WO2021012504A1

WO2021012504A1 - Intelligent traffic-based road information prompt method, device, server, and medium

Info

Publication number: WO2021012504A1
Application number: PCT/CN2019/117417
Authority: WO
Inventors: 曾燕玲; 杨晟
Original assignee: 平安科技（深圳）有限公司
Priority date: 2019-07-25
Filing date: 2019-11-12
Publication date: 2021-01-28
Also published as: CN110473418A; CN110473418B

Abstract

An intelligent traffic-based road information prompt method, a device, a server, and a storage medium. Said method comprises: acquiring automobile insurance case reporting information, the automobile insurance case reporting information at least including an accident situation image and an accident site (S1); evaluating, according to the accident situation image, the risk level of the accident site (S2); marking the accident site according to the risk level of the accident site, so as to obtain information of a high-risk accident site and information of a low-risk accident site (S3); and sending to a relevant user warning information including the high-risk accident site (S4). By means of the method above, accident blackspots can be counted, a damage level of an automobile in an accident is determined by means of machine learning, and according to the automobile damage level, a user is reminded of the danger level of the accident blackspots, so that the user would pay more attention, and the user can also be reminded to avoid the accident blackspots of a high danger level as much as possible when going out, thereby reducing traffic accidents.

Description

Road information prompt method, device, server and medium based on smart traffic

This application claims to be submitted to the Chinese Patent Office on July 25, 2019. The application number is 201910678463.6. The application titled "Dangerous Road Section Identification Method, Device, Server and Storage Medium" is the priority of the Chinese patent application, the entire content of which is incorporated by reference. In this application.

Technical field

This application relates to the field of computer technology, in particular to a method, device, server and storage medium for prompting road information based on smart traffic.

Background technique

The function of the vehicle safety early warning system or device is to remind the driver of the possible danger, so that the driver can be more vigilant and standardize the operation to achieve the purpose of avoiding danger. At present, some navigation devices or systems can provide warning functions for dangerous road sections. For example, when the vehicle is driving to a known dangerous road section, the system sends out some kind of warning message to remind the driver to slow down, which will greatly improve driving safety and Ensure the personal safety of drivers and passengers. However, in the prior art, the driver can only be reminded based on whether the current road section is an accident-prone area and whether there is a rockfall.

Summary of the invention

In view of the above, it is necessary to propose a road information prompt method, device, server, and storage medium based on smart transportation, which can assess the risk level of the accident site based on the car insurance report information, so as to remind users to avoid high-risk roads.

A method for prompting road information based on smart transportation. The method includes: acquiring auto insurance report information, wherein the auto insurance report information includes at least an auto insurance image and an accident location; and according to the damage level and location of the vehicle in the auto insurance image The surrounding environment information of the accident location and/or the current number of accident occurrences in the accident location evaluate the risk level of the accident location; mark the accident location according to the risk level of the accident location to obtain a high risk Information on the location of the accident and information on the location of the low-risk accident; sending warning information including the location of the high-risk accident to relevant users.

A road information prompting device based on smart transportation. The device includes: an acquisition module for acquiring auto insurance report information, wherein the auto insurance report information includes at least an auto insurance image and an accident location; The damage level of the vehicle in the car insurance image, the surrounding environment information of the accident place, and/or the current number of accidents in the accident place evaluate the risk level of the accident place; the marking module is used to evaluate the risk level of the accident place according to the accident The risk level of the place of occurrence marks the place of occurrence of the accident to obtain information on the place of occurrence of high-risk accidents and information of the place of occurrence of low-risk accidents; a sending module is used to send warning information including the place of occurrence of the high-risk accident to relevant users.

A server includes a processor and a memory, and the processor is configured to execute at least one computer-readable instruction stored in the memory to implement the following steps: obtain car insurance report information, wherein the car insurance report information includes at least a car insurance image And the place where the accident occurred; assess the risk level of the accident place according to the damage level of the vehicle in the car insurance image, the surrounding environment information of the accident place and/or the current number of accident occurrences in the accident place; The risk level of the accident occurrence location marks the accident occurrence location to obtain high-risk accident occurrence location information and low-risk accident occurrence location information; and send warning information including the high-risk accident occurrence location to relevant users.

A non-volatile readable storage medium that stores at least one computer readable instruction, and when the at least one computer readable instruction is executed by a processor, the following steps are implemented: Obtain a car insurance report Information, wherein the car insurance report information includes at least a car insurance image and the location of the accident; according to the damage level of the vehicle in the car insurance image, the surrounding environment information of the accident location and/or the current accident in the accident location Evaluate the risk level of the accident location based on the number of occurrences; mark the accident location based on the risk level of the accident location to obtain information on the location of high-risk accidents and information on the location of low-risk accidents; send information including the high-risk accident The warning information of the place of occurrence to the relevant user.

It can be seen from the above technical solutions that the intelligent transportation-based road information prompt method, device, server, and storage medium provided in this application evaluate the risk level of the accident site based on the car insurance image information, and mark the risk level of the accident site Send warning messages including locations of high-risk accidents to relevant users. It can not only count accident-prone areas, and remind users of the risk levels of the accident-prone areas based on the level of vehicle damage in the accident, so that users can get enough attention, but also remind users to avoid the high-risk accident-prone areas as much as possible when traveling. , Thereby reducing the occurrence of traffic accidents.

Description of the drawings

FIG. 1 is a flowchart of a method for prompting road information based on smart traffic according to Embodiment 1 of the present application.

2 is a flowchart of a method for improving the clarity level of a car insurance image in a road information prompt method based on smart traffic provided in Embodiment 1 of the present application.

FIG. 3 is a flowchart of a method for determining the risk level of an accident location according to the vehicle damage level in the car insurance image in the road information prompt method based on smart traffic provided by Embodiment 1 of the present application.

Fig. 4 is a diagram of functional modules in a preferred embodiment of a road information prompting device based on smart traffic of the present application provided in the second embodiment of the present application.

FIG. 5 is a schematic diagram of a server provided in Embodiment 3 of the present application.

Detailed ways

Example one

FIG. 1 is a flowchart of a method for prompting road information based on smart traffic according to Embodiment 1 of the present application. According to different needs, the execution order in this flowchart can be changed, and some steps can be omitted.

Step S1: Acquire car insurance report information, where the car insurance report information includes at least a car insurance image and an accident location.

In this embodiment, the server may obtain report information from the mobile terminal. The mobile terminal may be a smart terminal such as a mobile phone, a tablet computer, a personal digital assistant, a wearable device (for example, a smart watch, smart glasses), or any other suitable electronic device. The report information may include the car insurance image and the place where the accident occurred, and may also include the owner's information, the license plate number of the dangerous vehicle, the time of the accident, and the reason for the accident. The car insurance image may be video information or image information taken by a car owner.

In other embodiments, the car insurance image may be video information or image information collected on site by an operator (such as a surveyor), and the operator sends the car insurance image to a database of another system (such as an insurance company system). The server may obtain the car insurance image from the other system database. The car insurance image is associated with the place where the accident occurred.

