A method of detecting a hydraulic axle of a vehicle and a detection system

Abstract

The application provides a detection method and system for a hydraulic axle of a vehicle, wherein the method comprises: acquiring real-time weighing data and an axle triggering signal when the vehicle passes through an axle group type dynamic weighing platform; determining a first time when a scale on each axle is triggered and a second time when a scale is triggered according to the axle triggering signal; acquiring an axle load of each axle and an axle group load of each axle group according to the real-time weighing data; when the vehicle has an abnormal hydraulic axle operation behavior, replacing the axle group load of the corresponding axle group with the sum of the axle loads of the axles in the axle group according to the difference between the axle load of each axle and the axle group load of the axle group and the vehicle passing speed, so as to restore the vehicle weight. Through the application, the accuracy of dynamic weighing of the vehicle is improved by identifying the abnormal hydraulic axle operation behavior and restoring the weight.

Description

Technical Field

[0001] This application relates to the field of vehicle dynamic weighing measurement technology, specifically to a method and system for detecting vehicle hydraulic axles, which is particularly suitable for vehicle dynamic weighing in scenarios such as highway overload control and highway entrance weighing. Background Technology

[0002] Currently, axle group dynamic weighing systems are widely used in highway overload control and expressway entrance weighing. Compared to axle weighing methods, axle group weighing reduces the number of weighing operations, lowers cumulative errors, and offers higher weighing accuracy. Compared to vehicle-mounted dynamic weighing systems, its platform length is shorter (typically 4-6 meters), and its construction and maintenance costs are only one-quarter of those of vehicle-mounted systems, making it highly cost-effective. Therefore, it has been widely adopted and used throughout the country.

[0003] However, with the widespread adoption of axle-mounted dynamic weighing systems, methods of cheating using hydraulic axles that exploit their weighing principle are increasing. Unscrupulous vehicles, by illegally installing hydraulic lifting devices, manipulate the system when passing over the weighing platform, briefly raising some axles and transferring the weight they should be bearing to other axles or releasing it outside the weighing system. Existing systems can only output the final weighing data and cannot precisely monitor the weight changes as the vehicle passes over the weighing platform. This results in the system measuring a significantly lower weight than the actual value when a vehicle uses hydraulic cheating devices, yet it cannot detect the cheating or effectively reconstruct the true weight, causing serious economic losses and safety risks to highway operations and management. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method and system for detecting a vehicle hydraulic shaft, so as to overcome at least one of the above-mentioned defects.

[0005] In a first aspect, embodiments of this application provide a method for detecting a vehicle's hydraulic axle. The method includes: acquiring real-time weighing data and axle trigger signals when a vehicle passes through an axle group dynamic weighing platform; determining a first moment when each axle is put on the scale and a second moment when it is removed from the scale based on the axle trigger signals; acquiring the axle weight of each axle and the axle group weight of each axle group based on the real-time weighing data; and, based on the difference between the axle weight of each axle and the axle group weight of its corresponding axle group, and in conjunction with the vehicle's passing speed, replacing the axle group weight of the corresponding axle group with the sum of the axle weights of all axles in the axle group to restore the vehicle's weight if the vehicle exhibits abnormal hydraulic axle operation.

[0006] In one optional embodiment of this application, the real-time weighing data includes upper scale sensor data and lower scale sensor data, and the axle trigger signal includes upper scale trigger signal and lower scale trigger signal.

[0007] In one optional embodiment of this application, the first moment of each shaft being put on the scale and the second moment of being put off the scale are determined in the following way: when the on-scale trigger signal rises from below the on-scale threshold to above the on-scale threshold, it is determined that a new shaft is put on the scale, and this moment is recorded as the first moment; when the off-scale trigger signal falls from above the off-scale threshold to below the off-scale threshold, it is determined that a new shaft is put off the scale, and this moment is recorded as the second moment.

[0008] In one optional embodiment of this application, the axle weight of each shaft is obtained by: continuously collecting a set number of weighing sensor data after a first moment as a temporary weight array for that shaft; and calculating the average of the temporary weight array to obtain the axle weight of that shaft.

[0009] In one optional embodiment of this application, the weight of each axle group is obtained in the following manner: when the first moment occurs, the total weight data of the weighing platform is recorded, which is the sum of the data of the upper weighing sensor and the data of the lower weighing sensor; recording continues until the second moment of the last axle of the axle group occurs, and a temporary total weight array of the axle group is obtained; the temporary total weight array is subjected to anti-shake processing to obtain the weight of the axle group.

[0010] In one optional embodiment of this application, the anti-shake processing includes: sorting the values ​​in the temporary total weight array; removing the values ​​with a predetermined ratio at the beginning and a predetermined ratio at the end after sorting; and calculating the average value of the remaining values ​​to obtain the weight of the shaft group.

[0011] In one optional embodiment of this application, the vehicle passing speed is obtained by: calculating the time difference based on the sampling point sequence number corresponding to the first and second moments of the same axle; calculating the instantaneous speed of the axle in combination with the length of the weighing platform; and taking the average value of the instantaneous speeds of each axle as the vehicle passing speed.

[0012] In one optional embodiment of this application, the step of determining whether there is abnormal hydraulic axle control behavior based on the difference between the axle weight of each axle and the weight of the axle group, combined with the vehicle's passing speed, includes: calculating the difference between the sum of the axle weights of each axle in the axle group and the weight of the axle group; if the difference exceeds a first threshold, then a weight logic abnormality is determined; acquiring the real-time total weight data of the axle group during the weighing process, and counting the number of abnormal points where the deviation from the final axle group weight exceeds a third threshold; if the number of abnormal points exceeds a fourth threshold, then a process fluctuation abnormality is determined; if the vehicle's passing speed is lower than a second threshold, then a speed abnormality is determined; when the weight logic abnormality, process fluctuation abnormality, and speed abnormality are all satisfied simultaneously, then it is determined that the vehicle has abnormal hydraulic axle control behavior.

[0013] Secondly, this application also provides a detection system for vehicle hydraulic axles. The system includes: an axle group dynamic weighing platform, wherein an upper axle trigger and a lower axle trigger are respectively provided at the upper and lower weighing ends of the platform, and weighing sensors are provided at the four corners of the platform; a data acquisition unit, connected to the upper axle trigger, the lower axle trigger, and the weighing sensors, for acquiring axle trigger signals and real-time weighing data when a vehicle passes; and a processing unit, connected to the data acquisition unit, configured to: determine the first moment when each axle is put on the scale and the second moment when it is put off the scale based on the axle trigger signals; acquire the axle weight of each axle and the axle group weight of each axle group based on the real-time weighing data; determine whether there is abnormal hydraulic axle operation based on the difference between the axle weight of each axle and the axle group weight of its respective axle group, combined with the vehicle passing speed; and if so, replace the axle group weight of the corresponding axle group with the sum of the axle weights of all axles in the corresponding axle group to restore the vehicle weight.

[0014] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the method described above are performed.

