Traffic accident estimation method, device and equipment and storage medium

Abstract

The invention relates to the technical field of big data processing, and discloses a traffic accident estimation method, device and equipment and a storage medium, and the method comprises the steps: obtaining the speed data of a target vehicle and a vehicle image; recognizing a target vehicle model according to the vehicle image; calculating a vehicle speed factor of a traffic accident of the target vehicle according to the vehicle speed data; and carrying out weight calculation according to the vehicle speed factor and an influence factor corresponding to the target vehicle model to obtain a traffic accident occurrence probability. According to the invention, the accuracy of traffic accident estimation can be effectively improved, so that the efficiency of traffic accident estimation is improved.

Description

Technical Field

[0001] The present invention relates to the field of big data processing technology, and in particular to a traffic accident prediction method, device, electronic device and computer-readable storage medium. Background Art

[0002] With the acceleration of urbanization and the sharp increase in the number of vehicles, modern cities are facing increasingly severe traffic challenges, among which the frequent occurrence of traffic accidents is particularly prominent. Traffic accidents not only directly threaten the lives and safety of the public, but also indirectly hinder the healthy development of the social economy.

[0003] However, the existing technologies for traffic accident prediction mostly focus on the single-dimensional analysis of driver behavior, ignoring the high complexity and diversity of human behavior, resulting in limited accuracy of prediction results, which in turn weakens the potential for widespread application of this method. At the same time, although big data processing technology has great potential when facing massive traffic data, it is often accompanied by high computing costs and time consumption, and low prediction efficiency. In summary, the existing traffic accident prediction methods have the problems of insufficient prediction accuracy and low prediction efficiency. Summary of the invention

[0004] The present invention provides a traffic accident prediction method, device and computer-readable storage medium, the main purpose of which is to solve the problems of insufficient accuracy and low prediction efficiency of traffic accident prediction.

[0005] To achieve the above object, the present invention provides a traffic accident prediction method, comprising:

[0006] Obtain the speed data and vehicle image of the target vehicle;

[0007] Identify a target vehicle type according to the vehicle image;

[0008] Calculating a speed factor of the target vehicle causing a traffic accident based on the speed data;

[0009] The probability of a traffic accident occurring is obtained by performing weight calculation based on the vehicle speed factor and the influencing factor corresponding to the target vehicle model.

[0010] In some embodiments, identifying the target vehicle model according to the vehicle image includes:

[0011] Selecting images to be identified one by one from the vehicle images;

[0012] Performing image preprocessing on the image to be recognized to obtain a target recognition image;

[0013] Calculating the similarity between the target recognition image and a preset vehicle model template image;

[0014] The target vehicle model corresponding to the image to be identified is matched according to the similarity.

[0015] In some embodiments, the calculating the similarity between the target recognition image and a preset vehicle model template image includes:

[0016] Converting the image to be recognized into an image vector to be recognized;

[0017] Converting the vehicle model template image into a template image vector;

[0018] The similarity is calculated according to the to-be-recognized image vector and the template image vector.

[0019] In some embodiments, the calculating the speed factor of the target vehicle causing the traffic accident based on the vehicle speed data includes:

[0020] Performing weighted calculation on the speed data of the target vehicle to obtain a comprehensive speed;

[0021] The probability of the target vehicle having a traffic accident is calculated according to the comprehensive vehicle speed and a preset critical speed.

[0022] In some embodiments, the calculating the probability of the target vehicle having a traffic accident according to the comprehensive vehicle speed and a preset critical speed includes:

[0023] Determine the target model according to different critical speeds corresponding to the comprehensive vehicle speed;

[0024] The probability of the target vehicle having a traffic accident is calculated according to the target model.

[0025] In some embodiments, determining the target model according to different critical speeds corresponding to the comprehensive vehicle speed includes:

[0026] If the comprehensive vehicle speed is lower than a preset first critical speed, determining the corresponding target model to be a first model;

[0027] If the comprehensive vehicle speed is between the first critical speed and the second critical speed, determining the corresponding target model to be the second model;

[0028] If the comprehensive vehicle speed is between the second critical speed and the third critical speed, determining the corresponding target model to be the third model;

[0029] If the comprehensive vehicle speed is higher than the third critical speed, the corresponding target model is determined to be the fourth model.

