Vehicle weight measuring method and device in commercial vehicle driving state and electronic equipment

Abstract

The invention provides a vehicle weight measurement method and device in a commercial vehicle driving state and electronic equipment, and relates to the technical field of vehicle detection, and the method comprises the steps: obtaining a sensor signal set of a commercial vehicle, the sensor signal set comprises a longitudinal vehicle speed signal, a longitudinal acceleration signal, a longitudinal driving force signal, a transverse acceleration signal, a transverse force signal, a vertical acceleration signal, a vertical force signal and a suspension system restoring force signal; performing signal analysis processing on the sensor signal set through a preset commercial vehicle three-degree-of-freedom dynamic model, and determining the longitudinal vehicle weight, the transverse vehicle weight and the vertical vehicle weight of the commercial vehicle; and carrying out weighted fusion processing on the longitudinal vehicle weight, the transverse vehicle weight and the vertical vehicle weight to determine an initial vehicle weight measurement value, and carrying out smoothing processing on the initial vehicle weight measurement value through a Kalman filtering method to determine a target vehicle weight measurement value. According to the invention, the accuracy of vehicle weight measurement in a driving state can be significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle detection, and in particular to a vehicle weight measurement method, device and electronic equipment for a commercial vehicle in a driving state. Background Art

[0002] In the modern transportation industry, vehicle weight management of commercial vehicles is crucial. For logistics, transportation and heavy-load vehicles, real-time and accurate vehicle weight information will directly affect transportation safety, fuel efficiency and protection of road infrastructure. At present, relevant technologies have proposed that vehicle weight can be measured through static equipment such as floor scales or weighing platforms. However, the above solutions require the vehicle to stop at a specific location and state when weighing, which is not suitable for vehicle weight monitoring during dynamic driving, and cannot meet the needs of dynamic weighing of commercial vehicles and real-time monitoring of vehicle load changes during driving. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a method, device and electronic equipment for measuring the vehicle weight of a commercial vehicle in a driving state, which can significantly improve the accuracy of the vehicle weight measurement in a driving state.

[0004] In a first aspect, an embodiment of the present invention provides a method for measuring the vehicle weight of a commercial vehicle in a driving state, the method comprising: obtaining a sensor signal set of the commercial vehicle, wherein the sensor signal set comprises: a longitudinal vehicle speed signal, a longitudinal acceleration signal, a longitudinal driving force signal, a lateral acceleration signal, a lateral force signal, a vertical acceleration signal, a vertical force signal and a suspension system restoring force signal; performing signal analysis and processing on the sensor signal set by presetting a three-degree-of-freedom dynamic model of the commercial vehicle to determine the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight of the commercial vehicle; performing weighted fusion processing on the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight to determine an initial vehicle weight measurement value, and smoothing the initial vehicle weight measurement value by a Kalman filter method to determine a target vehicle weight measurement value.

[0005] In one embodiment, a preset three-degree-of-freedom dynamic model of a commercial vehicle includes: a longitudinal vehicle weight measurement model, a lateral vehicle weight measurement model and a vertical vehicle weight measurement model. By using the preset three-degree-of-freedom dynamic model of the commercial vehicle, a sensor signal set is subjected to signal analysis and processing to determine the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight of the commercial vehicle, including: determining the longitudinal vehicle weight based on the longitudinal vehicle speed signal, the longitudinal acceleration signal and the longitudinal driving force signal through the longitudinal vehicle weight measurement model; determining the lateral vehicle weight based on the lateral acceleration signal and the lateral force signal through the lateral vehicle weight measurement model; and determining the vertical vehicle weight based on the vertical acceleration signal, the vertical force signal and the suspension system restoring force signal through the vertical vehicle weight measurement model.

[0006] In one embodiment, the step of determining the longitudinal vehicle weight based on the longitudinal vehicle speed signal, the longitudinal acceleration signal and the longitudinal driving force signal by means of the longitudinal vehicle weight measurement model comprises: sending the longitudinal vehicle speed signal, the longitudinal acceleration signal and the longitudinal driving force signal to the longitudinal vehicle weight measurement model to determine the longitudinal vehicle weight, wherein the longitudinal vehicle weight measurement model is expressed as:

[0007]

[0008] Among them, m x F is the longitudinal weight of the commercial vehicle; t is the longitudinal driving force of the commercial vehicle; C D is the air resistance coefficient; A is the frontal area; Ψ is the road resistance coefficient; g is the acceleration of gravity; δ is the vehicle rotation mass conversion coefficient; a x is the longitudinal acceleration.

[0009] In one embodiment, the step of determining the lateral vehicle weight based on the lateral acceleration signal and the lateral force signal by the lateral vehicle weight measurement model includes: determining the quotient of the lateral force and the lateral acceleration of the commercial vehicle as the lateral vehicle weight.

[0010] In one embodiment, the step of determining the vertical vehicle weight through a vertical vehicle weight measurement model based on a vertical acceleration signal, a vertical force signal and a suspension system restoring force signal includes: determining the vertical vehicle weight as the quotient of the difference between the vertical force of the commercial vehicle and the suspension system restoring force and the vertical acceleration.

[0011] In one embodiment, the longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight are weighted and fused to determine the initial vehicle weight measurement value, including: obtaining a preset weight coefficient set, wherein the preset weight coefficient set includes weight coefficients corresponding to the longitudinal, lateral and vertical directions of the commercial vehicle, and the sum of each weight coefficient is equal to 1; and determining the sum of the products of the longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight and their corresponding weight coefficients as the initial vehicle weight measurement value.

