Farmland pavement spectrum measurement method based on multi-sensor coupling

Abstract

The invention relates to a farmland pavement spectrum measurement method based on multi-sensor coupling. The method specifically comprises the following steps: carrying a farmland pavement spectrum measurement platform comprising a laser displacement sensor, an acceleration sensor and a tilt angle sensor on agricultural machinery; the agricultural machinery is driven to run at a constant speed on the road surface to be tested, the measurement data of the laser displacement sensor, the acceleration sensor and the tilt angle sensor are collected in real time and processed, and the vertical distance of the laser displacement sensor relative to the horizontal datum line at each measurement position is calculated; according to the obtained vertical distance of each measurement position, constructing a road surface unevenness height sequence; inputting the road surface unevenness height sequence, the sampling point number and the sampling frequency into a power spectrum analysis model to obtain a road surface unevenness curve graph and a road surface power spectrum density map; and comparing the power spectrum density value output by the power spectrum analysis model with the pavement grade in the national standard to determine the farmland pavement grade.

Description

Technical Field

[0001] The present invention relates to the field of agricultural field soil and pavement spectrum testing, and in particular to a farmland and pavement spectrum measurement method based on multi-sensor coupling. Background Art

[0002] As a way to describe farmland pavement excitation, the farmland pavement spectrum is one of the excitation (input) conditions for agricultural machinery vibration modeling and control. It is also the basis for obtaining the vibration characteristics of agricultural machinery. Accurately obtaining farmland pavement excitation directly affects the results of agricultural machinery dynamic characteristics analysis, and thus affects the vibration control and dynamic design of agricultural machinery.

[0003] To this end, the invention patent application with application publication number CN 115218830A discloses "a testing device, testing method and system for farmland pavement spectrum", in which the testing method uses a non-contact measurement laser displacement sensor to measure the elevation (displacement) changes of the four wheels and the vertical elevation changes of the center of mass of the whole machine respectively, and uses MATLAB to calculate the average value and standard deviation of the elevation data. The weight coefficient of each wheel and center of mass is obtained by the coefficient of variation method, and the weighted average value is taken as the elevation result of the whole machine. The weighted elevation data is then subjected to discrete Fourier transform to obtain the farmland pavement spectrum.

[0004] However, in the above test method, the unevenness of the road surface will excite the tires, causing the entire test device to vibrate and pitch. This will cause the data measured by the laser displacement sensor to not be the actual road surface unevenness, resulting in excessive errors in the final farmland road surface spectrum (or power spectrum density value). Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a farmland pavement spectrum measurement method based on multi-sensor coupling. The farmland pavement spectrum measurement method can realize real-time measurement of the pavement spectrum when agricultural machinery passes through the soft ground of the farmland, and the measurement accuracy is higher.

[0006] The technical solution of the present invention to solve the above technical problems is:

[0007] A method for measuring farmland pavement spectrum based on multi-sensor coupling includes the following steps:

[0008] Step S1: A farmland pavement spectrum measurement platform including a laser displacement sensor, an acceleration sensor, and an inclination sensor is mounted on agricultural machinery;

[0009] Step S2: driving the agricultural machinery to travel at a constant speed on the road surface to be tested, and collecting measurement data from the laser displacement sensor, acceleration sensor, and inclination sensor in real time;

[0010] Step S3: Processing the real-time measurement data from the laser displacement sensor, acceleration sensor, and inclination sensor to calculate the vertical distance of the laser displacement sensor relative to the horizontal reference line at each measurement position, and constructing a road surface roughness height sequence based on the obtained vertical distance at each measurement position;

[0011] Step S4: Input the road surface roughness height sequence, number of sampling points, and sampling frequency into the power spectrum analysis model to obtain a road surface roughness curve and a road surface power spectrum density diagram; at the same time, compare the power spectrum density value output by the power spectrum analysis model with the road surface grade in the national standard to determine the farmland road surface grade.

[0012] Preferably, in step S1, the farmland pavement spectrum measurement platform includes a laser displacement sensor, an acceleration sensor, an inclination sensor, a data acquisition card and a host computer, wherein:

[0013] The laser displacement sensor is used to measure the elevation of the four wheels of the agricultural machinery relative to the ground. The laser displacement sensor is provided in four groups, and the four groups of laser displacement sensors are respectively installed on the front axle and the rear axle of the agricultural machinery;

[0014] The acceleration sensors are used to measure the vibration displacement of the body of the agricultural machinery relative to the road surface. The acceleration sensors are in four groups, and the four groups of acceleration sensors are respectively installed on the steering knuckles connected to the front axle and the rear axle of the agricultural machinery;

[0015] The tilt sensor is used to measure the pitch of the agricultural machinery body relative to the ground, and the tilt sensor is installed on the body in front of the driver's seat with its front facing upwards;

[0016] The data acquisition card is used to collect measurement data of the laser displacement sensor, acceleration sensor and inclination sensor in real time, and upload the collected data information to the host computer;

[0017] The host computer is used to process the received data information to obtain and display road surface roughness data.

