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

Abstract

This application discloses a method, device, electronic device, and storage medium for vehicle inspection and early warning on highways. The method includes: collecting first metering data, first data axis data, and first image data of a target vehicle within a first preset time period; correcting the first metering data and first data axis data using a high-speed dynamic intelligent metering instrument to obtain second metering data and second data axis data; inspecting the target vehicle based on the second metering data, second data axis data, and first image data to obtain a target inspection result; determining the target inspection fraud level corresponding to the target inspection result when the target vehicle meets preset conditions; and generating target alarm information based on the target inspection fraud level. Using the embodiments of this application can improve the accuracy of vehicle inspection and early warning on highways.

Description

Technical Field

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

[0002] Highways are an inevitable product of economic development and play a crucial role in the transportation industry. Compared with ordinary roads, highways have significant advantages such as higher speeds, greater traffic capacity, lower transportation costs, and improved driving safety and comfort. However, the high speeds of vehicles on highways make vehicle inspections more difficult.

[0003] Currently, vehicle inspection and warning methods on highways use devices such as light curtains and inductive loop detectors to determine the direction of vehicle travel. However, drivers may also engage in cheating during inspections, interfering with the process. For example, drivers may run over wheel axle detectors to interfere with the determination of the direction of travel and axle type identification. This can cause traditional vehicle inspection and warning methods to fail to accurately identify the actual behavior of the vehicle, resulting in misjudgments, generating incorrect warning information, or even failing to generate any warning information at all.

[0004] Therefore, improving the accuracy of vehicle inspection and early warning on highways is an urgent issue that needs to be addressed. Summary of the Invention

[0005] This application provides a method, device, electronic device, and storage medium for vehicle inspection and early warning on highways, which can improve the accuracy of vehicle inspection and early warning on highways.

[0006] In a first aspect, embodiments of this application provide a highway vehicle inspection and early warning method, 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:

[0007] The metering module collects first metering data of the target vehicle within a first preset time period; the first preset time period is a period from when the target vehicle appears on the target inspection platform until it leaves the target inspection platform.

[0008] The first axis data of the target vehicle within the first preset time period is collected by the axis detection module.

[0009] Obtain the first device attribute parameters of the metering module; obtain the second device attribute parameters of the number axis detection module;

[0010] The high-speed dynamic intelligent metering instrument corrects the first metering data according to the first device attribute parameters to obtain the second metering data;

[0011] The high-speed dynamic intelligent metering instrument corrects the first number axis data according to the second device attribute parameters to obtain the second number axis data;

[0012] The target speed of the target vehicle is determined based on the second data axis.

[0013] The target acquisition interval is determined based on the target driving speed and the first preset time period.

[0014] The camera module acquires image data of the target vehicle within the first preset time period at the target acquisition interval to obtain first image data.

[0015] The target vehicle is inspected based on the second measurement data, the second data axis data, and the first image data to obtain the target inspection result;

[0016] Obtain the target vehicle permit information corresponding to the target vehicle;

[0017] Based on the target inspection results and the target vehicle permit information, determine whether the target vehicle meets the preset conditions;

[0018] When the target vehicle meets the preset conditions, the target inspection fraud level corresponding to the target inspection result is determined;

[0019] Based on the target inspection fraud level, a target alarm message is generated, which includes the inspection fraud behavior and penalty measures corresponding to the target vehicle.

[0020] Secondly, embodiments of this application provide a highway vehicle inspection and early warning device, 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 data acquisition unit, an inspection unit, and an alarm unit, wherein:

[0021] The acquisition unit is used to acquire first measurement data of the target vehicle within a first preset time period through the measurement module; the first preset time period is a time period from when the target vehicle appears on the target inspection platform until it leaves the target inspection platform; acquire first axis data of the target vehicle within the first preset time period through the axis detection module; obtain first device attribute parameters of the measurement module; obtain second device attribute parameters of the axis detection module; correct the first measurement data according to the first device attribute parameters using the high-speed dynamic intelligent measurement instrument to obtain second measurement data; correct the first axis data according to the second device attribute parameters using the high-speed dynamic intelligent measurement instrument to obtain second axis data; determine the target driving speed of the target vehicle based on the second axis data; determine the target acquisition interval based on the target driving speed and the first preset time period; and acquire image data of the target vehicle within the first preset time period through the camera module at the target acquisition interval to obtain first image data.

[0022] The inspection unit is configured to inspect the target vehicle based on the second measurement data, the second data axis data, and the first image data to obtain a target inspection result; acquire the target vehicle permit information corresponding to the target vehicle; determine whether the target vehicle meets preset conditions based on the target inspection result and the target vehicle permit information; and determine the target inspection cheating level corresponding to the target inspection result when the target vehicle meets the preset conditions.

[0023] The alarm unit is used to generate target alarm information based on the target inspection cheating level. The target alarm information includes the inspection cheating behavior and penalty measures corresponding to the target vehicle.

[0024] Thirdly, this application provides an electronic device, including: a processor and a memory, the memory being used to store one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps in the first aspect of this application.

[0025] Fourthly, this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0026] Fifthly, this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package.

[0027] Implementing this application will have the following beneficial effects:

[0028] As can be seen, the highway vehicle inspection and early warning method described in this application can effectively reduce measurement errors and improve data accuracy by acquiring data from the metering module and the digital axis detection module within a first preset time period and correcting it in conjunction with equipment attribute parameters. This provides strong data support for subsequent vehicle inspections. In addition, combining the target inspection results with the target vehicle's permit information to determine whether the vehicle meets the preset conditions is equivalent to further considering the vehicle's legal permit range on the basis of identifying inspection fraud behavior. This generates alarm information that matches the actual situation of the vehicle, thereby improving the accuracy of vehicle inspection and early warning on highways. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0030] Figure 1 This is a scenario application diagram of a vehicle inspection system provided in an embodiment of this application;

[0031] Figure 2 This is a schematic diagram of the structure of a vehicle inspection system provided in an embodiment of this application;

[0032] Figure 3 This is a flowchart of a highway vehicle inspection and early warning method provided in an embodiment of this application;

[0033] Figure 4 This is a functional unit block diagram of a highway vehicle inspection and early warning device provided in an embodiment of this application;

[0034] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0036] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0037] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0038] The electronic devices described in the embodiments of this application may include vehicle inspection systems.

[0039] The following will explain some of the technical terms used in this application:

[0040] Vehicle inspection fraud: In this application, vehicle inspection fraud refers to the act of a vehicle attempting to evade normal toll collection, supervision, or obtain illegal benefits through improper means at highway entrances and exits. For example, repeatedly reversing or running over specific locations, the driver attempts to find the "weak point" of the weighing equipment by repeatedly reversing and moving forward, or deliberately running over specific locations on the weighing platform, such as edges or corners, to affect the weighing accuracy and make the weighing result lower, thereby evading normal toll collection.

[0041] High-speed dynamic intelligent metering instruments are devices specifically designed for highway environments, enabling dynamic measurement and intelligent analysis of vehicle-related data. Unlike traditional static metering equipment, they adapt to the rapid movement of traffic on highways, continuously monitoring parameters such as vehicle 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 vehicle's weight without requiring the vehicle to come to a complete stop. Furthermore, for vehicle position information, it can work in conjunction with a digital axis detection module to accurately determine the vehicle's coordinates on the highway, providing a basis for vehicle trajectory analysis and detecting fraudulent activities.

[0042] A digital axis sensor is a sensor used to measure physical quantities such as position, displacement, and velocity of an object along a digital axis. In vehicle inspection systems, digital axis sensors can be used in conjunction with weighing sensors to measure the vehicle's position and velocity along a digital axis, thereby achieving accurate measurement of the vehicle's weight. For example, when a vehicle passes over a weighing platform, the digital axis sensor can determine the position of the vehicle's front and rear axles, while the weighing sensor measures the weight of the vehicle at different positions, and the total weight of the vehicle is calculated using an algorithm. Digital axis sensors can also be used to detect whether the vehicle is completely stationary on the weighing platform and to detect any fraudulent activities during the weighing process, such as movement or tilting of the vehicle.

[0043] Please see Figure 1 , Figure 1 This is a scenario application diagram of a vehicle inspection system provided in an embodiment of this application; as shown... Figure 1 As shown, vehicle inspection systems are installed at highway toll booths to accurately measure relevant vehicle data and monitor for potential fraudulent inspection activities. It should be noted that highways can be dynamic roads.

