WO2020000834A1

WO2020000834A1 - Traffic accident handling method and system, and server

Info

Publication number: WO2020000834A1
Application number: PCT/CN2018/113400
Authority: WO
Inventors: 刘志龙
Original assignee: 平安科技（深圳）有限公司
Priority date: 2018-06-28
Filing date: 2018-11-01
Publication date: 2020-01-02
Also published as: CN109242739A

Abstract

Provided in the embodiments of the present application are a traffic accident handling method and system, and a server. In the embodiments of the present application, the method comprises: receiving traffic accident scene information, acquiring a specified incident analysis model, and generating traffic accident liability identification information based on the specified incident analysis model according to the traffic accident scene information. The traffic accident liability identification information can be automatically intelligently generated by using the specified incident analysis model, without the need for traffic policemen and insurance personnel carrying out an investigation at the scene and without the need for people in the traffic accident to wait at the scene, which saves on labour costs so that the accident handling time is reduced and the accident handling speed is improved, and also greatly reduces the waiting time of people in the traffic accident so that the people in the traffic accident without serious problems can leave the scene of the accident quickly, thereby avoiding traffic congestion or shortening the congestion time, and thus solving the problems of high labour costs and a more time-consuming traffic accident handling flow in the prior art to a certain extent.

Description

Traffic accident processing method, system and server

This application claims priority from a Chinese patent application filed with the Chinese Patent Office on June 28, 2018, with application number 201810690318.5, and the invention name is "Traffic Accident Processing Method, System, and Server", the entire contents of which are incorporated herein by reference. in.

Technical field

The present application relates to the field of communication technologies, and in particular, to a method, a system, and a server for processing a traffic accident.

Background technique

At present, with the continuous increase in the number of domestic cars in our country, the traffic pressure on highways is getting heavier. For example, roads in large and medium-sized cities are often congested, especially during peak periods such as commuting and holidays. When a traffic accident occurs, the congestion situation becomes more serious.

At present, when a traffic accident occurs, the handling process is as follows: the relevant personnel of the traffic accident report the incident. After receiving the report, the transportation department and the insurance company assign personnel to the scene of the traffic accident to conduct an on-site investigation (such as taking pictures). Liability, issue a certificate of responsibility for traffic accidents. Alternatively, the traffic police subsequently retrieved the surveillance video of the accident scene and combined the site investigation information to determine the responsibility of the accident a few days after the occurrence of the traffic accident, and issued a traffic accident responsibility certificate.

In the above process, the stop of the accident vehicle itself caused the congestion of the road section of the accident, and the arrival of traffic police vehicles and insurance company vehicles will further exacerbate the congestion of the road section of the accident. During this process, both the transportation department and the insurance company need to appoint personnel to visit the site in person, and the labor cost is large. In addition, since the accident is conducted by manual site investigation, the accident processing time is long, and the relevant personnel need to spend more waiting time.

Application content

The embodiments of the present application provide a method, a system, and a server for processing a traffic accident, which are used to solve the problems of high-cost and time-consuming labor in the process of handling a traffic accident in the prior art.

In a first aspect, an embodiment of the present application provides a method for processing a traffic accident, where the method includes:

Receive traffic accident scene information;

Obtaining a specified accident analysis model, which is obtained based on a deep learning algorithm and trained by using known traffic accident scene information and known traffic accident responsibility identification information corresponding to the known traffic accident scene information;

Based on the designated accident analysis model, traffic accident responsibility determination information is generated according to the traffic accident scene information.

In a second aspect, an embodiment of the present application provides a traffic accident processing system, where the system includes:

A receiving module for receiving traffic accident scene information;

An acquisition module, configured to acquire a designated accident analysis model, which is based on a deep learning algorithm and is obtained by training using known traffic accident scene information and known traffic accident responsibility identification information corresponding to the known traffic accident scene information. of;

A generating module is configured to generate traffic accident responsibility determination information based on the specified accident analysis model and according to the traffic accident scene information.