[0018] Preferably, in step S3, the process of processing the real-time measurement data of the laser displacement sensor, the acceleration sensor and the inclination sensor is as follows:

[0019] According to the pitch angle detected by the inclination sensor, the measurement value of the laser displacement sensor is processed to obtain the vertical distance measured by the laser displacement sensor;

[0020] The measured value from the acceleration sensor is integrated twice to obtain the vertical vibration displacement of the agricultural machinery.

[0021] Preferably, before performing secondary integration on the measured value in the acceleration sensor, it is necessary to remove the DC component when converting the measured value into a digital value. The specific steps are as follows:

[0022] An average value of the measured values from the acceleration sensor is obtained, and a difference between the measured value and the average value is obtained as a new acceleration value.

[0023] Preferably, the new acceleration value is integrated twice using the Simpson integration method to obtain the vertical vibration displacement.

[0024] Preferably, in step S3, a first difference between the height value of the farmland pavement spectrum measurement platform in the geographic coordinate system and the height value of the standard road in the geographic coordinate system is obtained, and a second difference between the vertical distance measured by the laser displacement sensor and the vertical vibration displacement is obtained; and a third difference between the first difference and the second difference is obtained as the vertical distance of the laser displacement sensor at the measurement position relative to the horizontal reference line.

[0025] Preferably, in step S2, the laser displacement sensor and the acceleration sensor respectively use laser displacement sensors and acceleration sensors with different high and low frequency performances to detect high-frequency signals and low-frequency signals, wherein the high-frequency signal is a signal greater than 5 Hz; the low-frequency signal is a signal lower than 5 Hz.

[0026] Preferably, high-frequency acceleration signals and low-frequency acceleration signals are measured respectively by acceleration sensors with different high- and low-frequency performances, and then the high- and low-frequency acceleration displacement signals are obtained by using the quadratic integration method; for the displacement signal collected by the laser displacement sensor, the low-frequency laser displacement signal is extracted by low-pass filtering, and the high-frequency laser displacement signal is extracted by high-pass filtering; combined with the measured inclination signal, the low-frequency road surface roughness and high-frequency road surface roughness are obtained respectively according to the inertial reference road surface roughness measurement principle; on the premise of ensuring that the sampling time or spatial position of the low-frequency road surface roughness and the high-frequency road surface roughness correspond one-to-one, the low-frequency road surface roughness height sequence and the high-frequency road surface roughness height sequence of the corresponding points are added to obtain the final road surface roughness height sequence.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. The farmland pavement spectrum measurement method based on multi-sensor coupling of the present invention measures the elevation change of the wheels in the front and rear axles of the agricultural machinery through a laser displacement sensor, then measures the vibration of the agricultural machinery body through an acceleration sensor, and measures the pitch change of the agricultural machinery body through an inclination sensor. Finally, the vertical distance of the laser displacement sensor relative to the horizontal reference line at each measurement position is calculated, and a road surface roughness height sequence is constructed based on the obtained vertical distance of each measurement position; the road surface roughness height sequence, the number of sampling points and the sampling frequency are input into a power spectrum analysis model to obtain a road surface roughness curve diagram and a road surface power spectrum density diagram; at the same time, the power spectrum density value output by the power spectrum analysis model is compared with the road surface grade in the national standard to determine the farmland pavement grade.

[0029] 2. The farmland pavement spectrum measurement method based on multi-sensor coupling of the present invention can realize real-time measurement of the pavement spectrum of the farmland soft ground when agricultural machinery passes through the farmland soft ground, and display it in real time on the host computer, thereby achieving the analysis of road surface roughness, pavement spectrum data and prediction of vehicle damage.

[0030] 3. The farmland pavement spectrum measurement method based on multi-sensor coupling of the present invention has higher measurement accuracy and efficiency and is more convenient to operate. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of the farmland pavement spectrum measurement method based on multi-sensor coupling of the present invention.

[0032] Figure 2 The schematic diagram of the road roughness measurement based on inertial reference.

