Vehicle passing control method and system based on defect detection

Abstract

The invention discloses a vehicle passing control method and system based on defect detection. The method comprises the steps of obtaining a vehicle identifier of a passing vehicle which is about to pass through target checkpoint equipment; determining vehicle defect information corresponding to the passing vehicle according to the vehicle identifier based on preset vehicle detection historical data; predicting a passing scene corresponding to the passing vehicle according to the equipment information of the target checkpoint equipment and the vehicle identifier; calculating a passing risk parameter corresponding to the passing vehicle based on a risk identification algorithm according to the vehicle defect information and the passing scene; the passing risk parameter indicates the risk degree of allowing the passing vehicle to pass through the target checkpoint equipment. Therefore, the monitoring efficiency and accuracy of the vehicle passing risk can be improved, an accurate risk estimation reference is provided for passing control, and the safety and the intelligent degree of the vehicle passing control are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a vehicle traffic control method and system based on defect detection. Background Art

[0002] With the development of intelligent recognition algorithms, more and more vehicle production or maintenance organizations have begun to adopt more intelligent and automated production control technologies. Among them, since there are multiple links in vehicle defect detection, the vehicle needs to pass through different areas during the entire process or after completion. How to achieve vehicle traffic control more accurately and safely has become an important technical issue. When performing vehicle traffic control, most existing technologies only consider the vehicle's own identification such as the license plate number or the driver's identity identification such as facial images. When applied to vehicle multi-link detection scenarios, they do not fully combine vehicle defect recognition and traffic scenarios to achieve traffic control. Obviously, the accuracy and safety of its traffic control are insufficient. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a vehicle traffic control method and system based on defect detection, which can improve the monitoring efficiency and accuracy of vehicle traffic risks, provide accurate risk estimation reference for traffic control, and improve the safety and intelligence of vehicle traffic control.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a vehicle traffic control method based on defect detection, the method comprising:

[0005] Obtaining the vehicle identification of a vehicle that intends to pass through the target checkpoint device;

[0006] Based on the preset vehicle inspection history data and according to the vehicle identification, determining the vehicle defect information corresponding to the passing vehicle;

[0007] Predicting a traffic scene corresponding to the passing vehicle based on the device information of the target checkpoint device and the vehicle identification;

[0008] According to the vehicle defect information and the passage scenario, based on a risk identification algorithm, a passage risk parameter corresponding to the passing vehicle is calculated; the passage risk parameter indicates the risk level of allowing the passing vehicle to pass through the target checkpoint device.

[0009] As an optional embodiment, in the first aspect of the present invention, the vehicle identification includes at least one of vehicle sensor data, vehicle license plate, vehicle inspection number and facial images of people in the vehicle; the vehicle sensor data includes at least one of vehicle image data, vehicle sound data, vehicle light reflection three-dimensional model data and vehicle temperature and humidity data.

[0010] As an optional embodiment, in the first aspect of the present invention, determining the vehicle defect information corresponding to the passing vehicle according to the vehicle identification based on the preset vehicle detection history data includes:

[0011] Perform matching in a vehicle detection history database based on the vehicle identification to obtain a matching result;

[0012] When the matching result indicates that a matching record exists, obtaining a plurality of vehicle detection records obtained by matching;

[0013] When the matching result is that there is no matching record, determining at least one similar vehicle based on the vehicle identifier of multiple detected vehicles in the vehicle detection history database, and obtaining vehicle detection records corresponding to all similar vehicles;

[0014] According to the vehicle inspection record, vehicle defect information corresponding to the passing vehicle is determined.

[0015] As an optional embodiment, in the first aspect of the present invention, determining at least one similar vehicle from a plurality of detected vehicles in the vehicle detection history database based on the vehicle identification includes:

[0016] Inputting the vehicle sensor data of the passing vehicle into a trained detection elapsed time prediction neural network to obtain the detection elapsed time corresponding to the passing vehicle; the detection elapsed time prediction neural network is trained using a training data set including a plurality of training vehicle sensor data and corresponding time distance annotations of the last detection time to the current time;

[0017] Calculate the difference between the current time point and the detection time to obtain the possible last detection time corresponding to the passing vehicle;

[0018] For each detected vehicle in the vehicle detection history database, obtaining vehicle data and a historically most recent detection time point corresponding to the detected vehicle;

[0019] Calculating a first similarity between the vehicle data and the vehicle identification;

[0020] Calculating a first time difference between the most recent historical detection time point and the possible last detection time;

[0021] Calculating a ratio of the first similarity to the first time difference to obtain a similarity parameter corresponding to the detected vehicle;

[0022] The detected vehicles whose similarity parameter is greater than a parameter threshold are determined as similar vehicles.

[0023] As an optional embodiment, in the first aspect of the present invention, determining the vehicle defect information corresponding to the passing vehicle based on the vehicle inspection record includes:

[0024] For each vehicle detection record, calculating a second time difference between the detection time corresponding to the vehicle detection record and the current time point;

[0025] Calculating a second similarity between the detected vehicle information corresponding to the vehicle detection record and the vehicle identifier;

[0026] Calculate the difference between the number of defects detected in the vehicle inspection record and the average number; the average number is the average number of defects detected in all vehicle inspection records;

[0027] Calculating the product of the second time difference and the quantity difference, calculating the ratio of the second similarity to the product, and obtaining the record priority corresponding to the vehicle detection record;

[0028] The detected defect in the vehicle inspection record with the highest record priority is determined as the vehicle defect information corresponding to the passing vehicle.