The car insurance image may include a general term for various graphics or images, usually referring to images with visual effects, and generally may include images on paper media, negatives or photos, televisions, projectors, or computer screens. The car insurance image described in this embodiment may include computer image data stored on a readable storage medium after being captured by a camera or camera device, and may include various types of computer images such as vector graphics, bitmaps, static and dynamic images.

Preferably, as shown in FIG. 2, after obtaining the car insurance report information, the smart traffic-based road information prompt method can also improve the clarity level of the car insurance image. The method for improving the clarity level of the car insurance image includes:

S10: Calculate the first clarity level of the car insurance image.

In this embodiment, the first sharpness level of the car insurance image can be calculated through a gray-scale change function, a gradient function, or an image gray-scale entropy function. The gray-scale change function, the gradient function, or the image gray-scale entropy function are existing techniques for calculating image clarity, and will not be repeated here.

S11: Compare whether the first definition level is lower than a preset definition level. When the first definition level is lower than the preset definition level, step S12 is executed; when the first definition level is higher than the preset definition level, step S2 is executed.

S12: Enhance the sharpness level of the car insurance image to obtain a new car insurance image, and calculate a second sharpness level of the new car insurance image.

In this embodiment, the method of enhancing the clarity level of the car insurance image to obtain a new car insurance image includes:

a: Calculate the high frequency and low frequency components in the car insurance image.

Specifically, low-pass filtering is performed on the spatial signal of the car insurance image to obtain the low-frequency component of the car insurance image, and the spatial signal of the car insurance image is subjected to a difference calculation to obtain the high-frequency component of the car insurance image.

b: Recognizing the high-frequency components in the car insurance image, and performing enhancement processing on the recognized high-frequency components.

Specifically, the high-frequency components of the car insurance image are identified and then classified, the noise, details, small edges, and large edges in the high-frequency components are separated, and then the noise and details in the high-frequency components , Small edges and large edges are enhanced.

The enhancing processing of the identified high-frequency components includes:

b1: Calculate the dynamic threshold of coring noise reduction, and judge whether the points in the car insurance image belong to noise;

By comparing the absolute value of the high-frequency components of the points of the car insurance image with the dynamic threshold of coring noise reduction, it is judged whether the points of the car insurance image belong to noise; if the points in the car insurance image are high If the absolute value of the frequency component is less than the coring noise reduction threshold, it is confirmed that the point is noise, and step b2 is executed; if the absolute value of the high frequency component of the point in the car insurance image is greater than or equal to the coring noise reduction threshold , Confirm that the point is not noise, and go to step b3.

b2: Set the value of the high-frequency component corresponding to the point to 0.

By setting the value of the high-frequency component corresponding to the point to 0, small high-frequency noise is suppressed to achieve the purpose of coring noise reduction.

b3: Apply a nonlinear high-frequency enhancement curve to enhance the high-frequency components of the car insurance image.

After the nonlinear high-frequency enhancement curve is processed, the details in the high-frequency component, the different regions corresponding to the small edges and the large edges can be processed to different degrees, and the enhanced image obtained thereby has a smooth and natural transition, and The monotonicity of high-frequency components is maintained.

c: superimpose the enhanced high frequency component and the low frequency component to obtain a new car insurance image.

After enhancing the definition of the car insurance image, a new car insurance image is obtained, and the second definition of the new car insurance image is calculated. It is understandable that the calculation method of the second definition of the new car insurance image is consistent with the calculation method of the first definition of the car insurance image, and will not be repeated here.

S13: Compare whether the second sharpness level is lower than the preset sharpness level. When the second sharpness level is lower than the preset sharpness level, return to step S12; when the second sharpness level is higher than the preset sharpness level, perform step S2.

It is understandable that before calculating the first clarity level of the car insurance image, the method for prompting road information based on smart traffic may further include the step of performing data preprocessing on the car insurance image, wherein the data The preprocessing process includes: analog-to-digital conversion, binarization, image smoothing, transformation, enhancement, restoration, filtering, etc.

In this embodiment, the clarity of the auto insurance image uploaded by the user is adjusted to obtain an auto insurance image that meets the claim settlement requirements, which can improve the work efficiency of the self-assisted compensation system. It can also avoid the trouble that the user re-uploads the car insurance image when the clarity of the car insurance image uploaded by the user does not meet the claims requirements, thereby improving the user experience.

It is understandable that after obtaining the car insurance report information, the car insurance image is associated with the location of the accident and then stored in the database of the server.

Step S2: Evaluate the risk level of the accident place based on the damage level of the vehicle in the car insurance image, the surrounding environment information of the accident place and/or the current accident occurrence frequency of the accident place.

In this embodiment, the risk level of the accident site can be evaluated according to the damage level of the vehicle in the car insurance image and/or the surrounding environment information of the accident site.

In the first embodiment, the damage level of the vehicle in the car insurance image is acquired according to the car insurance image, and the risk level of the accident site is evaluated according to the damage level.

Specifically, as shown in FIG. 3, the method of assessing the risk level of the accident site based on the damage level of the vehicle in the car insurance image and the current number of accident occurrences in the accident site includes:

Step S21: Acquire the damage level of the vehicle in the car insurance image.

In this embodiment, the damage area recognition model generated by pre-training is called to recognize the car insurance image to obtain vehicle damage area information; the damage area of the damage area is calculated according to the vehicle damage area information; the vehicle damage area and the damage The area is input into a preset calculation model and the calculation result is obtained, wherein the preset calculation model is the product of the damage area and the weight value of the damage area plus the damage area; when the calculation result is greater than or equal to the predicted Set a value to confirm that the damage level of the vehicle in the car insurance image is high; when the calculation result is less than the preset value, it is confirmed that the damage level of the vehicle in the car insurance image is low.

Specifically, the damage area recognition model is called to identify the damage area of the car insurance image, and then the damage area of the damage area is calculated, and a preset calculation model is used to determine the vehicle in the car insurance image according to the damage area and the corresponding damage area. The level of damage that occurred.

In this embodiment, a recognition model for recognizing the damaged area in the car insurance image may be pre-trained and generated, and the recognition model may be one of multiple models related to image processing.

Preferably, the damage area recognition model is a convolutional neural network model.

Generally speaking, the vehicle damage area may include a first area, a second area, a third area, a fourth area, and a fifth area. The first area is a direct collision damage area (also referred to as a primary damage area); the second area is an indirect collision damage area (also referred to as a secondary damage area); and the third area is a mechanical damage area, namely Damaged areas such as automobile mechanical parts, power transmission system parts, accessories, etc.; the fourth area is the passenger compartment area, and various damages to the car compartment, including interior parts, lights, control devices, operating devices, and decorative layers; The fifth area is the exterior and paint area, that is, damage to the exterior parts of the car body and various external parts.

Preferably, the training process of the damage area recognition model includes:

1) Obtain a preset number of auto insurance image samples;

2) Extracting a preset ratio of auto insurance images from the auto insurance image samples as the sample images to be trained, and using the remaining auto insurance image samples in the preset number of auto insurance image samples as the sample images to be verified;

3) Perform model training using each sample image to be trained to generate the convolutional neural network model, and use each sample image to be verified to verify the generated convolutional neural network model;

4) If the verification pass rate is greater than or equal to the preset threshold, the training is completed; otherwise, the number of auto insurance image samples is increased to perform training and verification again.