[0044] The target inspection platform, located on the highway, is one of the core components of the vehicle inspection system. It comprises a metering module and a scale detection module, used to measure the vehicle's weight and position along the scale. The metering module uses sensors (e.g., metering sensors) to accurately measure the vehicle's weight as it passes the weighbridge, providing crucial information for determining whether a vehicle is overloaded and calculating toll fees. The scale detection module includes a first scale sensor and a second scale sensor, used to monitor changes in the vehicle's position and movement along the scale, such as its direction of travel and speed.

[0045] The high-speed dynamic intelligent metering instrument is typically installed in a control room or other easily accessible location near the toll station. It communicates with the metering module and the digital axis detection module on the target inspection platform, receiving and processing data from these modules. The high-speed dynamic intelligent metering instrument may include a display screen to show the results of vehicle fraud detection for easy viewing by staff. The camera module is used to collect image information of the vehicle, providing data support for the vehicle inspection system.

[0046] Thus, the vehicle inspection system enables accurate measurement of vehicles and effective monitoring of fraudulent inspection activities, providing important technical support for the management and operation of highways.

[0047] It should 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. Alternatively, the control module can be an application installed on the staff's mobile phone, computer or other devices.

[0048] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a vehicle inspection system provided in an embodiment of this application. As can be seen, the vehicle inspection system includes: a target inspection platform, a high-speed dynamic intelligent metering instrument, a control module, and a camera module.

[0049] The target inspection platform includes a metrology module and a digital axis detection module. When a vehicle passes over the platform, the metrology module accurately measures its weight. For example, it uses sensors to detect the pressure of the vehicle on the weighing platform, converting the pressure signal into an electrical signal for processing and calculation to obtain the vehicle's weight data, i.e., metrology data. The digital axis detection module detects the vehicle's position, speed, and direction of travel along the digital axis, obtaining digital axis data. Both metrology and digital axis data provide crucial information for vehicle measurement and inspection. Additionally, the vehicle inspection system can use a camera module to photograph the vehicle, obtaining image data.

[0050] It should be explained that the metering module may include at least one weighing sensor, and the number axis detection module may include at least one number axis sensor.

[0051] 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.

[0052] The control module analyzes and judges the vehicle's measurement data, axis data, and image data to identify any cheating behavior. By analyzing the measurement and axis data, it determines whether the vehicle is cheating by interfering with the weighing equipment, repeatedly reversing, or other methods. The control module can also display the collected measurement data and alarm information, providing staff with intuitive vehicle measurement information and alerts to cheating behavior. For example, when cheating is detected, the control module can communicate with staff through its display screen, indicator lights, or audible alarms to prevent successful cheating.

[0053] Please see Figure 3 , Figure 3 This is a flowchart of a highway vehicle inspection and early warning method provided in an embodiment of this application. The method is applied to a vehicle inspection system, which 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:

[0054] S301. Collect first measurement data of the target vehicle within a first preset time period through the measurement module; the first preset time period is a time period from when the target vehicle appears on the target inspection platform until it leaves the target inspection platform.

[0055] In this embodiment, when a target vehicle enters the target inspection platform, the metering module can begin acquiring metering data (e.g., weight value) at the start of a first preset time period until the end of the first preset time period, obtaining multiple metering data points, i.e., the first metering data, and transmitting the first metering data 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.

[0056] S302. The first number axis data of the target vehicle within the first preset time period is collected by the number axis detection module.

[0057] In this embodiment, when the target vehicle enters the target inspection platform, the data acquisition module can begin acquiring data axis data (e.g., vehicle position coordinates, vehicle speed, etc.) at the start of a first preset time period, continuing until the end of the first preset time period, thus obtaining multiple data axis data, i.e., the first data axis data, and transmitting the first data axis data to the high-speed dynamic intelligent metering instrument. The data axis detection module can be physically and / or communicatively connected to the high-speed dynamic intelligent metering instrument.

[0058] S303. Obtain the first device attribute parameters of the metering module; obtain the second device attribute parameters of the number axis detection module.

[0059] In this embodiment of the application, the second device attribute parameter can be at least one of the following: resolution, error amplitude, effective detection distance, etc., which are not limited here.

[0060] In a specific embodiment, the first device attribute parameters of the measurement module can be obtained first, and then the second device attribute parameters of the number axis detection module can be obtained. Specifically, the second device attribute parameter can be the error range. The device manual or technical document of the number axis detection module can be consulted. The device manual or technical document will clearly list various attribute parameters of the device, including the error range. The second device attribute parameter can be obtained from the device manual or technical document. For example, assuming that the device manual states that the measurement error of the number axis detection module is within ±2 cm, then its error range can be determined to be 4 cm.

[0061] Optionally, step S303, obtaining the first device attribute parameters of the metering module, may include the following steps:

[0062] A1. Obtain the target device type corresponding to the metering module;

[0063] A2. Determine the reference first device attribute parameters and the first error range corresponding to the target device type;

[0064] A3. Obtain the actual measurement data and theoretical measurement data of the metering module within the 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;

[0065] A4. Determine the second error range corresponding to the measurement module based on the actual measurement data and the theoretical measurement data;

[0066] A5. Determine the reference error deviation of the metering module based on the second error range and the first error range;

[0067] A6. Obtain the target usage time and standard usage time corresponding to the metering module;

[0068] A7. Determine the difference between the target usage time and the standard usage time to obtain the target usage time difference;

[0069] A8. Determine the first optimization factor corresponding to the target duration difference;

[0070] A9. Optimize the reference error deviation according to the first optimization factor to obtain the target error deviation;

[0071] A10. Determine the second optimization factor corresponding to the target error deviation;

[0072] A11. Optimize the reference first device attribute parameters according to the second optimization factor to obtain the first device attribute parameters.

[0073] In this embodiment of the 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 in advance or defaulted, and the second preset time period represents a period of time in the past.

[0074] In a specific embodiment, the target device type corresponding to the metering module can be obtained first. Specifically, the device technical document of the metering module can be queried, which will clearly list various device information (e.g., device type). The target device type can be obtained from the device technical document. Next, the reference first device attribute parameters and the first error range corresponding to the target device type can be determined. Specifically, a preset mapping relationship between device types and first device attribute parameters can be stored in advance, and the reference first device attribute parameters corresponding to the target device type can be determined based on the mapping relationship. Alternatively, a preset mapping relationship between device types and error ranges can be stored in advance, 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 a second preset time period can be obtained. Specifically, the vehicle inspection system can have a metering database that stores historical metering data and corresponding theoretical metering data of the metering module. Then, a database query language (e.g., 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, queries can be performed based on time range, vehicle identification, metering module number, etc., to obtain the required data.

[0075] Furthermore, a second error range corresponding to the measurement module can be determined based on actual and theoretical measurement data. Specifically, for each actual measurement value in the actual measurement data, the error value between it and the corresponding theoretical measurement data is calculated: Error value = Actual measurement value - Theoretical measurement value. This yields multiple error values. The maximum and minimum error values ​​among these multiple error values ​​are then obtained, and the second error range is determined based on the maximum and minimum error values. Next, the reference error deviation corresponding to the measurement module can be determined based on the second and first error ranges. Finally, the target usage time and standard usage time corresponding to the measurement module can be obtained. The standard usage time represents the lifespan of the metering module. The standard usage time can be determined based on the target device type. For example, a pre-stored mapping relationship between device types and usage times can be used to determine the standard usage time corresponding to the target device type. Then, the installation records, operation logs, and other data of the metering module can be checked. Based on this data, the actual usage time of the metering module from installation to the present can be determined, which is the target usage time. For example, if the metering module was installed in January 2020, 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.

[0076] Next, the difference between the target usage time and the standard usage time can be calculated. The specific calculation formula is as follows:

[0077] Target usage time difference = Target usage time - Standard usage time;

[0078] The target duration difference can be obtained from the above formula. Then, the first optimization factor corresponding to the target duration difference can be determined. Specifically, a pre-stored mapping relationship between the duration difference and the optimization factor can be used to determine the first optimization factor corresponding to the target duration difference. The value range of the first optimization factor can be -0.2 to 0.2. Next, the reference error deviation can be optimized based on the first optimization factor. The specific calculation formula is as follows:

[0079] Target error deviation = Reference error deviation × (1 + First optimization factor);

[0080] The target error deviation can be obtained from the above formula. Then, the second optimization factor corresponding to the target error deviation can be determined. Specifically, a pre-stored mapping relationship between the error deviation and the optimization factor can be used to determine the second optimization factor corresponding to the target error deviation. The value range of the second optimization factor can be -0.3 to 0.3. Finally, the reference first device attribute parameters can be optimized according to the second optimization factor. The specific calculation formula is as follows:

[0081] First equipment attribute parameter = Reference first equipment attribute parameter × (1 + Second optimization factor);

[0082] The first device attribute parameters can be obtained from the above formula.