[0030] In some embodiments, the weight calculation is performed based on the vehicle speed factor and the vehicle type factor corresponding to the target vehicle type to obtain the probability of a traffic accident, including:

[0031] Calculate the average of the vehicle model factors according to the vehicle model factors corresponding to the target vehicle model;

[0032] The probability of a traffic accident is obtained by performing weight calculation based on the vehicle speed factor and the average of the vehicle model factor.

[0033] In order to solve the above problems, the present invention further provides a traffic accident prediction device, the device comprising:

[0034] Speed ​​measurement module, used to obtain the speed data and vehicle image of the target vehicle;

[0035] A vehicle type recognition module, used to recognize a target vehicle type according to the vehicle image;

[0036] An algorithm module, used to calculate the speed factor of the target vehicle causing the traffic accident based on the speed data;

[0037] The early warning module is used to perform weight calculation based on the vehicle speed factor and the influencing factor corresponding to the target vehicle model to obtain the probability of traffic accidents.

[0038] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0039] at least one processor; and,

[0040] a memory communicatively connected to the at least one processor; wherein,

[0041] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned traffic accident prediction method.

[0042] In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned traffic accident prediction method.

[0043] The present invention can effectively track the target vehicle and enhance safety by acquiring the speed data and vehicle image of the target vehicle and identifying the vehicle model based on the vehicle image; the speed factor of the target vehicle causing a traffic accident is calculated based on the speed data, and the probability of traffic accidents within different speed ranges can be more accurately predicted; the probability of traffic accidents is obtained by weighting the speed factor and the influencing factor corresponding to the target vehicle model, and the probability of traffic accidents can be scientifically estimated, thereby improving the efficiency and accuracy of traffic accident prediction. Therefore, the traffic accident prediction method, device, electronic device and computer-readable storage medium proposed by the present invention can solve the problems of insufficient prediction accuracy and low prediction efficiency in existing traffic accident prediction methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flow chart of a traffic accident prediction method provided by an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a process of identifying vehicle types through vehicle image according to an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of a module of a traffic accident prediction device provided by an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of the structure of an electronic device of a traffic accident prediction device provided by one embodiment of the present invention.

[0048] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, and to fully understand and implement how the present disclosure applies technical means to solve technical problems and achieve the corresponding technical effects, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. The embodiments of the present disclosure and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the scope of protection of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0050] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0051] An embodiment of the present application provides a method for predicting traffic accidents, and the execution subject of the method for predicting traffic accidents includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the device provided by the embodiment of the present application. In other words, the method for predicting traffic accidents can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0052] Reference Figure 1 FIG. 1 is a flow chart of a traffic accident prediction method provided by an embodiment of the present invention; Figure 2 A schematic diagram of a process for identifying vehicle types by vehicle image provided by an embodiment of the present invention.

[0053] In this embodiment, the traffic accident prediction method includes:

[0054] S1. Obtaining the speed data and vehicle image of the target vehicle.

[0055] In the embodiment of the present invention, the target vehicle refers to vehicles traveling at a similar distance on the same road, such as the front and rear vehicles on the same road.

[0056] The speed data of the target vehicle described in the present invention can be obtained through a speed sensor, such as a wheel speed sensor, which measures the rotation speed of the wheel through a group of electromagnets passing through a coil. The movement of the gear teeth causes the magnetic flux to change over time, thereby inducing a proportional AC voltage signal in the coil. The voltage signal is converted into a digital pulse signal, and the speed data of the target vehicle is obtained.

[0057] Furthermore, based on the GPS positioning tracker installed on the target vehicle, the data of the GPS positioning tracker can be remotely accessed through a computer platform to obtain real-time data such as vehicle speed.

