WO2023008540A1

WO2023008540A1 - Wearable information processing device and program

Info

Publication number: WO2023008540A1
Application number: PCT/JP2022/029209
Authority: WO
Inventors: 晶鈴木; 洋平鈴木
Original assignee: 晶鈴木; 洋平鈴木
Priority date: 2021-07-28
Filing date: 2022-07-28
Publication date: 2023-02-02
Also published as: JPWO2023008540A1; JP7239126B1

Abstract

The present invention automatically generates a situation report regarding an accident or incident that a person wearing a wearable device has been involved in.　The present invention comprises the following units connected via a network to a video processing device 40 that stores camera video from a wearable device 30 as video data, a map processing device 50 that stores map data, and a mobile terminal device 20 that stores situation data including GPS data and time-of-day data at the time of occurrence of an accident or incident: a situation data acquisition unit 122 for acquiring situation data; a video data acquisition unit 121 for acquiring video data on the basis of the time-of-day data; a map data acquisition unit 123 for acquiring map data on the basis of the GPS data; a situation outline generation unit 125 for generating a situation outline on the basis of at least the video data and the map data; and a situation report generation unit 126 for generating a situation report including the situation outline.

Description

Wearable information processing device and program

The present invention relates to a wearable information processing device and a program that generate a situation report of an accident or incident using information such as camera images of the wearable device.

In recent years, there have been many cases of collisions caused by using mobile devices such as smartphones while driving. On the other hand, by attaching a drive recorder to an automobile and storing images of driving, it has become possible to understand the circumstances of automobile collisions by watching the images. For example, in Patent Literature 1, by storing a camera image of a drive recorder, it can be used for post-event analysis of the cause of an accident and the like.

Japanese Patent Application Laid-Open No. 2006-306153

By the way, collisions caused by the use of mobile devices occur not only in automobiles, but also between bicycles, motorcycles and people, and between people. However, since the drive recorder as disclosed in Patent Document 1 is installed in a vehicle, even if it can store images of vehicle accidents, it can also store images of accidents that do not involve vehicles, such as accidents between bicycles and accidents between bicycles and pedestrians. can't remember. Moreover, it is difficult to accurately convey the situation because the victim is upset at the scene of the accident.

In consideration of such circumstances, the present invention uses wearable information such as a camera image of a wearable device worn by a person to automatically generate a status report of an accident or incident in which the person is involved. An object is to provide a wearable information processing device and a program.

In order to solve the above problems, a device according to the present invention is a wearable information processing device that processes information including a camera video from a wearable device worn by a person, and stores the camera video of the wearable device as video data. A mobile terminal connected via a network to a video processing device, a map processing device that stores map data, and a mobile terminal that can collect situation data including time data and GPS data at the time of occurrence of an accident or incident. A situation data acquisition unit that acquires situation data from the device, a video data acquisition unit that acquires video data based on time data from the video processing device, and a map data acquisition unit that acquires map data based on GPS data from the map processing device. , a schematic situation map generator for generating a schematic situation map based on at least the video data and the map data; and a situation report generator for generating a situation report of the accident or incident including the schematic situation map.

According to the wearable information processing device of the present invention, it is possible to automatically generate a situation report of an accident or incident based on a camera image from a wearable device worn by a person. In this way, by using a camera image from a wearable device worn by a person, it is possible to automatically generate not only a situation report of an accident in which the person is involved, but also a situation report of an incident such as a snatch. In addition, video data can be obtained from the time data when an accident or incident occurs, map data can be obtained from GPS data, and even a schematic diagram of the situation can be automatically generated. can also generate on-the-fly situation reports that accurately convey the situation on the ground.

In a preferred aspect of the present invention, the camera image is an image of the surroundings of the person wearing the wearable device. According to this aspect, since the image of the surroundings of the person wearing the wearable device is used as the camera image, for example, in the case of an accident in which a bicycle collides, it is possible to know from which direction the bicycle collided. You can also see which direction the snatcher is coming from.

In a preferred aspect of the present invention, the schematic situation diagram generation unit detects an image of the target that caused the accident or the incident from the video data acquired by the video data acquisition unit, replaces the target image with a predetermined symbol, and displays the image on the map. A schematic map of the site situation shown in the upper position is generated, a map of the vicinity of the site including the position of the GPS data is generated from the map data acquired by the map data acquisition unit, and a schematic situation map is generated from the schematic map of the site situation and the map of the vicinity of the site. According to this aspect, an image of a target that caused an accident or incident (for example, a vehicle such as a bicycle, a motorcycle, a car, or a person such as a pedestrian or a runner) is detected, and the target image is replaced with a predetermined symbol and displayed on the map. Since a schematic situation map can be automatically generated from the schematic map of the site situation indicated by the position of and the map of the vicinity of the site, compared to simply pasting the image of the video data to the situation report, the process of processing the image to the schematic situation map is saved. be able to. In this way, a situation report is automatically generated that includes a schematic diagram of the situation that can be sent to the health company or the police without editing, so even if you are upset about being involved in an accident or incident, you can accurately report the situation. can tell In addition, since it is possible to automatically generate a schematic situation map by replacing the images of the person or vehicle that caused the accident or incident with a predetermined symbol, it is possible to directly match the schematic situation map of a court decision, etc., and paste the image of the person or vehicle as it is. Collation accuracy can be greatly improved compared to the case of attaching.

In a preferred aspect of the present invention, the wearable device is connected to the mobile terminal device, and the video processing device receives camera video from the wearable device via the mobile terminal device and generates a plurality of video data for each predetermined video time. , and the image data acquisition unit acquires image data including at least the image at the time of the time data from among the plurality of image data. According to this aspect, the camera video from the wearable device is divided and stored as a plurality of video data for each predetermined video time. It is easy to identify images before and after the time of occurrence.

In a preferred aspect of the present invention, before generating the schematic site situation diagram, the schematic situation diagram generation unit determines whether or not a plurality of images of a person or a vehicle that is a target candidate has been detected from the video data acquired by the video data acquisition unit. If it is determined that only one image of a person or vehicle that is a candidate for the target is detected, that one image is specified as the target that caused the accident or incident, and a schematic map of the site situation and the site When it is determined that a plurality of images of a person or a vehicle, which are candidates for a target, have been detected by generating a map of the vicinity and a schematic diagram of the situation, candidates for the target are identified by comparing the video data with previous video data. Detect the position and movement of each person or vehicle that becomes the target, generate an object candidate map, and identify the object that caused the accident or incident from among multiple images based on the position and movement of the object candidate map , to generate a map of the site situation and a map of the vicinity of the site and a map of the situation. According to this aspect, even when a plurality of vehicles or persons are shown in the video data, it is possible to automatically identify the object that caused the accident or incident. In addition, by comparing video data based on when an accident or incident occurred with video data before that, the position and movement of a person or vehicle, which is a target candidate, can be detected. As a result, the target can be specified based on the position and movement of the target candidate, so the target can be specified more accurately than when the target is specified simply from the difference in size of the target in the image. can be identified.

In a preferred aspect of the present invention, the portable terminal device is provided with an impact sensor for detecting an impact, and when the impact sensor detects an impact, it is possible to select whether an accident has occurred, an incident has occurred, or neither. The accident case selection screen is displayed on the display unit, and the situation data acquisition unit acquires situation data including the information that the occurrence of the accident or the occurrence of the incident is selected from the portable terminal device. Even if an impact is detected, there are cases where an accident or incident does not occur, such as when the portable terminal device is simply dropped. According to this aspect, the situation data acquired from the portable terminal device by the situation data acquisition unit includes information that the occurrence of an accident or an incident has been selected. Since the report can be prevented from being automatically generated, it is possible to suppress wasteful generation of the status report.

In a preferred embodiment of the present invention, the situation report generation unit determines whether it is an accident or an incident based on the situation data from the portable terminal device. form), and if it is determined to be an incident, a situation report is generated from a predetermined incident occurrence status report form. According to this aspect, it is possible to automatically generate a situation report according to the form of each accident or incident, thereby saving the trouble of recreating the situation report later according to a predetermined form.

In a preferred embodiment of the present invention, a judicial case database that stores judicial precedent data including schematic situation diagrams is compared with the schematic situation diagram generated by the schematic situation diagram generation unit and the schematic situation diagrams included in the judicial cases in the schematic situation diagrams to make the schematic situation diagrams similar. a percentage-of-fault report generator for selecting a case, extracting percentage-of-failure from the selected case, and generating a percentage-of-fault report including a schematic of the situation of the selected case and the percentage-of-fault. According to this aspect, since the judicial precedent database is compared with the schematic situation diagram, it is easier to find similar judicial precedents than when searching for judicial precedents by text search. Since it is possible to automatically generate a percentage-of-fault report based on case law data, it is possible to know the percentage of fault in similar cases, making it easier to file lawsuits and decide whether to settle, and for insurance companies to decide whether to apply insurance.

In a preferred embodiment of the present invention, the judicial precedent database is divided into an accident judicial precedent database and an incident judicial precedent database. If so, it selects a precedent with a similar outline of the situation from the accident precedent database, and if it determines that it is a case, selects a precedent with a similar outline of the situation from the case precedent database. According to this aspect, in the case of an accident, a judicial precedent with a similar schematic diagram of the situation is selected from the accident judicial precedent database, and in the case of a case, a judicial precedent with a similar schematic diagram of the situation is selected from the case judicial precedent database. Precedents can be selected, and appropriate precedents and crime types (articles, etc.) can be selected according to the case.

In order to solve the above-described problems, a program of the present invention is a program for causing a computer to execute a process of generating a situation report of an accident or incident performed by a wearable information processing device, wherein the wearable information processing device is a wearable device worn on a person. A video processing device that stores camera video of the device as video data, a map processing device that stores map data, a mobile terminal device that can collect situation data including time data and GPS data when an accident or incident occurs, is connected via a network, and the situation report generating process includes a step of acquiring situation data from a mobile terminal device, a step of acquiring video data based on time data from a video processing device, and a step of acquiring GPS data from a map processing device. generating a situation diagram based at least on the video data and the map data; and generating a situation report including the situation diagram. This allows the computer to function as a wearable information processing device for generating a situation report of an accident or incident.