[0012] In one embodiment, the initial vehicle weight measurement value is smoothed by the Kalman filtering method, and the step of determining the target vehicle weight measurement value includes: determining the predicted vehicle weight at the current moment and the prediction error covariance matrix corresponding to the predicted vehicle weight according to a preset state transfer matrix and the initial vehicle weight measurement value at the previous moment; determining the Kalman gain according to the prediction error covariance matrix, and using the Kalman gain and the observation value to update the initial vehicle weight measurement value at the current moment, determining the updated initial vehicle weight measurement value, and the error covariance matrix corresponding to the initial vehicle weight measurement value, and using the error covariance matrix to perform an iterative loop to make the vehicle weight measurement value close to the true value; performing error correction processing on the vehicle weight determined after the iterative loop ends by the least squares method to determine the target vehicle weight measurement value.

[0013] In one embodiment, the step of determining the Kalman gain based on the prediction error covariance matrix includes: obtaining the observation value at the current moment, and determining the Kalman gain based on the observation value and the prediction error covariance matrix, wherein the observation value includes: an observation matrix and an observation noise covariance matrix, and the Kalman gain is used to reflect the importance of the initial vehicle weight measurement value at the current moment to the corrected predicted vehicle weight.

[0014] In one embodiment, the step of determining the Kalman gain according to the observation value and the prediction error covariance matrix includes: sending the observation matrix, the observation noise covariance matrix and the prediction error covariance matrix to a preset Kalman gain calculation model to determine the Kalman gain, wherein the preset Kalman gain calculation model is expressed as:

[0015] K k =P k|k-1 ·H T ·(H·P k|k-1 ·H T +R) -1

[0016] Among them, K k is the Kalman gain at time k, H is the observation matrix, R is the observation noise covariance matrix, P k|k-1 is the prediction error covariance matrix at time k.

[0017] In a second aspect, an embodiment of the present invention further provides a vehicle weight measurement device for a commercial vehicle in a driving state, the device comprising: a detection module, which obtains a sensor signal set of the commercial vehicle, wherein the sensor signal set comprises: a longitudinal vehicle speed signal, a longitudinal acceleration signal, a longitudinal driving force signal, a lateral acceleration signal, a lateral force signal, a vertical acceleration signal, a vertical force signal and a suspension system restoring force signal; a vehicle weight measurement module, which performs signal analysis and processing on the sensor signal set by presetting a three-degree-of-freedom dynamic model of the commercial vehicle to determine the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight of the commercial vehicle; a vehicle weight data fusion module, which performs weighted fusion processing on the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight to determine an initial vehicle weight measurement value, and smoothes the initial vehicle weight measurement value by a Kalman filter method to determine a target vehicle weight measurement value.

[0018] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the first aspect.

[0019] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0020] The embodiments of the present invention bring the following beneficial effects:

[0021] The embodiment of the present invention provides a vehicle weight measurement method, device and electronic equipment for a commercial vehicle in a driving state. After obtaining a sensor signal set of the commercial vehicle, the method performs signal analysis and processing on the sensor signal set by presetting a three-degree-of-freedom dynamic model of the commercial vehicle to determine the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight of the commercial vehicle, and then performs weighted fusion processing on the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight to determine an initial vehicle weight measurement value, and smoothes the initial vehicle weight measurement value through the Kalman filter method to determine a target vehicle weight measurement value. The embodiment of the present invention can effectively and accurately measure the weight of the commercial vehicle through longitudinal, lateral and vertical dynamic analysis of the commercial vehicle and fusion of multiple algorithms, meet the dynamic weight measurement requirements of the commercial vehicle, and effectively support the load management and driving safety monitoring of the vehicle.

[0022] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0023] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 A schematic diagram of the structure of a vehicle weight measurement system for a commercial vehicle in a driving state provided by an embodiment of the present invention;

[0026] Figure 2 A schematic flow chart of a method for measuring vehicle weight of a commercial vehicle in a driving state provided by an embodiment of the present invention;

[0027] Figure 3A schematic diagram of vehicle weight data fusion provided by an embodiment of the present invention;

[0028] Figure 4 A schematic diagram of the structure of a vehicle weight measurement device for a commercial vehicle in a driving state provided by an embodiment of the present invention;

[0029] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] At present, in the modern transportation industry, the weight management of commercial vehicles is crucial. For logistics, transportation and heavy-load vehicles, real-time and accurate vehicle weight information directly affects transportation safety, fuel efficiency and protection of road infrastructure. However, existing vehicle weight measurement technologies mostly rely on static equipment such as floor scales or weighing platforms. Although these methods are more accurate under specific conditions, they often have limitations in practical applications. For example, weighing with a floor scale requires the vehicle to stop at a specific location and state, which is not suitable for vehicle weight monitoring during dynamic driving. For commercial vehicles, dynamic weighing and real-time monitoring of vehicle load changes during driving are particularly important.

