Dynamic truck scale automatic calibration method

Abstract

The invention discloses a dynamic truck scale automatic calibration method, and relates to the technical field of metering, and the method comprises the steps: collecting pressure data generated when a vehicle passes through a truck scale in real time through the truck scale; the method comprises the following steps: acquiring and recording dynamic information when a vehicle passes through a truck scale through speed measuring equipment, wherein the dynamic information comprises the speed and acceleration of the vehicle; collecting and recording a plurality of groups of data, and converging into a data set; the data of the data set is preprocessed, abnormal values are removed, and smoothing processing is carried out on the data; according to the dynamic motor truck scale automatic calibration method provided by the invention, the pressure data generated when the vehicle passes through the motor truck scale and the dynamic information of the vehicle are collected in real time, so that the actual action condition of the vehicle on the motor truck scale can be reflected more comprehensively, and the calibration accuracy and reliability are improved; the method has the advantages of high calibration accuracy, strong adaptability, high efficiency and good convenience, and is of great significance for improving the measurement accuracy and reliability of the dynamic truck scale.

Description

Technical Field

[0001] The invention relates to the technical field of measurement, and in particular to an automatic calibration method for a dynamic truck scale. Background Art

[0002] In order to solve the problem that in the existing dynamic truck scale technology, the weight measurement of the vehicle often depends on the static calibration result of the truck scale, that is, when the truck scale is installed or regularly maintained, it is calibrated with a standard vehicle of known weight to ensure the accuracy and reliability of the truck scale. However, in actual use, due to the influence of environmental factors such as temperature, humidity, equipment aging, sensor accuracy changes and other factors, the measurement accuracy of the truck scale will gradually decrease, resulting in a deviation between the measurement result and the actual vehicle weight. When measuring the weight of a moving vehicle, the dynamic truck scale also needs to consider the dynamic characteristics of the vehicle, such as speed, acceleration, etc. These factors will affect the measurement results. Traditional calibration methods often ignore these dynamic factors, or simply correct them through empirical formulas, resulting in less than ideal calibration results. In this regard, we propose an automatic calibration method for a dynamic truck scale. Summary of the invention

[0003] In order to solve the above technical problems, a dynamic vehicle scale automatic calibration method is provided. This technical solution solves the above problems.

[0004] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0005] A dynamic vehicle scale automatic calibration method, comprising:

[0006] Use the truck scale to collect real-time pressure data generated when the vehicle passes through the truck scale;

[0007] The dynamic information of the vehicle passing through the truck scale is obtained and recorded by the speed measuring device, and the dynamic information includes: the speed and acceleration of the vehicle;

[0008] Collect and record several sets of data and aggregate them into data sets;

[0009] Preprocess the data of the data set, remove outliers and smooth the data;

[0010] Based on the processed data, a relationship model between vehicle weight and sensor output is established, the real-time monitoring data is input into the model, the estimated weight of the current vehicle is output, the calibration parameters are calculated through the weight recognized by the truck scale and the estimated weight, and the weight display value of the dynamic truck scale is automatically calibrated through the calibration parameters.

[0011] Preferably, the real-time collection of pressure data generated when a vehicle passes through the truck scale by the truck scale specifically includes:

[0012] Install sensors evenly distributed at the four corners and the middle of the weighing platform of the truck scale;

[0013] Use wired connection to transmit the data information collected by the sensor;

[0014] Install a vehicle detection device to detect whether a vehicle has entered the truck scale area. When a vehicle is detected to have entered, the sensor is triggered to start data collection;

[0015] When a vehicle passes through the truck scale, the pressure sensor will sense the pressure exerted by the vehicle on the scale in real time, collect the sensor signal at a preset sampling frequency, record the pressure data at each moment during the vehicle's passage, and convert the pressure signal into an electrical signal for external transmission.