[0033] Figure 3 This is the structural block diagram of the farmland pavement spectrum measurement platform.

[0034] Figure 4 This is the flow chart for high and low frequency measurement of road roughness. DETAILED DESCRIPTION

[0035] The present invention will be described in further detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0036] See also Figures 1-4 The farmland pavement spectrum measurement method based on multi-sensor coupling of the present invention comprises the following steps:

[0037] Step S1: A farmland pavement spectrum measurement platform including a laser displacement sensor, an acceleration sensor, and an inclination sensor is mounted on agricultural machinery; the farmland pavement spectrum measurement platform includes a laser displacement sensor, an acceleration sensor, an inclination sensor, a data acquisition card, and a host computer, wherein:

[0038] The laser displacement sensor is used to measure the elevation of the four wheels of agricultural machinery relative to the ground. The laser displacement sensor is provided in four groups, which are respectively installed on the front and rear axles of the agricultural machinery and measure the elevation of the four wheels relative to the ground using the triangulation laser principle.

[0039] The acceleration sensor indirectly measures the vibration displacement of the body of the agricultural machine relative to the road surface through the upward acceleration of the body of the agricultural machine. The acceleration sensor is provided in four groups, and the four groups of acceleration sensors are respectively installed on the steering knuckles connected to the front axle and the rear axle of the agricultural machine;

[0040] The tilt sensor is used to measure the pitch of the agricultural machinery body relative to the ground, thereby indirectly measuring the displacement of the agricultural machinery body. The tilt sensor is installed on the body in front of the driver's seat with its front facing upwards;

[0041] The data acquisition card is used to collect measurement data of the laser displacement sensor, acceleration sensor and inclination sensor in real time, and upload the collected data information to the host computer;

[0042] The host computer is used to process the received data information to obtain and display road surface roughness data.

[0043] The specific steps for building the farmland pavement spectrum measurement platform are as follows:

[0044] (1) The stainless steel mounting bracket for installing the laser displacement sensor is installed on the front and rear axles, and then the laser displacement sensor is fixed; the acceleration sensor is fixed to the front and rear axles by M2.5 screws, and the inclination sensor is fixed to the vehicle body in front of the driver by bolts; the laser displacement sensor, acceleration sensor, and inclination displacement sensor are connected to the data acquisition card located in the cab through corresponding wires. In order to reduce the interference of vehicle body vibration on the transmission signal, the wires are fixed to the frame with insulating tape; the data acquisition card and the host computer are both placed in the cab, and the road surface roughness data is obtained through real-time acquisition.

[0045] (2) Use wires to connect the positive and negative poles of the vehicle power supply to the positive and negative poles of the sine wave inverter to convert the 24V DC power of the vehicle power supply into 220V AC power. The 220VDC converted by the sine wave inverter is used to power each sensor through the following voltage stabilization modules:

[0046] The laser displacement sensor is powered by a 24-12DC DC voltage regulator module;

[0047] The accelerometer is powered by a 6-24VDC DC voltage regulator module;

[0048] The tilt sensor is powered by a 6-24VDC DC voltage regulator module.

[0049] (3) Check whether each functional module is working properly, that is, after the laser displacement sensor, acceleration sensor, and inclination sensor are installed, conduct equipment availability test first. Among them, the laser displacement sensor emits laser according to the principle of triangulation displacement measurement, and then receives reflected laser to measure data; the acceleration sensor measures acceleration through vehicle body vibration; the inclination sensor measures pitch angle through vehicle body tilt, ensuring that all devices can measure data before proceeding with subsequent operations.

[0050] (4) Determine the reference point of the laser displacement sensor. The agricultural machinery is set at the starting point of the road surface to be tested. The laser displacement sensor is adjusted to 0, and the current elevation data from the ground is used as the reference point of the entire measurement process, that is, the relative zero point of the measurement system.

[0051] (5) Set the sampling frequency and number of sampling points on the data acquisition card, set the system parameters, name the sampled data, set the save path, debug the parameters of each acquisition channel and the stop time. When selecting the sampling frequency, the maximum road excitation frequency needs to be determined according to the vehicle speed.

[0052]

[0053] If the vehicle speed is 5 km / h, find the maximum road excitation frequency f max =13.8Hz, so the sampling frequency should be selected as 50Hz to ensure the accuracy of data collection.

