Information processing device

Abstract

To provide an information processing device capable of accurately predicting a deterioration state of a road surface and achieving long life of a road and reduction in road budget.SOLUTION: An information processing device includes a satellite image acquisition unit 200, a vehicle extraction unit 210, an analysis unit 220, and a prediction unit 230. The satellite image acquisition unit 200 acquires a satellite image. The vehicle extraction unit 210 extracts a large vehicle having a size equal to or larger than a predetermined value from the satellite image acquired by the satellite image acquisition unit 200. The analysis unit 220 detects a road on which a large vehicle travels by a predetermined value or more in the satellite image, and analyzes vehicle data on the detected road. The prediction unit 230 predicts a deterioration state of a road surface based on analysis results by the analysis unit 220.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an information processing device. [Background technology]

[0002] It is known that an increase in the number of large vehicles traveling on a road accelerates road surface deterioration and shortens the road's lifespan. Therefore, Patent Document 1 discloses a method for estimating the volume of large vehicle traffic based on data from traffic volume sensors and predicting road surface conditions. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-288665 Summary of the Invention [Problem to be solved by the invention]

[0004] In the method disclosed in Patent Document 1, the traffic volume of large vehicles is estimated, so if there is a difference between the estimated volume and the actual traffic volume of large vehicles, the accuracy of the predicted value will decrease.

[0005] In consideration of the above, the present invention aims to provide an information processing device that can accurately predict the deterioration state of road surfaces, and that can achieve a longer road life and a reduction in road budgets. [Means for solving the problem]

[0006] The information processing device according to the present invention as set forth in claim 1 includes a satellite image acquisition unit that acquires satellite images, a vehicle extraction unit that extracts large vehicles having a size equal to or greater than a predetermined value from the satellite images acquired by the satellite image acquisition unit, an analysis unit that detects roads on which the number of large vehicles traveling equal to or greater than a predetermined value is detected in the satellite images and analyzes vehicle data on the roads, and a prediction unit that predicts the deterioration state of the road surface based on the analysis results by the analysis unit. an operating day acquisition unit that acquires data indicating operating days of the business office from the business office through which the large vehicle travels; Equipped with The satellite image acquisition unit acquires satellite images acquired on the business days of the business office indicated by the data acquired by the business day acquisition unit. do.

[0007] The information processing device according to the present invention, as set forth in claim 1, extracts large vehicles of a size equal to or greater than a predetermined value from satellite images and detects roads on which the number of large vehicles traveling on the road exceeds a predetermined value. This makes it possible to grasp the number of large vehicles actually traveling on the road from satellite images. Furthermore, the notification device according to the present invention, as set forth in claim 1, analyzes vehicle data on roads on which the number of large vehicles traveling on the road exceeds a predetermined value and predicts the deterioration state of the road surface based on the analysis results. Therefore, the deterioration state of the road surface can be predicted based on vehicle data on roads on which a large number of large vehicles actually travel, thereby enabling accurate prediction of the deterioration state of the road surface. Furthermore, accurate prediction of the deterioration state of the road surface allows for optimization of road infrastructure and lifecycle costs, thereby realizing longer road lifespans and reduced road budgets. Furthermore, the information processing device according to the present invention as set forth in claim 1 acquires satellite images taken on the working days of the business office where the large vehicle is traveling. Therefore, it is possible to acquire satellite images taken on the days when the large vehicle is actually traveling, and therefore it is possible to more accurately predict the deterioration state of the road surface.

[0008] The information processing device of the present invention described in claim 2 has the configuration described in claim 1, in which the satellite image acquisition unit acquires weather information associated with the acquired satellite image, and acquires the satellite image acquired during a sunny day based on the acquired weather information.

[0009] The information processing device according to the present invention as set forth in claim 2 acquires satellite images acquired during the daytime on clear skies, thereby enabling the acquisition of clearer satellite images, which makes it easier to extract large vehicles from the acquired satellite images, thereby enabling the prediction of road surface deterioration with greater accuracy.