[0016] Preferably, the calculation method of the pressure involved in the truck scale is:

[0017] Among them, the method for calculating the pressure of a single sensor is:

[0018]

[0019] In the formula, P represents the pressure value of the sensor, V represents the electrical signal output by the sensor, and k represents the pressure sensitivity coefficient;

[0020] Among them, the method for calculating the total pressure of the truck scale is:

[0021]

[0022] Where P total It represents the total pressure value of the truck scale, n represents the total number of pressure sensors, i represents the i-th sensor, P i Represents the pressure value P measured by the i-th sensor;

[0023] The average pressure is calculated to reflect the pressure distribution of the vehicle on the truck scale. The calculation method of the average pressure is:

[0024]

[0025] Where P avg Indicates the average pressure value.

[0026] Preferably, obtaining and recording dynamic information of a vehicle passing through a truck scale by means of a speed measuring device specifically includes:

[0027] Install the radar speed meter and acceleration sensor in a fixed position near the truck scale;

[0028] When a vehicle passes through the truck scale, the speed measuring device starts working and obtains the vehicle's speed data and acceleration data information in real time;

[0029] The vehicle speed calculation formula is:

[0030]

[0031] In the formula, f0 represents the frequency of electromagnetic waves emitted by the speed meter, f represents the frequency of the reflected electromagnetic waves, c represents the propagation speed of the electromagnetic waves, and s represents the speed of the vehicle relative to the speed measuring device;

[0032] The vehicle acceleration calculation formula is:

[0033]

[0034] In the formula, a represents the acceleration of the vehicle, j represents the jth moment, t represents time, and s j represents the speed of the vehicle at the jth moment, t j Represents the time corresponding to the jth moment.

[0035] Preferably, preprocessing the data of the data set, removing outliers and smoothing the data specifically includes:

[0036] The data collected by the sensor is set as a data point, and the data collected by several sensors is a data set. The data set is represented by {x1,x2,...,x h}, calculate the mean and standard deviation of the data set;

[0037] The mean calculation formula is:

[0038]

[0039] In the formula, represents the mean of the data collected by several sensors, h represents the total number of data points, l represents the lth data point, x represents the data point, and x l represents the lth data point x;

[0040] The standard deviation calculation formula is:

[0041]

[0042] In the formula, b represents the standard deviation of the data collected by several sensors;

[0043] For a dataset {x1,x2,...,x h}, assuming that the window size is m, the smoothed data sequence is {y1,y2,y3,...,y h-m+1};

[0044] The moving average calculation formula is:

[0045]

[0046] In the formula, y l Represents the smoothed data.

[0047] Preferably, establishing a relationship model between vehicle weight and sensor output based on the processed data specifically includes:

[0048] Arrange the preprocessed and smoothed data set to ensure that the data set contains the pressure data, speed, acceleration and corresponding known vehicle weight information when the vehicle passes through the truck scale;

[0049] Divide the data set into a training set and a test set. The training set is used to build the model, and the test set is used to evaluate the performance of the model.

[0050] Construct a relationship model between vehicle weight and sensor output, where the model expression is:

[0051]

[0052] Where W represents the vehicle weight, β0,β1,...,β n ,γ1,γ2 represent the regression coefficients to be determined, ∈ represents the error term;

[0053] Use the training set data to train the model and estimate the regression coefficient by the least squares method. For each set of data in the training set, substitute it into the model to obtain an equation. By solving the equation for all training data, the residual sum of squares is minimized to determine the value of the regression coefficient.

[0054] Use the test set data to evaluate the trained model and calculate the prediction error of the model on the test set;

[0055] When the model is evaluated to meet the requirements, it is applied to the real-time monitoring data.

[0056] Preferably, the residual sum of squares is calculated as:

[0057]

[0058] In the formula, SSE represents the residual sum of squares, W e Indicates the actual vehicle weight, Represents the vehicle weight predicted by the model.