[0032] Traditional weighing methods are difficult to meet the dynamic monitoring needs of commercial vehicles. The main reason is that the vehicle is subject to a lot of external interference during driving, such as uneven road surface, inertia force when the vehicle turns or accelerates suddenly, changes in the center of gravity, etc. These factors will cause fluctuations and instability in the measurement data. In addition, commercial vehicles often need to be transported over long distances. Under various complex road conditions, a single measuring device or method is difficult to ensure the continuity and accuracy of the data. Therefore, the industry urgently needs a vehicle weight measurement technology that can work stably during vehicle driving, which can not only provide real-time data during dynamic driving, but also accurately reflect the actual weight of the vehicle.

[0033] Based on this, the vehicle weight measurement method, device and electronic equipment for commercial vehicles in driving state provided by the present invention can collect dynamic data of the vehicle in the longitudinal, lateral and vertical directions, preliminarily integrate these data using the weighted average method, and dynamically adjust the weight according to the driving state. After that, the Kalman filter method is used to further smooth the data, eliminate high-frequency noise, and ensure the stability and continuity of the data stream. Finally, the historical data is fitted and analyzed by the least squares method, the error is corrected, and the accuracy of the vehicle weight measurement is improved, so as to output stable and reliable vehicle weight measurement values ​​under different driving conditions, providing strong support for the safe operation and performance optimization of the vehicle.

[0034] To facilitate understanding of this embodiment, a vehicle weight measurement method for a commercial vehicle in a driving state disclosed in an embodiment of the present invention is first described in detail. The method is applied to a vehicle weight measurement system for a commercial vehicle in a driving state. To facilitate understanding of the vehicle weight measurement system for a commercial vehicle in a driving state, an embodiment of the present invention provides a structural schematic diagram of a vehicle weight measurement system for a commercial vehicle in a driving state, such as Figure 1 As shown, the vehicle weight measurement system of the commercial vehicle in the driving state includes: a detection mechanism, a vehicle weight measurement unit and a fusion algorithm processing unit. After the detection mechanism collects the corresponding sensor signal, the detection mechanism sends the signal to the vehicle weight measurement unit. The vehicle measurement unit measures the longitudinal, lateral and vertical vehicle weights of the commercial vehicle according to the input sensor signals. Then, the vehicle weight measurement unit sends the three measured vehicle weights to the fusion algorithm processing unit. After receiving the three vehicle weights, the fusion algorithm processing unit uses the weighted average method for preliminary fusion, and then uses the Kalman filter to smooth the data. Finally, the least squares method is used to correct the error to obtain the final vehicle weight measurement value, thereby realizing accurate measurement of the vehicle weight.

[0035] The detection mechanism is used to detect the longitudinal vehicle speed signal, the longitudinal acceleration signal, the longitudinal driving force signal, the lateral acceleration signal, the lateral force signal, the vertical acceleration signal, the vertical force signal and the suspension system restoring force signal. In one embodiment, the longitudinal acceleration signal and the longitudinal force signal are used to measure the longitudinal vehicle weight, the lateral acceleration signal and the lateral force signal are used to measure the lateral vehicle weight, and the vertical acceleration signal, the vertical force signal and the suspension system restoring force signal are used to measure the vertical vehicle weight.

[0036] The vehicle weight measurement unit includes: a longitudinal vehicle weight measurement model, a lateral vehicle weight measurement model and a vertical vehicle weight measurement model. The vehicle weight measurement unit measures the longitudinal, lateral and vertical vehicle weights based on the longitudinal vehicle speed signal, longitudinal acceleration signal, longitudinal driving force signal, lateral acceleration signal, lateral force signal, vertical acceleration signal, vertical force signal and suspension system restoring force signal provided by the detection mechanism through the three-degree-of-freedom dynamics model of the commercial vehicle, and then sends the three vehicle weights to the fusion algorithm processing unit.

[0037] Specifically, for the measurement of the longitudinal dynamic vehicle weight of a commercial vehicle, the longitudinal vehicle speed is first obtained through a vehicle speed sensor, and then a high-precision acceleration sensor is installed near the center of gravity of the commercial vehicle. The sensor can respond to the acceleration fluctuations of the vehicle in the longitudinal direction in real time and sensitively, and convert these dynamic changes into electrical signals for output, and then obtain the corresponding driving force according to the drive system of the commercial vehicle; after successfully obtaining the precise measurement values ​​of the vehicle speed, longitudinal acceleration and longitudinal driving force, the mass measurement value of the vehicle in the longitudinal direction can be measured using Newton's second law based on the road resistance coefficient and vehicle rotational mass conversion coefficient obtained from the experiment.

[0038] In addition, for the measurement of the lateral dynamic vehicle weight of a commercial vehicle, a high-precision acceleration sensor is first installed near the center of gravity of the commercial vehicle. This sensor can respond to the acceleration fluctuations of the vehicle in the lateral direction in real time and sensitively, and convert these dynamic changes into electrical signals for output. Then, corresponding piezoelectric sensors are installed on the left and right sides of the commercial vehicle respectively. This sensor can highly sensitively capture the lateral interaction force between the wheels and the ground, and convert these physical force signals into electrical signals that are easy to process and transmit. After successfully obtaining the accurate measurement values ​​of the lateral force and lateral acceleration, Newton's second law can be used to measure the mass measurement value of the vehicle in the lateral direction.