In a third aspect, an embodiment of the present application provides a server, where the server includes:

processor;

A memory for storing the processor-executable instructions;

The processor is configured to:

Receive traffic accident scene information;

Obtaining a specified accident analysis model, which is obtained by training based on a deep learning algorithm using known traffic accident scene information and known traffic accident responsibility identification information corresponding to the known traffic accident scene information;

According to a fourth aspect, an embodiment of the present application provides a computer non-volatile readable storage medium. The computer non-volatile readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

Receive traffic accident scene information;

The embodiments of the present application have the following beneficial effects:

In the embodiment of the present application, by receiving traffic accident scene information, obtaining a designated accident analysis model trained based on a deep learning algorithm, based on the designated accident analysis model, and generating traffic accident responsibility identification information based on the traffic accident scene information, the designated accident analysis model can be used. Automatically and intelligently generate traffic accident responsibility identification information, no traffic police and insurance personnel are required to visit the scene, and no traffic accident personnel are required to wait at the scene. On the one hand, it saves manpower costs, reduces the time for accident handling, and improves the speed of accident handling. On the one hand, the waiting time of traffic accident personnel is greatly reduced, so that traffic accident personnel without major disturbance can quickly leave the accident scene, avoiding causing traffic congestion or shortening the congestion time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a first example flowchart of a traffic accident processing method according to an embodiment of the present application.

FIG. 2 is a second example flowchart of a traffic accident processing method provided by an embodiment of the present application.

FIG. 3 is a third exemplary flowchart of a traffic accident processing method according to an embodiment of the present application.

FIG. 4 is a fourth exemplary flowchart of a traffic accident processing method provided by an embodiment of the present application.

FIG. 5 is a structural block diagram of a traffic accident processing system according to an embodiment of the present application.

FIG. 6 is a simplified block diagram of a server provided by an embodiment of the present application.

detailed description

In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

Example one

The embodiment of the present application provides a method for processing a traffic accident. The traffic accident processing method provided in the embodiment of the present application may be executed on a server of a transportation department.

FIG. 1 is a first example flowchart of a traffic accident processing method according to an embodiment of the present application. As shown in FIG. 1, in this embodiment, the method for handling a traffic accident may include the following steps:

S101: Receive traffic accident scene information.

S102. Acquire a specified accident analysis model. The specified accident analysis model is based on a deep learning algorithm and is obtained by training using known traffic accident responsibility information corresponding to known traffic accident scene information and known traffic accident scene information.

S103. Based on the designated accident analysis model, the traffic accident responsibility identification information is generated according to the traffic accident scene information.

The traffic accident scene information may include pictures and videos of the traffic accident scene. For example, when a traffic accident occurs, the accident scene environment, the scene photos of the vehicles involved, and monitoring video can be used as traffic accident scene information.

The source of the traffic accident scene information received in step S101 may be a traffic monitoring device on the road section where the traffic accident scene is located, or a personal handheld device such as a mobile phone or a tablet of the car owner involved.

For example, when a traffic accident occurs, the traffic monitoring equipment on the road segment where the traffic accident is located can collect the scene picture in real time, and upload the scene picture to the server of the transportation department together with the stored video of the time period of the traffic accident. The server can receive traffic accident scene information. The operation of collecting and uploading pictures and / or videos by the traffic monitoring device may be actively performed based on the judgment result of its own judgment program, or it may be passively triggered upon receiving an instruction issued by the background system of the traffic department.

Correspondingly, the method for receiving traffic accident scene information may be: receiving traffic accident scene information collected by a traffic monitoring device on a road section where the traffic accident scene is located.

For example, when a traffic accident occurs, car owners (also known as traffic accident parties), passers-by, etc. can take live pictures or live videos through personal handheld devices such as mobile phones and tablets, and then send live pictures and / or live videos through the network Send to the server of the transportation department.

The server may be provided with a dedicated interface for receiving information uploaded by personal handheld devices of the car owner and passerby involved. For example, the interface may be a data upload interface of the official website of the transportation department, or an application (Application) installed on a personal handheld device, or an interface subroutine on the application.

Correspondingly, the method for receiving traffic accident scene information may be: receiving traffic accident scene information uploaded by a personal handheld device.

It should be noted that the above is only an example of the way to receive traffic accident scene information, and is not intended to limit the way to receive traffic accident scene information. In applications, you can set up to receive traffic accident scene information according to the actual situation and needs Specific way.

It can be seen that, based on step S101, the background server of the transportation department can quickly and easily obtain the information of the traffic accident scene without having to assign personnel to the scene of the traffic accident to conduct a manual on-site survey, which will help shorten the processing time of the accident and improve the efficiency of the accident. It also saves labor costs.

The designated accident analysis model in step S102 may be pre-trained. In an exemplary implementation process, obtaining a specified accident analysis model may include: obtaining multiple sets of training data, each set of training data including known traffic accident scene information and known traffic accidents corresponding to the known traffic accident scene information Responsibility determination information; extract known traffic accident scene information from multiple sets of training data as input data for a designated accident analysis model; extract known traffic accident responsibility determination information from multiple sets of training data as output data for a specified accident analysis model ; Use input data and output data to train a deep learning algorithm model to generate a designated accident analysis model.