Exemplarily, suppose that 100,000 sample pictures of claims auto insurance images are obtained. Extract the pre-set ratio of sample pictures of the claim insurance policy as the training set, and use the remaining auto insurance image sample pictures in the preset number of auto insurance image sample pictures as the test set. The number of auto insurance image sample pictures in the training set is greater than the auto insurance image samples in the test set The number of pictures, for example, 80% of the car insurance image sample pictures are used as the training set, and the remaining 20% of the car insurance image sample pictures are used as the test set.

When training the convolutional neural network model for the first time, the parameters of the convolutional neural network model are trained with default parameters, and the parameters are continuously adjusted during the training process. The verified sample image verifies the generated convolutional neural network model. If the verification pass rate is greater than or equal to the preset threshold, for example, the pass rate is greater than or equal to 98%, the training ends, and the trained convolutional neural network model is Identify the damage area recognition model of the vehicle damage area in the car insurance image; if the verification pass rate is less than a preset threshold, for example, less than 98%, increase the number of auto insurance image samples and re-execute the above steps until the verification pass rate is greater than or Equal to the preset threshold.

During the test, the trained convolutional neural network model is used to identify the damage area of a preset number (such as ten) of auto insurance image samples randomly selected from the auto insurance image samples in the test set, and the recognition results are compared with manual The confirmed vehicle damage level results are compared to evaluate the recognition effect of the trained convolutional neural network model.

After identifying the damaged area of the vehicle in the car insurance image, the method for prompting road information based on smart traffic includes the step of calculating the size of the damaged area of the damaged area.

It is understandable that when calculating the damage area of the damage area, it is necessary to first calculate the damage area of the vehicle in the car insurance image, and then calculate the actual area size of the damage area according to a certain ratio. According to the damage area, the weight value corresponding to the damage area, and the corresponding damage area, a preset calculation model is used to determine the level of damage to the vehicle in the car insurance image.

Exemplarily, the preset calculation model may be the product of the damage area and the weight value of the damage area plus the damage area. The level of damage to the vehicle in the car insurance image is confirmed according to the size of the calculation result of the preset calculation model. When the calculation result is greater than or equal to the preset value, it is confirmed that the damage level of the vehicle in the car insurance image is high; when the calculation result is less than the preset value, it is confirmed that the damage level of the vehicle in the car insurance image is low .

Step S22: Obtain historical accident data of the place where the accident occurred, where the historical accident data resulted in including the number of accidents and the damage level of the vehicle in the historical accident.

In this embodiment, the database of the server stores car insurance images and accident locations in historical report information. It is understandable that the historical accident data also includes the time when the accident occurred.

Step S23: Determine whether the current number of accidents in the accident place is greater than a preset number, and determine whether the damage level of the vehicle in the car insurance image and the current number of accidents is higher than a first preset level.

In this embodiment, in order to solve the problem that in the prior art, the user is only prompted that a certain area is an accident-prone area, and the user cannot pay enough attention to it, traffic accidents still occur in the accident-prone area. This solution not only counts the number of accidents that occur in the accident site, but also determines the damage level of the vehicle in the accident, and associates the number of accidents with the vehicle damage level to assess whether the lot is a high-risk accident site. .

Specifically, when the current number of accident occurrences at the place where the accident occurred is less than or equal to the preset number, or the damage level of the vehicle in the current number of accident occurrences and the damage level of the vehicle in the car insurance image are both lower than the first preset level, It is confirmed that the car accident at the place where the accident occurred is an accident and is not the place where a high-risk accident occurs. There is no need to deliberately remind the user to execute step S24 to mark the place where the accident occurs as a low-risk accident place; when the place where the accident occurs is currently When the number of accidents is greater than the preset number, and the damage level of the vehicle in the current number of accidents and the damage level of the vehicle in the car insurance image are both higher than the first preset level, confirm that the accident site is a high-risk accident site , A serious traffic accident is prone to occur, and step S25 is executed to mark the place of the accident as a place of high-risk accident.

It should be noted that if the current number of accidents at the accident location is less than or equal to the preset number, or the damage level of the vehicle in the current number of accidents is higher than the first preset level, and the vehicle in the car insurance image When the damage level of is lower than the first preset level, it is confirmed that the car accident at the location of the accident is an accident and is not a high-risk accident location. There is no need to deliberately remind the user. Step S24 is performed to mark the accident location as low-risk. The place where the accident occurred.

In the second embodiment, the surrounding environment information of the accident place may be obtained according to the car insurance image, and the risk level of the accident place may be evaluated according to the surrounding environment information of the accident place.

Specifically, the method for assessing the risk level of the accident site according to the surrounding environment information of the accident site includes:

(1) Identify the road environment information in the car insurance image; the road environment information includes whether there are foreign objects (such as gravel) on the road, whether the road is rugged, and whether it is a sharp turn, and so on. In this embodiment, the road surface environment information in the car insurance image is recognized by an image recognition method. The image recognition method is an existing technology and will not be repeated here.

(2) Judging the road surface condition level according to the road surface environment information;

When there is a foreign object on the road surface or the road is rough or the current road is a sharp turn, it is determined that the road surface is in bad condition and is prone to traffic accidents; when there is no foreign object on the road surface and the road surface is flat and the current road is not a sharp turn, it is determined The road surface is in good condition and traffic accidents are unlikely to occur.

(3) Obtaining historical accident data of the place where the accident occurred, where the historical accident data resulted in including the number of accidents and the road surface condition level in the historical accident;

(4) Determine whether the current number of accident occurrences in the place where the accident occurred is greater than the preset number, and determine whether the road condition level in the current number of accident occurrences is higher than the second preset level;

Specifically, when the current number of accident occurrences at the accident location is less than or equal to the preset number, or the road condition level in the current accident occurrence number is lower than the second preset level, it is confirmed that the traffic accident at the accident location belongs to Unexpected situation is not the place where the high-risk accident occurs. There is no need to deliberately remind the user, and the process goes to step S3; when the current number of accidents in the place where the accident occurs is greater than the preset number, and the road condition level in the current number of accidents is higher than the second expected When setting the level, it is confirmed that the accident site is a high-risk accident site and is prone to serious traffic accidents, and the process goes to step S3.

In the third embodiment, the road information prompt method based on smart traffic may be based on the current number of accidents in the accident place, the damage level of the vehicle in the car insurance image, and the surrounding environment of the accident place Information to assess the risk level of the place where the accident occurred.

Specifically, if the current number of accident occurrences at the accident place is greater than the preset number, and the damage level of the vehicle is higher than the first preset level, or the current accident occurrence number at the accident place is greater than When the preset number of times and it is determined that the road surface condition level in the surrounding environment information is higher than the second preset level, step S3 is executed; if the current number of accident occurrences at the accident site is less than the preset number, and When the damage level of the vehicle is lower than the first preset level, and it is determined that the road surface condition level in the surrounding environment information is lower than the second preset level, step S3 is executed.