[0083] In this way, by continuously acquiring actual and theoretical measurement data and determining the error range and deviation, the performance changes of the measurement module can be monitored in real time. Optimizing the equipment attribute parameters based on these changes allows the measurement module to more accurately adapt to actual working conditions, thereby improving measurement accuracy.

[0084] Optionally, step A5, determining the reference error deviation of the metering module based on the second error range and the first error range, may include the following steps:

[0085] B1. Determine the overlap range between the second error range and the first error range to obtain the target overlap range;

[0086] B2. Determine the first reference error deviation corresponding to the target overlap range;

[0087] B3. Obtain the first upper limit of error and the first lower limit of error for the first error range;

[0088] B4. Obtain the second upper limit of error and the second lower limit of error within the second error range;

[0089] B5. Determine the upper limit difference between the first error upper limit and the second error upper limit to obtain the target upper limit difference;

[0090] B6. Determine the difference between the first lower error limit and the second lower error limit to obtain the target lower limit difference;

[0091] B7. Determine the first influencing factor corresponding to the difference in the target upper limit;

[0092] B8. Determine the second influencing factor corresponding to the difference in the target lower limit;

[0093] B9. Adjust the first reference error deviation according to the first influence factor and the second influence factor to obtain the reference error deviation.

[0094] In this embodiment, the error deviation represents the degree of deviation between the actual error range and the theoretical error range of the metering module.

[0095] 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 the first error range is -5% to 10% and the second error range is -10% to 5%, then the target overlap range is -5% to 5%. Next, 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, followed by the first amplitude of the first error range. Then, the first reference error deviation can be calculated based on the target amplitude and the first amplitude. The specific calculation formula is as follows:

[0096] First reference error deviation = |first amplitude - target amplitude| / first amplitude × 100%;

[0097] The first reference error deviation can be obtained from the above formula. For example, if the first amplitude corresponding to -5% to 10% is 15%, and the target amplitude corresponding to -5% to 5% is 10%, then the first reference error deviation = |15% - 10%| / 15% × 100% ≈ 33.3%. Furthermore, the first upper and lower limits of the first error range can be obtained, as well as the second upper and lower limits of the second error range. Next, the upper limit difference between the first and second upper limits can be calculated using the following formula:

[0098] Target upper limit difference = First error upper limit - Second error upper limit;

[0099] The target upper limit difference can be obtained from the above formula; next, the lower limit difference between the first lower limit and the second lower limit is calculated using the following formula:

[0100] Target lower limit difference = first lower limit of error - second lower limit of error;

[0101] The target lower limit difference can be obtained from the above formula. Next, the first influence factor corresponding to the target upper limit difference can be determined. Specifically, a pre-stored mapping relationship between the upper limit difference and the influence factor can be used to determine the first influence factor corresponding to the target upper limit difference. Similarly, a pre-stored mapping relationship between the lower limit difference and the influence factor can be used to determine the second influence factor corresponding to the target lower limit difference. The values ​​of both the first and second influence factors can range from -0.12 to 0.12. Finally, the first reference error deviation can be adjusted based on the first and second influence factors. The specific calculation formula is as follows:

[0102] Reference error deviation = First reference error deviation × (1 + First impact factor) × (1 + Second impact factor);

[0103] The reference error deviation can be obtained from the above formula.

[0104] Thus, by determining the first reference error deviation corresponding to the target overlap range, a specific numerical indicator is provided for further error analysis. This indicator can intuitively reflect the degree of deviation between the actual error and the expected error of the measurement module, which helps to quickly assess the performance of the measurement module. In addition, the first and second influencing factors are determined based on the difference between the upper and lower limits of the target, and then the first reference error deviation is adjusted to fully consider the different influences of the upper and lower limits of the error range, making the final reference error deviation more accurate.

[0105] S304. The high-speed dynamic intelligent metering instrument corrects the first metering data according to the first device attribute parameters to obtain the second metering data.

[0106] In this embodiment of the application, a high-speed dynamic intelligent metering instrument can analyze and correct the first metering data according to the first device attribute parameters, thereby obtaining the second metering data.

[0107] Optionally, the step of correcting the first measurement data based on the first device attribute parameters using the high-speed dynamic intelligent metering instrument to obtain the second measurement data may include the following steps:

[0108] S41. Determine the first correction coefficient corresponding to the first device attribute parameter;

[0109] S42. Obtain the first part of the error range that is greater than zero in the second error range, and the second part of the error range that is less than zero in the second error range;

[0110] S43. Determine the first error amplitude corresponding to the first part of the error range;

[0111] S44. Determine the second error amplitude corresponding to the second part of the error range;

[0112] S45. When the first error amplitude is greater than the second error amplitude, the first measurement data is reverse-corrected according to the first correction coefficient to obtain the second measurement data;

[0113] S46. When the first error amplitude is equal to the second error amplitude, the second measurement data is determined based on the first measurement data;

[0114] S47. When the first error amplitude is less than the second error amplitude, the first measurement data is positively corrected according to the first correction coefficient to obtain the second measurement data.

[0115] In this embodiment, a first correction coefficient corresponding to the first device attribute parameter can be determined. Specifically, a pre-stored mapping relationship between the device attribute parameter and the correction coefficient can be used to determine the first correction coefficient corresponding to the first device attribute parameter. The value range of the first correction coefficient can be 0 to 0.25. Next, a first part of the error range that is greater than zero and a second part of the error range that is less than zero can be obtained. For example, assuming the second error range is -10% to 5%, then the first part of the error range is 0 to 5%, and the second part of the error range is -10% to 0. Next, a first error amplitude corresponding to the first part of the error range can be determined. Specifically, the maximum and minimum values ​​in the first part of the error range can be obtained, and the first error amplitude can be obtained by subtracting the minimum value from the maximum value. For example, if the first part of the error range is 0 to 5%, then the first error amplitude is equal to 5% minus 0, that is, the first error amplitude is 5%. Next, a 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, and will not be described again here.

[0116] When the first error amplitude is greater than the second error amplitude, it indicates that the measurement data detected by the measurement module is too large. The first measurement data is then corrected in reverse according to the first correction coefficient. The specific calculation formula is as follows:

[0117] Second measurement data = First measurement data × (1 - First correction factor);

[0118] The second measurement data can be obtained based on the above formula.

[0119] When the first error range equals the second error range, it means that the measurement data detected by the measurement module is accurate and no correction is needed. The first measurement data can be directly used as the second measurement data.

[0120] When the first error amplitude is less than the second error amplitude, it indicates that the measurement data detected by the measurement module is too small. The first measurement data is then positively corrected according to the first correction coefficient. The specific calculation formula is as follows:

[0121] Second measurement data = First measurement data × (1 + First correction factor);

[0122] The second measurement data can be obtained based on the above formula.

[0123] Thus, by determining the first correction coefficient corresponding to the first equipment attribute parameter, the measurement data can be adjusted in a targeted manner according to different equipment conditions and measurement requirements, thereby improving the accuracy of the measurement data. In addition, different correction methods are adopted according to the 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.

[0124] S305. The first number axis data is corrected by the high-speed dynamic intelligent metering instrument according to the second device attribute parameters to obtain the second number axis data.

[0125] In this embodiment, a high-speed dynamic intelligent metering instrument can determine the second correction coefficient corresponding to the second device attribute parameter. Specifically, based on the mapping relationship between the device attribute parameter and the correction coefficient, the second correction coefficient corresponding to the second device attribute parameter can be determined. The value range of the second correction coefficient can be -0.12 to 0.12. Then, the first number line data can be directly corrected based on the second correction coefficient. The specific calculation formula is as follows:

[0126] Second number line data = First number line data × (1 + Second correction factor);

[0127] The second number line data can be obtained according to the above formula, or the method for obtaining the second number line data can be the same as the method for obtaining the second measurement data.

[0128] S306. Determine the target speed of the target vehicle based on the second number axis data.

[0129] In this embodiment of the application, the second number line data may include multiple number line signals. Two consecutive number line signals can be randomly extracted from the second number line data, and the first time difference between them can be calculated. Then, based on the characteristics of the number line signals, the distance represented by each signal, that is, the first distance between the two number line signals, can be determined. For example, if each pulse represents a wheel rotation of 1 degree and the radius of the wheel is known, the arc length corresponding to each pulse can be calculated. Then, the target driving speed can be obtained by dividing the first distance by the first time difference.

[0130] S307. Determine the target acquisition interval based on the target driving speed and the first preset time period.