In this way, based on deep learning algorithms, a large number of known traffic accident scene information and known traffic accident responsibility identification information corresponding to these known traffic accident scene information can be trained to obtain a designated accident analysis model.

It should be noted that the larger the number of training data sets, the more accurate the specified accident analysis model trained.

The following example illustrates the process of obtaining the specified accident analysis model. Assuming that the deep learning algorithm model uses a convolutional neural network model, the acquisition process of the specified accident analysis model may be: 10,000 sets of known traffic accident scene information that has occurred and known traffic accidents corresponding to the known traffic accident scene information Responsibility identification information is used as training data. Several parameters are set for the convolutional neural network model. These parameters are unknown before training. The known traffic accident scene information in the training data is used as the input of the convolutional neural network model. The known traffic accident liability identification information is used as the output of the convolutional neural network model to solve the parameter values of the parameters. At this time, the parameter values of these parameters are known. These parameter values determine a specific volume. Product neural network model, which can be used as a designated accident analysis model.

In step S102, generating traffic accident responsibility determination information based on the traffic accident scene information based on the designated accident analysis model may include: inputting traffic accident scene information into the designated accident analysis model as input data; obtaining output data of the designated accident analysis model , Use this output data as traffic accident liability identification information.

Among them, the traffic accident liability confirmation information may be a traffic accident liability confirmation.

Among them, the content of traffic accident liability identification information may include:

Basic situation of road traffic accident parties, vehicles, roads and traffic environment;

The road traffic accident happened;

Evidence of road traffic accidents and analysis of their causes;

The party's fault and liability or cause of the road traffic accident;

The name and date of the traffic management department of the public security organ that made the determination of a road traffic accident.

It should be noted that in specific applications, the specific content contained in the traffic accident liability determination information can be adjusted according to needs or actual conditions, such as adding, reducing, or modifying the corresponding content.

Through step S102, the background server of the transportation department can automatically and intelligently analyze and judge the traffic accident, and then determine the responsibility of the traffic accident. In this way, there is no longer a need for traffic police to rush to the scene of a traffic accident, and the responsibility for a traffic accident can be determined manually, thereby reducing the time for handling the accident and increasing the speed of handling the accident.

According to steps S101 and S102, it can be known that the embodiment of the present application can automatically and intelligently blame a traffic accident based on remotely collected traffic accident scene information. Therefore, if a person is not injured or slightly injured in a traffic accident, and the vehicle is damaged When it is not big, after the relevant personnel (such as the car owner or passerby) collect and upload the traffic accident scene information, the car owner concerned can leave without having to stay on the scene, so as to avoid causing traffic congestion or shortening the congestion time. When a traffic accident occurs on a section where traffic monitoring equipment such as a camera is installed, the traffic monitoring equipment automatically collects and reports the traffic accident scene information, and the car owner involved may not even need to stay on the scene. In this way, road congestion caused by traffic accidents is avoided. This is of great significance to the traffic conditions of roads with heavy traffic and roads during peak periods.

In other embodiments of the present application, after generating the traffic accident liability determination information, the generated traffic accident liability determination information may be further processed, such as storing, publishing, and sending to the parties involved in the traffic accident.

Based on this, the embodiment of the present application proposes a traffic accident processing method flow shown in FIG. 2.

FIG. 2 is a second example flowchart of a traffic accident processing method provided by an embodiment of the present application. As shown in FIG. 2, in this embodiment, the method for processing a traffic accident may include the following steps:

S201. Receive traffic accident scene information.

S202: Acquire a designated accident analysis model. The designated accident analysis model is obtained by training based on a deep learning algorithm and using known traffic accident responsibility information corresponding to known traffic accident scene information and known traffic accident scene information.

S203: Based on the designated accident analysis model, generate traffic accident responsibility determination information according to traffic accident scene information, and execute steps S204, S205, and / or S206.

S204: Store the traffic accident responsibility determination information in the designated storage location, and execute step S207.

S205: Publish the information about the responsibility of the traffic accident to the designated media platform, and the process ends.

S206: Send the traffic accident responsibility determination information to the mobile device of the traffic accident party, and the process ends.

S207. Receive a download request for downloading the traffic accident liability determination information from the client.

S208. In response to the download request, obtain traffic accident liability determination information from a designated storage location.

S209. Send the traffic accident responsibility determination information to the client.

Through step S204, the traffic department can store the traffic accident responsibility determination information in order to record the traffic accident and provide a basis for future inquiry.

In step S205, the designated media platform may be the official website of the transportation department. Through step S205, the traffic department can publish the information about the responsibility of the traffic accident to the official website for the relevant personnel to check.