[0010] The information processing device of the present invention described in claim 3 is configured as described in claim 1 or claim 2, wherein the satellite image acquisition unit acquires weather information associated with the acquired satellite image, and the prediction unit assigns a weighting to the predicted deterioration state of the road surface according to the weather based on the weather information acquired by the satellite image acquisition unit.

[0011] For example, satellite images taken during the day on a clear day have higher illuminance than satellite images taken on rainy or cloudy days, making it easier to extract large vehicles than satellite images taken on rainy or cloudy days. On the other hand, satellite images taken in the evening on a rainy or cloudy day have lower illuminance than satellite images taken during a clear day, making it more difficult to extract large vehicles than satellite images taken during a clear day. Therefore, the information processing device according to the present invention, as set forth in claim 3, assigns a weighting to the predicted road surface deterioration state according to the weather based on meteorological information associated with the satellite image. This allows the reliability of the extraction of large vehicles in the satellite image to be reflected in the predicted road surface deterioration state, making it possible to more accurately predict the road surface deterioration state.

[0012] The information processing device of the present invention described in claim 4 has the configuration described in any one of claims 1 to 3, wherein the vehicle extraction unit extracts at least one of trucks and buses, and vehicles of a predetermined weight or more as the large vehicle.

[0013] In the information processing device according to the present invention, at least one of trucks, buses, and vehicles of a predetermined weight or more is extracted as a large vehicle. Therefore, when a vehicle of a predetermined weight or more is extracted as a large vehicle, for example, a vehicle that is relatively small but carries a large amount of cargo is also extracted as a large vehicle. Therefore, heavy vehicles that are thought to affect road surface deterioration can also be reflected in the prediction of road surface deterioration state. [Effects of the Invention]

[0016] As described above, the information processing device according to the present invention has the excellent effect of being able to accurately predict the deterioration state of road surfaces, and achieving a longer road life and a reduction in road budgets. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a diagram illustrating an example of a schematic configuration of an information processing system according to a first embodiment of the present invention. [Figure 2] 2 is a block diagram showing the hardware configuration of a user terminal according to the first embodiment of the present invention. FIG. [Figure 3] 2 is a block diagram showing the hardware configuration of a center server according to the first embodiment of the present invention. FIG. [Figure 4] 2 is a block diagram showing an example of the functional configuration of a CPU in a center server according to the first embodiment of the present invention. FIG. [Figure 5] 3 is a flowchart showing an example of the flow of information processing according to the first embodiment of the present invention. [Figure 6] FIG. 10 is a block diagram illustrating an example of the functional configuration of a CPU in a center server according to a second embodiment of the present invention. [Figure 7] 10 is a flowchart showing an example of the flow of information processing according to the second embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] An information processing system 100 according to a first embodiment of the present invention will be described below with reference to the accompanying drawings. As shown in FIG. 1, the information processing system 100 of this embodiment is configured to include a satellite server 10, a center server 20, and a user terminal 30. The center server 20 is an example of an information processing device. Note that the number of user terminals 30 included in the information processing system 100 is not limited to the number shown in FIG. 1. The satellite server 10, the center server 20, and the user terminal 30 are each connected to one another via a network CN1.

[0019] The satellite server 10 stores satellite images, which are images of the ground taken from the sky by artificial satellites or aircraft. Specifically, the satellite server 10 stores the satellite images, the date and time the satellite images were taken, the weather information for the day the satellite images were taken, and the location where the satellite images were taken, in association with each other. The "weather information" includes information such as illuminance and whether or not there is rain, and specifically includes information distinguishing between sunny, rainy, and cloudy weather, as an example.

[0020] (user terminal) The user terminal 30 is a terminal such as a smartphone or a computer owned by a user.

[0021] 2, the user terminal 30 includes a central processing unit (CPU) 30A, a read-only memory (ROM) 30B, a random access memory (RAM) 30C, an input unit 30E, a display unit 30F, and a communication interface (I / F) 30G. The CPU 30A, ROM 30B, RAM 30C, input unit 30E, display unit 30F, and communication I / F 30G are connected to each other via an internal bus 30H so that they can communicate with each other. In addition to the ROM 30B, the user terminal 30 may also include a non-volatile memory such as an SD card.

