CN106205146A - Vehicle information acquisition and processing system and method based on dual-vector magnetic sensor - Google Patents

Abstract

The invention provides a vehicle information acquisition and processing system based on a dual-vector magnetic sensor, which is characterized by comprising the following components: a front-end dual-vector magnetic sensor section including two vector magnetoresistive sensors S1 and S2 for converting a magnetic field signal generated by the motion of the vehicle into an analog electrical signal; the signal acquisition part is used for converting the analog electric signals output by the front-end dual-vector magnetic sensor part into digital signals; a vehicle information determination part for determining the characteristic quantity of the digital signal from the signal acquisition part, finding a numerical value suitable for processing, and determining vehicle information including the presence or absence of a vehicle, the vehicle running direction, the vehicle speed and the vehicle length; a result output section for outputting the vehicle information.

Description

Vehicle information acquisition and processing system and method based on dual-vector magnetic sensor

Technical Field

The invention relates to a vehicle information acquisition and processing system and method based on a dual-vector magnetic sensor, and belongs to the field of intelligent traffic and signal processing.

Background

With the high-speed development of economy in China, the economy of people is continuously improved, the automobile process is accelerated, and the situation of few vehicles and multiple paths brings great challenges to urban traffic. In order to solve the existing problems, people focus on the development of the intelligent traffic field. In the field of intelligent transportation, vehicle detection is the most basic and important link in the field of intelligent transportation.

The vehicle detection technology commonly used in the world today mainly comprises a toroidal coil, a video, a microwave, a magneto-resistive sensor and the like. The loop coil technology is mostly applied to urban traffic intersections and high-speed intersections, has high detection precision and good reliability, but is complex to install and maintain and damages the road surface. Video detection is mainly applied to intersections, the current traffic situation can be clearly seen, but the detection precision is greatly influenced by weather and light, and a lens is easily influenced by road surface dust. The microwave is mostly applied to speed measurement of high-speed road sections, is convenient to install and long in service life, and is easily influenced by the surrounding environment. The magnetic resistance sensor has the characteristics of small volume, low cost, convenience in installation and maintenance and the like, so that the magnetic resistance sensor is more suitable for being used as a current vehicle identification front-end sensor.

At present, common magnetoresistive sensor vehicle detection algorithms mainly include a fixed threshold algorithm, an adaptive threshold algorithm, a state machine detection algorithm and the like. Detecting whether the vehicle exists or not, identifying the vehicle by using one or two sensors, estimating the vehicle speed, and finally identifying the vehicle type by using a pattern identification or threshold classification algorithm. But in the aspect of decision basis, a single criterion is used, and the processing accuracy is poor.

Disclosure of Invention

According to one aspect of the invention, a vehicle information collecting and processing system based on a dual-vector magnetic sensor is provided, which is characterized by comprising:

the front-end dual-vector magnetic sensor portion, which includes two vector magnetoresistive sensors S1 and S2, converts magnetic field signals generated by the motion of the vehicle into analog electrical signals,

a signal acquisition part for converting the analog electric signal output by the front end dual-vector magnetic sensor part into a digital signal,

a vehicle information determination section for determining the characteristic amount of the digital signal from the signal acquisition section, finding a numerical value suitable for processing, and determining vehicle information including the presence or absence of the vehicle, the vehicle traveling direction, the vehicle speed, the vehicle length,

a result output section for outputting the vehicle information.

According to another aspect of the invention, a vehicle information collecting and processing method based on a dual-vector magnetic sensor is provided, which is characterized by comprising the following steps:

A) with the front dual-vector magnetic sensor section, which includes two vector magnetoresistive sensors S1 and S2, magnetic field signals generated by vehicle motion are converted into analog electrical signals,

B) converts the analog electric signal output by the front-end dual-vector magnetic sensor part into a digital signal,

C) the digital signal from the signal acquisition part is subjected to characteristic quantity determination, a numerical value suitable for processing is found, and vehicle information including the existence of the vehicle, the vehicle running direction, the vehicle speed and the vehicle length is determined and determined,

D) the vehicle information is output by a result output section.

Drawings

FIG. 1 is a schematic diagram of the system of the present invention.

Fig. 2 is a schematic view of the installation of the sensor array of the present invention.

FIG. 3 is a block flow diagram of the algorithm of the present invention.

Detailed Description

The invention adopts the double-vector magnetic sensor, provides a multi-criterion processing method, makes better compromise from the aspects of system complexity and performance, and provides a novel vehicle information processing method which has better robustness and lower complexity.

The vehicle information acquisition and processing system based on the dual-vector magnetic sensor comprises a vehicle information acquisition part and a processing part of the dual-vector magnetic sensor. The vehicle information acquisition system comprises a front-end dual-vector magnetic sensor part, a signal acquisition part, a processor part and a result output acquisition part. The processing section includes a vehicle information determination section. The following description is made separately.

