Intelligent agricultural machine operation speed monitoring method and system based on multi-sensing information fusion

Abstract

The invention discloses an intelligent agricultural machine operation speed monitoring method and system based on multi-sensing information fusion. The method comprises the steps that first speed data measured by an encoder speed measuring device, second speed data measured by a GPS speed measuring device and third speed data measured by a Doppler radar speed measuring device are acquired; obtaining the operation speed of the agricultural machine based on the three kinds of speed data, specifically, processing the speed data by using a weighted average filtering algorithm, and fusing the processed three kinds of speed data to obtain a first prediction speed; fusing the three kinds of speed data by using a Kalman filtering algorithm to obtain a second predicted speed; and performing data fusion on the first prediction speed and the second prediction speed through a D-S evidence theory algorithm, and taking a fused speed prediction result as the operation speed of the agricultural machine. The speed prediction result obtained by the data fusion method is more accurate than the prediction result obtained by a single data fusion method.

Description

Technical Field

[0001] The present invention relates to the technical field of speed monitoring of agricultural machinery, and in particular to an intelligent agricultural machinery operation speed monitoring method and system based on multi-sensor information fusion. Background Art

[0002] In the development of modern agricultural machinery automation, the accuracy of speed measurement has a vital impact on the operation quality of agricultural machinery. When agricultural machinery is working in the field, its driving speed and operating speed need to be dynamically adjusted according to the terrain, soil conditions and operation requirements to ensure that agricultural machinery can efficiently and accurately complete tasks such as sowing, tilling, and spraying. However, due to the complexity of the agricultural operating environment, such as variable ground resistance, humidity changes, and slope differences between different plots, it is often difficult to accurately obtain and predict speed by relying solely on a single sensor or measurement method.

[0003] In the prior art, speed measurement mainly relies on sensors such as encoders, GPS modules, and Doppler radars to measure the travel speed of the machine, and these sensors are greatly affected by the environment. For example, when the agricultural machinery is traveling on soft soil or slippery ground, the wheel speed sensor (encoder) may be inaccurate due to slipping, and when the GPS signal is weak, the data generated by the GPS module may also be inaccurate or intermittent.

[0004] In the prior art, in the electronic control system of the seed drill disclosed in patent CN 208590238 U, the speed value is used to determine whether to use the ground wheel signal or the GPS / Beidou signal. This processing method is relatively simple, and the speed accuracy is still low. In the machine speed measurement method disclosed in patent CN118549672A, the method of distributing weights is disclosed to process the ground wheel speed and positioning speed. In addition, in the field of robotics, there is a data fusion method such as Kalman filtering to process multiple sensor signals. These methods still have occasional inaccurate data, and there is still room for improvement in the stability of speed measurement. Summary of the invention

[0005] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides an intelligent agricultural machinery operation speed monitoring method and system based on multi-sensor information fusion, which can improve the accuracy of machine speed detection and the stability of speed measurement.

[0006] Technical solution: To achieve the above purpose, the intelligent agricultural machinery operation speed monitoring method based on multi-sensor information fusion of the present invention comprises:

[0007] Acquire three kinds of speed data, namely, first speed data a1 measured by an encoder speed measuring device, second speed data a2 measured by a GPS speed measuring device, and third speed data a3 measured by a Doppler radar speed measuring device;

[0008] The operating speed of agricultural machinery is obtained based on the three speed data;

[0009] It is characterized in that: the agricultural machinery operation speed obtained based on the three speed data includes:

[0010] The speed data are processed by using a weighted average filtering algorithm, and the three speed data are fused to obtain a first predicted speed V1;

[0011] The three speed data are fused using the Kalman filter algorithm to obtain the second predicted speed V2;

[0012] The first predicted speed V1 and the second predicted speed V2 are fused by using the DS evidence theory algorithm, and the fused speed prediction result V is used as the operating speed of the agricultural machinery.

