, and air pressure. Furthermore, this weather data may be data measured directly by various measuring instruments, or may be data obtained by indirect calculation from data on operations related to the natural phenomenon, such as the number of times the windshield wipers are operated.

The data communication device 20 can be a wide variety of existing devices capable of data communication. For example, a data communication device dedicated to the data communication according to this embodiment can be installed and fixed in the vehicle 11 as the data communication device 20. Alternatively, a general-purpose data communication terminal such as a tablet or smartphone with a dedicated app installed can be installed in the vehicle 11 and used as the data communication device 20.

The vehicle position information acquisition device 13 is configured as a receiver of a satellite positioning system that enables communication with satellites (positioning satellites) for a satellite positioning system such as GPS. In this case, the vehicle position information acquisition device 13 is configured as a car navigation system, for example. The vehicle position information acquisition device 13 acquires (calculates) the position of the vehicle 11 by communicating with the positioning satellite, and transmits the acquired position information of the vehicle 11 to the data communication device 20 as position data of the vehicle 11. The data communication device 20 that receives the position data is capable of transmitting the position data to the data processing device 14 together with the above-mentioned electric field strength data and weather data. Of course, the vehicle position information acquisition device 13 may be provided in the vehicle 11 as a dedicated device separate from the car navigation system.

As shown in FIG. 1, the data processing device 14 has a data storage unit 21 and a data processing unit 22. Of these, the data storage unit 21 stores position data, electric field strength data, and weather data transmitted from multiple vehicles 11. These data are transmitted, for example, from each vehicle 11 at predetermined intervals. Furthermore, when there is data transmitted from a source other than the vehicle 11, this data is stored in the data storage unit 21. In the example shown in FIG. 1, weather data provided by a weather information provider 23 is received and stored in the data storage unit 21. This weather data includes, for example, data related to actual lightning strikes (area, time, strength, etc.).

The data processing unit 22 performs lightning strike occurrence prediction processing based on various data transmitted by the data communication device 20 and stored in the data storage unit 21.

Here, the lightning strike occurrence prediction process (specifically, execution of a program for predicting the occurrence of lightning strikes) can be performed using, for example, AI (artificial intelligence). Specifically, first, various data stored in the data storage unit 21 are used as teacher data, and the data processing unit 22 generates a learning model that outputs information on the area, time, and strength of lightning strikes (learning model generation step P1). At this time, the teacher data used are the position data and electric field strength data of each vehicle 11, and data on lightning strikes that have actually occurred (hereinafter simply referred to as lightning strike data). In other words, the position data and electric field strength data corresponding to past lightning strike data, that is, the electric field strength data in the area and time when lightning strikes actually occurred, are extracted from the various data stored in the data storage unit 21 using the corresponding position data. Then, a learning model is generated that uses the extracted electric field strength data and other weather data as inputs and lightning strike data as output. For the lightning strike data, for example, data on past lightning strikes provided by the weather information providing agency 23 can be used as shown in FIG. 1.

After generating a learning model for the occurrence of lightning strikes in this manner, the occurrence of lightning strikes is predicted using this learning model (lightning strike occurrence prediction step P2). Specifically, the data processing unit 22 executes a program that inputs the position data, electric field strength data, and other meteorological data of each vehicle 11 into the learning model generated in the learning model generation step, and outputs at least one of the area, time, and strength of the lightning strike as a predicted result of the occurrence of the lightning strike. This allows information regarding the predicted result of the occurrence of the lightning strike to be obtained.

Next, an example of a lightning strike prediction method using the lightning strike prediction system 10 configured as described above will be described with reference to Figures 1 to 3.

Figure 3 shows a flowchart for explaining the steps of a lightning prediction method using the lightning prediction system 10. As shown in this flowchart, the lightning prediction method according to this embodiment includes a first data acquisition step S1 for acquiring electric field intensity data, position data, and weather data from a plurality of vehicles 11, a second data acquisition step S2 for acquiring weather data from sources other than the vehicles 11, a lightning prediction step S3 for predicting the occurrence of a lightning strike based on the various acquired data, and a lightning prediction information provision step S4 for providing information on the lightning prediction result to necessary facilities 24 (e.g., various production facilities such as semiconductor factories) based on the lightning prediction result, if necessary.

