Quartz type dynamic truck scale automatic weighing system and weighing method thereof

Abstract

The application discloses a quartz type dynamic truck scale automatic weighing system and a weighing method thereof, relates to the technical field of dynamic truck scale automatic weighing, and effectively removes noise interference and improves data quality through signal preprocessing and peak value feature extraction; based on the calculation of a signal difference matrix and a time difference matrix, the system can analyze the dynamic load change of a vehicle in a sensor array area; in combination with a load distribution model constructed according to the passing speed and the wheelbase of the vehicle, the system provides a spatial dimension dynamic load evaluation capability; finally, by comparing the dynamic change L(x) with a standard load distribution threshold BZ, the system realizes accurate identification of the cheating behavior of the vehicle, generates a cheating behavior judgment result R, improves the reliability and anti-cheating capability of dynamic weighing, provides an intelligent solution for traffic management and freight supervision, significantly reduces manual intervention, and improves the detection efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the field of automatic weighing technology for dynamic truck scales, specifically to an unattended, anti-cheating quartz-type dynamic truck scale automatic weighing system and its weighing method. Background Technology

[0002] Dynamic weighing technology is an important branch of the transportation sector, widely used in road transport management and freight vehicle supervision. Among them, quartz dynamic truck scales, as a key device in dynamic weighing systems, are gradually gaining an important position in dynamic weighing scenarios due to their high sensitivity and stability.

[0003] Specifically, quartz dynamic truck scales can measure the weight of vehicles in real time while they are in motion by sensing changes in their dynamic loads, thus avoiding the efficiency problems of traditional static weighing methods. At the application level, unattended dynamic weighing systems are gradually becoming the direction of industry development, especially in highway truck overload detection, logistics park cargo weight monitoring, and intelligent transportation system traffic flow control, where quartz dynamic truck scales are being widely deployed.

[0004] While quartz dynamic truck scales have achieved a high level of automation, they still have shortcomings in handling abnormal vehicle load distribution. Because the frequency signals acquired by sensors at different times can vary significantly, a single sensor struggles to accurately capture the changing trajectory of a vehicle's dynamic load. This deficiency is particularly pronounced when the loads on the front and rear axles are inconsistent or when the load ratio is adjusted as the vehicle passes. Existing systems typically rely on fixed algorithms to process vehicle weight distribution information, but the accuracy of these algorithms is limited when faced with complex load distribution scenarios. Furthermore, the lack of signal timing analysis capabilities from multiple rows of sensors limits the effectiveness of existing systems in identifying cheating behaviors, such as vehicles adjusting axle load ratios to circumvent overload detection, making it difficult to effectively meet the anti-cheating requirements of unattended weighing systems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a quartz-type dynamic truck scale automatic weighing system and its weighing method, solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the first aspect of the present invention provides an automatic weighing method for a quartz dynamic truck scale, comprising the following steps:

[0007] S1. Collect frequency band signals of the vehicle as it passes through the vehicle dynamically using multiple rows of quartz sensors, record the output data of the sensors in the time series, and perform signal preprocessing on the output data to obtain the preprocessed signal vector T.

[0008] S2. Based on the time series feature extraction of the preprocessed signal vector T, the peak points and feature positions of the sensor signal are identified, and feature points (Pi, ti) are formed. After integration, a peak feature set P is generated.

[0009] S3. Based on the peak feature set P, analyze the signal differences of different sensors in the time series, calculate the relative signal offset △Pij and time difference △tij of adjacent sensors, and form the signal difference matrix △P and the time difference matrix △t.

[0010] S4. Based on the signal difference matrix △P and the time difference matrix △t, and combined with the vehicle speed v and wheelbase d, a vehicle load distribution model is constructed to determine the dynamic change L(x) of the vehicle load distribution on the x-axis.

[0011] S5. Based on the preset standard load distribution threshold BZ for vehicle type, and anomaly assessment of dynamic changes L(x), generate vehicle cheating behavior judgment result R.

[0012] Preferably, S1 specifically includes S11;

[0013] S11. Collect frequency band signal data when a vehicle passes through the array using multiple rows of quartz sensors, and record the output signals of the sensors in the time series {S1, S2, S3, ..., Sn}. Each sensor i corresponds to a collection output signal Si(f, t), which describes the signal strength at frequency f and time t. The collection range of the frequency band signal data is the time interval [t0, t1] during which the vehicle passes through the sensor array.