Among them, the designated media platform can further provide an interface for downloading traffic accident liability identification information, so that relevant personnel can easily download the traffic accident liability identification information from the designated media platform.

Through step S206, after generating the traffic accident liability determination information, the server can automatically send the traffic accident liability determination information to the mobile device of the traffic accident party. For example, a short message containing the contents of the traffic accident liability determination information is sent to the party's mobile phone. In this way, the parties to a traffic accident can be informed of the results of the traffic accident in a timely manner, and the parties to a traffic accident are not required to go to the relevant departments to collect them, saving time for both parties and the transportation department.

Through steps S207 to S209, the parties to a traffic accident can actively request to download the traffic accident liability determination information through the client without having to go to the relevant department to collect it, saving the time of the parties.

It should be noted that, in other embodiments, any one or two steps of step S204, step S205, and step S206 may be performed instead of all of them. In addition, S204, S205, and S206 are only step numbers, and do not indicate the execution order of the steps. The embodiment of the present application does not limit the execution order of steps S204, S205, and S206.

When the server receives traffic accident scene information, it indicates that a traffic accident has occurred. At this time, the server can actively report to a designated agency (such as an insurance company), which further reduces user operations and saves time for the user.

Based on this, the embodiment of the present application proposes a traffic accident processing method flow shown in FIG. 3.

FIG. 3 is a third exemplary flowchart of a traffic accident processing method according to an embodiment of the present application. As shown in FIG. 3, in this embodiment, the method for handling a traffic accident may include the following steps:

S301. Receive traffic accident scene information.

S302. Acquire a specified accident analysis model. The specified accident analysis model is based on a deep learning algorithm and is trained by using known traffic accident scene information and known traffic accident responsibility information corresponding to the known traffic accident scene information.

S303. Based on the designated accident analysis model, and based on the traffic accident scene information, generate traffic accident liability identification information.

S304. In response to receiving the traffic accident scene information, analyze the traffic accident scene information to determine a corresponding designated agency.

S305: Generate report information.

S306. Send the report information to the designated agency.

Among them, the designated agency may be an insurance company, a transportation department, and so on.

Through steps S304 to S306, the server automatically reports to the designated agency after receiving the information of the traffic accident scene, and does not require the relevant personnel to call the designated agency to report the case, which facilitates the parties involved in the accident and saves processing time for the parties. In this way, the length of stay of the parties at the scene is further reduced, and the probability of traffic congestion is reduced.

In an exemplary implementation process, the traffic accident scene information includes the location of the traffic accident; analyzing the traffic accident scene information to determine the corresponding designated agency includes: determining that the traffic accident location has jurisdiction according to the traffic accident location The right traffic management agency; or, according to the location of the traffic accident, obtain first aid resources of all hospitals within the designated area centered on the location of the traffic accident; according to the first aid resources, select free from all hospitals within the designated area The hospital with emergency resources is the candidate hospital; from the candidate hospitals, the hospital closest to the location where the traffic accident occurred is determined as the target hospital, and the target hospital is the designated institution.

In an exemplary implementation process, the method may further include: sending an emergency resource retrieval instruction to the target hospital, the emergency resource retrieval instruction is used to retrieve the ambulance and medical resources of the target hospital, and the emergency resource retrieval instruction is Includes address information for the location of the traffic accident.

In some traffic accidents, there may be cases where people are seriously injured and they cannot leave the site themselves and need rescue. At this time, not only need to report to the transportation department and insurance company, but also ask for rescue. In the embodiment of the present application, it may be judged whether a rescue situation is needed, and then whether the rescue is needed is determined according to the judgment result.

Based on this, the embodiment of the present application proposes a traffic accident processing method flow shown in FIG. 4.

FIG. 4 is a fourth exemplary flowchart of a traffic accident processing method provided by an embodiment of the present application. As shown in FIG. 4, in this embodiment, the method for handling a traffic accident may include the following steps:

S401. Receive traffic scene information.

S402. Acquire a designated accident analysis model. The designated accident analysis model is obtained by training based on a deep learning algorithm and using known traffic accident responsibility information corresponding to known traffic accident scene information and known traffic accident scene information.

S403. Based on the designated accident analysis model, generate traffic accident liability identification information based on traffic accident scene information.

S404. Determine whether rescue is needed based on the information of the traffic accident scene.

S405: If rescue is needed, send a rescue request message to the designated rescue agency.