According to the present invention, by using wearable information such as camera images of a wearable device worn by a person, it is possible to store images of people, bicycles, motorcycles, and accidents and incidents between people. A status report of an accident or incident can be automatically generated based on this video.

It is a figure which shows the structure of the wearable information processing system of 1st Embodiment. 2 is a block diagram of the wearable information processing system of FIG. 1; FIG. 4 is a data table showing a specific example of video data; It is a data table showing a specific example of user data. 4 is a flowchart for explaining processing of the entire system of the first embodiment; It is a figure which shows the specific example of an accident case selection screen. It is a figure which shows the specific example of a report instruction|indication screen. It is a figure which shows the specific example of the form of an accident occurrence situation report. It is a figure which shows the specific example of the form of an incident occurrence situation report. FIG. 6 is a flow chart showing a specific example of the status report generation processing of FIG. 5; FIG. FIG. 11 is a flow chart showing a specific example of the schematic situation diagram generation process of FIG. 10; FIG. It is a figure which shows the specific example of the video data selected by the situation schematic drawing production|generation process. It is a figure which shows the specific example of the site situation schematic drawing produced|generated by the situation schematic drawing production|generation process. It is a figure which shows the specific example of the near-site map produced|generated by the situation schematic drawing production|generation process. It is a figure which shows the specific example of the schematic situation map produced|generated by the schematic situation map production|generation process. It is a block diagram of the wearable information processing system of 2nd Embodiment. 9 is a flowchart for explaining processing of the entire system of the second embodiment; FIG. 11 is a flow chart showing a specific example of fault rate report generation processing; FIG. FIG. 10 is a diagram showing a specific example of a form for an Accident-Negligence Percentage Report; FIG. 10 is a diagram showing a specific example of the form of the Incident Negligence Percentage Report. It is a figure which shows the specific example of the form of a case constituent element report. FIG. 10 is a diagram showing another specific example of the accident case selection screen; FIG. 10 is a diagram showing another specific example of the report instruction screen; FIG. 11 is a flowchart showing a specific example of target specifying processing according to a modification; FIG. FIG. 10A is a diagram showing an example of images in the object specifying process, where (a) is an omnidirectional image and (b) is an image generated from the omnidirectional image. 26A is an omnidirectional image, and FIG. 26B is an image generated from the omnidirectional image. FIG. FIG. 10 is a diagram showing a specific example of a target candidate diagram; FIG. 10 is a diagram showing a specific example of a target candidate verification diagram;

<First Embodiment>
A first embodiment of the present invention will be described below with reference to the drawings. In the first embodiment, a wearable information processing system 100 including the wearable information processing device 10 of the present invention is illustrated. FIG. 1 is a diagram showing the configuration of a wearable information processing system 100 according to the first embodiment. A wearable information processing system 100 of FIG. 1 includes a wearable information processing device 10 , a mobile terminal device 20 , a video processing device 40 and a map processing device 50 .

The wearable information processing device 10 is configured so that the mobile terminal device 20, the video processing device 40, and the map processing device 50 can communicate with each other via a network N such as the Internet. The wearable information processing device 10, the video processing device 40, and the map processing device 50 may be composed of a personal computer or a cloud server. Each of the wearable information processing device 10, the image processing device 40, and the map processing device 50 may be configured to perform distributed processing by a plurality of devices, or may be configured by a plurality of virtual machines provided in one server device. may

The mobile terminal device 20 is a portable information processing device used by the user. The mobile terminal device 20 is, for example, a smart phone, a tablet, a PDA (Personal Digital Assistant), or the like. Although the case where one mobile terminal device 20 is connected to the network N is illustrated, two or more mobile terminal devices 20 may be connected.

A wearable device 30 can be connected to the mobile terminal device 20 via a wireless communication network based on a communication standard such as Bluetooth (registered trademark). The wearable device 30 is equipped with a camera 34 and can be worn on a person's body to take camera images of the person's surroundings. A wearable device 30 exemplified in the illustration of FIG. 1 is an ear hook type that is worn on the ear, and is provided with a camera 34 capable of capturing omnidirectional camera images.

The wearable information processing device 10 automatically generates a status report of an accident or incident based on wearable information (camera image around the body, etc.). The wearable information processing device 10 of the first embodiment is configured by a server computer having the mobile terminal device 20 as a client. The video processing device 40 receives the camera video from the wearable device 30 via the mobile terminal device 20 and stores it as video data. The map processing device 50 stores map data, and transmits map data of the vicinity of the site including the latitude and longitude of the received GPS data to the wearable information processing device 10 .

FIG. 2 is a block diagram of the wearable information processing system 100 of FIG. The mobile terminal device 20 shown in FIG. 2 includes a communication section 21, a control section 22, a storage section 23, a camera 24, a microphone 25, a sensor section 26, an input section 27, a display section 28, and the like. The control unit 22, the communication unit 21, the storage unit 23, the camera 24, the microphone 25, the sensor unit 26, the input unit 27, and the display unit 28 are each connected to the bus line 20L, and can exchange data (information) with each other. is.

The communication unit 21 is connected to the network N by wire or wirelessly, and transmits and receives data (information) to and from the wearable information processing device 10 and the video processing device 40 . The communication unit 21 functions as a communication interface for the Internet or an intranet, and is capable of communication using, for example, TCP/IP, Wi-Fi (registered trademark), and Bluetooth (registered trademark). The communication unit 21 also transmits and receives data (information) to and from the wearable device 30 . The communication unit 21 can be connected to the wearable device 30 via a wireless communication network based on a communication standard such as Bluetooth (registered trademark).

The control unit 22 comprehensively controls the mobile terminal device 20 as a whole. The control unit 22 is composed of an integrated circuit such as an MPU (Micro Processing Unit). The control unit 22 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), and a ROM (Read Only Memory). The control unit 22 loads necessary programs into the ROM and executes the programs using the RAM as a work area, thereby performing various processes.

The storage unit 23 is an example of a storage medium (computer-readable tangible storage medium: a tangible storage medium) that stores various programs executed by the control unit 22 and data used by these programs. The storage unit 23 is configured by a storage device such as a hard disk, an optical disk, or a magnetic disk. The configuration of the storage unit 23 is not limited to these, and the storage unit 23 may be configured by a semiconductor memory such as RAM or flash memory. For example, the storage unit 23 can be configured with an SSD (Solid State Drive).

The storage unit 23 includes a program storage unit 231, a data storage unit 232, and the like. A plurality of application programs to be executed by the control unit 22 are stored in the program storage unit 231 . Program storage unit 231 stores a dedicated application program for performing wearable information processing in cooperation with wearable device 30 , wearable information processing device 10 , and video processing device 40 . This dedicated application program is a program that can be downloaded to the mobile terminal device 20 via the network N. FIG. The data storage unit 232 stores various data used by application programs.

The control unit 22 includes a sensor data acquisition unit 221, an impact detection unit 222, an impact data collection unit 223, a situation data collection unit 224, and the like. The sensor data acquisition unit 221 acquires sensor data (such as acceleration data) from the sensor unit 26 . The sensor unit 26 includes an impact sensor (for example, an acceleration sensor) that detects impact of the mobile terminal device 20 . The impact detection unit 222 detects impact of the mobile terminal device 20 based on sensor data (acceleration data, etc.) acquired by the sensor data acquisition unit 221 . For example, acceleration data is monitored based on sensor data, and impact is detected when the acceleration exceeds a predetermined value. This predetermined value of acceleration may be changeable by user input. When the impact detection unit 222 detects an impact, the impact data collection unit 223 collects time data at the time of the impact (when the impact is detected), GPS data at the time of impact, etc. as impact data.

The control unit 22 transmits the impact data collected by the impact data collection unit 223 to the wearable information processing device 10 through the communication unit 21 . When the wearable information processing device 10 receives the impact data, the wearable information processing device 10 transmits a push notification to the mobile terminal device 20 . The situation data collection unit 224 can collect situation data by displaying an accident case selection screen, a situation input screen, and the like on the display unit 28 by push notification.

The accident case selection screen is a screen where you can select, for example, "Is there an incident?", "Is there an accident?" The situation input screen is a screen on which situation explanations and detailed information can be input, such as the appearance of the other party, the signal situation, and what kind of accident or incident has occurred. This situation input screen may be configured to allow input in the form of a questionnaire. The data (information) input from the input unit 27 through the accident case selection screen, the situation input screen, etc. are collected as situation data. The situation data includes impact data such as acceleration data, time data, and GPS data at the time of impact.

The input unit 27 is, for example, a touch panel or buttons. The input unit 27 may be an external input device (keyboard, mouse, etc.) connected to the mobile terminal device 20 via Bluetooth (registered trademark). The display unit 28 is a liquid crystal display, an organic EL display, or the like, and displays various information according to instructions from the control unit 22 . The display unit 28 here exemplifies a liquid crystal display with a touch panel. The buttons of the input section 27 may be buttons displayed on the display section 28 .

A wearable device 30 in FIG. 2 includes a communication unit 31, a camera 34, and the like. The communication unit 31 transmits and receives data (information) to and from the mobile terminal device 20 . The communication unit 31 can be connected to the mobile terminal device 20 via a wireless communication network based on a communication standard such as Bluetooth (registered trademark).

The camera 34 is attached to the human body and can take camera images of the surroundings (360 degrees horizontally). The camera 34 is composed of a CCD (Charge Coupled Device) camera or the like capable of taking an omnidirectional camera image. Note that the camera 34 may not necessarily be an omnidirectional camera. Any wearable device 30 may be used as long as it has a camera 34 capable of capturing camera images of the surroundings including the front, rear, left, and right of the human body. For example, a plurality of cameras 34 may be used to capture camera images around the body. Also, the type of wearable device 30 is not limited to the ear hook type shown in the figure. As long as the camera 34 capable of capturing images of the surroundings of the human body is attached, it may be of any type, such as a glasses type, a headphone type, or a neck type. The camera 34 is not limited to a CCD camera, and may be a web camera, an IoT camera, or the like.