[0039] Furthermore, for the vertical dynamic vehicle weight measurement of commercial vehicles, a high-precision acceleration sensor is first installed near the center of gravity of the commercial vehicle. The sensor can respond to the acceleration fluctuations of the vehicle in the vertical direction in real time and sensitively, and convert these dynamic changes into electrical signals for output; then, corresponding piezoelectric sensors are installed at key positions of the commercial vehicle suspension system. The sensor can respond to the vertical force borne by the suspension system with high sensitivity, and convert it into electrical signals for output. In order to improve the accuracy and robustness of the measurement, a multi-point arrangement strategy is often adopted, that is, multiple sensors are installed at different suspension points, and the system error is eliminated by the differential measurement method; then, the corresponding force sensor is used to measure the restoring force of the suspension system. After successfully obtaining the accurate measurement values ​​of the vertical force, vertical acceleration and restoring force of the suspension system, Newton's second law can be used to calculate the mass measurement value of the vehicle in the vertical direction.

[0040] After receiving the longitudinal, lateral and vertical vehicle weight signals sent by the vehicle weight measurement unit, the fusion algorithm processing unit uses the weighted average method for preliminary fusion, then uses the Kalman filter method for smoothing, and finally uses the least squares method for error correction to obtain the final vehicle weight measurement value. That is to say, after obtaining the vehicle weight data in the three degrees of freedom directions through the vehicle weight measurement module, it is necessary to determine the longitudinal, lateral and vertical weight coefficients. Due to the different driving conditions of the vehicle, the fluctuation degree of the mass data in each direction will be different; when the vehicle accelerates or decelerates, the longitudinal vehicle weight data may show a larger fluctuation due to the change of dynamic load. In case of large fluctuation, the weight coefficient of the longitudinal vehicle weight in the weighted average calculation method can be appropriately reduced to reduce its potential impact on the final vehicle weight measurement value; when the vehicle is turning, the mass data in the lateral direction may become more important due to the effect of centrifugal force. At this time, the weight coefficient in the lateral direction needs to be increased to more accurately reflect the mass characteristics of the vehicle in this direction; after determining the reasonable weight coefficient, the mass measurement values ​​in the longitudinal, lateral and vertical directions can be weighted averaged according to these weights, thereby ensuring that the data in each direction can have an appropriate impact on the total mass measurement value according to its importance.

[0041] based on Figure 1 The structural diagram of the vehicle weight measurement system when the commercial vehicle is in driving state is shown in FIG. 1 , and the vehicle weight measurement method when the commercial vehicle is in driving state is described in detail in the embodiment of the present invention. Figure 2 The flowchart of a method for measuring vehicle weight when a commercial vehicle is in a driving state is shown in FIG. 1 , and the method mainly includes the following steps S202 to S206:

[0042] Step S202, obtaining a sensor signal set of the commercial vehicle, wherein the sensor signal set includes: a longitudinal vehicle speed signal, a longitudinal acceleration signal, a longitudinal driving force signal, a lateral acceleration signal, a lateral force signal, a vertical acceleration signal, a vertical force signal and a suspension system restoring force signal.

[0043] Step S204: by presetting the three-degree-of-freedom dynamics model of the commercial vehicle, the sensor signal set is subjected to signal analysis and processing to determine the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight of the commercial vehicle. The presetting three-degree-of-freedom dynamics model of the commercial vehicle includes: a longitudinal vehicle weight measurement model, a lateral vehicle weight measurement model and a vertical vehicle weight measurement model. In one embodiment, the three-degree-of-freedom vehicle weight measurement of the commercial vehicle is specifically described in (1) to (3) as follows:

[0044] (1) The longitudinal vehicle weight is determined based on the longitudinal vehicle speed signal, the longitudinal acceleration signal and the longitudinal driving force signal through the longitudinal vehicle weight measurement model. That is, the longitudinal vehicle speed signal, the longitudinal acceleration signal and the longitudinal driving force signal are sent to the longitudinal vehicle weight measurement model to determine the longitudinal vehicle weight. The longitudinal vehicle weight measurement model is expressed as:

[0045]

[0046] Among them, m x is the longitudinal weight of the commercial vehicle, in kg; F t is the longitudinal driving force of the commercial vehicle, in N; C D is the air resistance coefficient; A is the frontal area, in m2; Ψ is the road resistance coefficient; g is the acceleration of gravity, in m / s2; δ is the vehicle rotation mass conversion coefficient; a x is the longitudinal acceleration, in m / s2; V X is the longitudinal speed.

[0047] (2) The lateral vehicle weight is determined based on the lateral acceleration signal and the lateral force signal through the lateral vehicle weight measurement model. That is, the quotient of the lateral force and the lateral acceleration of the commercial vehicle is determined as the lateral vehicle weight, which can be specifically expressed as:

[0048]

[0049] Among them, m y is the lateral weight of the commercial vehicle, in kg; F y is the resultant force of the commercial vehicle in the lateral direction, in N; a y is the lateral acceleration in m / s2.

[0050] (3) The vertical vehicle weight is determined by the vertical vehicle weight measurement model based on the vertical acceleration signal, the vertical force signal and the suspension system restoring force signal. That is, the vertical vehicle weight is determined as the quotient of the difference between the vertical force of the commercial vehicle and the suspension system restoring force and the vertical acceleration. Specifically, it can be expressed as:

[0051]

[0052] Among them, m z is the vertical weight of the commercial vehicle, in kg; F z is the resultant force of the commercial vehicle in the vertical direction, in N; F spring is the restoring force of the suspension system, in N; a z is the vertical acceleration in m / s2.

