Expressway vehicle checking and early warning method and device, electronic equipment and storage medium

Abstract

The invention discloses a highway vehicle checking and early warning method and device, electronic equipment and a storage medium. The method comprises the steps that first measurement data, first number axis data and first image data of a target vehicle within a first preset time period are collected; correcting the first metering data and the first number axis data through a high-speed dynamic intelligent metering instrument to obtain second metering data and second number axis data; checking the target vehicle according to the second measurement data, the second number axis data and the first image data to obtain a target checking result; when the target vehicle meets a preset condition, determining a target check cheating level corresponding to the target check result; and generating target alarm information according to the target check cheating level. By adopting the embodiment of the invention, the accuracy of checking and early warning the vehicle on the expressway can be improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle safety warning technology, and in particular to a highway vehicle inspection warning method, device, electronic equipment and storage medium. Background Art

[0002] Expressways are an inevitable product of economic development and play a pivotal role in the transportation industry. Compared with ordinary roads, expressways have outstanding advantages such as high driving speed, large traffic capacity, low transportation cost, safe driving, and comfort. However, due to the high speed of vehicles on expressways, it is difficult to inspect vehicles.

[0003] At present, the vehicle inspection and early warning method on the highway uses light curtains, ground sensing coils and other equipment to judge the direction of vehicle travel. However, drivers may also cheat in inspections and interfere with vehicle inspections. For example, drivers may interfere with the direction of travel judgment and axle type recognition by running over axle identifiers, resulting in the inability of traditional vehicle inspection and early warning methods to accurately identify the actual behavior of the vehicle, leading to misjudgments, generation of erroneous early warning information, or even no early warning information being generated.

[0004] Therefore, how to improve the accuracy of vehicle inspection and early warning on highways needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present application provide a method, device, electronic device and storage medium for early warning of vehicle inspection on a highway, which can improve the accuracy of early warning of vehicle inspection on a highway.

[0006] In a first aspect, an embodiment of the present application provides a highway vehicle inspection warning method, which is applied to a vehicle inspection system, wherein the vehicle inspection system includes: a control module, a target inspection platform, a high-speed dynamic intelligent metering instrument, and a camera module, wherein the target inspection platform includes a metering module and a digital axis detection module, and the method includes: The first measurement data of the target vehicle in a first preset time period is collected by the measurement module; the first preset time period is a time period from when the target vehicle appears on the target inspection platform to when it leaves the target inspection platform; Collecting first axis data of the target vehicle in the first preset time period by the axis detection module; Acquire a first device attribute parameter of the metering module; acquire a second device attribute parameter of the digital axis detection module; Correcting the first metering data according to the first device attribute parameter by the high-speed dynamic intelligent metering instrument to obtain second metering data; Correcting the first digital axis data according to the second device attribute parameter by the high-speed dynamic intelligent metering instrument to obtain second digital axis data; Determining a target driving speed of the target vehicle according to the second number axis data; Determining a target collection interval according to the target driving speed and the first preset time period; The camera module collects image data of the target vehicle within the first preset time period at the target collection interval to obtain first image data; Inspect the target vehicle according to the second measurement data, the second number axis data and the first image data to obtain a target inspection result; Obtaining target vehicle permission information corresponding to the target vehicle; Determine whether the target vehicle meets the preset conditions according to the target inspection result and the target vehicle permission information; When the target vehicle meets the preset condition, determining a target inspection cheating level corresponding to the target inspection result; Target alarm information is generated according to the target inspection cheating level, and the target alarm information includes the inspection cheating behavior and punishment measures corresponding to the target vehicle.

[0007] In the second aspect, the embodiment of the present application provides a highway vehicle inspection warning device, which is applied to a vehicle inspection system, wherein the vehicle inspection system includes: a control module, a target inspection platform, a high-speed dynamic intelligent metering instrument, and a camera module, wherein the target inspection platform includes a metering module and a digital axis detection module, and the device includes: a collection unit, an inspection unit, and an alarm unit, wherein: The acquisition unit is used to acquire first metering data of the target vehicle in a first preset time period through the metering module; the first preset time period is a time period from the appearance of the target vehicle on the target inspection platform to the departure of the target inspection platform; acquire first number axis data of the target vehicle in the first preset time period through the number axis detection module; acquire first device attribute parameters of the metering module; acquire second device attribute parameters of the number axis detection module; correct the first metering data according to the first device attribute parameters through the high-speed dynamic intelligent metering instrument to obtain second metering data; correct the first number axis data according to the second device attribute parameters through the high-speed dynamic intelligent metering instrument to obtain second number axis data; determine the target driving speed of the target vehicle according to the second number axis data; determine the target acquisition interval according to the target driving speed and the first preset time period; acquire image data of the target vehicle in the first preset time period at the target acquisition interval through the camera module to obtain first image data; The inspection unit is used to inspect the target vehicle according to the second measurement data, the second number axis data and the first image data to obtain a target inspection result; obtain target vehicle permission information corresponding to the target vehicle; determine whether the target vehicle meets a preset condition according to the target inspection result and the target vehicle permission information; when the target vehicle meets the preset condition, determine a target inspection cheating level corresponding to the target inspection result; The alarm unit is used to generate target alarm information according to the target inspection cheating level, and the target alarm information includes the inspection cheating behavior and punishment measures corresponding to the target vehicle.

[0008] In a third aspect, the present application provides an electronic device comprising: a processor and a memory, wherein the memory is used to store one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the present application.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the present application.

[0010] In a fifth aspect, the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the present application. The computer program product may be a software installation package.

[0011] The implementation of this application has the following beneficial effects: It can be seen that the highway vehicle inspection and early warning method described in the present application can effectively reduce measurement errors and improve data accuracy by acquiring data from the metering module and the axis detection module within the first preset time period and correcting them in combination with the equipment attribute parameters. This provides strong data support for subsequent vehicle inspections. In addition, the target inspection results are combined with the target vehicle license information to determine whether the vehicle meets the preset conditions, which is equivalent to further considering the legal license scope of the vehicle itself on the basis of identifying inspection cheating behavior, thereby generating alarm information that conforms to the actual situation of the vehicle, and further improving the accuracy of vehicle inspection and early warning on highways. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0013] Figure 1 This is a scenario application diagram of a vehicle inspection system provided by an embodiment of the present application; Figure 2 It is a structural schematic diagram of a vehicle inspection system provided in an embodiment of the present application; Figure 3 It is a flow chart of a highway vehicle inspection and early warning method provided by an embodiment of the present application; Figure 4 This is a functional unit composition block diagram of a highway vehicle inspection and warning device provided by an embodiment of the present application; Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0014] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0016] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0017] The electronic device described in the embodiments of the present application may include a vehicle inspection system.

[0018] The following is an explanation of some professional terms involved in this application: Vehicle inspection cheating: In this application, vehicle inspection cheating refers to the behavior of vehicles trying to evade normal charging, supervision or obtain illegal benefits through improper means at highway entrances and exits, such as repeatedly reversing or running over specific locations. The driver repeatedly reverses and moves forward to try to find the "weak points" of the weighing equipment, or deliberately runs over specific locations of the scale, such as edges or corners, to affect the weighing accuracy and make the weighing results lower, thereby evading normal charges.

[0019] High-speed dynamic intelligent metering instrument: It is a device specially used in highway scenarios, which can dynamically measure and intelligently analyze and process vehicle-related data. Unlike traditional static metering equipment, it can adapt to the characteristics of fast-moving traffic on highways and continuously monitor vehicle parameters such as weight, speed, and position. For example, when a vehicle passes through the dynamic weighing area of ​​a highway toll station, the instrument can quickly and accurately measure the weight of the vehicle without the vehicle completely stopping; in addition, for the vehicle's location information, it can cooperate with the digital axis detection module to accurately determine the vehicle's position coordinates on the highway, providing a basis for the vehicle's driving trajectory analysis and the judgment of cheating behavior.

[0020] Axis sensor: A sensor used to measure physical quantities such as the position, displacement, and speed of an object in the direction of the axis. In the vehicle inspection system, the axis sensor can be used in conjunction with the metering sensor to measure the position and speed of the vehicle in the direction of the axis, thereby accurately measuring the weight of the vehicle. For example, when a vehicle passes through a weighing platform, the axis sensor can determine the position of the front and rear axles of the vehicle, while the metering sensor measures the weight of the vehicle at different positions and calculates the total weight of the vehicle through an algorithm. The axis sensor can also be used to detect whether the vehicle is completely stopped on the weighing platform, and whether there is any inspection cheating, such as the movement or tilt of the vehicle during the weighing process.