[0029] As an optional embodiment, in the first aspect of the present invention, the vehicle defect information includes a defect type corresponding to at least one vehicle part; the vehicle part is a door, front, body, chassis, roof, rear, trunk, interior space or wheel.

[0030] As an optional embodiment, in the first aspect of the present invention, predicting the traffic scene corresponding to the passing vehicle based on the device information of the target checkpoint device and the vehicle identification includes:

[0031] Inputting the device information of the target checkpoint device into a trained first traffic scene prediction neural network to obtain a first scene prediction result corresponding to the target checkpoint device; the first traffic scene prediction neural network is trained using a training data set including a plurality of training device information and corresponding traffic scene annotations; the device information includes device location, device model, device hardware parameters, and device software parameters;

[0032] Inputting the vehicle identification into a trained second traffic scene prediction neural network to obtain a second scene prediction result corresponding to the passing vehicle; the second traffic scene prediction neural network is trained using a training data set including a plurality of training vehicle identifications and corresponding traffic scene annotations;

[0033] Calculate the intersection of the first scenario prediction result and the second scenario prediction result to obtain the passage scenario corresponding to the passing vehicle; the passage scenario is the factory entry inspection scenario, the inspection link transfer scenario, the factory exit scenario after inspection, the factory exit scenario with a fault, or the factory exit mission execution scenario.

[0034] As an optional embodiment, in the first aspect of the present invention, the calculating, based on the vehicle defect information and the traffic scenario and a risk identification algorithm, the traffic risk parameter corresponding to the passing vehicle includes:

[0035] Determining a first reference threshold corresponding to the traffic scenario based on a preset mathematical correspondence between the scenario and the risk threshold;

[0036] Determining a second reference threshold corresponding to the vehicle defect information based on a preset mathematical correspondence between defects and risk thresholds;

[0037] Calculating a weighted average of the first reference threshold and the second reference threshold to obtain a risk reference threshold;

[0038] Inputting the vehicle defect information and the traffic scenario into a trained traffic risk prediction neural network to obtain an output predicted risk value; the traffic risk prediction neural network is trained using a training data set including a plurality of training vehicle defect information and training traffic scenarios and corresponding traffic risk annotations;

[0039] The difference between the predicted risk value and the risk reference threshold is calculated to obtain a passing risk parameter corresponding to the passing vehicle.

[0040] A second aspect of an embodiment of the present invention discloses a vehicle traffic control system based on defect detection, the system comprising:

[0041] An acquisition module, used to acquire a vehicle identification of a vehicle that intends to pass through a target checkpoint device;

[0042] A determination module, configured to determine vehicle defect information corresponding to the passing vehicle according to the vehicle identification based on preset vehicle detection history data;

[0043] A prediction module, configured to predict a traffic scene corresponding to the passing vehicle based on the device information of the target checkpoint device and the vehicle identification;

[0044] A calculation module is used to calculate a passage risk parameter corresponding to the passing vehicle based on the vehicle defect information and the passage scenario and a risk identification algorithm; the passage risk parameter indicates the risk level of allowing the passing vehicle to pass through the target checkpoint device.

[0045] As an optional embodiment, in the second aspect of the present invention, the vehicle identification includes at least one of vehicle sensor data, vehicle license plate, vehicle inspection number and facial images of people in the vehicle; the vehicle sensor data includes at least one of vehicle image data, vehicle sound data, vehicle light reflection three-dimensional model data and vehicle temperature and humidity data.

[0046] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines the vehicle defect information corresponding to the passing vehicle according to the vehicle identification based on preset vehicle detection history data includes:

[0047] Perform matching in a vehicle detection history database based on the vehicle identification to obtain a matching result;

[0048] When the matching result indicates that a matching record exists, obtaining a plurality of vehicle detection records obtained by matching;

[0049] When the matching result is that there is no matching record, determining at least one similar vehicle based on the vehicle identifier of multiple detected vehicles in the vehicle detection history database, and obtaining vehicle detection records corresponding to all similar vehicles;

[0050] According to the vehicle inspection record, vehicle defect information corresponding to the passing vehicle is determined.

[0051] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines at least one similar vehicle based on the vehicle identifier of the plurality of detected vehicles in the vehicle detection history database includes:

[0052] Inputting the vehicle sensor data of the passing vehicle into a trained detection elapsed time prediction neural network to obtain the detection elapsed time corresponding to the passing vehicle; the detection elapsed time prediction neural network is trained using a training data set including a plurality of training vehicle sensor data and corresponding time distance annotations of the last detection time to the current time;

[0053] Calculate the difference between the current time point and the detection time to obtain the possible last detection time corresponding to the passing vehicle;

[0054] For each detected vehicle in the vehicle detection history database, obtaining vehicle data and a historically most recent detection time point corresponding to the detected vehicle;

[0055] Calculating a first similarity between the vehicle data and the vehicle identification;

[0056] Calculating a first time difference between the most recent historical detection time point and the possible last detection time;

[0057] Calculating a ratio of the first similarity to the first time difference to obtain a similarity parameter corresponding to the detected vehicle;

[0058] The detected vehicles whose similarity parameter is greater than a parameter threshold are determined as similar vehicles.

