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

Abstract

The invention 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 aims to effectively remove noise interference and improve data quality through signal preprocessing and peak value feature extraction. Based on the calculation of the signal difference matrix and the time difference matrix, the system can analyze the dynamic load change of the vehicle in the sensor array area. A load distribution model is built by combining the vehicle passing speed and the wheelbase, and the dynamic load evaluation capability of the spatial dimension is provided; finally, by comparing the dynamic change L (x) with a standard load distribution threshold value BZ, accurate recognition of vehicle cheating behaviors is achieved, a cheating behavior judgment result R is generated, the reliability and the anti-cheating capacity of dynamic weighing are improved, an intelligent solution is provided for traffic management and freight supervision, manual intervention is remarkably reduced, and the working efficiency is improved. And the detection efficiency and accuracy are improved.

Description

Technical Field

[0001] The invention relates to the technical field of automatic weighing of dynamic automobile scales, and in particular to an unattended anti-cheating quartz dynamic automobile scale automatic weighing system and a weighing method thereof. Background Art

[0002] Dynamic weighing technology is one of the important branches in the field of transportation and is widely used in road transportation management and freight vehicle supervision. Among them, quartz dynamic vehicle scale, as a key device in the dynamic weighing system, gradually occupies an important position in the dynamic weighing scene due to its high sensitivity and stability.

[0003] Specifically, quartz dynamic truck scales can sense the dynamic load changes of vehicles and measure their weight in real time during driving, thus avoiding the efficiency problems of traditional static weighing methods. At the application level, unattended dynamic weighing systems have gradually become the direction of industry development, especially in the detection of overloaded trucks on highways, the weight monitoring of cargo in logistics parks, and the vehicle flow control of intelligent transportation systems. Quartz dynamic truck scales are widely deployed.

[0004] Although quartz-type dynamic truck scales have achieved a high level of automation, they still have shortcomings when dealing with abnormal vehicle load distribution. Since the frequency band signals obtained by the sensor at different time points may be quite different, it is difficult for a single sensor to accurately capture the changing trajectory of the vehicle's dynamic load. This deficiency is particularly evident when the loads on the front and rear axles of the vehicle are inconsistent or the load ratio is adjusted when the vehicle passes. Existing systems usually rely on fixed algorithms to process vehicle weight distribution information, but the accuracy of the algorithm is limited when faced with complex load distribution scenarios. In addition, the lack of signal timing analysis capabilities for multiple rows of sensors makes the existing system limited in identifying cheating behaviors, such as vehicles circumventing overload detection by adjusting the axle load ratio, and it is difficult to effectively meet the anti-cheating needs of unattended weighing. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a quartz-type dynamic truck scale automatic weighing system and a weighing method thereof, which solve the problems mentioned in the background technology.

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

[0007] S1. Collect frequency band signals of vehicles passing dynamically through multiple rows of quartz sensors, record output data of the sensors in time series, and perform signal preprocessing on the output data to obtain a preprocessed signal vector T;

[0008] S2, extracting time series features from the preprocessed signal vector T, identifying the signal peak points and feature positions of the sensor, forming feature points (Pi, ti), and generating a peak feature set P after integration;

[0009] S3, analyzing the signal differences of different sensors in time series based on the peak feature set P, calculating the relative signal offset △Pij and time difference △tij of adjacent sensors, and forming a signal difference matrix △P and a time difference matrix △t;

[0010] S4, constructing a vehicle load distribution model based on the signal difference matrix ΔP and the time difference matrix Δt, combined with the vehicle passing speed v and the wheelbase d, and determining 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 of the vehicle type, an abnormal evaluation is performed on the dynamic change L(x) to generate a vehicle cheating behavior judgment result R.

[0012] Preferably, the S1 specifically includes S11;

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

[0014] The acquisition 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] represents 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 collected 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, the S1 specifically also includes S12;

[0017] The S12 comprises: 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, wherein the frequency band range [f1, f2] represents the interval range of frequency analysis;

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

[0019] Preferably, the S2 specifically includes S21;

[0020] The S21 includes: performing time series analysis on the preprocessed signal vector T and 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 a feature point (Pi, ti) of each sensor.

[0021] Preferably, the S2 specifically also includes S22;

[0022] The S22 comprises: integrating each of the obtained characteristic points (Pi, ti), determining the dynamic load change characteristics of the vehicle in the detection area, and constructing a complete peak characteristic set P;

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

[0024]

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

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

[0027] Preferably, in S3, the relative signal offset ΔPij is obtained by a calculation formula of 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 calculation formula Δtij=ti-tj, where ti and tj represent the time positions of sensor i and sensor j respectively.