[0021] See also Figure 1 , Figure 1 is a scenario application diagram of a vehicle inspection system provided by an embodiment of the present application; Figure 1 As shown, at the toll booth of the expressway, a vehicle inspection system is provided to accurately measure relevant data of the vehicle and monitor possible inspection cheating. It should be explained that the expressway can be a dynamic highway.

[0022] Among them, the target inspection platform is located on the highway and is one of the core parts of the vehicle inspection system. It includes a metering module and an axis detection module, which are used to measure the weight of the vehicle and its position in the axis direction. The metering module can accurately measure the weight data of the vehicle when it passes through the weighing platform through technical means such as sensors (for example, metering sensors), providing an important basis for determining whether the vehicle is overloaded and calculating tolls. The axis detection module includes a first axis sensor and a second axis sensor, which are used to monitor the position change and movement state of the vehicle in the axis direction, such as the vehicle's driving direction, speed and other information.

[0023] Among them, the high-speed dynamic intelligent metering instrument is usually installed in the control room near the toll station or other locations that are convenient for operation and monitoring. It communicates with the metering module and the axis detection module on the target inspection platform, and receives and processes data from these modules. The high-speed dynamic intelligent metering instrument can include a display screen, which can be used to display the vehicle's cheating identification results for easy viewing by staff. Among them, the camera module is used to collect image information of the vehicle and provide data support for the vehicle inspection system.

[0024] In this way, the vehicle inspection system can achieve accurate measurement of vehicles and effective monitoring of inspection cheating, providing important technical support for the management and operation of highways.

[0025] It needs to be explained that the control module of the vehicle inspection system can be located in the control center of the toll station or integrated with the high-speed dynamic intelligent metering instrument, or the control module can be an application installed on the staff's mobile phone, computer and other devices.

[0026] See also Figure 2 , Figure 2 This is a structural diagram of a vehicle inspection system provided in an embodiment of the present application. It can be seen that the vehicle inspection system includes: a target inspection platform, a high-speed dynamic intelligent metering instrument, a control module, and a camera module.

[0027] Among them, the target inspection platform includes a metering module and a digital axis detection module. When a vehicle passes through the target inspection platform, the weight of the vehicle can be accurately measured by the metering module. For example, the pressure of the vehicle on the scale is sensed by a metering sensor and other equipment, and the pressure signal is converted into an electrical signal for processing and calculation to obtain the vehicle's weight data, that is, the metering data. In addition, the digital axis detection module can also detect the position, speed, and direction of travel of the vehicle in the digital axis direction to obtain the digital axis data. The metering data and digital axis data can provide important basis for the measurement and inspection of the vehicle. In addition, the vehicle inspection system can also shoot the vehicle through the camera module to obtain the image data of the vehicle.

[0028] It should be explained that the metering module may include at least one weighing sensor, and the axis detection module may include at least one axis sensor.

[0029] Among them, the high-speed dynamic intelligent metering instrument is used to receive data from the metering module and the digital axis detection module, and to correct, analyze and store the data. By correcting the collected data, the accuracy and reliability of the data can be improved.

[0030] Among them, the control module is used to analyze and judge the vehicle's measurement data, axis data and image data to identify whether there is any inspection cheating. By analyzing the measurement data and axis data, it is determined whether the vehicle cheats by interfering with the weighing equipment, repeatedly reversing, etc. The control module can also display the collected measurement data and alarm information to provide intuitive vehicle measurement information and inspection cheating behavior prompts to the staff. For example, when the vehicle is detected to have inspection cheating behavior, the control module can convey the alarm information to the staff through its own display screen, indicator light or sound alarm to prevent the vehicle from successfully cheating.

[0031] See also Figure 3 , Figure 3 The present invention provides a flowchart of a highway vehicle inspection and early warning method provided by an embodiment of the present invention. The method is applied to a vehicle inspection system. The vehicle inspection system includes: a control module, a target inspection platform, a high-speed dynamic intelligent metering instrument and a camera module. The target inspection platform includes a metering module and a digital axis detection module. The method includes but is not limited to the following steps: S301. Collect first metering data of a target vehicle in a first preset time period through the metering module; the first preset time period is a time period from when the target vehicle appears on the target inspection platform to when it leaves the target inspection platform.

[0032] In the embodiment of the present application, when the target vehicle enters the target inspection platform, the metering data (e.g., weight value) of the metering module can be obtained at the start time of the first preset time period until the end time of the first preset time period, and multiple metering data, i.e., first metering data, are obtained, and the first metering data is transmitted to the high-speed dynamic intelligent metering instrument. The metering module can be physically and / or communicatively connected to the high-speed dynamic intelligent metering instrument.

[0033] S302. Collect first axis data of the target vehicle in the first preset time period through the axis detection module.

[0034] In the embodiment of the present application, when the target vehicle enters the target inspection platform, the axis data (e.g., the position coordinates of the vehicle, the vehicle speed, etc.) of the axis detection module can be obtained at the start time of the first preset time period until the end time of the first preset time period, and multiple axis data, i.e., the first axis data, are obtained, and the first axis data is transmitted to the high-speed dynamic intelligent metering instrument. The axis detection module can be physically and / or communicatively connected to the high-speed dynamic intelligent metering instrument.

[0035] S303, obtaining a first device attribute parameter of the metering module; and obtaining a second device attribute parameter of the number axis detection module.

[0036] In the embodiment of the present application, the second device attribute parameter may be at least one of the following: resolution, error amplitude, effective detection distance, etc., which are not limited here.

[0037] In a specific embodiment, the first device attribute parameter of the metering module can be obtained first, and then the second device attribute parameter of the digital axis detection module can be obtained. Specifically, the second device attribute parameter can be an error margin. The device manual or technical document of the digital axis detection module can be queried. The device manual or technical document will clearly list various attribute parameters of the device, including the error margin. The second device attribute parameter is obtained from the device manual or technical document. For example, assuming that the device manual states that the measurement error of the digital axis detection module is within the range of ±2 cm, it can be determined that the error margin is 4 cm.

[0038] Optionally, step S303, obtaining the first device attribute parameter of the metering module may include the following steps: A1. Obtain the target device type corresponding to the metering module; A2. Determine a reference first device attribute parameter and a first error range corresponding to the target device type; A3. Acquire actual metering data and theoretical metering data of the metering module within a second preset time period; the end time of the second preset time period is earlier than the start time of the first preset time period; A4. Determine a second error range corresponding to the metering module according to the actual metering data and the theoretical metering data; A5. Determine a reference error deviation corresponding to the metering module according to the second error range and the first error range; A6. Obtain the target usage time and standard usage time corresponding to the metering module; A7. Determine the difference between the target usage time and the standard usage time to obtain a target time difference; A8. Determine a first optimization factor corresponding to the target duration difference; A9. Optimize the reference error deviation according to the first optimization factor to obtain a target error deviation; A10, determining a second optimization factor corresponding to the target error deviation; A11. Optimize the reference first device attribute parameter according to the second optimization factor to obtain the first device attribute parameter.

[0039] In the embodiment of the present application, the first device attribute parameter can be at least one of the following: measurement accuracy, resolution, data update frequency, range, etc., which are not limited here; the second preset time period can be preset or defaulted in advance, and the second preset time period represents a period of time in the past.

[0040] In a specific embodiment, the target device type corresponding to the metering module can be first obtained. Specifically, the device technical document of the metering module can be queried. The device technical document will clearly list various device information of the device (for example, the device type), and the target device type can be obtained from the device technical document; then, the reference first device attribute parameter and the first error range corresponding to the target device type can be determined. Specifically, the mapping relationship between the preset device type and the first device attribute parameter can be pre-stored, and the reference first device attribute parameter corresponding to the target device type can be determined based on the mapping relationship. Of course, the mapping relationship between the preset device type and the error range can also be pre-stored, and the first error range corresponding to the target device type can be determined based on the mapping relationship; then, the actual metering data and theoretical metering data of the metering module within the second preset time period can be obtained. Specifically, the vehicle inspection system can have a metering database, and the metering database stores the historical metering data of the metering module and the corresponding theoretical metering data. Then, the database query language (for example, SQL language) can be used to retrieve the actual metering data and theoretical metering data corresponding to the metering module within the second preset time period from the metering database. For example, the query can be performed according to conditions such as time range, vehicle identification, metering module number, etc., so as to obtain the required data.