Step S3, marking the accident occurrence location according to the risk level of the accident occurrence location, and obtaining information about the occurrence location of a high-risk accident and a low-risk accident occurrence location.

Specifically, in the first embodiment, when the current number of occurrences of accidents at the location of the accident is less than or equal to the preset number, or the damage level of the vehicle in the current number of accidents is lower than the first preset level, the accident is confirmed The car accident at the place of occurrence is an accident, not the place where a high-risk accident occurs. There is no need to deliberately remind the user to mark the place of the accident as a place of low-risk accident; when the current number of accidents at the place of the accident is greater than the preset number, And when the damage level of the vehicle in the current number of accidents is higher than the first preset level, it is confirmed that the accident site is a high-risk accident site and is prone to serious traffic accidents, and the accident site is marked as a high-risk accident occurrence Ground.

In the second embodiment, when the current number of accident occurrences at the location of the accident is less than or equal to the preset number, or the road condition level in the current number of accident occurrences is lower than the second preset level, the location of the accident is confirmed A car accident is an accident and is not a high-risk accident location. There is no need to deliberately remind the user to mark the accident location as a low-risk accident location; when the current accident occurrence number in the accident location is greater than the preset number, and the current When the road condition level in the number of accidents is higher than the second preset level, it is confirmed that the accident site is a high-risk accident site and is prone to serious traffic accidents, and the accident site is marked as a high-risk accident site.

In the third embodiment, if the current number of accident occurrences at the accident place is greater than the preset number, and the damage level of the vehicle is higher than the first preset level, or the current accident place When the number of accidents is greater than the preset number and it is determined that the road surface condition level in the surrounding environment information is higher than the second preset level, the location of the accident is marked as a high-risk accident location; if the accident occurs The current number of accidents at the locality is less than the preset number, and the damage level of the vehicle is lower than the first preset level, and the road surface condition level in the determination that the surrounding environment information is lower than the second preset When setting the level, mark the place where the accident occurred as a low-risk accident place.

Step S4: Send a warning message including the location of the high-risk accident to the relevant user.

In this embodiment, when it is determined that the place where the accident occurred is the place where the high-risk accident occurred, the detailed information including the place where the high-risk accident occurred can be sent to the user who signed the insurance contract with the relevant insurance company, so that the user can be prompted In future trips, try to avoid the place where the high-risk accident occurs, so as to reduce the occurrence of traffic accidents and also reduce the number of claims for insurance companies.

Of course, it is also possible to send detailed information including the location of the low-risk accident to the user who has signed an insurance contract with the relevant insurance company, so that the user can be prompted to preferentially select the location of the low-risk accident during future trips, thereby reducing traffic The accident happened.

Preferably, after marking the location of the accident as the location of a high-risk accident, the information of the location of the high-risk accident may be sent to the navigation system. Therefore, when the user drives the vehicle to arrive at the location of the high-risk accident, the navigation system can send voice prompt information to remind the user that the vehicle is about to arrive at the location of the high-risk accident. As a result, the user's vigilance can be increased, and the user is reminded to be more careful when driving the vehicle through the place where the high-risk accident occurs, so as to avoid traffic accidents. It is also possible to provide the user with a route that avoids the place where the high-risk accident occurs when the user uses the navigation system to plan a travel route.

Preferably, the method for prompting road information based on smart traffic can also prompt related users in combination with the time of the accident.

When it is determined that the place of occurrence of the accident is the place of occurrence of a high-risk accident, the time of occurrence of the accident in the historical accident data of the place of occurrence of the accident is acquired.

If the accident occurrence time in the historical accident data is concentrated in a certain time period, for example, 5:00-9:00 in the morning, then the relevant users are reminded to avoid the high-risk accident when traveling in the time period. This can reduce the occurrence of traffic accidents and reduce the number of claims for insurance companies.

Preferably, the method for prompting road information based on smart traffic can also remind related users in combination with weather conditions when the accident occurs.

When it is determined that the location of the accident is the location of a high-risk accident, the weather conditions at the time of the accident in the accident location in the historical accident data are acquired.

If the weather in the place where the accident occurred in the historical accident data is bad, such as heavy fog, or heavy rain, or heavy snow, the user is reminded to avoid the place where the high-risk accident occurs in bad weather, thereby It can reduce the occurrence of traffic accidents and reduce the number of claims for insurance companies.

In summary, the smart transportation-based road information prompt method provided by this application includes acquiring auto insurance report information, where the auto insurance report information includes at least an auto insurance image and an accident location; the accident occurrence is evaluated based on the auto insurance image The risk level of the place; mark the place of the accident according to the risk level of the place of the accident; send warning information including the place of the high-risk accident to relevant users. It can count the accident-prone areas and remind users of the risk levels of the accident-prone areas according to the level of vehicle damage in the accident, so that users can get enough attention, and remind users to avoid the high-risk accident-prone areas as much as possible when traveling, thereby Can reduce the occurrence of traffic accidents.

The above are only specific implementations of this application, but the scope of protection of this application is not limited to this. For those of ordinary skill in the art, without departing from the creative concept of this application, they can also make Improvements, but these all belong to the scope of protection of this application.

In the following, the functional modules and hardware structure of the server that implement the above-mentioned smart transportation-based road information prompting method are introduced in conjunction with FIG. 4 and FIG. 5.

Example two

FIG. 4 is a diagram of functional modules in a preferred embodiment of a road information prompting device based on smart traffic in this application.

In some embodiments, the smart traffic-based road information prompting device 40 (for ease of description, hereinafter referred to as "road information prompting device 40") runs in a server. The road information prompting device 40 may include multiple functional modules composed of program code segments. The program code of each program segment in the road information prompting device 40 can be stored in the memory and executed by at least one processor to execute (see Figure 1 and related descriptions for details) the road information prompt function based on smart traffic .

In this embodiment, the road information prompting device 40 can be divided into multiple functional modules according to the functions it performs. The functional modules may include: an acquisition module 401, an evaluation module 402, a marking module 403, and a sending module 404. The module referred to in this application refers to a series of computer-readable instruction segments that can be executed by at least one processor and can complete fixed functions, and are stored in a memory. In some embodiments, the functions of each module will be detailed in subsequent embodiments.

The acquisition module 401 is configured to acquire auto insurance report information, where the auto insurance report information includes at least a car insurance image and a place where an accident occurred.

Preferably, after acquiring the car insurance report information, after acquiring the car insurance report information, the smart transportation-based road information prompt device 40 can also improve the clarity level of the car insurance image. The method for improving the clarity level of the car insurance image includes:

(1) Calculate the first sharpness level of the car insurance image;

In this embodiment, the first sharpness level of the car insurance image can be calculated by calculations such as a grayscale change function, a gradient function, or an image grayscale entropy function. The gray-scale change function, the gradient function, or the image gray-scale entropy function are existing techniques for calculating image clarity, and will not be repeated here.