[0029] Preferably, in S3, the signal difference matrix ΔP is calculated and determined by the following matrix arrangement and generation method:

[0030]

[0031] The time difference matrix Δt is calculated and determined by the following matrix arrangement generation method:

[0032]

[0033] The signal difference matrix △P and the time difference matrix △t are symmetrical, and are specifically matrices of n*n dimensions, where n represents 1 to n sensors, and their own signals and time differences are always 0.

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

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

[0036]

[0037] Where L(x) represents the dynamic change, specifically the dynamic change of the vehicle load on the x-axis in 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 time position difference between adjacent sensors.

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

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

[0040] Wherein, 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, x∈[x0,x1] represents that x is any point in the interval from x0 to x1, where x0 represents the starting point coordinate of the sensor coverage area, and x1 represents the end point coordinate of the sensor coverage area;

[0041] The abnormal evaluation of the dynamic change L(x) includes:

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

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

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

[0045] Among them, 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 inspection fails, and the unique identification of the currently inspected vehicle is marked as abnormal. At the same time, the sound and light alarm device is triggered to remind the driver to pay attention, and then the abnormal log is recorded; when the vehicle cheating behavior judgment result R=0, it is determined that the vehicle does not have abnormal load distribution, and the vehicle inspection passes.

[0046] A second aspect of the present invention provides a quartz-type dynamic vehicle 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 collects frequency band signals of vehicles passing dynamically through multiple rows of quartz sensors, records output data of the sensors in a time series, and performs signal preprocessing on the output data to obtain a preprocessed signal vector T;

[0048] The signal extraction module extracts time series features from the preprocessed signal vector T, identifies the signal peak points and feature positions of the sensor, forms feature points (Pi, ti), and obtains a 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 a signal difference matrix △P and a time difference matrix △t;

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

[0051] The vehicle assessment module performs an abnormal assessment on the dynamic change L(x) according to the standard load distribution threshold BZ preset for the vehicle type, and obtains the vehicle cheating behavior judgment result R.

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

[0053] (1) Through signal preprocessing and peak feature extraction, the system effectively removes noise interference and improves data quality. Based on the calculation of signal difference matrix and 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 passing speed and wheelbase, it provides dynamic load assessment capabilities in the spatial dimension. Finally, by comparing the dynamic change L(x) with the standard load distribution threshold BZ, it realizes accurate identification of vehicle cheating behavior and generates cheating behavior judgment result R, which improves the reliability and anti-cheating ability of dynamic weighing, provides intelligent solutions 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, the signal difference analysis is performed and the 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, and reflects the spatial variation law of vehicle load distribution from multi-dimensional dynamic features. Through the matrix arrangement of the dynamic features of adjacent sensors, the originally discrete sensor signal offset △Pij and time difference △tij are converted into signal difference matrix △P and time difference matrix △t with global correlation. This matrix expression 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 modeling of the system through matrix symmetry, thereby improving the robustness and anomaly detection capability of the system in load distribution analysis, and laying a solid foundation for subsequent load modeling and anomaly evaluation.

[0055] (3) By combining the signal difference matrix △P, the time difference matrix △t, the vehicle passing speed v and the 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, and by comparing it with the standard load distribution threshold BZ, the uniformity and abnormality of the vehicle load distribution are evaluated. Not only can the abnormal area be accurately identified in the spatial distribution characteristics of the vehicle dynamic load, but also the comprehensive analysis of local abnormalities and overall distribution consistency is ensured through logical quantifier judgment. Especially in the case of complex and diverse vehicle cheating behaviors, it effectively avoids missed judgments or misjudgments caused by single-point errors or lack of dynamic analysis capabilities in traditional weighing methods, and provides a high-precision, full-coverage intelligent solution for anti-cheating detection in unattended scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of a flow chart of a quartz-type dynamic truck scale automatic weighing method of the present invention;

[0057] Figure 2 The present invention is a schematic diagram of the steps of a quartz-type dynamic truck scale automatic weighing system. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0059] Example 1

[0060] The present invention provides an unattended anti-cheating quartz dynamic vehicle scale automatic weighing method, please refer to Figure 1 :Comprising the following steps:

[0061] S1. Collect frequency band signals of vehicles passing dynamically through multiple rows of quartz sensors, record output data of the sensors in a time series, and perform signal preprocessing on the output data to obtain a preprocessed signal vector T.

[0062] S2. Extract the time series features of the preprocessed signal vector T, identify the signal peak points and feature positions of the sensor, form feature points (Pi, ti), and obtain the peak feature set P after integration.