[0041] Furthermore, the second error range corresponding to the metering module can be determined according to the actual metering data and the theoretical metering data. Specifically, for each actual metering value in the actual metering data, the error value between it and the corresponding theoretical metering data is calculated, and the error value = actual metering value - theoretical metering value. Thus, multiple error values ​​are obtained, and the maximum error value and the minimum error value among the multiple error values ​​are obtained. The second error range is determined according to the maximum error value and the minimum error value; then, the reference error deviation corresponding to the metering module can be determined according to the second error range and the first error range; then, the target usage time and the standard usage time corresponding to the metering module can be obtained. Specifically, The standard usage time indicates the service life of the metering module, and the standard usage time can be determined according to the target device type. For example, the mapping relationship between the preset device type and the usage time can be pre-stored, and the standard usage time corresponding to the target device type can be determined based on the mapping relationship. Then, the installation record, operation log and other data of the metering module can be checked. Based on these data, the actual usage time of the metering module from installation to the present can be determined, that is, the target usage time. For example, if the metering module is installed in January 2020 and the current time is January 2024, and the operation log records that it is still running as of the current time, then the target usage time is approximately four years.

[0042] Next, the difference between the target usage time and the standard usage time can be calculated. The specific calculation formula is as follows: Target duration difference = target usage duration - standard usage duration; According to the above formula, the target duration difference can be obtained; then, the first optimization factor corresponding to the target duration difference can be determined. Specifically, the mapping relationship between the preset duration difference and the optimization factor can be pre-stored, and the first optimization factor corresponding to the target duration difference can be determined based on the mapping relationship. The value range of the first optimization factor can be -0.2~0.2; then, the reference error deviation can be optimized according to the first optimization factor. The specific calculation formula is as follows: Target error deviation = reference error deviation × (1 + first optimization factor); According to the above formula, the target error deviation can be obtained; then, the second optimization factor corresponding to the target error deviation can be determined. Specifically, the mapping relationship between the preset error deviation and the optimization factor can be pre-stored, and the second optimization factor corresponding to the target error deviation can be determined based on the mapping relationship. The value range of the second optimization factor can be -0.3~0.3; finally, the reference first device attribute parameter can be optimized according to the second optimization factor. The specific calculation formula is as follows: First device attribute parameter = reference first device attribute parameter × (1 + second optimization factor); The first device attribute parameter can be obtained according to the above formula.

[0043] In this way, by continuously acquiring actual measurement data and theoretical measurement data and determining the error range and error deviation, the performance changes of the measurement module can be monitored in real time. By optimizing the device attribute parameters according to these changes, the measurement module can be more accurately adapted to the actual working conditions, thereby improving the accuracy of measurement.

[0044] Optionally, step A5, determining the reference error deviation corresponding to the metering module according to the second error range and the first error range, may include the following steps: B1. Determine an overlap range between the second error range and the first error range to obtain a target overlap range; B2. determining a first reference error deviation corresponding to the target overlap range; B3. Obtaining a first error upper limit and a first error lower limit of the first error range; B4. Obtaining a second error upper limit and a second error lower limit of the second error range; B5. determining an upper limit difference between the first error upper limit and the second error upper limit to obtain a target upper limit difference; B6. determining a lower limit difference between the first error lower limit and the second error lower limit to obtain a target lower limit difference; B7. Determine a first influencing factor corresponding to the target upper limit difference; B8. Determine a second influencing factor corresponding to the target lower limit difference; B9. Adjust the first reference error deviation according to the first influencing factor and the second influencing factor to obtain the reference error deviation.

[0045] In the embodiment of the present application, the error deviation degree indicates the degree of deviation between the actual error range of the metering module and the theoretical error range.

[0046] In a specific embodiment, the overlap range between the second error range and the first error range can be determined first to obtain the target overlap range. For example, assuming that the first error range is -5%~10% and the second error range is -10%~5%, the target overlap range is -5%~5%. Then, the first reference error deviation corresponding to the target overlap range can be determined. Specifically, the target amplitude of the target overlap range can be obtained first, and then the first amplitude of the first error range can be obtained. Then, the first reference error deviation can be calculated according to the target amplitude and the first amplitude. The specific calculation formula is: The first reference error deviation = |first amplitude - target amplitude| / first amplitude × 100%; According to the above formula, the first reference error deviation can be obtained. For example, the first amplitude corresponding to -5%~10% is 15%, and the target amplitude corresponding to -5%~5% is 10%. Then the first reference error deviation = |15%-10%| / 15%×100%≈33.3%; further, the first error upper limit and the first error lower limit of the first error range can be obtained, and the second error upper limit and the second error lower limit of the second error range can also be obtained; then, the upper limit difference between the first error upper limit and the second error upper limit can be calculated. The specific calculation formula is: Target upper limit difference = first error upper limit - second error upper limit; According to the above formula, the target upper limit difference can be obtained; then, the lower limit difference between the first error lower limit and the second error lower limit is calculated, and the specific calculation formula is: Target lower limit difference = first error lower limit - second error lower limit; According to the above formula, the target lower limit difference can be obtained; then, the first influencing factor corresponding to the target upper limit difference can be determined. Specifically, the mapping relationship between the preset upper limit difference and the influencing factor can be pre-stored, and the first influencing factor corresponding to the target upper limit difference can be determined based on the mapping relationship; similarly, the mapping relationship between the preset lower limit difference and the influencing factor can also be pre-stored, and the second influencing factor corresponding to the target lower limit difference can be determined based on the mapping relationship, wherein the value range of the first influencing factor and the second influencing factor can both be -0.12~0.12; finally, the first reference error deviation can be adjusted according to the first influencing factor and the second influencing factor, and the specific calculation formula is: Reference error deviation = first reference error deviation × (1 + first impact factor) × (1 + second impact factor); The reference error deviation can be obtained according to the above formula.

[0047] In this way, by determining the first reference error deviation corresponding to the target overlap range, a specific numerical indicator is provided for further error analysis, which can intuitively reflect the degree of deviation between the actual error of the metering module and the expected error, and is helpful to quickly evaluate the performance of the metering module. In addition, the first influencing factor and the second influencing factor are determined according to the target upper limit difference and the target lower limit difference, and then the first reference error deviation is adjusted, which fully considers the different influences of the upper and lower limits of the error range, so that the final reference error deviation is more accurate.

[0048] S304: Correct the first metering data according to the first device attribute parameter by using the high-speed dynamic intelligent metering instrument to obtain second metering data.

[0049] In the embodiment of the present application, the high-speed dynamic intelligent metering instrument may analyze and correct the first metering data according to the first device attribute parameter, thereby obtaining the second metering data.

[0050] Optionally, the correcting the first metering data according to the first device attribute parameter by the high-speed dynamic intelligent metering instrument to obtain the second metering data may include the following steps: S41, determining a first correction coefficient corresponding to the first device attribute parameter; S42, obtaining a first part of the error range that is greater than zero in the second error range, and a second part of the error range that is less than zero in the second error range; S43, determining a first error range corresponding to the first partial error range; S44, determining a second error range corresponding to the second partial error range; S45. When the first error amplitude is greater than the second error amplitude, reversely correct the first measurement data according to the first correction coefficient to obtain the second measurement data; S46. When the first error amplitude is equal to the second error amplitude, determining the second measurement data according to the first measurement data; S47: When the first error amplitude is smaller than the second error amplitude, positively correct the first measurement data according to the first correction coefficient to obtain the second measurement data.

[0051] In an embodiment of the present application, a first correction coefficient corresponding to a first device attribute parameter can be determined. Specifically, a mapping relationship between a preset device attribute parameter and a correction coefficient can be pre-stored, and the first correction coefficient corresponding to the first device attribute parameter can be determined based on the mapping relationship. The value range of the first correction coefficient can be 0~0.25; then, a first part of the error range greater than zero in the second error range and a second part of the error range less than zero in the second error range can be obtained. For example, assuming that the second error range is -10%~5%, then the first part of the error range is 0~5%, and the second part of the error range is -10%~0; then, a first error amplitude corresponding to the first part of the error range can be determined. Specifically, the maximum value and the minimum value in the first part of the error range can be obtained, and the first error amplitude can be obtained by subtracting the maximum value from the minimum value. For example, if the first part of the error range is 0~5%, then the first error amplitude is equal to 5% minus 0, that is, the first error amplitude is 5%; then, the second error amplitude corresponding to the second part of the error range can be determined. Specifically, the method for obtaining the second error amplitude can be the same as the method for obtaining the first error amplitude, which will not be repeated here.

[0052] When the first error amplitude is greater than the second error amplitude, it indicates that the metering data detected by the metering module is too large, and the first metering data is reversely corrected according to the first correction coefficient. The specific calculation formula is as follows: Second measurement data = first measurement data × (1-first correction coefficient); The second measurement data can be obtained according to the above formula.