[0131] In this embodiment, the target driving speed determines the distance the vehicle travels per unit time. The purpose of acquiring vehicle images is to clearly and completely record the vehicle's state or details during its movement. If the vehicle is traveling at a high speed, the acquisition interval needs to be shortened to avoid image blurring and information loss; if the vehicle is traveling at a low speed, the acquisition interval can be appropriately extended. Therefore, the first acquisition interval can be determined based on the target driving speed. For example, a preset mapping relationship between driving speed and acquisition interval can be stored in advance. Based on this mapping relationship, the first acquisition interval corresponding to the target driving speed is determined. The number of images that can be acquired in a first preset time period is determined based on the first acquisition interval to obtain the first number of images. If the number of first images is greater than or equal to the preset number of images, the first acquisition interval is determined as the target acquisition interval. If the number of first images is less than the preset number of images, the target acquisition interval can be obtained by dividing the duration corresponding to the first preset time period by the preset number of images.

[0132] S308. The camera module acquires image data of the target vehicle within the first preset time period at the target acquisition interval to obtain first image data.

[0133] In this embodiment of the application, within a first preset time period, the target vehicle can be photographed by the camera module at target acquisition intervals to obtain first image data.

[0134] S309. The target vehicle is inspected based on the second measurement data, the second number axis data and the first image data to obtain the target inspection result.

[0135] In this embodiment of the application, the control module can perform inspection and analysis based on the second measurement data, the second data axis data and the first image data to identify whether the target vehicle has any 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.

[0136] Optionally, step S309, which involves inspecting the target vehicle based on the second measurement data and the second data axis data to obtain the target inspection result, may include the following steps:

[0137] C1. Sample the second measurement data to obtain multiple measurement data, each measurement data corresponding to a first sampling time;

[0138] C2. Based on the multiple measurement data and the first sampling time corresponding to each measurement data, a curve fitting is performed to obtain a first measurement curve; the horizontal axis of the first measurement curve is time, and the vertical axis is the measurement value;

[0139] C3. Match the first measurement curve with the abnormal curves in the preset abnormal curve library to obtain multiple matching degrees; each matching degree corresponds to an abnormal curve; the preset abnormal curve library includes multiple abnormal curves, and each abnormal curve corresponds to a type of cheating detection behavior.

[0140] C4. Determine the maximum value among the multiple matching degrees, and determine the first verification result based on the maximum value;

[0141] C5. Obtain the measurement data that is greater than the first preset measurement value from the second measurement data to obtain m measurement data; m is a natural number.

[0142] C6. Determine the second inspection result of the target vehicle based on the m measurement data;

[0143] C7. Inspect the target vehicle based on the second number axis data to obtain a third inspection result;

[0144] C8. Inspect the target vehicle based on the first image data to obtain a fourth inspection result;

[0145] 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.

[0146] In this embodiment of the application, the preset abnormal curve library, the first preset measurement value, and the preset matching degree threshold can all be preset in advance or defaulted.

[0147] In a specific embodiment, the second measurement data can be sampled at equal intervals to obtain multiple measurement data. Then, the first sampling time of each measurement data can be obtained to obtain multiple first sampling times. Based on the multiple measurement data and the corresponding first sampling times in the multiple first sampling times, multiple first coordinate points are obtained. Then, a curve fitting method (e.g., polynomial fitting method, spline method, etc.) can be used to fit these multiple first coordinate points to obtain a first measurement curve. Then, the first measurement curve can be matched with abnormal curves in a preset abnormal curve library to obtain multiple matching degrees. Specifically, all abnormal curves in the preset abnormal curve library can be obtained first to obtain multiple abnormal curves. Then, a suitable matching algorithm (e.g., cosine similarity algorithm, dynamic time warping algorithm) can be selected to calculate the matching degree between the first measurement curve and each of these multiple abnormal curves to obtain multiple matching degrees.

[0148] Furthermore, the maximum value among these multiple matching degrees can be selected, and the first verification result can be determined based on 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, the target abnormal curve corresponding to the maximum value is obtained. Then, the first verification cheating behavior corresponding to the target abnormal curve is determined, that is, the first verification result is that the first verification cheating behavior exists. The first verification cheating behavior can be verification cheating behaviors such as reversing before getting off the scale or reversing after passing through the scale. 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 measurement curve and the abnormal curve is not high, and the first verification result is that the first verification cheating behavior does not exist. Then, the second measurement data that is greater than the first preset measurement value can be obtained. The measurement data is used to obtain m measurement data points. Based on these m measurement data points, a second inspection result for the target vehicle is determined. Next, the target vehicle can be inspected based on the second data axis and the first image data to obtain a third and fourth inspection result. Finally, based on the first, second, third, and fourth inspection results, it can be determined which inspection cheating behaviors exist in the target vehicle. Based on these inspection cheating behaviors, the target inspection result is obtained. For example, assuming the first inspection result is that there is a first inspection cheating behavior, the second inspection result is that there is a second inspection cheating behavior, and the third inspection result is that there is no third inspection cheating behavior, then the target inspection result can be: the target vehicle has both first and second inspection cheating behaviors.

[0149] It should be explained that the second type of cheating during inspection can be cheating during overweight inspection, and the third type of cheating during inspection can be cheating during reverse driving inspection.

[0150] Thus, by combining the results of the four checks to determine the final target check result, it is equivalent to performing multiple verifications on the 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, by verifying each other through multiple methods, the risk of misjudgment can be greatly reduced and the reliability of the identification results can be improved.

[0151] Optionally, step C6, determining the second inspection result of the target vehicle based on the m measurement data, may include the following steps:

[0152] D1. Determine the average measurement value corresponding to the m measurement data;

[0153] D2. When the average measurement value is greater than the second preset measurement value, the second inspection result is determined to be a case of cheating in the overweight inspection; the second preset measurement value is greater than the first preset measurement value.

[0154] D3. When the average measurement value is less than or equal to the second preset measurement value, the second inspection result is determined to be that there is no cheating behavior in the overweight inspection.

[0155] In this embodiment of the application, the average value of 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 indicates that the measurement value of the target vehicle is too large and there is an abnormal situation, so the second inspection result can be determined to be that there is cheating behavior in the overweight inspection.

[0156] 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 the normal range, and it can be determined that the second inspection result is that there is no cheating behavior in the overweight inspection.

[0157] Optionally, step C7, which involves inspecting the target vehicle based on the second data axis to obtain a third inspection result, may include the following steps:

[0158] E1. Sample the second number line data to obtain multiple number line data, each number line data corresponding to a second sampling time;

[0159] E2. Based on the multiple number axis data and the second sampling time corresponding to each number axis data, a curve fitting is performed to obtain a first number axis curve; the horizontal axis of the first number axis curve is time, and the vertical axis is the number axis value;

[0160] E3. Determine the coordinate time corresponding to the preset number axis value in the first number axis curve to obtain multiple coordinate times; the preset number axis value is used to represent the target vehicle stably entering or leaving the target inspection platform.

[0161] E4. Obtain 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;

[0162] E5. Determine the first measurement value corresponding to the first coordinate time in the second measurement data;

[0163] E6. Determine the second measurement value corresponding to the second coordinate time in the second measurement data;

[0164] E7. When the second measurement value is less than or equal to the first measurement value, the third inspection result is determined to be that there is no reverse driving inspection cheating behavior.

[0165] E8. When the second measurement value is greater than the first measurement value, obtain the target time difference between the first coordinate time and the second coordinate time;

[0166] E9. When the target time difference is greater than the preset time difference, the third inspection result is determined to be the existence of the reverse driving inspection cheating behavior;

[0167] E10. When the target time difference is less than or equal to the preset time difference, the third verification result is determined to be that there is no reverse driving verification cheating behavior.

[0168] In this embodiment, the preset time difference can be preset in advance or defaulted. The preset time difference is used to determine whether the target vehicle is engaging in reverse driving to cheat during inspection.

[0169] In a specific embodiment, the second number axis data can be sampled to obtain multiple number axis data. Then, the second sampling time of each number axis data can be obtained to obtain multiple second sampling times. Based on the multiple number axis data and the corresponding second sampling times in the multiple second sampling times, multiple second coordinate points are obtained. Then, a curve fitting method (e.g., polynomial fitting method, spline method, etc.) can be used to fit these multiple second coordinate points to obtain a first number axis curve. Further, the coordinate time corresponding to the preset number axis value in the first number axis curve can be determined to obtain multiple coordinate times. Specifically, the first curve equation of the first number axis curve can be obtained, and the preset number axis value can be substituted into the first curve equation to solve for multiple coordinate times.