[0015] The vehicle hydraulic axle detection method and system provided in this application acquire real-time weighing data and axle trigger signals when a vehicle passes through an axle group dynamic weighing platform; determine the first moment each axle enters the scale and the second moment it exits the scale based on the axle trigger signals; acquire the axle weight of each axle and the axle group weight of each axle group based on the real-time weighing data; determine whether there is abnormal hydraulic axle operation behavior based on the difference between the axle weight of each axle and the axle group weight of its corresponding axle group, combined with the vehicle's passing speed; if so, replace the axle group weight of that axle group with the sum of the axle weights of all axles in the corresponding axle group to restore the vehicle weight. This application improves the accuracy of vehicle dynamic weighing by identifying abnormal hydraulic axle behavior and restoring weight.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the vehicle hydraulic shaft detection method provided in this application embodiment; Figure 2 A flowchart for determining the first moment when each shaft is on the scale and the second moment when it is off the scale, provided for embodiments of this application; Figure 3 A flowchart for obtaining the axle weight of each axle provided in an embodiment of this application; Figure 4 A flowchart for obtaining the weight of each shaft group provided in this application embodiment; Figure 5 A waveform diagram of a normal six-axle vehicle passing through an axle-group dynamic weighing platform, as provided in an embodiment of this application. Figure 6 A waveform diagram of a six-axle vehicle exhibiting abnormal hydraulic shaft control behavior passing through an axle-group dynamic weighing platform, provided as an embodiment of this application. Figure 7 A flowchart for obtaining vehicle passing speed is provided as an embodiment of this application; Figure 8 A flowchart for determining abnormal hydraulic axle control behavior in a vehicle, provided in this application embodiment; Figure 9 This is a schematic diagram of a vehicle hydraulic shaft detection system provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0020] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of vehicle inspection technology.

[0021] Research has revealed that while existing axle-mounted dynamic weighing systems offer advantages such as low cost and high accuracy, they suffer from significant technical flaws in practical applications. When a vehicle is briefly lifted via a hydraulic lifting device during the weighing process, the weight it should bear is transferred to other axles or released outside the weighing system, resulting in a significantly lower measured vehicle weight or axle assembly weight than the actual value. Furthermore, existing systems can only output the final weighing data and cannot precisely monitor the weight change trajectory of the vehicle as it passes over the weighing platform. They cannot identify abnormal weight fluctuations caused by hydraulic cheating, nor can they effectively reconstruct the true weight.

[0022] Overloaded and oversized transport severely damages highway infrastructure and easily leads to major traffic accidents, making the innovation of overload control technology crucial. Currently, dynamic weighing systems are mainly divided into three types: axle weighing, axle group weighing, and whole vehicle weighing. Among them, axle group dynamic weighing systems, with their moderate platform length, low cost, and high accuracy, have been widely used in recent years.

[0023] However, unscrupulous vehicles exploit the cumulative principle of axle-mounted weighing systems by illegally installing hydraulic lifting devices. When passing over the weighbridge, they manipulate the system to briefly raise some axles, transferring the weight that should be borne to other axles or releasing it off the weighbridge, resulting in a significantly lower weighing result than the actual value. This type of cheating is characterized by active manipulation, concealed actions, and instantaneous weight changes. Existing systems can only output inaccurate data and cannot identify the cheating behavior, let alone restore the true weight, causing serious economic losses and safety risks to highway operation and management.

[0024] Based on this, embodiments of this application provide a method and system for detecting a vehicle hydraulic shaft, in order to identify abnormal operating behavior of the hydraulic shaft and correct misaligned weights, thereby improving the accuracy of dynamic weighing.

[0025] This application provides a method for detecting and restoring the weight of abnormal hydraulic axle control behavior in vehicles based on an axle-group dynamic weighing system. In this method, load cells are located at the four corners of the axle-group weighing platform, with the weighing direction perpendicular to the vehicle's travel direction, for collecting real-time weighing data and performing filtering. A vehicle separator is located 200mm in front of the weighing platform to distinguish the data belonging to consecutive vehicles. Axle triggers are located at the upper and lower ends of the weighing platform to control the number of axles on the upper and lower scales and to track changes in axle weight and axle group weight in real time. Starting from the first axle triggering the upper scale trigger, 20-point mean filtering is applied to each set of sensor data to smooth out the effects of vehicle vibration and construct a data waveform; simultaneously, the weight change of each axle is recorded. When a vehicle axle triggers the lower scale trigger, the waveform and weight change are input into the detection algorithm. If abnormal hydraulic axle control behavior is detected, the current axle group weighing weight is replaced with the accumulated axle weight to restore vehicle weighing accuracy.

[0026] This application includes two parts: the detection of abnormal hydraulic shaft operation and the restoration of vehicle weight.

[0027] During the system initialization phase, all sensors are initialized after power-on. The vehicle separator, upper scale axle trigger, lower scale axle trigger, and four corner load cells enter real-time data acquisition mode, with all sensors synchronously sampling at a high frequency of 1000 points per second. When a truck approaches, the truck front first triggers the vehicle separator, and the vehicle separator signal G[i] changes from '1' (no truck) to '0' (truck present). The system immediately marks the sampling point index at this time as the starting point i_start of the single-vehicle data segment and begins to cyclically record the following data: G[i] is the vehicle separator state; C1[i] is the upper scale axle trigger signal; C2[i] is the lower scale axle trigger signal; D1[i] is the real-time weight value of the upper scale load cell in kg; D2[i] is the real-time weight value of the lower scale load cell in kg. Simultaneously, the system calculates derived data in real time: total weight of the weighing platform D3[i] = D1[i] + D2[i]; difference between consecutive points before and after the upper weighing sensor A1[i] = D1[i+1] - D1[i]; difference between consecutive points before and after the lower weighing sensor A2[i] = D2[i+1] - D2[i]. A1[i] and A2[i] are filtered, retaining only values ​​within a preset range (Vmin, Vmax) to eliminate obvious noise points.

[0028] For ease of description, the following variables are defined: The real-time data point G[i] of the vehicle separator takes the value 0 or 1, where 0 represents a vehicle present and 1 represents no vehicle present; the upper axle trigger signal C1[i]; the lower axle trigger signal C2[i]; the real-time value of the upper sensor D1[i]; the real-time value of the lower sensor D2[i]; the total weight of the weighing platform D3[i] = D1[i] + D2[i]; the difference between the upper and lower sensors A1[i] = D1[i+1] - D1[i]; the difference between the lower and upper sensors A2[i] = D2[i+1] - D2[i]. The upper and lower axle thresholds Vup and Vdown are set. The upper axle number n, the lower axle number w, and the axle group number m are defined. The real-time weight record for each axle is ZhouWeightTemp[n][i], and the real-time weight record for each axle group is ZhouZuWeightTemp[m][i]. The starting axle number for each axle group is ZhouZuStaNum[m], and the ending axle number is ZhouZuEndNum[m]. Finally, the weights of each axle, ZhouWeight[n], and each axle group, ZhouZuWeight[m], are calculated. The total vehicle weight can be obtained by summing the axle group weights: CarWeight = ΣZhouZuWeight[m], or by summing the axle weights: CarWeight = ΣZhouWeight[n].