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

[0064] S4. Based on the signal difference matrix ΔP and the time difference matrix Δt, a vehicle load distribution model is constructed in combination with the vehicle passing speed v and the wheelbase d to obtain the dynamic change L(x) of the vehicle load distribution on the x-axis.

[0065] S5. According to the preset standard load distribution threshold BZ of the vehicle type, an abnormal evaluation is performed on the dynamic change L(x) to 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 anti-cheating automated detection process. This method can significantly solve the deficiencies raised in the background introduction, including the limitations of 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 signal difference matrix and 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 passing speed and wheelbase, it provides a dynamic load evaluation capability in the spatial dimension; finally, by comparing the dynamic change L(x) with the standard load distribution threshold BZ, the accurate identification of vehicle cheating behavior is achieved, and the cheating behavior judgment result R is generated, which improves the reliability and anti-cheating ability of dynamic weighing, and provides intelligent solutions for traffic management and freight supervision, significantly reducing manual intervention and improving detection efficiency and accuracy.

[0067] Example 2

[0068] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11 and S12.

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

[0070] The acquisition 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], indicating that the signal acquisition time range is determined by the dynamic time of the vehicle passing through the sensor array.

[0071] The collected output signals Si(f, t) are integrated to obtain an initial signal set S={S1(f, t), S2(f, t), S3(f, t), ..., Sn(f, t)}.

[0072] S12. Perform signal preprocessing on the initial signal set S, including frequency band filtering preprocessing and noise reduction preprocessing, and then extract the effective signal components within the frequency band range [f1, f2] to 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, wherein the frequency band range [f1, f2] represents the interval range of frequency analysis.

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

[0074]

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

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

[0077] The S2 includes S21 and S22.

[0078] S21. Perform time series analysis on the preprocessed signal vector T and extract features from the time series signal Ti(t) of each sensor to obtain the signal peak point Pi and the time position ti, and integrate the signal peak point Pi and the time position ti to form a feature point (Pi, ti) of each sensor.

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

[0080]

[0081] Wherein, Pi represents the peak intensity of the i-th sensor signal, specifically represents 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 represents the time t when the local upper limit value in the time series signal Ti(t) appears.

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

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

[0087]

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

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

[0090] In this embodiment, the original frequency band signal when the vehicle passes dynamically is collected by multiple rows of quartz sensors, and the initial signal set S is subjected to frequency band filtering and noise reduction through signal preprocessing. The system effectively extracts the effective signal components within the frequency band range [f1, f2] to form a standardized time series signal Ti(t). Subsequently, through the time series analysis and feature extraction of step S21, the system can accurately identify the peak point Pi and time position ti of each sensor signal, and by integrating the feature points (Pi, ti) of all sensors, construct the peak feature set P of the vehicle in the detection area, which not only significantly reduces the data distortion problem caused by environmental noise and frequency interference during the multi-sensor detection process, but also greatly improves the feature extraction accuracy of the dynamic load change of the vehicle. Especially in the scenario where the vehicle passes at high speed or the load distribution is complex, 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 explained in Example 1, please refer to Figure 1 , specifically: S3 includes S31 and S32.

[0093] S31, analyzing the signal differences of different sensors in time series based on the peak feature set P, and calculating the relative signal offset △Pij and time difference △tij of adjacent sensors;

[0094] The relative signal offset ΔPij is obtained by the calculation 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 calculation formula Δtij=ti-tj, where ti and tj represent the time positions of sensor i and sensor j respectively.

[0096] S32, performing matrix arrangement on the signal offset ΔPij and the time difference Δtij, respectively constructing a signal difference matrix ΔP and a time difference matrix Δt, to reflect the dynamic characteristics of vehicle load distribution.

[0097] The signal difference matrix ΔP is obtained by the following matrix arrangement and generation method:

[0098]

[0099] The time difference matrix △t is obtained by the following matrix arrangement and generation method:

[0100]

[0101] The signal difference matrix △P and the time difference matrix △t are symmetrical, and are specifically matrices of n*n dimensions, where n represents 1 to n sensors, and their own signals and time differences are always 0.