1 vehicle information acquisition part

(1) Front-end dual-vector magnetic sensor section

The part comprises two three-dimensional magnetic sensors at a certain distance and necessary signal amplification, filtering and driving circuits for completing the conversion from magnetic field signals to electric signals. The basic principle of the vehicle information processing system based on the magneto-resistive sensor is to acquire the variation of the earth magnetic field before and after a vehicle passes by using the magneto-resistive sensor and further analyze the variation to obtain vehicle information. The physical mechanism of the magnetic field cutting device is that the ferromagnetic substance contained in a common vehicle cuts the existing magnetic field distribution of the earth during the driving process, and the magnetic field is changed. The magnetic field disturbance caused by vehicle passing is a low-frequency signal with the intensity of about nT to uT, and a magnetoresistive sensor is suitable for being adopted. There are three options for magnetoresistive sensors, AMR (anisotropic magneto resistance), GMR (Giant magneto resistance) and TMR (Tunnel magneto resistance). In contrast, AMR is more suitable for the method of the present invention because of its definite directionality, moderate sensitivity, and high cost/performance ratio. Because the original output electric signals of the magnetoresistors are weak, the signals need to be amplified. Since the magnetic field through which the vehicle passes is a low-frequency alternating magnetic field, a filtering process is also necessary to remove the interference information of the environmental magnetic field. Finally, the output signal of the part is processed by a driving circuit to be transmitted to a signal acquisition part of a lower stage.

(2) Signal acquisition part

The signal acquisition part completes the analog-digital conversion function and is used for converting the analog signal output by the front-end sensor into a digital signal required by the processor part. According to one embodiment of the invention, the signal acquisition portion comprises a data acquisition card; according to another embodiment of the present invention, the signal acquisition part includes an ADC (Analog-Digital converter) circuit. The signal acquisition part meets the requirements of low noise and conversion precision, and reduces the electrical noise brought by the signal acquisition part.

(3) Vehicle information determination section

According to one embodiment of the present invention, the vehicle information determination section includes a processor loaded with a corresponding processing application. In embodiments according to the invention, a computer or other embedded platform is employed. When the real-time requirement is considered, a DSP (digital signal processor) type execution hardware platform with a strong operation function and data throughput is generally selected.

A more detailed description of the vehicle information determination portion is given below.

(4) Result output section

The result output section is an output instruction section and/or a data transmission section of the vehicle information. The output part covers the general content of the vehicle information, including the existence of the vehicle, the driving direction of the vehicle, the speed of the vehicle, the length of the vehicle and the like. When the instruction section is output, the result output section is also provided with a display function and an instruction function. When acting as a data transfer section, the result output section transfers the general contents of the vehicle information to the other end through the communication interface and protocol. In practice, the other end is a traffic guidance center or a data summarization center.

2 vehicle information determination section

The vehicle information acquisition and processing system determines the characteristic quantity of the acquired data and finds a numerical value suitable for calculation. And determining the vehicle information, and determining the relevant information of the vehicle, including the existence of the vehicle, the vehicle running direction, the vehicle speed, the vehicle length and the like.

The vehicle information determining section includes a data preprocessing section, a vehicle presence or absence discriminating section, a vehicle direction discriminating section, an average vehicle speed measuring section, and a vehicle length measuring section. The following description will be made separately.

(1) Data preprocessing section

According to the installation of the two vector magnetoresistive sensors at the front end, the original data is 6 paths, and the magnetic field signals of the two sensors in fixed XYZ axes are respectively marked as S1.X, S1.Y, S1.Z, S2.X, S2.Y and S2.Z (S1 represents one of the magnetoresistive sensors, and S2 represents the other). The signal is first filtered to eliminate the bursty interference signal. Then, the filtered signals are further operated to remove the direct current component of the data volume, and 6 alternating variables delta S are obtained respectively₁.X、ΔS₁.Y、ΔS₁.Z、ΔS₂.X、ΔS₂Y and Δ S₂Z. Further processing to obtain 6 derivatives respectively of Δ S₁.TMI、ΔS₂.TMI、ΔS₁₂TMI, grad.x, grad.y and grad.z, the mathematical definitions of which are shown in formulas (1) to (6).

ΔS₁₂.TMI＝ΔS₁.TMI-ΔS₂TMI formula (3)

From the physical concept, Δ S₁TMI and Δ S₂TMI reflects the total intensity of the alternating field in the magnetic field measured by the two sensors as a vector (Δ S)₁.X,ΔS₁.Y,ΔS₁Z) and (Δ S)₂.X,ΔS₂.Y,ΔS₂A two-norm of Z); delta S₁₂TMI reflects the difference in the total intensity of the alternating field of the two sensors; the magnetic field of the two sensors in the XYZ coordinate direction is reflected by the amount of change with distance, typically referred to as gradient values.

In the actual judgment, a relatively large amount of the 6 derived quantities needs to be found out and used as a main basis for judgment. And comparing the TMI values on the XYZ axes, and finding the axis with the largest TMI value as a sensitive axis so as to calculate data of the sensitive axis.