(S1) First Data Acquisition Step In this step, position data, electric field strength data, and weather data such as temperature, rainfall, and wind speed (hereinafter, for convenience, referred to as first weather data) of each vehicle 11 are acquired from a plurality of vehicles 11 that are targets of data acquisition. Specifically, the electric field strength of the space in which the vehicle 11 is located is detected by the above-mentioned electric field strength detection device 12, and information on the detected electric field strength is transmitted to the data processing device 14 as electric field strength data by the data communication device 20. Similarly, position information of the vehicle 11 acquired by the vehicle position information acquisition device 13 is transmitted to the data processing device 14 as position data by the data communication device 20. In addition, weather information detected by various sensors and the like (not shown) is transmitted to the data processing device 14 as first weather data by the data communication device 20. The data processing device 14 that receives these various data accumulates the received various data in the data accumulation unit 21. In this way, electric field strength data and first weather data at a predetermined time at a plurality of locations within a predetermined area are acquired.

(S2) Second Data Acquisition Step In this step, meteorological data (hereinafter, for convenience, referred to as second meteorological data) including data on lightning strikes that have occurred in the past within a specified area (lightning strike data) is acquired from the meteorological information provider 23. The data processing device 14 accumulates the acquired meteorological data (second meteorological data) such as the lightning strike data in the data accumulation unit 21. As a result, the lightning strike data at a specified time within the specified area, the electric field intensity data for the same area and at the same time, and various meteorological data are accumulated in the data accumulation unit 21 in a mutually associated state.

(S3) Lightning Occurrence Prediction Step In this step, the data processing unit 22 performs a process of predicting the occurrence of lightning in a predetermined area based on the various data acquired in steps S1 and S2. Specifically, the data processing unit 22 executes a program that inputs various data and outputs a lightning occurrence prediction result. Any program can be executed here, and in this embodiment, a lightning occurrence prediction program using the above-mentioned learning model is executed. That is, in the learning model generation step P1, a learning model regarding the occurrence of lightning is generated using the past electric field strength data, position data, weather data, and lightning data acquired in steps S1 and S2 as teacher data. Thereafter, the data processing unit 22 executes a program that inputs the electric field strength data, position data, and weather data acquired at a predetermined time into the learning model and outputs at least one of the area, time, and strength of lightning that will occur after the predetermined time as a lightning occurrence prediction result. As a result, information regarding the result of the lightning occurrence prediction after the predetermined time is acquired.

(S4) Step of Providing Prediction Information In this step, information about the lightning strike occurrence prediction result obtained in step S3 is provided to the facility 24 that needs it. In this way, the lightning strike occurrence prediction information can be utilized in the facility 24 that needs it (for example, to implement advance measures against lightning strikes, including the possibility of implementing them).

As described above, the lightning occurrence prediction system 10 according to this embodiment can obtain a large amount of electric field intensity data linked to the position data by acquiring electric field intensity data and position data from multiple vehicles 11 over a very wide range. Therefore, by predicting the occurrence of lightning based on this large amount of electric field intensity data, it is possible to perform lightning predictions that are much more reliable than conventional lightning warning techniques. In addition, this position data and electric field intensity data can be collected (stored) by utilizing the data communication device 20 already installed in the vehicle 11, so it is possible to build the above-mentioned prediction system 10 without incurring a significant increase in cost.

In addition, in this embodiment, the data processing unit 22 acquires from the vehicle 11 the location data, the electric field strength data, and data related to meteorological information for the area in which the location data was acquired, and also acquires meteorological data including data related to lightning strikes that have actually occurred from the meteorological information provider 23. Based on the various acquired data, the occurrence of lightning strikes is predicted, making it possible to predict lightning strikes with even greater accuracy.

In addition, in this embodiment, the electric field strength around the vehicle 11 can be detected by using the electric field strength detection device 12 equipped with the field effect transistor 15. That is, according to this electric field strength detection device 12, the antenna 16 receives the electric field in the space in which the antenna 16 is placed, and a voltage V2 corresponding to the state of the received electric field (electric field strength) is applied to the gate electrode 15a. Since a certain correlation is observed between the electric field strength and the voltage V2 (gate voltage) applied to the gate electrode 15a, the electric field strength around the vehicle 11 can be accurately detected by accurately evaluating the fluctuation of the current I between the source electrode 15b and the drain electrode 15c that occurs when the voltage V2 is applied to the gate electrode 15a. Here, in this embodiment, the antenna 16 is a body metal part of the vehicle 11, specifically the door outer 11a, so that the electric field can be sensed over a very large area. Therefore, the electric field strength can be detected with high sensitivity, and it is possible to predict the occurrence of a lightning strike with higher accuracy. In addition, the field effect transistor 15 is very inexpensive and easy to obtain, and by using a metal body part of the vehicle 11 (here, the door outer 11a) as the antenna 16, the effort and cost of preparing a separate antenna 16 can be reduced. Also, the bias power supply 17 can use an existing power supply (such as an ECU power supply) installed in the vehicle 11, so in this respect too, the electric field intensity detection device 12 can be manufactured at low cost.