(2) Compare the first definition level with a preset definition level; when the first definition level is lower than the preset definition level, enhance the definition level of the car insurance image to Obtain a new car insurance image, and calculate the second sharpness level of the new car insurance image; when the first sharpness level is higher than the preset sharpness level, according to the damage of the vehicle in the car insurance image The level, the surrounding environment information of the accident place and/or the current number of accident occurrences of the accident place evaluate the risk level of the accident place.

The enhancing processing of the identified high-frequency components includes:

Calculate the dynamic threshold of coring noise reduction, and determine whether the points in the car insurance image belong to noise.

By comparing the absolute value of the high-frequency components of the points of the car insurance image with the dynamic threshold of coring noise reduction, it is determined whether the points of the car insurance image belong to noise.

If the absolute value of the high frequency component of the point in the car insurance image is less than the coring noise reduction threshold, it is confirmed that the point is noise, and the value of the high frequency component corresponding to the point is set to 0. By setting the value of the high-frequency component corresponding to the point to 0, small high-frequency noise is suppressed to achieve the purpose of coring noise reduction.

If the absolute value of the high-frequency component of the point in the car insurance image is greater than or equal to the cored noise reduction threshold, it is confirmed that the point is not noise, and a nonlinear high-frequency enhancement curve is applied to perform the high-frequency component of the car insurance image. Enhanced. After the nonlinear high-frequency enhancement curve is processed, the details in the high-frequency component, the different regions corresponding to the small edges and the large edges can be processed to different degrees, and the enhanced image obtained thereby has a smooth and natural transition, and The monotonicity of high-frequency components is maintained.

(3) Compare the second definition level with the preset definition level; when the second definition level is lower than the preset definition level, enhance the definition of the car insurance image Level to obtain a new car insurance image, and calculate the second clarity level of the new car insurance image; when the second clarity level is higher than the preset clarity level, evaluate the The risk level of the place where the accident occurred.

The evaluation module 402 is configured to evaluate the risk level of the accident place according to the damage level of the vehicle in the car insurance image, the surrounding environment information of the accident place and/or the current number of accident occurrences in the accident place .

Specifically, the method for assessing the risk level of the accident site based on the damage level of the vehicle in the car insurance image and the current number of accident occurrences in the accident site includes:

(a) Acquire the damage level of the vehicle in the car insurance image.

Preferably, the training process of the damage area recognition model includes:

1) Obtain a preset number of auto insurance image samples;

After identifying the damaged area of the vehicle in the car insurance image, the evaluation module 402 is further used to calculate the size of the damaged area of the damaged area.

Exemplarily, the preset calculation model may be the product of the damage area and the weight value of the damage area plus the damage area. The level of damage to the vehicle in the car insurance image is confirmed according to the size of the calculation result of the preset calculation model. When the calculation result is greater than or equal to the preset value, it is confirmed that the damage level of the vehicle in the car insurance image is high; when the calculation result is less than the preset value, the damage level of the vehicle in the car insurance image is confirmed low.

(b) Acquiring historical accident data of the place where the accident occurred, wherein the historical accident data resulted in including the number of accidents and the damage level of the vehicle in the historical accident.

(c) Determine whether the current number of accidents at the accident site is greater than a preset number, and determine whether the damage level of the vehicle in the car insurance image and the current number of accidents is higher than the first preset level.

Specifically, when the current number of accident occurrences in the place where the accident occurred is less than or equal to the preset number, or the damage level of the vehicle in the current number of accidents and the damage level of the vehicle in the car insurance image are both lower than the first preset level , Confirming that the car accident occurred at the place of the accident is an accident, not the place where the high-risk accident occurred, and there is no need to deliberately remind the user to mark the place of the accident as the place of low-risk accident; the current number of accidents when the accident occurred When the number of accidents is greater than the preset number, and the damage level of the vehicle in the current number of accidents and the damage level of the vehicle in the car insurance image are higher than the first preset level, it is confirmed that the accident site is a high-risk accident site and is prone to occur For a serious traffic accident, mark the place where the accident occurred as a place where a high-risk accident occurred.

It should be noted that if the current number of accidents at the accident location is less than or equal to the preset number, or the damage level of the vehicle in the current number of accidents is higher than the first preset level, and the vehicle in the car insurance image When the damage level of is lower than the first preset level, it is confirmed that the car accident at the place of the accident is an accident and is not a high-risk accident place, and there is no need to deliberately remind the user to mark the place of the accident as a low-risk accident place.

(1) Identify road environment information in the car insurance image. The road environment information includes whether there are foreign objects (such as gravel) on the road surface, whether the road surface is bumpy, whether it is a sharp turn, and so on. In this embodiment, the road surface environment information in the car insurance image is recognized by an image recognition method. The image recognition method is an existing technology and will not be repeated here.

(2) Judging the road surface condition level based on the road surface environment information.

(3) Obtain historical accident data of the place where the accident occurred, wherein the historical accident data causes the number of accidents to be included and the road surface condition level in the historical accident.

(4) Determine whether the current number of accident occurrences in the place where the accident occurred is greater than the preset number, and determine whether the road surface condition level in the current number of accident occurrences is higher than a second preset level.

Specifically, when the current number of accident occurrences at the place where the accident occurred is less than or equal to the preset number, or the road condition level in the current number of accident occurrences is lower than the second preset level, it is confirmed that the car accident at the place where the accident occurs is an accident If the situation is not a high-risk accident location, there is no need to deliberately remind the user to mark the accident location according to the risk level of the accident location; when the current accident occurrence number of the accident location is greater than the preset number, and the current accident occurrence When the road condition level is higher than the second preset level in the number of times, it is confirmed that the accident site is a high-risk accident site and is prone to serious traffic accidents, and the accident site is marked according to the risk level of the accident site.

In the third embodiment, the road information prompting device 40 may use the current number of accident occurrences in the accident place, the damage level of the vehicle in the car insurance image, and the surrounding environment information of the accident place. Assess the risk level of the place where the accident occurred.

Specifically, if the current number of accident occurrences at the accident place is greater than the preset number, and the damage level of the vehicle is higher than the first preset level, or the current accident occurrence number at the accident place is greater than When the preset number of times and the road surface condition level in the surrounding environment information is determined to be higher than the second preset level, the accident location is marked according to the risk level of the accident location; The current number of accidents is less than the preset number of times, the damage level of the vehicle is lower than the first preset level, and the road surface condition level in the determining the surrounding environment information is lower than the second preset level When the accident occurs, mark the accident occurrence location according to the risk level of the accident occurrence location.

The marking module 403 is used to mark the place of the accident according to the risk level of the place of the accident to obtain information on the place of occurrence of high-risk accidents and information of the place of occurrence of low-risk accidents.

In the second embodiment, when the current number of accident occurrences in the accident place is less than or equal to the preset number, or the road surface condition level in the current number of accident occurrences is lower than the second preset level, the occurrence of the accident is confirmed A car accident in a locality is an accident and is not a high-risk accident location. There is no need to deliberately remind the user to mark the accident location as a low-risk accident location; when the current accident occurrence number in the accident location is greater than the preset number, and When the road condition level in the current number of accidents is higher than the second preset level, it is confirmed that the accident site is a high-risk accident site and is prone to serious traffic accidents, and the accident site is marked as a high-risk accident site.