Through steps S404 and S405, the server can intelligently determine whether rescue is needed based on the traffic accident scene information, and actively send rescue request information to the designated rescue agency when rescue is needed, so that the designated rescue agency can rush to the scene to treat the injured as soon as possible. This not only reduces the number of rescue operations required for the parties, but also provides a solution for situations where the parties cannot automatically ask for assistance, ensuring that injured people can be treated as soon as possible.

Among them, the designated rescue agency may be a hospital.

Among them, the rescue request information may include information on the location of the accident.

In an exemplary implementation process, the traffic accident scene information includes traffic accident scene images; judging whether rescue is needed based on the traffic accident scene information may include: extracting a bloodstain image from the traffic accident scene image to obtain an extraction result; and according to the extraction result To determine whether there is blood on the scene of a traffic accident; if there is blood on the scene of a traffic accident, determine that rescue is needed; or, if there is no blood on the scene of a traffic accident, determine that rescue is not required.

In an exemplary implementation process, judging whether rescue is needed based on traffic accident scene information may include: judging whether the traffic accident scene information shows that the vehicle owner's stay time exceeds the specified duration; if the traffic accident scene information shows that the vehicle owner's stay time exceeds the specified duration, It is determined that rescue is required; or, if the information on the scene of the traffic accident shows that the owner's stay time does not exceed the specified time, it is determined that rescue is not required.

In other embodiments of the present application, it is also possible to comprehensively determine whether rescue is required in combination with the above two methods, or to determine whether rescue is required based on factors other than blood stains and dwell time, which is not limited in this application.

In summary, the traffic accident processing method provided in the embodiment of the present application receives traffic accident scene information, obtains a specified accident analysis model trained based on a deep learning algorithm, and generates a traffic accident based on the traffic accident scene information based on the specified accident analysis model. Responsibility determination information can use the specified accident analysis model to automatically and intelligently generate traffic accident responsibility determination information, without the need for traffic police and insurance personnel to conduct on-site investigations, and no need for traffic accident personnel to wait on the scene, on the one hand saving labor costs and reducing accidents The processing time improves the speed of accident handling. On the other hand, the waiting time of traffic accident personnel is greatly reduced, so that traffic accident personnel without major disturbance can quickly leave the accident site, avoiding traffic congestion or shortening the congestion time.

Example two

The embodiment of the present application provides a traffic accident processing system, and the traffic accident processing system is configured to execute the traffic accident processing method of the foregoing embodiment.

FIG. 5 is a structural block diagram of a traffic accident processing system according to an embodiment of the present application. As shown in FIG. 5, in this embodiment, the traffic accident processing system includes:

The receiving module 510 is configured to receive traffic accident scene information.

The obtaining module 520 is configured to obtain a specified accident analysis model. The specified accident analysis model is based on a deep learning algorithm and is trained by using known traffic accident scene information and known traffic accident responsibility determination information corresponding to the known traffic accident scene information.

A generating module 530 is configured to generate traffic accident liability identification information based on traffic accident scene information based on a designated accident analysis model.

In an exemplary implementation process, the system may further include: a storage module, configured to store traffic accident responsibility determination information to a specified storage location.

In an exemplary implementation process, the system may further include: a request receiving module for receiving a download request for downloading the traffic accident responsibility determination information from the client; and an obtaining module for responding to the download request from a specified storage location Acquiring traffic accident liability determination information; a first sending module is used to send traffic accident liability determination information to a client.

In an exemplary implementation process, the system may further include a publishing module for publishing traffic accident responsibility determination information to a designated media platform.

In an exemplary implementation process, the system may further include: a second sending module, configured to send traffic accident responsibility determination information to the mobile device of the traffic accident party.

In an exemplary implementation process, the system may further include: an analysis module configured to analyze the traffic accident scene information in response to receiving the traffic accident scene information to determine a corresponding designated agency; a report information generation module, It is used to generate report information; a third sending module is used to send report information to a designated agency.

In an exemplary implementation process, the traffic accident scene information includes the location where the traffic accident occurred; when the analysis module is used to analyze the traffic accident scene information to determine the corresponding designated agency, it can be used to: Location, determine the traffic management agency that has jurisdiction over the location of the traffic accident; or, based on the location of the traffic accident, obtain first aid resources for all hospitals within the designated area centered on the location of the traffic accident; Among all the hospitals in the area, a hospital having free emergency resources is selected as an alternative hospital. From the alternative hospitals, a hospital closest to the place where a traffic accident occurs is determined as a target hospital, and the target hospital is a designated institution.

