CN109163790B - Vehicle dynamic weighing system and method based on multiple sensors - Google Patents
Vehicle dynamic weighing system and method based on multiple sensors Download PDFInfo
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- CN109163790B CN109163790B CN201810997241.6A CN201810997241A CN109163790B CN 109163790 B CN109163790 B CN 109163790B CN 201810997241 A CN201810997241 A CN 201810997241A CN 109163790 B CN109163790 B CN 109163790B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
- G01G19/02—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles
- G01G19/03—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles for weighing during motion
Abstract
The invention discloses a vehicle dynamic weighing system and a method based on multiple sensors, wherein the system comprises a sensor module, a data acquisition system and a main control module; the sensor module comprises a plurality of weighing sensors arranged on the weighing platform and is used for measuring weight data of each axle of the vehicle passing through the weighing platform in real time; the data acquisition system is used for acquiring weight data of all the weighing sensors, storing the data and sending the data to the main control module; the main control module comprises an algorithm subsystem, the algorithm subsystem is used for processing the weight data and fusing the data of the weighing sensors by using a weighted fusion algorithm to obtain a weight coefficient; the total weight information of the vehicle is obtained by judging the position of each axle of the vehicle and then adding the corresponding sensor values multiplied by the weighting coefficients. The system can obtain the relevant information of the vehicle and improve the measurement precision of the vehicle weight.
Description
Technical Field
The invention belongs to the technical field of vehicle weighing, and particularly relates to a vehicle dynamic weighing system and method based on multiple sensors.
Background
With the rapid development of the road transportation industry, the vehicle dynamic weighing technology has become a key technology and development direction for vehicle load measurement. The dynamic weighing of the vehicle is to weigh the vehicle on the premise that the vehicle does not stop or decelerate. Compared with the traditional static weighing measurement method, the dynamic weighing of the vehicle has the characteristics of high speed, high efficiency and small influence on the throughput capacity of the road, and can obtain the total weight of the vehicle and the related information such as the number of axles, the axle weight, the vehicle speed and the like.
For dynamic weighing of vehicles, the currently common data processing methods mainly include: filtering methods, EMD (empirical mode decomposition) methods, and the like. Because the vehicle axle load signal and the vehicle vibration interference signal are low-frequency signals, the simple filtering method can remove the low-frequency interference and cause the condition that partial axle load information is lost; although the EMD can effectively decompose nonlinear and non-stationary signals, the capacity of processing data in real time is poor, and the requirement of practical application is difficult to meet.
Therefore, it is very critical to design a multi-sensor vehicle weight extraction algorithm with high precision and real-time data processing capability.
Disclosure of Invention
The invention provides a vehicle dynamic weighing system and a vehicle dynamic weighing method based on multiple sensors, aiming at the existing problems, the system can process data in real time, can obtain vehicle related information and improve the vehicle weight measurement precision.
In order to achieve the purpose, the invention adopts the technical scheme that:
a vehicle dynamic weighing system based on multiple sensors comprises a sensor module, a data acquisition system and a main control module;
the sensor module comprises a plurality of weighing sensors arranged on the weighing platform and is used for measuring weight data of each axle of the vehicle passing through the weighing platform in real time;
the data acquisition system is used for acquiring weight data of all the weighing sensors, storing the data and sending the data to the main control module;
the main control module comprises an algorithm subsystem, the algorithm subsystem is used for processing the weight data and fusing the data of the weighing sensors by using a weighted fusion algorithm to obtain a weight coefficient; the total weight information of the vehicle is obtained by judging the position of each axle of the vehicle and then adding the corresponding sensor values multiplied by the weighting coefficients.
As a further improvement of the invention, the weighing platform is formed by connecting a plurality of weighing platforms in sequence, and a group of weighing sensors are respectively arranged at the front end and the rear end of each weighing platform.
As a further improvement of the present invention, the sensor module further includes a vehicle separator for determining whether a vehicle is driven into the weighing system.