[0170] Next, the earliest first and second coordinate times among multiple coordinate times can be obtained. Specifically, the multiple coordinate times can be sorted in chronological order to obtain the first coordinate time sequence. The first and second coordinate times in the first coordinate time sequence are then 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 can be substituted into the second curve equation to obtain the first measurement value. Next, the second measurement value corresponding to the second coordinate time in the second measurement data can be determined. Specifically, the method for obtaining the second measurement value can be the same as that for the first measurement value.

[0171] 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 cheating behavior in the inspection. In other words, the third inspection result is that there is no cheating behavior in the reverse driving inspection.

[0172] When the second measurement value is greater than the first measurement value, it indicates that the target vehicle is traveling in the opposite direction. The target time difference between the first coordinate time and the second coordinate time can be obtained. The specific calculation formula is as follows:

[0173] Target time difference = |first coordinate time -second coordinate time|;

[0174] 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 driving in the opposite direction and the reverse driving time is relatively long. Then it can be determined that the third inspection result is that there is a reverse driving inspection cheating behavior.

[0175] When the target time difference is less than or equal to the preset time difference, the third inspection result is determined to be that there is no reverse driving inspection cheating behavior.

[0176] Thus, by comprehensively analyzing the second axis data and the second measurement data, information about vehicle behavior can be obtained from different perspectives. The axis data reflects the changes in the vehicle's position on the weighing platform, while the measurement data reflects the vehicle's weight information. Combining these two types of data allows for a more comprehensive understanding of the vehicle's driving status and improves the accuracy of reverse driving inspections.

[0177] Optionally, step C8, which involves inspecting the target vehicle based on the first image data to obtain a fourth inspection result, may include the following steps:

[0178] F1. Determine the target vehicle type and cargo information corresponding to the target vehicle based on the target vehicle permit information;

[0179] F2. The first image data is identified using image recognition technology to obtain the first type of goods;

[0180] F3. Determine whether the first cargo type is compliant based on the cargo loading information;

[0181] F4. If compliant, determine the cargo loading space corresponding to the target vehicle type, resulting in n cargo loading spaces; n is a positive integer.

[0182] F5. Segment the first image data based on the n cargo loading spaces to obtain n segmented image data;

[0183] F6. 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;

[0184] F7. Determine the vehicle meter value corresponding to the target vehicle type;

[0185] F8. Determine the target pre-estimated value based on the vehicle measurement value and the n pre-estimated values;

[0186] F9. Obtain the target measurement deviation based on the deviation between the target estimated value and the average measurement value;

[0187] F10. When the target measurement deviation is greater than the preset deviation threshold, the fourth inspection result is determined to be a system detection anomaly.

[0188] In this embodiment of the application, the target vehicle type may include one of the following: small truck, medium truck, large truck, etc., without limitation; the preset deviation threshold can be preset in advance or defaulted.

[0189] In a specific embodiment, the target vehicle type and cargo information can be obtained from the target vehicle's permit information. The cargo information refers to the cargo that the target vehicle is allowed to load. Then, the first image data can be identified using image recognition technology to obtain the first cargo type. Specifically, feature extraction (e.g., shape feature extraction, texture feature extraction, color feature extraction, etc.) can be performed on the first image data to obtain the first feature data. The first feature data is then input into a preset classification model to obtain the first cargo type. Next, it can be determined whether the first cargo type belongs to the permitted cargo in the cargo information. If it does, the first cargo type is determined to be compliant; if it does not, the first cargo type is determined to be non-compliant. An alarm message can be directly generated to alert the vehicle inspection system staff that the cargo loaded on the target vehicle is abnormal.

[0190] If compliant, the cargo loading space corresponding to the target vehicle type is determined, resulting in n cargo loading spaces. Specifically, a pre-stored mapping relationship between vehicle types and cargo loading spaces can be used to determine the n cargo loading spaces corresponding to the target vehicle type. Next, the first image data can be segmented based on these n cargo loading spaces, resulting in n segmented image data. Specifically, the location data of these n cargo loading spaces within the target vehicle can be obtained first, resulting in n location data. Then, a region growing segmentation algorithm can be used to segment the first image data based on these n location data, resulting in n segmented image data. Further, the estimated cargo measurement value corresponding to each segmented image data can be determined, resulting in n estimated cargo measurement values. Next, the vehicle measurement value corresponding to the target vehicle type can be determined. For example, a pre-stored mapping relationship between vehicle types and measurement values ​​can be used to determine the vehicle measurement value corresponding to the target vehicle type. Then, the vehicle measurement value and the n estimated values ​​can be superimposed to obtain the measurement value sum, which is the target estimated value. Finally, the deviation between the target estimated value and the average measurement value can be calculated.

[0191] Target measurement deviation = |Target estimated value - Average measurement value| / Target estimated value;

[0192] 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 anomaly.

[0193] Thus, by comparing the deviation between the target estimated value and the average measured value, anomalies in the system can be detected. If the deviation exceeds a preset deviation threshold, it is determined to be a system anomaly, which can promptly identify problems such as sensor malfunctions and image recognition errors. For example, if the actual cargo load of a vehicle differs significantly from the system's estimated load, it may be due to an error in the image recognition algorithm or a malfunction in the vehicle's measuring equipment. This timely anomaly detection ensures the accuracy of the inspection results and avoids incorrect judgments caused by system errors.

[0194] 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:

[0195] G1. Obtain the first segmented image data; the first segmented image data is any one of the n segmented image data.

[0196] G2. Determine the area of ​​the first cargo in the first segmented image data;

[0197] G3. Determine the first shooting angle corresponding to the first segmented image data;

[0198] G4. Determine the first area compensation coefficient corresponding to the first shooting angle;

[0199] G5. Adjust the area of ​​the first cargo according to the first area compensation coefficient to obtain the area of ​​the second cargo;

[0200] G6. Determine the estimated cargo measurement value corresponding to the first segmented image data based on the second cargo area and the first cargo type.

[0201] In this embodiment, firstly, first segmented image data can be acquired. Next, the area of ​​the first cargo in the first segmented image data can be determined. Specifically, an edge detection algorithm can be used to detect edges in the first segmented image data to obtain an edge image. Then, contour extraction is performed, connecting 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. Next, the first shooting angle corresponding to the first segmented image data can be determined. Specifically, the shooting time of the first segmented image can be determined first, and then the shooting angle of the camera model at that shooting time can be obtained to obtain the first shooting angle. Further, a first area compensation coefficient corresponding to the first shooting angle can be determined. For example, a preset mapping relationship between shooting angles and area compensation coefficients can be stored in advance. Based on this mapping relationship, the first area compensation coefficient corresponding to the first shooting angle is determined. The value range of the first area compensation coefficient can be -0.12 to 0.12. Then, the area of ​​the first cargo can be adjusted according to the first area compensation coefficient, as follows:

[0202] Second cargo area = First cargo area × (1 + First area compensation coefficient);

[0203] According to the above formula, the area of ​​the second cargo can be obtained. Then, the estimated cargo volume corresponding to the first segmented image data can be determined based on the area of ​​the second cargo and the first cargo type. Specifically, the cargo volume can be estimated based on the area of ​​the second cargo. For example, assuming that the shape of the cargo can be approximated as a cuboid, and the area of ​​the second cargo is known to be S (assuming it is the projected area of ​​the cargo on a certain plane), and assuming that the length, width, and height of the cargo are a, b, and c respectively, if the area S is known to be the area of ​​the base of the cargo (i.e., S = a × b), and the height c of the cargo is obtained through other means (such as the proportional relationship in the image or prior knowledge), then the cargo volume V = S × c. Then, the density corresponding to the first cargo type can be obtained, and the density can be multiplied by the cargo volume to obtain the estimated cargo volume corresponding to the first segmented image data.

[0204] Thus, 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 stretched or compressed, and the area compensation coefficient can adjust for this distortion according to the change of angle, thereby obtaining a second cargo area that is closer to the actual cargo area.

[0205] In this embodiment of the application, first segmented image data can be obtained first; then, the area of ​​the first cargo in the first segmented image data can be determined.

[0206] Determine the first shooting angle corresponding to the first segmented image data;

[0207] Determine the first area compensation coefficient corresponding to the first shooting angle;

[0208] The area of ​​the first cargo is adjusted according to the first area compensation coefficient to obtain the area of ​​the second cargo;

[0209] The estimated cargo measurement value corresponding to the first segmented image data is determined based on the second cargo area and the first cargo type.

[0210] S310. Obtain the target vehicle license information corresponding to the target vehicle.

[0211] In this embodiment, the target vehicle permit information 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 target vehicle permit information from a preset database based on the vehicle identification number to obtain the target vehicle permit information.