In an exemplary implementation process, when the acquisition module 520 is used to acquire a specified accident analysis model, the acquisition module 520 may be used to: acquire multiple sets of training data, each set of training data including known traffic accident scene information and the known traffic accident Known traffic accident responsibility identification information corresponding to the scene information; extract known traffic accident scene information from multiple sets of training data as input data for a designated accident analysis model; extract known traffic accident liability identification information from multiple sets of training data, As the output data of the specified accident analysis model; use the input data and output data to train the deep learning algorithm model to generate the specified accident analysis model.

In an exemplary implementation process, the system may further include: a determination module for determining whether rescue is needed based on traffic accident scene information; and a fourth sending module for sending a rescue request to a designated rescue agency if rescue is needed information.

In an exemplary implementation process, the traffic accident scene information includes a traffic accident scene image; when the judgment module is used to determine whether rescue is required based on the traffic accident scene information, it may be specifically used to: extract a bloodstain image from the traffic accident scene image To obtain the extraction result; to determine whether there is blood on the scene of the traffic accident according to the extraction result; if there is blood on the scene of the traffic accident, it is necessary to rescue;

In an exemplary implementation process, when the judgment module is used to determine whether rescue is needed based on traffic accident scene information, it can be specifically used to: determine whether the traffic accident scene information shows that the vehicle owner's stay time exceeds the specified duration; if the traffic accident scene information If the vehicle owner stays longer than the specified time, it is determined that rescue is needed; or if the traffic accident scene information shows that the vehicle owner stays less than the specified time, it is determined that no rescue is needed.

The traffic accident processing system provided in the embodiment of the present application receives traffic accident scene information, obtains a specified accident analysis model trained based on a deep learning algorithm, and generates traffic accident responsibility determination information based on the specified information of the accident accident scene based on the specified accident analysis model. Can use the specified accident analysis model to automatically and intelligently generate traffic accident responsibility determination information, no need for traffic police and insurance personnel to visit the scene, no need for traffic accident personnel to wait on the scene, on the one hand, save labor costs, reduce accident processing time, improve The speed of accident handling is greatly reduced. On the other hand, the waiting time of traffic accident personnel is greatly reduced, so that the traffic accident personnel without major disturbance can quickly leave the scene of the accident, avoiding causing traffic congestion or shortening the congestion time.

Example three

An embodiment of the present application provides a server. The server includes: a processor; a memory for storing processor-executable instructions; the processor is configured to: receive traffic accident scene information; obtain a designated accident analysis model; Based on a deep learning algorithm, trained using known traffic accident scene information and known traffic accident liability identification information corresponding to the known traffic accident scene information; based on a designated accident analysis model, generating traffic accident liability based on the traffic accident scene information Authorization information.

FIG. 6 is a simplified block diagram of a server provided by an embodiment of the present application. Referring to FIG. 6, the server 600 may include a processor 601 connected to one or more data storage tools, and the data storage tool may include a storage medium 606 and a memory unit 604. The server 600 may further include an input interface 605 and an output interface 607 for communicating with another device or system. The program code executed by the CPU of the processor 601 may be stored in the memory unit 604 or the storage medium 606.

The processor 601 in the server 600 calls the program code stored in the memory unit 604 or the storage medium 606 and performs the following steps:

Receive traffic accident scene information;

Obtain the specified accident analysis model. The specified accident analysis model is based on a deep learning algorithm and is trained using known traffic accident responsibility information corresponding to known traffic accident scene information and known traffic accident scene information.

Based on the designated accident analysis model, the traffic accident responsibility identification information is generated based on the traffic accident scene information.

In an exemplary implementation process, the processor 601 may be further configured to: store the traffic accident responsibility determination information to a designated storage location.

In an exemplary implementation process, the processor 601 may be further configured to:

Receiving a download request for downloading traffic accident liability determination information from a client;

In response to the download request, obtain traffic accident liability determination information from a designated storage location;

Send traffic accident responsibility determination information to the client.

Publish traffic accident liability determination information to designated media platforms.

Send traffic accident liability determination information to the mobile device of the traffic accident party.

In response to receiving traffic accident scene information, analyze the traffic accident scene information to determine the corresponding designated agency;

Generate report information;

Send report information to the designated agency.

In an exemplary implementation process, the traffic accident scene information includes the location where the traffic accident occurred; the processor 601 may also be configured to:

Based on the location of the traffic accident, determine the traffic management agency that has jurisdiction over the location of the traffic accident; or,

According to the location of the traffic accident, obtain the first-aid resources of all hospitals within the designated area centered on the location of the traffic accident; according to the first-aid resources, select the hospital with free emergency resources from all the hospitals within the designated area as the first-aid resources. Candidate hospitals: From among the alternative hospitals, determine the hospital closest to the location where the traffic accident occurred as the target hospital and the target hospital as the designated institution.