[0053] When the first error amplitude is equal to the second error amplitude, it indicates that the metering data detected by the metering module is accurate and does not need to be corrected, and the first metering data can be directly used as the second metering data; When the first error amplitude is smaller than the second error amplitude, it indicates that the metering data detected by the metering module is too small, and the first metering data is positively corrected according to the first correction coefficient. The specific calculation formula is as follows: The second measurement data = the first measurement data × (1 + the first correction coefficient); The second measurement data can be obtained according to the above formula.

[0054] In this way, by determining the first correction coefficient corresponding to the first device attribute parameter, the measurement data can be adjusted in a targeted manner according to different equipment conditions and measurement requirements to improve the accuracy of the measurement data. In addition, different correction methods are adopted according to the size relationship between the first error amplitude and the second error amplitude, so that the vehicle inspection system can maintain good performance under different error conditions, thereby enhancing the adaptability of the system.

[0055] S305. Correct the first digital axis data according to the second device attribute parameter by using the high-speed dynamic intelligent metering instrument to obtain second digital axis data.

[0056] In the embodiment of the present application, the second correction coefficient corresponding to the second device attribute parameter can be determined by the high-speed dynamic intelligent metering instrument. Specifically, the second correction coefficient corresponding to the second device attribute parameter can be determined based on the mapping relationship between the above-mentioned device attribute parameter and the correction coefficient. The value range of the second correction coefficient can be -0.12~0.12. Then, the first number axis data can be directly corrected according to the second correction coefficient. The specific calculation formula is as follows: Second number axis data = first number axis data × (1 + second correction coefficient); The second number axis data may be obtained according to the above formula, or the method for obtaining the second number axis data may be the same as the above method for obtaining the second measurement data.

[0057] S306. Determine a target driving speed of the target vehicle according to the second number axis data.

[0058] In an embodiment of the present application, the second number axis data may include multiple number axis signals, and two consecutive number axis signals may be randomly extracted from the second number axis data to calculate the first time difference between them. Then, the distance represented by each signal, that is, the first distance between the two number axis signals, may be determined based on the characteristics of the number axis signal. For example, if each pulse represents a 1 degree rotation of the wheel and the radius of the wheel is known, the arc length corresponding to each pulse may be calculated, and then the target driving speed may be obtained by dividing the first distance by the first time difference.

[0059] S307: Determine a target collection interval according to the target driving speed and the first preset time period.

[0060] In the embodiment of the present application, the target driving speed determines the distance that the vehicle moves in a unit time. The purpose of collecting vehicle images is to clearly and completely record the status or details of the vehicle during the driving process. If the vehicle is driving fast, in order to avoid image blur and information omission, the collection interval needs to be shortened; if the vehicle is driving slow, the collection interval can be appropriately extended, so the first collection interval can be determined according to the target driving speed. For example, the mapping relationship between the preset driving speed and the collection interval can be pre-stored, and the first collection interval corresponding to the target driving speed is determined based on the mapping relationship. The number of images that can be collected in the first preset time period is determined according to the first collection interval to obtain the first image number. If the first image number is greater than or equal to the preset image number, the first collection interval is determined as the target collection interval. If the first image number is less than the preset image number, the duration corresponding to the first preset time period can be divided by the preset image number to obtain the target collection interval.

[0061] S308. Collect image data of the target vehicle within the first preset time period at the target collection interval through the camera module to obtain first image data.

[0062] In the embodiment of the present application, within the first preset time period, the target vehicle can be photographed by the camera module at the target acquisition interval to obtain the first image data.

[0063] S309: Inspect the target vehicle according to the second measurement data, the second number axis data and the first image data to obtain a target inspection result.

[0064] In an embodiment of the present application, the control module can perform inspection and analysis based on the second measurement data, the second number axis data and the first image data to identify whether the target vehicle has inspection cheating behavior and obtain the target inspection result; wherein, the inspection cheating behavior may include at least one of the following: overweight inspection cheating behavior, reverse driving inspection cheating behavior, system detection abnormality, etc., which are not limited here.

[0065] Optionally, step S309, checking the target vehicle according to the second measurement data and the second number axis data to obtain a target checking result, may include the following steps: C1. Sampling the second measurement data to obtain a plurality of measurement data, each measurement data corresponding to a first sampling time; C2. Performing curve fitting according to the plurality of metering data and the first sampling time corresponding to each metering data to obtain a first metering curve; the horizontal axis of the first metering curve is time, and the vertical axis is the metering value; C3. Matching the first metering curve with an abnormal curve in a preset abnormal curve library to obtain a plurality of matching degrees; each matching degree corresponds to an abnormal curve; the preset abnormal curve library includes a plurality of abnormal curves, and each abnormal curve corresponds to a cheating behavior; C4. Determine a maximum value among the multiple matching degrees, and determine a first inspection result according to the maximum value; C5. Obtain measurement data that is greater than the first preset measurement value in the second measurement data to obtain m measurement data; m is a natural number; C6. Determine a second inspection result of the target vehicle according to the m measurement data; C7. Check the target vehicle according to the second number axis data to obtain a third checking result; C8. Inspect the target vehicle according to the first image data to obtain a fourth inspection result; C9. Determine the target inspection result based on the first inspection result, the second inspection result, the third inspection result and the fourth inspection result.

[0066] In the embodiment of the present application, the preset abnormal curve library, the first preset measurement value, and the preset matching degree threshold can all be preset or defaulted in advance.

[0067] In a specific embodiment, the second metering data may be sampled equidistantly to obtain a plurality of metering data, and then the first sampling time of each metering data in the plurality of metering data may be obtained to obtain a plurality of first sampling times; a plurality of first coordinate points may be obtained by combining the plurality of metering data and the corresponding first sampling times in the plurality of first sampling times, and then a curve fitting method (for example, a polynomial fitting method, a spline method, etc.) may be used to fit the plurality of first coordinate points to obtain a first metering curve; then the first metering curve may be matched with abnormal curves in a preset abnormal curve library to obtain a plurality of matching degrees, and specifically, all abnormal curves in the preset abnormal curve library may be first obtained to obtain a plurality of abnormal curves, and then a suitable matching algorithm (for example, a cosine similarity algorithm, a dynamic time warping algorithm) may be selected to calculate the matching degree between the first metering curve and each abnormal curve in the plurality of abnormal curves to obtain a plurality of matching degrees.

[0068] Furthermore, a maximum value among the multiple matching degrees can be selected, and a first inspection result can be determined according to the maximum value. Specifically, the maximum value can be compared with a preset matching degree threshold. If the maximum value is greater than the preset matching degree threshold, a target abnormal curve corresponding to the maximum value is obtained, and then, a first inspection cheating behavior corresponding to the target abnormal curve is determined, that is, the first inspection result is that a first inspection cheating behavior exists, and the first inspection cheating behavior can be reversed without getting off the scale, reversed after getting off the scale, and other inspection cheating behaviors. Conversely, if the maximum value is less than or equal to the preset matching degree threshold, it means that the matching degree between the first metering curve and the abnormal curve is not high, and the first inspection result is that there is no first inspection cheating behavior. Then, the second metering data that is greater than the first preset metering value can be obtained. Measurement data is obtained, and a second inspection result of the target vehicle is determined according to the m measurement data; then, the target vehicle can also be inspected according to the second number axis data and the first image data to obtain a third inspection result and a fourth inspection result; finally, it can be determined according to the first inspection result, the second inspection result, the third inspection result and the fourth inspection result which inspection cheating behaviors exist in the target vehicle, and a target inspection result is obtained based on these inspection cheating behaviors. For example, assuming that the first inspection result is that the first inspection cheating behavior exists, the second inspection result is that the second inspection cheating behavior exists, and the third inspection result is that the third inspection cheating behavior does not exist, then the target inspection result can be: the target vehicle has the first inspection cheating behavior and the second inspection cheating behavior.

[0069] It needs to be explained that the second inspection cheating behavior can be overweight inspection cheating behavior, and the third inspection cheating behavior can be reverse driving inspection cheating behavior.

[0070] In this way, the final target inspection result is determined by combining the four inspection results, which is equivalent to multiple verifications of inspection cheating behavior. If there is only one identification method, it may lead to wrong conclusions due to the limitations of the method itself or misjudgment. However, through the mutual verification of multiple methods, the risk of misjudgment can be greatly reduced and the reliability of the identification results can be improved.