[0059] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines the vehicle defect information corresponding to the passing vehicle based on the vehicle inspection record includes:

[0060] For each vehicle detection record, calculating a second time difference between the detection time corresponding to the vehicle detection record and the current time point;

[0061] Calculating a second similarity between the detected vehicle information corresponding to the vehicle detection record and the vehicle identifier;

[0062] Calculate the difference between the number of defects detected in the vehicle inspection record and the average number; the average number is the average number of defects detected in all vehicle inspection records;

[0063] Calculating the product of the second time difference and the quantity difference, calculating the ratio of the second similarity to the product, and obtaining the record priority corresponding to the vehicle detection record;

[0064] The detected defect in the vehicle inspection record with the highest record priority is determined as the vehicle defect information corresponding to the passing vehicle.

[0065] As an optional embodiment, in the second aspect of the present invention, the vehicle defect information includes a defect type corresponding to at least one vehicle part; the vehicle part is a door, front, body, chassis, roof, rear, trunk, interior space or wheel.

[0066] As an optional embodiment, in the second aspect of the present invention, the prediction module predicts the specific manner of the passage scene corresponding to the passing vehicle based on the device information of the target checkpoint device and the vehicle identification, including:

[0067] Inputting the device information of the target checkpoint device into a trained first traffic scene prediction neural network to obtain a first scene prediction result corresponding to the target checkpoint device; the first traffic scene prediction neural network is trained using a training data set including a plurality of training device information and corresponding traffic scene annotations; the device information includes device location, device model, device hardware parameters, and device software parameters;

[0068] Inputting the vehicle identification into a trained second traffic scene prediction neural network to obtain a second scene prediction result corresponding to the passing vehicle; the second traffic scene prediction neural network is trained using a training data set including a plurality of training vehicle identifications and corresponding traffic scene annotations;

[0069] Calculate the intersection of the first scenario prediction result and the second scenario prediction result to obtain the passage scenario corresponding to the passing vehicle; the passage scenario is the factory entry inspection scenario, the inspection link transfer scenario, the factory exit scenario after inspection, the factory exit scenario with a fault, or the factory exit mission execution scenario.

[0070] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the calculation module calculates the passage risk parameter corresponding to the passing vehicle based on the vehicle defect information and the passage scenario and a risk identification algorithm includes:

[0071] Determining a first reference threshold corresponding to the traffic scenario based on a preset mathematical correspondence between the scenario and the risk threshold;

[0072] Determining a second reference threshold corresponding to the vehicle defect information based on a preset mathematical correspondence between defects and risk thresholds;

[0073] Calculating a weighted average of the first reference threshold and the second reference threshold to obtain a risk reference threshold;

[0074] Inputting the vehicle defect information and the traffic scenario into a trained traffic risk prediction neural network to obtain an output predicted risk value; the traffic risk prediction neural network is trained using a training data set including a plurality of training vehicle defect information and training traffic scenarios and corresponding traffic risk annotations;

[0075] The difference between the predicted risk value and the risk reference threshold is calculated to obtain a passing risk parameter corresponding to the passing vehicle.

[0076] A third aspect of the present invention discloses another vehicle traffic control system based on defect detection, the system comprising:

[0077] a memory storing executable program code;

[0078] a processor coupled to the memory;

[0079] The processor calls the executable program code stored in the memory to execute part or all of the steps in the vehicle traffic control method based on defect detection disclosed in the first aspect of the present invention.

[0080] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the vehicle traffic control method based on defect detection disclosed in the first aspect of the present invention.

[0081] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0082] The present invention can determine the vehicle defect information corresponding to the passing vehicle according to the vehicle identification based on the preset vehicle detection historical data, and then accurately predict the passage scenario according to the device information and vehicle identification of the target checkpoint device, so as to accurately calculate the vehicle's passage risk based on the vehicle defect information and the passage scenario, thereby improving the monitoring efficiency and accuracy of vehicle passage risk, providing an accurate risk estimation reference for passage control, and improving the safety and intelligence of vehicle passage control. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0084] Figure 1 It is a flow chart of a vehicle traffic control method based on defect detection disclosed in an embodiment of the present invention.

[0085] Figure 2 It is a structural diagram of a vehicle passage control system based on defect detection disclosed in an embodiment of the present invention.

[0086] Figure 3 It is a structural diagram of another vehicle passage control system based on defect detection disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0087] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0088] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

[0089] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0090] The present invention discloses a vehicle traffic control method and system based on defect detection. This method can determine the corresponding vehicle defect information of a passing vehicle based on its identification, based on preset historical vehicle detection data. It then accurately predicts the traffic scenario based on the target checkpoint's device information and vehicle identification, accurately calculating the vehicle's traffic risk based on the vehicle defect information and traffic scenario. This method improves the efficiency and accuracy of monitoring vehicle traffic risk, provides an accurate risk estimation reference for traffic control, and enhances the safety and intelligence of vehicle traffic control. These are described in detail below.

[0091] Example 1

[0092] See also Figure 1 , Figure 1 This is a flow chart of a vehicle traffic control method based on defect detection disclosed in an embodiment of the present invention. Figure 1 The vehicle traffic control method based on defect detection described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the vehicle traffic control method based on defect detection may include the following operations:

[0093] 101. Obtain a vehicle identification of a vehicle that intends to pass through the target checkpoint device.

[0094] 102. Based on the preset vehicle inspection history data and according to the vehicle identification, the vehicle defect information corresponding to the passing vehicle is determined.