[0029] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for detecting a vehicle hydraulic shaft provided in an embodiment of this application. Figure 1 As shown in the embodiment of this application, the method for detecting a vehicle hydraulic shaft includes: S101. Obtain real-time weighing data and axle trigger signals when the vehicle passes through the axle-type dynamic weighing platform.

[0030] Real-time weighing data includes data from the upper and lower scale sensors, and axle trigger signals include upper and lower scale trigger signals.

[0031] In this step, raw data is collected as vehicles pass by using a sensor array deployed on an axle-mounted dynamic weighing platform. Weighing sensors are installed at the four corners of the platform, outputting data from the upper and lower scales respectively to monitor the load on the platform in real time. Simultaneously, axle triggers are embedded at the upper and lower scale ends of the platform, outputting upper and lower scale trigger signals to detect whether an axle has entered or left the platform. Furthermore, a vehicle separator is located at the front of the platform to identify the arrival and departure of vehicles, thus defining the data collection area for a single vehicle. All sensors sample synchronously at a high frequency of 1000 points per second, ensuring that subtle changes during the dynamic weighing process of vehicles are captured.

[0032] The collected raw data first undergoes preprocessing, such as smoothing the trigger signals and sensor data using a moving average filtering algorithm to eliminate high-frequency interference from vehicle vibration, road surface unevenness, and electrical noise. The filtered data curves are more stable and accurately reflect the load change trend of the axle as it passes the platform, laying a reliable data foundation for subsequent accurate axle event identification and weight calculation. This step achieves high-precision digital recording of the entire vehicle passage process and is a prerequisite for all subsequent analyses.

[0033] To further eliminate vehicle vibration and random noise interference, this application performs filtering processing on the collected sensor data. Specifically, with the number of sampling points i of each sensor as the X-axis, and the upper scale axle trigger value C1[i], the lower scale axle trigger value C2[i], the upper scale sensor data value D1[i], and the lower scale sensor data value D2[i] as the Y-axis values, the 20-point averaging method is used to optimize the curve smoothness of each sensor data:

[0034] The values ​​obtained by averaging the 20 points were used to replace the values ​​of the original data collection points C1[i], C2[i], D1[i], and D2[i] one by one to form a filtered data graph. The filtered data curve is more stable and can truly reflect the load change trend when the axle passes through the platform, laying a reliable data foundation for subsequent accurate identification of axle events and weight calculation.

[0035] Specifically, this application employs a 20-point moving average filtering algorithm to smooth the original data. For each sampling point i, a 20-point moving average filter is applied to the four original data curves C1[i], C2[i], D1[i], and D2[i]. Taking D1[i] as an example, its smoothed value D1_smooth[i] is calculated using the following formula: D1_smooth[i] = (D1[i-9]+ D1[i-8] + ... + D1[i]+ ... + D1[i+9] + D1[i+10]) / 20 For sampling points near the data boundaries, special boundary point processing methods are used (such as copying edge values ​​or shortening the filter window). The smoothed data is stored in a new array (such as C1_smooth[i], C2_smooth[i], D1_smooth[i], D2_smooth[i]) for all subsequent analyses. The 20-point moving average filter effectively suppresses interference from high-frequency mechanical vibrations and random electrical noise, making the data curves smoother and more stable.

[0036] S102. Determine the first moment when each axle is put on the scale and the second moment when it is taken off the scale based on the axle trigger signal.

[0037] This step accurately captures the timing information of each axle passing over the weighing platform based on the transition characteristics of the upper and lower scale trigger signals. The axle triggers are installed at the upper and lower ends of the weighing platform. When an axle passes over it, it outputs a high-level signal, and when no axle passes over it, it outputs a low-level signal.

[0038] By monitoring the rising and falling edges of these two signals in real time and comparing them with preset thresholds, the instants when each shaft enters and leaves the weighing platform are identified and recorded as the first and second moments, respectively. These two moments are stored as high-precision sampling point numbers, forming the basis for shaft counting, shaft group division, weight data extraction, and vehicle speed calculation in the entire method. Through this triggering mechanism, this application can establish the weighing timeline of each shaft with millisecond-level accuracy, providing a reliable time reference for subsequent refined analysis.

[0039] In the weighing event detection, the system continuously monitors the smoothed weighing trigger signal C1_smooth[i]. When it detects that the value first jumps from below the weighing threshold Vup and stably exceeds Vup, this moment is recorded as the "first moment" T1_n of the nth axis. The system immediately performs the following operations: records the smoothed value D1_smooth[T1_n] of the weighing sensor at this time as the reference point dl; increments the weighing axis counter by 1, i.e., n=n+1; creates a temporary array ZhouWeightTemp[n]

[100] , and stores 100 consecutive D1_smooth[i] values ​​starting from index T1_n+1 into this array as the original weight trajectory of this axis. This event usually marks the start of a new axle group, so the axle group counter is incremented by 1, i.e., m = m + 1; the starting axle number of the axle group is recorded as ZhouZuStaNum[m] = n; a temporary dynamic array ZhouZuWeightTemp[m][] is created, and the smoothed total weight of the subsequent weighing platform starting from time T1_n is continuously appended to this array.

[0040] In the weighing event detection, the system continuously monitors the smoothed weighing trigger signal C2_smooth[i]. When it detects that the value first drops from above the weighing threshold Vdn and stabilizes below Vdn, this moment is recorded as the "second moment" T2_n of the w-th axis (corresponding to the n-th axis on the weighing). The system immediately performs the following operations: records the smoothed value D2_smooth[T2_n] of the weighing sensor at this time as a reference point; increments the weighing axis counter by 1, i.e., w = w + 1; records the end axis number of this axis group, ZhouZuEndNum[m] = w; and stops appending data to the temporary array ZhouZuWeightTemp[m][] of the current axis group. At this time, the array completely contains the smoothed total weight values ​​of the weighing platform corresponding to all sampling points from the start of the first axis on the weighing of the m-th axis group to the end of the weighing of the last axis (the w-th axis).

[0041] Please see Figure 2 , Figure 2 A flowchart illustrating the determination of the first moment when each shaft is on the scale and the second moment when it is off the scale, provided for embodiments of this application. (See attached flowchart.) Figure 2 As shown, the first moment of weighing each shaft and the second moment of weighing each shaft are determined in the following way: S201. When the weighing trigger signal rises from below the weighing threshold to above the weighing threshold, it is determined that a new shaft is being weighed, and this moment is recorded as the first moment.

[0042] The system continuously acquires the analog or digital signals output by the axle triggers of the vehicle entering the weighbridge, and performs necessary filtering to eliminate transient interference. The weighbridge threshold is a pre-calibrated voltage or value, typically set to the midpoint between the base value of the axle-less state signal and the peak value of the axle-present state signal, or slightly higher than the base value to ensure anti-interference capability. During vehicle passage, the weighbridge trigger signal value at the current sampling point is continuously compared with the weighbridge threshold.

[0043] When one axle of the vehicle reaches the weighing platform, the weighing trigger signal rapidly rises from a stable low level. The moment the signal value first exceeds the weighing threshold, it is determined that a new axle has entered the weighing platform. At this point, the index number of the current sampling point is immediately recorded as the first moment for that axle, and the weighing trigger state is marked as "axle present" to avoid repeated triggering of the same axle due to signal jitter. The recorded first moment is accurate to the millisecond and will serve as the starting point for subsequent weight data extraction for that axle, as well as the marker for the start of recording for the axle group to which that axle belongs.