[0058] In the embodiment of the present invention, the obtained vehicle speed data facilitates the subsequent calculation of the vehicle speed factor of the target vehicle involved in the traffic accident, thereby improving the efficiency of the overall traffic accident prediction.

[0059] The present invention can capture vehicle images in real time through road monitoring or vehicle driving recorders, and remotely access the road monitoring and vehicle driving recorders in a preset video monitoring system to obtain vehicle images.

[0060] In the embodiment of the present invention, by acquiring the speed data and the vehicle image of the target vehicle, the speed data and the vehicle image information can be stored in a preset database, so as to reduce the data search time and improve the calculation efficiency when calculating based on the above data.

[0061] S2. Identify the target vehicle model according to the vehicle image.

[0062] In the embodiment of the present invention, the target vehicle model may refer to the front and rear vehicle models on the same road, and the vehicle models may be divided into buses, trucks, commercial vehicles, sedans, SUVs, sports cars, and the like.

[0063] In the embodiment of the present invention, the step of identifying the target vehicle type according to the vehicle image includes:

[0064] Selecting images to be identified one by one from the vehicle images;

[0065] Performing image preprocessing on the image to be recognized to obtain a target recognition image;

[0066] Calculating the similarity between the target recognition image and a preset vehicle model template image;

[0067] The target vehicle model corresponding to the image to be identified is matched according to the similarity.

[0068] In detail, the image preprocessing includes resizing, graying and denoising the image to be identified so as to more effectively compare it with the vehicle model template image, thereby improving the efficiency of subsequent similarity calculation.

[0069] In the embodiment of the present invention, the calculating the similarity between the target recognition image and the preset vehicle model template image includes:

[0070] Converting the image to be recognized into an image vector to be recognized;

[0071] Converting the vehicle model template image into a template image vector;

[0072] The similarity is calculated according to the to-be-recognized image vector and the template image vector.

[0073] The similarity between the image to be identified and the preset vehicle model template image is calculated using the following formula:

[0074] W=|xy i ∣

[0075] Where x is the image vector to be identified obtained by transforming the image to be identified, and y is i is the template image vector corresponding to the i-th vehicle model in the vehicle model template image, and W is the similarity.

[0076] In detail, the image to be identified and the vehicle model template image can be converted into vector form using image frequency-inverse document frequency or N-gram model, so as to facilitate subsequent similarity calculation, thereby improving the accuracy of vehicle model identification.

[0077] In an embodiment of the present invention, the target vehicle type can be identified based on the vehicle image, so that the target vehicle can be effectively tracked, thereby improving the accuracy and timeliness of traffic law enforcement. By identifying high-risk vehicle types, traffic accidents can be prevented, safety can be enhanced, and rescue efficiency can be improved.

[0078] S3. Calculate the speed factor of the target vehicle causing the traffic accident based on the speed data.

[0079] In the embodiment of the present invention, the vehicle speed factor refers to the probability of a traffic accident occurring at different critical speeds of the target vehicle, and the different critical speeds include a first critical speed, a second critical speed, and a third critical speed.

[0080] In detail, the first critical speed can be set to 60Km / h. In actual application scenarios, a driving speed below 60Km / h is considered to be a safer speed under most road conditions, and in many road types (such as urban viaducts), 60Km / h is also used as a symbol of speed measurement; generally speaking, when the driving speed is lower than this speed, it is easier for the driver to control the vehicle and grasp the road conditions; when it is higher than this speed, the driver's difficulty in controlling the vehicle increases.

[0081] The second critical speed can be set to 90 km / h. It is known that under the same conditions, the faster the speed, the longer the braking distance. From the perspective of braking distance, it is estimated that when the speed is 90 km / h, the braking distance of most family cars has reached about 35 meters. When the driving speed exceeds 90 km / h, the braking distance will increase significantly, and the probability of traffic accidents will be greatly increased compared to the first critical speed.