[0014] The acquired output signal Si(f, t) represents the original signal strength of the i-th sensor at the frequency f and the time t, where i = 1, 2, 3, ..., n represents 1 to n sensors, and t ∈ [t0, t1] indicates that the signal acquisition time range is determined by the dynamic time of the vehicle passing through the sensor array;

[0015] By integrating all the acquired output signals Si(f,t), an initial signal set S = {S1(f,t), S2(f,t), S3(f,t), ..., Sn(f,t)} is obtained.

[0016] Preferably, S1 further includes S12;

[0017] S12 includes: extracting effective signal components within the frequency band range [f1, f2] and forming a standardized time series signal Ti(t), wherein the time series signal Ti(t) represents the preprocessed signal time series of the i-th sensor at time t, and the frequency band range [f1, f2] represents the interval range of frequency analysis;

[0018] By integrating the preprocessed time series signal Ti(t), a preprocessed signal vector T is obtained, specifically T = {Ti1(t), Ti2(t), T3(t), ..., Tn(t)}.

[0019] Preferably, S2 specifically includes S21;

[0020] S21 includes: performing time series analysis on the preprocessed signal vector T, extracting features from the time series signal Ti(t) of each sensor, determining the signal peak point Pi and the time position ti, and integrating the signal peak point Pi and the time position ti to form the feature point (Pi, ti) of each sensor.

[0021] Preferably, S2 further includes S22;

[0022] S22 includes: integrating each obtained feature point (Pi, ti) to determine the dynamic load change characteristics of the vehicle in the detection area, and constructing a complete set of peak features P;

[0023] The peak feature set P is calculated and determined using the following integrated formula:

[0024]

[0025] In the formula, ∪ represents the union symbol, specifically indicating that multiple elements are combined into a single set. This means that from the 1st sensor to the nth sensor, the feature points (Pi, ti) of each sensor are added one by one to the peak feature set P;

[0026] The peak feature set P is specifically P = {(P1, t1), (P2, t2), (P3, t3), ..., (Pn, tn)}.

[0027] Preferably, in step S3, the relative signal offset ΔPij is obtained by the formula ΔPij=Pi-Pj, where Pi and Pj represent the signal peak points of sensor i and sensor j, respectively.

[0028] The time difference Δtij is obtained by the formula Δtij=ti-tj, where ti and tj represent the time positions of sensor i and sensor j, respectively.

[0029] Preferably, in step S3, the signal difference matrix ΔP is calculated and determined using the following matrix rearrangement method:

[0030]

[0031] The time difference matrix Δt is calculated and determined using the following matrix rearrangement method:

[0032]

[0033] The signal difference matrix ΔP and the time difference matrix Δt are symmetric, specifically n*n dimension matrices, where n represents 1 to n sensors, and their own signal and time difference are always 0.

[0034] Preferably, in step S4, the vehicle speed v is specifically determined by a speed sensor monitoring the dynamic driving speed of the vehicle within the sensor array area; the wheelbase d is the wheelbase parameter between the front and rear wheels of the vehicle.

[0035] The dynamic change L(x) is obtained through the following calculation formula:

[0036]

[0037] In the formula, L(x) represents dynamic change, specifically the dynamic change of vehicle load on the x-axis within the sensor array area, △P(i, i+1) represents the difference in signal peak points between adjacent sensors, and △t(i, i+1) represents the difference in time position between adjacent sensors.

[0038] Preferably, in step S5, the standard load distribution threshold BZ is preset within a standard range for different vehicles, and the range is defined by an upper limit and a lower limit; wherein, the standard load distribution threshold BZ is specifically represented as follows:

[0039] BZ={[Lmin(x),Lmax(x)]|x∈[x0,x1]};

[0040] In the formula, Lmin(x) represents the lower limit of the standard load distribution threshold BZ on the x-axis, Lmax(x) represents the upper limit of the standard load distribution threshold BZ on the x-axis, and x∈[x0,x1] means that x is any point in the interval from x0 to x1, where x0 represents the starting coordinate of the sensor coverage area and x1 represents the ending coordinate of the sensor coverage area.

[0041] The anomaly assessment of the dynamically changing L(x) includes:

[0042] Determine whether the dynamic change L(x) falls within the standard load distribution threshold;

[0043] If so, the result of the vehicle cheating behavior judgment is determined to be 0;

[0044] If not, then the result of the vehicle cheating behavior judgment is determined to be 1;

[0045] Specifically, when the vehicle cheating behavior judgment result R=1, it is determined that the vehicle has abnormal load distribution and abnormal cheating behavior, the vehicle fails the inspection, the unique identifier of the currently detected vehicle is marked as abnormal, the audible and visual alarm device is triggered to remind the driver, and the abnormality is recorded in the log; when the vehicle cheating behavior judgment result R=0, it is determined that the vehicle does not have abnormal load distribution, the vehicle passes the inspection.

