CN114563069B - Comprehensive high-precision intelligent vehicle real-time weighing method and system - Google Patents
Comprehensive high-precision intelligent vehicle real-time weighing method and system Download PDFInfo
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- CN114563069B CN114563069B CN202210253963.7A CN202210253963A CN114563069B CN 114563069 B CN114563069 B CN 114563069B CN 202210253963 A CN202210253963 A CN 202210253963A CN 114563069 B CN114563069 B CN 114563069B
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Classifications
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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
- G01G19/035—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 using electrical weight-sensitive devices
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G3/00—Weighing apparatus characterised by the use of elastically-deformable members, e.g. spring balances
- G01G3/12—Weighing apparatus characterised by the use of elastically-deformable members, e.g. spring balances wherein the weighing element is in the form of a solid body stressed by pressure or tension during weighing
- G01G3/14—Weighing apparatus characterised by the use of elastically-deformable members, e.g. spring balances wherein the weighing element is in the form of a solid body stressed by pressure or tension during weighing measuring variations of electrical resistance
Abstract
The invention discloses a comprehensive high-precision intelligent vehicle real-time weighing method, which comprises the following steps: strain resistance sensors are respectively arranged on a plurality of axles for bearing on a vehicle, and each strain resistance sensor is a Wheatstone full-bridge circuit consisting of four resistance strain gauges; s02, collecting and amplifying the output voltage signal change of the Wheatstone full-bridge circuit by a differential amplifying circuit, and adjusting the voltage signal change by a zeroing and attenuating circuit; s03, performing analog-to-digital conversion on the amplified and adjusted voltage signal through an AD chip of the weighing controller, and then transmitting the voltage signal to a singlechip MCU of the weighing controller for data processing; s04, the singlechip MCU transmits the weight data obtained after processing to a nixie tube display screen for display. The invention further provides a comprehensive high-precision intelligent vehicle real-time weighing system. The comprehensive high-precision intelligent vehicle real-time weighing method and system provided by the invention can display the vehicle cargo weight in real time, and have the advantages of safety, reliability, lower cost and high precision.
Description
Technical Field
The invention relates to a comprehensive high-precision intelligent vehicle real-time weighing system, and belongs to the technical field of weighing systems.
Background
The heavy vehicle turns over due to overload, the news layer of the damaged road is endless, and the overload overruns and brings infinite potential safety hazard for road driving safety. With the coming of the digital era and the rapid development of the technological revolution of the new logistics industry, the research on a real-time weighing system of a vehicle is focused. Through investigation and analysis, the existing vehicle-mounted weighing system has a plurality of defects. The traditional weighing mode is to weigh the wagon balance, and the wagon balance must be weighed to a fixed place, so that the weighing is very troublesome; the automobile body is characterized in that the automobile body is also realized by using a shaft pin type sensor, the shaft pin type sensor replaces bolts at the joint of a plate spring and a girder on a truck, the height and the structure of the automobile body are not changed, the installation is also huge in engineering, and strictly speaking, the shaft pin type sensor replaces a very important stressed part, the design of an original factory is destroyed, the illegal modification can be calculated, and once the part has a problem, the original manufacturer of the automobile cannot bear the problem caused by the fact; the traditional strain type weighing sensor is used for carrying out a vehicle-mounted real-time weighing scheme, and is mainly characterized in that a common weighing sensor is arranged between a carriage and a frame, one end of the sensor is fixed on a newly added part of the frame, and the other end of the sensor is suspended and used for supporting the carriage, which is equivalent to reforming the carriage into a scale pan. Therefore, when the automobile is additionally installed, the whole carriage is required to be lifted, the automobile structure can be damaged, the gravity center of the automobile main body is lifted, and potential safety hazards are caused. In summary, the existing vehicle-mounted weighing system is difficult to balance in three aspects of precision, cost and reliability, and meanwhile, the existing vehicle-mounted weighing system has the problems of troublesome installation, potential safety hazard and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a comprehensive high-precision intelligent vehicle real-time weighing method and a system thereof, which can display the vehicle cargo weight in real time and have the advantages of safety, reliability, lower cost and high precision.