When the wearable device 30 is connected to the mobile terminal device 20 , it activates the camera 34 and transmits the camera image to the mobile terminal device 20 . The mobile terminal device 20 transmits camera video from the wearable device 30 to the video processing device 40 .

The video processing device 40 includes a communication section 41, a control section 42, a storage section 43, and the like. The control unit 42, the communication unit 41, and the storage unit 43 are each connected to the bus line 40L, and can exchange data (information) with each other. The communication unit 41 is connected to the network N by wire or wirelessly, and transmits and receives data (information) to and from the wearable information processing device 10 and the mobile terminal device 20 . The communication unit 41 functions as a communication interface for the Internet or an intranet, and is capable of communication using, for example, TCP/IP, Wi-Fi (registered trademark), and Bluetooth (registered trademark).

The control unit 42 comprehensively controls the entire video processing device 40 . The control unit 42 is composed of an integrated circuit such as an MPU. The control unit 42 includes a CPU, RAM, and ROM. The control unit 42 loads a necessary program into the ROM and executes the program using the RAM as a work area, thereby performing various processes.

The storage unit 43 is an example of a storage medium that stores various programs executed by the control unit 42 and data used by these programs. The storage unit 43 is configured by a storage device such as a hard disk, an optical disk, or a magnetic disk. The configuration of the storage unit 43 is not limited to these, and the storage unit 43 may be configured by a semiconductor memory such as RAM or flash memory. For example, the storage unit 43 can be configured with an SSD (Solid State Drive).

The storage unit 43 stores the camera video from the wearable device 30 received via the mobile terminal device 20 as a plurality of video data 432 for each predetermined video time. Specifically, the storage unit 43 stores a plurality of video data for each predetermined video time, such as the data table in FIG. In the data table of FIG. 3, a user ID, video data, video date and time, video time, location, etc. are associated and stored.

The video date and time in FIG. 3 are the shooting date and video start time of the video data 432 . Since FIG. 3 illustrates a case where the video time is set to 5 minutes, video data for every 5 minutes is stored. The video time can be set, and is not limited to 5 minutes, and may be set any time such as every minute or every few seconds. The video time can be freely changed by user input from the mobile terminal device 20 .

Also, the video time may be changed according to the user's speed. The user's speed can be detected by the acceleration sensor of the mobile terminal device 20 . For example, the video time can be changed depending on whether the user is walking or riding a bicycle, and the faster the user is, the shorter the video time can be. According to this, the video data at the time of impact does not become too long or conversely too short, and the video data of the optimum video time corresponding to the speed of the user can be obtained. The location is the shooting location of the video data, and stores, for example, the location specified from the GPS data at the start of the video. Note that the video data 432 is not limited to the data table shown in FIG. 3, and the items of the data table are not limited to those shown in FIG.

Note that the storage unit 43 includes a program storage unit, a data storage unit, and the like (not shown). Programs executed by the control unit 42 are stored in the program storage unit. Various data used by the program are stored in the data storage unit. The storage unit 43 may be composed of a hard disk, or may be composed of a semiconductor memory such as a flash memory. For example, the storage unit 43 can be configured with an SSD.

The control unit 42 includes a time data acquisition unit 421 and a video data selection unit 422 . The time data acquisition unit 421 acquires time data at the time of impact from the wearable information processing device 10 . The image data selection unit 422 acquires image data including at least the image at that time based on the time data at the time of impact from a plurality of image data as shown in FIG. The acquired video data is transmitted to the wearable information processing device 10 via the communication unit 41 .

The map processing device 50 includes a communication section 51, a control section 52, a storage section 53, and the like. The control unit 52, the communication unit 51, and the storage unit 53 are each connected to the bus line 50L, and can exchange data (information) with each other. The communication unit 51 is connected to the network N by wire or wirelessly, and transmits and receives data (information) to and from the wearable information processing device 10 . The communication unit 51 functions as a communication interface for the Internet or an intranet, and is capable of communication using, for example, TCP/IP, Wi-Fi (registered trademark), and Bluetooth (registered trademark).

The control unit 52 comprehensively controls the map processing device 50 as a whole. The control unit 52 is composed of an integrated circuit such as an MPU. The control unit 52 includes a CPU, RAM, and ROM. The control unit 52 loads a necessary program into the ROM and executes the program using the RAM as a work area, thereby performing various processes.

The storage unit 53 is an example of a storage medium that stores various programs executed by the control unit 52 and data used by these programs. The storage unit 53 is configured by a storage device such as a hard disk, an optical disk, or a magnetic disk. The configuration of the storage unit 53 is not limited to these, and the storage unit 53 may be configured by semiconductor memory such as RAM and flash memory. For example, the storage unit 53 can be configured with an SSD (Solid State Drive).

The storage unit 53 stores map data 532 and the like. Note that the storage unit 53 includes a program storage unit, a data storage unit, and the like (not shown). Programs executed by the control unit 52 are stored in the program storage unit. Various data used by the program are stored in the data storage unit. The storage unit 53 may be composed of a hard disk, or may be composed of a semiconductor memory such as a flash memory. For example, the storage unit 53 can be configured with an SSD.

The control unit 52 includes a GPS data acquisition unit 521 and a map data selection unit 522. The GPS data acquisition unit 521 acquires GPS data at the time of impact from the wearable information processing device 10 . The map data selection unit 522 acquires map data including a map of at least the latitude and longitude based on the acquired GPS data. The acquired map data is transmitted to the wearable information processing device 10 via the communication unit 51 .

The wearable information processing device 10 in FIG. 2 includes a communication unit 11, a control unit 12, and a storage unit 14. The control unit 12, the communication unit 11, the control unit 12, and the storage unit 14 are each connected to the bus line 10L, and can exchange data (information) with each other.

The communication unit 11 is connected to the network N by wire or wirelessly, and transmits and receives data (information) among the mobile terminal device 20, the video processing device 40, and the map processing device 50. The communication unit 11 functions as a communication interface for the Internet or an intranet, and is capable of communication using TCP/IP, Wi-Fi (registered trademark), Bluetooth (registered trademark), or the like.

The control unit 12 comprehensively controls the wearable information processing apparatus 10 as a whole. The control unit 12 is composed of an integrated circuit such as an MPU. The control unit 12 includes a CPU, RAM, and ROM. The control unit 12 loads a necessary program into the ROM and executes the program using the RAM as a work area, thereby performing various processes.

The storage unit 14 is a computer-readable storage medium that stores various programs executed by the control unit 12 and data used by these programs. The storage unit 14 is configured by a storage device such as a hard disk, an optical disk, or a magnetic disk. The configuration of the storage unit 14 is not limited to these, and the storage unit 14 may be configured by a semiconductor memory such as RAM or flash memory. For example, the storage unit 14 can be configured with an SSD (Solid State Drive).

The storage unit 14 includes a program storage unit 231 and a data storage unit 232. Programs to be executed by the control unit 12 are stored in the program storage unit 231 . The data storage unit 232 stores various data used by the program. The control unit 12 reads necessary programs from the program storage unit 15 and executes various processes.

The data storage unit 16 stores user data 161, acquired data 162, generated data 163, and the like. In addition to the above, the data storage unit 16 stores a form of an accident occurrence status report 126A illustrated in FIG. 8, a form of an incident occurrence status report 126B illustrated in FIG. 9, and the like. The user data 161 stores user ID, name, telephone number, address, type of wearable device, report destination, etc., as in the data table of FIG. 4, for example. These user data 161 are pre-stored as previously input by the user. “Type of wearable device” is the type of wearable device 30 connected to mobile terminal device 20 .

For example, ear hook type, glasses type, headphone type, and neck type are stored in the "wearable device type" item in FIG. The “report destination” is an insurance company, the police, or the like that reports the occurrence of an accident or incident. When the report destination is registered, the situation report and data automatically generated by the wearable information processing device 10 can be automatically sent from the mobile terminal device 20 according to the user's instruction (for example, clicking a button on the application). . The user data 161 is not limited to the data table shown in FIG. 4, and the items of the data table are not limited to those shown in FIG.

The acquired data 162 is various data acquired by the wearable information processing device 10 via the communication unit 11 . The acquired data 162 includes video data 432 acquired from the video processing device 40, situation data acquired from the portable terminal device 20, map data 532 acquired from the map processing device 50, and the like. The generated data 163 is data generated by the wearable information processing device 10 . The generated data 163 includes situation schematic data and situation report data generated by the wearable information processing device 10 .

The control unit 12 includes a video data acquisition unit 121, a situation data acquisition unit 122, a map data acquisition unit 123, a schematic situation diagram generation unit 125, and a situation report generation unit 126. The video data acquisition unit 121 acquires video data (for example, video data at the time of impact) from the video processing device 40 via the communication unit 11 . The situation data acquisition unit 122 acquires situation data (for example, situation data including time data at the time of impact and GPS data) from the mobile terminal device 20 via the communication unit 11 . The map data acquisition unit 123 acquires map data (for example, map data at the time of impact) from the map processing device 50 via the communication unit 11 .

The schematic situation diagram generation unit 125 generates a schematic situation diagram based on at least the video data acquired by the video data acquisition unit 121 and the map data by the map data acquisition unit 123 . The schematic situation map includes, for example, a schematic accident situation map in the case of an accident and a schematic incident situation map in the case of an incident. Specifically, the schematic situation diagram generation unit 125 automatically generates a schematic accident situation diagram in the case of an accident, and automatically generates a schematic incident situation diagram in the case of an incident. The schematic situation diagram generated by the schematic situation diagram generation unit 125 is stored as generated data 163 in the data storage unit 16 .