[0059] Preferably, the regression coefficient calculation formula is:

[0060] [β0,β1,...,β n ,γ1,γ2]=(G R G) -1 G R U

[0061] In the formula, G represents the independent variable matrix, and U represents the dependent variable vector, that is, the actual vehicle weight vector.

[0062] Preferably, calculating calibration parameters by using the weight recognized by the truck scale and the estimated weight, and automatically calibrating the weight display value of the dynamic truck scale by using the calibration parameters specifically includes:

[0063] When a vehicle passes through the truck scale, sensor data, speed and acceleration are collected in real time;

[0064] Substitute the real-time collected data into the established model to calculate the estimated weight of the current vehicle;

[0065] The weight is measured by the truck scale display, and the weight display value of the dynamic truck scale is automatically calibrated through the calibration parameters.

[0066] Preferably, the calculation formula for calculating the calibration parameters is:

[0067]

[0068] Where C represents the calibration parameter, Represents the estimated weight of the current vehicle, W d Indicates the weight displayed by the truck scale.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] An automatic calibration method for a dynamic truck scale proposed in the present invention can more comprehensively reflect the actual effect of the vehicle on the truck scale by real-time collecting pressure data generated when the vehicle passes through the truck scale and the dynamic information of the vehicle, thereby improving the accuracy and reliability of the calibration. A relationship model between vehicle weight and sensor output is established based on the collected data. The model can consider the influence of various factors on the measurement results, including environmental factors, equipment aging, changes in sensor accuracy and dynamic characteristics of the vehicle. Therefore, it can better adapt to actual conditions, improve measurement accuracy and stability, and realize automatic calibration of the dynamic truck scale without manual intervention, which greatly improves the efficiency and convenience of calibration. It has high calibration accuracy, strong adaptability, high efficiency and good convenience, and is of great significance for improving the measurement accuracy and reliability of the dynamic truck scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 These are the method steps of the present invention. DETAILED DESCRIPTION

[0072] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0073] Reference Figure 1 As shown, a dynamic vehicle scale automatic calibration method comprises:

[0074] The truck scale can collect real-time pressure data generated when the vehicle passes through the truck scale. The high-precision sensor installed on the truck scale will keenly sense the pressure exerted by the vehicle on the scale and convert it into an electronic signal for subsequent processing. These pressure data reflect the impact of the vehicle on the truck scale under different conditions, providing important basic information for accurately measuring the vehicle weight.

[0075] Speed ​​measuring equipment is used to obtain and record dynamic information of vehicles passing through the truck scale. The dynamic information here mainly includes the speed and acceleration of the vehicle. Professional speed measuring equipment can accurately measure the vehicle's driving speed and acceleration changes when it passes through the truck scale. This dynamic information is crucial for a comprehensive understanding of the vehicle's driving status and accurate calibration of the truck scale;

[0076] Collect and record several sets of data, and aggregate these data from different aspects into a complete data set, which includes the pressure data collected by the truck scale, the speed and acceleration data obtained by the speed measuring equipment. By continuously collecting and accumulating data, it can provide rich samples for subsequent analysis and processing;

[0077] The data of this data set is preprocessed. During the preprocessing process, on the one hand, outliers should be removed. Through scientific statistical methods and judgment criteria, data points that are obviously inconsistent with the overall data trend should be identified and removed from the data set to ensure the accuracy and reliability of the data. On the other hand, the data should be smoothed and appropriate algorithms and techniques should be used to reduce noise and fluctuations in the data to make the data more stable and continuous.

[0078] Based on the processed data, a relationship model between vehicle weight and sensor output is established, and the real-time monitoring data is input into this model. The model will calculate the estimated weight of the current vehicle based on the established relationship, and calculate the calibration parameters by comparing the weight recognized by the truck scale with the estimated weight. Then, these calibration parameters are used to automatically calibrate the weight display value of the dynamic truck scale, making the measurement results of the truck scale more accurate and reliable.