[0212] S311. Determine whether the target vehicle meets the preset conditions based on the target inspection results and the target vehicle permit information.

[0213] In this embodiment of the application, the preset conditions can be preset in advance or defaulted. For example, the preset condition can be: the target vehicle has cheating behavior during the inspection.

[0214] In a specific embodiment, key information such as vehicle type, actual load, driving speed, and inspection results can be extracted from the target inspection results. This key information is then compared with the target vehicle's permit information to check for consistency. If they are consistent, it means that the target vehicle's driving behavior complies with the permit, which means it does not meet the preset conditions.

[0215] If there is a discrepancy, such as the actual load not matching the permitted load, or the driving route not matching the permitted route, it indicates that the target vehicle's driving behavior does not comply with the permit, that is, it meets the preset conditions.

[0216] S312. When the target vehicle meets the preset conditions, determine the target inspection fraud level corresponding to the target inspection result.

[0217] In this embodiment of the application, when the target vehicle meets the preset conditions, the target inspection fraud level corresponding to the target inspection result is determined. Specifically, the target fraud type of the target vehicle (e.g., overloading fraud type, toll evasion fraud type, detour fraud type, technical fraud type, etc.) can be determined based on the target inspection result, and then the target inspection fraud level can be determined based on the target fraud type. For example, a preset mapping relationship between fraud types and fraud levels can be stored in advance, and the target inspection fraud level corresponding to the target fraud type can be determined based on the mapping relationship.

[0218] If the target vehicle does not meet the preset conditions, it means that the inspection results do not contain any inspection fraud, and there is no need to issue an alarm for the target vehicle.

[0219] S313. Generate target alarm information based on the target inspection cheating level, wherein the target alarm information includes the inspection cheating behavior and penalty measures corresponding to the target vehicle.

[0220] In this embodiment of the application, in addition to the inspection cheating behavior and penalty measures corresponding to the target vehicle, the alarm information may also include the following data: vehicle identification information (such as license plate number), detection time and detection location, degree of deviation of measurement data, etc., which are not limited here.

[0221] In a specific embodiment, target alarm information can be generated based on the target verification cheating level. Specifically, corresponding target alarm information can be generated according to the level of the target verification cheating. A preset mapping relationship between cheating levels and alarm information can be stored in advance. Based on this mapping relationship, the target alarm information corresponding to the target verification cheating level is determined. 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 SMS, email, system pop-up, prompt voice, etc. The target alarm information is transmitted to the staff to guide them to impose cheating penalties or further detect verification cheating behavior on the target vehicle. For example, assuming that the target inspection result is that the target vehicle has the first verification cheating behavior, the staff can be prompted by a prompt voice. The target alarm information could be: "Dear XX employee, vehicle with license plate number XXX was detected to have the first verification cheating behavior at a certain location and time. The vehicle's measurement data deviates from the actual weight by 10%. According to XX regulations, it needs to be fined XX and its XX permit revoked."

[0222] Implementing this application will have the following beneficial effects:

[0223] As can be seen, the highway vehicle inspection and early warning method described in this application can effectively reduce measurement errors and improve data accuracy by acquiring data from the metering module and the digital axis detection module within a first preset time period and correcting it in conjunction with equipment attribute parameters. This provides strong data support for subsequent vehicle inspections. In addition, combining the target inspection results with the target vehicle's permit information to determine whether the vehicle meets the preset conditions is equivalent to further considering the vehicle's legal permit range on the basis of identifying inspection fraud behavior. This generates alarm information that matches the actual situation of the vehicle, thereby improving the accuracy of vehicle inspection and early warning on highways.

[0224] Please see Figure 4 , Figure 4This is a functional unit block diagram of a highway vehicle inspection and early warning device 400 provided in this application embodiment. It is applied to a vehicle inspection system, which 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 data acquisition unit 401, an inspection unit 402, and an alarm unit 403, wherein:

[0225] The acquisition unit 401 is used to acquire first measurement data of the target vehicle within a first preset time period through the measurement module; the first preset time period is a time period from when the target vehicle appears on the target inspection platform until it leaves the target inspection platform; acquire first axis data of the target vehicle within the first preset time period through the axis detection module; obtain first device attribute parameters of the measurement module; obtain second device attribute parameters of the axis detection module; correct the first measurement data according to the first device attribute parameters using the high-speed dynamic intelligent measurement instrument to obtain second measurement data; correct the first axis data according to the second device attribute parameters using the high-speed dynamic intelligent measurement instrument to obtain second axis data; determine the target driving speed of the target vehicle based on the second axis data; determine the target acquisition interval based on the target driving speed and the first preset time period; and acquire image data of the target vehicle within the first preset time period through the camera module at the target acquisition interval to obtain first image data.

[0226] The inspection unit 402 is used to inspect the target vehicle based on the second measurement data, the second data axis data, and the first image data to obtain a target inspection result; acquire the target vehicle permit information corresponding to the target vehicle; determine whether the target vehicle meets preset conditions based on the target inspection result and the target vehicle permit information; and determine the target inspection cheating level corresponding to the target inspection result when the target vehicle meets the preset conditions.

[0227] The alarm unit 403 is used to generate target alarm information based on the target inspection cheating level. The target alarm information includes the inspection cheating behavior and penalty measures corresponding to the target vehicle.

[0228] Optionally, in the step of inspecting the target vehicle based on the second measurement data, the second data axis data, and the first image data to obtain the target inspection result, the inspection unit 402 is specifically used for:

[0229] The second measurement data is sampled to obtain multiple measurement data, and each measurement data corresponds to a first sampling time;

[0230] A first measurement curve is obtained by performing curve fitting based on the multiple measurement data and the first sampling time corresponding to each measurement data; the horizontal axis of the first measurement curve is time, and the vertical axis is the measurement value.

[0231] The first measurement curve is matched with the abnormal curves in the preset abnormal curve library to obtain multiple matching degrees; each matching degree corresponds to an abnormal curve; the preset abnormal curve library includes multiple abnormal curves, and each abnormal curve corresponds to a type of cheating detection behavior.

[0232] Determine the maximum value among the plurality of matching degrees, and determine the first verification result based on the maximum value;

[0233] Obtain the measurement data that is greater than the first preset measurement value from the second measurement data to obtain m measurement data; m is a natural number.

[0234] The second inspection result of the target vehicle is determined based on the m measurement data;

[0235] The target vehicle is inspected based on the second data axis, and a third inspection result is obtained;

[0236] The target vehicle is inspected based on the first image data to obtain a fourth inspection result;

[0237] The target inspection result is determined based on the first inspection result, the second inspection result, the third inspection result, and the fourth inspection result.

[0238] Optionally, in determining the second inspection result of the target vehicle based on the m measurement data, the inspection unit 402 is specifically used for:

[0239] Determine the average measurement value corresponding to the m measurement data;

[0240] When the average measurement value is greater than the second preset measurement value, the second inspection result is determined to be an overweight inspection fraud; the second preset measurement value is greater than the first preset measurement value.

[0241] When the average measurement value is less than or equal to the second preset measurement value, the second inspection result is determined to be that there is no cheating behavior in the overweight inspection.

[0242] Optionally, in the step of inspecting the target vehicle based on the first image data to obtain a fourth inspection result, the inspection unit 402 is specifically used for:

[0243] The target vehicle type and cargo information corresponding to the target vehicle are determined based on the target vehicle permit information;

[0244] The first image data is identified using image recognition technology to obtain the first type of goods;

[0245] Determine whether the first cargo type is compliant based on the cargo loading information;

[0246] If compliant, determine the cargo loading space corresponding to the target vehicle type, resulting in n cargo loading spaces; n is a positive integer;

[0247] The first image data is segmented based on the n cargo loading spaces to obtain n segmented image data;

[0248] 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;

[0249] Determine the vehicle meter value corresponding to the target vehicle type;

[0250] The target pre-estimated value is determined based on the vehicle measurement value and the n pre-estimated values;

[0251] The target measurement deviation is obtained based on the deviation between the target estimated value and the average measurement value.

[0252] When the target measurement deviation is greater than a preset deviation threshold, the fourth inspection result is determined to be a system detection anomaly.

[0253] Optionally, in determining the estimated cargo measurement value corresponding to each of the n segmented image data to obtain n estimated cargo measurement values, the inspection unit 402 is specifically used for:

[0254] Obtain first segmented image data; the first segmented image data is any one of the n segmented image data.