[0022] The CPU 30A is a central processing unit that executes various programs and controls various parts. That is, the CPU 30A reads out programs from the ROM 30B and executes the programs using the RAM 30C as a working area.

[0023] The ROM 30B stores various programs and various data. The RAM 30C serves as a working area for temporarily storing programs or data.

[0024] The input unit 30E is, for example, a keyboard, a push-button numeric keypad, a touchpad, or the like, and is used to input various types of information with the user's fingers.

[0025] The display unit 30F is, for example, a liquid crystal display, and displays various types of information. The display unit 30F may be provided as a touch display that also serves as the input unit 30E.

[0026] The communication I / F 20G is an interface for connecting to the network CN1.

[0027] (Center server) 3, the center server 20 includes a CPU 20A, a ROM 20B, a RAM 20C, and a communication I / F 20G. The CPU 20A, the ROM 20B, the RAM 20C, and the communication I / F 20G are connected to each other via an internal bus 20H so as to be able to communicate with each other.

[0028] The CPU 20A is a central processing unit that executes various programs and controls each part. That is, the CPU 20A reads out a program from the ROM 20B and executes the program using the RAM 20C as a work area.

[0029] The ROM 20B stores various programs and various data. The RAM 20C temporarily stores programs or data as a working area.

[0030] In this embodiment, the ROM 20B stores an information processing program, which is a program for realizing each function of the center server 20.

[0031] The communication I / F 20G is an interface for connecting to the network CN1.

[0032] Fig. 4 is a block diagram showing an example of the functional configuration of CPU 20A. As shown in Fig. 4, CPU 20A has a satellite image acquisition unit 200, a vehicle extraction unit 210, an analysis unit 220, and a prediction unit 230. Each functional configuration is realized by CPU 20A reading and executing an information processing program stored in ROM 20B.

[0033] The satellite image acquisition unit 200 acquires satellite images from the satellite server 10 via the communication I / F 20G. In the present embodiment, as an example, the satellite image acquisition unit 200 acquires satellite images taken at a predetermined target location on a predetermined target date and time. The target location and target date and time may be predetermined by the user terminal 30, or may be predetermined by an administrator of the center server 20, for example. The target location and target date and time can be changed via the communication I / F 30G and the communication I / F 20G by input from the input unit 30E of the user terminal 30, for example.

[0034] In this embodiment, the satellite image acquisition unit 200 transmits a preset target location, target date, and target time to the satellite server 10. Then, the satellite server 10 transmits to the center server 20 satellite images taken at the target location at the target time on the target date received from the satellite image acquisition unit 200. In this embodiment, the target date and time are, for example, daytime for one consecutive week, and, for example, 10:00 AM and 2:00 PM are used as the target times. As a result, the satellite image acquisition unit 200 acquires satellite images of the target location taken at 10:00 AM and 2:00 PM for one consecutive week. Note that the target date and time are not limited to 10:00 AM and 2:00 PM, and the satellite image acquisition unit 200 may continuously acquire satellite images taken during the day. Note that, in this embodiment, daytime is defined as 8:00 AM to 3:00 PM as an example.

[0035] The vehicle extraction unit 210 extracts large vehicles having a size equal to or greater than a predetermined value from the satellite image. In this embodiment, specifically, the vehicle extraction unit 210 extracts trucks, buses, and vehicles having a weight equal to or greater than a predetermined value as large vehicles. The extraction of trucks and buses by the vehicle extraction unit 210 may be performed by, for example, generating templates in advance by photographing multiple types of trucks and buses from above and performing matching with these templates.

[0036] Furthermore, the extraction of vehicles of a predetermined weight or more by the vehicle extraction unit 210 may, for example, be performed by placing a code such as a two-dimensional code or mark on the top of a vehicle that may be of a predetermined weight or more, such as a delivery vehicle, that identifies the vehicle as being of a predetermined weight or more, and extracting this code from the satellite image, thereby extracting the vehicle on which this code is placed as a vehicle of a predetermined weight or more. Note that the method for extracting large vehicles from satellite imagery is not particularly limited, and any known technology can be used.