[0079] The calculation method for the pressure involved in the truck scale is:

[0080] Among them, the method for calculating the pressure of a single sensor is:

[0081]

[0082] In the formula, P represents the pressure value of the sensor, V represents the electrical signal output by the sensor, and k represents the pressure sensitivity coefficient;

[0083] Among them, the method for calculating the total pressure of the truck scale is:

[0084]

[0085] Where P total It represents the total pressure value of the truck scale, n represents the total number of pressure sensors, i represents the i-th sensor, P i Represents the pressure value P measured by the i-th sensor;

[0086] The average pressure is calculated to reflect the pressure distribution of the vehicle on the truck scale. The calculation method of the average pressure is:

[0087]

[0088] Where P avg Indicates the average pressure value.

[0089] The dynamic information obtained by the speed measuring device when the vehicle passes through the truck scale includes:

[0090] Install the radar speed meter and acceleration sensor in a fixed position near the truck scale;

[0091] When a vehicle passes through the truck scale, the speed measuring device starts working and obtains the vehicle's speed data and acceleration data information in real time;

[0092] The vehicle speed calculation formula is:

[0093]

[0094] In the formula, f0 represents the frequency of electromagnetic waves emitted by the speed meter, f represents the frequency of the reflected electromagnetic waves, c represents the propagation speed of the electromagnetic waves, and s represents the speed of the vehicle relative to the speed measuring device;

[0095] The vehicle acceleration calculation formula is:

[0096]

[0097] In the formula, a represents the acceleration of the vehicle, j represents the jth moment, t represents time, and s j represents the speed of the vehicle at the jth moment, t j Represents the time corresponding to the jth moment.

[0098] Preprocess the data of the data set, remove outliers and smooth the data, including:

[0099] The data collected by the sensor is set as a data point, and the data collected by several sensors is a data set. The data set is represented by {x1,x2,...,x h}, calculate the mean and standard deviation of the data set;

[0100] The mean calculation formula is:

[0101]

[0102] In the formula, represents the mean of the data collected by several sensors, h represents the total number of data points, l represents the lth data point, x represents the data point, and x l represents the lth data point x;

[0103] The standard deviation calculation formula is:

[0104]

[0105] In the formula, b represents the standard deviation of the data collected by several sensors;

[0106] For a dataset {x1,x2,...,x h}, assuming that the window size is m, the smoothed data sequence is {y1,y2,y3,...,y h-m+1};

[0107] The moving average calculation formula is:

[0108]

[0109] In the formula, y l Represents the smoothed data.

[0110] Construct a relationship model between vehicle weight and sensor output, where the model expression is:

[0111]

[0112] Where W represents the vehicle weight, β0,β1,...,β n ,γ1,γ2 represent the regression coefficients to be determined, ∈ represents the error term;

[0113] Use the training set data to train the model and estimate the regression coefficient by the least squares method. For each set of data in the training set, substitute it into the model to obtain an equation. By solving the equation for all training data, the residual sum of squares is minimized to determine the value of the regression coefficient.

[0114] Use the test set data to evaluate the trained model and calculate the prediction error of the model on the test set;

[0115] When the model is evaluated to meet the requirements, it is applied to the real-time monitoring data.

[0116] The residual sum of squares is calculated as:

[0117]

[0118] In the formula, SSE represents the residual sum of squares, W e Indicates the actual vehicle weight, Represents the vehicle weight predicted by the model.

[0119] The regression coefficient calculation formula is:

[0120] [β0,β1,...,β n ,γ1,γ2]=(G R G) -1 G R U

[0121] In the formula, G represents the independent variable matrix, and U represents the dependent variable vector, that is, the actual vehicle weight vector.