[0044] In a preferred embodiment, when the weighing trigger signal first exceeds the weighing threshold, the system records the value of the weighing sensor D1[i] at the current moment as d_l, and simultaneously switches the weighing trigger state to "with shaft". Subsequently, the system continues to monitor the weighing trigger signal, and when it first drops from the "with shaft" state to below the weighing threshold, it records the value of the weighing sensor D1[i] as d_r.

[0045] If d_r-d_l>d_r / 2, then it is confirmed that the axis has been stably mounted on the scale. At this time, the axis number n=n+1 and the axis group number m=m+1 are updated, and the 100 consecutive D1[i] data starting from the first moment are stored in the temporary weight array ZhouWeightTemp[n][i] of the axis. At the same time, the starting axis number of the axis group ZhouZuStaNum[m]=n is recorded, and the total weight of the scale D3[i] is stored in real time in the temporary total weight array ZhouZuWeightTemp[m][i] of the axis group.

[0046] S202. When the downscale trigger signal drops from above the downscale threshold to below the downscale threshold, it is determined that the new shaft is downscaled, and this moment is recorded as the second moment.

[0047] Similar to the weighing detection, the signal value of the weighing axle trigger is monitored in real time and compared with a pre-calibrated weighing threshold. The weighing threshold is calibrated in the same way as the weighing threshold, falling between the base value of the axle-less signal and the peak value of the axle-borne signal. The weighing trigger signal value at the current sampling point is continuously compared with the weighing threshold.

[0048] When the axle reaches the lower end of the weighing platform and is about to completely leave, the lowering trigger signal begins to drop from a stable high level. The moment the signal value first falls below the lowering threshold, it is determined that the axle has completely left the weighing platform. At this point, the index number of the current sampling point is immediately recorded as the second moment for that axle, and the state of the lowering trigger is marked as "no axle," preparing for subsequent axle lowering detection.

[0049] The second recorded moment marks the end of the weighing process for that axle, serving as the termination point for capturing the axle's weight data and the marker for the end of recording for the axle group to which that axle belongs. Combining the first and second recorded moments for the same axle, the total time it takes for the axle to pass through the weighing platform can be calculated, which can then be used for vehicle speed calculation.

[0050] In a preferred embodiment, when the scale trigger signal first drops below the scale threshold, the system records the value of the scale sensor D2[i] at the current moment as d_l, and simultaneously switches the scale trigger state to "shaftless".

[0051] Subsequently, the lower scale trigger signal was monitored. When it first rose from the shaftless state to the upper scale threshold, the value of the lower scale sensor D2[i] was recorded as d_r.

[0052] If d_l-d_r>d_l / 2, then it is confirmed that the shaft has been stably removed from the scale. At this time, the shaft number w=w+1 is updated, and the end shaft number of the shaft group ZhouZuEndNum[m]=w is recorded. At the same time, data is stopped from being added to the temporary total weight array ZhouZuWeightTemp[m][i] of the shaft group.

[0053] S103. Based on real-time weighing data, obtain the weight of each shaft and the weight of each shaft group.

[0054] In this step, the individual weight of each axle and the total weight of each axle group are calculated using the weighing sensor data collected in step S101 and the event time determined in step S102. The real-time output data from the upper and lower weighing sensors, combined with the time boundaries of the first and second moments, form the basis for calculating the axle weight and the weight of the axle group.

[0055] For single axle load, after the first moment for each axle, the data from the weighing sensor during a stable period after the axle is placed on the scale is extracted, and the instantaneous fluctuations caused by vehicle vibration are eliminated by averaging to obtain the stable weight value of the axle.

[0056] For the weight of the shaft group, starting from the first moment of the first shaft of each shaft group, the total weight data of the weighing platform obtained by adding the data of the upper weighing sensor and the data of the lower weighing sensor in real time is continuously recorded until the second moment of the last shaft of the shaft group, forming a temporary total weight array that reflects the entire process of load change of the shaft group.

[0057] To address the drastic weight fluctuations that could result from hydraulic cheating, the array is de-jittered. Extreme values ​​are removed, and the average value is calculated to obtain the stable weight value for the shaft assembly. This step, through a differentiated data processing strategy, ensures weighing accuracy under normal operating conditions while preserving the abnormal fluctuation characteristics that might be caused by cheating, providing reliable data support for subsequent cheating detection.

[0058] Please see Figure 3 , Figure 3 This is a flowchart illustrating the process of obtaining the weight of each shaft as provided in an embodiment of this application. Figure 3 As shown, the axle weight of each axle is obtained in the following way: S301. After the first moment, continuously collect a set number of weighing sensor data as a temporary weight array for the axis.

[0059] After recording the first moment for a specific shaft, the acquisition of axle load data for that shaft is immediately initiated. The data collected is the real-time weight value output by the weighing sensor after that moment. The weighing sensor is located at the bottom of the weighing platform and directly senses the load applied when the shaft presses onto the platform.

[0060] A predetermined number of data points, typically 100, are continuously collected at a fixed sampling frequency, corresponding to a duration of 0.1 seconds (sampling frequency 1000 points / second). These 100 consecutive data points constitute a temporary weight array for the shaft, where each value represents the instantaneous load applied to the weighing sensor at a corresponding minute moment. This array comprehensively records the weight change trajectory of the shaft from its initial contact with the weighing platform to the point where the load stabilizes, providing the raw data foundation for subsequently obtaining the stable shaft weight value.

[0061] S302. Calculate the mean of the temporary weight array to obtain the axle weight of the axle.

[0062] After data acquisition is completed, the arithmetic mean of all values ​​in the temporary weight array is calculated. Because vehicles inevitably vibrate up and down when dynamically passing over the weighing platform, the instantaneous value output by the weighing sensor fluctuates around the actual axle load, and this fluctuation has randomness and symmetry.

[0063] By calculating the average of 100 consecutive sampling points, positive and negative fluctuations cancel each other out, thus effectively restoring the true static weight of the shaft. The calculated average value is the final axle load of the shaft, which is stored in the axle load array for subsequent use. This axle load value not only represents the actual load-bearing weight of the shaft, but will also be used for weight logic consistency verification in subsequent steps, that is, compared with the weight of the shaft group in which it belongs, as an important basis for judging whether there is any abnormal operation behavior of the hydraulic shaft.

[0064] In a preferred embodiment, the axle load calculation uses a 100-point average to further reduce weighing errors caused by vehicle vibration. The axle load calculation formula is as follows:

[0065] in, Let z be the weight of the nth axis, and z be the sampling point number. The weight is the number of the z-th sampling point on the n-th axis.

[0066] Please see Figure 4 , Figure 4 This is a flowchart illustrating the process of obtaining the weight of each shaft group according to an embodiment of this application. Figure 4 As shown, the weight of each axle group is obtained in the following way: S401. When the first moment occurs, start recording the total weight data of the weighing platform.