The situation report generation unit 126 generates a situation report including a schematic diagram of the situation. The situation report includes, for example, an accident occurrence situation report in the case of an accident and an incident occurrence situation report in the case of an incident. Specifically, in the event of an accident, the situation report generation unit 126 automatically inputs a schematic diagram of the accident situation into the column "Schematic diagram of the accident situation" in the form of the accident occurrence situation report 126A in FIG. Based on the situation data from the mobile terminal device 20, the situation report generation unit 126 automatically fills in the relevant data in the relevant columns (columns for detailed accident information and accident situation explanation) in the form of the accident occurrence situation report 126A in FIG. to enter. Thus, the situation report generator 126 automatically generates an accident occurrence situation report 126A as shown in FIG.

In the case of an incident, the situation report generation unit 126 automatically enters the incident situation diagram into the incident situation diagram column of the form of the incident occurrence situation report 126B in FIG. Based on the situation data from the mobile terminal device 20, the situation report generation unit 126 automatically fills in the relevant data in the corresponding fields (fields for the detailed information on the incident and the description of the incident situation) in the form of the incident occurrence status report 126B shown in FIG. to enter. In this way, the situation report generator 126 automatically generates an incident occurrence situation report 126B as shown in FIG.

FIG. 5 is a flowchart for explaining the overall processing of the wearable information processing system 100 according to the first embodiment. As shown in FIG. 5, the wearable information processing system 100 executes processing while exchanging data between the wearable information processing device 10, the mobile terminal device 20, the video processing device 40, and the map processing device 50. FIG.

As shown in FIG. 5, first, in step S10, the mobile terminal device 20 determines whether or not a dedicated application program has been started. This dedicated application program is a dedicated application program for portable terminal device 20 to perform wearable information processing in cooperation with wearable device 30 , wearable information processing device 10 and video processing device 40 .

When the portable terminal device 20 determines in step S10 that the dedicated application program has been activated, it asks the user to confirm connection with the wearable device 30 . When the user activates the wearable device 30 worn on the human body (for example, the ear in the case of the ear hook type), the wearable device 30 is connected to the mobile terminal device 20 via Bluetooth (registered trademark) or the like. When connected to the mobile terminal device 20 , the wearable device 30 activates the camera 34 and transmits the camera video 34 a to the video processing device 40 . Then, the video processing device 40 stores the camera video 34a as a plurality of video data 432 for each predetermined video time (for example, every five minutes in FIG. 3).

Next, in step S11, the control unit 22 of the mobile terminal device 20 determines whether or not an impact has been detected. When the control unit 22 of the mobile terminal device 20 determines that the impact has not been detected, the impact detection determination is continued. Specifically, the control unit 22 monitors the sensor data (acceleration data) acquired by the sensor data acquisition unit 221, and detects an impact when the impact detection unit 222 determines that the acceleration exceeds a predetermined value, for example.

When the controller 22 of the mobile terminal device 20 determines in step S11 that an impact has been detected, it collects the impact data 223a and transmits the impact data 223a to the wearable device 30. The impact data 223 a is collected by the impact data collection section 223 of the control section 22 . The impact data 223a includes time data at the time of impact, GPS data at the time of impact, acceleration data at the time of impact, and the like.

The wearable device 30 transmits a push notification P to the mobile terminal device 20 upon receiving the impact data 223a. When the mobile terminal device 20 receives the push notification P, it determines whether it is an accident or an incident in step S12. Specifically, the mobile terminal device 20 displays an accident case selection screen as shown in FIG. 6 on the display unit 28, for example. The accident case selection screen shown in FIG. 6 is a display for informing the user that there was an impact and asking the user to select "occurrence of accident", "occurrence of incident", or "neither".

If the user selects "neither" on the display screen of FIG. 6 in step S12, the mobile terminal device 20 continues to determine impact detection, assuming that this impact is not an accident. On the other hand, if the user selects "occurrence of an accident" or "occurrence of an incident" on the display screen of FIG. In the situation input process, for example, an accident questionnaire is displayed on the display unit 28 if an accident has occurred, and an incident questionnaire is displayed on the display unit 28 if an incident has occurred.

The accident questionnaire is a questionnaire for obtaining data to be entered in the form of the accident occurrence status report 126A illustrated in FIG. Among the data to be entered in the form of the accident occurrence status report 126A, for example, unknown data that cannot be obtained from the data (sensor data, weather data, time data, etc.) that can be acquired by the mobile terminal device 20 is entered by the user in the accident questionnaire. Can be supplemented with data. The unknown data here includes, for example, traffic conditions and signal conditions at the time of the accident occurrence, data of the other party (address, name, telephone number, etc.), explanation of the accident situation, and the like among the detailed accident information shown in FIG. By putting these unknown data in the form of questions in the accident questionnaire, it is possible to make it easier for the user to input them. In the explanation of the accident situation in the questionnaire, for example, by explaining the case of a hit-and-run dealer, the situation before and after the accident can be understood, so that it is possible to prevent damage due to unjust billing and the like.

The incident questionnaire is a questionnaire for acquiring data to be entered in the incident occurrence status report 126B form illustrated in FIG. Among the data to be entered in the incident occurrence status report 126B form, for example, unknown data that cannot be obtained from the data (sensor data, weather data, time data, etc.) that can be obtained by the mobile terminal device 20 is entered by the user in the incident questionnaire. Can be supplemented with data. The unknown data here includes, for example, the site situation and crowd at the time of the incident occurrence, the other party's data (age, gender, appearance, etc.) and the description of the incident situation among the incident detailed information shown in FIG. By putting these unknown data in the form of questions in the case questionnaire, it is possible to make it easier for the user to input them. In the explanation of the case situation in the questionnaire, for example, by explaining the case of false accusation, the situation before and after the occurrence can be understood, so that damage such as false accusation can be prevented.

Next, the mobile terminal device 20 transmits the situation data 122a at the time of impact to the wearable information processing device 10. The situation data 122a at the time of impact includes time data 224a at the time of impact, GPS data at the time of impact, accident case selection data (data selected from the accident case selection screen in FIG. 6), questionnaire input data, sensor data ( Acceleration data from impact sensors, etc.), weather data at the time of impact, and other data necessary for generating a situation report.

When the wearable information processing device 10 receives the situation data 122 a at the time of impact via the communication unit 11 , it transmits the time data 224 a at the time of impact from the situation data 122 a to the video processing device 40 .

When the video processing device 40 receives the time data 224 a at the time of impact via the communication unit 41 , the video processing device 40 converts the video data 422 a at the time of impact including the video at the time of the time data 224 a to the video data 432 stored in the storage unit 43 . and transmits it to the wearable information processing device 10 . The wearable information processing device 10 receives the image data 422 a at the time of impact via the communication unit 11 .

In this way, the image data acquisition unit 121 of the wearable information processing device 10 acquires the image of the time of the time data 224a from the plurality of image data 432 (camera images of the wearable device 30) stored in the storage unit 43 of the image processing device 40. Image data 422a at the time of impact is acquired. Note that the image data acquiring unit 121 may acquire image data including the image at the time of the time data 224a at the time of impact and image data before and after that as the image data at the time of impact 422a.

Also, when the wearable information processing device 10 receives the situation data 122 a at the time of impact via the communication unit 11 , it transmits the GPS data 224 b at the time of impact from the situation data 122 a to the map processing device 50 .

When the map processing device 50 receives the GPS data 224b at the time of the impact via the communication unit 51, the map processing device 50 extracts a map including the latitude and longitude of the GPS data 224b at the time of the impact from the map data 532 stored in the storage unit 50. The map data 522a at the time of impact of the range is acquired and transmitted to the wearable information processing device 10. FIG. The wearable information processing device 10 receives the map data 522a at the time of impact via the communication unit 11 .

In this way, the map data acquisition unit 123 of the wearable information processing device 10 acquires map data including a map of latitude and longitude of the GPS data 224b at the time of impact from the map processing device 50. The map data acquisition unit 123 acquires a map of a predetermined range around the latitude and longitude of the GPS data 224b at the time of impact as map data 522a at the time of impact. The predetermined range is set in advance, and the predetermined setting may be changed by the user's input from the mobile terminal device 20 .

Next, when the wearable information processing device 10 acquires the image data 422a at the time of impact from the video processing device 40 and acquires the map data 522a at the time of impact from the map processing device 50, the wearable information processing device 10 executes a situation report generation process in step S20. . The situation report generation process automatically generates a situation report (accident occurrence situation report or incident occurrence situation report) based on the situation data 122a at the time of impact, the video data 422a at the time of impact, and the map data 522a at the time of impact. It is a process to The details of this status report generation process will be described later.

The situation report data 126 a (accident occurrence situation report or incident occurrence situation report data) generated by the wearable information processing device 10 is transmitted to the mobile terminal device 20 via the communication unit 11 . When receiving the situation report data 126a, the mobile terminal device 20 displays the situation report (accident occurrence situation report or incident occurrence situation report) on the display unit 28 in step S14, and determines whether or not to notify in step S15. to judge. Specifically, the mobile terminal device 20 displays the notification instruction screen shown in FIG. The report instruction screen shown in FIG. 7 displays pre-registered insurance companies, etc., and is a display for allowing the user to select "report" or "not report" to the insurance company.

If the user selects "report" on the display screen of FIG. 7 in step S15, the mobile terminal device 20 notifies the pre-registered insurance company or the like in step S16. At this time, the situation report data 126a (accident occurrence situation report or incident occurrence situation report data) may be transmitted together with the report. The report destination is not limited to the pre-stored insurance company or the like. Police and fire stations may also be included. By reporting to the police and transmitting the status report data 126a at the same time, the police can also grasp the accident at an early stage. A series of processing is terminated by the notification in step S16, and the transmission of the camera image is stopped.

On the other hand, if the user selects "do not report" on the display screen of FIG. 7 in step S15, the mobile terminal device 20 continues to determine impact detection without reporting. A series of such processes by the wearable information processing system 100 are executed until the dedicated application program of the mobile terminal device 20 is terminated.