[0095] 103. Predict the traffic scenario corresponding to the passing vehicle based on the device information and vehicle identification of the target checkpoint device.

[0096] 104. Based on the vehicle defect information and traffic scenario, and based on the risk identification algorithm, the traffic risk parameters corresponding to the passing vehicle are calculated.

[0097] Optionally, the passage risk parameter indicates the risk level of allowing the vehicle to pass through the target checkpoint device.

[0098] It can be seen that the above-mentioned embodiment of the invention can determine the vehicle defect information corresponding to the passing vehicle according to the vehicle identification based on the preset vehicle detection historical data, and then accurately predict the passage scenario according to the device information and vehicle identification of the target checkpoint device, so as to accurately calculate the vehicle's passage risk based on the vehicle defect information and the passage scenario, thereby improving the monitoring efficiency and accuracy of vehicle passage risks, providing an accurate risk estimation reference for passage control, and improving the safety and intelligence of vehicle passage control.

[0099] As an optional embodiment, in the above steps, the vehicle identification includes at least one of vehicle sensor data, vehicle license plate, vehicle inspection number and facial images of people in the vehicle; the vehicle sensor data includes at least one of vehicle image data, vehicle sound data, vehicle light reflection three-dimensional model data and vehicle temperature and humidity data.

[0100] It can be seen that through the above optional embodiments, the specific content of the vehicle identification is limited, which can fully characterize the characteristics of passing vehicles, and is subsequently used to accurately predict vehicle defects and communication scenarios, assist in improving the monitoring efficiency and accuracy of vehicle passage risks, provide accurate risk estimation reference for passage control, and improve the safety and intelligence of vehicle passage control.

[0101] As an optional embodiment, in the above steps, based on preset vehicle inspection history data and according to the vehicle identification, determining the vehicle defect information corresponding to the passing vehicle includes:

[0102] Matching is performed in a vehicle detection history database based on the vehicle identification to obtain a matching result;

[0103] When the matching result indicates that a matching record exists, multiple matching vehicle detection records are obtained;

[0104] When the matching result is that there is no matching record, determining at least one similar vehicle based on the vehicle identification of multiple detected vehicles in the vehicle detection history database, and obtaining vehicle detection records corresponding to all similar vehicles;

[0105] According to the vehicle inspection records, the vehicle defect information corresponding to the passing vehicles is determined.

[0106] It can be seen that through the above-mentioned optional embodiments, it is possible to match the vehicle detection history database based on the vehicle identification or to obtain accurate vehicle detection records based on the determination of similar vehicles, so as to make precise defect determination, facilitate subsequent risk assessment, and assist in improving the monitoring efficiency and accuracy of vehicle traffic risks, provide accurate risk estimation reference for traffic control, and improve the safety and intelligence of vehicle traffic control.

[0107] As an optional embodiment, in the above step, determining at least one similar vehicle based on the vehicle identifications of multiple detected vehicles in the vehicle detection history database includes:

[0108] Inputting the vehicle sensor data of the passing vehicle into a trained detection elapsed time prediction neural network to obtain the detection elapsed time corresponding to the passing vehicle; optionally, the detection elapsed time prediction neural network is trained using a training data set including a plurality of training vehicle sensor data and corresponding time distance annotations of the last detection from the current time;

[0109] Calculate the difference between the current time point and the detection time to obtain the possible last detection time corresponding to the passing vehicle;

[0110] For each detected vehicle in the vehicle detection history database, obtain the vehicle data and the most recent historical detection time point corresponding to the detected vehicle;

[0111] Calculating a first similarity between the vehicle data and the vehicle identification;

[0112] Calculate the first time difference between the most recent historical detection time point and the possible last detection time;

[0113] Calculating a ratio of the first similarity to the first time difference to obtain a similarity parameter corresponding to the detected vehicle;

[0114] The detected vehicles whose similarity parameters are greater than the parameter threshold are determined as similar vehicles.

[0115] It can be seen that through the above-mentioned optional embodiments, similar vehicles can be accurately screened out based on the vehicle data of the inspected vehicle and the proximity between the inspection time point and the passing vehicles to obtain accurate vehicle inspection records, so as to make accurate defect determinations, facilitate subsequent risk assessments, and assist in improving the monitoring efficiency and accuracy of vehicle traffic risks, provide accurate risk estimation references for traffic control, and improve the safety and intelligence of vehicle traffic control.

[0116] As an optional embodiment, in the above step, determining the vehicle defect information corresponding to the passing vehicle based on the vehicle inspection record includes:

[0117] For each vehicle detection record, calculate a second time difference between the detection time corresponding to the vehicle detection record and the current time point;

[0118] Calculating a second similarity between the detected vehicle information and the vehicle identifier corresponding to the vehicle detection record;

[0119] Calculate the difference between the number of defects detected in the vehicle inspection record and the average number; optionally, the average number is the average number of defects detected in all vehicle inspection records;

[0120] Calculate the product of the second time difference and the quantity difference, calculate the ratio of the second similarity and the product, and obtain the record priority corresponding to the vehicle detection record;

[0121] The detected defects in the vehicle inspection record with the highest record priority are determined as the vehicle defect information corresponding to the passing vehicle.