In order to solve the technical problems, the invention adopts the following technical scheme:
a comprehensive high-precision intelligent vehicle real-time weighing method comprises the following steps:
s01, strain resistance sensors are respectively arranged on a plurality of axles for bearing on a vehicle, the strain resistance sensors are Wheatstone full-bridge circuits formed by four resistance strain gauges, and the input ends of the Wheatstone full-bridge circuits are provided with power sources by a weighing controller;
s02, collecting and amplifying the output voltage signal change of the Wheatstone full-bridge circuit by a differential amplifying circuit, and adjusting the voltage signal change by a zeroing and attenuating circuit;
s03, performing analog-to-digital conversion on the amplified and adjusted voltage signal through an AD chip of the weighing controller, and then transmitting the voltage signal to a singlechip MCU of the weighing controller for data processing;
s04, the singlechip MCU transmits the weight data obtained after processing to a nixie tube display screen for display, and controls the singlechip MCU through a control key.
In S01, the first and third resistance strain gauges are positioned at one side in the left-right direction in the four resistance strain gauges, the second and fourth resistance strain gauges are positioned at the other side in the left-right direction, and the relation is satisfied
Wherein V is S Power supply for weighing controller, V O1 The voltage output by the full-bridge Wheatstone circuit formed by four resistance strain gages, k is the coefficient of the strain gages, epsilon 1 ，ε 2 ，ε 3 ，ε 4 The strain amounts of the first, second, third, and fourth resistive strain gages, respectively.
In S02, the signals collected by the input end of the AD chip are voltage signals after differential amplification, thereby meeting the requirements of
Wherein V is 1 And V 2 For two output voltages of a Wheatstone full-bridge circuit, R 1 And R is 2 R is equal to the set proportional resistance p To match resistance, R f For proportional reference resistance, the resistance is based on R 1 And R is 2 Is dependent on the resistance value of V O2 Is the magnitude of the voltage signal after differential amplification.
The number of axles for bearing is at least two, and the weight value calculated by the MCU is the average value of the weights weighed on a plurality of axles.
And (3) calibrating before weighing, wherein the singlechip MCU adopts multistage calibration, and establishes a linear relation between the strain resistance sensor signal and the weighing weight.
The MCU predicts the signal by adopting a Kalman filtering algorithm, compares the current signal with the predicted signal and calculates covariance, and then obtains the next predicted signal, the predicted signal of each time forms an output sequence, the output sequence is a stable signal obtained after filtering, and the Kalman filtering formula is specifically as follows:
EVN＝ECR*EVP+KG*(MV-EVP)
let Kalman gain be KG, this estimated value be EVN, last estimated value be EVP, current estimated covariance ECN, next estimated value be ECX, measured value be MV, current measured covariance be MCN, next measured covariance be MCX, estimated change ratio be ECR.
The utility model provides a synthesize real-time weighing system of high accuracy intelligent vehicle, includes and sets up strain resistance sensor on the axletree at a plurality of, strain resistance sensor comprises wheatstone full bridge circuit, including four resistance strain gauge, four the resistance strain gauge is geometric symmetry and distributes about the axletree, wheatstone full bridge circuit's input is connected with the power that weighing controller provided, wheatstone full bridge circuit's output has connected gradually differential amplifier circuit and zero setting and decay circuit, decay circuit with weighing controller's input is connected, weighing controller includes AD chip and singlechip MCU, AD chip's input with decay circuit is connected, AD chip's output with singlechip MCU's input is connected, singlechip MCU is connected with digital tube display screen and control button.
The AD chip model is AD1256, the singlechip MCU model is STM32F103C8T6.
The resistance strain gauge comprises a substrate, the substrate is a PEEK film, a cover plate is arranged on the substrate, a sensitive grid is arranged between the substrate and the cover plate, and the substrate and the sensitive grid as well as the substrate and the cover plate are fixed through an adhesive.