The transmission of the camera image by the camera 34 of the wearable device 30 may be stopped at a predetermined timing. For example, when the camera 34 of the wearable device 30 is turned off, when the power of the wearable device 30 is turned off, when the connection with the wearable device 30 is turned off, etc., transmission of the camera image is stopped. good too. The timing of stopping transmission of camera images is not limited to this.

Also, the video processing device 40 may delete the video data 432 stored in the storage unit 43 at a predetermined timing. For example, the video data 432 may be deleted at the timing when the dedicated application program of the mobile terminal device 20 is terminated or the power of the wearable device 30 is turned off. The timing of deleting the video data 432 is not limited to this. Also, the video data 432 may be deleted after being saved for a certain period of time. The retention period of the video data 432 may be freely changed by user input from the mobile terminal device 20 .

Next, the situation report generation processing performed by the wearable information processing device 10 in step S20 of FIG. 5 described above will be described with reference to the drawings. FIG. 10 is a flow chart showing a specific example of the status report generation process. The situation report generation process of FIG. The necessary program is read and executed.

First, the control unit 12 determines whether the situation data acquisition unit 122 acquired the situation data 122a at the time of impact from the portable terminal device 20 in step S210 of FIG. If the control unit 12 determines that the situation data 122a at the time of impact has not been acquired, it returns to the process of step S210. The unit 121 determines whether the image data 422a at the time of impact has been acquired from the image processing device 40 .

If the controller 12 determines in step S210 that the image data 422a at the time of impact has not been acquired, the process returns to step S210. Then, it is determined whether the map data acquisition unit 123 has acquired the map data 522a at the time of impact from the map processing device 50 .

If the control unit 12 determines in step S220 that the map data 522a at the time of impact has not been acquired, the process returns to step S210. to determine whether it is an accident or not. Specifically, the control unit 12 determines whether the event is an accident or an incident based on the accident case selection data (data selected from the accident case selection screen in FIG. 6) among the situation data from the portable terminal device 20 .

When the control unit 12 determines that there is an accident in step S240, it acquires the form of the accident occurrence status report 126A of FIG. 8 from the data storage unit 16 in step S242. If the controller 12 determines in step S240 that there is no accident, it determines in step S250 whether there is an incident.

If the control unit 12 determines in step S250 that there is no incident, it returns to the process of step S220, and if it determines in step S250 that there is an incident, in step S252 the incident occurrence status report 126B of FIG. form is obtained from the data storage unit 16, and a schematic situation diagram generation process is performed in step S260.

Here, a specific example of the schematic situation diagram generation process will be described with reference to FIGS. 11 to 15. FIG. FIG. 11 is a flow chart showing a specific example of the schematic situation diagram generation process. FIG. 12 is a diagram showing a specific example of video data selected in the schematic situation diagram generation process. FIG. 13 is a diagram showing a specific example of the schematic site situation map generated by the schematic situation map generation process. FIG. 14 is a diagram showing a specific example of the near-site map generated by the schematic situation diagram generation process. FIG. 15 is a diagram showing a specific example of the schematic situation diagram generated by the schematic situation diagram generation process.

12 to 15, if the user is A pA (Party pA), when A starts crossing the crosswalk with a green light, a bicycle driven by BpB (Party pB) enters the crosswalk and hits A. This is an example of generating a schematic situation diagram for an accident of collision. This is the case where Party A is the victim and Party B on the bicycle is the perpetrator.

11 is a process for automatically generating a schematic situation map (a schematic accident situation map or a schematic incident situation map) based on the video data 422a at the time of impact, the map data 522a at the time of impact, and the situation data 122a at the time of impact. A predetermined program is read out and executed by the schematic situation diagram generator 125 .

As shown in FIG. 11, the schematic situation diagram generation unit 125 selects a video for schematic diagram generation in step S261. Specifically, an image (still image) for schematic drawing generation as shown in FIG. 12 is selected from the image data 422a at the time of impact. FIG. 12 shows a video (still image) of a predetermined time (for example, several seconds) before the time of impact. According to FIG. 12, it is a schematic diagram generation image 422b when B's bicycle is about to enter the pedestrian crossing. In this way, it is preferable to select the image several seconds before the time of the impact, instead of the image at the time of the impact, as the schematic diagram generation image 422b.

For example, the schematic situation diagram generation unit 125 detects B and the bicycle appearing in the image at the time of the impact from the image data 422a at the time of impact, and selects the image several seconds before the B in the image. Persons and vehicles, such as B and the bicycle driven by B, are detected from the video using a trained model trained by machine learning or AI (artificial intelligence), for example.

Next, in step S262, the schematic situation diagram generation unit 125 generates a schematic site situation diagram from the selected schematic diagram generation image 422b. For example, as shown in FIG. 13, the schematic diagram 422c of the site situation is a schematic diagram of a predetermined range including the bicycles driven by User A and the bicycles driven by User A several seconds before, centering on User A's head. is replaced with A will be replaced with a symbol representing a person, and B and a bicycle will be replaced with a predetermined symbol representing a bicycle. In this way, when a person is riding a vehicle, the person and the vehicle are not replaced with individual symbols, but the person and the vehicle are collectively replaced with a symbol representing the vehicle. As for the predetermined symbols, for example, if symbols such as people, bicycles, and motorbikes are determined in an accident occurrence situation report, the symbols are stored in advance in the data storage unit 16, and users A and B are replaced with those symbols. In this manner, since a schematic situation diagram can be generated using predetermined symbols, the situation report automatically generated in this embodiment can be submitted to various places as a formal document as it is.

In the schematic diagram 422c of the site situation in FIG. 13, the positions of users A and B can be determined by analyzing the GPS data 224b and video data at the time of impact. Since the image data of this embodiment is a 360-degree camera image, the approximate direction and distance of B can be known from the image several seconds before. Since the latitude and longitude of User A's position can be obtained from GPS data, the approximate GPS data of User B can be calculated from User B's direction and distance. In this way, the positions of users A and B on the map (GPS data) can be replaced. Note that the distance between User A and User B may be calculated from the size of User B's image by recognizing an image several seconds before.

Next, the schematic situation map generator 125 selects a map for schematic map generation in step S263, and generates a near-site map from the map for schematic map generation in step S264. Specifically, a range of a map for schematic drawing generation as shown in FIG. 14 is selected from the map data 522a at the time of the impact, and a near-site map 522b is generated. FIG. 14 is a near-site map 522b including the map positions of users A and B in FIG. In FIG. 14, the dotted line overlaps the range of the schematic diagram 422c of the site situation, in which the position of A and the position of B several seconds before the impact are indicated by x.

Subsequently, in step S265, the schematic situation diagram generation unit 125 generates the schematic situation diagram 125a from the schematic site situation diagram 422c and the site vicinity map 522b. Specifically, the schematic situation diagram generation unit 125 superimposes the schematic site situation diagram 422c as shown in FIG. 13 on the site vicinity map 522b as shown in FIG. 14 to generate the schematic situation diagram 125a as shown in FIG.

Then, in step S266, the schematic situation diagram generation unit 125 stores the schematic situation diagram 125a in the data storage unit 16 as the generated data 163, ends the series of schematic situation diagram generation processing, and proceeds to the processing of step S270 in FIG.

At step S270 in FIG. 10, the control unit 12 generates a status report. Specifically, when it is determined that there is an accident in step S240, the situation report generation unit 126 inputs the schematic diagram of the accident situation and the corresponding data into the form of the accident occurrence situation report 126A shown in FIG. is automatically generated. On the other hand, if it is determined to be an incident in step S240, the situation report generation unit 126 inputs the incident situation diagram and relevant data into the incident occurrence situation report 126B form shown in FIG. 9, and automatically generates the incident occurrence situation report 126B. Generate with

Next, in step S280, the control unit 12 stores the generated status report (accident occurrence status report 126A or incident occurrence status report 126B) in the data storage unit 16 as the generated data 163, and performs a series of status report generation processing. finish.

According to the first embodiment, it is possible to automatically generate a situation report of an accident or incident based on the camera image from the wearable device 30 worn by the user. In this way, by using the camera image from the wearable device 30 worn by the user, the user can see not only when the user is driving a car but also when walking, riding a bicycle or a motorcycle. The image of the surroundings can be stored. Therefore, it is possible to automatically generate not only a status report of an accident in which the user is involved, but also a status report of an incident such as a snatch. As a result, it is possible to automatically generate a situation report of an accident or an incident that cannot be generated from a camera image mounted on an automobile, for example, an accident or incident between a person and a bicycle or a motorcycle, or an accident or incident between a person and a person.

Moreover, video data can be obtained from the time data when an accident or incident occurs (at the time of impact in the first embodiment), map data can be obtained from GPS data, and a schematic diagram of the situation can be automatically generated. Even if you are caught up in and upset, you can generate a situation report on the spot that accurately conveys the situation on the ground. As a result, the circumstances of accidents and incidents can be quickly and accurately reported to insurance companies and the police, and early resolution of accidents and incidents can be expected.

In addition, since the image around the user wearing the wearable device 30 is used as the camera image, for example, in the case of an accident in which a bicycle collides, it is possible to know from which direction the bicycle collided. You can also see which direction the snatcher is coming from.

Furthermore, according to this embodiment, at least an image of an object (for example, a vehicle such as a bicycle, a motorcycle, a car, or a person such as a pedestrian or a runner) that caused an accident or an incident is detected from image data around the user, and the image of the object is detected. Since the target image can be replaced with a predetermined symbol and the situation map can be automatically generated from the site situation map indicated by the position on the map and the site area map, compared to simply pasting the image of the video data to the situation report, It is possible to omit the trouble of processing the image into a schematic diagram of the situation. In this way, a situation report is automatically generated that includes a schematic diagram of the situation that can be sent to the health company or the police without editing, so even if you are upset about being involved in an accident or incident, you can accurately report the situation. can tell

Further, according to the present embodiment, since the camera video from the wearable device 30 is divided and stored as a plurality of video data for each predetermined video time, compared to the case of storing without dividing into a plurality of video data, It is easy to identify images before and after the time when an accident or incident occurred. Further, even if an impact is detected, there may be cases where an accident or incident does not occur, such as when the mobile terminal device 20 is simply dropped. According to this embodiment, the situation data acquired from the portable terminal device 20 by the situation data acquiring unit 122 includes information indicating that an accident or an incident has occurred. In this case, the status report can be prevented from being automatically generated, so wasteful generation of the status report can be suppressed.