[0082] The third critical speed can be set to 120 km / h. At this time, the vehicle speed has reached a relatively high level. Under the same conditions, the probability of traffic accidents occurring at the third critical speed is greatly increased compared with the first critical speed and the second critical speed. On the other hand, since many road factors (road friction coefficient, guardrail strength, slope, curvature radius, sign size, etc.) are designed according to the 120 km / h standard, if the driving speed is lower than the third critical speed, the speed and many road factors are still within the adaptation range. On the contrary, if the driving speed is higher than the third critical speed, the speed and many road factors are no longer compatible, and the probability of traffic accidents increases accordingly.

[0083] In the embodiment of the present invention, the step of calculating the speed factor of the target vehicle causing the traffic accident based on the vehicle speed data includes:

[0084] Performing weighted calculation on the speed data of the target vehicle to obtain a comprehensive speed;

[0085] The probability of the target vehicle having a traffic accident is calculated according to the comprehensive vehicle speed and a preset critical speed.

[0086] In detail, the speed data of the target vehicle is calculated in a weighted manner to obtain a comprehensive speed. Since the speed information of the target vehicles is highly independent, for weighted matching, the present invention mainly focuses on analyzing the speed of the rear vehicle, so the weight of the speed data of the rear vehicle should be greater. Under comprehensive analysis, the speed data of the front and rear adjacent vehicles obtained by the present invention are distributed with a weight of 2:1 (rear vehicle: front vehicle), thereby obtaining a comprehensive speed.

[0087] In the embodiment of the present invention, the calculating the probability of the target vehicle having a traffic accident according to the comprehensive vehicle speed and the preset critical speed includes:

[0088] Determine the target model according to different critical speeds corresponding to the comprehensive vehicle speed;

[0089] The probability of the target vehicle having a traffic accident is calculated according to the target model.

[0090] In an embodiment of the present invention, a target model is determined according to different critical speeds corresponding to the comprehensive vehicle speed to predict the probability and severity of a traffic accident. Calculating the probability of a traffic accident occurring to the target vehicle according to different models can improve prediction accuracy and reduce the occurrence of accidents.

[0091] In the embodiment of the present invention, determining the target model according to different critical speeds corresponding to the comprehensive vehicle speed includes:

[0092] If the comprehensive vehicle speed is lower than a preset first critical speed, determining the corresponding target model to be a first model;

[0093] If the comprehensive vehicle speed is between the first critical speed and the second critical speed, determining the corresponding target model to be the second model;

[0094] If the comprehensive vehicle speed is between the second critical speed and the third critical speed, determining the corresponding target model to be the third model;

[0095] If the comprehensive vehicle speed is higher than the third critical speed, the corresponding target model is determined to be the fourth model.

[0096] In an embodiment of the present invention, the first model may be a logistic regression model, which is based on the assumption that the sample follows a Bernoulli distribution and is solved by methods such as maximum likelihood estimation and gradient descent. Its output is a probability value ranging from 0 to 1, which represents the possibility of an event occurring. The logistic regression model is a very important model in machine learning and statistics because it provides a method for directly modeling the probability of an event occurring. The logistic regression model is not only important in theory, but also widely used in practice. For example, in marketing, logistic regression can be used to predict the possibility of a customer purchasing a certain product; in medical research, it can be used to predict the probability of a certain disease occurring, etc. The simplicity and effectiveness of the logistic regression model make it one of the preferred methods for dealing with binary classification problems.

[0097] The logistic regression model is mainly composed of a linear model and a logistic function. The output of the linear model is mapped to between 0 and 1 through the logistic function, thereby realizing the calculation of the probability of traffic accidents.

[0098] Furthermore, the vehicle speed data are combined in a linear combination manner using the logistic regression model to form a linear prediction value, and the linear prediction value is converted into a probability value using a logistic function, thereby obtaining the traffic accident probability corresponding to the vehicle speed data.