[0013] Furthermore, the weighted average filtering algorithm is used to process each speed data, and the processed three speed data are fused to obtain the first predicted speed V1, which includes:

[0014] Obtain the speed data a corresponding to each of the i The time series of (t) are respectively denoted as

[0015] A i ={a i (t1),a i (t2),…,a i (t n )};

[0016] Among them, t1, t2, …, t n is the time point; i=1,2,3;

[0017] The definition corresponds to the time series A i The weight of each element in ω(j);

[0018] The processed speed data corresponding to the current moment is obtained using the following formula:

[0019]

[0020] Where c is the current time point, j is the index of the historical data point in the weighted moving sliding window;

[0021] The current first predicted speed V1 is obtained by weighted calculation based on the processed three speed data:

[0022]

[0023] Among them, α, β, and γ are weight coefficients.

[0024] Furthermore, corresponding to the time series A i The weight ω(j) of each element in is specifically:

[0025]

[0026] Among them, ω(j) represents the value corresponding to a j is the weight of ; σ is the standard deviation.

[0027] Furthermore, the Kalman filter algorithm is used to fuse the three speed data to obtain the second predicted speed V2, including:

[0028] Define the state variable of the system as the current speed prediction value x k , the measurement vector is the three speed data z provided by the sensor k =[a1,a2,a3] T , the state transfer matrix A k =1, control variable matrix B k = 0, the transformation matrix H from the state vector to the measurement vector k =[1,1,1] T / 3, system interference covariance matrix Q, and measurement interference covariance matrix R;

[0029] Initialize the initial value of the state variable Initialize the state covariance matrix P0;

[0030] At each time step k, the velocity is first predicted according to the state transfer equation, and the prior state estimate at the current moment is calculated:

[0031]

[0032] And calculate the prior state covariance:

[0033]

[0034] The Kalman gain is calculated from the measurement equation:

[0035]

[0036] Use the Kalman gain to update the posterior state estimate of the current velocity:

[0037]

[0038] And update the posterior state covariance:

[0039]

[0040] in: and The posterior state estimates P at time k-1 and time k are k-1 and P k , respectively, the posterior estimated covariance at time k-1 and time k; is the prior estimated covariance at time k; K k is the Kalman gain;

[0041] The posterior state estimate for each time step As the second predicted speed V2 after fusion.

[0042] Furthermore, the data fusion of the first predicted speed V1 and the second predicted speed V2 is performed by using the DS evidence theory algorithm, and the fused speed prediction result V is used as the operating speed of the agricultural machinery, including:

[0043] Define the recognition framework Θ = {V1, V2} and construct the power set 2 Θ ={Φ,{V1},{V2},{V1,V2}};

[0044] Define basic trust assignment functions m1 and m2 for each subset in the identification framework Θ, where m1({V1}) and m2({V2}) represent the direct trust values ​​of V1 and V2 respectively, and m1({V1, V2}) and m2({V1, V2}) represent the trust values ​​of both, reflecting the uncertainty part. The basic trust allocation function satisfies the following conditions:

[0045]

[0046] in, That is to say and m(A) is the basic trust allocation function corresponding to subset A, where A is a non-empty subset;

[0047] Calculate the conflict coefficient K to measure the degree of conflict between V1 and V2; the formula is:

[0048]

[0049] Among them: A i and B j Represent the focal element of basic trust allocation functions m1 and m2 respectively;

[0050] The Dempster combination rule is used to calculate the value of the fused basic trust allocation function m(A) for each non-empty subset A:

[0051]

[0052] According to the fused basic trust allocation function m(A), the final speed prediction result V is calculated.

[0053] The intelligent agricultural machinery operation speed monitoring system based on multi-sensor information fusion includes an encoder speed measuring device, a GPS speed measuring device and a Doppler radar speed measuring device, and also includes a controller connected to the three speed measuring devices. The controller can implement the above-mentioned intelligent agricultural machinery operation speed monitoring method based on multi-sensor information fusion.