The invention has the beneficial effects that: the comprehensive high-precision intelligent vehicle real-time weighing method and the system thereof provided by the invention have the advantages that the original structure of the vehicle is not changed, the safety of the vehicle is not influenced, a high-precision Wheatstone full-bridge circuit is adopted instead of the Wheatstone quarter-bridge circuit for weighing and measuring the traditional resistance strain gauge, the four resistance strain gauges can be used for changing the resistance instead of a single resistance strain gauge, compared with the Wheatstone quarter-bridge circuit, more variable items are provided, the interference of pressing or pulling, bending, shearing or torsion stress and the like can be effectively eliminated, the real-time weighing of the vehicle with higher precision can be realized, the weighing equipment is directly arranged on an axle without changing the structure of the vehicle body along with the vehicle, and the safety and the installation convenience are ensured; the calibration is carried out before weighing, and the single chip microcomputer MCU adopts multi-stage calibration, so that the system error can be reduced, and the measurement precision is further improved; the MCU adopts Kalman filtering algorithm, and has the characteristics of high precision and strong stability.
Drawings
FIG. 1 is a system connection diagram of a comprehensive high-precision intelligent vehicle real-time weighing system of the invention;
FIG. 2 is a front view of an axle having a resistive strain gauge mounted thereon in accordance with the present invention;
FIG. 3 is a schematic diagram of a Wheatstone full bridge circuit constructed from four resistive strain gages in accordance with the present invention;
FIG. 4 is a circuit configuration diagram of the differential amplifying circuit of the present invention;
FIG. 5 shows a sampling part of the AD chip and a MCU control part of the singlechip;
FIG. 6 is a comparison between a single stage calibration and an actual value for a multi-stage calibration of the present invention;
FIG. 7 is a graph showing the effect of Kalman filtering employed in the present invention.
Reference numerals in the drawings are as follows: 1-a first chip resistor strain gauge; 2-a second chip resistor strain gauge; 3-a third patch resistor strain gauge; 4-a fourth chip resistor strain gauge; 5-axle; 6-vehicle.
Detailed Description
The present invention will be further described with reference to the accompanying drawings, and the following examples are only for more clearly illustrating the technical aspects of the present invention, and are not to be construed as limiting the scope of the present invention.
As shown in fig. 1, the invention provides a comprehensive high-precision intelligent vehicle real-time weighing system, when cargoes are loaded on a vehicle frame, the change of the output end of a full-bridge circuit of a strain resistance sensor can be triggered through the deformation of an axle, and the weight of a vehicle is displayed on a nixie tube in a vehicle cab through the calculation of a Kalman filtering algorithm of a weighing controller. The specific structure comprises a strain resistance sensor, a weighing controller, a protective shell of the weighing controller and an external power supply, wherein the strain resistance sensor is formed by four resistance strain gauges arranged on a plurality of axles, and the external power supply is used for supplying power to the weighing controller. The input end of the strain resistance sensor is provided with a power supply by a weighing controller, the strain resistance sensor is composed of a Wheatstone full-bridge circuit, the output end reflects weight through a voltage signal, the voltage signal is processed through a differential amplifying circuit and a zeroing and attenuating circuit, the processed voltage signal is sampled by an AD chip and then is connected with the input end of a singlechip MCU of the weighing controller, and the signal acquired by the AD chip is the voltage signal after differential amplification. The differential amplifying circuit carries out differential amplification on the signal output by the strain resistance sensor, so that the voltage signal is easier to measure and process. The weighing controller realizes a Kalman filtering algorithm and multi-stage calibration; the weighing controller is connected with a nixie tube display screen and control keys which are arranged in a vehicle cab.
As shown in fig. 2, the strain resistance sensor is actually a full-bridge wheatstone circuit formed by four resistance strain gages geometrically symmetric in the upper and lower directions and the left and right directions of the axle of the patch. The resistance strain gauge consists of a sensitive grid, a substrate, a cover plate, an adhesive and the like.