In addition, the situation report generation unit 126 of this embodiment determines whether it is an accident or an incident based on the situation data from the mobile terminal device 20, and if it determines that it is an accident, it reports the situation from a predetermined accident occurrence situation report form. A report is generated, and if it is determined to be an incident, a situation report is generated from a predetermined incident occurrence situation report form. In this manner, a situation report can be automatically generated in accordance with the form of each accident or incident, saving the trouble of recreating the situation report later in accordance with the prescribed form.

<Second embodiment>
A second embodiment of the present invention will be described. Constituent elements having substantially the same functional configuration in each form illustrated below are denoted by the same reference numerals, thereby omitting redundant description. In the first embodiment, the case of automatically generating a situation report of an accident or incident was exemplified, but in the second embodiment, not only the situation report but also the percentage of fault report of the accident or incident is automatically generated. Illustrate the case. Note that the configuration of the wearable information processing apparatus 10 of the second embodiment is the same as that of the first embodiment, so detailed description thereof will be omitted.

FIG. 16 is a diagram showing a schematic configuration of the wearable information processing system 100 of the second embodiment, and corresponds to FIG. The storage unit 14 of FIG. 16 includes an accident precedent database 17 and a case precedent database 18 . The data storage unit 16 also stores a form of an accident negligence ratio report 127A illustrated in FIG. 19 and a form of an incident negligence ratio report 127B illustrated in FIG. The controller 12 of FIG. 16 includes a fault rate report generator 127 .

The fault rate report generation unit 127 selects judicial precedents similar to the situation report generated by the situation report generation unit 126, and automatically generates a fault rate report based on the selected judicial precedents. Specifically, in the case of an accident, the fault rate report generator 127 selects from the accident precedent database 17 a precedent similar to the accident situation advertisement data of the accident occurrence situation report 126A, and based on the selected precedent, generates a report in FIG. An exemplary accident fault percentage report 127A is automatically generated. On the other hand, in the case of a case, the fault rate report generation unit 127 selects from the case precedent database 18 similar precedents to the incident situation report data of the incident occurrence situation report 126B, and shows an example in FIG. 20 based on the selected precedents. Automatically generate the incident fault rate report 127B.

FIG. 17 is a flowchart for explaining the overall processing of the wearable information processing system 100 according to the second embodiment, and corresponds to FIG. In the process of FIG. 17, a fault rate report generation process is performed in step S30.

Here, the fault rate report generation processing in step S30 will be described with reference to the drawings. FIG. 18 is a flow chart showing a specific example of fault rate report generation processing. 18 is executed by reading a necessary program from the program storage unit 15 by the control unit 12 (such as the fault ratio report generating unit 127).

First, in step S310, the fault rate report generation unit 127 acquires the situation report data 126a (accident situation report data or incident situation report data) generated in the situation report generation process (step S20 in FIG. 17) from the data storage unit 16. .

Next, the fault rate report generator 127 determines whether or not the accident situation report data has been acquired in step S320. The form of the rate report 127A is obtained from the data store 16;

Then, in step S324, the fault rate report generation unit 127 collates the accident precedent database 17 and selects precedents similar to the accident situation report data. Specifically, the rough accident situation diagrams included in the accident situation report data and the rough accident situation diagrams contained in the case examples in the accident case case database 17 are collated, and judicial precedents with similar schematic accident situation diagrams are selected. A learned model by machine learning or AI (artificial intelligence) can be used for collation of the schematic diagram of the accident situation.

It should be noted that the selection of judicial precedents in step S324 was exemplified by collating accident situation diagrams, but is not limited to this. For example, data (detailed information on the accident, explanation of the incident situation, etc.) other than the schematic diagram of the accident situation in the accident situation report data may be collated.

After selecting the judicial precedent in step S324, the fault ratio report generation unit 127 extracts the fault ratio from the judicial precedent selected in step S340. For example, it extracts the basic fault ratio and the fault ratio evaluation based on the correction element data as shown in the column of "failure ratio" of the case law information of the accident in FIG. The selected judicial precedent may be displayed as a reference judicial precedent in the remarks column of FIG. 19, for example. Correction factor data includes, for example, whether the accident site is a "main road" or "residential area" as shown in FIG. The percent fault rating is a number displayed corresponding to each of these modifier data. FIG. 19 is a specific example of fault rate evaluation of correction factor data for A. In FIG. Correction factor data and fault rate evaluation are not limited to those illustrated. Modifier data and percent fault assessment for B may also be displayed, depending on the case law chosen, for example.

Next, in step S350, the fault rate report generation unit 127 automatically generates a fault rate report (for example, the accident fault rate report 127A in FIG. 19). Specifically, detailed accident information, an accident situation diagram, an explanation of the accident situation, etc. are extracted from the accident situation report data, and entered into the form of the accident/fault ratio report 127A. In addition, a schematic diagram of the accident situation is extracted from the judicial precedent selected in step S324, and is entered in the form of the accident-fault ratio report 127A together with the fault ratio (basic fault ratio, correction element data, fault ratio evaluation, etc.) extracted in step S340.

Next, in step S360, the fault rate report generation unit 127 stores the generated fault rate report (for example, the accident fault rate report 127A in FIG. 19) in the data storage unit 16 as the generated data 163, and generates a series of fault rate reports. exit.

On the other hand, if the fault rate report generator 127 determines in step S320 that the accident situation report data has not been acquired, it determines in step S330 whether or not the incident situation report data has been acquired. If the fault rate report generator 127 determines in step S330 that the incident situation report data has not been acquired, the process returns to step S310.

When the fault rate report generation unit 127 determines in step S330 that the incident situation report data has been acquired, it acquires the form of the fault rate report 127B of FIG. 20 from the data storage unit 16 in step S332.

Then, in step S334, the fault rate report generator 127 collates the case precedent database 18 and selects precedents similar to the case status report data. Specifically, the schematic diagram of the case situation included in the case situation report data and the schematic diagram of the case situation contained in the cases in the case precedent database 18 are collated to select cases with similar schematic diagrams of the case situation. A trained model by machine learning or AI (artificial intelligence) can be used for collation of the schematic diagram of the incident situation.

It should be noted that the selection of judicial precedents in step S334 was exemplified by collating the schematic diagram of the case situation, but it is not limited to this. For example, data (incident detailed information, incident situation explanation, etc.) other than the incident situation schematic diagram of the incident situation report data may be collated.

After selecting the judicial precedent in step S334, the fault ratio report generation unit 127 extracts the fault ratio from the judicial precedent selected in step S340. For example, the basic percentage of negligence and the type of judicial precedent as shown in the "percentage of fault" column of the judicial precedent information of the case in FIG. 20 are extracted. Case types include, for example, "theft," "injury," "robbery," and "robbery resulting in injury," as shown in FIG. For example, if the selected judicial precedent includes these characters, "o" is displayed, and if these characters are not included, "x" is displayed. It should be noted that notation of judicial precedent type is not limited to these. In case precedents, the percentage of negligence is often the ratio of victim 0:perpetrator 100, and there are cases where the percentage of negligence is not indicated. This can be expressed as The judicial precedents selected in step S334 may be displayed as reference judicial precedents in the remarks column.

Next, in step S350, the fault rate report generation unit 127 automatically generates a fault rate report (for example, the incident fault rate report 127B in FIG. 20). Specifically, the detailed information of the incident, the illustration of the incident situation, the description of the incident situation, etc. are extracted from the incident situation report data, and entered into the form of the incident negligence ratio report 127B. In addition, a schematic diagram of the case situation is extracted from the judicial precedent selected in step S334, and entered into the form of the case fault ratio report 127B together with the fault ratio (basic fault ratio, correction element data, fault ratio evaluation, etc.) extracted in step S340.

Next, in step S360, the negligence ratio report generator 127 stores the generated negligence ratio report (for example, the incident negligence ratio report 127B in FIG. 20) in the data storage unit 16 as the generated data 163 of the negligence ratio report data 127a, End the series of percent fault report generation. As shown in FIG. 17, fault rate report data 127a (data of accident fault rate report or incident fault rate report) generated by the wearable information processing device 10 is transmitted to the mobile terminal device 20 via the communication unit 11. be done. When the mobile terminal device 20 receives the fault rate report data 127a, in step S14, the situation report (accident occurrence situation report or incident occurrence situation report) and the fault rate report (accident fault rate report or incident fault rate report) ) is displayed on the display unit 28, and it is determined whether or not to notify in step S15. Since subsequent processing is the same as in FIG. 5, detailed description is omitted.

As described above, according to the second embodiment, similar to the first embodiment, it is possible to automatically generate a situation report of an accident or incident from the camera image of the wearable device 30, and further to automatically generate a fault rate report. . Moreover, since the judicial precedent database (accident judicial precedent database 17 or incident judicial precedent database 18) is collated with a rough situation diagram, similar judicial precedents can be found more easily than when searching for judicial precedents by text search. In addition, since it is possible to automatically generate a schematic situation map by replacing the images of people and vehicles with predetermined symbols, it is possible to directly compare with the schematic situation charts of court cases, etc., compared to pasting the images of people and vehicles as they are. Matching accuracy can be greatly improved. Since it is possible to automatically generate a percentage-of-fault report based on case law data, it is possible to know the percentage of fault in similar cases, making it easier to file lawsuits and decide whether to settle, and for insurance companies to decide whether to apply insurance.