The sending module 404 is configured to send warning information including the location of the high-risk accident to relevant users.

Preferably, the road information prompting device 40 can also prompt related users in combination with the time of the accident.

When it is determined that the place of occurrence of the accident is the place where a high-risk accident occurs, obtain the time of the accident in the historical accident data of the place where the accident occurred; if the time of the accident in the historical accident data is concentrated in a certain time period, for example , From 5:00 to 9:00 in the morning, the relevant users are reminded to avoid travel where the high-risk accident occurs during the time period, thereby reducing the occurrence of traffic accidents and reducing the number of claims for insurance companies.

Preferably, the road information prompting device 40 can also remind related users in combination with weather conditions when the accident occurs.

In summary, the road information prompting device 40 provided in the present application can count accident-prone areas, and remind users of the risk levels of the accident-prone areas according to the level of vehicle damage in the accident, so that the user can get enough attention and remind the user to do everything possible when traveling. It is possible to avoid the high-risk accident-prone areas, thereby reducing the occurrence of traffic accidents.

The above-mentioned integrated unit implemented in the form of a software function module may be stored in a nonvolatile readable storage medium. The above-mentioned software function module is stored in a storage medium, and includes several instructions to make a computer device (which can be a personal computer, a dual-screen device, or a network device, etc.) or a processor to execute the various embodiments of this application Method part.

Example three

FIG. 5 is a schematic diagram of a server provided in Embodiment 3 of this application.

The server 5 includes: a database 51, a memory 52, at least one processor 53, computer readable instructions 54 stored in the memory 52 and executable on the at least one processor 53 and at least one communication bus 55.

When the at least one processor 53 executes the computer readable instruction 54, the steps in the above embodiment of the method for prompting road information based on smart traffic are implemented.

Exemplarily, the computer-readable instructions 54 may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 52 and executed by the at least one processor 53 Execute to complete this application. The one or more modules/units may be a series of computer-readable instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer-readable instructions 54 in the server 5.

The server 5 is a device that can automatically perform numerical calculation and/or information processing in accordance with pre-set or stored instructions. Its hardware includes, but is not limited to, a microprocessor and an application specific integrated circuit (ASIC) , Field-Programmable Gate Array (FPGA), Digital Processor (Digital Signal Processor, DSP), embedded equipment, etc. Those skilled in the art can understand that the schematic diagram 5 is only an example of the server 5, and does not constitute a limitation on the server 5. It may include more or less components than those shown in the figure, or combine some components, or different components. For example, the server 5 may also include input and output devices, network access devices, buses, etc.

The database (Database) 51 is a warehouse built on the server 5 to organize, store and manage data according to a data structure. Databases are usually divided into three types: hierarchical database, network database and relational database. In this embodiment, the database 51 is used to store the car insurance image information.

The at least one processor 53 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processors, DSP), application specific integrated circuits (ASICs). ), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 53 can be a microprocessor, or the processor 53 can also be any conventional processor, etc. The processor 53 is the control center of the server 5, and connects each of the entire server 5 through various interfaces and lines. section.

The memory 52 may be used to store the computer-readable instructions 54 and/or modules/units, and the processor 53 can run or execute the computer-readable instructions and/or modules/units stored in the memory 52, and The data stored in the memory 52 is called to realize various functions of the server 5. The memory 31 includes Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), and Erasable Programmable Read-Only Memory (EPROM) , One-time Programmable Read-Only Memory (OTPROM), Electronically-Erasable Programmable Read-Only Memory (EEPROM), CD-ROM (Compact Disc Read- Only Memory, CD-ROM) or other optical disk storage, magnetic disk storage, tape storage, or any other non-volatile readable storage medium that can be used to carry or store data.

The memory 52 stores program codes, and the at least one processor 53 can call the program codes stored in the memory 52 to perform related functions. For example, the modules (acquisition module 401, evaluation module 402, marking module 403, and sending module 404) described in FIG. 4 are program codes stored in the memory 52 and executed by the at least one processor 53 , So as to realize the functions of the various modules to achieve the purpose of road information prompt based on smart traffic.

If the integrated modules/units of the server 5 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile readable storage medium. Based on this understanding, this application implements all or part of the processes in the above-mentioned embodiments and methods, and can also be completed by instructing relevant hardware by computer-readable instructions. The computer-readable instructions can be stored in a non-volatile memory. In the read storage medium, when the computer-readable instructions are executed by the processor, the steps of the foregoing method embodiments can be implemented. Wherein, the computer-readable instruction includes computer-readable instruction code, and the computer-readable instruction code may be in the form of source code, object code, executable file, or some intermediate form. The non-volatile readable medium may include: any entity or device capable of carrying the computer readable instruction code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read -Only Memory).

Although not shown, the server 5 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 53 through a power management system, thereby implementing management through the power management system. Functions such as charging, discharging, and power management. The power supply may also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power supply status indicators and other arbitrary components. The server 5 may also include a Bluetooth module, a Wi-Fi module, etc., which will not be repeated here.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit them. Although the application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the application can be Modifications or equivalent replacements are made without departing from the spirit of the technical solution of the present application.

Claims

A road information prompt method based on smart traffic, characterized in that the method includes:

Acquiring auto insurance report information, where the auto insurance report information includes at least a car insurance image and a place where the accident occurred;

Assessing the risk level of the accident place according to the damage level of the vehicle in the car insurance image, the surrounding environment information of the accident place and/or the current number of accident occurrences in the accident place;

Mark the location of the accident according to the risk level of the location of the accident, and obtain information on the location of high-risk accidents and information on the location of low-risk accidents;

Send warning information including the location of the high-risk accident to the relevant users.
The method for prompting road information based on smart traffic according to claim 1, wherein after said obtaining the information of a car insurance report, the method further comprises:

Calculating the first clarity level of the car insurance image;

Comparing the first definition level with a preset definition level;

When the first definition level is lower than the preset definition level, enhancing the definition level of the car insurance image to obtain a new car insurance image, and calculating the second definition level of the new car insurance image;

Comparing the second sharpness level with the preset sharpness level;

When the second sharpness level is higher than the preset sharpness level, the execution is performed according to the damage level of the vehicle in the car insurance image, the surrounding environment information of the accident site and/or the accident occurrence The current number of accidents in the place is a step to evaluate the risk level of the place where the accident occurred.
The method for prompting road information based on smart traffic according to claim 1, wherein the risk of the accident site is evaluated according to the damage level of the vehicle in the car insurance image and the current number of accident occurrences in the accident site The levels include:

Acquiring the damage level of the vehicle in the car insurance image;

Acquiring historical accident data of the place where the accident occurred, where the historical accident data includes the number of accidents and the damage level of the vehicle in the historical accident;

Judging whether the current number of accident occurrences in the accident place is greater than a preset number, and judging whether the damage level of the vehicle in the car insurance image and the current number of accidents is higher than the first preset level;