As a further improvement of the invention, the data acquisition system comprises an amplifying circuit, an analog-to-digital converter, a data acquisition system main control module, a communication module and a data storage system;
the amplifying circuit is used for amplifying the acquired original weight data;
the analog-to-digital converter is used for performing analog-to-digital conversion on the amplified data;
the data acquisition system main control module is used for collecting all data after analog-to-digital conversion and sending the data to the communication module and the data storage system;
the data storage system is used for storing data;
and the communication module is used for sending the data to the algorithm subsystem.
As a further improvement of the invention, the algorithm subsystem is also used to calculate the gross weight, axle count and speed of the vehicle:
the total weight of the vehicle is obtained by judging the weighing weight of each axle of the vehicle and adding the corresponding sensor values;
the number of the axles is obtained by the number of times of axle signals on a front sensor of a first weighing block in the signal duration of the vehicle separator;
the axle weight is the difference between the weight of the rear weighting block and the front weighting block of the upper block of the corresponding axle;
the vehicle speed is obtained by the time difference of sudden change caused by the weight on the shaft and the weight on the lower shaft, and the average value of the vehicle speed on each shaft is the vehicle speed.
A dynamic weighing method of a vehicle based on multiple sensors comprises the following steps:
measuring original weight data of each axle of the vehicle when the axle passes through the weighing platform;
performing de-reference processing and filtering preprocessing on the original weight data;
processing the weight data, and fusing the data of the weighing sensors by using a weighted fusion algorithm to obtain a weight coefficient; the total weight information of the vehicle is obtained by judging the position of each axle of the vehicle and then adding the corresponding sensor values multiplied by the weighting coefficients.
The processing steps of the weight data are as follows: the detection and identification of the axle signals are carried out by adopting a linear analysis method, the processes of weighing weight blocks on the vehicle and weighing weight blocks below the vehicle are identified, and characteristic values of each process are extracted.
As a further improvement of the invention, sampling is carried out at intervals, axle signals are detected and identified by processing weight data and adopting a linear analysis method, processes of weighing weight blocks on a vehicle and weighing weight blocks below the vehicle are identified, and characteristic values of each process are extracted.
As a further improvement of the invention, the method for linear analysis comprises the following specific steps:
the sampled value waveform is represented as:
(t1,y1),(t2,y2),(t3,y3),...,(tn,yn)
wherein, tnIndicating the nth point, y, collected by the sensor from the sampling instantnIs the sample value of the nth point; selection (t)1，y1) As a starting point, when a subsequent point (t)n++2,yn+2) And front point (t)n+1,yn+1) Is not more than the set α value, connect (t)n,yn) And (t)n+2,yn+2) Substitution (t)n,yn) And (t)n+1,yn+1) The line segment between (t) when the difference between the sampled values exceeds αn,yn) And (t)n+1,yn+1) The line segment between ends at (t)n+1,yn+1) As a starting point, this process is repeated;
the processed waveform of the sampled value is divided into the following sections:
S1，S2，...，Sk,...,Sn
Sk＝(tk b,yk b),(tk e,yk e)
wherein (t)k b,yk b) Represents the starting point of line segment k, (t)k e,yk e) Representing the end point of the line segment k;
calculating the slope of each line segment by the algorithm, and then selecting the line segment with the large absolute value of the slope as an axis signal: the signal with k being greater than th is the on-axis weighing block signal, k is the slope of the line segment, and th is the selected positive threshold; the signal of k < ts) is a weight weighing signal under the shaft, k is the slope of a line segment, and ts is a selected positive threshold;
setting an own axle signal threshold value for each vehicle, and acquiring the peak value y of a sensor in front of a first weighing block on a first axle of the vehiclehLet positive threshold th be 0.13 x yhLet negative threshold ts equal to 0.11 × yh；
The driving direction of the vehicle is judged by the slope of the front and rear sensor signals: when the current collected signal is an on-axis weighing block signal or the rear sensor signal is an off-axis block signal, the vehicle runs forwards; when the current collected signal is an off-axis weight signal or a rear sensor signal is an on-axis weight signal, the vehicle runs in the opposite direction;
and converting the signal of the weighing sensor into a 0-1 signal by using the set threshold value.