[0102] In this embodiment, based on the peak feature set P, the signal difference analysis is performed and the signal difference matrix and time difference matrix are constructed, and the system realizes a comprehensive description from single-point features to multi-sensor collaborative dynamic features. By calculating the relative signal offset △Pij and time difference △tij of adjacent sensors, the dynamic load change trend of the vehicle in different sensor areas is clarified; and by arranging the signal offset △Pij and time difference △tij in the form of a symmetric matrix, the signal difference matrix △P and the time difference matrix △t are generated, reflecting the spatial change law of the vehicle load distribution from the multi-dimensional dynamic characteristics. The symmetry and n×n dimension of the matrix ensure the panoramic modeling of the signal difference and time relationship between sensors. Through the matrix arrangement of the dynamic features of adjacent sensors, the originally discrete sensor signal offset △Pij and time difference △tij are converted into signal difference matrix △P and time difference matrix △t with global correlation. This matrix expression 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 modeling of the system through matrix symmetry, thereby improving the robustness and anomaly detection capability of the system in load distribution analysis, and laying a solid foundation for subsequent load modeling and anomaly evaluation.

[0103] Example 4

[0104] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S4 includes S41.

[0105] S41. Based on the signal difference matrix ΔP and the time difference matrix Δt, a vehicle load distribution model is constructed in combination with the vehicle passing speed v and the wheelbase d to obtain the dynamic change L(x) of the vehicle load distribution on the x-axis.

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

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

[0108]

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

[0110] The S5 includes S51.

[0111] S51. According to the preset standard load distribution threshold BZ of the vehicle type, an abnormality assessment is performed on the dynamic change L(x) to obtain the vehicle cheating behavior judgment result R.

[0112] The standard load distribution threshold BZ is preset with 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 expressed as follows:

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

[0115] Where 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, x∈[x0,x1] represents that x is any point in the interval from x0 to x1, where x0 represents the starting point coordinate of the sensor coverage area, and x1 represents the end point coordinate of the sensor coverage area.

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

[0117]

[0118] In the formula, represents an existential quantifier, specifically indicating that there is a certain x that satisfies the subsequent condition, which is used to determine whether there is at least one position x that makes the dynamic change L(x) exceed the standard threshold range; It represents a universal quantifier, specifically indicating that for all positions x∈[x0,x1] in the detection range, the subsequent conditions are all met, which is used to determine whether the dynamic change L(x) fully meets the standard threshold range in the entire sensor area.

[0119] When the vehicle cheating behavior judgment result R=1, the vehicle is detected to have abnormal load distribution and abnormal cheating behavior, the vehicle fails the inspection, the unique identification of the current inspected vehicle is marked as abnormal, and the sound and light alarm device is triggered to remind the driver to pay attention, and then the abnormal log is recorded.

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

[0121] In this embodiment, by combining the signal difference matrix △P, the time difference matrix △t, the vehicle passing speed v and the 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, and by comparing with the standard load distribution threshold BZ, the uniformity and abnormality of the vehicle load distribution are evaluated. Not only can the abnormal area be accurately identified in the spatial distribution characteristics of the vehicle dynamic load, but also the comprehensive analysis of local abnormalities and overall distribution consistency is ensured through logical quantifier judgment. Especially in the case of complex and diverse vehicle cheating behaviors, such as evading detection by dynamically adjusting the axle load ratio, it effectively avoids the missed judgment or misjudgment caused by single-point errors or lack of dynamic analysis capabilities in traditional weighing methods, and provides a high-precision, full-coverage 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 evaluation module and a vehicle evaluation module.

[0124] The signal acquisition module collects frequency band signals of vehicles passing dynamically through multiple rows of quartz sensors, records output data of the sensors in a 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 peak points and feature positions 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 time series based on the peak feature set P, calculates the relative signal offset △Pij and time difference △tij of adjacent sensors, and forms a signal difference matrix △P and a time difference matrix △t.

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

[0128] The vehicle assessment module performs an abnormal assessment on the dynamic change L(x) according to the standard load distribution threshold BZ preset for the vehicle type, and obtains the vehicle cheating behavior judgment result R.

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

Claims

1. A quartz-type dynamic truck scale automatic weighing method, characterized in that: The following steps are involved: S1. Collect frequency band signals of vehicles passing dynamically through multiple rows of quartz sensors, record output data of the sensors in time series, and perform signal preprocessing on the output data to obtain a preprocessed signal vector T; S2, extracting time series features from the preprocessed signal vector T, identifying the signal peak points and feature positions of the sensor, forming feature points (Pi, ti), and generating a peak feature set P after integration; S3, analyzing the signal differences of different sensors in time series based on the peak feature set P, calculating the relative signal offset △Pij and time difference △tij of adjacent sensors, and forming a signal difference matrix △P and a time difference matrix △t; S4, constructing a vehicle load distribution model based on the signal difference matrix ΔP and the time difference matrix Δt, combined with the vehicle passing speed v and the wheelbase d, and determining the dynamic change L(x) of the vehicle load distribution on the x-axis; S5. Based on the preset standard load distribution threshold BZ of the vehicle type, an abnormal evaluation is performed on the dynamic change L(x) to generate a vehicle cheating behavior judgment result R.