[0067] The total weight data of the weighing platform is the sum of the data from the upper and lower scale sensors.

[0068] Upon detecting the first shaft of a certain shaft group, the recording of weight data for that shaft group is immediately initiated. The total weight data of the weighing platform is output in real time by the load cells installed at the four corners of the weighing platform, specifically the sum of the data from the upper and lower load cells.

[0069] The upper weighing sensor is located at the bottom of the weighing platform's weighing end, sensing the load entering the platform; the lower weighing sensor is located at the bottom of the weighing platform's lower weighing end, sensing the load about to leave the platform. Adding these two sensors together gives the total load borne by the entire weighing platform at the current moment. Starting from the sampling point corresponding to the first moment, the total weight data of the weighing platform at each sampling point is sequentially stored in a newly created temporary array for that shaft group. The start time of this array is precisely synchronized with the moment the shaft group begins to form, ensuring that subsequently recorded data completely covers the entire weighing process of that shaft group.

[0070] S402. Continue recording until the second moment of the last axis of the axis group occurs, and obtain the temporary total weight array of the axis group.

[0071] After recording is started, the total weight data of the weighing platform at each sampling point is continuously appended to the temporary array established in step S401. This recording process spans the entire period during which all shafts in the shaft group pass through the weighing platform, that is, from the moment the first shaft in the shaft group starts to be weighed until the moment the last shaft in the shaft group is completely unloaded.

[0072] When the second moment of detecting the last shaft in the shaft group in step S202 is reached, data writing to the temporary array is immediately stopped. At this time, the array contains the total weight value of the weighing platform at each sampling moment during the complete time period from the formation of the shaft group to its complete departure. This array completely records the load change process of the shaft group during the common weighing period, including stable data during the normal load phase, as well as instantaneous weight jump data that may be caused by hydraulic cheating, providing a complete original data foundation for subsequent anti-shake processing and cheating detection.

[0073] S403. Perform anti-jitter processing on the temporary total weight array to obtain the weight of the shaft group.

[0074] After obtaining the temporary total weight array formed in step S402, the array is subjected to anti-shake processing to eliminate abnormal fluctuation interference caused by vehicle vibration and cheating actions.

[0075] Preferably, the anti-shake processing includes: sorting the values ​​in the temporary total weight array; removing the values ​​with the first and last set proportions after sorting; and calculating the average of the remaining values ​​to obtain the weight of the shaft group.

[0076] In a preferred embodiment, the specific steps of the anti-shake processing are as follows: First, all data points in the temporary total weight array ZhouZuWeightTemp[m][i] are quickly sorted according to their numerical values; then, the number of points to be removed for anti-shake processing is calculated as f = i × 5%, and the first f maximum values ​​and the last f minimum values ​​are removed from the sorted array; finally, the arithmetic mean of the remaining i–2f intermediate data points is calculated to obtain the final axis group weight. The axis group weight calculation formula is as follows:

[0077] in, Let be the weight of the m-th axis group, and i be the number of remaining data points after anti-shake processing. Let be the weight of the i-th data point in the m-th axis group.

[0078] Image stabilization should be performed according to the following steps: First, sort all data points in the temporary total weight array in ascending order of their numerical values. Then, calculate the number of data points to be removed according to a preset percentage (e.g., 5%), and remove the maximum value points at the beginning and the minimum value points at the end from the sorted array. Finally, calculate the arithmetic mean of the remaining middle data points, and this mean is the final weight of the axis group.

[0079] The purpose of removing extreme values ​​is to eliminate abnormally high and low values ​​that may be caused by instantaneous impacts, vehicle jumps, or rapid movements of hydraulic devices. These outliers, if included in the averaging, would distort the true axle assembly weight. This process effectively counteracts the fluctuation interference caused by random vibrations while retaining abnormal fluctuation traces that may reflect cheating characteristics (because abnormal fluctuations will be reflected in the statistics of the number of outliers, used for judgment in subsequent step S104), ultimately obtaining a stable and reliable axle assembly weight value for vehicle weight calculation and cheating detection.

[0080] S104. Based on the difference between the axle weight of each axle and the axle group weight of its corresponding axle group, and combined with the vehicle's speed, if the vehicle exhibits abnormal hydraulic axle control behavior, replace the axle group weight of that axle group with the sum of the axle weights of all axles in that axle group to restore the vehicle's weight.

[0081] In this step, information from three dimensions—axle load, axle group load, and vehicle speed—is comprehensively used to identify abnormal hydraulic axle control behavior through a multi-condition joint judgment method.

[0082] First, using the calculated axle weights and axle group weights, a weight logic consistency check is performed on each axle group: the sum of the axle weights of each individual axle within the axle group is compared with the axle group weight directly measured. If the difference significantly exceeds the normal fluctuation range, it indicates that weight transfer or concealment may have occurred during the weighing process of that axle group. Second, the fluctuation characteristics of the real-time total weight data of the axle group during the weighing process are analyzed: a temporary total weight array of the axle group is extracted, and the deviation of the instantaneous total weight from the final axle group weight at each moment is examined. If large-scale and continuous abnormal fluctuations occur, it indicates that the axle group has experienced abnormal load changes, which is consistent with the load redistribution characteristics caused by the instantaneous action of the hydraulic device. Finally, vehicle speed is introduced as an auxiliary criterion: since the operation of the hydraulic cheating device requires a certain action time, cheating vehicles often choose to pass at low speeds to ensure the effective operation of the device. Therefore, when the vehicle speed is lower than the set threshold, the suspicion of cheating is further increased.

[0083] Only when all three abnormal characteristics mentioned above are detected simultaneously is it ultimately determined that the shaft assembly exhibits abnormal hydraulic shaft operation behavior. This multi-condition joint judgment mechanism effectively avoids misjudgments caused by occasional abnormalities in a single indicator, improves the accuracy and reliability of detection, and ensures that subsequent weight recovery processing is triggered only when cheating characteristics are indeed present.

[0084] In a preferred embodiment, for a shaft group that is determined to have abnormal hydraulic shaft operation behavior, the specific method of weight recovery is as follows: replace the original shaft group weight ZhouZuWeight[m] with the sum of the weights of each shaft in the shaft group, sum_zhouweight, thereby realizing weight correction and recovery.

[0085] When an abnormal hydraulic axle manipulation is detected in a particular axle group, this step initiates the weight recovery mechanism. Since the cheating behavior causes the direct weighing result (axle group weight) of that axle group to be significantly lower than the actual load, and the axle weight of each individual axle within that axle group is measured individually when each axle passes through the sensor, unaffected by the cheating transfer, replacing the contaminated original axle group weight with the sum of the axle weights within that axle group restores the true weight that the axle group should bear. For axle groups not detected as cheating, their original axle group weight calculation results are retained. Finally, the weights of all axle groups (recovered or original values) are summed to obtain the total vehicle weight.

[0086] If it is determined that there is no abnormal handling behavior in the axle group, the original axle group weight calculated in step S403 is retained without any replacement. The original axle group weight is directly included in the subsequent accumulation of the vehicle weight.