(2) Vehicle presence/absence determination section

And adopting a multi-basis weighting judgment method for judging whether the vehicle exists or not. In the previous step, 6 quantities, respectively Δ S, were calculated₁.TMI、ΔS₂.TMI、ΔS₁₂TMI, grad.x, grad.y and grad.z. The 6 quantities can be weighted by increasing the weighting factor lambda₁-λ₆And calculating according to the formula (7) to obtain pass _ score. When it is necessary to note, the weighting factor λ₁-λ₆Generally, the process of average weighting is not used, but a method of weighting according to specific gravity is used. The determination of a particular weighting value may be determined using a relative change in 6 quantities. If a certain quantity varies more, indicating that the quantity is more sensitive, the corresponding weighting value should be greater. In general, λ₃The specific gravity of (A) will be relatively large, λ₁And λ₂Next, the method is described. When the vehicle is judged to be in existence, the difference value of the total intensity of the alternating fields of the sensors is used as a main basis for the vehicle to pass, the total intensity of the alternating fields in the magnetic field measured by the two sensors is used as a secondary basis, and the gradient value in each direction is used as a smaller basis. The vehicle is judged according to the weighting of the six quantities, the passing of the vehicle can be judged only by meeting the main basis and one of the secondary bases, and otherwise, the passing of the vehicle is not judged

$\begin{array}{c}pass\_score={\mathrm{\λ}}_1*{\mathrm{\ΔS}}_1.TMI+{\mathrm{\λ}}_2*{\mathrm{\ΔS}}_2.TMI+{\mathrm{\λ}}_3*{\mathrm{\ΔS}}_{12}.TMI+\\ {\mathrm{\λ}}_4*Grad.X+{\mathrm{\λ}}_5*Grad.Y+{\mathrm{\λ}}_6*Grad.Z\end{array}---\left(7\right)$

The value of pass score is compared to a preset threshold. If the threshold value is larger than the threshold value, the vehicle passes through, otherwise, the vehicle does not pass through. In one embodiment of the invention, a fixed threshold algorithm is adopted, and the specific threshold is obtained through multiple actual measurements of a scene through which the automobile passes.

(3) Vehicle direction discriminating section

In the judgment of the vehicle direction, Grad.X, Grad.Y and Grad.Z are used as main basis for judgment. And obtaining the driving direction of the vehicle according to the changing slopes of the three quantities and integrating the three slopes. If the gradient of Grad.X is positive, the certain driving direction is the positive direction of the X axis; otherwise, it is negative X-axis. And the same method is used for judging the driving directions of the Y axis and the Z axis. Finally, comprehensively judging the driving directions of the three coordinate axes

(4) Average vehicle speed measuring section

The signals S1.X, S1.Y, S1.Z, S2.X, S2.Y and S2.Z after the output of the sensor is filtered are converted by a larger amount, namely the main basis of judgment, binarization processing is carried out to remove some invalid data points in the data,

and selecting a judging threshold St as a main basis for judging, and converting the waveform into a normalized square wave (1,0), wherein the value of the Nth sampling point on the sensitive axis in the normalized square wave is selected by the following method:

$S=\left\{\begin{array}{cc}1,& \mathrm{Sn}<\mathrm{St}\\ 0,& \mathrm{Sn}>\mathrm{St}\end{array}\right.$

in the formula: sn is the value at the sampling point N; st is the set waveform threshold. When selecting, the two sensors should consider the large change amount, and should keep the data direction of the judgment basis as the same direction.

Using the data of a certain sensor after binarization, calculating the difference between the number of points of adjacent 01 changes, and combining the sampling rate to calculate the time difference T of the vehicle passing through the two sensors, as shown in formula 7. Where N2 and N1 are the number of points at which the sensors 2 and 1 exhibit a change in level 01, respectively, and S is the sampling rate.

T＝(N2-N1)/S (7

The average speed of the vehicle is calculated according to equation 8, where L is the distance of the dual sensors in the sensitive direction, which is a known quantity, and T is calculated in equation 7. Calculating the time between the vehicle passing the two sensors according to:

V＝L/T (8)

(5) vehicle length measuring part

The length of the characteristic curve waveform reflects the time of the vehicle passing through the sensor, and the length of the characteristic curve is scaled in proportion to the vehicle speed or the length of the vehicle. The time difference of the same vehicle passing through 2 sensors is recorded by using the sensors, the waveform is normalized to eliminate the influence of the vehicle speed on a time axis, and the normalized waveform length is in direct proportion to the length of the whole vehicle. If T1 and T2 are the times when the first sensor detects the presence and the departure of the vehicle, respectively, and T3 and T4 are the times when the second sensor detects the presence and the departure of the vehicle, respectively, the vehicle passes through the average time of one sensor, which is shown in formula (9), the average vehicle speed is obtained, and the length of the vehicle can be obtained from the time and the average vehicle speed, which is shown in formula (10).

T＝((T2－T1)+(T4－T3))/2 (9)

length＝V*T (10)

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in detail below with reference to specific embodiments.