[0046] A second aspect of the present invention provides a quartz-type dynamic truck scale automatic weighing system, comprising a signal acquisition module, a signal extraction module, a difference calculation module, a change evaluation module, and a vehicle evaluation module;

[0047] The signal acquisition module acquires frequency band signals of the vehicle as it passes through multiple rows of quartz sensors, records the sensor output data in time series, and performs signal preprocessing on the output data to obtain the preprocessed signal vector T.

[0048] The signal extraction module extracts time-series features from the preprocessed signal vector T, identifies the peak points and feature locations of the sensor signal, forms feature points (Pi, ti), and obtains the peak feature set P after integration.

[0049] The difference calculation module analyzes the signal differences of different sensors in the time series based on the peak feature set P, calculates the relative signal offset △Pij and time difference △tij of adjacent sensors, and forms the signal difference matrix △P and the time difference matrix △t.

[0050] The change assessment module constructs a vehicle load distribution model based on the signal difference matrix △P and the time difference matrix △t, and combines the vehicle passing speed v and wheelbase d to obtain the dynamic change L(x) of the vehicle load distribution on the x-axis.

[0051] The vehicle evaluation module performs anomaly evaluation on the dynamic change L(x) based on the preset standard load distribution threshold BZ of the vehicle type, and obtains the vehicle cheating behavior judgment result R.

[0052] The present invention has the following beneficial effects:

[0053] (1) By preprocessing the signal and extracting peak features, the system effectively removes noise interference and improves data quality. Based on the calculation of the signal difference matrix and the time difference matrix, the system can analyze the dynamic load changes of the vehicle in the sensor array area. Combined with the load distribution model constructed by the vehicle's speed and wheelbase, it provides the ability to evaluate the dynamic load in the spatial dimension. Finally, by comparing the dynamic change L(x) with the standard load distribution threshold BZ, the system can accurately identify vehicle cheating behavior and generate the cheating behavior judgment result R, which improves the reliability and anti-cheating ability of dynamic weighing, provides an intelligent solution for traffic management and freight supervision, significantly reduces manual intervention, and improves detection efficiency and accuracy.

[0054] (2) Based on the peak feature set P, signal difference analysis is performed and signal difference matrix and time difference matrix are constructed. The system realizes a comprehensive description from single-point features to multi-sensor collaborative dynamic features. The spatial variation law of vehicle load distribution is reflected from multi-dimensional dynamic features. Through the matrix organization of dynamic features of adjacent sensors, the originally discrete sensor signal offset △Pij and time difference △tij are transformed into a signal difference matrix △P and a time difference matrix △t with global correlation. This matrix expression method can not only accurately capture the dynamic change trend of vehicle load distribution, but also eliminate the influence of single-point anomalies on the overall system modeling through matrix symmetry. Thus, the robustness and anomaly detection capability of the system are improved in load distribution analysis, laying a solid foundation for subsequent load modeling and anomaly assessment.

[0055] (3) By combining the signal difference matrix ΔP, time difference matrix Δt, vehicle speed v, and wheelbase d, a vehicle load distribution model is constructed to obtain the dynamic change L(x) of the vehicle load distribution on the x-axis. The uniformity and anomalies of the vehicle load distribution are evaluated by comparing it with the standard load distribution threshold BZ. This not only accurately identifies abnormal areas in the spatial distribution characteristics of the vehicle's dynamic load but also ensures a comprehensive analysis of local anomalies and overall distribution consistency through logical quantifier judgments. Especially in situations involving complex and diverse vehicle cheating behaviors, it effectively avoids missed or false judgments caused by single-point errors or lack of dynamic analysis capabilities in traditional weighing methods, providing a high-precision, fully-covered intelligent solution for anti-cheating detection in unattended scenarios. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating an automatic weighing method for a quartz-type dynamic truck scale according to the present invention.