Figure 4 shows a conceptual diagram of a power transmission accident prediction system 50 according to a second embodiment of the present invention. This power transmission accident prediction system 50 includes the lightning strike prediction system 10 shown in Figure 1, and is capable of storing data related to the location information of power transmission equipment provided by a specified data providing service organization 51 in a data storage unit 21, and is capable of executing a program for predicting the occurrence of a power transmission accident due to a lightning strike based on the stored location data, electric field strength data, and the location data of the power transmission equipment in a data processing unit 22. The functions of the data processing device 14 other than those described above, and the configuration for acquiring electric field strength data, location data, and weather data from the vehicle 11 (electric field strength detection device 12, vehicle location information acquisition device 13, data communication device 20) are the same as those in the first embodiment, so detailed explanations will be omitted.

Figure 5 shows a flowchart for explaining the procedure of the method for predicting the occurrence of a power transmission accident due to a lightning strike using the power transmission accident prediction system 50. As shown in this flowchart, the power transmission accident prediction method according to this embodiment includes a first data acquisition step S5 for acquiring electric field strength data, position data, and first weather data from a plurality of vehicles 11, a second data acquisition step S6 for acquiring second weather data from other than the vehicles 11, a lightning strike occurrence prediction step S7 for predicting the occurrence of a lightning strike based on the various acquired data, a third data acquisition step S8 for acquiring position data of the power transmission equipment, a power transmission accident occurrence prediction step S9 for predicting the occurrence of a power transmission accident based on various data including the position data of the power transmission equipment, and a prediction information provision step S10 for providing information on the prediction of the occurrence of a power transmission accident to a necessary facility 24 based on the result of the prediction of the occurrence of the power transmission accident. Of these, steps S5 to S7 are the same as steps S1 to S3 in the first embodiment, so detailed explanations will be omitted.

(S8) Third data acquisition step In this step, data on the location information of the power transmission facility, specifically, data on the location information of the power transmission line, is acquired from a predetermined data providing service institution 51. The data processing device 14 accumulates the acquired location data of the power transmission facility in the data accumulation unit 21.

(S9) Power Transmission Accident Prediction Step In this step, the data processing unit 22 predicts the occurrence of a power transmission accident due to the occurrence of a lightning strike in a specified area based on the various data acquired in steps S5, S6, and S8. Specifically, the data processing unit 22 executes a program that inputs various data and outputs a power transmission accident prediction result. Here, any program can be executed, and for example, after outputting a prediction result regarding the area where lightning strikes will occur using the same program as in the first embodiment, the data processing unit 22 executes a program that determines whether or not the occurrence of a lightning strike will affect the operation of power transmission equipment such as a power transmission line, in other words, whether or not lightning will strike the power transmission equipment or its surroundings, based on the output prediction result regarding the area where lightning strikes will occur and the position information of the power transmission equipment acquired in step S8. This allows the prediction result of the occurrence of a power transmission accident due to a lightning strike to be obtained.

Here, an example of the effect of a lightning strike on the operation of the power transmission equipment is a power outage or voltage sag in the facility 24 receiving power from the power transmission equipment (power transmission line). In this case, the data processing unit 22 executes the above-mentioned power transmission accident prediction program to obtain the predicted result of the power outage or voltage sag in the facility 24 due to a lightning strike.

(S10) Prediction Information Providing Step In this step, information on the results of the power transmission accident prediction obtained in step S9 is provided to the facility 24 that needs it. In this way, the power transmission accident prediction information can be utilized in the facility 24 that needs it (for example, for implementing advance measures against lightning strikes, including the possibility of determining whether or not to do so).

As described above, the power transmission accident prediction system 50 according to this embodiment can predict the occurrence of a lightning strike using data related to the power transmission equipment in addition to the position data and electric field strength data, thereby making it possible to accurately predict the possibility of lightning striking the power transmission equipment or its surroundings. This makes it possible to predict the occurrence of a power transmission accident due to a lightning strike with a higher probability.

In addition, in this embodiment, by predicting the occurrence of a lightning strike using data on the location information of the power transmission line as a power transmission facility, it is possible to predict with a high degree of accuracy which power transmission line will be struck by lightning or which is most likely to affect the operation of the power transmission line. Therefore, it is possible to identify in advance the facilities that will experience a power outage or voltage drop when lightning strikes a power transmission line or its surroundings, making it easy to take preventive measures.