[0037] The analysis unit 220 detects roads in the satellite image where the number of large vehicles traveling is greater than or equal to a predetermined value, and analyzes vehicle data for the detected roads. Specifically, the analysis unit 220 detects roads in the satellite image where a predetermined number of large vehicles extracted by the vehicle extraction unit 210 have traveled per predetermined road range (for example, 1 km, etc.). The analysis unit 220 analyzes the classification of vehicles traveling on the roads detected in the satellite image (for example, large vehicles, passenger cars, light vehicles, etc.). Note that publicly known technology can be used to classify the vehicles.

[0038] The prediction unit 230 predicts the deterioration state of the road surface based on the analysis results by the analysis unit 220. Specifically, as an example, on a road where the number of large vehicles traveling is equal to or greater than a predetermined value, the prediction unit 230 can calculate the proportion of large vehicles among all traveling vehicles as the degree of road surface deterioration. That is, on the road shown in satellite images taken at 10:00 AM and 2:00 PM for one consecutive week, the proportion of large vehicles among all traveling vehicles is calculated as the degree of road surface deterioration.

[0039] Specifically, as an example, if all the vehicles traveling are large vehicles, the deterioration level is set to 100%, and if 50% of the vehicles traveling are large vehicles, the deterioration level is set to 50%. In this embodiment, the range of the deterioration level value is set to 0% to 100%, but the present invention is not limited to this, and a deterioration level in a different value range may be used. The predicted road surface deterioration state, i.e., the deterioration level, is output to the user terminal 30 via the communication I / F 20G and the communication I / F 30G.

[0040] The prediction unit 230 may also assign a weighting factor to the predicted road surface deterioration state according to the weather based on weather information associated with the satellite image. In this case, the satellite image acquisition unit 200 acquires weather information associated with the acquired satellite image. Specifically, for example, if the prediction unit 230 calculates the deterioration level to be 50%, the satellite image acquisition unit 200 acquires weather information associated with the satellite image used for this calculation, i.e., the satellite image used to detect the number of large vehicles. Based on the weather information acquired by the satellite image acquisition unit 200, the prediction unit 230 multiplies the deterioration level by a weighting factor of, for example, "1" if the acquired weather information indicates clear weather, "0.8" if the acquired weather information indicates cloudy weather, or "0.5" if the acquired weather information indicates rainy weather. Note that the value of the weighting factor is not limited to this, and different values ​​may be used. The weighting factor may also be changeable as appropriate.

[0041] In addition, the prediction unit 230 may predict the deterioration state of the road surface by inputting the analysis results analyzed by the analysis unit 220 into a deterioration state prediction model that has been trained using the analysis results by the analysis unit 220 and the actual degree of deterioration of the road surface as a data set.

[0042] Next, the flow of the prediction process for predicting the deterioration state of the road surface will be explained using Figure 5. The CPU 20A reads out a prediction program from the ROM 20B, loads it into the RAM 20C, and executes it to perform the prediction process. The satellite server 10 successively acquires and stores satellite data.

[0043] 5, first, in step S11, the satellite image acquisition unit 200 acquires a satellite image as described above. Next, in step S12, the vehicle extraction unit 210 extracts large vehicles from the satellite image acquired from the satellite server 10 as described above.

[0044] In step S13, analysis unit 220 detects roads on which the number of large vehicles traveling is equal to or greater than a predetermined value, as described above. Next, analysis unit 220 determines whether or not any roads have been detected. If no roads have been detected (step S14; NO), analysis unit 220 proceeds to step S11.

[0045] On the other hand, if a road is detected in step S14 (step S14; YES), in step S15, analysis unit 220 analyzes the vehicle data traveling on the detected road as described above. Next, in step S16, prediction unit 230 predicts the deterioration state of the road surface based on the analysis result of analysis unit 220 in step S15 as described above, and the example process ends.

[0046] Next, the effects of the center server 20 as an information processing device in the first embodiment will be described.