[0071] Optionally, step C6, determining the second inspection result of the target vehicle according to the m measurement data, may include the following steps: D1. Determine the average measurement value corresponding to the m measurement data; D2. When the average measurement value is greater than a second preset measurement value, determining that the second inspection result is an overweight inspection cheating behavior; the second preset measurement value is greater than the first preset measurement value; D3. When the average measurement value is less than or equal to the second preset measurement value, determining that the second inspection result is that there is no overweight inspection cheating behavior.

[0072] In the embodiment of the present application, the average value of the m measurement data can be calculated according to the average value calculation formula to obtain the average measurement value; when the average measurement value is greater than the second preset measurement value, it means that the measurement value of the target vehicle is too large and there is an abnormality, then the second inspection result can be determined as cheating in overweight inspection; When the average measurement value is less than or equal to the second preset measurement value, it indicates that the measurement value of the target vehicle is within a normal range, and it can be determined that the second inspection result does not include any cheating in the overweight inspection.

[0073] Optionally, step C7, checking the target vehicle according to the second number axis data to obtain a third checking result, may include the following steps: E1. Sampling the second axis data to obtain a plurality of axis data, each axis data corresponding to a second sampling time; E2. Performing curve fitting according to the plurality of digital axis data and the second sampling time corresponding to each digital axis data to obtain a first digital axis curve; the horizontal axis of the first digital axis curve is time, and the vertical axis is the digital axis value; E3. Determine the coordinate time corresponding to the preset axis value in the first axis curve to obtain multiple coordinate times; the preset axis value is used to indicate that the target vehicle stably enters or leaves the target inspection platform; E4. Obtain the earliest first coordinate time and second coordinate time among the multiple coordinate times; the first coordinate time is earlier than the second coordinate time; E5. Determine a first measurement value corresponding to the first coordinate time in the second measurement data; E6. Determine a second measurement value corresponding to the second coordinate time in the second measurement data; E7. When the second measurement value is less than or equal to the first measurement value, determining that the third inspection result is that there is no reverse driving inspection cheating behavior; E8. When the second measurement value is greater than the first measurement value, obtaining a target time difference between the first coordinate time and the second coordinate time; E9. When the target time difference is greater than the preset time difference, determining that the third inspection result is that there is cheating in the reverse driving inspection; E10. When the target time difference is less than or equal to the preset time difference, determining that the third inspection result is that there is no cheating in the reverse driving inspection.

[0074] In the embodiment of the present application, the preset time difference can be preset in advance or defaulted, and the preset time difference is used to determine whether the target vehicle has cheated in the reverse driving inspection.

[0075] In a specific embodiment, the second axis data can be sampled to obtain multiple axis data, and then the second sampling time of each axis data in the multiple axis data can be obtained to obtain multiple second sampling times; multiple axis data and corresponding second sampling times in the multiple second sampling times are combined to obtain multiple second coordinate points, and then a curve fitting method (for example, a polynomial fitting method, a spline method, etc.) can be used to fit the multiple second coordinate points to obtain a first axis curve; further, the coordinate time corresponding to the preset axis value in the first axis curve can be determined to obtain multiple coordinate times. Specifically, the first curve equation of the first axis curve can be obtained, and the preset axis value can be substituted into the first curve equation to solve it to obtain multiple coordinate times.

[0076] Next, the earliest first coordinate time and second coordinate time among the multiple coordinate times can be obtained. Specifically, the multiple coordinate times can be sorted in chronological order to obtain a first coordinate time order, and the coordinate times ranked first and second in the first coordinate time order are selected to obtain the first coordinate time and the second coordinate time; then, the first measurement value corresponding to the first coordinate time in the second measurement data can be determined. For example, the second curve equation of the first measurement curve can be obtained, and the first coordinate time is substituted into the second curve equation to obtain the first measurement value; then, the second measurement value corresponding to the second coordinate time in the second measurement data can be determined. Specifically, the second measurement value can be obtained in the same way as the first measurement value.

[0077] When the second measurement value is less than or equal to the first measurement value, it indicates that the target vehicle is traveling in the forward direction and there is no inspection cheating behavior, that is, the third inspection result is that there is no reverse driving inspection cheating behavior; When the second measurement value is greater than the first measurement value, it indicates that the target vehicle is traveling in the opposite direction, and the target time difference between the first coordinate time and the second coordinate time can be obtained. The specific calculation formula is as follows: Target time difference = |first coordinate time - second coordinate time|; The target time difference is obtained according to the above formula. When the target time difference is greater than the preset time difference, it means that the target vehicle is traveling in the reverse direction and the reverse driving time is relatively long. Then, it can be determined that the third inspection result is a cheating behavior in the reverse driving inspection. When the target time difference is less than or equal to the preset time difference, it is determined that the third inspection result is that there is no cheating in the reverse driving inspection.

[0078] In this way, through the comprehensive analysis of the second axis data and the second measurement data, information about the vehicle behavior can be obtained from different angles. The axis data reflects the position change of the vehicle on the scale, while the measurement data reflects the weight information of the vehicle. Combining these two types of data for judgment can provide a more comprehensive understanding of the vehicle's driving status and improve the accuracy of reverse driving inspections.

[0079] Optionally, step C8, checking the target vehicle according to the first image data to obtain a fourth checking result, may include the following steps: F1. Determine the target vehicle type and loadable cargo information corresponding to the target vehicle according to the target vehicle permission information; F2. Identify the first image data by using image recognition technology to obtain a first cargo type; F3. Determine whether the first cargo type is compliant according to the loadable cargo information; F4. If the conditions are met, determine the cargo loading space corresponding to the target vehicle type to obtain n cargo loading spaces; n is a positive integer; F5. Segment the first image data based on the n cargo loading spaces to obtain n segmented image data; F6. Determine the estimated cargo measurement value corresponding to each of the n segmented image data to obtain n estimated cargo measurement values; F7. Determine the vehicle measurement value corresponding to the target vehicle type; F8. determining a target estimated quantity value according to the vehicle measurement value and the n estimated quantity values; F9. Obtaining a target measurement deviation according to a deviation between the target estimated measurement value and the average measurement value; F10. When the target measurement deviation is greater than a preset deviation threshold, determining that the fourth inspection result is a system detection abnormality.

[0080] In the embodiment of the present application, the target vehicle type may include one of the following: a small truck, a medium truck, a large truck, etc., which are not limited here; the preset deviation threshold can be preset or defaulted in advance.

[0081] In a specific embodiment, the target vehicle type and loadable cargo information can be obtained from the target vehicle permission information, and the loadable cargo information indicates the information of the cargo that the target vehicle is allowed to load; then, the first image data can be identified by image recognition technology to obtain the first cargo type. Specifically, feature extraction (for example, shape feature extraction, texture feature extraction, color feature extraction, etc.) can be performed on the first image data to obtain first feature data, and the first feature data can be input into a preset classification model to obtain the first cargo type; then, it can be determined whether the first cargo type belongs to the permitted cargo in the loadable cargo information. If so, it is determined that the first cargo type is compliant; if not, it is determined that the first cargo type is non-compliant, and an alarm message can be directly generated to prompt the staff of the vehicle inspection system that there is an abnormality in the cargo loaded on the target vehicle.

[0082] If it is compliant, the cargo loading space corresponding to the target vehicle type is determined to obtain n cargo loading spaces. Specifically, the mapping relationship between the preset vehicle type and the cargo loading space can be pre-stored, and the n cargo loading spaces corresponding to the target vehicle type are determined based on the mapping relationship. Then, the first image data can be segmented based on the n cargo loading spaces to obtain n segmented image data. Specifically, the position data of the n cargo loading spaces in the target vehicle can be first obtained to obtain n position data. Then, the first image data can be segmented using a region growing segmentation algorithm based on the n position data to obtain n segmented image data; further, the estimated cargo measurement value corresponding to each segmented image data in the n segmented image data can be determined to obtain n estimated cargo measurement values; then, the vehicle measurement value corresponding to the target vehicle type can be determined. For example, the mapping relationship between the preset vehicle type and the measurement value can be pre-stored, and the vehicle measurement value corresponding to the target vehicle type can be determined based on the mapping relationship. Then, the vehicle measurement value and the n estimated measurement values ​​can be superimposed to obtain the measurement value sum, that is, the target estimated measurement value; then, the deviation between the target estimated measurement value and the average measurement value can be calculated. Target measurement deviation = |target estimated measurement value - average measurement value| / target estimated measurement value; According to the above formula, the target measurement deviation can be obtained; when the target measurement deviation is greater than the preset deviation threshold, the fourth inspection result is determined to be a system detection abnormality.