[0054] Beneficial effects: The intelligent agricultural machinery operation speed monitoring method and system based on multi-sensor information fusion of the present invention has the following beneficial effects:

[0055] The present invention realizes accurate monitoring of the operation speed of agricultural machinery through multi-sensor information fusion, and comprehensively utilizes weighted average filtering, Kalman filtering and DS evidence theory algorithms to give full play to the complementary advantages of different sensor data. Weighted average filtering improves the smoothness and noise resistance of the data, Kalman filtering enhances the adaptability to dynamic changes and prediction accuracy, and DS evidence theory further integrates different prediction results to improve the reliability and accuracy of decision-making. The overall solution has high robustness and real-time performance, can effectively cope with complex agricultural machinery operation environments, and improve the accuracy and stability of operating speed monitoring. The agricultural machinery operation speed monitoring method and system are suitable for monitoring methods that can be used for agricultural machinery operations such as sowing, spraying, and harvesting. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of the method for monitoring the operation speed of agricultural machinery;

[0057] Figure 2 The figure is a schematic diagram of the specific process of obtaining the operating speed of agricultural machinery based on three speed data. DETAILED DESCRIPTION

[0058] The present invention will be further described below in conjunction with the accompanying drawings.

[0059] The intelligent agricultural machinery operation speed monitoring method based on multi-sensor information fusion of the present invention is based on an agricultural machinery operation speed monitoring system. The agricultural machinery operation speed monitoring system includes an encoder speed measuring device, a GPS speed measuring device and a Doppler radar speed measuring device, and also includes a controller connected to the three speed measuring devices. The controller can implement the following intelligent agricultural machinery operation speed monitoring method based on multi-sensor information fusion.

[0060] like Figure 1 As shown, the method comprises the following steps S1-S2:

[0061] Step S1, obtaining three kinds of speed data, namely, first speed data a1 measured by an encoder speed measuring device, second speed data a2 measured by a GPS speed measuring device, and third speed data a3 measured by a Doppler radar speed measuring device;

[0062] Step S2, obtaining the operating speed of the agricultural machinery based on the three speed data;

[0063] like Figure 2 As shown, the step S2 includes the following steps S201-S203:

[0064] Step S201, using a weighted average filtering algorithm to process each speed data, and fusing the processed three speed data to obtain a first predicted speed V1;

[0065] Step S202, using a Kalman filter algorithm to fuse the three speed data to obtain a second predicted speed V2;

[0066] Step S203: fusing the first predicted speed V1 and the second predicted speed V2 using the DS evidence theory algorithm, and using the fused speed prediction result V as the operating speed of the agricultural machinery.

[0067] The present invention realizes accurate monitoring of the operation speed of agricultural machinery through multi-sensor information fusion, and comprehensively utilizes weighted average filtering, Kalman filtering and DS evidence theory algorithms to give full play to the complementary advantages of different sensor data. Weighted average filtering improves the smoothness and noise resistance of data, Kalman filtering enhances the adaptability to dynamic changes and prediction accuracy, and DS evidence theory further integrates different prediction results to improve the reliability and accuracy of decision-making. The overall solution has high robustness and real-time performance, can effectively cope with complex agricultural machinery operation environments, and improve the accuracy and stability of operating speed monitoring.

[0068] The above-mentioned step S201 uses a weighted average filtering algorithm to process each speed data, and fuses the processed three speed data to obtain the first predicted speed V1, which includes the following steps S301-S304:

[0069] Step S301, obtaining the speed data a corresponding to each of the speed data a i The time series of (t) are respectively denoted as

[0070] A i ={a i (t1),a i (t2),…,a i (t n )};

[0071] Among them, t1, t2, …, t n is the time point; i=1,2,3;

[0072] Step S302, define the time series A i The weight of each element in ω(j);

[0073] Step S303, using the following formula to obtain the processed speed data corresponding to the current moment:

[0074]

[0075] Where c is the current time point, j is the index of the historical data point in the weighted moving sliding window, and the sliding window only contains the current time point and the previous k data points;

[0076] Step S304: Perform weighted calculation based on the processed three speed data to obtain the current first predicted speed V1:

[0077]

[0078] Among them, α, β, and γ are weight coefficients used to indicate the importance of each sensor speed data.