[0057] Figure 2 This is a schematic diagram of the steps of an automatic weighing system for a quartz dynamic truck scale according to the present invention. Detailed Implementation

[0058] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0059] Example 1

[0060] This invention provides an unattended, anti-cheating automatic weighing method for a quartz dynamic truck scale. Please refer to [link / reference]. Figure 1 Includes the following steps:

[0061] S1. Collect frequency band signals of the vehicle as it passes by using multiple rows of quartz sensors, record the output data of the sensors in the time series, and perform signal preprocessing on the output data to obtain the preprocessed signal vector T.

[0062] S2. Based on the time series feature extraction of the preprocessed signal vector T, the peak points and feature positions of the sensor signal are identified, and feature points (Pi, ti) are formed. After integration, the peak feature set P is obtained.

[0063] S3. Based on the peak feature set P, analyze the signal differences of different sensors in the time series, calculate the relative signal offset △Pij and time difference △tij of adjacent sensors, and form the signal difference matrix △P and the time difference matrix △t.

[0064] S4. Based on the signal difference matrix △P and the time difference matrix △t, and by combining the vehicle speed v and wheelbase d, a vehicle load distribution model is constructed to obtain the dynamic change L(x) of the vehicle load distribution on the x-axis.

[0065] S5. Based on the preset standard load distribution threshold BZ for vehicle type, perform anomaly assessment on dynamic change L(x) and obtain the vehicle cheating behavior judgment result R.

[0066] In this embodiment, through steps S1 to S5, signal acquisition and preprocessing, time series feature extraction, signal difference analysis, load distribution model construction, and anomaly detection are gradually completed, forming a complete automated anti-cheating detection process. This method can significantly solve the shortcomings mentioned in the background introduction, including limitations in dynamic load data analysis, insufficient multi-sensor collaborative analysis capabilities, and low accuracy in load anomaly identification. Through signal preprocessing and peak feature extraction, the system effectively removes noise interference and improves data quality. Based on the calculation of the signal difference matrix and time difference matrix, the system can analyze the dynamic load changes of the vehicle in the sensor array area. Combining the load distribution model constructed by the vehicle's passing speed and wheelbase, it provides a spatial dynamic load assessment capability. Finally, by comparing the dynamic change L(x) with the standard load distribution threshold BZ, accurate identification of vehicle cheating behavior is achieved, generating a cheating behavior judgment result R, improving the reliability and anti-cheating capability of dynamic weighing, providing an intelligent solution for traffic management and freight supervision, significantly reducing manual intervention, and improving detection efficiency and accuracy.

[0067] Example 2

[0068] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: S1 includes S11 and S12.

[0069] S11. Collect frequency band signal data when the vehicle passes by dynamically through multiple rows of quartz sensors, and record the output signals of the sensors in the time series {S1, S2, S3, ..., Sn}. Each sensor i corresponds to a collection output signal Si(f, t) to describe the signal strength at frequency f and time t. The collection range of the frequency band signal data is the time interval [t0, t1] during which the vehicle passes through the sensor array.

[0070] The acquired output signal Si(f, t) represents the original signal strength of the i-th sensor at the frequency f and the time t, where i = 1, 2, 3, ..., n represents 1 to n sensors, and t ∈ [t0, t1] indicates that the signal acquisition time range is determined by the dynamic time of the vehicle passing through the sensor array.

[0071] By integrating the acquired output signals Si(f,t), an initial signal set S = {S1(f,t), S2(f,t), S3(f,t), ..., Sn(f,t)} is obtained.

[0072] S12. Perform signal preprocessing on the initial signal set S, including frequency band filtering preprocessing and noise reduction preprocessing, then extract the effective signal components within the frequency band range [f1, f2], and form a standardized time series signal Ti(t). The time series signal Ti(t) represents the preprocessed signal time series of the i-th sensor at time t, where the frequency band range [f1, f2] represents the interval range of frequency analysis.

[0073] The time series signal Ti(t) is preprocessed using the following frequency filtering and noise reduction methods:

[0074]

[0075] In the formula, W(f) represents the integration of frequency f over the frequency band [f1, f2], W(f) represents the frequency weighting function, which is used to highlight the signal within a specific frequency range, and df represents the integral element of frequency f, which specifically represents the accumulation of the frequency signal.

[0076] By integrating the preprocessed time series signal Ti(t), a preprocessed signal vector T is obtained, specifically T = {Ti1(t), Ti2(t), T3(t), ..., Tn(t)}.

[0077] S2 includes S21 and S22.

[0078] S21. Based on the time series analysis of the preprocessed signal vector T, and the feature extraction of the time series signal Ti(t) of each sensor, the signal peak point Pi and the time position ti are obtained, and the signal peak point Pi and the time position ti are integrated to form the feature point (Pi, ti) of each sensor.