[0255] Determine the area of ​​the first cargo in the first segmented image data;

[0256] Determine the first shooting angle corresponding to the first segmented image data;

[0257] Determine the first area compensation coefficient corresponding to the first shooting angle;

[0258] The area of ​​the first cargo is adjusted according to the first area compensation coefficient to obtain the area of ​​the second cargo;

[0259] The estimated cargo measurement value corresponding to the first segmented image data is determined based on the second cargo area and the first cargo type.

[0260] Optionally, in the step of inspecting the target vehicle based on the second data axis to obtain a third inspection result, the inspection unit 402 is specifically used for:

[0261] The second number line data is sampled to obtain multiple number line data, and each number line data corresponds to a second sampling time.

[0262] Curve fitting is performed based on the multiple number axis data and the second sampling time corresponding to each number axis data to obtain a first number axis curve; the horizontal axis of the first number axis curve is time, and the vertical axis is the number axis value;

[0263] Determine the coordinate time corresponding to the preset number axis value in the first number axis curve to obtain multiple coordinate times; the preset number axis value is used to represent the target vehicle stably entering or leaving the target inspection platform.

[0264] Obtain 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.

[0265] Determine the first measurement value corresponding to the first coordinate time in the second measurement data;

[0266] Determine the second measurement value corresponding to the second coordinate time in the second measurement data;

[0267] When the second measurement value is less than or equal to the first measurement value, the third inspection result is determined to be that there is no reverse driving inspection cheating behavior;

[0268] When the second measurement value is greater than the first measurement value, the target time difference between the first coordinate time and the second coordinate time is obtained;

[0269] When the target time difference is greater than the preset time difference, the third inspection result is determined to indicate that the reverse driving inspection cheating behavior exists;

[0270] When the target time difference is less than or equal to the preset time difference, the third verification result is determined to be that there is no reverse driving verification cheating behavior.

[0271] Optionally, in acquiring the first device attribute parameters of the metering module, the acquisition unit 401 is specifically used for:

[0272] Obtain the target device type corresponding to the metering module;

[0273] Determine the reference first device attribute parameters and the first error range corresponding to the target device type;

[0274] Obtain the actual measurement data and theoretical measurement data of the metering module within the 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;

[0275] The second error range corresponding to the measurement module is determined based on the actual measurement data and the theoretical measurement data.

[0276] The reference error deviation of the metering module is determined based on the second error range and the first error range.

[0277] Obtain the target usage time and standard usage time corresponding to the metering module;

[0278] The difference between the target usage time and the standard usage time is determined to obtain the target usage time difference;

[0279] Determine the first optimization factor corresponding to the target duration difference;

[0280] The reference error deviation is optimized based on the first optimization factor to obtain the target error deviation;

[0281] Determine the second optimization factor corresponding to the target error deviation;

[0282] The reference first device attribute parameters are optimized according to the second optimization factor to obtain the first device attribute parameters.

[0283] In specific implementations, the highway vehicle inspection and early warning device 400 described in the embodiments of the present invention can also execute other implementation methods described in the highway vehicle inspection and early warning method provided in the embodiments of the present invention, which will not be repeated here.

[0284] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device includes a processor, a memory, a communication interface, and one or more programs. The processor, memory, and communication interface are interconnected via a bus. The one or more programs are stored in the memory and configured to be executed by the processor. The one or more programs include instructions for executing other embodiments described in the highway vehicle inspection and early warning method provided in the above embodiments of the present invention, which will not be repeated here.

[0285] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0286] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0287] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0288] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0289] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0290] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0291] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0292] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0293] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for early warning of vehicle inspection on highways, characterized in that, An application is made in a vehicle inspection system, the vehicle inspection system comprising: a control module, a target inspection platform, a high-speed dynamic intelligent metering instrument, and a camera module, the target inspection platform comprising a metering module and a digital axis detection module, the method comprising: The metering module collects first metering data of the target vehicle within a first preset time period; the first preset time period is a period from when the target vehicle appears on the target inspection platform until it leaves the target inspection platform; the first metering data includes the weight value of the target vehicle. The first axis data of the target vehicle within the first preset time period is acquired by the axis detection module; the first axis data includes the position coordinates of the target vehicle. Obtain the first device attribute parameters of the measurement module; obtain the second device attribute parameters of the number axis detection module; the first device attribute parameters include at least one of the following: measurement accuracy, resolution, data update frequency, and measurement range; the second device attribute parameters include at least one of the following: resolution, error amplitude, and effective detection distance. The high-speed dynamic intelligent metering instrument corrects the first metering data according to the first device attribute parameters to obtain the second metering data; The high-speed dynamic intelligent metering instrument corrects the first number axis data according to the second device attribute parameters to obtain the second number axis data. Specifically, a preset mapping relationship between device attribute parameters and correction coefficients is stored in advance. Based on the mapping relationship, the second correction coefficient corresponding to the second device attribute parameters is determined. The first number axis data is corrected according to the second correction coefficient to obtain the second number axis data. The target speed of the target vehicle is determined based on the second data axis. The target acquisition interval is determined based on the target driving speed and the first preset time period. Specifically, a preset mapping relationship between driving speed and acquisition interval is stored in advance. The first acquisition interval corresponding to the target driving speed is determined based on the mapping relationship. The number of images that can be acquired in the first preset time period is determined based on the first acquisition interval to obtain the first number of images. If the number of the first images is greater than or equal to the preset number of images, the first acquisition interval is determined as the target acquisition interval. If the number of the first images is less than the preset number of images, the target acquisition interval is obtained by dividing the duration corresponding to the first preset time period by the preset number of images. The camera module acquires image data of the target vehicle within the first preset time period at the target acquisition interval to obtain first image data. The target vehicle is inspected based on the second measurement data, the second data axis data, and the first image data to obtain the target inspection result; Obtain the target vehicle permit information corresponding to the target vehicle; Based on the target inspection results and the target vehicle permit information, determine whether the target vehicle meets the preset conditions; When the target vehicle meets the preset conditions, the target inspection fraud level corresponding to the target inspection result is determined; Based on the target inspection fraud level, a target alarm information is generated, which includes the inspection fraud behavior and penalty measures corresponding to the target vehicle. The step of obtaining second measurement data by correcting the first measurement data based on the first device attribute parameters using the high-speed dynamic intelligent metering instrument includes: Determine the first correction coefficient corresponding to the first device attribute parameter; Obtain the second error range corresponding to the metering module; Obtain the first portion of the error range that is greater than zero in the second error range, and the second portion of the error range that is less than zero in the second error range; Determine the first error amplitude corresponding to the first part of the error range; Determine the second error magnitude corresponding to the second part of the error range; When the first error amplitude is greater than the second error amplitude, the first measurement data is reversed according to the first correction coefficient to obtain the second measurement data; When the first error amplitude is equal to the second error amplitude, the second measurement data is determined based on the first measurement data; When the first error amplitude is less than the second error amplitude, the first measurement data is positively corrected according to the first correction coefficient to obtain the second measurement data. The step of inspecting the target vehicle based on the second measurement data, the second data axis data, and the first image data to obtain the target inspection result includes: The second measurement data is sampled to obtain multiple measurement data, and each measurement data corresponds to a first sampling time; A first measurement curve is obtained by performing curve fitting based on the multiple measurement data and the first sampling time corresponding to each measurement data; the horizontal axis of the first measurement curve is time, and the vertical axis is the measurement value. The first measurement curve is matched with the abnormal curves in the preset abnormal curve library to obtain multiple matching degrees; each matching degree corresponds to an abnormal curve; the preset abnormal curve library includes multiple abnormal curves, and each abnormal curve corresponds to a type of cheating detection behavior. Determine the maximum value among the plurality of matching degrees, and determine the first verification result based on the maximum value; Obtain the measurement data that is greater than the first preset measurement value from the second measurement data to obtain m measurement data; m is a natural number. The second inspection result of the target vehicle is determined based on the m measurement data; The target vehicle is inspected based on the second data axis, and a third inspection result is obtained; The target vehicle is inspected based on the first image data to obtain a fourth inspection result; The target inspection result is determined based on the first inspection result, the second inspection result, the third inspection result, and the fourth inspection result.

2. The method as described in claim 1, characterized in that, The step of determining the second inspection result of the target vehicle based on the m measurement data includes: Determine the average measurement value corresponding to the m measurement data; When the average measurement value is greater than the second preset measurement value, the second inspection result is determined to be an overweight inspection fraud; 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, the second inspection result is determined to be that there is no cheating behavior in the overweight inspection.