[0053] Step S206, weighted fusion processing is performed on the longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight to determine the initial vehicle weight measurement value, and the initial vehicle weight measurement value is smoothed by the Kalman filter method to determine the target vehicle weight measurement value. In one embodiment, a preset weight coefficient set can be obtained, and the sum of the products of the longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight and their corresponding weight coefficients is determined as the initial vehicle weight measurement value, wherein the preset weight coefficient set includes the weight coefficients corresponding to the longitudinal, lateral and vertical directions of the commercial vehicle, and the sum of each weight coefficient is equal to 1, which can be specifically expressed as:

[0054] m initial =w x ·m x +w y ·m y +w z ·m z

[0055] In the formula, m initial is the initial vehicle weight measurement value, in kg; w x 、w y 、w z They are the vehicle weight coefficients in the longitudinal, lateral and vertical directions, satisfying w x +w y +w z =1.

[0056] The vehicle weight measurement method of the commercial vehicle in the driving state provided by the embodiment of the present invention can significantly improve the accuracy of the vehicle weight measurement in the driving state.

[0057] When the initial vehicle weight measurement value is smoothed by the Kalman filtering method and the target vehicle weight measurement value is determined, the Kalman filter needs to be designed first: the actual driving state and force characteristics of the commercial vehicle need to be considered in designing the Kalman filter, that is, the state transfer matrix is ​​designed according to the trajectory of the vehicle weight state evolving over time. The state transfer matrix is ​​closely related to factors such as the vehicle's acceleration, speed, and driving environment. Then, an observation matrix is ​​designed to map the actual vehicle weight state of the commercial vehicle to the observation space (that is, the data space measured by the sensor). In addition, a control matrix is ​​used to consider the influence of external control inputs (such as braking force, driving force, etc.) on the vehicle weight state.

[0058] In practical applications, the implementation of the Kalman filter is an iterative process. It predicts the current state based on the optimal measurement value at the previous moment, and then uses the observation value at the current moment to correct the predicted value to obtain the optimal measurement value at the current moment. Specifically, the prediction step is to predict the mass measurement value and its error covariance matrix at the current moment based on the state transfer matrix and the optimal vehicle weight measurement value at the previous moment. The update step is to calculate the Kalman gain based on the observation value at the current moment and the observation noise covariance matrix. This gain reflects the importance of the observation value for correcting the predicted value. Then, the Kalman gain and the observation value are used to update the optimal vehicle weight measurement value and the error covariance matrix at the current moment. This process is repeated continuously to form a closed-loop feedback system, which allows the vehicle weight measurement value to gradually approach the true value while reducing the interference of high-frequency noise, see Figure 3 The schematic diagram of a vehicle weight data fusion is shown. The embodiment of the present invention also provides an implementation method of vehicle weight data fusion. For details, see (1) to (4) below:

[0059] (1) After obtaining the initial vehicle weight measurement value, Kalman filtering is used to smooth the vehicle weight data. Kalman filtering includes two steps: prediction and update. The prediction step includes vehicle weight prediction and covariance prediction. That is, according to the preset state transfer matrix and the initial vehicle weight measurement value at the previous moment, the predicted vehicle weight at the current moment and the prediction error covariance matrix corresponding to the predicted vehicle weight are determined. For details, see (A) to (B) below:

[0060] (A) The vehicle weight prediction formula is:

[0061]

[0062] in, is the predicted vehicle weight measurement at time k (based on the information at time k-1); A is the state transition matrix, which is used to describe the evolution of the system state from time k-1 to time k; is the optimal vehicle weight measurement value at time k-1; B is the control input matrix, which is used to describe the impact of the control input on the system state; u k is the control input at time k, representing the external control quantity of the system at that time.

[0063] (B) The covariance prediction formula is:

[0064] P k|k-1 =A·P k-1|k-1 ·A T +Q

[0065] Among them, P k|k-1 is the prediction error covariance matrix at time k (indicating prediction uncertainty); P k-1|k-1is the most accurate error covariance matrix at time k-1; Q is the process noise covariance matrix, which describes the influence of the internal process noise of the system.

[0066] (2) The Kalman gain can be determined according to the prediction error covariance matrix, that is, the observation value at the current moment is obtained, and the Kalman gain is determined according to the observation value and the prediction error covariance matrix, wherein the observation value includes: an observation matrix and an observation noise covariance matrix, and the Kalman gain is used to reflect the importance of the initial vehicle weight measurement value at the current moment to the correction of the predicted vehicle weight. In one embodiment, the observation matrix, the observation noise covariance matrix and the prediction error covariance matrix can be sent to a preset Kalman gain calculation model to determine the Kalman gain, wherein the preset Kalman gain calculation model is expressed as:

[0067] K k =P k|k-1 ·H T ·(H·P k|k-1 ·H T +R) -1

[0068] Among them, K k is the Kalman gain at time k, which is used to adjust the prediction result and weigh the relative credibility of the prediction and observation; H is the observation matrix, which is used to map the state to the observation space to reflect the relationship between the vehicle weight and the observation data (for example, the acceleration measured by the sensor); R is the observation noise covariance matrix, which is used to describe the influence of measurement noise, P k|k-1 is the prediction error covariance matrix at time k.