[0122] The calibration parameters are calculated by the weight recognized by the truck scale and the estimated weight. The weight display value of the dynamic truck scale is automatically calibrated by the calibration parameters, including:

[0123] When a vehicle passes through the truck scale, sensor data, speed and acceleration are collected in real time;

[0124] Substitute the real-time collected data into the established model to calculate the estimated weight of the current vehicle;

[0125] The weight is measured by the truck scale display, and the weight display value of the dynamic truck scale is automatically calibrated through the calibration parameters.

[0126] The calculation formula for calculating the calibration parameters is:

[0127]

[0128] Where C represents the calibration parameter, Represents the estimated weight of the current vehicle, W d Indicates the weight displayed by the truck scale.

[0129] The use process of the present invention is as follows: pressure data of a vehicle passing through is collected by a truck scale, dynamic information such as vehicle speed and acceleration is obtained by a speed measuring device, multiple groups of data are collected to form a data set, which is pre-processed to remove abnormal values ​​and smoothed, a vehicle weight and sensor output relationship model is established based on the processed data, real-time monitoring data is input into the model to obtain an estimated weight, calibration parameters are calculated using the truck scale identification weight and the estimated weight, and the dynamic truck scale weight display value is automatically calibrated.

[0130] In summary, the advantages of the present invention are: real-time calibration, improved accuracy, and establishment of a relationship model based on vehicle dynamic information; comprehensive consideration of dynamic factors to improve accuracy in a more practical manner; high degree of automation, automatic collection and processing of data, and calculation of calibration parameters to adjust display values; strong adaptability, and accurate calibration for different vehicles and environments; and improved equipment service life to avoid error accumulation and damage to the equipment.

[0131] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A dynamic vehicle scale automatic calibration method, characterized in that: include: Use the truck scale to collect real-time pressure data generated when the vehicle passes through the truck scale; The dynamic information of the vehicle passing through the truck scale is obtained and recorded by the speed measuring device, and the dynamic information includes: the speed and acceleration of the vehicle; Collect and record several sets of data and aggregate them into data sets; Preprocess the data of the data set, remove outliers and smooth the data; Based on the processed data, a relationship model between vehicle weight and sensor output is established, the real-time monitoring data is input into the model, the estimated weight of the current vehicle is output, the calibration parameters are calculated through the weight recognized by the truck scale and the estimated weight, and the weight display value of the dynamic truck scale is automatically calibrated through the calibration parameters.

2. The method for automatic calibration of a dynamic truck scale according to claim 1, characterized in that: The pressure data generated by the vehicle passing through the truck scale is collected in real time through the truck scale, including: Install sensors evenly distributed at the four corners and the middle of the weighing platform of the truck scale; Use wired connection to transmit the data information collected by the sensor; Install a vehicle detection device to detect whether a vehicle has entered the truck scale area. When a vehicle is detected to have entered, the sensor is triggered to start data collection; When a vehicle passes through the truck scale, the pressure sensor will sense the pressure exerted by the vehicle on the scale in real time, collect the sensor signal at a preset sampling frequency, record the pressure data at each moment during the vehicle's passage, and convert the pressure signal into an electrical signal for external transmission.

3. The method for automatic calibration of a dynamic truck scale according to claim 2, characterized in that: The calculation method for the pressure involved in the truck scale is: Among them, the method for calculating the pressure of a single sensor is: In the formula, P represents the pressure value of the sensor, V represents the electrical signal output by the sensor, and k represents the pressure sensitivity coefficient; Among them, the method for calculating the total pressure of the truck scale is: Where P total It represents the total pressure value of the truck scale, n represents the total number of pressure sensors, i represents the i-th sensor, P i Represents the pressure value P measured by the i-th sensor; The average pressure is calculated to reflect the pressure distribution of the vehicle on the truck scale. The calculation method of the average pressure is: Where P avg Indicates the average pressure value.