When the current number of accident occurrences at the accident location is less than or equal to the preset number, or the damage level of the vehicle in the car insurance image and the damage level of the vehicle in the current number of accidents are both lower than the first preset level, Mark the place where the accident occurred as a low-risk accident place;

When the current number of accidents in the place where the accident occurred is greater than the preset number, and the damage level of the vehicle in the car insurance image and the damage level of the vehicle in the current number of accidents are both higher than the first preset level, the accident The place of occurrence is marked as the place where a high-risk accident occurred.
The method for prompting road information based on smart traffic according to claim 3, wherein the step of obtaining the damage level of the vehicle in the car insurance image comprises:

Call the damage area recognition model generated by pre-training to recognize the car insurance image, and obtain vehicle damage area information;

Calculating the damaged area of the damaged area according to the vehicle damage area information;

Inputting the damage area and damage area of the vehicle into a preset calculation model to obtain a calculation result, wherein the preset calculation model is the product of the damage area and the weight value of the damage area plus the damage area;

Judging whether the calculation result is greater than or equal to a preset value;

When the calculation result is greater than or equal to the preset value, it is confirmed that the damage level of the vehicle in the car insurance image is high;

When the calculation result is less than the preset value, it is confirmed that the damage level of the vehicle in the car insurance image is low.
The road information prompt method based on smart transportation according to claim 3, wherein the risk level of the accident place is evaluated according to the surrounding environment information of the accident place and the current number of accident occurrences in the accident place include:

Identifying road environment information in the car insurance image, where the road environment information includes whether there are foreign objects on the road, whether the road is rugged, and whether it is a sharp turn;

Judging the road surface condition level according to the road surface environment information;

Acquiring historical accident data of the place where the accident occurred, wherein the historical accident data includes at least the number of accidents and the road surface condition level in the historical accident;

Judging whether the current number of accident occurrences in the place where the accident occurred is greater than the preset number, and judging whether the road condition level in the current number of accident occurrences is higher than a second preset level;

When the current accident occurrence frequency of the accident occurrence place is less than or equal to the preset number, or the road condition level in the current accident occurrence frequency is lower than the second preset level, the accident occurrence place is marked as a low-risk accident occurrence Land

When the current number of accident occurrences at the location of the accident is greater than the preset number, and the road condition level in the current number of accident occurrences is higher than the second preset level, mark the location of the accident as a high-risk accident occurrence Ground.
The method for prompting road information based on smart traffic according to claim 5, wherein:

If the current number of accident occurrences at the accident location is greater than the preset number, and the damage level of the vehicle is higher than the first preset level, or the current accident occurrence number at the accident location is greater than the predicted number Setting the number of times and determining that the road surface condition level in the surrounding environment information is higher than the second preset level, marking the accident site as a high-risk accident site;

If the current number of accident occurrences at the accident site is less than or equal to the preset number, and the damage level of the vehicle is lower than the first preset level, and the determination of the road surface condition level in the surrounding environment information When the level is lower than the second preset level, the accident occurrence location is marked as a low risk accident occurrence location.
The method for prompting road information based on smart traffic according to claim 5, wherein said judging said road surface condition level according to said road surface environment information comprises:

When there is a foreign body on the road surface or the road surface is bumpy or the current road is a sharp turn, determining that the road surface condition level is high;

When there is no foreign matter on the road surface and the road surface is flat and the current road is not a sharp turn, it is determined that the road surface condition level is low.
A road information prompting device based on smart traffic, characterized in that the device includes:

An acquisition module for acquiring auto insurance report information, where the auto insurance report information includes at least a car insurance image and a place where the accident occurred;

An evaluation module, configured to evaluate the risk level of the accident place according to the damage level of the vehicle in the car insurance image, the surrounding environment information of the accident place and/or the current number of accident occurrences in the accident place;

The marking module is used to mark the place of occurrence of the accident according to the risk level of the place of occurrence of the accident to obtain information on the place of occurrence of high-risk accidents and information of the place of occurrence of low-risk accidents;

The sending module is used to send warning information including the location of the high-risk accident to relevant users.
A server, characterized in that the server includes a processor and a memory, and the processor is configured to implement the following steps when executing at least one computer-readable instruction stored in the memory:

Acquiring auto insurance report information, where the auto insurance report information includes at least a car insurance image and a place where the accident occurred;

Assessing the risk level of the accident place according to the damage level of the vehicle in the car insurance image, the surrounding environment information of the accident place and/or the current number of accident occurrences in the accident place;

Mark the location of the accident according to the risk level of the location of the accident, and obtain information on the location of high-risk accidents and information on the location of low-risk accidents;

Send warning information including the location of the high-risk accident to the relevant users.
9. The server according to claim 9, wherein after said obtaining the car insurance report information, the processor is further configured to implement the following steps when executing the at least one computer-readable instruction:

Calculating the first clarity level of the car insurance image;

Comparing the first definition level with a preset definition level;

When the first definition level is lower than the preset definition level, enhancing the definition level of the car insurance image to obtain a new car insurance image, and calculating the second definition level of the new car insurance image;

Comparing the second sharpness level with the preset sharpness level;

When the second sharpness level is higher than the preset sharpness level, the execution is performed according to the damage level of the vehicle in the car insurance image, the surrounding environment information of the accident site and/or the accident occurrence The current number of accidents in the place evaluates the risk level of the accident place.
The server according to claim 9, wherein when evaluating the risk level of the accident site based on the damage level of the vehicle in the car insurance image and the current number of accident occurrences in the accident site, the The processor executes the at least one computer-readable instruction to implement the following steps:

Acquiring the damage level of the vehicle in the car insurance image;

Acquiring historical accident data of the place where the accident occurred, where the historical accident data includes the number of accidents and the damage level of the vehicle in the historical accident;

Judging whether the current number of accident occurrences in the accident place is greater than a preset number, and judging whether the damage level of the vehicle in the car insurance image and the current number of accidents is higher than the first preset level;

When the current number of accident occurrences at the accident location is less than or equal to the preset number, or the damage level of the vehicle in the car insurance image and the damage level of the vehicle in the current number of accidents are both lower than the first preset level, Mark the place where the accident occurred as a low-risk accident place;

When the current number of accidents in the place where the accident occurred is greater than the preset number, and the damage level of the vehicle in the car insurance image and the damage level of the vehicle in the current number of accidents are both higher than the first preset level, the accident The place of occurrence is marked as the place where a high-risk accident occurred.
The server according to claim 11, wherein when acquiring the damage level of the vehicle in the car insurance image, the processor executes the at least one computer-readable instruction to implement the following steps:

Call the damage area recognition model generated by pre-training to recognize the car insurance image, and obtain vehicle damage area information;

Calculating the damaged area of the damaged area according to the vehicle damage area information;

Inputting the damage area and damage area of the vehicle into a preset calculation model to obtain a calculation result, wherein the preset calculation model is the product of the damage area and the weight value of the damage area plus the damage area;

Judging whether the calculation result is greater than or equal to a preset value;

When the calculation result is greater than or equal to the preset value, it is confirmed that the damage level of the vehicle in the car insurance image is high;