[0122] It can be seen that through the above-mentioned optional embodiments, it is possible to obtain accurate vehicle inspection records based on the calculation and threshold screening of the information similarity between the vehicle information in each historical vehicle inspection record and the vehicle information of the vehicle to be inspected, so as to make precise defect determination, facilitate subsequent risk assessment, and assist in improving the monitoring efficiency and accuracy of vehicle traffic risks, provide accurate risk estimation reference for traffic control, and improve the safety and intelligence of vehicle traffic control.

[0123] As an optional embodiment, in the above steps, the vehicle defect information includes a defect type corresponding to at least one vehicle part; the vehicle part is a door, front, body, chassis, roof, rear, trunk, interior space or wheel.

[0124] It can be seen that through the above-mentioned optional embodiments, the details of the vehicle defect information are limited, so that the vehicle defect information can accurately characterize the defect situation of the vehicle, facilitate the subsequent accurate calculation of traffic risk, assist in improving the monitoring efficiency and accuracy of vehicle traffic risk, provide accurate risk estimation reference for traffic control, and improve the safety and intelligence of vehicle traffic control.

[0125] As an optional embodiment, in the above step, predicting the traffic scene corresponding to the passing vehicle based on the device information and vehicle identification of the target checkpoint device includes:

[0126] Inputting the device information of the target checkpoint device into a trained first traffic scene prediction neural network to obtain a first scene prediction result corresponding to the target checkpoint device; optionally, the first traffic scene prediction neural network is trained using a training data set including a plurality of training device information and corresponding traffic scene annotations; the device information includes device location, device model, device hardware parameters, and device software parameters;

[0127] Inputting the vehicle identification into the trained second traffic scene prediction neural network to obtain a second scene prediction result corresponding to the passing vehicle; optionally, the second traffic scene prediction neural network is trained using a training data set including multiple training vehicle identifications and corresponding traffic scene annotations;

[0128] Calculate the intersection of the prediction results of the first scenario and the prediction results of the second scenario to obtain the passage scenario corresponding to the passing vehicle; the passage scenario is the factory entry inspection scenario, the inspection link transfer scenario, the factory exit scenario after inspection, the factory exit scenario due to failure, or the factory exit mission scenario.

[0129] It can be seen that through the above optional embodiments, two trained scene prediction neural networks can be used to perform scene prediction based on checkpoint equipment information and vehicle information respectively, and determine the accurate traffic scene based on intersection calculation, which is convenient for subsequent accurate traffic risk calculation, assists in improving the monitoring efficiency and accuracy of vehicle traffic risk, provides an accurate risk estimation reference for traffic control, and improves the safety and intelligence of vehicle traffic control.

[0130] As an optional embodiment, in the above steps, the calculation of the corresponding traffic risk parameters of the passing vehicle based on the risk identification algorithm according to the vehicle defect information and the traffic scenario includes:

[0131] Determine a first reference threshold corresponding to the traffic scenario based on a mathematical correspondence between a preset scenario and a risk threshold;

[0132] Determining a second reference threshold corresponding to the vehicle defect information based on a preset mathematical correspondence between the defect and the risk threshold;

[0133] Calculating a weighted average of the first reference threshold and the second reference threshold to obtain a risk reference threshold;

[0134] Inputting the vehicle defect information and traffic scenarios into a trained traffic risk prediction neural network to obtain an output predicted risk value; optionally, the traffic risk prediction neural network is trained using a training data set including a plurality of training vehicle defect information and training traffic scenarios and corresponding traffic risk annotations;

[0135] Calculate the difference between the predicted risk value and the risk reference threshold to obtain the corresponding traffic risk parameter of the passing vehicle.

[0136] It can be seen that through the above optional embodiments, the risk thresholds corresponding to vehicle defects and traffic scenarios can be determined respectively based on the preset risk threshold mathematical relationship to accurately calculate the reference threshold, and then the risk value is predicted based on the neural network, and the accurate traffic risk is determined based on the difference calculation, so that the calculated risk value takes into account the possible risk level when the traffic scenario and vehicle defects exist alone, thereby improving the monitoring efficiency and accuracy of vehicle traffic risks, providing an accurate risk estimation reference for traffic control, and improving the safety and intelligence of vehicle traffic control.

[0137] Example 2

[0138] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a vehicle traffic control system based on defect detection disclosed in an embodiment of the present invention. Figure 2 The vehicle traffic control system based on defect detection described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the vehicle traffic control system based on defect detection may include:

[0139] The acquisition module 201 is used to acquire the vehicle identification of a vehicle that intends to pass through the target checkpoint device.

[0140] The determination module 202 is used to determine the vehicle defect information corresponding to the passing vehicle based on the preset vehicle detection history data and the vehicle identification.

[0141] The prediction module 203 is used to predict the traffic scene corresponding to the passing vehicle based on the device information and vehicle identification of the target checkpoint device.

[0142] The calculation module 204 is used to calculate the passing risk parameters corresponding to the passing vehicle based on the vehicle defect information and the passing scenario and the risk identification algorithm.

[0143] Optionally, the passage risk parameter indicates the risk level of allowing the vehicle to pass through the target checkpoint device.

[0144] It can be seen that the above-mentioned embodiment of the invention can determine the vehicle defect information corresponding to the passing vehicle according to the vehicle identification based on the preset vehicle detection historical data, and then accurately predict the passage scenario according to the device information and vehicle identification of the target checkpoint device, so as to accurately calculate the vehicle's passage risk based on the vehicle defect information and the passage scenario, thereby improving the monitoring efficiency and accuracy of vehicle passage risks, providing an accurate risk estimation reference for passage control, and improving the safety and intelligence of vehicle passage control.