As a further improvement of the invention, the extraction axle is divided into an upper weighing block and a lower weighing block on the axle:
first, two queues are constructed: axleonblk, waittocheck and an array: axle; according to an axis detection algorithm, when a signal of weighing the weight on an axis is obtained, recording the information of the current weighing weight, and pressing the information into an axleonblk queue; when obtaining an off-axis block signal, transferring the axle signal from the axlenblk queue to the waittocheck queue; judging whether a vehicle arrives according to the change of a vehicle separator signal, and constructing a two-dimensional array axle of the number of axles x n according to the number of axles; and sequentially storing the axle signals in the waittocheck queue into axle, and further storing the related information into the array.
As a further improvement of the invention, the method also comprises the steps of calculating the whole weight, the axle number and the speed of the vehicle; the specific calculation method is as follows:
the total weight of the vehicle is obtained by judging the weighing weight of each axle of the vehicle and adding the corresponding sensor values;
the number of the axles is obtained by the number of times of axle signals on a front sensor of a first weighing block in the signal duration of the vehicle separator;
the axle weight is the difference between the weight of the rear weighting block and the front weighting block of the upper block of the corresponding axle;
the vehicle speed is obtained by the time difference of sudden change caused by the weight on the shaft and the weight on the lower shaft, and the average value of the vehicle speed on each shaft is the vehicle speed.
The invention has the following advantages and beneficial effects:
the dynamic weighing system for the vehicle comprises a plurality of weighing sensors, wherein the weighing sensors are used for measuring weight data of each axle of the vehicle passing through a weighing platform in real time; the weighting processing is carried out to obtain the total weight, the total weight is specifically realized by an algorithm subsystem in the main control module, the weight data are processed firstly, and the total weight information of the vehicle is obtained by judging the positions of each axle of the vehicle and then multiplying the corresponding sensor values by the weight coefficients for addition. After the data of the multiple weighing block sensors are processed by the algorithm subsystem, the interference caused by noise and vehicle vibration can be effectively overcome, axle signals are extracted, and finally, relevant information such as the number of axles, the weight of each axle, the weight of the whole vehicle, the driving speed of the vehicle and the like is obtained. The test shows that the average error of the measured axle weight is 1.37 percent, and the average precision of the measured total weight is 0.24 percent.
Furthermore, two groups of two bridge sensors are arranged at the front end and the rear end of the weighing platform respectively, and the bridge sensors are good in linearity, simple to install and suitable for practical operation, so that the bridge sensors are selected as the weighing sensors.
Further, the data of the symmetric retransmission sensor is amplified and subjected to analog-to-digital conversion, and then is transmitted to the data storage module. Through the connection of the Ethernet, the algorithm subsystem processes the data, extracts the axle signals and finally obtains the relevant information of the vehicle such as the integral weight, the axle number, the speed and the like.
The control method disclosed by the invention has the advantages that the weight data are processed, and the data of a plurality of weighing sensors are fused by using a weighted fusion algorithm to obtain a weight coefficient; the total weight information of the vehicle is obtained by judging the position of each axle of the vehicle and then adding the corresponding sensor values multiplied by the weighting coefficients. A multi-weighing sensor method is adopted, and data of a plurality of weighing sensors are fused by a weighted fusion algorithm. The weighted fusion algorithm can carry out weighted average on redundant information provided by a plurality of sensors, has the capability of processing dynamic original data in real time, and is very suitable for a vehicle dynamic weighing system. Through a plurality of experiments, a proper weight coefficient is finally selected. The method can obtain high-precision vehicle gross weight data.