[0054] Step S2: driving the agricultural machinery to travel at a constant speed on the road surface to be tested, and collecting measurement data from the laser displacement sensor, acceleration sensor, and inclination sensor in real time;

[0055] In this embodiment, the agricultural machinery enters the test road at a speed of 5 km / h, and the experimental test distance is selected as 100 m. The road conditions are good and basically cover the common roads where agricultural machinery works and travels. Multiple measurements are performed on three road conditions, namely gravel, soft soil, and hard soil, to ensure that the measurement error is reduced and the road conditions of the test road are increased.

[0056] When the agricultural machinery is at the starting point of the test range, click to start acquisition on the operation interface of the farmland pavement spectrum measurement platform, and end the acquisition of the farmland pavement spectrum measurement platform in time when the measurement range ends; the data information collected by the data acquisition card is uploaded to the host computer and saved through the test software LabVIEW in the host computer.

[0057] Step S3: Processing the real-time measurement data from the laser displacement sensor, acceleration sensor, and inclination sensor to calculate the vertical distance of the laser displacement sensor relative to the horizontal reference line at each measurement position, and constructing a road surface roughness height sequence based on the obtained vertical distance at each measurement position;

[0058] Taking the horizontal reference line as the reference, the prerequisite for obtaining the road surface roughness height sequence is to calculate the vertical distance Δz(t) of the laser displacement sensor relative to the horizontal reference line at each measurement position; since the road surface roughness will excite the wheel tires, thereby causing the body of the agricultural machinery to vibrate and pitch, the data measured by the laser displacement sensor will not be the actual road surface roughness. Therefore, the road surface roughness needs to be obtained by integrating the vertical distance z4(t) measured by the laser displacement sensor and the measurement value of the acceleration sensor twice to obtain the vertical vibration displacement z2(t) and the pitch angle θ of the body of the agricultural machinery. y (t) Jointly decide on the following formula:

[0059] Δz(t)=z1-z0-z3(t);

[0060] =z1-z0-[z4(t)cos(θy(t))-z2(t)];

[0061] Where: z1 is the height of the farmland pavement spectrum measurement platform in the geographic coordinate system; z0 is the height of the standard road in the geographic coordinate system, that is, the height of the horizontal reference line;

[0062] From the above formula, we can see that the size of Δz(t) depends on the vertical vibration displacement z2(t) obtained by quadratic integration of the acceleration sensor measurement value;

[0063] Assume that the acceleration measured by the acceleration sensor at time t is a(t), then the component of the acceleration a(t) in the direction perpendicular to the horizontal reference line is a(t)θ y (t), where θ y (t) can be measured by the inclination sensor, so the vertical vibration displacement z2(t):

[0064] z2(t)=∫0 t ∫0 t a(t)θy(t)dtdt;

[0065] To obtain the displacement by performing a quadratic integration on the acceleration signal, three steps are required: de-averaging, integration, and de-trending.

[0066] The vertical vibration of agricultural machinery is measured by an acceleration sensor. During the transmission process, the acceleration value needs to be filtered and converted into a digital value through A / D conversion. There must be a DC component in it. The DC component will affect the integration accuracy, thus causing errors. Therefore, the present invention uses the mean value of the acceleration signal as an estimate of the DC component and removes it from the original acceleration signal. The specific method requires calculating the mean value of the acceleration.

[0067]

[0068] Then subtract this average value from the acceleration value:

[0069]

[0070] From the above formula, we can know that a′ i This is the new acceleration value after removing the DC component.

[0071] The acceleration after removing the DC component is integrated once to obtain the velocity, and integrated twice to obtain the displacement. The present invention adopts the Simpson integration method to obtain the displacement of a′ i For quadratic integration, the Simpson integration method is more accurate than the trapezoidal integration method, and the results are more precise.

[0072] Among them, Simpson integral formula:

[0073]

[0074] Where, k = 1.2…(N-1);

[0075] Calculate velocity from acceleration using Simpson's method:

[0076]

[0077] Calculate displacement from acceleration using Simpson's method:

[0078]

[0079] In actual measurement, sensor zero drift caused by ambient temperature fluctuations, unstable sensor integration performance, and environmental interference can cause the integration result to deviate from the theoretical baseline, generating a trend term that deviates from the baseline and varies with time. Because the trend term has a significant impact on the change result, especially the trend term after quadratic integration, which can completely distort the displacement, detrending the term is necessary. The commonly used detrending method is the polynomial least squares method, which works as follows:

[0080] Assume that the actual measured acceleration value is a(1,2,…,N), and its corresponding time series is t(1,2,…,N).