[0087] After all axle groups have undergone the above judgment and processing, the weight values ​​of all axle groups are summed to obtain the total weight of the vehicle. Among them, the weight of the axle groups judged to be cheating is the corrected weight after replacement, while the weight of the normal axle groups is the original weight.

[0088] This differentiated approach not only corrects the weight loss caused by cheating but also ensures that the weighing results of normal vehicles are not interfered with, making the final output vehicle weight closer to the actual load of the vehicle and improving the measurement accuracy of the axle group dynamic weighing system in complex cheating environments.

[0089] After the vehicle completely leaves the weighing platform, the vehicle separator signal G[i] returns to '1', marking the end of single-vehicle data collection. The system summarizes the weight values ​​of all axle groups, where the axle groups judged to be cheating have been replaced with the corrected weight, and the normal axle groups retain their original weights. The total vehicle weight CarWeight = Σ(ZhouZuWeight[1]+ ZhouZuWeight[2] +... + ZhouZuWeight[M]) is calculated. At the same time, the system outputs complete weighing information, including the number of axles, the vehicle's passing speed, whether there is any abnormal hydraulic axle operation, and the specific cheating axle group identifier, for subsequent management or law enforcement use.

[0090] For further details, please refer to Figure 5 and Figure 6 , Figure 5 This is a waveform diagram of a normal six-axle vehicle passing through an axle-group dynamic weighing platform, as provided in an embodiment of this application. Figure 6 This is a waveform diagram of a six-axle vehicle exhibiting abnormal hydraulic shaft control behavior passing through an axle-group dynamic weighing platform, as provided in an embodiment of this application.

[0091] In both graphs, the horizontal axis represents the sampling point number, corresponding to the time axis (sampling frequency 1000 points / second); the vertical axis represents the weight value, in kilograms (kg). The curves in the graphs have the following meanings: the grating is the vehicle separator signal, used to identify vehicle entry and exit; the axle is the axle trigger signal, used to identify the time of axle loading and unloading; the total weight is the sum of the loading and unloading sensor values, D3[i]; the first, second, third, and fourth channels are the raw outputs of the four corner weighing sensors; three plus four is the sum of the third and fourth sensor values; five plus six represents other channel combinations. Figure 5 Curve 5 in the middle is the axle waveform under normal vehicle passage, reflecting the weight change characteristics of each axle when passing through the weighing platform under normal traffic conditions; Figure 6 Curve 6 represents the waveform of the hydraulic axle during vehicle operation. It clearly shows the weight distribution change caused by abnormal hydraulic axle control behavior. Specifically, the axle assembly weight waveform exhibits abnormal fluctuations, contrasting sharply with the normal waveform, which matches the characteristics described by the abnormal process fluctuation criterion in step S104 of this application. Through comparison... Figure 5 and Figure 6 This allows for a direct verification of the effectiveness of the triple criterion detection method provided in this application, namely, the ability to accurately identify abnormal hydraulic shaft operation behavior and correct misaligned weight.

[0092] Please see Figure 7 , Figure 7 A flowchart illustrating the process of obtaining vehicle speed, provided as an embodiment of this application. Figure 7 As shown, the vehicle speed is obtained in the following way: S501. Calculate the time difference based on the sampling point sequence numbers corresponding to the first and second moments on the same axis.

[0093] The first moment of each axis being put on the scale and the second moment of being put off the scale are recorded separately. Both moments are stored in the form of sampling point numbers. The sampling point number is a time index during the data acquisition process. Since sampling is performed at a fixed frequency (e.g., 1000 points per second), the time interval between adjacent sampling points is fixed. For the same axis, the sampling point number corresponding to the first moment is denoted as t1, and the sampling point number corresponding to the second moment is denoted as t2.

[0094] By calculating the difference between t2 and t1, the number of sampling points the shaft passes through from the moment it starts to step onto the scale until it completely steps off can be obtained. Dividing this number by the sampling frequency (1000 points / second) yields the actual time difference Δt taken for the shaft to pass through the scale platform, in seconds. This time difference is the fundamental data for subsequent calculations of the shaft's instantaneous velocity, and its accuracy directly determines the accuracy of the velocity calculation.

[0095] S502. Based on the length of the weighing platform, calculate the instantaneous speed of the axle, and take the average of the instantaneous speeds of all axles as the vehicle's passing speed.

[0096] After obtaining the time difference Δt, the speed is calculated using the known effective length L of the weighing platform. The platform length is a fixed parameter of the shaft-type dynamic weighing platform, typically between 4 and 6 meters, and this value is calibrated and stored during installation.

[0097] For each shaft, its instantaneous velocity V_axle across the weighing platform is calculated using the formula V_axle = L / Δt, in meters per second (m / s). For ease of practical application, the velocity unit is usually converted to kilometers per hour (km / h). In a preferred embodiment, the time difference between the shaft's passage across the weighing platform is Δt = (t2 - t1) / 1000 seconds, and the instantaneous velocity V_n = L / Δt, in meters per second (m / s). The formula for converting the velocity to km / h is: V = L / ((t2 - t1) / 1000) × 3.6.

[0098] The calculated speeds of all axles of the vehicle are arithmetically averaged to obtain the overall vehicle speed, V_vehicle, which serves as the final speed characteristic. This speed value is used not only for the speed anomaly criterion in step S104 (e.g., comparison with a threshold of 10 km / h), but also for monitoring vehicle driving status and assisting in the analysis of weighing data. By accurately calculating the vehicle speed, low-speed driving, a behavioral characteristic strongly correlated with hydraulic cheating, can be identified, providing a crucial basis for the final comprehensive judgment.

[0099] Please see Figure 8 , Figure 8A flowchart illustrating the determination of abnormal hydraulic axle control behavior in a vehicle, provided as an embodiment of this application. Figure 8 As shown, the following methods are used to determine if the vehicle exhibits abnormal hydraulic shaft control behavior: S601. Calculate the difference between the sum of the axle loads of each axle in the axle group and the total weight of the axle group.

[0100] First, obtain the relevant data for the current axle group from the calculation results, including the axle weight of each individual axle within the group and the total weight of the axle group itself. The axle weight of each axle within the group is calculated by averaging the sensor data after each axle is weighed and stored in the axle weight array; the total weight of the axle group is obtained by applying anti-shake processing to the temporary total weight array and stored in the axle group weight array.

[0101] Based on the recorded start and end axle numbers of the axle group, the axle weights of all individual axles within that axle group are extracted from the axle weight array and summed item by item. Then, the difference between this sum and the total axle group weight is calculated. This difference reflects the degree of deviation between the total axle group weight obtained by direct weighing and the total axle group weight obtained by summing individual axles. This difference serves as the basis for subsequent weight logic anomaly detection; its magnitude directly indicates whether there is any suspicious weight loss or transfer during the axle group weighing process.

[0102] S602. If the difference exceeds the first threshold, the weight logic is determined to be abnormal.