[0069] (3) In addition to the above-mentioned Kalman gain calculation, the updating step also includes vehicle weight update and covariance update, that is, the initial vehicle weight measurement value at the current moment is updated using the Kalman gain and the observation value, and the updated initial vehicle weight measurement value and the error covariance matrix corresponding to the initial vehicle weight measurement value are determined, so that the error covariance matrix is ​​used for iterative cycles to make the vehicle weight measurement value close to the true value. In one embodiment, the vehicle weight update formula is:

[0070]

[0071] in, is the optimal state measurement at time k (the updated result after combining the observed value); z k is the observed value (actual measurement value) at time k; It is the difference between the measured value and the predicted value, indicating the gap between the current measurement and the prediction.

[0072] In one implementation, the covariance update formula is:

[0073] P k|k =(IKk ·H)·P k|k-1

[0074] Among them, P k|k is the optimal error covariance matrix at time k (the updated uncertainty after combining the observed values); I is the unit matrix, which is used to maintain dimensional consistency.

[0075] (4) After obtaining the vehicle weight value after Kalman filter smoothing, the least squares method is used to perform error correction on the vehicle weight determined after the iteration cycle to determine the target vehicle weight measurement value. The least squares method minimizes the sum of squares of the measurement error, fits the data smoothed by Kalman filter with the historical data, and outputs a stable final vehicle weight measurement value m. final , specifically:

[0076]

[0077] in, is each measurement value after smoothing by Kalman filter; n is the number of historical measurements.

[0078] In one embodiment, based on the vehicle weight data after Kalman filtering smoothing, in order to further improve the accuracy of vehicle weight measurement of commercial vehicles, it is necessary to introduce the least squares method for error correction. The first is the collection and management of historical data. In the daily operation of commercial vehicles, as the vehicle travels, the total mass measurement values ​​at each moment are continuously generated and recorded, forming a rich historical data set. Database or time series analysis methods are usually used to effectively manage these historical data to ensure the integrity, accuracy and traceability of the data; then, a suitable mathematical model is constructed, which can accurately reflect the law of vehicle weight changes over time and consider various possible sources of error. Finally, the gradient descent method is used to solve the least squares problem to obtain the final vehicle weight measurement value.

[0079] In summary, the present invention can measure the vehicle weight of the commercial vehicle in the three degrees of freedom directions of longitudinal, lateral and vertical, and perform algorithm fusion to effectively and accurately measure the weight of the commercial vehicle and meet the dynamic weight measurement requirements of the commercial vehicle. In addition, the weighted average method, Kalman filtering and least squares method are applied to the vehicle weight measurement to obtain more accurate and stable vehicle weight measurement values ​​under different working conditions.

[0080] Regarding the vehicle weight measurement method for a commercial vehicle in a driving state provided in the aforementioned embodiment, an embodiment of the present invention provides a vehicle weight measurement device for a commercial vehicle in a driving state, see Figure 4 The structure diagram of a vehicle weight measuring device for a commercial vehicle in a driving state is shown in FIG. 1 , and the device includes the following parts:

[0081] The detection module 402 acquires a sensor signal set of the commercial vehicle, wherein the sensor signal set includes: a longitudinal vehicle speed signal, a longitudinal acceleration signal, a longitudinal driving force signal, a lateral acceleration signal, a lateral force signal, a vertical acceleration signal, a vertical force signal, and a suspension system restoring force signal;

[0082] The vehicle weight measurement module 404 performs signal analysis and processing on the sensor signal set by presetting a three-degree-of-freedom dynamic model of the commercial vehicle to determine the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight of the commercial vehicle;

[0083] The vehicle weight data fusion module 406 performs weighted fusion processing on the longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight to determine the initial vehicle weight measurement value, and smoothes the initial vehicle weight measurement value through the Kalman filter method to determine the target vehicle weight measurement value.

[0084] The vehicle weight measurement device for the commercial vehicle in the driving state provided in the embodiment of the present application can significantly improve the accuracy of the vehicle weight measurement in the driving state.

[0085] In one embodiment, the preset three-degree-of-freedom dynamic model of the commercial vehicle includes: a longitudinal vehicle weight measurement model, a lateral vehicle weight measurement model and a vertical vehicle weight measurement model. When performing signal analysis and processing on the sensor signal set through the preset three-degree-of-freedom dynamic model of the commercial vehicle to determine the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight of the commercial vehicle, the above-mentioned vehicle weight measurement module 404 is also used to: determine the longitudinal vehicle weight through the longitudinal vehicle weight measurement model based on the longitudinal vehicle speed signal, the longitudinal acceleration signal and the longitudinal driving force signal; determine the lateral vehicle weight through the lateral vehicle weight measurement model based on the lateral acceleration signal and the lateral force signal; determine the vertical vehicle weight through the vertical vehicle weight measurement model based on the vertical acceleration signal, the vertical force signal and the suspension system restoring force signal.

[0086] In one implementation manner, when the step of determining the longitudinal vehicle weight based on the longitudinal vehicle speed signal, the longitudinal acceleration signal and the longitudinal driving force signal by the longitudinal vehicle weight measurement model is performed, the vehicle weight measurement module 404 is further used to: send the longitudinal vehicle speed signal, the longitudinal acceleration signal and the longitudinal driving force signal to the longitudinal vehicle weight measurement model to determine the longitudinal vehicle weight, wherein the longitudinal vehicle weight measurement model is expressed as:

[0087]

[0088] Among them, m x F is the longitudinal weight of the commercial vehicle; t is the longitudinal driving force of the commercial vehicle; C D is the air resistance coefficient; A is the frontal area; Ψ is the road resistance coefficient; g is the acceleration of gravity; δ is the vehicle rotation mass conversion coefficient; a x is the longitudinal acceleration; VX is the longitudinal speed.