Further, the judicial precedent database of the second embodiment is divided into an accident judicial precedent database 17 and a case precedent database 18, and the fault rate report generator 127 judges whether it is an accident or a case based on the situation data from the mobile terminal device 20, If it is determined to be a case, it selects a precedent with a similar outline of the situation from the accident precedent database, and if it is determined to be a case, selects a precedent with a similar outline of the situation from the case precedent database. According to this, in the case of an accident, a judicial precedent with a similar schematic diagram of the situation is selected from the accident judicial precedent database 17, and in the case of a case, a judicial precedent with a similar schematic diagram of the situation is selected from the case judicial precedent database 18. It is possible to select appropriate judicial precedents and appropriate judicial precedents and crime types (articles, etc.) according to the case.

<Modification>
The present invention is not limited to the above-described embodiments, and various applications and modifications described below are possible. Moreover, it is also possible to appropriately combine one or a plurality of arbitrarily selected aspects of these modifications and each of the above-described embodiments. In addition, it is clear that a person skilled in the art can conceive of various modifications or modifications within the scope described in the claims, and these naturally belong to the technical scope of the present invention. It is understood.

(1) In the above-described second embodiment, in the case of a case, a case constituent element report 127C as shown in FIG. 21 is created, and in step S14 of FIG. You may make it display on the apparatus 20. FIG. The case constituent element report 127C in FIG. 21 displays the case guilt presumption instead of the fault rate in the fault rate column of FIG. For example, the case is estimated based on the judicial precedent selected in step S334 of FIG. The presumption of crimes includes "theft", "injury", "robbery", "robbery resulting in injury", etc., as shown in FIG. If there is no injury, there is a possibility of "theft", but if the victim's crime is suppressed, there is also a possibility of "robbery". If there is an injury, there is a possibility of "injury", "robbery", and "robbery resulting in injury", but there is also a possibility of combined crime of "theft" and "injury". If a robbery is committed and the victim is injured during the robbery, it may be a robbery resulting in injury. Also, in the case of "theft", if there is no assault, there is a high possibility that only theft will be committed, but if there is assault, there is a possibility that "theft" and "assault" are combined. In this way, possible case charges can be inferred from judicial precedents. As explained in the second embodiment, in the case of a case, the ratio of negligence is often 0 to the perpetrator and 100 to the perpetrator, and there are cases where the ratio of negligence is not indicated. The document 127C may be created automatically. It should be noted that this case guilt presumption may be included in the case fault rate report 127C.

(2) In the first and second embodiments, the emergency button is displayed on the display unit 28 while the mobile terminal device 20 is executing a dedicated application program for performing wearable information processing. may The emergency button is a button for displaying the accident case selection screen even if the mobile terminal device 20 does not detect an impact. When the emergency button is pressed, for example, an accident case selection screen shown in FIG. 22 is displayed on the display unit 28.

In this case, step S11 in FIGS. 10 and 18 assumes that the impact has been detected or the emergency button has been pressed. When the emergency button is pressed" can be read and applied. Specifically, for example, data at the time of impact in FIGS. 10 and 18 is read as data at the time of pressing the emergency button. The situation data (time data, GPS data, sensor data) at the time of impact is read as the situation data (time data, GPS data, sensor data) at the time of pressing the emergency button. The image data at the time of impact is read as the image data at the time of pressing the emergency button, and the map data at the time of impact is read as the map data at the time of pressing the emergency button.

In this way, by enabling the accident case selection screen to be displayed on the mobile terminal device 20 by pressing the emergency button, even in the case of an accident or incident in which the mobile terminal device 20 has almost no impact, such as a stalker, the situation can be reported and Percentage fault reports can be automatically generated.

(3) In the report in step S16 of FIGS. 10 and 18, when reporting to the police, a report instruction screen as shown in FIG. With this, it is possible to select whether or not to report to the police when an accident or incident occurs. A situation report may also be sent to the police along with the report to the police. As a result, it is possible to expect prompt resolution of accidents and incidents. In particular, when the emergency button is pressed, a screen for instructing to report to the police is displayed, making it possible to respond to emergencies.

(4) In the schematic situation diagram generation process of FIG. 11, a case is illustrated in which a video (still image) of a predetermined time (for example, several seconds) before the time of impact is selected from the video data 422a at the time of impact. As the image a predetermined time before the time of impact, an image generated from part of the omnidirectional image, which is the image data 422a, as shown in FIG. The omnidirectional image shown in FIG. 25 may be used as it is. Recognize the person or vehicle appearing in the omnidirectional video and identify it as the object that caused the accident or incident involving User A.

In Fig. 12, an example is shown in which a single bicycle driven by User A is shown in User A's omnidirectional video. In this way, when only one target candidate appears in the omnidirectional video, it is easy to identify the target. However, in an actual scene, there are cases where a plurality of vehicles and people are captured in the omnidirectional video. In this case, it is necessary to identify the object (vehicle or person) that caused the accident or incident involving user A from among the vehicles or person appearing in the omnidirectional video.

Here, the target specifying processing according to the modified example of the present invention will be described with reference to FIGS. 24 to 28. FIG. FIG. 24 is a flowchart illustrating a specific example of target identification processing. 25 to 28, similar to FIGS. 12 to 15, when user A pA (Party pA) starts crossing the crosswalk with a green light, a bicycle driven by Party pB (Party pB) enters the crosswalk. Then, the case of an accident in which the car collides with the instep is illustrated. In addition, in FIGS. 25 to 28, in order to make the explanation easier to understand, only B's bicycle is displayed, and other vehicles and people are omitted.

　Figures 25(a) and 26(a) are omnidirectional images based on the image data 422a. 25(b) and 26(b) are images generated from the omnidirectional images. Figures 25(a) and (b) are images several seconds before the impact or pressing of the emergency button, and Figures 26(a) and (b) are several seconds before Figures 25(a) and (b). It is a video of FIG. 27 is a diagram showing a specific example of the target candidate diagram 422d, showing the positions and movements of A and B based on the images of FIGS. FIG. 28 is a diagram showing a specific example of the target candidate verification diagram 422e, in which the position mX of the accident or incident (position at the time of impact or pressing of the emergency button) is superimposed on FIG. In FIGS. 27 and 28, m1 of A and t1 of B are respectively the latitude and longitude of the GPS data of A and B based on the omnidirectional video of FIG. 25(a). m2 of A and t2 of B are respectively the latitude and longitude of the GPS data of A and B based on the omnidirectional video of FIG. 26(a).

The target specifying process in FIG. 24 is executed by reading out a predetermined program by the control unit 12 before executing the schematic situation diagram generation process in FIG. As shown in FIG. 24, the control unit 12 first selects an image for schematic diagram generation in step S461. Specifically, the control unit 12 selects an omnidirectional image (for example, FIG. 25A) of a predetermined time (eg, several seconds before) the time of impact from the image data 422a at the time of impact.

Next, in step S462, the control unit 12 detects vehicles and persons from the selected omnidirectional video, and determines whether or not there are multiple persons or vehicles that can be candidates for causing an accident or incident. Specifically, the control unit 12 detects an image of a person or vehicle from the omnidirectional video, and determines whether or not multiple images of a person or vehicle have been detected. If the control unit 12 determines in step S462 that only one target candidate (image of a person or vehicle) is detected, the control unit 12 terminates the target specifying process, shifts to the schematic situation diagram generation process of FIG. 11, and proceeds to step S462. If it is determined that a plurality of target candidates (images of persons or vehicles) have been detected, the process moves to step S463.

Next, in step S463, the control unit 12 extracts an omnidirectional image (for example, FIG. 26A) a predetermined time (for example, several seconds earlier) than the omnidirectional image in FIG. 24 selected in step S461 from the image data 422a. elect. Then, in step S464, the control unit 12 calculates the positions (latitude and longitude of GPS data) of the person or vehicle that is the target candidate from the two selected omnidirectional images (Fig. 25(a) and Fig. 26(a)). Then, an object candidate drawing 422d in FIG. 27 is generated, and the position and movement of the object candidate are detected.

Here, a specific example of a method of calculating the position (latitude and longitude of GPS data) of a person or vehicle that is a target candidate will be described. For example, the omnidirectional video in FIG. 25(a) is the video around user A, so the center m1 of the omnidirectional video can be the position of user A. In this case, the camera image may be adjusted so that the center of the omnidirectional image matches the position of the user. Then, the position from A to B is indicated by a vector (m1→t1). The direction of this vector is the direction of B with respect to A, and the magnitude (length) of the vector corresponds to the distance between A and B. Using a learning model that has been trained in advance by associating vectors (direction and distance) to multiple objects in the omnidirectional video with vectors (direction and distance) from the actual instep to the object, the actual Calculate the direction and distance of B. If the direction and distance to Party A can be specified for Party B, the latitude and longitude of Party B's GPS data can be obtained based on the latitude and longitude of Party A's GPS data. The method of calculating the latitude and longitude of the GPS data of B is not limited to the above, and may be calculated by another known method from the omnidirectional video. As for the latitude and longitude of the GPS data of Party A, those received from the portable terminal device 20 are used as described above.

Next, in step S465, the control unit 12 determines whether or not the target candidate is approaching user A. Specifically, as shown in FIG. 27, the control unit determines whether or not the target candidate is approaching user A based on the position and movement of the target candidate obtained from the two omnidirectional images. For example, whether or not it is approaching is determined by whether or not the direction of movement is toward the course of user A.

For example, in the target candidate diagram 422d of FIG. 27, the direction of movement of B's bicycle (t2→t1) is toward the course of user A (m2→m1), so it can be determined that User A is approaching. On the other hand, even if a vehicle or a person is captured, if it is stationary, the position should not change, so it is known that it is not a target candidate. Also, if the direction of movement is in the direction away from the course of the instep, it can be seen that it is not a target candidate. As described above, according to the object identification process, even if a plurality of persons or vehicles are photographed, it is possible to automatically determine whether or not user A is a candidate for the object that caused the accident or incident.