Drawings
FIG. 1 is a schematic diagram of the overall system;
FIG. 2 is a block diagram of a communication architecture of the system;
FIG. 3 is a graph of measured values of the front sensor;
FIG. 4 is a graph of measured values for the rear sensor;
FIG. 5 is a main flow diagram of the system;
fig. 6 axle sorting diagram.
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings, but the embodiments of the invention are not limited thereto.
The overall structure of the vehicle dynamic weighing system of the invention is shown in figure 1 and mainly comprises three parts: 1. a sensor module (load cell); 2. a data acquisition system (realizes the acquisition of all sensor data); 3. and the main control module (performs data processing and algorithm operation to realize user interaction). The data acquisition system is connected with the main control module through a gigabit Ethernet to realize communication.
The data acquisition system finishes the acquisition work of the sensor signals, stores the acquired data and sends the data to the main control module through the serial port and the Ethernet.
The main control module mainly comprises an algorithm subsystem and a user interaction system. The algorithm subsystem processes the sensor signals to obtain the integer weight, the axle number and the speed, and sends the information to the User interaction system through a User Datagram Protocol (UDP). The user interaction system performs the tasks of displaying, accessing data and configuring the system. The algorithm subsystem comprises axle signal detection, vehicle separator signal processing, weighing block processing system, vehicle queue state detection, data storage and data transmission. Finally, the system realizes the output of the vehicle information through the user interaction system of the main control module. Meanwhile, the user interface and the data acquisition system, and the data acquisition system and the algorithm subsystem are communicated through UDP to complete the work of receiving and sending data and sending instructions. The communication structure of the system is shown in fig. 2.
The multi-sensor vehicle weight extraction algorithm mainly realizes the calculation of the whole weight, the axle number and the speed by processing the acquired original data. The principle is as follows: first, each load cell signal is processed using a linear analysis method (i.e., the sensor signal waveform is represented by a number of connected line segments). And the running state of the vehicle on each weighing block can be analyzed by combining the signals of the vehicle separator, and the method comprises the following steps: the number of axles included in the vehicle, the time for each axle to enter and leave each weighing block, the numerical value of the weighing sensor at each moment, and the like. By using the information and processing the signals of the symmetrical retransmission sensors, the number of axles, the axle weight, the total weight and the average running speed of the vehicle can be obtained.
The invention adopts bridge sensors, and a group of sensors (comprising two bridge sensors) are respectively arranged at the front end and the rear end of a weighing platform. When each axle of the vehicle passes through the weighing platform, the front and rear sets of sensors respectively measure one set of data, as shown in fig. 3 and 4. When one axle of the vehicle enters the weighing block, the sampling value can be suddenly changed as seen from the measured value graph of the front sensor, and the measured value of the front sensor is gradually reduced along with the backward movement of the axle as the vehicle continues to run until the next axle of the vehicle enters the weighing block, and the sampling value can be suddenly changed. The change process of the measured quantity value of the rear sensor is opposite to the change process of the measured quantity value of the front sensor: as an axle of the vehicle gradually moves backwards on the weighing block, the sampling value of the rear sensor gradually becomes larger, and when the axle leaves the weighing block, the rear sensor generates a downward sudden change. The weight of the object on the platform can be represented by the sum of the front and rear sensors.
Since the signal sampled by the sensor has a certain reference, the ADC of each module needs to be de-referenced first to perform subsequent signal processing, and the main flow chart is shown in fig. 5. The invention adopts a median filtering method, and has better effect of removing impact noise.
The axle detection is the core of the multi-sensor vehicle weight extraction algorithm. After the reference is removed, the sampling signal still contains a large noise signal. In this case, it is difficult to correctly extract the axle signal. And an IIR low-pass Elliptic filter with good transition band characteristics is adopted to preprocess the acquired signal and realize the filtering smoothing processing of the original signal.