FIG. 1 is a schematic diagram of the system of the present invention. The vehicle information acquisition and processing system is composed of a front-end dual-vector magnetic sensor part, a signal acquisition part, a processor part and a result output acquisition part. The front end double-vector magnetic sensor part consists of two three-dimensional magnetic sensors at a certain distance and necessary signal amplification, filtering and driving circuits and is used for completing the conversion from a magnetic field signal to an electric signal.

The signal acquisition part completes the analog-digital conversion function and is used for converting the analog signal output by the front-end sensor into a digital signal required by the processor part. The specific implementation method can utilize a data acquisition card or various ADC (Analog-Digital converter) circuits. The performance of the converter is required to meet the requirements of low noise and conversion precision, and the electrical noise caused by the converter is reduced.

The processor section is a carrier that operates for the vehicle information determination section, and is used to implement the vehicle information determination process. A computer or other embedded platform may generally be chosen. In consideration of real-time requirements, a processing execution hardware platform such as a DSP (digital signal processor) with a strong operation function and data throughput is generally selected.

The result output section is an output instruction section or a data transmission section of the vehicle information. The output part is to cover the general content of the vehicle information, such as whether the vehicle exists, the driving direction of the vehicle, the speed of the vehicle, the length of the vehicle, etc. For example, the indicating part is a device with a display function and an indicating function for outputting. If it is a data transfer part, the general contents of the vehicle information should be transferred to the other end through some kind of communication interface and protocol. In practice, the other end is a traffic guidance center or a data summarization center.

Fig. 2 is a schematic view of the installation of the sensor array of the present invention. The coordinate system adopts a rectangular coordinate system, the origin is O, and the coordinate axis is XYZ. It is assumed here that the direction of travel of the cart coincides with the Y-axis forward direction. The sensors S1 and S2 are arbitrarily placed in the coordinate system, and their signal outputs are S1X, S1Y, S1Z, S2X, S2Y, and S2Z. The distance differences in the XYZ axis directions of the two sensors are dX, dY, and dZ, respectively. These three distances are determined at the sensor layout, known quantities for the signal processing process, and are used to calculate the gradient values.

FIG. 3 is a block flow diagram of the processing operations of the present invention. Viewed from the flow chart, the processing operations comprise:

(1) smoothing filtering

The signal is first filtered to eliminate the bursty interference signal. Considering real-time performance and processor capability, the present processing method employs mean-smooth filtering to process the raw sensor signal, as shown in fig. 3. The method is suitable for processing dynamic signals, not only can well weaken the burr noise, but also is simple in calculation. The smoothed waveform diagram shows that after the detection signal of the vehicle passing through the sensor is smoothed, the burr phenomenon is effectively weakened, and the characteristic waveform of the vehicle is retained. When the average value smoothing filter is used for filtering the burr noise, the adjacent lane vehicles can be filtered to a certain degree to generate a waveform with smaller amplitude and shorter wave width.

(2) Removing direct current

Further operating the filtered signals, removing the direct current component of the data volume to respectively obtain 6 alternating variables delta S₁.X、ΔS₁.Y、ΔS₁.Z、ΔS₂.X、ΔS₂Y and Δ S₂.Z。

(3) Calculate 6 derived quantities

Further processing to obtain 6 derivatives respectively of Δ S₁.TMI、ΔS₂.TMI、ΔS₁₂TMI, grad.x, grad.y and grad.z, the mathematical definitions of which are shown in formulas (1) to (6).

ΔS₁₂.TMI＝ΔS₁.TMI-ΔS₂TMI formula (3)

(4) Vehicle presence/absence determination

And adopting a multi-basis weighting judgment method for judging whether the vehicle exists or not. In the previous step, 6 quantities, respectively Δ S, were calculated₁.TMI、ΔS₂.TMI、ΔS₁₂TMI, grad.x, grad.y and grad.z. The 6 quantities can be weighted by increasing the weighting factor lambda₁-λ₆And calculating according to the formula (7) to obtain pass _ score. When it is necessary to note, the weighting factor λ₁-λ₆Generally, the process of average weighting is not used, but a method of weighting according to specific gravity is used. The determination of a particular weighting value may be determined using a relative change in 6 quantities. If a certain quantity varies more, indicating that the quantity is more sensitive, the corresponding weighting value should be greater. In general, λ₃The specific gravity of (A) will be relatively large, λ₁And λ₂Next, the method is described. When the vehicle is judged to be in existence, the difference value of the total intensity of the alternating fields of the sensors is used as a main basis for the vehicle to pass, the total intensity of the alternating fields in the magnetic field measured by the two sensors is used as a secondary basis, and the gradient value in each direction is used as a smaller basis. The vehicle is judged according to the weighting of the six quantities, the passing of the vehicle can be judged only by meeting the main basis and one of the secondary bases, and otherwise, the passing of the vehicle is not judged

$\begin{array}{c}pass\_score={\mathrm{\&lambda;}}_1*{\mathrm{\&Delta;S}}_1.TMI+{\mathrm{\&lambda;}}_2*{\mathrm{\&Delta;S}}_2.TMI+{\mathrm{\&lambda;}}_3*\\ {\mathrm{\&Delta;S}}_{12}.TMI+{\mathrm{\&lambda;}}_4*Grad.X+{\mathrm{\&lambda;}}_5*Grad.Y+{\mathrm{\&lambda;}}_6*Grad.Z\end{array}---\left(7\right)$

The value of pass score is compared to a preset threshold. If the threshold value is larger than the threshold value, the vehicle passes through, otherwise, the vehicle does not pass through. In the aspect of processing operation, a fixed threshold algorithm is adopted, and a specific threshold is obtained through multiple times of actual measurement of a scene through which an automobile passes.