3. The method as described in claim 2, characterized in that, The step of inspecting the target vehicle based on the first image data to obtain a fourth inspection result includes: The target vehicle type and cargo information corresponding to the target vehicle are determined based on the target vehicle permit information; The first image data is identified using image recognition technology to obtain the first type of goods; Determine whether the first cargo type is compliant based on the cargo loading information; If compliant, determine the cargo loading space corresponding to the target vehicle type, resulting in n cargo loading spaces; n is a positive integer; The first image data is segmented 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; Determine the vehicle meter value corresponding to the target vehicle type; The target estimated quantity value is determined based on the vehicle measurement value and the n estimated cargo measurement values; The target measurement deviation is obtained based on the deviation between the target estimated 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 anomaly.

4. The method as described in claim 3, characterized in that, The step of determining the estimated cargo measurement value corresponding to each segmented image data in the n segmented image data to obtain n estimated cargo measurement values ​​includes: Obtain first segmented image data; the first segmented image data is any one of the n segmented image data. Determine the area of ​​the first cargo in the first segmented image data; Determine the first shooting angle corresponding to the first segmented image data; Determine the first area compensation coefficient corresponding to the first shooting angle; The area of ​​the first cargo is adjusted according to the first area compensation coefficient to obtain the area of ​​the second cargo; The estimated cargo measurement value corresponding to the first segmented image data is determined based on the second cargo area and the first cargo type.

5. The method according to any one of claims 1-4, characterized in that, The step of inspecting the target vehicle based on the second data axis to obtain a third inspection result includes: The second number line data is sampled to obtain multiple number line data, and each number line data corresponds to a second sampling time. Curve fitting is performed based on the multiple number axis data and the second sampling time corresponding to each number axis data to obtain a first number axis curve; the horizontal axis of the first number axis curve is time, and the vertical axis is the number axis value; The coordinate time corresponding to the preset number axis value in the first number axis curve is determined to obtain multiple coordinate times; the preset number axis value is used to determine whether the target vehicle stably enters or leaves the target inspection platform. If the number axis value corresponding to the target vehicle is greater than or equal to the preset number axis value, it is determined that the target vehicle has entered the target inspection platform; otherwise, if the number axis value corresponding to the target vehicle is less than the preset number axis value, it is determined that the target vehicle has left the target inspection platform. Obtain 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. Specifically, sort the multiple coordinate times in chronological order to obtain the first coordinate time order, and select the first and second coordinate times in the first coordinate time order to obtain the first coordinate time and the second coordinate time. Determine the first measurement value corresponding to the first coordinate time in the second measurement data; Determine the 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, the third inspection result is determined to be that there is no reverse driving inspection cheating behavior; When the second measurement value is greater than the first measurement value, the target time difference between the first coordinate time and the second coordinate time is obtained; When the target time difference is greater than the preset time difference, the third inspection result is determined to indicate that the reverse driving inspection cheating behavior exists; When the target time difference is less than or equal to the preset time difference, the third verification result is determined to be that there is no reverse driving verification cheating behavior.

6. The method according to any one of claims 1-4, characterized in that, The step of obtaining the first device attribute parameters of the metering module includes: Obtain the target device type corresponding to the metering module; Determine the reference first device attribute parameters and the first error range corresponding to the target device type; Obtain the actual measurement data and theoretical measurement data of the metering module within the 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; The second error range corresponding to the measurement module is determined based on the actual measurement data and the theoretical measurement data; The reference error deviation of the metering module is determined based on the second error range and the first error range. Obtain the target usage time and standard usage time corresponding to the metering module; The difference between the target usage time and the standard usage time is determined to obtain the target usage time difference; Determine the first optimization factor corresponding to the target duration difference; The reference error deviation is optimized based on the first optimization factor to obtain the target error deviation; Determine the second optimization factor corresponding to the target error deviation; The reference first device attribute parameters are optimized according to the second optimization factor to obtain the first device attribute parameters.

7. A highway vehicle inspection and early warning device, characterized in that, This device is applied to a vehicle inspection system, which 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 data acquisition unit, an inspection unit, and an alarm unit, wherein: The acquisition unit is used to acquire first measurement data of the target vehicle within a first preset time period through the measurement module; the first preset time period is a time period from when the target vehicle appears on the target inspection platform until it leaves the target inspection platform; the first measurement data includes the weight value of the target vehicle; acquire first data axis data of the target vehicle within the first preset time period through the data axis detection module; the first data axis data includes the position coordinates of the target vehicle; obtain first device attribute parameters of the measurement module; obtain second device attribute parameters of the data axis detection module; the first device attribute parameters include at least one of the following: measurement accuracy, resolution, data update frequency, and range; the second device attribute parameters include at least one of the following: resolution, error amplitude, and effective detection distance; correct the first measurement data according to the first device attribute parameters using the high-speed dynamic intelligent measurement instrument to obtain second measurement data; correct the first data axis data according to the second device attribute parameters using the high-speed dynamic intelligent measurement instrument to obtain second data axis data, specifically, pre-store preset... The mapping relationship between the device attribute parameters and correction coefficients is established. Based on this mapping relationship, a second correction coefficient corresponding to the second device attribute parameter is determined. The first data axis data is corrected according to the second correction coefficient to obtain the second data axis data. The target driving speed of the target vehicle is determined according to the second data axis data. The target acquisition interval is determined according to the target driving speed and the first preset time period. Specifically, a preset mapping relationship between driving speed and acquisition interval is stored in advance. Based on this mapping relationship, a first acquisition interval corresponding to the target driving speed is determined. The number of images that can be acquired in the first preset time period is determined according to the first acquisition interval to obtain the first number of images. If the number of first images is greater than or equal to the preset number of images, the first acquisition interval is determined as the target acquisition interval. If the number of first images is less than the preset number of images, the target acquisition interval is obtained by dividing the duration corresponding to the first preset time period by the preset number of images. The image data of the target vehicle within the first preset time period is acquired by the camera module at the target acquisition interval to obtain the first image data. The inspection unit is configured to inspect the target vehicle based on the second measurement data, the second data axis data, and the first image data to obtain a target inspection result; acquire the target vehicle permit information corresponding to the target vehicle; determine whether the target vehicle meets preset conditions based on the target inspection result and the target vehicle permit information; and determine the target inspection cheating level corresponding to the target inspection result when the target vehicle meets the preset conditions. The alarm unit is used to generate target alarm information based on the target inspection cheating level, and the target alarm information includes the inspection cheating behavior and penalty measures corresponding to the target vehicle. Specifically, in the step of correcting the first measurement data based on the first device attribute parameters using the high-speed dynamic intelligent metering instrument to obtain the second measurement data, the acquisition unit is used for: Determine the first correction coefficient corresponding to the first device attribute parameter; Obtain the second error range corresponding to the metering module; Obtain the first portion of the error range that is greater than zero in the second error range, and the second portion of the error range that is less than zero in the second error range; Determine the first error amplitude corresponding to the first part of the error range; Determine the second error magnitude corresponding to the second part of the error range; When the first error amplitude is greater than the second error amplitude, the first measurement data is reversed according to the first correction coefficient to obtain the second measurement data; When the first error amplitude is equal to the second error amplitude, the second measurement data is determined based on the first measurement data; When the first error amplitude is less than the second error amplitude, the first measurement data is positively corrected according to the first correction coefficient to obtain the second measurement data. Specifically, in the step of inspecting the target vehicle based on the second measurement data, the second data axis data, and the first image data to obtain the target inspection result, the inspection unit is used for: The second measurement data is sampled to obtain multiple measurement data, and each measurement data corresponds to a first sampling time; A first measurement curve is obtained by performing curve fitting based on the multiple measurement data and the first sampling time corresponding to each measurement data; the horizontal axis of the first measurement curve is time, and the vertical axis is the measurement value. The first measurement curve is matched with the abnormal curves in the preset abnormal curve library to obtain multiple matching degrees; each matching degree corresponds to an abnormal curve; the preset abnormal curve library includes multiple abnormal curves, and each abnormal curve corresponds to a type of cheating detection behavior. Determine the maximum value among the plurality of matching degrees, and determine the first verification result based on the maximum value; Obtain the measurement data that is greater than the first preset measurement value from the second measurement data to obtain m measurement data; m is a natural number. The second inspection result of the target vehicle is determined based on the m measurement data; The target vehicle is inspected based on the second data axis, and a third inspection result is obtained; The target vehicle is inspected based on the first image data to obtain a fourth inspection result; The target inspection result is determined based on the first inspection result, the second inspection result, the third inspection result, and the fourth inspection result.

8. An electronic device, characterized in that, include: A processor and a memory, the memory being used to store one or more programs and configured to be executed by the processor, the programs including instructions for performing the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, A computer program for storing electronic data interchange is provided, wherein the computer program causes a computer to perform the method as described in any one of claims 1-6.

Images