The weighing method comprises the following specific steps:
the method firstly samples the sensor data once every 2ms, and the sampling frequency is 500 Hz. Through the analysis of the sampling points, the processes of weighing the weight on the vehicle and weighing the weight down are identified, and characteristic values of each process are extracted.
To facilitate detection and identification of the axle signal, the axle detection algorithm of the present invention employs a linear analysis method (i.e., the sensor signal waveform is represented by a number of connected line segments). This way of describing the waveform of the sensor signal has the following advantages: 1. the characteristics of these connected line segments are readily available, such as slope, length, amplitude, etc.; 2. by analyzing the characteristics of these line segments, the information we are interested in can be known, including: the time of weighing the weight on the axle, the time of weighing the weight down, the duration of the axle on the weighing weight and the like.
The sensor sample value waveform can be written as:
(t1,y1),(t2,y2),(t3,y3),...,(tn,yn)
wherein, tnIndicating the nth point, y, collected by the sensor from the sampling instantnIs the sample value of the nth point. Selection (t)1，y1) As a starting point, when a subsequent point (t)n++2,yn+2) And front point (t)n+1,yn+1) Is not more than the set α value, connect (t)n,yn) And (t)n+2,yn+2) Substitution (t)n,yn) And (t)n+1,yn+1) The line segment between (t) when the difference between the sampled values exceeds αn,yn) And (t)n+1,yn+1) The line segment between ends at (t)n+1,yn+1) This process is repeated as a starting point.
In order to meet the real-time requirement of the system, when the difference between the sampling values of the subsequent point and the starting point of the line segment is less than deltay, the line segment is considered as a straight line. From practical considerations, inDuring the period that the vehicle passes through the weighing blocks, each weighing block is only on average
The time (n being the total number of weighing blocks) is informative, and the time per weighing block is less informative in view of the throughput of the road of the day. Therefore, the method can save a large amount of calculation and analysis time. The processed waveform diagram of the sensor sampling value is divided into the following sections:
S1，S2，...，Sk,...,Sn
Sk＝(tk b,yk b),(tk e,yk e)
wherein (t)k b,yk b) Represents the starting point of line segment k, (t)k e,yk e) Representing the end of line segment k.
Through the algorithm, the slope of each line segment can be calculated, and then the line segment with the large absolute value of the slope is selected as an axis signal: k is a radical of>th (k is the slope of the line segment, th is the selected positive threshold) is taken as an on-axis weighing block signal; k is a radical of<the ts (k is the slope of the line segment and ts is the selected negative threshold) signal is the off-axis weight signal. In practical cases, the vehicle models passing through the dynamic weighing system are different, including: two-axle, three-axle and six-axle vehicles, etc. Even the same type of vehicle is classified into an empty state, a full state, and the like. Therefore, it is obviously not scientific to choose fixed th and ts, and the algorithm uses a dynamic threshold method to set an own axle signal threshold for each vehicle. The method comprises the following specific steps: obtaining the peak value y of the front sensor of the first weighing block on the first shaft of the vehiclehLet positive threshold th be 0.13 x yhLet negative threshold ts equal to 0.11 × yh. Furthermore, the traveling direction (actual condition) of the vehicle can be determined from the slope of the front and rear sensor signals. When the current sensor signal is an on-axis weighing block signal (namely the slope is positive) or the rear sensor signal is an off-axis block signal (namely the slope is negative), the vehicle runs in the positive direction; when in useWhen the front sensor signal is the off-axis weight signal (i.e. the slope is negative) or the rear sensor signal is the on-axis weight signal (i.e. the slope is positive), the vehicle runs in the reverse direction. The design uses a Schmitt trigger to convert a weighing sensor signal into a 0-1 signal by using a set threshold.