(5) Judging the direction of travel

In the method for judging the vehicle direction, in one embodiment of the invention, the data after filtering is used for respectively comparing the difference value of each coordinate axis of two sensors XYZ, and then the driving direction of the vehicle is judged by using the difference value and a preset coefficient. Since the starting point of each coordinate axis of the sensor response is different, a preset coefficient is required to balance the difference of the starting points with the environmental influence factors.

(5) Binary vehicle passing information

The length and the internal structure of different vehicle types are different, the amount and the position of the contained ferromagnetic substances are different greatly, and therefore the geomagnetic disturbance waveform caused when the vehicle passes through the sensor is different greatly. However, vehicles of the same type are similar in structure, and key attributes of waveforms are similar, so that characteristic quantities of vehicle types can be reflected on disturbance waveforms. Considering that factors such as vehicle speed and vehicle chassis distance detection system height also have great influence on the waveform, the data after filtering needs to be subjected to binarization processing. And invalid data points in the data are removed, so that the influence of the environment and the vehicle on the collected data is reduced as much as possible.

The sensitive axis disturbance waveform data can be utilized for processing, and the waveform is converted into a normalized square wave (1,0) by taking the sensitive axis disturbance waveform data as a standard. The selection method of the value at the Nth sampling point on the sensitive axis in the normalized square wave comprises the following steps:

$S=\left\{\begin{array}{cc}1,& \mathrm{Sn}<\mathrm{St}\\ 0,& \mathrm{Sn}>\mathrm{St}\end{array}\right.$

in the formula: sn is the value at the sampling point N; st is the set waveform threshold. The peaks/troughs in the normalized square wave correspond to the sampling points where the sensitive axis magnetic signal is severely distorted, i.e. the moment when the vehicle drive shaft or engine passes over the sensor, since the engine is often mounted close to the drive shaft, overlapping the drive shaft in the normalized square wave. Therefore, the number N of wave crests/wave troughs can completely represent the number of transmission shafts of the vehicle.

(6) Calculating average vehicle speed

And respectively calculating the point number of the low level of the binarized data before the high level changes, calculating the time of the vehicle passing between the two sensors by using the difference of the two point numbers and the sampling rate, and obtaining the average speed of the vehicle by using the relative distance between the sensors. See formulas (1.1) and (1.2).

T＝(N2-N1)/100 (1.1)

V＝L/T (2.2)

(7) The length of the waveform of the vehicle length characteristic curve is calculated to reflect the time that the vehicle passes through the sensor, and the length of the waveform expands and contracts in proportion to the speed of the vehicle or the length of the vehicle. The time difference of the same vehicle passing through 2 sensors is recorded by using the sensors, the waveform is normalized to eliminate the influence of the vehicle speed on a time axis, and the normalized waveform length is in direct proportion to the length of the whole vehicle. When T1 and T2 are the times when the first sensor detects the presence and the departure of the vehicle, respectively, and T3 and T4 are the times when the second sensor detects the presence and the departure of the vehicle, respectively, the average time of the vehicle passing through one sensor is shown in formula (1.3), the average vehicle speed is obtained, and the length of the vehicle can be obtained from the time T and the average vehicle speed, shown in formula (1.4).

T＝((T2－T1)+(T4－T3))/2 (1.3)

length＝V*T (1.4)

Advantages and benefits of the invention include

The invention has the following advantages:

(1) the vehicle information passing through the sensor is recorded and stored in real time, the stability is high, the flexibility of the device is strong, the device can be built at any time, and the device is not influenced by places.

(2) The discrimination operation is novel and accurate, and has a certain filtering function on environmental interference. The misjudgment can be effectively avoided, and the vehicle information is calculated relatively accurately.

Claims (10)

1. Vehicle information gathers and processing system based on dual vector magnetic sensor, its characterized in that includes:

the front-end dual-vector magnetic sensor portion, which includes two vector magnetoresistive sensors S1 and S2, converts magnetic field signals generated by the motion of the vehicle into analog electrical signals,

a signal acquisition part for converting the analog electric signal output by the front end dual-vector magnetic sensor part into a digital signal,

a vehicle information determination section for determining the characteristic amount of the digital signal from the signal acquisition section, finding a numerical value suitable for processing, and determining vehicle information including the presence or absence of the vehicle, the vehicle traveling direction, the vehicle speed, the vehicle length,

a result output section for outputting the vehicle information.