[0081] Polynomial functions

[0082]

[0083] k=(1,2,…,N)

[0084] Determine the function The unknown coefficients b0, b1, b2, ..., b m , so that the function The sum of squares of the errors with the acceleration value a is minimized:

[0085]

[0086] Find b0, b1, b2, ..., b that satisfies the minimum value of E. m The specific value of m is determined according to the actual signal. When the acceleration is integrated once to calculate the velocity, m=2 when the trend term is removed; when the acceleration is integrated twice to calculate the displacement, m=3 when the trend term is removed.

[0087] In this embodiment, the road surface wavelength range to be measured is 0.01 to 100 m. When the vehicle speed during testing is 5 m / s, the relationship between frequency, wavelength, and speed (f = v / λ) indicates that the required time frequency range to be measured is 0.05 to 500 Hz. The longitudinal section curve of the road surface (road shape curve) contains signals ranging from low to high frequencies. A sensor using only a single frequency cannot cover all signal frequency bands. Therefore, the present invention divides the measurement signal into two parts: low-frequency and high-frequency signals. Low-frequency signals (less than 5 Hz) can be measured using sensors with good low-frequency performance, while high-frequency signals (greater than 5 Hz) can be measured using sensors with better high-frequency performance.

[0088] High-frequency and low-frequency acceleration signals are measured using accelerometers with different high- and low-frequency performance, respectively. The high- and low-frequency acceleration displacement signals are then derived using a quadratic integration method. The displacement signals collected by the laser displacement sensor are low-pass filtered (≤5Hz) to extract the low-frequency laser displacement signal, and high-pass filtered (>5Hz) to extract the high-frequency laser displacement signal. Combined with the measured inclination signal, the low-frequency and high-frequency road roughness measurements are obtained based on the inertial reference road roughness measurement principle. By ensuring a one-to-one correspondence between the sampling times or spatial locations of the low-frequency and high-frequency road roughness measurements, the low-frequency and high-frequency road roughness height sequences at the corresponding points are summed to obtain the final road roughness.

[0089] Step S4: Input the road surface roughness height sequence, number of sampling points, and sampling frequency into the power spectrum analysis model to obtain a road surface roughness curve and a road surface power spectrum density diagram; at the same time, compare the power spectrum density value output by the power spectrum analysis model with the road surface grade in the national standard to determine the farmland road surface grade.

[0090] Since the road surface roughness cannot be completely reconstructed by the time domain characteristic parameters, it is necessary to analyze the road surface roughness height sequence from the frequency domain structure by constructing the power spectrum density method.

[0091] Road roughness fitting expression:

[0092]

[0093] Where: n is the spatial frequency; n0 is the reference spatial frequency, generally 0.1; G d (n0) is the road surface power spectrum density value corresponding to the reference spatial frequency; G d (n) is the random road surface power spectrum density corresponding to n, and the frequency index of the graded road surface spectrum is W=2.

[0094] The program that generates the road surface spectrum is input into the software to obtain the road surface roughness curve and the road surface power spectrum density diagram, namely the farmland road surface spectrum; at the same time, the power spectrum density value output by the power spectrum analysis model is compared with the road surface grade in the national standard to determine the farmland road surface grade.

[0095] Finally, a MATLAB program was written based on the above-mentioned road surface power spectrum density formula to design an acquisition system that can display the collected road surface elevation and road surface spectrum in real time, realizing the real-time acquisition function of road surface roughness data.

[0096] The present invention measures the elevation changes of the front and rear axles of agricultural machinery, and uses acceleration sensors and inclination sensors to measure the vibration displacement of the vehicle body. The real-time road roughness of the agricultural machinery is measured through the road roughness measurement principle based on inertial reference. Then, the power spectrum density of the vehicle-mounted farmland road surface is obtained by using MATLAB according to the method of constructing power spectrum density.

[0097] The above is a preferred embodiment of the present invention, but the embodiment of the present invention is not limited to the above content. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A method for measuring farmland pavement spectrum based on multi-sensor coupling, characterized in that: The following steps are involved: Step S1: A farmland pavement spectrum measurement platform including a laser displacement sensor, an acceleration sensor, and an inclination sensor is mounted on agricultural machinery; Step S2: driving the agricultural machinery to travel at a constant speed on the road surface to be tested, and collecting measurement data from the laser displacement sensor, acceleration sensor, and inclination sensor in real time; Step S3: Processing the real-time measurement data from the laser displacement sensor, acceleration sensor, and inclination sensor to calculate the vertical distance of the laser displacement sensor relative to the horizontal reference line at each measurement position, and constructing a road surface roughness height sequence based on the obtained vertical distance at each measurement position; Step S4: Input the road surface roughness height sequence, number of sampling points, and sampling frequency into the power spectrum analysis model to obtain a road surface roughness curve and a road surface power spectrum density diagram; at the same time, compare the power spectrum density value output by the power spectrum analysis model with the road surface grade in the national standard to determine the farmland road surface grade.