[0099] In an embodiment of the present invention, the second model can be a recurrent neural network model. The recurrent neural network is a type of recurrent neural network that takes sequence data as input, recursively in the evolution direction of the sequence, and all nodes (recurrent units) are connected in a chain. The recurrent neural network has memory, parameter sharing and Turing completeness, so it has certain advantages when learning the nonlinear characteristics of the sequence; the recurrent neural network model is mainly composed of an input layer, i.e., a data preprocessing layer, a hidden layer and an output layer. The input layer receives the input of the target data, and the hidden layer is responsible for processing the input, and then generates the prediction result of the model according to the output of the hidden layer.

[0100] Furthermore, the data processing layer is used to process missing values ​​and normalize the vehicle speed data so that the vehicle speed data is normalized to a range of 0-1, and the processed data is then input into the recurrent neural network model to obtain the traffic accident probability corresponding to the vehicle speed data.

[0101] In an embodiment of the present invention, the third model may be a recursive neural network model, which is a neural network model that uses a recursive structure for calculation. Unlike a traditional feedforward neural network, a recursive neural network can process input data with a recursive structure. The key idea of ​​a recursive neural network is to calculate the representation of the overall structure by recursively combining substructures; a classic recursive neural network model is Tree-LSTM, which is widely used in natural language processing. Tree-LSTM recursively combines the representation of child nodes into the representation of parent nodes to construct the representation of the entire sentence. This recursive combination method enables Tree-LSTM to model the grammatical structure of a sentence, thereby extracting semantic information from the sentence.

[0102] The recursive neural network model is mainly composed of an input layer, a long short-term memory network layer, a fully connected layer and an output layer, and a gate mechanism is introduced to better capture long-term dependencies.

[0103] Furthermore, the vehicle speed data is cleaned, missing values, outliers, etc. are processed, and the cleaned vehicle speed data is normalized to extract target features and construct feature vectors based on them. The feature vectors are input into the recursive neural network model to obtain the traffic accident probability corresponding to the vehicle speed data.

[0104] In an embodiment of the present invention, the fourth model can be a hybrid model, which is a model that combines a convolutional neural network model CNN and a long short-term memory neural network LSTM; LSTM is good at capturing long-term dependencies in sequence data, while CNN is good at extracting local features of image data. By combining the advantages of both, the model can take into account the temporal information and spatial information of the data at the same time, reduce parameters and reduce the risk of overfitting, thereby providing more accurate predictions, better performance and higher training efficiency.

[0105] Furthermore, the vehicle speed data is preprocessed to obtain vehicle speed sequence data, the vehicle speed sequence data is subjected to feature extraction through multiple convolutional layers, the extracted features are pooled and fused to obtain a feature map, the feature map is converted into a one-dimensional vector and input into the LSTM layer, thereby mapping the traffic accident probability corresponding to the vehicle speed data.

[0106] In the embodiment of the present invention, since the generalization capabilities of the first model, the second model, the third model and the fourth model are different, different models are selected according to different critical speeds in order to optimize the use of computing resources and improve the accuracy of traffic accident prediction based on the calculation of traffic accident probability.

[0107] In the embodiment of the present invention, the speed factor of the target vehicle causing the traffic accident is calculated based on the vehicle speed data, so that the probability of traffic accidents within different speed ranges can be predicted more accurately, thereby improving the efficiency of traffic accident prediction.

[0108] S4. Perform weight calculation based on the vehicle speed factor and the vehicle type factor corresponding to the target vehicle type to obtain the probability of a traffic accident.

[0109] In the embodiment of the present invention, the vehicle type factor corresponding to the target vehicle type refers to the probability coefficient of traffic accidents occurring in different vehicle types. The higher the vehicle type factor is, the higher the probability of a traffic accident occurring in the vehicle is.

[0110] In the embodiment of the present invention, the bus model factor can be set to 0.2. From the perspective of obtaining a driver's license, there are strict regulations on age, physical condition, driving experience, driver's license level, etc. to obtain the qualification to drive a bus. Objectively speaking, the bus that meets the above regulations and can be driven on the road should have the lowest safety level compared to other models. From the perspective of subjective consciousness, generally speaking, the passenger capacity of a bus is the largest compared to other models, and the bus driver needs to observe carefully at all times to ensure the safety of the passengers. Based on the above analysis, under the same conditions, the fixed value of the bus model should be set to the lowest compared to other models.