2. The vehicle information collection and processing system according to claim 1, wherein the vehicle information determination section includes:

a data preprocessing part for filtering the 6 magnetic field signals S1.x, S1.y, S1.z, S2.x, S2.y and S2.z of the two vector magnetoresistive sensors under the fixed XYZ-axis coordinates to eliminate the sudden interference signals, and then removing the dc component of the filtered 6 magnetic field signals to obtain 6 alternating variables Δ S respectively₁.X、ΔS₁.Y、ΔS₁.Z、ΔS₂.X、ΔS₂Y and Δ S₂Z, and further obtaining 6 derivatives each of which is Δ S₁.TMI、ΔS₂.TMI、ΔS₁₂TMI, grad.x, grad.y and grad.z:

${\mathrm{\&Delta;S}}_1.TMI=\sqrt{{({\mathrm{\&Delta;S}}_1.X)}^2+{({\mathrm{\&Delta;S}}_1.Y)}^2+{({\mathrm{\&Delta;S}}_1.X)}^2}---\left(1\right)$

${\mathrm{\&Delta;S}}_2.TMI=\sqrt{{({\mathrm{\&Delta;S}}_2.X)}^2+{({\mathrm{\&Delta;S}}_2.Y)}^2+{({\mathrm{\&Delta;S}}_2.X)}^2}---\left(2\right)$

ΔS₁₂.TMI＝ΔS₁.TMI-ΔS₂.TMI (3)

$Grad.X=\frac{{\mathrm{\&Delta;S}}_1.X-{\mathrm{\&Delta;S}}_2.X}{\mathrm{\&Delta;}X}---\left(4\right)$

$Grad.Y=\frac{{\mathrm{\&Delta;S}}_1.Y-{\mathrm{\&Delta;S}}_2.Y}{\mathrm{\&Delta;}Y}---\left(5\right)$

$Grad.Z=\frac{{\mathrm{\&Delta;S}}_1.Z-{\mathrm{\&Delta;S}}_2.Z}{\mathrm{\&Delta;}Z}---\left(6\right)$

wherein,

ΔS₁TMI and Δ S₂TMI characterizes the total strength of the alternating field in the magnetic field measured by the two sensors as a vector (Δ S)₁.X,ΔS₁.Y,ΔS₁.Z)And (Δ S)₂.X,ΔS₂.Y,ΔS₂A two-norm of Z); delta S₁₂TMI characterizes the difference in the total intensity of the alternating field of the two sensors; the changes of the magnetic fields of the two sensors with distance in the XYZ coordinate directions, i.e. the gradient values, are characterized by Grad.X, Grad.Y and Grad.Z, and

the data preprocessing part finds the amount with larger transformation in the 6 derived quantities as the main basis of judgment, compares TMI values on XYZ axes, finds the axis with the largest TMI value as the sensitive axis, and determines the sensitive axis,

-a vehicle presence judging section for judging presence or absence of a vehicle using a multiple-basis weighted judgment method, comprising:

by a weighting factor lambda₁-λ₆According to the following steps:

$\begin{array}{c}pass\_score={\mathrm{\&lambda;}}_1*{\mathrm{\&Delta;S}}_1.TMI+{\mathrm{\&lambda;}}_2*{\mathrm{\&Delta;S}}_2.TMI+{\mathrm{\&lambda;}}_3*{\mathrm{\&Delta;S}}_{12}.TMI+\\ {\mathrm{\&lambda;}}_4*Grad.X+{\mathrm{\&lambda;}}_5*Grad.Y+{\mathrm{\&lambda;}}_6*Grad.Z\end{array}---\left(7\right)$

for Δ S₁.TMI、ΔS₂.TMI、ΔS₁₂TMI, grad.x, grad.y and grad.z were weighted to obtain the amount pas score,

comparing the value of pass score with a preset threshold value, judging that the vehicle passes when the value is larger than the threshold value, otherwise judging that the vehicle does not pass, wherein the threshold value is determined by experience,

-a vehicle direction discriminating section for determining a driving direction of the vehicle based on a gradient of change of the three quantities using the grad.x, the grad.y and the grad.z as a criterion, and determining the corresponding driving direction as an X-axis positive direction if the gradient of the grad.x is positive; otherwise, determining that the corresponding driving direction is the negative direction of the X axis; determining the driving directions of a Y axis and a Z axis according to Grad.Y and Grad.Z in the same way, and comprehensively judging the driving directions of three coordinate axes; respectively determining the difference value of each coordinate axis of the two sensors XYZ from the filtered data, judging the driving direction of the vehicle by using the difference value,