4. The method for automatic calibration of a dynamic truck scale according to claim 1, characterized in that: The dynamic information obtained by the speed measuring device when the vehicle passes through the truck scale includes: Install the radar speed meter and acceleration sensor in a fixed position near the truck scale; When a vehicle passes through the truck scale, the speed measuring device starts working and obtains the vehicle's speed data and acceleration data information in real time; The vehicle speed calculation formula is: In the formula, f0 represents the frequency of electromagnetic waves emitted by the speed meter, f represents the frequency of the reflected electromagnetic waves, c represents the propagation speed of the electromagnetic waves, and s represents the speed of the vehicle relative to the speed measuring device; The vehicle acceleration calculation formula is: In the formula, a represents the acceleration of the vehicle, j represents the jth moment, t represents time, and s j represents the speed of the vehicle at the jth moment, t j Represents the time corresponding to the jth moment.

5. The method for automatic calibration of a dynamic truck scale according to claim 1, characterized in that: Preprocess the data of the data set, remove outliers and smooth the data, including: The data collected by the sensor is set as a data point, and the data collected by several sensors is a data set. The data set is represented by {x1,x2,...,x h }, calculate the mean and standard deviation of the data set; The mean calculation formula is: In the formula, represents the mean of the data collected by several sensors, h represents the total number of data points, l represents the lth data point, x represents the data point, and x l represents the lth data point x; The standard deviation calculation formula is: In the formula, b represents the standard deviation of the data collected by several sensors; For a dataset {x1,x2,...,x h }, assuming that the window size is m, the smoothed data sequence is {y1,y2,y3,...,y h-m+1 }; The moving average calculation formula is: In the formula, y l Represents the smoothed data.

6. The method for automatic calibration of a dynamic truck scale according to claim 1, characterized in that: Based on the processed data, the relationship model between vehicle weight and sensor output is established, including: Arrange the preprocessed and smoothed data set to ensure that the data set contains the pressure data, speed, acceleration and corresponding known vehicle weight information when the vehicle passes through the truck scale; Divide the data set into a training set and a test set. The training set is used to build the model, and the test set is used to evaluate the performance of the model. Construct a relationship model between vehicle weight and sensor output, where the model expression is: Where W represents the vehicle weight, β0,β1,...,β n ,γ1,γ2 represent the regression coefficients to be determined, ∈ represents the error term; Use the training set data to train the model and estimate the regression coefficient by the least squares method. For each set of data in the training set, substitute it into the model to obtain an equation. By solving the equation for all training data, the residual sum of squares is minimized to determine the value of the regression coefficient. Use the test set data to evaluate the trained model and calculate the prediction error of the model on the test set; When the model is evaluated to meet the requirements, it is applied to the real-time monitoring data.

7. A dynamic vehicle scale automatic calibration method according to claim 6, characterized in that: The residual sum of squares is calculated as: In the formula, SSE represents the residual sum of squares, W e Indicates the actual vehicle weight, Represents the vehicle weight predicted by the model.

8. The method for automatic calibration of a dynamic truck scale according to claim 6, characterized in that: The regression coefficient calculation formula is: [β0, β1,..., β n ,γ1,γ2]=(G R G) -1 G R U In the formula, G represents the independent variable matrix, and U represents the dependent variable vector, that is, the actual vehicle weight vector.

9. The method for automatic calibration of a dynamic truck scale according to claim 1, characterized in that: The calibration parameters are calculated by the weight recognized by the truck scale and the estimated weight. The weight display value of the dynamic truck scale is automatically calibrated by the calibration parameters, including: When a vehicle passes through the truck scale, sensor data, speed and acceleration are collected in real time; Substitute the real-time collected data into the established model to calculate the estimated weight of the current vehicle; The weight is measured by the truck scale display, and the weight display value of the dynamic truck scale is automatically calibrated through the calibration parameters.

10. The method for automatic calibration of a dynamic truck scale according to claim 1, characterized in that: The calculation formula for calculating the calibration parameters is: Where C represents the calibration parameter, Represents the estimated weight of the current vehicle, W d Indicates the weight displayed by the truck scale.

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