[0047] The center server 20 of the first embodiment extracts large vehicles of a size equal to or greater than a predetermined value from satellite images and detects roads on which the number of large vehicles traveling on the road exceeds a predetermined value. Therefore, the number of large vehicles actually traveling on the road can be determined from the satellite images. Furthermore, the center server 20 of the present embodiment analyzes vehicle data on roads on which the number of large vehicles traveling on the road exceeds a predetermined value and predicts the deterioration state of the road surface based on the analysis results. Therefore, the deterioration state of the road surface can be predicted based on vehicle data on roads on which a large number of large vehicles actually travel, thereby enabling accurate prediction of the deterioration state of the road surface. Furthermore, accurate prediction of the deterioration state of the road surface allows optimization of road infrastructure and lifecycle costs, thereby realizing longer road lifespans and reduced road budgets.

[0048] Furthermore, the center server 20 of the first embodiment acquires satellite images acquired during the daytime on clear skies, so it can acquire clearer satellite images, which makes it easier to extract large vehicles from the acquired satellite images, and therefore makes it possible to more accurately predict the deterioration state of the road surface.

[0049] Furthermore, for example, satellite images taken on clear days have higher illuminance than satellite images taken on rainy or cloudy days, making it easier to extract large vehicles than satellite images taken on rainy or cloudy days. In contrast, satellite images taken on rainy or cloudy days have lower illuminance than satellite images taken on clear days, making it more difficult to extract large vehicles than satellite images taken on clear days. Therefore, the center server 20 of the first embodiment assigns a weighting to the predicted road surface deterioration state according to the weather based on the meteorological information associated with the satellite image. This allows the reliability of the extraction of large vehicles in the satellite image to be reflected in the predicted road surface deterioration state, making it possible to more accurately predict the road surface deterioration state.

[0050] Furthermore, the center server 20 of the first embodiment extracts trucks, buses, and vehicles of a predetermined weight or more as large vehicles. Therefore, when extracting vehicles of a predetermined weight or more as large vehicles, for example, vehicles that are relatively small but carry a large amount of cargo are also extracted as large vehicles. Therefore, heavy vehicles that are thought to affect road surface deterioration can also be reflected in the prediction of the road surface deterioration state.

[0051] Next, a center server 20-2 as an information processing device according to a second embodiment of the present invention will be described. In this embodiment, the same components as those in the first embodiment are denoted by the same reference numerals, and detailed description thereof will be omitted here.

[0052] As shown in FIG. 6, the CPU 20A-2 of the center server 20-2 of this embodiment further includes an operating day acquisition unit 240 in addition to the configuration of the CPU 20A of the center server 20 of the first embodiment.

[0053] The operating day acquisition unit 240 acquires data indicating the operating days of an office of a delivery company, construction company, or the like that operates large vehicles from the office. That is, the operating day acquisition unit 240 acquires data indicating the days on which the large vehicles actually operate. The satellite image acquisition unit 200 then acquires from the satellite server 10 satellite images acquired on the operating days of the office indicated by the data acquired by the operating day acquisition unit 240.

[0054] Next, the flow of the prediction process for predicting the deterioration state of the road surface will be explained using Figure 7. The CPU 20A-2 reads a prediction program from ROM 20B, loads it into RAM 20C, and executes it to perform the prediction process. The satellite server 10 successively acquires and stores satellite data.

[0055] 7, first, in step S21, the working day acquisition unit 240 acquires data indicating the working days of the business office as described above. Next, in step S22, the satellite image acquisition unit 200 acquires satellite images of the working days as described above. Note that steps S23 to S27 are similar to steps S12 to S16 shown in FIG. 5 of the first embodiment, and therefore detailed description thereof will be omitted here.

[0056] Next, the effects of the center server 20-2 as an information processing device in the second embodiment will be described.

[0057] According to the center server 20-2 of the second embodiment, satellite images taken on the working days of the sales office where the large vehicle runs are acquired. Therefore, it is possible to acquire satellite images taken on the days when the large vehicle actually runs, and therefore it is possible to more accurately predict the deterioration state of the road surface.