The extraction axle is divided into an upper weighting block and a lower weighting block of the axle. First, two queues are constructed: axleonblk, waittocheck and an array: axle. According to the axis detection algorithm, when a signal of the weight on the axis is obtained, information of the current weight (time of weight on the weight, weight calculation result, etc.) is recorded and pushed into the axleonblk queue as shown in fig. 6. When an off-axis block signal is obtained, the axle signal is transferred from the axlenblk queue to the waittocheck queue. According to the change of the signal of the vehicle separator, whether a vehicle arrives or not can be judged, and a two-dimensional array axle of the number of axles n (the number of weighing blocks) is constructed according to the number of axles. And storing the axle signals in the waittocheck queue into axle accordingly. To this end, the relevant information is stored in the array for subsequent calculations.
The movement of the vehicle on the dynamic weighing system is very complex, and the tire characteristics, suspension system, speed, acceleration and road surface flatness all bring vibrations to the vehicle body. In order to reduce the influence of the vibration on the weighing precision, the design adopts a multi-weighing-sensor method, and the data of a plurality of weighing sensors are fused by using a weighted fusion algorithm. The weighted fusion algorithm can carry out weighted average on redundant information provided by a plurality of sensors, has the capability of processing dynamic original data in real time, and is very suitable for a vehicle dynamic weighing system. Through a plurality of experiments, a proper weight coefficient is finally selected.
By using the above algorithm and the physical structure of the plurality of weighing sensors, the total weight of the vehicle can be obtained by judging the position of the vehicle, namely judging which weighing blocks each axle of the vehicle is on and then adding the corresponding sensor values, without adding the weights of the axles to obtain the total weight of the vehicle.
The axle number can be obtained by the number of times the axle signal is on the front sensor by the first weight during the duration of the vehicle decoupler signal.
The axle weight information can be recovered from the waveform diagrams of fig. 3 and 4, and the basic idea is that the difference value between the weight of the weighing block at the back of the corresponding axle and the weight of the weighing block at the front of the corresponding axle is the axle weight.
The time difference of sudden change brought by the upper weighing block and the lower weighing block is the duration time of the axle on the block, the length of the designed weighing block is known, the axle speed can be obtained by dividing the length of the designed weighing block and the length of the designed weighing block, and the average value of the axle speeds of the vehicle is the vehicle speed.
It should be noted that the above-mentioned embodiments are only preferred embodiments of the present invention, but the present invention is not limited by the above-mentioned embodiments, and any simple modifications, combinations, equivalent substitutions, alterations, and simplifications made to the above-mentioned embodiments without departing from the spirit and spirit of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A vehicle dynamic weighing system based on multiple sensors is characterized by comprising a sensor module, a data acquisition system and a main control module;
the sensor module comprises a plurality of weighing sensors arranged on the weighing platform and is used for measuring weight data of each axle of the vehicle passing through the weighing platform in real time;
the data acquisition system is used for acquiring weight data of all the weighing sensors, storing the data and sending the data to the main control module;
the main control module comprises an algorithm subsystem, the algorithm subsystem is used for processing the weight data and fusing the data of the weighing sensors by using a weighted fusion algorithm to obtain a weight coefficient; the total weight information of the vehicle is obtained by judging the position of each axle of the vehicle and adding the corresponding sensor values multiplied by the weight coefficients;
the processing steps of the weight data are as follows: detecting and identifying the axle signal by adopting a linear analysis method, identifying the processes of weighing weight blocks on the vehicle and weighing weight blocks below the vehicle, and extracting characteristic values of each process;
the extraction axletree divides into on the axletree weigh pouring weight and weigh pouring weight two parts down:
first, two queues are constructed: axleonblk, waittocheck and an array: axle; according to an axis detection algorithm, when a signal of weighing the weight on an axis is obtained, recording the information of the current weighing weight, and pressing the information into an axleonblk queue; when obtaining an off-axis block signal, transferring the axle signal from the axlenblk queue to the waittocheck queue; judging whether a vehicle arrives according to the change of a vehicle separator signal, and constructing a two-dimensional array axle of the number of axles x n according to the number of axles; and sequentially storing the axle signals in the waittocheck queue into axle, and further storing the related information into the array.