-an average vehicle speed measuring section for:

the signals S1.X, S1.Y, S1.Z, S2.X, S2.Y and S2.Z after the output of the sensor is filtered are converted by a larger amount, namely the main basis of judgment, binarization processing is carried out to remove some invalid data points in the data,

and selecting a judging threshold St as a main basis for judging, and converting the waveform into a normalized square wave (1,0), wherein the value of the Nth sampling point on the sensitive axis in the normalized square wave is selected by the following method:

in the formula: sn is the value at the sampling point N; st is a set waveform threshold value in which the direction of data on which the judgment is held is the same direction,

selecting data after binarization of data with larger change in the same direction of a certain sensor, calculating the difference between the point numbers of adjacent 01 changes, and calculating the time difference T of the vehicle passing through the two sensors by combining the sampling rate,

T＝(N2-N1)/S (7)

wherein N2 and N1 are the number of points at which level 01 changes occur in sensors 2 and 1, respectively, and S is the sampling rate to calculate the time for the vehicle to pass between the two sensors, according to:

V＝L/T (8)

the average speed of the vehicle is obtained from the relative distance between the sensors, where L is the distance of the dual sensors in the sensitive direction, a known quantity, and T is calculated in equation (7),

-a vehicle length measuring section for measuring the average vehicle speed based on the time T1 when the first sensor detects the presence of the vehicle, the time T2 when the first sensor detects the departure of the vehicle, the time T3 when the second sensor detects the presence of the vehicle, and the time T4 when the second sensor detects the departure of the vehicle, according to:

T＝((T2－T1)+(T4－T3))/2 (9)

length＝V*T (10)

the average time T for the vehicle to pass one sensor and the length of the vehicle are obtained.

3. The vehicle information collection and processing system according to claim 1 or 2, characterized in that:

the two vector magnetoresistive sensors S1 and S2 are selected from one of anisotropic magnetoresistors, giant magnetoresistors, and tunnel magnetoresistors.

4. The vehicle information collection and processing system according to claim 1 or 2, characterized in that:

the signal acquisition part comprises one selected from a data acquisition card and an analog-digital converter circuit,

the vehicle information determination portion includes one selected from the following devices:

a computer loaded with a corresponding processing application,

a digital signal processor loaded with a corresponding processing application.

5. The vehicle information collection and processing system according to claim 1 or 2, characterized in that:

the result output section includes at least one of the following means:

an output indication part with a display function and an indication function,

and the data transmission part is used for transmitting the vehicle information to at least one receiver through a communication interface and/or a protocol, wherein the at least one receiver comprises a traffic guidance center and/or a data summarization center.

6. The vehicle information acquisition and processing method based on the dual-vector magnetic sensor is characterized by comprising the following steps of:

A) with the front dual-vector magnetic sensor section, which includes two vector magnetoresistive sensors S1 and S2, magnetic field signals generated by vehicle motion are converted into analog electrical signals,

B) converts the analog electric signal output by the front-end dual-vector magnetic sensor part into a digital signal,

C) the digital signal from the signal acquisition part is subjected to characteristic quantity determination, a numerical value suitable for processing is found, and vehicle information including the existence of the vehicle, the vehicle running direction, the vehicle speed and the vehicle length is determined and determined,

D) the vehicle information is output by a result output section.

7. The vehicle information collection and processing method according to claim 6, wherein the step A) includes:

-filtering the 6 magnetic field signals S1.x, S1.y, S1.z, S2.x, S2.y and S2.z of the two vector magnetoresistive sensors under the fixed XYZ-axis coordinates to eliminate the sudden interference signals, and then removing the dc component of the filtered 6 magnetic field signals to obtain 6 alternating variables Δ S respectively₁.X、ΔS₁.Y、ΔS₁.Z、ΔS₂.X、ΔS₂Y and Δ S₂Z, and further obtaining 6 derivatives each of which is Δ S₁.TMI、ΔS₂.TMI、ΔS₁₂TMI, grad.x, grad.y and grad.z:

${\mathrm{\&Delta;S}}_1.TMI=\sqrt{{({\mathrm{\&Delta;S}}_1.X)}^2+{({\mathrm{\&Delta;S}}_1.Y)}^2+{({\mathrm{\&Delta;S}}_1.X)}^2}---\left(1\right)$

${\mathrm{\&Delta;S}}_2.TMI=\sqrt{{({\mathrm{\&Delta;S}}_2.X)}^2+{({\mathrm{\&Delta;S}}_2.Y)}^2+{({\mathrm{\&Delta;S}}_2.X)}^2}---\left(2\right)$

ΔS₁₂.TMI＝ΔS₁.TMI-ΔS₂.TMI (3)

$Grad.X=\frac{{\mathrm{\&Delta;S}}_1.X-{\mathrm{\&Delta;S}}_2.X}{\mathrm{\&Delta;}X}---\left(4\right)$

$Grad.Y=\frac{{\mathrm{\&Delta;S}}_1.Y-{\mathrm{\&Delta;S}}_2.Y}{\mathrm{\&Delta;}Y}---\left(5\right)$