[0079] The signal peak point Pi is obtained by the following calculation formula:

[0080]

[0081] In the formula, Pi represents the peak intensity of the i-th sensor signal, specifically the local upper limit value in the time series signal Ti(t), and max represents the maximum value function.

[0082] The time position ti is obtained by the following calculation formula:

[0083]

[0084] In the formula, argmax represents the independent variable that takes the maximum value, specifically representing the time t when the local upper limit value of the time series signal Ti(t) appears.

[0085] S22. Integrate each of the acquired feature points (Pi, ti) to obtain the dynamic load change characteristics of the vehicle in the detection area, and construct a complete peak feature set P.

[0086] The peak feature set P is obtained through the following integration formula:

[0087]

[0088] In the formula, ∪ represents the union symbol, specifically indicating that multiple elements are combined into a single set. This means that from the 1st sensor to the nth sensor, the feature points (Pi, ti) of each sensor are added one by one to the peak feature set P.

[0089] The peak feature set P is specifically P = {(P1, t1), (P2, t2), (P3, t3), ..., (Pn, tn)}.

[0090] In this embodiment, raw frequency signals are acquired by multiple rows of quartz sensors as the vehicle dynamically passes by. Signal preprocessing is used to filter and reduce noise in the initial signal set S, effectively extracting the effective signal components within the frequency range [f1, f2] to form a standardized time-series signal Ti(t). Subsequently, through time-series analysis and feature extraction in step S21, the system can accurately identify the peak point Pi and time position ti of each sensor signal. By integrating the feature points (Pi, ti) of all sensors, a peak feature set P of the vehicle in the detection area is constructed. This not only significantly reduces data distortion caused by environmental noise and frequency interference during multi-sensor detection but also greatly improves the accuracy of feature extraction for changes in the vehicle's dynamic load. Especially in scenarios where the vehicle passes at high speed or has a complex load distribution, the system can accurately capture the signal feature points (Pi, ti) of each sensor and comprehensively describe the dynamic load distribution characteristics of the vehicle through the construction of the peak feature set, providing high-quality basic data support for subsequent load distribution modeling and anomaly detection.

[0091] Example 3

[0092] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: S3 includes S31 and S32.

[0093] S31. Based on the peak feature set P, analyze the signal differences of different sensors in the time series, and calculate the relative signal offset ΔPij and time difference Δtij between adjacent sensors;

[0094] The relative signal offset ΔPij is obtained by the formula ΔPij=Pi-Pj, where Pi and Pj represent the signal peak points of sensor i and sensor j, respectively.

[0095] The time difference Δtij is obtained by the formula Δtij=ti-tj, where ti and tj represent the time positions of sensor i and sensor j, respectively.

[0096] S32. Perform matrix manipulation on the signal offset △Pij and the time difference △tij to construct the signal difference matrix △P and the time difference matrix △t respectively, and perform dynamic feature analysis to reflect the vehicle load distribution.

[0097] The signal difference matrix ΔP is obtained by the following matrix manipulation method:

[0098]

[0099] The time difference matrix Δt is obtained by the following matrix rearrangement method:

[0100]

[0101] The signal difference matrix ΔP and the time difference matrix Δt are symmetric, specifically n*n dimension matrices, where n represents 1 to n sensors, and their own signal and time difference are always 0.

[0102] In this embodiment, signal difference analysis is performed based on the peak feature set P, and a signal difference matrix and a time difference matrix are constructed. The system achieves a comprehensive description from single-point features to multi-sensor collaborative dynamic features. By calculating the relative signal offset ΔPij and time difference Δtij between adjacent sensors, the dynamic load change trend of the vehicle in different sensor regions is clarified. Furthermore, by organizing the signal offset ΔPij and time difference Δtij into a symmetric matrix, a signal difference matrix ΔP and a time difference matrix Δt are generated, reflecting the spatial variation law of vehicle load distribution from multi-dimensional dynamic features. The symmetry of the matrix and the n×n dimension ensure a panoramic modeling of the signal differences and temporal relationships between sensors. Through the matrix organization of the dynamic features of adjacent sensors, the originally discrete sensor signal offset ΔPij and time difference Δtij are transformed into a globally correlated signal difference matrix ΔP and time difference matrix Δt. This matrix representation not only accurately captures the dynamic change trend of vehicle load distribution but also eliminates the impact of single-point anomalies on the overall system modeling through matrix symmetry. This improves the robustness and anomaly detection capability of the system in load distribution analysis, laying a solid foundation for subsequent load modeling and anomaly assessment.