2. The multi-sensor based vehicle dynamic weighing system of claim 1, wherein the weighing platform is formed by connecting a plurality of weighing platforms in sequence, and each weighing platform has a group of weighing sensors at the front and rear ends.
3. The multi-sensor based vehicle dynamic weighing system of claim 1, wherein said sensor module further comprises a vehicle separator for determining whether a vehicle is driven into the weighing system.
4. The multi-sensor based dynamic vehicle weighing system of claim 1, wherein the data acquisition system comprises an amplification circuit, an analog-to-digital converter, a data acquisition system main control module, a communication module and a data storage system;
the amplifying circuit is used for amplifying the acquired original weight data;
the analog-to-digital converter is used for performing analog-to-digital conversion on the amplified data;
the data acquisition system main control module is used for collecting all data after analog-to-digital conversion and sending the data to the communication module and the data storage system;
the data storage system is used for storing data;
and the communication module is used for sending the data to the algorithm subsystem.
5. The multi-sensor based vehicle dynamic weighing system of claim 1, wherein said algorithm subsystem is further configured to calculate the gross weight, axle count and speed of the vehicle:
the total weight of the vehicle is obtained by judging the weighing weight of each axle of the vehicle and adding the corresponding sensor values;
the number of the axles is obtained by the number of times of axle signals on a front sensor of a first weighing block in the signal duration of the vehicle separator;
the axle weight is the difference between the weight of the rear weighting block and the front weighting block of the upper block of the corresponding axle;
the vehicle speed is obtained by the time difference of sudden change caused by the weight on the shaft and the weight on the lower shaft, and the average value of the vehicle speed on each shaft is the vehicle speed.
6. A vehicle dynamic weighing method based on multiple sensors is characterized by comprising the following steps:
measuring original weight data of each axle of the vehicle when the axle passes through the weighing platform;
performing de-reference processing and filtering preprocessing on the original weight data;
processing the weight data, and fusing the data of the weighing sensors by using a weighted fusion algorithm to obtain a weight coefficient; the total weight information of the vehicle is obtained by judging the position of each axle of the vehicle and adding the corresponding sensor values multiplied by the weight coefficients;
the processing steps of the weight data are as follows: detecting and identifying the axle signal by adopting a linear analysis method, identifying the processes of weighing weight blocks on the vehicle and weighing weight blocks below the vehicle, and extracting characteristic values of each process;
the extraction axletree divides into on the axletree weigh pouring weight and weigh pouring weight two parts down:
first, two queues are constructed: axleonblk, waittocheck and an array: axle; according to an axis detection algorithm, when a signal of weighing the weight on an axis is obtained, recording the information of the current weighing weight, and pressing the information into an axleonblk queue; when obtaining an off-axis block signal, transferring the axle signal from the axlenblk queue to the waittocheck queue; judging whether a vehicle arrives according to the change of a vehicle separator signal, and constructing a two-dimensional array axle of the number of axles x n according to the number of axles; and sequentially storing the axle signals in the waittocheck queue into axle, and further storing the related information into the array.