The resistance strain gauge comprises a substrate, a cover plate is arranged on the substrate, a sensitive grid is arranged between the substrate and the cover plate, and the substrate and the sensitive grid as well as the substrate and the cover plate are fixed through an adhesive. The resistance strain gauge consists of a sensitive grid, a lead, a substrate, a cover plate and an adhesive, wherein the sensitive grid is an important component of the strain gauge; the lead is a thin metal wire led out from the sensitive grid of the strain gauge; the substrate is used for maintaining the geometric shapes and the relative positions of the sensitive grid and the lead wires; the cover plate not only keeps the geometric shapes and the relative positions of the sensitive grid and the lead wires, but also can protect the sensitive grid; the adhesive is used for fixing the sensitive grid on the substrate and adhering the cover plate with the substrate. According to the test, new structures are designed on the gate length, gate width, gate spacing, leads and bonding pads of the sensitive gate, and meanwhile, a PEEK film with the minimum water absorption rate is selected as a substrate, and the structure is fully sealed, so that the structure has the functions of temperature self-compensation and creep self-compensation; has extremely low moisture absorption rate and better moisture resistance; creep deformation and zero return performance are excellent; the substrate has a certain toughness.
The first resistive strain gauge 1 and the third resistive strain gauge 3 are located on the left, and the second resistive strain gauge 2 and the fourth resistive strain gauge 4 are located on the right. The vehicle 6 is provided with at least two axles 5 for bearing weight, and the weight value calculated by the weighing controller is the average value of the weights weighed on the axles 5. Each axle shaft 5 forms a separate measuring channel. When the vehicle 6 is placed on a plurality of axles, each axle forms an independent measuring channel and has actual data, the obtained final weight value is the average value of the weights of the plurality of measuring channels, the systematic error is reduced, and the precision is effectively improved.
As shown in fig. 3, four resistive strain gages are connected by wires to form a wheatstone full bridge circuit. V (V) S Power supply for weighing controller, V O1 The voltage output by the full-bridge Wheatstone circuit formed by four resistance strain gages, k is the coefficient of the strain gages, epsilon 1 ，ε 2 ，ε 3 ，ε 4 The strain amounts of the four resistance strain gauges respectively, and the relation thereof satisfies
The measuring circuit adopts a Wheatstone full-bridge circuit capable of eliminating interference such as pressing or pulling, bending, shearing or torsion stress and the like, the output voltage change is collected and amplified by the differential amplifying circuit, and the voltage change is adjusted by adding the zeroing and attenuating circuit so as to improve the range and the precision of the whole weighing device. The circuit structure of the differential amplifying circuit is shown in FIG. 4, wherein the values of V1-V2 are equal to the voltage V output by a full-bridge Wheatstone circuit formed by four resistance strain gauges O1 。
Voltage V output by full-bridge wheatstone circuit composed of four resistance strain gauges O1 Voltage signal V amplified, zeroed and attenuated by differential circuit O2 The weighing controller is characterized in that the weighing controller is collected by an AD circuit and transmitted to a singlechip MCU of the weighing controller, and finally a nixie tube display screen arranged in a vehicle cab is controlled to display weight through calculation of the singlechip MCU, and various weighing functions can be realized through control keys.
The signals collected by the input end of the AD chip are voltage signals after differential amplification, thereby meeting the requirements of
Wherein V is 1 And V 2 For two output voltages of a Wheatstone full-bridge circuit, R 1 And R is 2 R is equal to the set proportional resistance p To match resistance, R f For proportional reference resistance, the resistance is based on R 1 And R is 2 Is dependent on the resistance value of V O2 Is the magnitude of the voltage signal after differential amplification.
The weighing controller is composed of an AD chip and a singlechip MCU, and the sampling part of the AD chip and the control part of the singlechip MCU are shown in figure 5. The filter has the functions of sampling voltage values, calculating weight values and filtering and stabilizing display weight. In the embodiment, the model of the AD chip is AD1256, and the model of the MCU is STM32F103C8T6.
The invention is calibrated before weighing, so that the weighing device has a definition on the weight, namely, a linear relation is established between the sensor and the weight, and then the vehicle is weighed according to the linear relation. The common weighing device adopts single-stage calibration, and the device adopts multi-stage calibration, so that the data is more accurate. The calibration process and the comparison of single stage calibration with multi-stage calibration and actual values are shown in fig. 6. The calibration weight may be chosen arbitrarily, but is on the order of one order of magnitude, typically between one tenth and one fifth of the weight of the weighing object.