[0111] The truck model factor can be set to 0.4. From the perspective of road knowledge and practice, trucks generally have a lower driving speed. Similarly, from the perspective of obtaining a driver's license, there are relatively strict regulations on driving skills, driving experience and other indicators to obtain the truck model qualification, but the regulations are relatively loose compared to those for buses (for example, in terms of driver's license level, bus models generally correspond to Class A driver's licenses, which are the highest level of motor vehicle driver's licenses; truck models generally correspond to Class B driver's licenses). Based on the above analysis, the fixed value of the truck model is set to 0.4.

[0112] To sum up, under the same conditions, the commercial vehicle model factor can be set to 0.6, the bridge vehicle and SUV model factor can be set to 0.7, and the sports car model factor can be set to 0.8.

[0113] In the embodiment of the present invention, the weight calculation is performed based on the vehicle speed factor and the vehicle type factor corresponding to the target vehicle type to obtain the probability of traffic accident occurrence, including:

[0114] Calculate the average of the vehicle model factors according to the vehicle model factors corresponding to the target vehicle model;

[0115] The probability of a traffic accident is obtained by performing weight calculation based on the vehicle speed factor and the average of the vehicle model factor.

[0116] In detail, for example, the front vehicle is a 99 km / h cross-country vehicle, and the rear vehicle is a 132 km / h sports car. If the average vehicle model factors of the cross-country vehicle and the sports car are calculated, because the vehicle model factor of the cross-country vehicle is 0.7 and the vehicle model factor of the sports car is 0.8, the calculated average vehicle model factor is 0.75.

[0117] In the embodiment of the present invention, under the same conditions, different vehicle models have an impact on the probability of traffic accidents, but the impact is far less than the effect of the vehicle speed factor under the same conditions, so the weight of the allocated vehicle speed factor is greater than the vehicle model factor corresponding to the target vehicle model.

[0118] For example, a weight of 0.1 may be assigned to the vehicle model factor, and a weight of 0.9 may be assigned to the vehicle speed factor. The result of multiplying the vehicle speed factor by 0.9 and the result of multiplying the vehicle model factor by 0.1 are added together to obtain the probability of a traffic accident.

[0119] In the embodiment of the present invention, by quantifying the vehicle speed factor and the vehicle model factor and calculating them in combination with their own weights, the probability of a traffic accident can be more scientifically estimated, thereby improving the accuracy and comprehensiveness of estimating the occurrence of traffic accidents.

[0120] The present invention can effectively track the target vehicle and enhance safety by acquiring the speed data and vehicle image of the target vehicle and identifying the vehicle model based on the vehicle image; the speed factor of the target vehicle causing a traffic accident is calculated based on the speed data, and the probability of traffic accidents within different speed ranges can be more accurately predicted; the probability of traffic accidents is obtained by weighting the speed factor and the influencing factor corresponding to the target vehicle model, and the probability of traffic accidents can be scientifically estimated, thereby improving the efficiency and accuracy of traffic accident prediction. Therefore, the traffic accident prediction method, device, electronic device and computer-readable storage medium proposed by the present invention can solve the problems of insufficient prediction accuracy and low prediction efficiency in existing traffic accident prediction methods.

[0121] like Figure 3 , which is a module diagram of a task delay processing device provided by an embodiment of the present disclosure.

[0122] The traffic accident prediction device 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the traffic accident prediction device 100 can include a speed measurement module 101, a vehicle type recognition module 102, an algorithm module 103, and an early warning module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0123] In this embodiment, the functions of each module / unit are as follows:

[0124] The speed measurement module 101 is used to obtain the speed data and vehicle image of the target vehicle;

[0125] The vehicle type recognition module 102 is used to recognize the target vehicle type according to the vehicle image;

[0126] The algorithm module 103 is used to calculate the speed factor of the target vehicle causing the traffic accident based on the vehicle speed data;

[0127] The early warning module 104 is used to perform weight calculation based on the vehicle speed factor and the influencing factor corresponding to the target vehicle model to obtain the probability of traffic accident occurrence.