[0089] In one embodiment, when performing the step of determining the lateral vehicle weight based on the lateral acceleration signal and the lateral force signal through the lateral vehicle weight measurement model, the vehicle weight measurement module 404 is also used to: determine the quotient of the lateral force and the lateral acceleration of the commercial vehicle as the lateral vehicle weight.

[0090] In one embodiment, when performing the step of determining the vertical vehicle weight based on the vertical acceleration signal, the vertical force signal and the suspension system restoring force signal through the vertical vehicle weight measurement model, the above-mentioned vehicle weight measurement module 404 is also used to: determine the vertical vehicle weight as the quotient of the difference between the vertical force of the commercial vehicle and the suspension system restoring force and the vertical acceleration.

[0091] In one embodiment, when performing weighted fusion processing on the longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight to determine the initial vehicle weight measurement value, the above-mentioned vehicle weight data fusion module 406 is also used to: obtain a preset weight coefficient set, wherein the preset weight coefficient set includes weight coefficients corresponding to the longitudinal, lateral and vertical directions of the commercial vehicle, and the sum of each weight coefficient is equal to 1; respectively determine the sum of the products of the longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight and their corresponding weight coefficients as the initial vehicle weight measurement value.

[0092] In one embodiment, when performing the step of smoothing the initial vehicle weight measurement value through the Kalman filtering method to determine the target vehicle weight measurement value, the above-mentioned vehicle weight data fusion module 406 is also used to: determine the predicted vehicle weight at the current moment and the prediction error covariance matrix corresponding to the predicted vehicle weight according to the preset state transfer matrix and the initial vehicle weight measurement value at the previous moment; determine the Kalman gain according to the prediction error covariance matrix, and use the Kalman gain and the observation value to update the initial vehicle weight measurement value at the current moment, determine the updated initial vehicle weight measurement value, and the error covariance matrix corresponding to the initial vehicle weight measurement value, and use the error covariance matrix to perform iterative loops to make the vehicle weight measurement value approach the true value; perform error correction processing on the vehicle weight determined after the iterative loop through the least squares method to determine the target vehicle weight measurement value.

[0093] In one embodiment, when performing the step of determining the Kalman gain based on the prediction error covariance matrix, the vehicle weight data fusion module 406 is also used to: obtain the observation value at the current moment, and determine the Kalman gain based on the observation value and the prediction error covariance matrix, wherein the observation value includes: the observation matrix and the observation noise covariance matrix, and the Kalman gain is used to reflect the importance of the initial vehicle weight measurement value at the current moment to the corrected predicted vehicle weight.

[0094] In one implementation, when performing the step of determining the Kalman gain according to the observation value and the prediction error covariance matrix, the vehicle weight data fusion module 406 is further used to: send the observation matrix, the observation noise covariance matrix and the prediction error covariance matrix to a preset Kalman gain calculation model to determine the Kalman gain, wherein the preset Kalman gain calculation model is expressed as:

[0095] K k =P k|k-1 ·H T ·(H·P k|k-1 ·H T +R) -1

[0096] Among them, K k is the Kalman gain at time k, H is the observation matrix, R is the observation noise covariance matrix, P k|k-1 is the prediction error covariance matrix at time k.

[0097] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0098] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0099] Figure 5 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 50, a memory 51, a bus 52 and a communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected via the bus 52; the processor 50 is used to execute an executable module stored in the memory 51, such as a computer program.

[0100] The memory 51 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 53 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0101] The bus 52 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0102] Among them, the memory 51 is used to store programs, and the processor 50 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 50 or implemented by the processor 50.

[0103] The processor 50 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 50. The above processor 50 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 51, and the processor 50 reads the information in the memory 51 and completes the steps of the above method in combination with its hardware.

[0104] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0105] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, electronic device, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0106] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for measuring vehicle weight when a commercial vehicle is in motion, characterized in that: The method comprises: Acquire a sensor signal set of the commercial vehicle, wherein the sensor signal set includes: a longitudinal vehicle speed signal, a longitudinal acceleration signal, a longitudinal driving force signal, a lateral acceleration signal, a lateral force signal, a vertical acceleration signal, a vertical force signal, and a suspension system restoring force signal; By presetting a three-degree-of-freedom dynamics model of the commercial vehicle, performing signal analysis and processing on the sensor signal set, the longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight of the commercial vehicle are determined; The longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight are weightedly fused to determine an initial vehicle weight measurement value, and the initial vehicle weight measurement value is smoothed by a Kalman filter method to determine a target vehicle weight measurement value.

2. The vehicle weight measurement method of a commercial vehicle in a driving state according to claim 1, characterized in that: The preset three-degree-of-freedom dynamics model of the commercial vehicle includes: a longitudinal vehicle weight measurement model, a lateral vehicle weight measurement model and a vertical vehicle weight measurement model. The step of performing signal analysis and processing on the sensor signal set by using the preset three-degree-of-freedom dynamics model of the commercial vehicle to determine the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight of the commercial vehicle includes: Determining the longitudinal vehicle weight based on the longitudinal vehicle speed signal, the longitudinal acceleration signal and the longitudinal driving force signal by the longitudinal vehicle weight measurement model; Determining the lateral vehicle weight based on the lateral acceleration signal and the lateral force signal by using the lateral vehicle weight measurement model; The vertical vehicle weight is determined by the vertical vehicle weight measurement model based on the vertical acceleration signal, the vertical force signal and the suspension system restoring force signal.