And after the calibration is finished, a formal weighing link is adopted. Because the voltage sampled by the AD chip is unstable, the singlechip MCU processes the voltage sampled by the AD chip by using a Kalman filtering algorithm and a multistage calibration method, so that the high-precision characteristic of the system is achieved. When weighing, the sensor receives pressure, the singlechip MCU predicts the signal by using a Kalman filtering algorithm, compares and calculates covariance between the current signal and the predicted signal, and then obtains the next predicted signal, thereby recursively estimating the predicted signal each time to form an output sequence. The output sequence is a stable signal obtained after filtering. And then establishing a linear relation according to the multi-stage calibration. The weight of the required weighing frame is obtained through a linear relation established in a calibration mode, meanwhile, the MCU controls a nixie tube display screen arranged in a vehicle cab to display the weight, and various weighing functions are realized through keys.
The Kalman filtering adopted by the invention is an algorithm for optimally estimating the system state by utilizing a linear system state equation and through system input and output observation data. The optimal estimate can also be seen as a filtering process, since the observed data includes the effects of noise and interference in the system. Kalman filtering is a data processing technique that removes noise to recover real data, and can estimate the state of a dynamic system from a series of data where measurement noise is present, with the measurement variance known. The method is convenient for computer programming realization, and can update and process the data collected on site in real time, so that the collected data is kept stable. In the invention, stable and high-precision weight values can be obtained through Kalman filtering.
Let Kalman gain be KG, this estimated value be EVN, last estimated value be EVP, current estimated covariance ECN, next estimated value be ECX, measured value be MV, current measured covariance be MCN, next measured covariance be MCX, estimated change ratio be ECR, we can obtain Kalman filter formula:
EVN＝ECR*EVP+KG*(MV-EVP)
as shown in fig. 6, the integrated high-precision intelligent vehicle real-time weighing system of the invention adopts multi-stage calibration. Compared with the single-stage calibration result of the line 2, the multi-stage calibration result of the line 1 is closer to the actual measurement value of the line 3, so that the system error is reduced, and the precision is further improved.
As shown in fig. 7, the comprehensive high-precision intelligent vehicle real-time weighing system utilizes a kalman filtering algorithm and utilizes a linear system state equation to perform optimal estimation on the system state through system input and output observation data. The scattered points with disorder are actual measured values, and stable values are output after Kalman filtering. Kalman filtering is a data processing technique that removes noise to recover real data, and can estimate the state of a dynamic system from a series of data where measurement noise is present, with the measurement variance known. The method is convenient for computer programming realization, and can update and process the data collected on site in real time, so that the collected data is kept stable. In the device, stable and high-precision weight values can be obtained through Kalman filtering.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (8)
1. A comprehensive high-precision intelligent vehicle real-time weighing method is characterized in that: the method comprises the following steps:
s01, strain resistance sensors are respectively arranged on a plurality of axles for bearing on a vehicle, each strain resistance sensor is composed of a Wheatstone full-bridge circuit, each strain resistance sensor comprises four resistance strain gauges, the four resistance strain gauges are distributed in a geometric symmetry mode in the up-down direction and the left-right direction of the axle, each strain resistance sensor is a Wheatstone full-bridge circuit composed of four resistance strain gauges, and the input end of each Wheatstone full-bridge circuit is provided with a power supply by a weighing controller;
s02, collecting and amplifying the output voltage signal change of the Wheatstone full-bridge circuit by a differential amplifying circuit, and adjusting the voltage signal change by a zeroing and attenuating circuit;
s03, performing analog-to-digital conversion on the amplified and adjusted voltage signal through an AD chip of the weighing controller, then transmitting the voltage signal to a singlechip MCU of the weighing controller for data processing, calibrating the voltage signal before weighing, wherein the singlechip MCU adopts multi-stage calibration, the calibration weight and the weight of a weighing object are in an order of magnitude which is between one tenth and one fifth of the weight of the weighing object, and establishing a linear relation between a strain resistance sensor signal and the weighing weight;
s04, controlling a singlechip MCU through a control key, predicting signals by the singlechip MCU through a Kalman filtering algorithm, comparing the current signals with the predicted signals, solving covariance, obtaining next predicted signals, forming an output sequence by each predicted signal, wherein the output sequence is a stable signal obtained after filtering, and transmitting the weight data obtained after processing to a nixie tube display screen for display by the singlechip MCU.