[0128] In some embodiments, when the speed measurement module 101 performs the function of obtaining the speed data and vehicle image of the target vehicle, the speed data of the target vehicle can be obtained through a speed sensor, such as a wheel speed sensor, which measures the rotation speed of the wheel through a group of electromagnets passing through a coil. The movement of the gear teeth causes the magnetic flux to change over time, thereby inducing a proportional AC voltage signal in the coil, and the voltage signal is converted into a digital pulse signal to obtain the speed data of the target vehicle.

[0129] In some embodiments, when the vehicle type recognition module 102 performs the function of recognizing the target vehicle type according to the vehicle image, it includes:

[0130] A recognition unit, used for selecting images to be recognized one by one from the vehicle images;

[0131] A preprocessing unit, used for performing image preprocessing on the image to be recognized to obtain a target recognition image;

[0132] A similarity calculation unit, used to calculate the similarity between the target recognition image and a preset vehicle model template image;

[0133] The vehicle type matching unit is used to match the target vehicle type corresponding to the image to be identified according to the similarity.

[0134] In some embodiments, when the algorithm module 103 performs the function of calculating the speed factor of the target vehicle causing the traffic accident according to the vehicle speed data, it includes:

[0135] A weighted calculation unit, used for performing weighted calculation on the speed data of the target vehicle to obtain a comprehensive speed;

[0136] The probability calculation unit is used to calculate the probability of the target vehicle having a traffic accident based on the comprehensive vehicle speed and a preset critical speed.

[0137] In some embodiments, when the early warning module 104 performs the function of performing weight calculation based on the vehicle speed factor and the vehicle type factor corresponding to the target vehicle type to obtain the probability of a traffic accident, it includes:

[0138] An average calculation unit, used to calculate the average of the vehicle model factors according to the vehicle model factors corresponding to the target vehicle model;

[0139] The weight calculation unit is used to perform weight calculation based on the vehicle speed factor and the average of the vehicle model factor to obtain the probability of traffic accident occurrence.

[0140] Each module in the traffic accident prediction device 100 described in the embodiment of the present invention is used in the same manner as described above. Figure 1 to Figure 2The same technical means as the traffic accident prediction method described in , and can produce the same technical effects, will not be repeated here.

[0141] On the basis of the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0142] like Figure 4 , is a schematic diagram of the electronic device structure of a traffic accident prediction method provided by an embodiment of the present disclosure.

[0143] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a traffic accident prediction program.

[0144] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, executing a traffic accident prediction program, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device and process data.

[0145] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the traffic accident prediction program, but also be used to temporarily store data that has been output or is to be output.

[0146] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0147] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0148] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0149] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0150] The traffic accident prediction program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0151] Obtain the speed data and vehicle image of the target vehicle;

[0152] Identify a target vehicle type according to the vehicle image;

[0153] Calculating a speed factor of the target vehicle causing a traffic accident based on the speed data;

[0154] The probability of a traffic accident occurring is obtained by performing weight calculation based on the vehicle speed factor and the influencing factor corresponding to the target vehicle model.

[0155] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0156] It should be noted that if software tools or components other than those of the Company appear in the embodiments of the present application, they are only used for illustration and do not represent actual use. In the several embodiments provided by the present invention, it should be understood that the disclosed devices and apparatuses can be implemented in other ways. For example, the system embodiments described above are merely illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0157] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0158] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0159] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0160] In some implementations of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0161] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0162] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0163] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0164] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0165] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0166] The blockchain referred to in this invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0167] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0168] The processor of the present invention may include, but is not limited to, one or more processors or microprocessors. Each processor may be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components to implement the method in the above embodiment.

[0169] The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, and the computer-readable storage medium may include but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0170] The computer-readable storage medium may also store at least one computer executable program / instruction, which may be, for example, a computer-readable instruction. The computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The computer-readable storage medium may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions stored on the computer-readable storage medium, the various methods described above may be performed.