[0103] Example 4

[0104] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: S4 includes S41.

[0105] S41. Based on the signal difference matrix △P and the time difference matrix △t, and by combining the vehicle speed v and wheelbase d, a vehicle load distribution model is constructed to obtain the dynamic change L(x) of the vehicle load distribution on the x-axis.

[0106] Specifically, the vehicle speed v is obtained by monitoring the dynamic driving speed of the vehicle within the sensor array area using a speed sensor; the wheelbase d is obtained by extracting the wheelbase parameter between the front and rear wheels of the vehicle from a vehicle type database.

[0107] The dynamic change L(x) is obtained through the following calculation formula:

[0108]

[0109] In the formula, L(x) represents dynamic change, specifically the dynamic change of vehicle load on the x-axis within the sensor array area, and evaluates whether the vehicle load is evenly distributed and whether there is any load abnormality or cheating. △P(i, i+1) represents the difference in signal peak points between adjacent sensors, and △t(i, i+1) represents the difference in time position between adjacent sensors.

[0110] S5 includes S51.

[0111] S51. Based on the preset standard load distribution threshold BZ for vehicle type, perform anomaly assessment on dynamic change L(x) and obtain the vehicle cheating behavior judgment result R.

[0112] The standard load distribution threshold BZ is preset within a standard range for different vehicles, and the range is limited by an upper limit and a lower limit.

[0113] The standard load distribution threshold BZ is specifically represented as follows:

[0114] BZ={[Lmin(x),Lmax(x)]|x∈[x0,x1]}

[0115] In the formula, Lmin(x) represents the lower limit of the standard load distribution threshold BZ on the x-axis, Lmax(x) represents the upper limit of the standard load distribution threshold BZ on the x-axis, and x∈[x0,x1] means that x is any point in the interval from x0 to x1, where x0 represents the starting coordinate of the sensor coverage area and x1 represents the ending coordinate of the sensor coverage area.

[0116] The vehicle cheating behavior judgment result R is obtained through the following comparison method:

[0117]

[0118] In the formula, The quantifier indicates the existence of a certain x that satisfies the subsequent conditions. It is used to determine whether there is at least one position x that causes the dynamic change L(x) to exceed the standard threshold range. This indicates a universal quantifier, specifically meaning that for all positions x∈[x0,x1] within the detection range, all subsequent conditions are true. It is used to determine whether the dynamic change L(x) completely conforms to the standard threshold range throughout the entire sensor area.

[0119] When the vehicle cheating behavior judgment result R=1, it is found that the vehicle has abnormal load distribution and abnormal cheating behavior. The vehicle fails the detection. The unique identifier of the currently detected vehicle is marked as abnormal, and the audible and visual alarm device is triggered to remind the driver. Then, the abnormality log is recorded.

[0120] When the vehicle cheating behavior judgment result R=0, it is determined that there is no abnormal load distribution in the vehicle, and the vehicle detection passes.

[0121] In this embodiment, a vehicle load distribution model is constructed by combining the signal difference matrix ΔP, the time difference matrix Δt, the vehicle speed v, and the wheelbase d. This model obtains the dynamic change L(x) of the vehicle load distribution along the x-axis and evaluates the uniformity and anomalies of the vehicle load distribution by comparing it with the standard load distribution threshold BZ. This not only accurately identifies abnormal areas in the spatial distribution characteristics of the vehicle's dynamic load but also ensures comprehensive analysis of both local anomalies and overall distribution consistency through logical quantifier judgments. Especially in situations involving complex and diverse vehicle cheating behaviors, such as circumventing detection by dynamically adjusting axle load ratios, it effectively avoids missed or false detections caused by single-point errors or lack of dynamic analysis capabilities in traditional weighing methods. This provides a high-precision, comprehensive, and intelligent solution for anti-cheating detection in unattended scenarios.

[0122] Example 5

[0123] An unattended, anti-cheating quartz dynamic truck scale automatic weighing system, please refer to... Figure 2 Specifically, it includes a signal acquisition module, a signal extraction module, a difference calculation module, a change assessment module, and a vehicle assessment module.

[0124] The signal acquisition module acquires frequency signals of the vehicle as it passes by using multiple rows of quartz sensors, records the sensor output data in time series, and performs signal preprocessing on the output data to obtain a preprocessed signal vector T.