[0145] As an optional embodiment, the vehicle identification includes at least one of vehicle sensor data, vehicle license plate, vehicle inspection number and facial images of people in the vehicle; the vehicle sensor data includes at least one of vehicle image data, vehicle sound data, vehicle light reflection three-dimensional model data and vehicle temperature and humidity data.

[0146] It can be seen that through the above optional embodiments, the specific content of the vehicle identification is limited, which can fully characterize the characteristics of passing vehicles, and is subsequently used to accurately predict vehicle defects and communication scenarios, assist in improving the monitoring efficiency and accuracy of vehicle passage risks, provide accurate risk estimation reference for passage control, and improve the safety and intelligence of vehicle passage control.

[0147] As an optional embodiment, the specific method of determining the vehicle defect information corresponding to the passing vehicle according to the vehicle identification based on the preset vehicle detection history data includes:

[0148] Matching is performed in a vehicle detection history database based on the vehicle identification to obtain a matching result;

[0149] When the matching result indicates that a matching record exists, multiple matching vehicle detection records are obtained;

[0150] When the matching result is that there is no matching record, determining at least one similar vehicle based on the vehicle identification of multiple detected vehicles in the vehicle detection history database, and obtaining vehicle detection records corresponding to all similar vehicles;

[0151] According to the vehicle inspection records, the vehicle defect information corresponding to the passing vehicles is determined.

[0152] It can be seen that through the above-mentioned optional embodiments, it is possible to match the vehicle detection history database based on the vehicle identification or to obtain accurate vehicle detection records based on the determination of similar vehicles, so as to make precise defect determination, facilitate subsequent risk assessment, and assist in improving the monitoring efficiency and accuracy of vehicle traffic risks, provide accurate risk estimation reference for traffic control, and improve the safety and intelligence of vehicle traffic control.

[0153] As an optional embodiment, the specific manner in which the determination module determines at least one similar vehicle based on the vehicle identifications of multiple detected vehicles in the vehicle detection history database includes:

[0154] Inputting the vehicle sensor data of the passing vehicle into a trained detection elapsed time prediction neural network to obtain the detection elapsed time corresponding to the passing vehicle; optionally, the detection elapsed time prediction neural network is trained using a training data set including a plurality of training vehicle sensor data and corresponding time distance annotations of the last detection from the current time;

[0155] Calculate the difference between the current time point and the detection time to obtain the possible last detection time corresponding to the passing vehicle;

[0156] For each detected vehicle in the vehicle detection history database, obtain the vehicle data and the most recent historical detection time point corresponding to the detected vehicle;

[0157] Calculating a first similarity between the vehicle data and the vehicle identification;

[0158] Calculate the first time difference between the most recent historical detection time point and the possible last detection time;

[0159] Calculating a ratio of the first similarity to the first time difference to obtain a similarity parameter corresponding to the detected vehicle;

[0160] The detected vehicles whose similarity parameters are greater than the parameter threshold are determined as similar vehicles.

[0161] It can be seen that through the above-mentioned optional embodiments, similar vehicles can be accurately screened out based on the vehicle data of the inspected vehicle and the proximity between the inspection time point and the passing vehicles to obtain accurate vehicle inspection records, so as to make accurate defect determinations, facilitate subsequent risk assessments, and assist in improving the monitoring efficiency and accuracy of vehicle traffic risks, provide accurate risk estimation references for traffic control, and improve the safety and intelligence of vehicle traffic control.

[0162] As an optional embodiment, the specific method in which the determination module determines the vehicle defect information corresponding to the passing vehicle based on the vehicle inspection record includes:

[0163] For each vehicle detection record, calculate a second time difference between the detection time corresponding to the vehicle detection record and the current time point;

[0164] Calculating a second similarity between the detected vehicle information and the vehicle identifier corresponding to the vehicle detection record;

[0165] Calculate the difference between the number of defects detected in the vehicle inspection record and the average number; optionally, the average number is the average number of defects detected in all vehicle inspection records;

[0166] Calculate the product of the second time difference and the quantity difference, calculate the ratio of the second similarity and the product, and obtain the record priority corresponding to the vehicle detection record;

[0167] The detected defects in the vehicle inspection record with the highest record priority are determined as the vehicle defect information corresponding to the passing vehicle.

[0168] It can be seen that through the above-mentioned optional embodiments, it is possible to obtain accurate vehicle inspection records based on the calculation and threshold screening of the information similarity between the vehicle information in each historical vehicle inspection record and the vehicle information of the vehicle to be inspected, so as to make precise defect determination, facilitate subsequent risk assessment, and assist in improving the monitoring efficiency and accuracy of vehicle traffic risks, provide accurate risk estimation reference for traffic control, and improve the safety and intelligence of vehicle traffic control.

[0169] As an optional embodiment, the vehicle defect information includes a defect type corresponding to at least one vehicle part; the vehicle part is a door, a front, a body, a chassis, a roof, a rear, a trunk, an interior space or a wheel.

[0170] It can be seen that through the above-mentioned optional embodiments, the details of the vehicle defect information are limited, so that the vehicle defect information can accurately characterize the defect situation of the vehicle, facilitate the subsequent accurate calculation of traffic risk, assist in improving the monitoring efficiency and accuracy of vehicle traffic risk, provide accurate risk estimation reference for traffic control, and improve the safety and intelligence of vehicle traffic control.