Obtain multiple sets of training data, each set of training data including known traffic accident scene information and known traffic accident responsibility identification information corresponding to the known traffic accident scene information;

Extract known traffic accident scene information from multiple sets of training data as input data for a designated accident analysis model;

Extract known traffic accident liability identification information from multiple sets of training data as output data for a designated accident analysis model;

Use input data and output data to train a deep learning algorithm model to generate a designated accident analysis model.

Determine whether rescue is required based on traffic accident scene information;

If rescue is needed, send a rescue request message to the designated rescue agency.

In an exemplary implementation process, the traffic accident scene information includes a traffic accident scene image; the processor 601 may also be configured to:

Extract the bloodstain image from the traffic accident scene image to get the extraction result;

According to the extraction results, determine whether there is blood on the scene of the traffic accident;

If there is blood on the scene of a traffic accident, it is determined that rescue is needed; or, if there is no blood on the scene of a traffic accident, it is determined that rescue is not required.

Determine whether the traffic accident scene information shows that the vehicle owner's stay time exceeds the specified time;

If the traffic accident scene information shows that the vehicle owner stays longer than the specified time, it is determined that rescue is needed; or if the traffic accident scene information shows that the vehicle owner stays time does not exceed the specified time, it is determined that rescue is not required.

Example 4

An embodiment of the present application provides a computer non-volatile readable storage medium. The computer non-volatile readable storage medium stores a computer program. When the computer program is executed by a processor, the following steps are implemented:

Receive traffic accident scene information;

Obtain the specified accident analysis model. The specified accident analysis model is based on a deep learning algorithm and is trained by using known traffic accident responsibility information corresponding to known traffic accident scene information and known traffic accident scene information.

In an exemplary implementation process, when the computer program is executed by the processor, the following steps are also implemented: storing the traffic accident liability determination information to a designated storage location.

In an exemplary implementation process, when a computer program is executed by a processor, the following steps are also implemented:

Send traffic accident responsibility determination information to the client.

Generate report information;

Send report information to the designated agency.

In an exemplary implementation process, the traffic accident scene information includes the location where the traffic accident occurred; when the computer program is executed by the processor, the following steps are also implemented:

According to the location of the traffic accident, obtain the first-aid resources of all hospitals within the designated area centered on the location of the traffic accident; according to the first-aid resources, select the hospital with free emergency resources from all hospitals within the designated area Candidate hospitals: From among the alternative hospitals, determine the hospital closest to the location where the traffic accident occurred as the target hospital and the target hospital as the designated institution.

In an exemplary implementation process, traffic accident scene information includes traffic accident scene images; when a computer program is executed by a processor, the following steps are also implemented:

The above are only preferred embodiments of this application, and are not intended to limit this application. Any modification, equivalent replacement, or improvement made within the spirit and principle of this application shall be included in this application Within the scope of protection.

Claims

A traffic accident processing method, characterized in that the method includes:

Receive traffic accident scene information;

Obtaining a specified accident analysis model, which is obtained by training based on a deep learning algorithm using known traffic accident scene information and known traffic accident responsibility identification information corresponding to the known traffic accident scene information;

Based on the designated accident analysis model, traffic accident responsibility determination information is generated according to the traffic accident scene information.
The method according to claim 1, further comprising:

Receiving a download request from a client to download the traffic accident responsibility determination information;

Acquiring the traffic accident responsibility determination information from the designated storage location in response to the download request;

Sending the traffic accident responsibility determination information to the client.
The method according to claim 1, further comprising:

In response to receiving the traffic accident scene information, analyzing the traffic accident scene information to determine a corresponding designated agency;

Generate report information;

Sending the report information to the designated agency.
The method according to claim 1, further comprising:

Determining whether rescue is needed based on the traffic accident scene information;

If rescue is needed, send a rescue request message to the designated rescue agency.
The method according to claim 4, wherein determining whether rescue is required based on the traffic accident scene information comprises:

The traffic accident scene information includes a traffic accident scene image;

Extracting a bloodstain image from the traffic accident scene image to obtain an extraction result;

Determining whether there is blood on the scene of the traffic accident according to the extraction result;

If there is blood on the scene of the traffic accident, it is determined that rescue is needed; or, if there is no blood on the scene of the traffic accident, it is determined that rescue is not required.
A traffic accident processing system, characterized in that the system includes:

A receiving module for receiving traffic accident scene information;

An acquisition module, configured to acquire a designated accident analysis model, which is based on a deep learning algorithm and is obtained by training using known traffic accident scene information and known traffic accident responsibility identification information corresponding to the known traffic accident scene information. of;