[0125] The signal extraction module extracts time-series features from the preprocessed signal vector T, identifies the signal peaks and feature locations of the sensor, forms feature points (Pi, ti), and obtains the peak feature set P after integration.

[0126] The difference calculation module analyzes the signal differences of different sensors in the time series based on the peak feature set P, calculates the relative signal offset △Pij and time difference △tij of adjacent sensors, and forms the signal difference matrix △P and the time difference matrix △t.

[0127] The change assessment module constructs a vehicle load distribution model based on the signal difference matrix △P and the time difference matrix △t, and combines the vehicle speed v and wheelbase d to obtain the dynamic change L(x) of the vehicle load distribution on the x-axis.

[0128] The vehicle evaluation module performs anomaly evaluation on the dynamic change L(x) based on the preset standard load distribution threshold BZ of the vehicle type, and obtains the vehicle cheating behavior judgment result R.

[0129] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An automatic weighing method for a quartz dynamic truck scale, characterized in that, Includes the following steps: S1. Collect frequency band signals of the vehicle as it passes through the vehicle dynamically using multiple rows of quartz sensors, record the output data of the sensors in the time series, and perform signal preprocessing on the output data to obtain a preprocessed signal vector T. S2. Based on the time series feature extraction of the preprocessed signal vector T, the peak points and feature positions of the sensor signal are identified, and feature points (Pi, ti) are formed. After integration, a peak feature set P is generated. S3. Based on the peak feature set P, analyze the signal differences of different sensors in the time series, calculate the relative signal offset △Pij and time difference △tij of adjacent sensors, and form the signal difference matrix △P and the time difference matrix △t. S4. Based on the signal difference matrix △P and the time difference matrix △t, and combined with the vehicle speed v and wheelbase d, a vehicle load distribution model is constructed to determine the dynamic change L(x) of the vehicle load distribution on the x-axis. S5. Based on the preset standard load distribution threshold BZ for vehicle type, and anomaly assessment of dynamic changes L(x), generate vehicle cheating behavior judgment result R.

2. The automatic weighing method for quartz dynamic truck scales according to claim 1, characterized in that, S1 specifically includes S11; S11. Collect frequency band signal data when a vehicle passes through the array using multiple rows of quartz sensors, and record the output signals of the sensors in the time series {S1, S2, S3, ..., Sn}. Each sensor i corresponds to a collection output signal Si(f, t), which describes the signal strength at frequency f and time t. The collection range of the frequency band signal data is the time interval [t0, t1] during which the vehicle passes through the sensor array. The acquired output signal Si(f, t) represents the original signal strength of the i-th sensor at the frequency f and the time t, where i = 1, 2, 3, ..., n represents 1 to n sensors, and t ∈ [t0, t1] indicates that the signal acquisition time range is determined by the dynamic time of the vehicle passing through the sensor array; By integrating all the acquired output signals Si(f,t), an initial signal set S = {S1(f,t), S2(f,t), S3(f,t), ..., Sn(f,t)} is obtained.

3. The automatic weighing method for quartz dynamic truck scales according to claim 2, characterized in that, S1 specifically includes S12; S12 includes: extracting effective signal components within the frequency band range [f1, f2] and forming a standardized time series signal Ti(t), wherein the time series signal Ti(t) represents the preprocessed signal time series of the i-th sensor at time t, and the frequency band range [f1, f2] represents the interval range of frequency analysis; By integrating the preprocessed time series signal Ti(t), a preprocessed signal vector T is obtained, specifically T = {Ti1(t), Ti2(t), T3(t), ..., Tn(t)}.

4. The automatic weighing method for quartz dynamic truck scales according to claim 3, characterized in that: S2 specifically includes S21; S21 includes: performing time series analysis on the preprocessed signal vector T, extracting features from the time series signal Ti(t) of each sensor, determining the signal peak point Pi and the time position ti, and integrating the signal peak point Pi and the time position ti to form the feature point (Pi, ti) of each sensor.

5. The automatic weighing method for quartz dynamic truck scales according to claim 4, characterized in that, Specifically, S2 also includes S22; S22 includes: integrating each obtained feature point (Pi, ti) to determine the dynamic load change characteristics of the vehicle in the detection area, and constructing a complete set of peak features P; The peak feature set P is calculated and determined using the following integrated formula: In the formula, ∪ represents the union symbol, specifically indicating that multiple elements are combined into a single set. This means that from the 1st sensor to the nth sensor, the feature points (Pi, ti) of each sensor are added one by one to the peak feature set P; The peak feature set P is specifically P = {(P1, t1), (P2, t2), (P3, t3), ..., (Pn, tn)}.