[0171] As an optional embodiment, the prediction module predicts the specific method of the traffic scene corresponding to the passing vehicle based on the device information and vehicle identification of the target checkpoint device, including:

[0172] Inputting the device information of the target checkpoint device into a trained first traffic scene prediction neural network to obtain a first scene prediction result corresponding to the target checkpoint device; optionally, the first traffic scene prediction neural network is trained using a training data set including a plurality of training device information and corresponding traffic scene annotations; the device information includes device location, device model, device hardware parameters, and device software parameters;

[0173] Inputting the vehicle identification into the trained second traffic scene prediction neural network to obtain a second scene prediction result corresponding to the passing vehicle; optionally, the second traffic scene prediction neural network is trained using a training data set including multiple training vehicle identifications and corresponding traffic scene annotations;

[0174] Calculate the intersection of the prediction results of the first scenario and the prediction results of the second scenario to obtain the passage scenario corresponding to the passing vehicle; the passage scenario is the factory entry inspection scenario, the inspection link transfer scenario, the factory exit scenario after inspection, the factory exit scenario due to failure, or the factory exit mission scenario.

[0175] It can be seen that through the above optional embodiments, two trained scene prediction neural networks can be used to perform scene prediction based on checkpoint equipment information and vehicle information respectively, and determine the accurate traffic scene based on intersection calculation, which is convenient for subsequent accurate traffic risk calculation, assists in improving the monitoring efficiency and accuracy of vehicle traffic risk, provides an accurate risk estimation reference for traffic control, and improves the safety and intelligence of vehicle traffic control.

[0176] As an optional embodiment, the calculation module calculates the corresponding traffic risk parameters of the passing vehicle based on the vehicle defect information and the traffic scenario and the risk identification algorithm, including:

[0177] Determine a first reference threshold corresponding to the traffic scenario based on a mathematical correspondence between a preset scenario and a risk threshold;

[0178] Determining a second reference threshold corresponding to the vehicle defect information based on a preset mathematical correspondence between the defect and the risk threshold;

[0179] Calculating a weighted average of the first reference threshold and the second reference threshold to obtain a risk reference threshold;

[0180] Inputting the vehicle defect information and traffic scenarios into a trained traffic risk prediction neural network to obtain an output predicted risk value; optionally, the traffic risk prediction neural network is trained using a training data set including a plurality of training vehicle defect information and training traffic scenarios and corresponding traffic risk annotations;

[0181] Calculate the difference between the predicted risk value and the risk reference threshold to obtain the corresponding traffic risk parameter of the passing vehicle.

[0182] It can be seen that through the above optional embodiments, the risk thresholds corresponding to vehicle defects and traffic scenarios can be determined respectively based on the preset risk threshold mathematical relationship to accurately calculate the reference threshold, and then the risk value is predicted based on the neural network, and the accurate traffic risk is determined based on the difference calculation, so that the calculated risk value takes into account the possible risk level when the traffic scenario and vehicle defects exist alone, thereby improving the monitoring efficiency and accuracy of vehicle traffic risks, providing an accurate risk estimation reference for traffic control, and improving the safety and intelligence of vehicle traffic control.

[0183] Example 3

[0184] See also Figure 3 , Figure 3 This is another vehicle traffic control system based on defect detection disclosed in an embodiment of the present invention. Figure 3The vehicle traffic control system based on defect detection is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the vehicle traffic control system based on defect detection may include:

[0185] A memory 301 storing executable program code;

[0186] a processor 302 coupled to the memory 301;

[0187] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the vehicle traffic control method based on defect detection described in the first embodiment.

[0188] Example 4

[0189] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the vehicle traffic control method based on defect detection described in the first embodiment.

[0190] Example 5

[0191] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the vehicle traffic control method based on defect detection described in Example 1.

[0192] The foregoing description of specific embodiments of the present disclosure is intended to illustrate a method for performing a multi-tasking process. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0193] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0194] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0195] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0196] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0197] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0198] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0199] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0200] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0201] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0202] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0203] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0204] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0205] Finally, it should be noted that the vehicle traffic control method and system based on defect detection disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A vehicle traffic control method based on defect detection, characterized in that: The method comprises: Obtaining the vehicle identification of a vehicle that intends to pass through the target checkpoint device; Based on the preset vehicle inspection history data and according to the vehicle identification, determining the vehicle defect information corresponding to the passing vehicle; Predicting a traffic scene corresponding to the passing vehicle based on the device information of the target checkpoint device and the vehicle identification; According to the vehicle defect information and the passage scenario, based on a risk identification algorithm, a passage risk parameter corresponding to the passing vehicle is calculated; the passage risk parameter indicates the risk level of allowing the passing vehicle to pass through the target checkpoint device.

2. The vehicle traffic control method based on defect detection according to claim 1, characterized in that: The vehicle identification includes at least one of vehicle sensor data, vehicle license plate, vehicle inspection number and facial image of people in the vehicle; the vehicle sensor data includes at least one of vehicle image data, vehicle sound data, vehicle light reflection three-dimensional model data and vehicle temperature and humidity data.