A generation module is configured to generate traffic accident responsibility determination information based on the traffic accident scene information based on the designated accident analysis model.
The system according to claim 6, further comprising:

A request receiving module, configured to receive a download request for downloading the traffic accident responsibility determination information from a client;

An obtaining module, configured to obtain the traffic accident responsibility determination information from the designated storage location in response to the download request;

A first sending module is configured to send the traffic accident responsibility determination information to the client.
The system according to claim 6, further comprising:

An analysis module, configured to analyze the traffic accident scene information in response to receiving the traffic accident scene information to determine a corresponding designated agency;

Report information generating module for generating report information;

A third sending module is configured to send the report information to the designated agency.
The system according to claim 6, further comprising:

A judging module, configured to judge whether rescue is needed according to the traffic accident scene information;

A fourth sending module is configured to send rescue request information to a designated rescue agency if rescue is needed.
The system according to claim 9, wherein the traffic accident scene information includes a traffic accident scene image; and the judgment module is specifically configured to:

Extracting a bloodstain image from the traffic accident scene image to obtain an extraction result;

Determining whether there is blood on the scene of the traffic accident according to the extraction result;

If there is blood on the scene of the traffic accident, it is determined that rescue is needed; or, if there is no blood on the scene of the traffic accident, it is determined that rescue is not required.
A server, characterized in that the server includes:

processor;

A memory for storing the processor-executable instructions;

The processor is configured to:

Receive traffic accident scene information;

Obtaining a specified accident analysis model, which is obtained by training based on a deep learning algorithm using known traffic accident scene information and known traffic accident responsibility identification information corresponding to the known traffic accident scene information;

Based on the designated accident analysis model, traffic accident responsibility determination information is generated according to the traffic accident scene information.
The server according to claim 11, wherein the processor is further configured to: receive a download request from a client to download the traffic accident responsibility determination information;

Acquiring the traffic accident responsibility determination information from the designated storage location in response to the download request;

Sending the traffic accident responsibility determination information to the client.
The server according to claim 11, wherein the processor is further configured to:

In response to receiving the traffic accident scene information, analyzing the traffic accident scene information to determine a corresponding designated agency;

Generate report information;

Sending the report information to the designated agency.
The server according to claim 11, wherein the processor is further configured to:

Determining whether rescue is needed based on the traffic accident scene information;

If rescue is needed, send a rescue request message to the designated rescue agency.
The server according to claim 14, wherein the traffic accident scene information includes a traffic accident scene image; and the processor is further configured to:

Extracting a bloodstain image from the traffic accident scene image to obtain an extraction result;

Determining whether there is blood on the scene of the traffic accident according to the extraction result;

If there is blood on the scene of the traffic accident, it is determined that rescue is needed; or, if there is no blood on the scene of the traffic accident, it is determined that rescue is not required.
A computer non-volatile readable storage medium stores a computer program, and is characterized in that, when the computer program is executed by a processor, the following steps are implemented:

Receive traffic accident scene information;

Obtaining a specified accident analysis model, which is obtained by training based on a deep learning algorithm using known traffic accident scene information and known traffic accident responsibility identification information corresponding to the known traffic accident scene information;

Based on the designated accident analysis model, traffic accident responsibility determination information is generated according to the traffic accident scene information.
The computer non-volatile readable storage medium according to claim 16, wherein when the computer program is executed by a processor, the following steps are further implemented:

Receiving a download request from a client to download the traffic accident responsibility determination information;

Acquiring the traffic accident responsibility determination information from the designated storage location in response to the download request;

Sending the traffic accident responsibility determination information to the client.
The computer non-volatile readable storage medium according to claim 16, wherein when the computer program is executed by a processor, the following steps are further implemented:

In response to receiving the traffic accident scene information, analyzing the traffic accident scene information to determine a corresponding designated agency;

Generate report information;

Sending the report information to the designated agency.
The computer non-volatile readable storage medium according to claim 16, wherein when the computer program is executed by a processor, the following steps are further implemented:

Determining whether rescue is needed based on the traffic accident scene information;

If rescue is needed, send a rescue request message to the designated rescue agency.
The computer non-volatile readable storage medium according to claim 19, wherein the traffic accident scene information includes a traffic accident scene image; and when the computer program is executed by a processor, the following steps are further implemented:

Extracting a bloodstain image from the traffic accident scene image to obtain an extraction result;

Determining whether there is blood on the scene of the traffic accident according to the extraction result;

If there is blood on the scene of the traffic accident, it is determined that rescue is needed; or, if there is no blood on the scene of the traffic accident, it is determined that rescue is not required.