When the calculation result is less than the preset value, it is confirmed that the damage level of the vehicle in the car insurance image is low.
The server according to claim 11, wherein when the risk level of the accident occurrence place is evaluated according to the surrounding environment information of the accident occurrence place and the current accident occurrence frequency of the accident occurrence place, the processing The device executes the at least one computer-readable instruction to implement the following steps:

Identifying road environment information in the car insurance image, where the road environment information includes whether there are foreign objects on the road, whether the road is rugged, and whether it is a sharp turn;

Judging the road surface condition level according to the road surface environment information;

Acquiring historical accident data of the place where the accident occurred, wherein the historical accident data includes at least the number of accidents and the road surface condition level in the historical accident;

Judging whether the current number of accident occurrences in the place where the accident occurred is greater than the preset number, and judging whether the road condition level in the current number of accident occurrences is higher than a second preset level;

When the current accident occurrence frequency of the accident occurrence place is less than or equal to the preset number, or the road condition level in the current accident occurrence frequency is lower than the second preset level, the accident occurrence place is marked as a low-risk accident occurrence Land

When the current number of accident occurrences at the location of the accident is greater than the preset number, and the road condition level in the current number of accident occurrences is higher than the second preset level, mark the location of the accident as a high-risk accident occurrence Ground.
The server according to claim 13, wherein the processor executing the at least one computer-readable instruction is further used to implement the following steps:

If the current number of accident occurrences at the accident location is greater than the preset number, and the damage level of the vehicle is higher than the first preset level, or the current accident occurrence number at the accident location is greater than the predicted number Setting the number of times and determining that the road surface condition level in the surrounding environment information is higher than the second preset level, marking the accident site as a high-risk accident site;

If the current number of accident occurrences at the accident site is less than or equal to the preset number, and the damage level of the vehicle is lower than the first preset level, and the determination of the road surface condition level in the surrounding environment information When the level is lower than the second preset level, the accident occurrence location is marked as a low risk accident occurrence location.
A non-volatile readable storage medium, wherein the non-volatile readable storage medium stores at least one computer readable instruction, and when the at least one computer readable instruction is executed by a processor, the following steps are implemented :

Acquiring auto insurance report information, where the auto insurance report information includes at least a car insurance image and a place where the accident occurred;

Assessing the risk level of the accident place according to the damage level of the vehicle in the car insurance image, the surrounding environment information of the accident place and/or the current number of accident occurrences in the accident place;

Mark the location of the accident according to the risk level of the location of the accident, and obtain information on the location of high-risk accidents and information on the location of low-risk accidents;

Send warning information including the location of the high-risk accident to the relevant users.
15. The storage medium according to claim 15, wherein after said obtaining the car insurance report information, the at least one computer readable instruction is executed by the processor to further implement the following steps:

Calculating the first clarity level of the car insurance image;

Comparing the first definition level with a preset definition level;

When the first definition level is lower than the preset definition level, enhancing the definition level of the car insurance image to obtain a new car insurance image, and calculating the second definition level of the new car insurance image;

Comparing the second sharpness level with the preset sharpness level;

When the second sharpness level is higher than the preset sharpness level, the execution is performed according to the damage level of the vehicle in the car insurance image, the surrounding environment information of the accident site and/or the accident occurrence The current number of accidents in the place evaluates the risk level of the accident place.
The storage medium of claim 15, wherein when the risk level of the accident site is evaluated according to the damage level of the vehicle in the car insurance image and the current number of accident occurrences in the accident site, The at least one computer readable instruction is executed by the processor to implement the following steps:

Acquiring the damage level of the vehicle in the car insurance image;

Acquiring historical accident data of the place where the accident occurred, where the historical accident data includes the number of accidents and the damage level of the vehicle in the historical accident;

Judging whether the current number of accident occurrences in the accident place is greater than a preset number, and judging whether the damage level of the vehicle in the car insurance image and the current number of accidents is higher than the first preset level;

When the current number of accident occurrences at the accident location is less than or equal to the preset number, or the damage level of the vehicle in the car insurance image and the damage level of the vehicle in the current number of accidents are both lower than the first preset level, Mark the place where the accident occurred as a low-risk accident place;

When the current number of accidents in the place where the accident occurred is greater than the preset number, and the damage level of the vehicle in the car insurance image and the damage level of the vehicle in the current number of accidents are both higher than the first preset level, the accident The place of occurrence is marked as the place where a high-risk accident occurred.
The storage medium according to claim 17, wherein when the damage level of the vehicle in the car insurance image is obtained, the at least one computer readable instruction is executed by the processor to implement the following steps:

Call the damage area recognition model generated by pre-training to recognize the car insurance image, and obtain vehicle damage area information;

Calculating the damaged area of the damaged area according to the vehicle damage area information;

Inputting the damage area and damage area of the vehicle into a preset calculation model to obtain a calculation result, wherein the preset calculation model is the product of the damage area and the weight value of the damage area plus the damage area;

Judging whether the calculation result is greater than or equal to a preset value;

When the calculation result is greater than or equal to the preset value, it is confirmed that the damage level of the vehicle in the car insurance image is high;

When the calculation result is less than the preset value, it is confirmed that the damage level of the vehicle in the car insurance image is low.
The storage medium according to claim 17, wherein when the risk level of the accident occurrence place is evaluated according to the surrounding environment information of the accident occurrence place and the current accident occurrence frequency of the accident occurrence place, the At least one computer readable instruction is executed by the processor to implement the following steps:

Identifying road environment information in the car insurance image, where the road environment information includes whether there are foreign objects on the road, whether the road is rugged, and whether it is a sharp turn;

Judging the road surface condition level according to the road surface environment information;

Acquiring historical accident data of the place where the accident occurred, wherein the historical accident data includes at least the number of accidents and the road surface condition level in the historical accident;

Judging whether the current number of accident occurrences in the place where the accident occurred is greater than the preset number, and judging whether the road condition level in the current number of accident occurrences is higher than a second preset level;

When the current accident occurrence frequency of the accident occurrence place is less than or equal to the preset number, or the road condition level in the current accident occurrence frequency is lower than the second preset level, the accident occurrence place is marked as a low-risk accident occurrence Land

When the current number of accident occurrences at the location of the accident is greater than the preset number, and the road condition level in the current number of accident occurrences is higher than the second preset level, mark the location of the accident as a high-risk accident occurrence Ground.
The storage medium of claim 19, wherein the at least one computer-readable instruction is executed by the processor to further implement the following steps:

If the current number of accident occurrences at the accident location is greater than the preset number, and the damage level of the vehicle is higher than the first preset level, or the current accident occurrence number at the accident location is greater than the predicted number Setting the number of times and determining that the road surface condition level in the surrounding environment information is higher than the second preset level, marking the accident site as a high-risk accident site;

If the current number of accident occurrences at the accident site is less than or equal to the preset number, and the damage level of the vehicle is lower than the first preset level, and the determination of the road surface condition level in the surrounding environment information When the level is lower than the second preset level, the accident occurrence location is marked as a low risk accident occurrence location.