[0058] [remarks] In the above embodiment, the satellite image acquisition unit 200 acquires satellite images taken during the day, i.e., at 10:00 AM and 2:00 PM, but the present invention is not limited to this. For example, if daytime is defined as 8:00 AM to 3:00 PM, satellite images taken outside of the daytime, e.g., at 4:00 PM, may be acquired. In this case, the prediction unit 230 may further assign a weight to the predicted road surface deterioration state based on the time the satellite image was acquired. For example, if the image was taken during the day, a weighting coefficient of "1" is used, and if the image was taken outside of the daytime, a weighting coefficient of "0.5" is used, which is then multiplied by the deterioration level. Note that the value of the weighting coefficient is not limited to this, and a different value may be used. Furthermore, the weighting coefficient may be changeable as appropriate.

[0059] In the above embodiment, the vehicle extraction unit 210 extracts trucks, buses, and vehicles of a predetermined weight or more as large vehicles, but it may also extract only trucks and buses as large vehicles, or only vehicles of a predetermined weight or more as large vehicles. The vehicles extracted as large vehicles can be changed as appropriate.

[0060] In the above embodiment, the center server 20 configured separately from the user terminal 30 is used as the information processing device, but the present invention is not limited to this example. A device built into the user terminal 30 may also be used as the information processing device.

[0061] In the above embodiment, satellite images are stored in the satellite server 10 configured separately from the center server 20, but the present invention is not limited to this example. Satellite images may be stored in a storage device such as the ROM 20B or storage included in the center server 20.

[0062] Furthermore, the processing performed by the CPU after reading the software (program) in the above-described embodiments may be performed by various processors other than the CPU. Examples of such processors include programmable logic devices (PLDs) (such as field-programmable gate arrays (FPGAs)) whose circuit configuration can be changed after fabrication, and dedicated electrical circuits such as application-specific integrated circuits (ASICs) that are processors with circuit configurations specifically designed to perform specific processing. The above-described processing may be performed by one of these various processors, or by a combination of two or more processors of the same or different types (e.g., multiple FPGAs, or a combination of a CPU and an FPGA). The hardware structure of these various processors is, more specifically, an electrical circuit that combines circuit elements such as semiconductor devices.

[0063] In the above embodiment, the programs are pre-stored (installed) in ROM, but the present invention is not limited to this. The programs may be provided in a form recorded on a recording medium such as a CD-ROM (Compact Disc Read Only Memory), a DVD-ROM (Digital Versatile Disc Read Only Memory), or a USB (Universal Serial Bus) memory. The programs may also be downloaded from an external device via a network.

[0064] The processing flow described in the above embodiment is also an example, and unnecessary steps may be deleted, new steps may be added, or the processing order may be changed within the scope of the gist of the invention.

[0065] Furthermore, the configurations of the satellite server 10, the center server 20, and the user terminal 30 described in the above embodiment are merely examples, and may be changed according to the situation within the scope of the spirit of the invention. [Explanation of symbols]

[0066] 20 Center server (information processing device) 200 Satellite image acquisition unit 210 Vehicle Extraction Department 220 Analysis Department 230 Prediction Department 240 Operating Day Acquisition Department

Claims

1. a satellite image acquisition unit that acquires satellite images; a vehicle extraction unit that extracts large vehicles having a size equal to or larger than a predetermined value from the satellite image acquired by the satellite image acquisition unit; an analysis unit that detects roads on which the number of large vehicles traveling is equal to or greater than a predetermined value in the satellite image and analyzes vehicle data on the roads; a prediction unit that predicts a deterioration state of the road surface based on the analysis result by the analysis unit; an operating day acquisition unit that acquires data indicating operating days of the business office from the business office through which the large vehicle travels; Equipped with The satellite image acquisition unit is an information processing device that acquires satellite images acquired on the working days of the business office indicated by the data acquired by the working day acquisition unit.

2. The information processing device according to claim 1 , wherein the satellite image acquisition unit acquires meteorological information associated with the acquired satellite image, and acquires the satellite image acquired on a clear day based on the acquired meteorological information.

3. the satellite image acquisition unit acquires meteorological information associated with the acquired satellite image, The information processing device according to claim 1 , wherein the prediction unit assigns a weighting to the predicted road surface deterioration state according to weather based on the meteorological information acquired by the satellite image acquisition unit.

4. The information processing device according to claim 1 , wherein the vehicle extraction unit extracts at least one of a truck, a bus, and a vehicle having a predetermined weight or more as the large vehicle.

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