[0103] The calculated difference is compared with a pre-set first threshold. This first threshold is an empirical value, preferably set to 30% of the axle group weight. This threshold is based on statistical data from a large number of normally passing vehicles. Under normal circumstances, due to vehicle vibration and weighing errors, there is a small deviation between the axle weight and the axle group weight, but it generally will not exceed this percentage. If the calculated difference is greater than 30% of the axle group weight, it indicates that the sum of the axle weights is significantly greater than the axle group weight, suggesting that a considerable portion of the load was not recorded during the period when the axle group was weighed together, raising serious suspicion of weight transfer or concealment. In this case, the weight logic anomaly flag for that axle group is set to valid for final comprehensive judgment. Through this judgment, an abnormal axle group with disrupted weight logic consistency is successfully identified, providing the first piece of evidence for subsequent comprehensive judgment.

[0104] In a preferred embodiment, the determination of weight logic anomalies is achieved as follows: based on the starting axis number ZhouZuStaNum[m] and the ending axis number ZhouZuEndNum[m] corresponding to axis group m, the axle weights of all single axes within the axis group are extracted from the axle weight array ZhouWeight[n], and the cumulative axle weights are calculated: = [ZhouZuStaNum[m]]+ [ZhouZuStaNum[m]+1]………+ ...+ [ZhouZuEndNum[m]-1]+ [ZhouZuEndNum[m]] If the difference between the accumulated sum and the weight of the shaft group ZhouZuWeight[m] satisfies: sum_zhouweight-ZhouZuWeight[m]>ZhouZuWeight[m]×30% Then the weight logic exception is determined to be true.

[0105] S603. Obtain the real-time total weight data of the shaft group during the weighing process, and count the number of abnormal points where the deviation from the final weight of the shaft group exceeds the third threshold.

[0106] Extract the real-time total weight data sequence of the current axle group from the recorded temporary total weight array. This sequence completely records the total weight of the weighing platform at each sampling moment during the entire process of the axle group from the first axle being put on the scale to the last axle being taken off the scale.

[0107] Simultaneously, the final weight of the axle group is obtained as a baseline value. Then, each data point in the real-time total weight array is traversed, and the absolute deviation between the instantaneous weight value at that point and the final axle group weight is calculated. It is then determined whether this deviation exceeds a preset third threshold. The third threshold is set to 30% of the axle group weight. Each data point with a deviation exceeding 30% is counted as an outlier. After the traversal is complete, the total number of outliers occurring during the weighing process is obtained. This statistical result quantifies the severity of load fluctuations in the axle group, providing a quantitative basis for subsequent abnormal fluctuation judgments.

[0108] S604. If the number of abnormal points exceeds the fourth threshold, the process fluctuation is determined to be abnormal.

[0109] The number of outliers obtained from the statistics is compared with the total number of points in the temporary total weight array of the axis group, and the proportion of outliers is calculated. If the proportion exceeds the preset fourth threshold, the process fluctuation is determined to be abnormal.

[0110] Preferably, the fourth threshold is set to 30% of the total number of points. This means that if, during the entire process of the axle group passing through the weighing platform, the instantaneous total weight deviates from the final axle group weight by more than 30% for more than 30% of the time period, it indicates that the load of the axle group has experienced abnormally drastic fluctuations. This large-scale, high-frequency weight jump is significantly different from the load change characteristics when a normal vehicle passes smoothly, but highly matches the load redistribution characteristics caused by the instantaneous action of the hydraulic device. At this time, the abnormal process fluctuation flag of the axle group is set to an active state.

[0111] In a preferred embodiment, the determination of abnormal process fluctuations is achieved by: comparing each value in the real-time total weight data of the axle group before anti-shake processing, ZhouZuWeightTemp[m][i], with the final axle group weight, ZhouZuWeight[m], and calculating the absolute value of the difference. If the following conditions are met: |ZhouZuWeightTemp[m][i]- [m]|> [m]×30% If the point is marked as an outlier, the number of outliers, a_err, is incremented by 1. After the traversal is complete, if the number of outliers, a_err, exceeds 30% of the total number of data points in the axis group (i.e., a_err > i × 30%), then the process fluctuation is considered abnormal.

[0112] S605. If the vehicle passes at a speed lower than the second threshold, the speed is determined to be abnormal.

[0113] The vehicle's passing speed is obtained from the calculation results of step S502. This speed is the average of the instantaneous speeds of each axle of the vehicle. Subsequently, this speed value is compared with a preset second threshold.

[0114] The second threshold is set at 10 km / h. This threshold is based on the working principle of the hydraulic cheating device: hydraulic lifting and releasing require a certain response and execution time, and the vehicle must maintain a low speed to ensure that the cheating action is effectively completed on the weighing platform. If the vehicle passes at a speed below 10 km / h, it indicates that the vehicle has the conditions to carry out hydraulic cheating and is suspected of speed anomalies. In this case, the speed anomaly indicator is set to active status. This judgment introduces driving characteristics strongly correlated with cheating behavior, providing a third piece of evidence for the final comprehensive judgment, while also effectively eliminating the possibility of false alarms from vehicles traveling normally on the highway.

[0115] S606. When the weight logic abnormality, process fluctuation abnormality, and speed abnormality are all met simultaneously, it is determined that the vehicle has abnormal hydraulic shaft control behavior.

[0116] The generated weight logic anomaly flag, process fluctuation anomaly flag, and speed anomaly flag are subjected to a logical AND operation. Only when all three flags are valid simultaneously is it determined that the current shaft group has abnormal hydraulic shaft operation behavior.

[0117] This triple-condition joint judgment mechanism is based on the multi-dimensional characteristics of hydraulic cheating behavior: abnormal weight logic reflects the total weight loss caused by cheating, abnormal process fluctuation reflects the load transients caused by cheating actions, and abnormal speed reflects the low-speed conditions required for cheating. These three conditions corroborate each other and are indispensable, effectively avoiding misjudgments caused by occasional anomalies in a single indicator (such as a single bounce caused by uneven road surface or normal low-speed passage). Once the judgment is valid, subsequent weight recovery processing is triggered to correct the weight of the contaminated original axle assembly. Through this comprehensive judgment process, a high accuracy rate of identification of hydraulic cheating behavior is achieved.

[0118] Compared with the existing axle group dynamic weighing method, the vehicle hydraulic axle detection method and detection system provided in this application improve the accuracy of dynamic weighing by analyzing the difference between axle weight and axle group weight, detecting real-time weight fluctuations, and jointly judging vehicle speed.

[0119] The purpose of this application is to provide a detection method capable of online real-time identification of abnormal hydraulic axle operation and automatic restoration of the vehicle's true weight. This method deeply integrates axle identification, dynamic weighing trajectory tracking, intelligent data analysis, and abnormal feature discrimination technologies. Through meticulous monitoring and data coupling analysis of the entire process of each axle's loading and unloading from the weighbridge, it extracts characteristic signals under abnormal operation behavior and performs weight correction.

[0120] Please see Figure 9 , Figure 9 This is a schematic diagram of a vehicle hydraulic shaft detection system provided in an embodiment of this application. Figure 9 As shown, the axle-type dynamic weighing platform used in this application includes: an upper axle trigger 1 embedded in the weighing end of the weighing platform, a lower axle trigger 2 embedded in the lower weighing end of the weighing platform, an upper weighing sensor 3 located at the bottom of the upper weighing end, a lower weighing sensor 4 located at the bottom of the lower weighing end, and a vehicle separator 7 located 200mm in front of the weighing end of the weighing platform.