[0083] In this way, by comparing the deviation between the target estimated measurement value and the average measurement value, the system abnormality can be detected. If the deviation is greater than the preset deviation threshold, it is determined that the system detection is abnormal, which can timely discover problems such as sensor failure and image recognition errors. For example, if the actual cargo volume of the vehicle differs too much from the cargo volume estimated by the system, it may be that the image recognition algorithm has an error, or the vehicle metering equipment has a fault. This timely abnormality detection can ensure the accuracy of the inspection results and avoid misjudgments caused by system errors.

[0084] Optionally, step F6, determining the estimated cargo measurement value corresponding to each of the n segmented image data to obtain n estimated cargo measurement values, may include the following steps: G1. Acquire first segmented image data; the first segmented image data is any segmented image data among the n segmented image data; G2. Determine the first cargo area in the first segmented image data; G3. Determine a first shooting angle corresponding to the first segmented image data; G4. Determine a first area compensation coefficient corresponding to the first shooting angle; G5. Adjust the first cargo area according to the first area compensation coefficient to obtain a second cargo area; G6. Determine an estimated cargo measurement value corresponding to the first segmented image data based on the second cargo area and the first cargo type.

[0085] In the embodiment of the present application, the first segmented image data may be obtained first; then, the first cargo area in the first segmented image data may be determined. Specifically, an edge detection algorithm may be used to perform edge detection on the first segmented image data to obtain an edge image, and then contour extraction may be performed to connect the edges in the edge image to form the contour of the cargo. The area of ​​the cargo is calculated based on the extracted contour to obtain the first cargo area. Then, the first shooting angle corresponding to the first segmented image data may be determined. Specifically, the shooting time of the first segmented image may be determined first, and then the shooting angle of the camera model at the shooting time may be obtained to obtain the first shooting angle. Further, the first area compensation coefficient corresponding to the first shooting angle may be determined. For example, a mapping relationship between a preset shooting angle and an area compensation coefficient may be pre-stored, and the first area compensation coefficient corresponding to the first shooting angle may be determined based on the mapping relationship. The value range of the first area compensation coefficient may be -0.12~0.12. Then, the first cargo area may be adjusted according to the first area compensation coefficient, as follows: Second cargo area = first cargo area × (1 + first area compensation coefficient); According to the above formula, the second cargo area can be obtained. Then, the estimated cargo measurement value corresponding to the first segmented image data can be determined based on the second cargo area and the first cargo type. Specifically, the cargo volume can be estimated according to the second cargo area. For example, assuming that the shape of the cargo can be approximated as a rectangular parallelepiped, and the second cargo area is known to be S (assuming it is the projection area of ​​the cargo on a certain plane), assuming that the length, width, and height of the cargo are a, b, and c respectively, if it is known that the area S is the bottom area of ​​the cargo (that is, S=a×b), and the height c of the cargo is obtained by other means (such as the proportional relationship in the image or prior knowledge), then the cargo volume V=S×c, and then, the density corresponding to the first cargo type can be obtained, and the density is multiplied by the cargo volume to obtain the estimated cargo measurement value corresponding to the first segmented image data.

[0086] In this way, by determining the first area compensation coefficient corresponding to the first shooting angle, the deviation in cargo area calculation caused by the shooting angle can be effectively corrected. For example, when the shooting angle is tilted, the projected area of ​​the cargo in the image may be elongated or compressed, and the area compensation coefficient can adjust this distortion according to the change in angle, thereby obtaining a second cargo area that is closer to the actual cargo area.

[0087] In the embodiment of the present application, the first segmented image data may be acquired first; then, the first cargo area in the first segmented image data may be determined; determining a first shooting angle corresponding to the first segmented image data; Determining a first area compensation coefficient corresponding to the first shooting angle; adjusting the first cargo area according to the first area compensation coefficient to obtain a second cargo area; An estimated cargo measurement value corresponding to the first segmented image data is determined based on the second cargo area and the first cargo type.

[0088] S310: Obtain target vehicle permission information corresponding to the target vehicle.

[0089] In an embodiment of the present application, the target vehicle permission information of the target vehicle can be obtained through the control module. Specifically, the control module can communicate with the electronic control unit in the target vehicle to obtain the vehicle identification number, and then query the permission information of the target vehicle from a preset database based on the vehicle identification number to obtain the target vehicle permission information.

[0090] S311. Determine whether the target vehicle meets a preset condition based on the target inspection result and the target vehicle permission information.

[0091] In the embodiment of the present application, the preset condition may be preset in advance or defaulted. For example, the preset condition may be: the target vehicle has cheated in the inspection.

[0092] In a specific embodiment, key information such as vehicle type, actual load, driving speed, inspection results, etc. can be extracted from the target inspection results, and these key information can be compared with the target vehicle's license information to check whether they are consistent. If they are consistent, it means that the driving behavior of the target vehicle is in compliance with the permit, that is, it does not meet the preset conditions.

[0093] If there is any inconsistency, for example, the actual load does not match the permitted load, the driving route does not match the permitted route, etc., then it means that the driving behavior of the target vehicle does not comply with the permit, that is, it meets the preset conditions.

[0094] S312: When the target vehicle meets the preset condition, determine the target inspection cheating level corresponding to the target inspection result.

[0095] In an embodiment of the present application, when the target vehicle meets the preset conditions, the target inspection cheating level corresponding to the target inspection result is determined. Specifically, the target cheating type of the target vehicle (for example, overloading cheating type, toll evasion cheating type, detour cheating type, technical cheating type, etc.) can be determined according to the target inspection result, and then the target inspection cheating level can be determined according to the target cheating type. For example, the mapping relationship between the preset cheating type and the cheating level can be pre-stored, and the target inspection cheating level corresponding to the target cheating type is determined based on the mapping relationship.

[0096] When the target vehicle does not meet the preset conditions, it means that the target inspection result does not contain inspection cheating behavior, and there is no need to alarm the target vehicle.

[0097] S313: Generate target alarm information according to the target inspection cheating level, wherein the target alarm information includes the inspection cheating behavior and punishment measures corresponding to the target vehicle.

[0098] In the embodiment of the present application, the target alarm information processing includes the inspection cheating behavior and punishment measures corresponding to the target vehicle. The alarm information may also include the following data: vehicle identification information (such as license plate number), inspection time and inspection location, degree of deviation of measurement data, etc., which are not limited here.

[0099] In a specific embodiment, target alarm information can be generated according to the target inspection cheating level. Specifically, corresponding target alarm information can be generated according to the target inspection cheating level. The mapping relationship between the preset cheating level and the alarm information can be pre-stored, and the target alarm information corresponding to the target inspection cheating level is determined based on the mapping relationship. Then, the target alarm information is sent to the staff of the vehicle inspection system as soon as possible. The target alarm information can be sent in the form of text messages, emails, system pop-ups, prompt voices, etc. The target alarm information is transmitted to the staff to guide the staff to impose cheating penalties on the target vehicle or further check the cheating behavior. For example, assuming that the target inspection result is that the target vehicle has the first inspection cheating behavior, the staff can be prompted by a prompt voice, and the target alarm information can be "Hello, xx employee, the vehicle with license plate number XXX was detected to have the first inspection cheating behavior at a certain place and time. The deviation between the measurement data of the vehicle and the actual weight has reached 10%. According to xx regulations, it is necessary to fine xx and revoke xx license."

[0100] The implementation of this application has the following beneficial effects: It can be seen that the highway vehicle inspection and early warning method described in the present application can effectively reduce measurement errors and improve data accuracy by acquiring data from the metering module and the axis detection module within the first preset time period and correcting them in combination with the equipment attribute parameters. This provides strong data support for subsequent vehicle inspections. In addition, the target inspection results are combined with the target vehicle license information to determine whether the vehicle meets the preset conditions, which is equivalent to further considering the legal license scope of the vehicle itself on the basis of identifying inspection cheating behavior, thereby generating alarm information that conforms to the actual situation of the vehicle, and further improving the accuracy of vehicle inspection and early warning on highways.