The above describes the first and second first embodiments of the present invention, but the lightning strike prediction system or the lightning strike prediction method using this system, or the power transmission accident prediction system or the power transmission accident prediction method using this system according to the present invention may have configurations other than those described above, as long as they do not deviate from the spirit of the invention.

For example, in the first embodiment, the data processing device 14 acquires electric field strength data, position data, and first weather data from a plurality of vehicles 11, and acquires second weather data including lightning data from the weather information provider 23, and performs lightning occurrence prediction based on these acquired various data. However, other forms are of course possible. For example, the data processing unit 22 may perform lightning occurrence prediction based only on the electric field strength data and position data acquired from each vehicle 11, and the first weather data. Alternatively, the data processing unit 22 may perform lightning occurrence prediction based only on the electric field strength data and position data acquired from each vehicle 11, and the second weather data provided by the weather information provider 23. Alternatively, data of a type other than the above-mentioned example data may be acquired together with the position data and electric field strength data, and lightning occurrence prediction may be performed based on these acquired data. In short, the type of data used for lightning occurrence prediction is arbitrary as long as it includes the position data and electric field strength data of the vehicle 11.

In the first embodiment, the electric field strength detection device 12 is provided with a field effect transistor 15, an antenna 16, and a bias power supply 17, and the antenna 16 is formed from a metal part of the body of the vehicle 11, but of course this is not limited to this. In other words, any known type of antenna may be used as the antenna 16 as long as it has sufficient sensitivity. Also, the electric field strength detection device 12 is not limited to the field effect transistor 15, and other known types of electric field strength detection equipment may be used.

In the first embodiment, the measuring device 18 of the electric field strength detection device 12 measures a current value corresponding to the state of the electric field around the vehicle 11, and the electric field strength is calculated from the measured current value by the electric field strength calculation unit 19. However, other configurations are also possible. For example, the electric field strength detection device 12 may acquire data that allows the electric field strength in the space around the vehicle 11 to be calculated by a predetermined calculation, such as the current value measured by the measuring device 18, and transmit the acquired data to the data processing device 14, and the transmitted data may be calculated by the electric field strength calculation unit 19 incorporated in the data processing device 14.

In the first and second embodiments, an example is given of providing information on the predicted results of a lightning strike or power transmission accident to a facility 24 that requires it, but information on the predicted results of a lightning strike or power transmission accident may be provided to, for example, an electric power company that has jurisdiction over the area in which the facility 24 is located.

In the above explanation, the prediction system according to the present invention has been described as being capable of predicting the occurrence of lightning strikes, but the prediction system according to the present invention can also be applied to predicting natural disasters, such as earthquakes, where fluctuations in electric field strength are recognized as a sign of the occurrence.

10 Lightning strike occurrence prediction system 11 Vehicle 11a Door outer 12 Electric field intensity detection device 13 Vehicle position information acquisition device 14 Data processing device 15 Field effect transistor 15a Gate electrode 15b Source electrode 15c Drain electrode 16 Antenna 17 Bias power supply 18 Measurement device 19 Electric field intensity calculation unit 20 Data communication device 21 Data storage unit 22 Data processing unit 23 Meteorological information providing agency 24 Facility 50 Power transmission accident occurrence prediction system 51 Data provision service agency S1 First data acquisition step S2 Second data acquisition step S3 Lightning strike occurrence prediction step S4 Lightning strike occurrence prediction information provision step S5 First data acquisition step S6 Second data acquisition step S7 Lightning strike occurrence prediction step S8 Third data acquisition step S9 Power transmission accident occurrence prediction step S10 Power transmission accident occurrence prediction information provision step

Claims (2)

A system for predicting the occurrence of lightning strikes, comprising:
a plurality of electric field strength detection devices provided in a plurality of vehicles and capable of detecting an electric field strength in a space in which each of the vehicles is located;
a vehicle position information acquisition device capable of acquiring position information of each of the vehicles;
a data processing device that accumulates the position information and the electric field strength as electric field strength distribution data at a predetermined time in a predetermined area and executes a predetermined process;
The data processing device is further configured to accumulate weather information acquired by each vehicle at the specified time as weather data, and to execute a process of predicting the occurrence of the lightning strike within the specified area based on the accumulated electric field strength distribution data for a specified period within the specified area and the weather data . A power transmission accident prediction system using the lightning strike occurrence prediction system according to claim 1 ,
The data processing device is a power transmission accident prediction system configured to be able to predict the occurrence of a power transmission accident due to a lightning strike based on the location data, the electric field strength distribution data, the weather data , and data related to location information of power transmission equipment.

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