6. The automatic weighing method for quartz dynamic truck scales according to claim 1, characterized in that: In S3, the relative signal offset ΔPij is obtained by the formula ΔPij=Pi-Pj, where Pi and Pj represent the signal peak points of sensor i and sensor j, respectively. The time difference Δtij is obtained by the formula Δtij=ti-tj, where ti and tj represent the time positions of sensor i and sensor j, respectively.

7. The automatic weighing method for quartz dynamic truck scales according to claim 6, characterized in that, In step S3, the signal difference matrix ΔP is calculated and determined using the following matrix rearrangement method: The time difference matrix Δt is calculated and determined using the following matrix rearrangement method: The signal difference matrix ΔP and the time difference matrix Δt are symmetric, specifically n*n dimension matrices, where n represents 1 to n sensors, and their own signal and time difference are always 0.

8. The automatic weighing method for quartz dynamic truck scales according to claim 7, characterized in that, In S4, the vehicle speed v is specifically determined by a speed sensor monitoring the vehicle's dynamic driving speed within the sensor array area; the wheelbase d is the wheelbase parameter between the front and rear wheels of the vehicle. The dynamic change L(x) is obtained through the following calculation formula: In the formula, L(x) represents dynamic change, specifically the dynamic change of vehicle load on the x-axis within the sensor array area, △P(i, i+1) represents the difference in signal peak points between adjacent sensors, and △t(i, i+1) represents the difference in time position between adjacent sensors.

9. The automatic weighing method for quartz dynamic truck scales according to claim 1, characterized in that, In step S5, the standard load distribution threshold BZ is preset within a standard range for different vehicles, and the range is defined by an upper and lower limit; specifically, the standard load distribution threshold BZ is expressed as follows: BZ={[Lmin(x),Lmax(x)]|x∈[x0,x1]}; In the formula, Lmin(x) represents the lower limit of the standard load distribution threshold BZ on the x-axis, Lmax(x) represents the upper limit of the standard load distribution threshold BZ on the x-axis, and x∈[x0,x1] means that x is any point in the interval from x0 to x1, where x0 represents the starting coordinate of the sensor coverage area and x1 represents the ending coordinate of the sensor coverage area. The anomaly assessment of the dynamically changing L(x) includes: Determine whether the dynamic change L(x) falls within the standard load distribution threshold; If so, the result of the vehicle cheating behavior judgment is determined to be 0; If not, then the result of the vehicle cheating behavior judgment is determined to be 1; Specifically, when the vehicle cheating behavior judgment result R=1, it is determined that the vehicle has abnormal load distribution and abnormal cheating behavior, the vehicle fails the inspection, the unique identifier of the currently detected vehicle is marked as abnormal, the audible and visual alarm device is triggered to remind the driver, and the abnormality is recorded in the log; when the vehicle cheating behavior judgment result R=0, it is determined that the vehicle does not have abnormal load distribution, the vehicle passes the inspection.

10. A quartz-type dynamic truck scale automatic weighing system, used to perform the method as described in any one of claims 1-9, characterized in that: It includes a signal acquisition module, a signal extraction module, a difference calculation module, a change assessment module, and a vehicle assessment module; The signal acquisition module acquires frequency band signals of the vehicle as it passes by dynamically through multiple rows of quartz sensors, records the sensor output data in time series, and performs signal preprocessing on the output data to determine the preprocessed signal vector T. The signal extraction module extracts time-series features from the preprocessed signal vector T, identifies the peak points and feature locations of the sensor signal, forms feature points (Pi, ti), and obtains the peak feature set P after integration. The difference calculation module analyzes the signal differences of different sensors in the time series based on the peak feature set P, calculates the relative signal offset △Pij and time difference △tij of adjacent sensors, and forms the signal difference matrix △P and the time difference matrix △t. The change assessment module constructs a vehicle load distribution model based on the signal difference matrix △P and the time difference matrix △t, and combines the vehicle passing speed v and wheelbase d to determine the dynamic change L(x) of the vehicle load distribution on the x-axis. The vehicle evaluation module performs anomaly evaluation on the dynamic change L(x) based on the preset standard load distribution threshold BZ for the vehicle type, and determines the judgment result R of vehicle cheating behavior.

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