2. The farmland road surface spectrum measurement method based on multi-sensor coupling according to claim 1 is characterized in that: In step S1, the farmland pavement spectrum measurement platform includes a laser displacement sensor, an acceleration sensor, an inclination sensor, a data acquisition card and a host computer, wherein: The laser displacement sensor is used to measure the elevation of the four wheels of the agricultural machinery relative to the ground. The laser displacement sensor is provided in four groups, and the four groups of laser displacement sensors are respectively installed on the front axle and the rear axle of the agricultural machinery; The acceleration sensors are used to measure the vibration displacement of the body of the agricultural machinery relative to the road surface. The acceleration sensors are in four groups, and the four groups of acceleration sensors are respectively installed on the steering knuckles connected to the front axle and the rear axle of the agricultural machinery; The tilt sensor is used to measure the pitch of the agricultural machinery body relative to the ground, and the tilt sensor is installed on the body in front of the driver's seat with its front facing upwards; The data acquisition card is used to collect measurement data of the laser displacement sensor, acceleration sensor and inclination sensor in real time, and upload the collected data information to the host computer; The host computer is used to process the received data information to obtain and display road surface roughness data.

3. The farmland road surface spectrum measurement method based on multi-sensor coupling according to claim 2 is characterized in that: In step S3, the real-time measurement data of the laser displacement sensor, acceleration sensor and inclination sensor are processed as follows: According to the pitch angle detected by the inclination sensor, the measurement value of the laser displacement sensor is processed to obtain the vertical distance measured by the laser displacement sensor; The measured value from the acceleration sensor is integrated twice to obtain the vertical vibration displacement of the agricultural machinery.

4. The farmland road surface spectrum measurement method based on multi-sensor coupling according to claim 3 is characterized in that: Before performing secondary integration on the measured value from the acceleration sensor, it is necessary to remove the DC component when converting the measured value into a digital quantity. The specific steps are as follows: An average value of the measured values from the acceleration sensor is obtained, and a difference between the measured value and the average value is obtained as a new acceleration value.

5. The farmland road surface spectrum measurement method based on multi-sensor coupling according to claim 4 is characterized in that: The new acceleration value is quadratically integrated using the Simpson integration method to obtain the vertical vibration displacement.

6. The farmland road surface spectrum measurement method based on multi-sensor coupling according to claim 4 is characterized in that: In step S3, a first difference between the height value of the farmland pavement spectrum measurement platform in the geographic coordinate system and the height value of the standard road in the geographic coordinate system is obtained, and a second difference between the vertical distance measured by the laser displacement sensor and the vertical vibration displacement is obtained; and a third difference between the first difference and the second difference is obtained as the vertical distance of the laser displacement sensor at the measurement position relative to the horizontal reference line.

7. The farmland road surface spectrum measurement method based on multi-sensor coupling according to claim 6 is characterized in that: In step S2, the laser displacement sensor and the acceleration sensor respectively use laser displacement sensors and acceleration sensors with different high and low frequency performances to detect high-frequency signals and low-frequency signals, wherein the high-frequency signal is a signal greater than 5 Hz; the low-frequency signal is a signal lower than 5 Hz.

8. The farmland road surface spectrum measurement method based on multi-sensor coupling according to claim 7 is characterized in that: High-frequency acceleration signals and low-frequency acceleration signals are measured respectively by acceleration sensors with different high- and low-frequency performance, and then the high- and low-frequency acceleration displacement signals are obtained using the quadratic integration method. For the displacement signal collected by the laser displacement sensor, the low-frequency laser displacement signal is extracted by low-pass filtering, and the high-frequency laser displacement signal is extracted by high-pass filtering. Combined with the measured inclination signal, the low-frequency road surface roughness and high-frequency road surface roughness are obtained respectively according to the inertial reference road surface roughness measurement principle. Under the premise of ensuring a one-to-one correspondence between the sampling time or spatial position of the low-frequency road surface roughness and the high-frequency road surface roughness, the low-frequency road surface roughness height sequence and the high-frequency road surface roughness height sequence of the corresponding points are added to obtain the final road surface roughness height sequence.

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