Next, in step S466, the control unit 12 verifies whether or not the target candidate is the target of an accident or incident. Specifically, the control unit 12 superimposes the position mX (the latitude and longitude of the GPS data) at the time of impact or pressing of the emergency button on the target candidate diagram 422d of FIG. Generate. Then, in step S467, the control unit 12 determines whether or not the position mX of the accident or incident (the position at the time of impact or pressing of the emergency button) is on the extension line (dotted arrow) of the target movement (t2→t1). do. In this case, there is a risk that the calculation accuracy of the GPS data for the location of B may affect the verification accuracy. Therefore, a certain tolerance may be provided according to the calculation accuracy of the GPS data of the position of B, for example, so that even if there is some deviation, it can be determined that it is on the extended line.

When the control unit 12 determines in step S467 that the position of the accident or incident is on the extension line of the movement of the target candidate, in step S468 the target candidate is identified as the target that caused the incident or accident. The above steps S465 to S468 are performed for each target candidate. The control unit 12 arbitrarily selects a target candidate in step S465 and performs steps S465 to S468 on the target candidate. If the target candidate does not approach in step S465 or if it is determined in step S467 that the position of the accident or incident is not on the extension line of the target candidate's movement, the process returns to step S464, and another arbitrary target candidate is determined. is selected, and steps S465 to S468 are performed.

When the target is specified in step S468, the control unit 12 ends the target specifying process and proceeds to the schematic situation diagram generation process of FIG. Note that the target identification process may be performed by the schematic situation diagram generation unit 125 . Since step S461 of the target specifying process and step S261 of the schematic situation drawing process overlap, when these target specifying process and the schematic situation drawing process are performed continuously, the process of step S261 in FIG. 11 is performed in the schematic situation drawing process. For example, the image shown in FIG. 25 selected in step S461 of FIG. 24 may be used as it is. Then, the object specifying process is performed before generating the site situation schematic diagram in step S262 of FIG.

According to the object identification processing according to such a modified example, even when a plurality of vehicles or persons are shown in the omnidirectional video, it is possible to automatically identify the object as the object that caused the accident or incident involving user A. . In addition, by comparing the omnidirectional video (video data) slightly before the occurrence of the accident or incident (at the time of impact or pressing the emergency button) with the omnidirectional video (video data) even before that, the target It can detect the position and motion of candidate people and vehicles. As a result, the target can be specified based on the position and movement of the target candidate, so the target can be specified more accurately than when the target is specified simply from the difference in size of the target in the image. can be identified.

(5) In the above-described first and second embodiments and modifications, the omnidirectional video selected in the schematic situation diagram generation process and the target identification process is obtained from the camera video from the wearable device 30 as an example. However, the information is not necessarily limited to this, and may be acquired from the image of the surveillance camera. For example, it is conceivable that the wearable device 30 may be damaged depending on the circumstances of an accident or incident. In such a case, the images before and after the damage may be supplemented from the images of surrounding surveillance cameras. By synthesizing the video of the surveillance camera so as to become the omnidirectional video of the user A, the schematic situation diagram generation processing and target identification processing of the above-described embodiment and modifications can be applied as they are.

(6) In the first embodiment, the second embodiment, and the modified example, the situation report in Japan was used as an example, but the present invention is not limited to this. For example, it can be applied to a foreign situation report by incorporating a foreign situation report form (template). In that case, the Japanese-based part can be automatically translated into the language of the country to match the form of the country. In addition, symbols of people and vehicles in schematic diagrams can be easily replaced by incorporating symbols designated by the country, if any. Moreover, as for the map information, by taking in the map information of the world as a matter of course, it is possible to replace the images of people and vehicles with predetermined symbols and generate a rough map of the site situation corresponding to the form of each country indicated by the position on the map. .

DESCRIPTION OF SYMBOLS 100... Wearable information processing system, 10... Wearable information processing apparatus, 10L... Bus line, 11... Communication part, 12... Control part, 14... Storage part, 15... Program storage part, 16... Data storage part, 17... Accident judgment Database 18 Case precedent database 20 Portable terminal device 20L Bus line 21 Communication unit 22 Control unit 23 Storage unit 24 Camera 25 Microphone 26 Sensor unit 27 Input Unit 28 Display unit 30 Wearable device 31 Communication unit 34 Camera 34a Camera image 40 Video processing device 40L Bus line 41 Communication unit 42 Control unit 43 Storage Unit 50 Map processing device 50L Bus line 51 Communication unit 52 Control unit 53 Storage unit 121 Video data acquisition unit 122 Situation data acquisition unit 122a Situation data 123 Map Data acquisition unit 125 Situation diagram generation unit 125a Situation diagram 126 Situation report generation unit 126a Situation report data 126A Accident occurrence situation report 126B Accident occurrence situation report 127 Negligence ratio report Generating unit 127A Accident negligence ratio report 127B Incident negligence ratio report 127C Incident constituent elements report 161 User data 162 Acquired data 163 Generated data 221 Sensor data acquisition unit 222 Impact detection unit 223 Impact data collection unit 223a Impact data 224 Situation data collection unit 224a Time data 224b Map data 231 Program storage unit 232 Data storage unit 421 Time data Acquisition unit 422 Video data selection unit 422a Video data at the time of impact 422b Schematic diagram generation video 422c Field situation schematic 422d Target candidate diagram 422e Target candidate verification diagram 432 Video data 521 ... GPS data acquisition unit 522 ... map data selection unit 522a ... map data at the time of impact 522b ... near site map 532 ... map data N ... network P ... push notification.

Claims

A wearable information processing device that processes information including camera images from a wearable device worn by a person,
A video processing device that stores camera video of the wearable device as video data, a map processing device that stores map data, and a mobile terminal device that can collect situation data including time data and GPS data when an accident or incident occurs. and connected over the network to
a situation data acquisition unit that acquires the situation data from the mobile terminal device;
a video data acquisition unit that acquires the video data based on the time data from the video processing device;
a map data acquisition unit that acquires the map data based on the GPS data from the map processing device;
a schematic situation map generation unit that generates a schematic situation map based on at least the video data and the map data;
a situation report generator that generates an accident or incident situation report including the situation diagram;
A wearable information processing device.
2. The wearable information processing device according to claim 1, wherein said camera image is an image of the surroundings of a person wearing said wearable device.
The situation diagram generation unit
An image of the object that caused the accident or incident is detected from the image data acquired by the image data acquisition unit, and a schematic map of the site situation is generated by replacing the image of the object with a predetermined symbol and showing the position on the map. ,
generating a site vicinity map including the position of the GPS data from the map data acquired by the map data acquisition unit;
3. The wearable information processing device according to claim 1, wherein a schematic situation map is generated from the schematic site situation map and the map near the site.
The wearable device is connected to the mobile terminal device,
The video processing device receives a camera video from the wearable device via the mobile terminal device and stores it as a plurality of video data for each predetermined video time,
The wearable information processing apparatus according to any one of claims 1 to 3, wherein the image data acquisition unit acquires image data including at least an image at the time of the time data from among the plurality of image data.
The situation diagram generation unit
determining whether or not a plurality of images of a person or a vehicle that is a candidate for the target is detected from the image data acquired by the image data acquisition unit before generating the schematic diagram of the site situation;
If it is determined that only one image of a person or vehicle that is a candidate for the object is detected, that one image is specified as the object that caused the accident or incident, and the schematic diagram of the scene and the vicinity of the scene are identified. Generate maps and situation diagrams,
When it is determined that a plurality of images of a person or vehicle that is a candidate for the object has been detected, each person or vehicle that is a candidate for the object is identified by comparing the image data with previous image data. Detect the position and movement of and generate a target candidate map,
wherein, based on the position and movement of the target candidate map, an object that caused the accident or incident is specified from among the plurality of images to generate the site situation schematic map and the site neighborhood map and situation schematic map. The wearable information processing device according to claim 3 or 4.
The mobile terminal device includes an impact sensor that detects an impact, and when the impact sensor detects an impact, an accident event selection screen is displayed for selecting either an accident, an incident, or neither. displayed on the display,
6. The wearable information processing according to any one of claims 1 to 5, wherein the situation data acquisition unit acquires the situation data including information that the occurrence of the accident or the occurrence of the incident is selected from the portable terminal device. Device.
The status report generation unit
judging whether it is an accident or an incident based on the situation data from the portable terminal device;
If it is determined that there is an accident, the situation report is generated from a predetermined accident occurrence situation report form,
7. The wearable information processing apparatus according to claim 6, wherein when it is determined that the incident is the incident, the situation report is generated from a predetermined incident occurrence situation report form.
a judicial precedent database that stores judicial precedent data including the schematic situation diagram;
The situation diagram generated by the situation diagram generation unit is collated with the situation diagram included in the cases in the case database to select judicial precedents similar to the situation diagram, extract the percentage of negligence from the selected judicial precedents, and select the judicial precedents. 8. The wearable information processing apparatus according to claim 6 or 7, further comprising: a fault rate report generator that generates a fault rate report including the situation diagram and the fault rate.
The precedent database is divided into an accident precedent database and a case precedent database,
The percent-of-failure report generator,
judging whether it is an accident or an incident based on the situation data from the portable terminal device;
If it is determined that it is an accident, select a precedent similar to the schematic situation diagram from the accident precedent database,
9. The wearable information processing apparatus according to claim 8, wherein when it is determined that the case is the case, a case precedent similar to the schematic situation diagram is selected from the case precedent database.
A program that causes a computer to execute a situation report generation process of an accident or incident performed by a wearable information processing device,
The wearable information processing device,
A video processing device that stores video data captured by a wearable device worn by a person, a map processing device that stores map data, and can collect situation data including time data and GPS data when an accident or incident occurs. connected via a network to a mobile terminal device such as
The status report generation process includes:
obtaining the situation data from the mobile terminal device;
obtaining the video data based on the time data from the video processing device;
obtaining the map data based on the GPS data from the map processing device;
generating a situation map based at least on said video data and said map data;
generating a status report including the status diagram;
A program that contains .