$Grad.Z=\frac{{\mathrm{\&Delta;S}}_1.Z-{\mathrm{\&Delta;S}}_2.Z}{\mathrm{\&Delta;}Z}---\left(6\right)$

wherein,

ΔS₁TMI andΔS₂TMI characterizes the total strength of the alternating field in the magnetic field measured by the two sensors as a vector (Δ S)₁.X,ΔS₁.Y,ΔS₁Z) and (Δ S)₂.X,ΔS₂.Y,ΔS₂A two-norm of Z); delta S₁₂TMI characterizes the difference in the total intensity of the alternating field of the two sensors; the changes of the magnetic fields of the two sensors with distance in the XYZ coordinate directions, i.e. the gradient values, are characterized by Grad.X, Grad.Y and Grad.Z, and

the data preprocessing part finds the amount with larger transformation in the 6 derived quantities as the main basis of judgment, compares TMI values on XYZ axes, finds the axis with the largest TMI value as the sensitive axis, and determines the sensitive axis,

-discriminating the presence or absence of a vehicle using a multi-basis weighted discrimination method comprising:

by a weighting factor lambda₁-λ₆According to the following steps:

$\begin{array}{c}pass\_score={\mathrm{\&lambda;}}_1*{\mathrm{\&Delta;S}}_1.TMI+{\mathrm{\&lambda;}}_2*{\mathrm{\&Delta;S}}_2.TMI+\\ {\mathrm{\&lambda;}}_3*{\mathrm{\&Delta;S}}_{12}.TMI+{\mathrm{\&lambda;}}_4*Grad.X+{\mathrm{\&lambda;}}_5*Grad.Y+{\mathrm{\&lambda;}}_6*Grad.Z\end{array}---\left(7\right)$

for Δ S₁.TMI、ΔS₂.TMI、ΔS₁₂TMI, grad.x, grad.y and grad.z were weighted to obtain the amount pas score,

comparing the value of pass score with a preset threshold value, judging that the vehicle passes when the value is larger than the threshold value, otherwise judging that the vehicle does not pass, wherein the threshold value is determined by experience,

-determining the direction of travel of the vehicle from the slopes of variation of the three quantities using grad.x, grad.y and grad.z as criteria, and if the slope of grad.x is positive, determining the corresponding direction of travel as the X-axis positive direction; otherwise, determining that the corresponding driving direction is the negative direction of the X axis; determining the driving directions of a Y axis and a Z axis according to Grad.Y and Grad.Z in the same way, and comprehensively judging the driving directions of three coordinate axes; respectively determining the difference value of each coordinate axis of the two sensors XYZ from the filtered data, judging the driving direction of the vehicle by using the difference value,

-measuring an average vehicle speed, comprising:

the signals S1.X, S1.Y, S1.Z, S2.X, S2.Y and S2.Z after the output of the sensor is filtered are converted by a larger amount, namely the main basis of judgment, binarization processing is carried out to remove some invalid data points in the data,

and selecting a judging threshold St as a main basis for judging, and converting the waveform into a normalized square wave (1,0), wherein the value of the Nth sampling point on the sensitive axis in the normalized square wave is selected by the following method:

in the formula: sn is the value at the sampling point N; st is a set waveform threshold value in which the direction of data on which the judgment is held is the same direction,

selecting data after binarization of data with larger change in the same direction of a certain sensor, calculating the difference between the point numbers of adjacent 01 changes, and calculating the time difference T of the vehicle passing through the two sensors by combining the sampling rate,

T＝(N2-N1)/S (7)

where N2 and N1 are the number of points at which level 01 changes occur at sensors 2 and 1, respectively, S is the sample rate to calculate the time between the vehicle passing over the two sensors,

according to the following steps:

V＝L/T (8)

the average speed of the vehicle is derived from the relative distance between the sensors, where L is the distance of the dual sensors in the sensitive direction, a known quantity, and T is calculated in equation (7),

-said average vehicle speed, according to the time T1 when the first sensor detects the presence of the vehicle, the time T2 when the first sensor detects the exit of the vehicle, the time T3 when the second sensor detects the presence of the vehicle, the time T4 when the second sensor detects the exit of the vehicle, according to:

T＝((T2－T1)+(T4－T3))/2 (9)

length＝V*T (10)

the average time T for the vehicle to pass one sensor and the length of the vehicle are obtained.

8. The vehicle information collection and processing method according to claim 6 or 7, characterized in that:

the two vector magnetoresistive sensors S1 and S2 are selected from one of anisotropic magnetoresistors, giant magnetoresistors, and tunnel magnetoresistors.

9. The vehicle information collection and processing method according to claim 1 or 2, characterized in that:

said step B) is performed using a signal acquisition section comprising a selected one of a data acquisition card and an analog-to-digital converter circuit,

said step C) is performed using a vehicle information determination section including one selected from the following devices:

a computer loaded with a corresponding processing application,

a digital signal processor loaded with a corresponding processing application.

10. The vehicle information collection and processing method according to claim 6 or 7, characterized in that:

the result output section includes at least one of the following means:

an output indication part with a display function and an indication function,

and the data transmission part is used for transmitting the vehicle information to at least one receiver through a communication interface and/or a protocol, wherein the at least one receiver comprises a traffic guidance center and/or a data summarization center.