[0101] See also Figure 4 , Figure 4 This is a functional unit composition block diagram of a highway vehicle inspection and early warning device 400 provided in an embodiment of the present application, which is applied to a vehicle inspection system. The vehicle inspection system includes: a control module, a target inspection platform, a high-speed dynamic intelligent metering instrument and a camera module. The target inspection platform includes a metering module and a digital axis detection module. The highway vehicle inspection and early warning device 400 includes: a collection unit 401, an inspection unit 402, and an alarm unit 403, wherein: The acquisition unit 401 is used to acquire first metering data of the target vehicle in a first preset time period through the metering module; the first preset time period is a time period from the appearance of the target vehicle on the target inspection platform to the departure of the target inspection platform; acquire first number axis data of the target vehicle in the first preset time period through the number axis detection module; acquire first device attribute parameters of the metering module; acquire second device attribute parameters of the number axis detection module; correct the first metering data according to the first device attribute parameters through the high-speed dynamic intelligent metering instrument to obtain second metering data; correct the first number axis data according to the second device attribute parameters through the high-speed dynamic intelligent metering instrument to obtain second number axis data; determine the target driving speed of the target vehicle according to the second number axis data; determine the target acquisition interval according to the target driving speed and the first preset time period; acquire image data of the target vehicle in the first preset time period at the target acquisition interval through the camera module to obtain first image data; The inspection unit 402 is used to inspect the target vehicle according to the second measurement data, the second number axis data and the first image data to obtain a target inspection result; obtain the target vehicle permission information corresponding to the target vehicle; determine whether the target vehicle meets the preset conditions according to the target inspection result and the target vehicle permission information; when the target vehicle meets the preset conditions, determine the target inspection cheating level corresponding to the target inspection result; The alarm unit 403 is used to generate target alarm information according to the target inspection cheating level, and the target alarm information includes the inspection cheating behavior and punishment measures corresponding to the target vehicle.

[0102] Optionally, in the aspect of inspecting the target vehicle according to the second measurement data, the second number axis data and the first image data to obtain a target inspection result, the inspection unit 402 is specifically used for: Sampling the second measurement data to obtain a plurality of measurement data, each measurement data corresponding to a first sampling time; Performing curve fitting according to the plurality of metering data and the first sampling time corresponding to each metering data to obtain a first metering curve; the horizontal axis of the first metering curve is time, and the vertical axis is the metering value; Matching the first metering curve with an abnormal curve in a preset abnormal curve library to obtain a plurality of matching degrees; each matching degree corresponds to an abnormal curve; the preset abnormal curve library includes a plurality of abnormal curves, each abnormal curve corresponds to a cheating behavior; Determine a maximum value among the multiple matching degrees, and determine a first inspection result according to the maximum value; Obtain measurement data greater than the first preset measurement value in the second measurement data to obtain m measurement data, where m is a natural number; Determine a second inspection result of the target vehicle according to the m measurement data; Checking the target vehicle according to the second number axis data to obtain a third checking result; Inspecting the target vehicle according to the first image data to obtain a fourth inspection result; The target inspection result is determined according to the first inspection result, the second inspection result, the third inspection result and the fourth inspection result.

[0103] Optionally, in determining the second inspection result of the target vehicle according to the m measurement data, the inspection unit 402 is specifically used to: Determine an average measurement value corresponding to the m measurement data; When the average measurement value is greater than a second preset measurement value, determining that the second inspection result is that there is overweight inspection cheating behavior; the second preset measurement value is greater than the first preset measurement value; When the average measurement value is less than or equal to the second preset measurement value, it is determined that the second inspection result is that there is no overweight inspection cheating behavior.

[0104] Optionally, in the aspect of inspecting the target vehicle according to the first image data to obtain a fourth inspection result, the inspection unit 402 is specifically configured to: Determine the target vehicle type and loadable cargo information corresponding to the target vehicle according to the target vehicle permission information; Identify the first image data by using image recognition technology to obtain a first cargo type; Determining whether the first cargo type is compliant according to the loadable cargo information; If it is compliant, determine the cargo loading space corresponding to the target vehicle type, and obtain n cargo loading spaces; n is a positive integer; Segmenting the first image data based on the n cargo loading spaces to obtain n segmented image data; Determine the estimated cargo measurement value corresponding to each segmented image data in the n segmented image data to obtain n estimated cargo measurement values; Determining a vehicle measurement value corresponding to the target vehicle type; Determining a target estimated quantity value based on the vehicle measurement value and the n estimated quantity values; Obtaining a target measurement deviation according to a deviation between the target estimated measurement value and the average measurement value; When the target measurement deviation is greater than a preset deviation threshold, the fourth inspection result is determined to be a system detection abnormality.

[0105] Optionally, in determining the estimated cargo measurement value corresponding to each of the n segmented image data to obtain the n estimated cargo measurement values, the checking unit 402 is specifically configured to: Acquire first segmented image data; the first segmented image data is any segmented image data among the n segmented image data; determining a first cargo area in the first segmented image data; determining a first shooting angle corresponding to the first segmented image data; Determining a first area compensation coefficient corresponding to the first shooting angle; adjusting the first cargo area according to the first area compensation coefficient to obtain a second cargo area; An estimated cargo measurement value corresponding to the first segmented image data is determined based on the second cargo area and the first cargo type.

[0106] Optionally, in the aspect of checking the target vehicle according to the second number axis data to obtain a third checking result, the checking unit 402 is specifically used for: Sampling the second axis data to obtain a plurality of axis data, each axis data corresponding to a second sampling time; Performing curve fitting according to the plurality of numerical axis data and the second sampling time corresponding to each numerical axis data to obtain a first numerical axis curve; the horizontal axis of the first numerical axis curve is time, and the vertical axis is the numerical axis value; Determine the coordinate time corresponding to the preset numerical axis value in the first numerical axis curve to obtain a plurality of coordinate times; the preset numerical axis value is used to indicate that the target vehicle stably enters or leaves the target inspection platform; Acquire the earliest first coordinate time and the earliest second coordinate time among the multiple coordinate times; the first coordinate time is earlier than the second coordinate time; Determine a first measurement value corresponding to the first coordinate time in the second measurement data; Determine a second measurement value corresponding to the second coordinate time in the second measurement data; When the second measurement value is less than or equal to the first measurement value, determining that the third inspection result is that there is no reverse driving inspection cheating behavior; When the second measurement value is greater than the first measurement value, obtaining a target time difference between the first coordinate time and the second coordinate time; When the target time difference is greater than the preset time difference, determining that the third inspection result is that there is cheating in the reverse driving inspection; When the target time difference is less than or equal to the preset time difference, it is determined that the third inspection result is that there is no cheating behavior in the reverse driving inspection.

[0107] Optionally, in the aspect of acquiring the first device attribute parameter of the metering module, the acquisition unit 401 is specifically used for: Obtaining a target device type corresponding to the metering module; Determine a reference first device attribute parameter and a first error range corresponding to the target device type; Acquire actual metering data and theoretical metering data of the metering module within a second preset time period; the end time of the second preset time period is earlier than the start time of the first preset time period; Determine a second error range corresponding to the metering module according to the actual metering data and the theoretical metering data; Determining a reference error deviation corresponding to the metering module according to the second error range and the first error range; Obtaining the target usage time and standard usage time corresponding to the metering module; Determine the difference between the target usage time and the standard usage time to obtain a target time difference; Determine a first optimization factor corresponding to the target duration difference; Optimizing the reference error deviation according to the first optimization factor to obtain a target error deviation; Determine a second optimization factor corresponding to the target error deviation; The reference first device attribute parameter is optimized according to the second optimization factor to obtain the first device attribute parameter.

[0108] In a specific implementation, the highway vehicle inspection and warning device 400 described in the embodiment of the present invention can also execute other implementation methods described in the highway vehicle inspection and warning method provided in the above embodiment of the present invention, which will not be repeated here.

[0109] See also Figure 5 , Figure 5It is a structural schematic diagram of an electronic device provided in an embodiment of the present application, the electronic device includes a processor, a memory, a communication interface and one or more programs, the processor, the memory and the communication interface are interconnected through a bus, the one or more programs are stored in the memory and are configured to be executed by the processor, the one or more programs include instructions for executing other implementation methods described in the highway vehicle inspection and warning method provided in the above embodiment of the present invention, which will not be repeated here.

[0110] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0111] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0112] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0113] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0114] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0115] The units described above as separate components may or may not be physically separated, and the components shown as units 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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or 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 software functional units.