2. The automatic weighing method of a quartz dynamic truck scale according to claim 1 is characterized in that: The S1 specifically includes S11; S11, using multiple rows of quartz sensors to collect frequency band signal data when the vehicle passes dynamically, and recording the output signals {S1, S2, S3, ..., Sn} of the sensors in a time series, each sensor i corresponds to a collected output signal Si (f, t), and describes the signal strength at frequency f and time t, and the collection range of the frequency band signal data is the time interval [t0, t1] when the vehicle passes through the sensor array; The acquisition 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] represents that the signal acquisition time range is determined by the dynamic time of the vehicle passing through the sensor array; By integrating all the collected 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 of a quartz dynamic truck scale according to claim 2 is characterized in that: The S1 specifically also includes S12; The S12 comprises: 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, wherein the frequency band range [f1, f2] represents the interval range of frequency analysis; The preprocessed signal vector T is obtained by integrating the preprocessed time series signal Ti(t), and the obtained preprocessed signal vector T is specifically T={Ti1(t), Ti2(t), T3(t), ..., Tn(t)}.

4. The automatic weighing method of a quartz dynamic truck scale according to claim 3 is characterized in that: The S2 specifically includes S21; The S21 includes: performing time series analysis on the preprocessed signal vector T and 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 a feature point (Pi, ti) of each sensor.

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

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

7. The automatic weighing method of a quartz dynamic truck scale according to claim 6 is characterized in that: In S3, the signal difference matrix ΔP is calculated and determined by the following matrix arrangement and generation method: The time difference matrix Δt is calculated and determined by the following matrix arrangement generation method: The signal difference matrix ΔP and the time difference matrix Δt are symmetrical, and are specifically matrices of n*n dimensions, where n represents 1 to n sensors, and their own signals and time differences are always 0.

8. The automatic weighing method of a quartz dynamic truck scale according to claim 7, characterized in that: In S4, the vehicle passing speed v is specifically determined by monitoring the dynamic driving speed of the vehicle in the sensor array area through a speed sensor; the wheelbase d is a wheelbase parameter between the front and rear wheels of the vehicle; The dynamic change L(x) is obtained by the following calculation formula: Where L(x) represents the dynamic change, specifically the dynamic change of the vehicle load on the x-axis in 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 time position difference between adjacent sensors.

9. The automatic weighing method of a quartz dynamic truck scale according to claim 1, characterized in that: In S5, the standard load distribution threshold BZ is preset with a standard range for different vehicles, and the range is limited by an upper limit and a lower limit; wherein the standard load distribution threshold BZ is specifically expressed as follows: BZ={[Lmin(x),Lmax(x)]|x∈[x0,x1]}; Wherein, 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, x∈[x0,x1] represents that x is any point in the interval from x0 to x1, where x0 represents the starting point coordinate of the sensor coverage area, and x1 represents the end point coordinate of the sensor coverage area; The abnormal evaluation of the dynamic change L(x) includes: Determining whether the dynamic change L(x) falls within the standard load distribution threshold; If yes, then the vehicle cheating behavior judgment result is determined to be 0; If not, the vehicle cheating behavior judgment result is determined to be 1; Among them, 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 inspection fails, and the unique identification of the currently inspected vehicle is marked as abnormal. At the same time, the sound and light alarm device is triggered to remind the driver to pay attention, and then the abnormal log is recorded; when the vehicle cheating behavior judgment result R=0, it is determined that the vehicle does not have abnormal load distribution, and the vehicle inspection passes.

10. A quartz dynamic truck scale automatic weighing system, used to perform the method as claimed in any one of claims 1 to 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 collects frequency band signals of vehicles passing dynamically through multiple rows of quartz sensors, records output data of the sensors in a time series, and performs signal preprocessing on the output data to determine a preprocessing signal vector T; The signal extraction module extracts time series features from the preprocessed signal vector T, identifies the signal peak points and feature positions of the sensor, forms feature points (Pi, ti), and obtains a 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 a signal difference matrix △P and a time difference matrix △t; The change evaluation module constructs a vehicle load distribution model based on the signal difference matrix △P and the time difference matrix △t, and then combines the vehicle passing speed v and the wheelbase d to determine the dynamic change L(x) of the vehicle load distribution on the x-axis; The vehicle assessment module performs an abnormal assessment on the dynamic change L(x) according to the standard load distribution threshold BZ preset for the vehicle type, and determines the vehicle cheating behavior judgment result R.

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