[0171] In addition, the computer device may also include (but not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.), etc.

[0172] The processor may communicate with external devices via an I / O bus via a wired or wireless network.

[0173] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product / computer program product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0174] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0175] It should be noted that in the present disclosure, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element limited by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0176] Although the embodiments disclosed in the present disclosure are as above, the above contents are only embodiments adopted for facilitating the understanding of the present disclosure and are not intended to limit the present disclosure. Any technician in the technical field to which the present disclosure belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present disclosure, but the scope of patent protection of the present disclosure shall still be subject to the scope defined in the attached claims.

Claims

1. A traffic accident prediction method, characterized in that: The method comprises: Obtaining the speed data and vehicle image of the target vehicle; identifying a target vehicle type according to the vehicle image; Calculating a speed factor of the target vehicle causing a traffic accident based on the speed data; The vehicle speed factor and the influencing factor corresponding to the target vehicle model are weighted to obtain the probability of a traffic accident.

2. A traffic accident prediction method as claimed in claim 1, characterized in that: The step of identifying a target vehicle type according to the vehicle image comprises: Selecting images to be identified one by one from the vehicle images; Performing image preprocessing on the image to be recognized to obtain a target recognition image; Calculating the similarity between the target recognition image and a preset vehicle model template image; The target vehicle model corresponding to the image to be identified is matched according to the similarity.

3. A traffic accident prediction method as claimed in claim 2, characterized in that: The calculating the similarity between the target recognition image and the preset vehicle model template image includes: Converting the image to be recognized into an image vector to be recognized; Converting the vehicle model template image into a template image vector; The similarity is calculated according to the difference between the to-be-recognized image vector and the template image vector.

4. A traffic accident prediction method as claimed in claim 1, characterized in that: The calculating the speed factor of the target vehicle causing the traffic accident according to the vehicle speed data includes: Performing weighted calculation on the speed data of the target vehicle to obtain a comprehensive speed; The probability of the target vehicle having a traffic accident is calculated according to the comprehensive vehicle speed and a preset critical speed.

5. A traffic accident prediction method as claimed in claim 4, characterized in that: The calculating the probability of the target vehicle having a traffic accident by using the comprehensive vehicle speed and a preset critical speed includes: Determine the target model according to different critical speeds corresponding to the comprehensive vehicle speed; The probability of the target vehicle having a traffic accident is calculated according to the target model.

6. A traffic accident prediction method as claimed in claim 5, characterized in that: Determining the target model according to different critical speeds corresponding to the comprehensive vehicle speed includes: If the comprehensive vehicle speed is lower than a preset first critical speed, determining the corresponding target model to be a first model; If the comprehensive vehicle speed is between the first critical speed and the second critical speed, determining the corresponding target model to be the second model; If the comprehensive vehicle speed is between the second critical speed and the third critical speed, determining the corresponding target model to be the third model; If the comprehensive vehicle speed is higher than the third critical speed, the corresponding target model is determined to be the fourth model.

7. A traffic accident prediction method as claimed in claim 1, characterized in that: The weight calculation is performed based on the vehicle speed factor and the vehicle type factor corresponding to the target vehicle type to obtain the probability of traffic accident occurrence, including: Calculate the average of the vehicle model factors according to the vehicle model factors corresponding to the target vehicle model; The probability of a traffic accident is obtained by performing weight calculation based on the vehicle speed factor and the average of the vehicle model factor.

8. A traffic accident prediction device, characterized in that: The device comprises: Speed ​​measurement module, used to obtain the speed data and vehicle image of the target vehicle; A vehicle type recognition module, used to recognize a target vehicle type according to the vehicle image; An algorithm module, used to calculate the speed factor of the target vehicle causing the traffic accident based on the speed data; The early warning module is used to perform weight calculation based on the vehicle speed factor and the influencing factor corresponding to the target vehicle model to obtain the probability of traffic accidents.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a traffic accident prediction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a traffic accident prediction method as described in any one of claims 1 to 7 is implemented.

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