[0117] If the above-mentioned integrated unit 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 memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0118] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A highway vehicle inspection and early warning method, characterized in that: Applied to a vehicle inspection system, the vehicle inspection system comprises: a control module, a target inspection platform, a high-speed dynamic intelligent metering instrument and a camera module, the target inspection platform comprises a metering module and a digital axis detection module, and the method comprises: The first measurement data of the target vehicle in a first preset time period is collected by the measurement module; the first preset time period is a time period from when the target vehicle appears on the target inspection platform to when it leaves the target inspection platform; Collecting first axis data of the target vehicle in the first preset time period by the axis detection module; Acquire a first device attribute parameter of the metering module; acquire a second device attribute parameter of the digital axis detection module; Correcting the first metering data according to the first device attribute parameter by the high-speed dynamic intelligent metering instrument to obtain second metering data; Correcting the first digital axis data according to the second device attribute parameter by the high-speed dynamic intelligent metering instrument to obtain second digital axis data; Determining a target driving speed of the target vehicle according to the second number axis data; Determining a target collection interval according to the target driving speed and the first preset time period; The camera module collects image data of the target vehicle within the first preset time period at the target collection interval to obtain first image data; Inspect the target vehicle according to the second measurement data, the second number axis data and the first image data to obtain a target inspection result; Obtaining target vehicle permission information corresponding to the target vehicle; Determine whether the target vehicle meets the preset conditions according to the target inspection result and the target vehicle permission information; When the target vehicle meets the preset condition, determining a target inspection cheating level corresponding to the target inspection result; Target alarm information is generated according to the target inspection cheating level, and the target alarm information includes the inspection cheating behavior and punishment measures corresponding to the target vehicle.

2. The method according to claim 1, characterized in that The step of inspecting the target vehicle according to the second measurement data, the second number axis data and the first image data to obtain a target inspection result includes: Sampling the second measurement data to obtain a plurality of measurement data, each measurement data corresponding to a first sampling time; Performing curve fitting according to the plurality of metering data and the first sampling time corresponding to each metering data to obtain a first metering curve; the horizontal axis of the first metering curve is time, and the vertical axis is the metering value; Matching the first metering curve with an abnormal curve in a preset abnormal curve library to obtain a plurality of matching degrees; each matching degree corresponds to an abnormal curve; the preset abnormal curve library includes a plurality of abnormal curves, each abnormal curve corresponds to a cheating behavior; Determine a maximum value among the multiple matching degrees, and determine a first inspection result according to the maximum value; Obtain measurement data greater than the first preset measurement value in the second measurement data to obtain m measurement data, where m is a natural number; Determine a second inspection result of the target vehicle according to the m measurement data; Checking the target vehicle according to the second number axis data to obtain a third checking result; Inspecting the target vehicle according to the first image data to obtain a fourth inspection result; The target inspection result is determined according to the first inspection result, the second inspection result, the third inspection result and the fourth inspection result.

3. The method according to claim 2, characterized in that Determining the second inspection result of the target vehicle according to the m measurement data includes: Determine an average measurement value corresponding to the m measurement data; When the average measurement value is greater than a second preset measurement value, determining that the second inspection result is that there is overweight inspection cheating behavior; the second preset measurement value is greater than the first preset measurement value; When the average measurement value is less than or equal to the second preset measurement value, it is determined that the second inspection result is that there is no overweight inspection cheating behavior.

4. The method according to claim 3, characterized in that The step of inspecting the target vehicle according to the first image data to obtain a fourth inspection result includes: Determine the target vehicle type and loadable cargo information corresponding to the target vehicle according to the target vehicle permission information; Identify the first image data by using image recognition technology to obtain a first cargo type; Determining whether the first cargo type is compliant according to the loadable cargo information; If it is compliant, determine the cargo loading space corresponding to the target vehicle type, and obtain n cargo loading spaces; n is a positive integer; Segmenting the first image data based on the n cargo loading spaces to obtain n segmented image data; Determine the estimated cargo measurement value corresponding to each segmented image data in the n segmented image data to obtain n estimated cargo measurement values; Determining a vehicle measurement value corresponding to the target vehicle type; Determining a target estimated quantity value based on the vehicle measurement value and the n estimated quantity values; Obtaining a target measurement deviation according to a deviation between the target estimated measurement value and the average measurement value; When the target measurement deviation is greater than a preset deviation threshold, the fourth inspection result is determined to be a system detection abnormality.

5. The method according to claim 4, characterized in that The step of determining the estimated cargo measurement value corresponding to each of the n segmented image data to obtain n estimated cargo measurement values ​​includes: Acquire first segmented image data; the first segmented image data is any segmented image data among the n segmented image data; determining a first cargo area in the first segmented image data; determining a first shooting angle corresponding to the first segmented image data; Determining a first area compensation coefficient corresponding to the first shooting angle; adjusting the first cargo area according to the first area compensation coefficient to obtain a second cargo area; An estimated cargo measurement value corresponding to the first segmented image data is determined based on the second cargo area and the first cargo type.

6. The method according to any one of claims 2 to 5, characterized in that: The step of checking the target vehicle according to the second number axis data to obtain a third checking result includes: Sampling the second axis data to obtain a plurality of axis data, each axis data corresponding to a second sampling time; Performing curve fitting according to the plurality of numerical axis data and the second sampling time corresponding to each numerical axis data to obtain a first numerical axis curve; the horizontal axis of the first numerical axis curve is time, and the vertical axis is the numerical axis value; Determine the coordinate time corresponding to the preset numerical axis value in the first numerical axis curve to obtain a plurality of coordinate times; the preset numerical axis value is used to indicate that the target vehicle stably enters or leaves the target inspection platform; Acquire the earliest first coordinate time and the earliest second coordinate time among the multiple coordinate times; the first coordinate time is earlier than the second coordinate time; Determine a first measurement value corresponding to the first coordinate time in the second measurement data; Determine a second measurement value corresponding to the second coordinate time in the second measurement data; When the second measurement value is less than or equal to the first measurement value, determining that the third inspection result is that there is no reverse driving inspection cheating behavior; When the second measurement value is greater than the first measurement value, obtaining a target time difference between the first coordinate time and the second coordinate time; When the target time difference is greater than the preset time difference, determining that the third inspection result is that there is cheating in the reverse driving inspection; When the target time difference is less than or equal to the preset time difference, it is determined that the third inspection result is that there is no cheating behavior in the reverse driving inspection.

7. The method according to any one of claims 1 to 5, characterized in that: The obtaining of the first device attribute parameter of the metering module includes: Obtaining a target device type corresponding to the metering module; Determine a reference first device attribute parameter and a first error range corresponding to the target device type; Acquire actual metering data and theoretical metering data of the metering module within a second preset time period; the end time of the second preset time period is earlier than the start time of the first preset time period; Determine a second error range corresponding to the metering module according to the actual metering data and the theoretical metering data; Determining a reference error deviation corresponding to the metering module according to the second error range and the first error range; Obtaining the target usage time and standard usage time corresponding to the metering module; Determine the difference between the target usage time and the standard usage time to obtain a target time difference; Determine a first optimization factor corresponding to the target duration difference; Optimizing the reference error deviation according to the first optimization factor to obtain a target error deviation; Determine a second optimization factor corresponding to the target error deviation; The reference first device attribute parameter is optimized according to the second optimization factor to obtain the first device attribute parameter.

8. A highway vehicle inspection warning device, characterized in that: Applied to a vehicle inspection system, the vehicle inspection system includes: a control module, a target inspection platform, a high-speed dynamic intelligent metering instrument and a camera module, the target inspection platform includes a metering module and a digital axis detection module, the device includes: a collection unit, an inspection unit, and an alarm unit, wherein: The acquisition unit is used to acquire first metering data of the target vehicle in a first preset time period through the metering module; the first preset time period is a time period from the appearance of the target vehicle on the target inspection platform to the departure of the target inspection platform; acquire first number axis data of the target vehicle in the first preset time period through the number axis detection module; acquire first device attribute parameters of the metering module; acquire second device attribute parameters of the number axis detection module; correct the first metering data according to the first device attribute parameters through the high-speed dynamic intelligent metering instrument to obtain second metering data; correct the first number axis data according to the second device attribute parameters through the high-speed dynamic intelligent metering instrument to obtain second number axis data; determine the target driving speed of the target vehicle according to the second number axis data; determine the target acquisition interval according to the target driving speed and the first preset time period; acquire image data of the target vehicle in the first preset time period at the target acquisition interval through the camera module to obtain first image data; The inspection unit is used to inspect the target vehicle according to the second measurement data, the second number axis data and the first image data to obtain a target inspection result; obtain target vehicle permission information corresponding to the target vehicle; determine whether the target vehicle meets a preset condition according to the target inspection result and the target vehicle permission information; when the target vehicle meets the preset condition, determine a target inspection cheating level corresponding to the target inspection result; The alarm unit is used to generate target alarm information according to the target inspection cheating level, and the target alarm information includes the inspection cheating behavior and punishment measures corresponding to the target vehicle.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store one or more programs and is configured to be executed by the processor, wherein the program includes instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 7.

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