2. The method for weighing the comprehensive high-precision intelligent vehicle in real time according to claim 1, wherein the method comprises the following steps: in S01, the first and third resistance strain gauges are positioned at one side in the left-right direction in the four resistance strain gauges, the second and fourth resistance strain gauges are positioned at the other side in the left-right direction, and the relation is satisfied
Wherein V is s Power supply for weighing controller, V o1 The voltage output by the full-bridge Wheatstone circuit formed by four resistance strain gages, k is the coefficient of the strain gages, epsilon 1 ，ε 2 ，ε 3 ，ε 4 The strain amounts of the first, second, third, and fourth resistive strain gages, respectively.
3. The method for weighing the comprehensive high-precision intelligent vehicle in real time according to claim 2, wherein the method comprises the following steps: in S02, the signals collected by the input end of the AD chip are voltage signals after differential amplification, thereby meeting the requirements of
Wherein V is 1 And V 2 For two output voltages of a Wheatstone full-bridge circuit, R 1 And R is 2 R is equal to the set proportional resistance p To match resistance, R f For proportional reference resistance, the resistance is based on R 1 And R is 2 Is dependent on the resistance value of V O2 Is the magnitude of the voltage signal after differential amplification.
4. The method for weighing the comprehensive high-precision intelligent vehicle in real time according to claim 1, wherein the method comprises the following steps: the number of axles for bearing is at least two, and the weight value calculated by the MCU is the average value of the weights weighed on a plurality of axles.
5. The method for weighing the comprehensive high-precision intelligent vehicle in real time according to claim 1, wherein the method comprises the following steps: the Kalman filtering formula is specifically as follows:
EVN＝ECR*EVP+KG*(MV-EVP)
let Kalman gain be KG, this estimated value be EVN, last estimated value be EVP, current estimated covariance ECN, next estimated value be ECX, measured value be MV, current measured covariance be MCN, next measured covariance be MCX, estimated change ratio be ECR.
6. A comprehensive high-precision intelligent vehicle real-time weighing system is characterized in that: including setting up a plurality of strain resistance sensor that sets up on the axletree, strain resistance sensor comprises wheatstone full-bridge circuit, including four resistance strain gauge, four the resistance strain gauge is in the axletree is geometric symmetry about and the direction is the distribution, wheatstone full-bridge circuit's input is connected with the power that weighing controller provided, wheatstone full-bridge circuit's output has connected gradually differential amplifier circuit and zero setting and decay circuit, decay circuit with weighing controller's input is connected, weighing controller includes AD chip and singlechip MCU, AD chip's input with decay circuit is connected, AD chip's output with singlechip MCU's input is connected, singlechip MCU is connected with digital tube display screen and control button.
7. The integrated high-precision intelligent vehicle real-time weighing system according to claim 6, wherein: the AD chip model is AD1256, the singlechip MCU model is STM32F103C8T6.
8. The integrated high-precision intelligent vehicle real-time weighing system according to claim 6, wherein: the resistance strain gauge comprises a substrate, the substrate is a PEEK film, a cover plate is arranged on the substrate, a sensitive grid is arranged between the substrate and the cover plate, and the substrate and the sensitive grid as well as the substrate and the cover plate are fixed through an adhesive.
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| PCT/CN2022/126960 WO2023173744A1 (en) | 2022-03-15 | 2022-10-24 | Comprehensive high-precision intelligent real-time vehicle weighing method and system |
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| WO2023173744A1 (en) | 2023-09-21 |
| CN114563069A (en) | 2022-05-31 |
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