3. The vehicle traffic control method based on defect detection according to claim 1, characterized in that: The determining of the vehicle defect information corresponding to the passing vehicle based on the preset vehicle detection history data and the vehicle identification includes: Perform matching in a vehicle detection history database based on the vehicle identification to obtain a matching result; When the matching result indicates that a matching record exists, obtaining a plurality of vehicle detection records obtained by matching; When the matching result is that there is no matching record, determining at least one similar vehicle based on the vehicle identifier of multiple detected vehicles in the vehicle detection history database, and obtaining vehicle detection records corresponding to all similar vehicles; According to the vehicle inspection record, vehicle defect information corresponding to the passing vehicle is determined.

4. The vehicle traffic control method based on defect detection according to claim 3 is characterized in that: The determining of at least one similar vehicle from a plurality of detected vehicles in the vehicle detection history database based on the vehicle identification includes: Inputting the vehicle sensor data of the passing vehicle into a trained detection elapsed time prediction neural network to obtain the detection elapsed time corresponding to the passing vehicle; the detection elapsed time prediction neural network is trained using a training data set including a plurality of training vehicle sensor data and corresponding time distance annotations of the last detection time to the current time; Calculate the difference between the current time point and the detection time to obtain the possible last detection time corresponding to the passing vehicle; For each detected vehicle in the vehicle detection history database, obtaining vehicle data and a historically most recent detection time point corresponding to the detected vehicle; Calculating a first similarity between the vehicle data and the vehicle identification; Calculating a first time difference between the most recent historical detection time point and the possible last detection time; Calculating a ratio of the first similarity to the first time difference to obtain a similarity parameter corresponding to the detected vehicle; The detected vehicles whose similarity parameter is greater than a parameter threshold are determined as similar vehicles.

5. The vehicle traffic control method based on defect detection according to claim 3 is characterized in that: The determining, based on the vehicle inspection record, vehicle defect information corresponding to the passing vehicle includes: For each vehicle detection record, calculating a second time difference between the detection time corresponding to the vehicle detection record and the current time point; Calculating a second similarity between the detected vehicle information corresponding to the vehicle detection record and the vehicle identifier; Calculate the difference between the number of defects detected in the vehicle inspection record and the average number; the average number is the average number of defects detected in all vehicle inspection records; Calculating the product of the second time difference and the quantity difference, calculating the ratio of the second similarity to the product, and obtaining the record priority corresponding to the vehicle detection record; The detected defect in the vehicle inspection record with the highest record priority is determined as the vehicle defect information corresponding to the passing vehicle.

6. The vehicle traffic control method based on defect detection according to claim 1, characterized in that: The vehicle defect information includes a defect type corresponding to at least one vehicle part; the vehicle part is a door, a front part, a body, a chassis, a roof, a rear part, a trunk, an interior space or a wheel.

7. The vehicle traffic control method based on defect detection according to claim 6, characterized in that: The predicting, based on the device information of the target checkpoint device and the vehicle identification, a corresponding traffic scene of the passing vehicle includes: Inputting the device information of the target checkpoint device into a trained first traffic scene prediction neural network to obtain a first scene prediction result corresponding to the target checkpoint device; the first traffic scene prediction neural network is trained using a training data set including a plurality of training device information and corresponding traffic scene annotations; the device information includes device location, device model, device hardware parameters, and device software parameters; Inputting the vehicle identification into a trained second traffic scene prediction neural network to obtain a second scene prediction result corresponding to the passing vehicle; the second traffic scene prediction neural network is trained using a training data set including a plurality of training vehicle identifications and corresponding traffic scene annotations; Calculate the intersection of the first scenario prediction result and the second scenario prediction result to obtain the passage scenario corresponding to the passing vehicle; the passage scenario is the factory entry inspection scenario, the inspection link transfer scenario, the factory exit scenario after inspection, the factory exit scenario with a fault, or the factory exit mission execution scenario.

8. The vehicle traffic control method based on defect detection according to claim 7, characterized in that: The calculating, based on the vehicle defect information and the traffic scenario and a risk identification algorithm, of a traffic risk parameter corresponding to the traffic vehicle includes: Determining a first reference threshold corresponding to the traffic scenario based on a preset mathematical correspondence between the scenario and the risk threshold; Determining a second reference threshold corresponding to the vehicle defect information based on a preset mathematical correspondence between defects and risk thresholds; Calculating a weighted average of the first reference threshold and the second reference threshold to obtain a risk reference threshold; Inputting the vehicle defect information and the traffic scenario into a trained traffic risk prediction neural network to obtain an output predicted risk value; the traffic risk prediction neural network is trained using a training data set including a plurality of training vehicle defect information and training traffic scenarios and corresponding traffic risk annotations; The difference between the predicted risk value and the risk reference threshold is calculated to obtain a passing risk parameter corresponding to the passing vehicle.

9. A vehicle traffic control system based on defect detection, characterized in that: The system comprises: An acquisition module, used to acquire a vehicle identification of a vehicle that intends to pass through a target checkpoint device; A determination module, configured to determine vehicle defect information corresponding to the passing vehicle according to the vehicle identification based on preset vehicle detection history data; A prediction module, configured to predict a traffic scene corresponding to the passing vehicle based on the device information of the target checkpoint device and the vehicle identification; A calculation module is used to calculate a passage risk parameter corresponding to the passing vehicle based on the vehicle defect information and the passage scenario and a risk identification algorithm; the passage risk parameter indicates the risk level of allowing the passing vehicle to pass through the target checkpoint device.

10. A vehicle traffic control system based on defect detection, characterized in that: The system comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the vehicle traffic control method based on defect detection as described in any one of claims 1 to 8.

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