7. The multi-sensor based vehicle dynamic weighing method according to claim 6, characterized in that the method of linear analysis comprises the following specific steps:
the sampled value waveform is represented as:
(t1,y1),(t2,y2),(t3,y3),...,(tn,yn)
wherein, tnIndicating the nth point, y, collected by the sensor from the sampling instantnIs the sample value of the nth point; selection (t)1，y1) As a starting point, when a subsequent point (t)n++2,yn+2) And front point (t)n+1,yn+1) Is not more than the set α value, connect (t)n,yn) And (t)n+2,yn+2) Substitution (t)n,yn) And (t)n+1,yn+1) The line segment between (t) when the difference between the sampled values exceeds αn,yn) And (t)n+1,yn+1) The line segment between ends at (t)n+1,yn+1) As a starting point, this process is repeated;
the processed waveform of the sampled value is divided into the following sections:
S1，S2，...，Sk,...,Sn
Sk＝(tk b,yk b),(tk e,yk e)
wherein (t)k b,yk b) Represents the starting point of line segment k, (t)k e,yk e) Representing the end point of the line segment k;
calculating the slope of each line segment by the algorithm, and then selecting the line segment with the large absolute value of the slope as an axis signal: the signal with k being larger than th is the signal of the on-axis weighing block, k is the slope of the line segment, and th is the selected positive threshold; k is less than ts) is a weight signal under the shaft, k is the slope of the line segment, and ts is a selected positive threshold;
setting an own axle signal threshold value for each vehicle, and acquiring the peak value y of a sensor in front of a first weighing block on a first axle of the vehiclehLet positive threshold th be 0.13 x yhLet negative threshold ts equal to 0.11 × yh；
The driving direction of the vehicle is judged by the slope of the front and rear sensor signals: when the current collected signal is an on-axis weighing block signal or the rear sensor signal is an off-axis block signal, the vehicle runs forwards; when the current collected signal is an off-axis weight signal or a rear sensor signal is an on-axis weight signal, the vehicle runs in the opposite direction;
and converting the signal of the weighing sensor into a 0-1 signal by using the set threshold value.
8. The multi-sensor based vehicle dynamic weighing method of claim 6, further comprising the steps of calculating the full weight, axle number and speed of the vehicle; the specific calculation method is as follows:
the total weight of the vehicle is obtained by judging the weighing weight of each axle of the vehicle and adding the corresponding sensor values;
the number of the axles is obtained by the number of times of axle signals on a front sensor of a first weighing block in the signal duration of the vehicle separator;
the axle weight is the difference between the weight of the rear weighting block and the front weighting block of the upper block of the corresponding axle;
the vehicle speed is obtained by the time difference of sudden change caused by the weight on the shaft and the weight on the lower shaft, and the average value of the vehicle speed on each shaft is the vehicle speed.
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| CN109668610A (en) * | 2019-01-11 | 2019-04-23 | 东南大学 | The system of vehicle dynamically weighting method and its use based on neural net regression |
| CN110702197A (en) * | 2019-11-26 | 2020-01-17 | 南京铁道职业技术学院 | Weighing measurement system for railway wagon and working method thereof |
| CN111442823B (en) * | 2020-05-09 | 2022-06-21 | 陕西四维衡器科技有限公司 | One-way three-lane vehicle weighing system and weighing method |
| CN112082628B (en) * | 2020-09-11 | 2021-12-14 | 锐马（福建）电气制造有限公司 | Livestock breeding Internet of things data acquisition system |
| CN113720429A (en) * | 2021-10-13 | 2021-11-30 | 武汉市路安电子科技集团有限公司 | Vehicle separation method based on axle data in dynamic truck scale |
| CN114993434B (en) * | 2022-05-20 | 2023-07-14 | 湖南科技大学 | Vehicle weight identification system and method based on EMD energy entropy of acoustic signals |
| CN114964445B (en) * | 2022-05-23 | 2023-05-02 | 西安交通大学 | Multi-module dynamic weighing method based on vehicle identification |
| CN115540987B (en) * | 2022-09-19 | 2023-07-14 | 蚌埠高灵传感系统工程有限公司 | Dynamic vehicle overload detection method and control device based on load sensor |
| CN115683296B (en) * | 2022-12-30 | 2023-04-18 | 广东泓胜科技股份有限公司 | Dynamic threshold processing method of road weighing sensor data and related equipment |
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| DE102011115235A1 (en) * | 2011-09-28 | 2013-03-28 | Bizerba Gmbh & Co. Kg | METHOD FOR GENERATING A TEACH DATA RECORD FOR DYNAMIC WEIGHING |
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