3. The vehicle weight measurement method of a commercial vehicle in a driving state according to claim 2, characterized in that: The step of determining the longitudinal vehicle weight by the longitudinal vehicle weight measurement model based on the longitudinal vehicle speed signal, the longitudinal acceleration signal and the longitudinal driving force signal comprises: The longitudinal vehicle speed signal, the longitudinal acceleration signal and the longitudinal driving force signal are sent to the longitudinal vehicle weight measurement model to determine the longitudinal vehicle weight, wherein the longitudinal vehicle weight measurement model is expressed as: Among them, mx is the longitudinal weight of the commercial vehicle; Ft is the longitudinal driving force of the commercial vehicle; CD is the air resistance coefficient; A is the frontal area; Ψ is the road resistance coefficient; g is the acceleration due to gravity; δ is the vehicle rotation mass conversion factor; ax is the longitudinal acceleration; V X is the longitudinal speed.

4. The vehicle weight measurement method of a commercial vehicle in a driving state according to claim 2, characterized in that: The step of determining the lateral vehicle weight based on the lateral acceleration signal and the lateral force signal by using the lateral vehicle weight measurement model comprises: The quotient of the lateral force and the lateral acceleration of the commercial vehicle is determined as the lateral vehicle weight.

5. The vehicle weight measurement method of a commercial vehicle in a driving state according to claim 2, characterized in that: The step of determining the vertical vehicle weight by the vertical vehicle weight measurement model based on the vertical acceleration signal, the vertical force signal and the suspension system restoring force signal comprises: The vertical vehicle weight is determined as the quotient of the difference between the vertical force of the commercial vehicle and the restoring force of the suspension system and the vertical acceleration.

6. The vehicle weight measurement method of a commercial vehicle in a driving state according to claim 1, characterized in that: The step of performing weighted fusion processing on the longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight to determine the initial vehicle weight measurement value comprises: Obtaining a preset weight coefficient set, wherein the preset weight coefficient set includes weight coefficients corresponding to the longitudinal, lateral and vertical directions of the commercial vehicle, and the sum of each weight coefficient is equal to 1; The sum of the products of the longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight and the corresponding weight coefficients is determined as the initial vehicle weight measurement value.

7. The vehicle weight measurement method of a commercial vehicle in a driving state according to claim 1, characterized in that: The step of smoothing the initial vehicle weight measurement value by a Kalman filter method to determine a target vehicle weight measurement value includes: Determine the predicted vehicle weight at the current moment and the prediction error covariance matrix corresponding to the predicted vehicle weight according to a preset state transfer matrix and the initial vehicle weight measurement value at the previous moment; Determine the Kalman gain according to the prediction error covariance matrix, and use the Kalman gain and the observation value to update the initial vehicle weight measurement value at the current moment, determine the updated initial vehicle weight measurement value, and the error covariance matrix corresponding to the initial vehicle weight measurement value, and use the error covariance matrix to iterate and cycle, so that the vehicle weight measurement value approaches the true value; The vehicle weight determined after the iteration cycle is completed is corrected by the least square method to determine the target vehicle weight measurement value.

8. The vehicle weight measurement method of a commercial vehicle in a driving state according to claim 7, characterized in that: The step of determining the Kalman gain according to the prediction error covariance matrix comprises: Obtain the observation value at the current moment, and determine the Kalman gain based on the observation value and the prediction error covariance matrix, wherein the observation value includes: an observation matrix and an observation noise covariance matrix, and the Kalman gain is used to reflect the importance of the initial vehicle weight measurement value at the current moment to correcting the predicted vehicle weight.

9. The vehicle weight measurement method of a commercial vehicle in a driving state according to claim 8, characterized in that: The step of determining the Kalman gain according to the observed value and the prediction error covariance matrix comprises: The observation matrix, the observation noise covariance matrix and the prediction error covariance matrix are sent to a preset Kalman gain calculation model to determine the Kalman gain, wherein the preset Kalman gain calculation model is expressed as: K k =P k|k-1 ·H T ·(H·P k|k-1 ·H T +R) -1 Among them, K k is the Kalman gain at time k, H is the observation matrix, R is the observation noise covariance matrix, and Pk|k-1 is the prediction error covariance matrix at time k.

10. A vehicle weight measuring device for a commercial vehicle in driving state, characterized in that: The device comprises: A detection module, which obtains a sensor signal set of the commercial vehicle, wherein the sensor signal set includes: a longitudinal vehicle speed signal, a longitudinal acceleration signal, a longitudinal driving force signal, a lateral acceleration signal, a lateral force signal, a vertical acceleration signal, a vertical force signal, and a suspension system restoring force signal; The vehicle weight measurement module performs signal analysis and processing on the sensor signal set by presetting a three-degree-of-freedom dynamics model of the commercial vehicle to determine the longitudinal vehicle weight, lateral vehicle weight and vertical vehicle weight of the commercial vehicle; The vehicle weight data fusion module performs weighted fusion processing on the longitudinal vehicle weight, the lateral vehicle weight and the vertical vehicle weight to determine an initial vehicle weight measurement value, and smoothes the initial vehicle weight measurement value through a Kalman filter method to determine a target vehicle weight measurement value.

11. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 9.

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