[0121] In one specific embodiment of this application, the length L of the axle assembly weighing platform is between 4 meters and 5.1 meters, for example, L=4 meters. All sensors (including the upper axle trigger, lower axle trigger, upper weighing sensor, and lower weighing sensor) are sampled at a frequency of 1000 points per second to ensure high-precision capture of subtle changes during the dynamic weighing process of the vehicle. This system is suitable for scenarios where vehicles pass at speeds from 0 km / h to 20 km / h, with a maximum rated axle assembly load of 60 tons and a maximum permissible overload of 100%.

[0122] With the above hardware configuration, the system can accurately capture the moment when each axle of the vehicle is put on and off the scale, as well as measure the load on the scale platform in real time with high precision, providing a reliable data foundation for subsequent axle weight calculation, axle group weight calculation, vehicle speed calculation, and abnormal operation behavior identification.

[0123] Furthermore, embodiments of this application also provide a detection system for a vehicle hydraulic axle, the system comprising: A shaft-type dynamic weighing platform, wherein the upper and lower weighing ends of the platform are respectively equipped with upper weighing axle triggers and lower weighing axle triggers, and weighing sensors are installed at the four corners of the platform; The data acquisition unit is connected to the upper scale axle trigger, the lower scale axle trigger, and the weighing sensor, respectively, and is used to collect the axle trigger signal and real-time weighing data when the vehicle passes by. The processing unit, connected to the data acquisition unit, is configured as follows: The first moment of each axle being put on the scale and the second moment of being taken off the scale are determined according to the axle trigger signal; the axle weight of each axle and the axle group weight of each axle group are obtained according to the real-time weighing data; based on the difference between the axle weight of each axle and the axle group weight of its axle group, and combined with the vehicle speed, if there is abnormal hydraulic axle operation behavior in the vehicle, the axle group weight of the corresponding axle group is replaced with the sum of the axle weights of all axles in the corresponding axle group to restore the vehicle weight.

[0124] Please see Figure 10 , Figure 10 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. Figure 10 As shown, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.

[0125] The memory 520 stores machine-readable instructions executable by the processor 510. When the electronic device 500 is running, the processor 510 and the memory 520 communicate via the bus 530. When the machine-readable instructions are executed by the processor 510, they can perform the operations described above. Figure 1 The steps of the vehicle hydraulic shaft detection method in the illustrated method embodiment can be found in the method embodiment for specific implementation, and will not be repeated here.

[0126] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0127] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0128] The units described 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.

[0129] In addition, 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.

[0130] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium 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 storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0131] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting a vehicle hydraulic axle, characterized in that, include: Acquire real-time weighing data and axle trigger signals when a vehicle passes through an axle-type dynamic weighing platform; The first moment when each axle is put on the scale and the second moment when it is taken off the scale are determined based on the axle trigger signal. Based on the real-time weighing data, obtain the weight of each shaft and the weight of each shaft group. Based on the difference between the axle weight of each axle and the axle group weight of its corresponding axle group, and combined with the vehicle's speed, if the vehicle exhibits abnormal hydraulic axle control behavior, the axle group weight of that axle group is replaced with the sum of the axle weights of all axles in that axle group to restore the vehicle's weight.

2. The method according to claim 1, characterized in that, The real-time weighing data includes data from the upper scale sensor and data from the lower scale sensor, and the axle trigger signal includes an upper scale trigger signal and a lower scale trigger signal.

3. The method according to claim 2, characterized in that, The first moment when each shaft is put on the scale and the second moment when it is removed from the scale are determined in the following way: When the weighing trigger signal rises from below the weighing threshold to above the weighing threshold, it is determined that a new shaft is being weighed, and this moment is recorded as the first moment. When the scale lowering trigger signal drops from above the scale lowering threshold to below the scale lowering threshold, it is determined that a new shaft is lowering, and this moment is recorded as the second moment.

4. The method according to claim 2, characterized in that, The weight of each shaft is obtained in the following way: After the first moment, a set number of weighing sensor data are continuously collected as a temporary weight array for that axis; The axle weight of the shaft is obtained by averaging the temporary weight array.

5. The method according to claim 2, characterized in that, The weight of each shaft group is obtained using the following method: When the first moment occurs, the total weight data of the weighing platform is recorded. The total weight data of the weighing platform is the sum of the data from the upper weighing sensor and the data from the lower weighing sensor. Continue recording until the second moment of the last axis in the axis group occurs, and obtain a temporary total weight array for the axis group; The temporary total weight array is de-jittered to obtain the weight of the shaft group.

6. The method according to claim 5, characterized in that, The image stabilization process includes: Sort the values ​​in the temporary total weight array; Remove the values ​​of the front and back set ratios after sorting; The average value of the remaining values ​​is used to obtain the weight of the shaft group.

7. The method according to claim 1, characterized in that, The vehicle's passing speed was obtained in the following way: Calculate the time difference based on the sampling point numbers corresponding to the first and second moments on the same axis; Calculate the instantaneous speed of the axle based on the length of the weighing platform, and take the average of the instantaneous speeds of all axles as the vehicle's passing speed.

8. The method according to claim 1, characterized in that, The method of determining whether there is abnormal hydraulic axle operation based on the difference between the axle load of each axle and the axle group load of its respective axle group, combined with the vehicle's speed, includes: Calculate the difference between the sum of the axle loads of all axles in the axle group and the total weight of the axle group. If the difference exceeds the first threshold, the weight logic is determined to be abnormal; Obtain the real-time total weight data of the shaft group during the weighing process, and count the number of abnormal points where the deviation from the final weight of the shaft group exceeds the third threshold. If the number of outliers exceeds the fourth threshold, the process fluctuation is determined to be abnormal. If the vehicle passes at a speed lower than the second threshold, the speed is determined to be abnormal; When the weight logic anomaly, process fluctuation anomaly, and speed anomaly are all met simultaneously, it is determined that the vehicle has abnormal hydraulic shaft operation behavior.

9. A detection system for a vehicle hydraulic axle, characterized in that, include: A shaft-type dynamic weighing platform, wherein the upper and lower weighing ends of the platform are respectively equipped with upper weighing axle triggers and lower weighing axle triggers, and weighing sensors are installed at the four corners of the platform; The data acquisition unit is connected to the upper scale axle trigger, the lower scale axle trigger, and the weighing sensor, respectively, and is used to collect the axle trigger signal and real-time weighing data when the vehicle passes by. The processing unit, connected to the data acquisition unit, is configured as follows: The first moment when each axle is put on the scale and the second moment when it is taken off the scale are determined based on the axle trigger signal. Based on the real-time weighing data, obtain the weight of each shaft and the weight of each shaft group. Based on the difference between the axle load of each axle and the axle load of the axle group, combined with the vehicle speed, it is determined whether there is any abnormal hydraulic axle operation. If it exists, the axle weight of the corresponding axle group is replaced with the sum of the axle weights of all axles in the axle group to restore the vehicle weight.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is in operation, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the method as described in any one of claims 1 to 8.

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