[0079] The above step S302 corresponds to time series A i The weight ω(j) of each element in is specifically:

[0080]

[0081] Among them, ω(j) represents the value corresponding to a j is the weight of ; σ is the standard deviation.

[0082] This weight function takes into account the distance between time point j and the current time point c. The closer the data point is, the higher the weight is, and the farther the data point is, the lower the weight is. In practical applications, an appropriate standard deviation σ is usually selected to control the width of the weight function, thereby balancing the smoothing effect and the response speed to the data points. Through the above-mentioned weighted average filtering algorithm, the calculated average value has a stronger response to recent data and a weaker response to distant data, thereby achieving a smoothing effect and avoiding the influence of abnormal data fluctuations.

[0083] The algorithm smoothes multi-sensor speed data through weighted moving average filtering, uses weight distribution to make recent data account for a higher proportion, improves the ability to respond to dynamic changes, and weakens noise interference. After the improvement, it only relies on current and historical data to ensure real-time performance, is suitable for smoothing and predicting speed signals in dynamic scenes, and improves the accuracy of data fusion and the stability of the system.

[0084] In the above step S304, in order to make the fusion speed more accurate, the weight coefficient needs to be dynamically adjusted, which specifically includes the following steps:

[0085] Step A1, according to the theoretical accuracy of the sensor and the typical applicable scenarios, initialize the weights α0, β0, γ0 for the encoder, GPS, and Doppler radar, and the initial weights meet the normalization condition: α0+β0+γ0=1;

[0086] Step A2: collect the speed data a1(t), a2(t), and a3(t) of each sensor, and use the sliding window to calculate the standard deviation σ of each sensor in the window. i :

[0087]

[0088] in: is the mean of the velocity data in the window, k is the window size, σ i The smaller the value, the higher the stability.

[0089] Step A3, based on Bayesian inference, combines historical measurement performance with current operating conditions to calculate the credibility P i :

[0090]

[0091] Where: E i P(a i |E i ) is the probability density of the speed measured in the current environment, P(E i ) is the prior probability of the environmental condition, P(a i ) is the marginal probability of the sensor velocity data;

[0092] Step A4, using external auxiliary sensor data (such as humidity, terrain detection) to calculate the suitability score S of each sensor i :

[0093] S i =f(H,T,C,P)

[0094] Function f is an empirical model for specific environmental variables or a suitability calculation formula obtained through training; H is humidity, T is terrain characteristics, C is temperature, and P is vibration intensity.

[0095] Step A5, integrate the stability, credibility and applicability scores to calculate the comprehensive score W of each sensor i :

[0096]

[0097] Among them, λ1, λ2, and λ3 are proportional factors for adjusting the contribution of the three weights, which are adjusted according to the actual scenario;

[0098] Step A6, normalize the comprehensive score and update the weights α, β, γ of each sensor:

[0099]

[0100] Step A7, using the updated weights to perform weighted fusion on the speed data to generate a first predicted speed V1 at the current moment.

[0101] This method realizes the adaptive fusion of multiple sensor data, significantly improving the accuracy, robustness and real-time performance of speed monitoring. During the operation of agricultural machinery, the weight distribution of each sensor can be dynamically adjusted according to the real-time environment to avoid global measurement errors caused by the failure of a single sensor. At the same time, by introducing the triple indicators of stability, credibility and environmental applicability, the system's adaptability to complex environments is effectively improved, ensuring the stable output of speed measurement. This technology is particularly suitable for scenarios where agricultural machinery operates under variable terrain conditions, providing reliable technical support for improving the efficiency and safety of intelligent agricultural machinery.

[0102] In the above step A4, the specific calculation formula for the suitability score is:

[0103]

[0104] Among them, X j is the measured value of the jth environmental variable, such as current humidity or terrain parameters; Represents the effect of sensor i on environmental variable X j The best fitness value of , which is usually set based on experiments or historical data; is the adaptation range parameter, which defines the sensor i in the variable X j The nonlinear function part adopts Gaussian distribution to reflect the sensitivity of sensor adaptability to environmental variables, where the adaptability score is at the best adaptability value. It reaches its maximum value near , and then gradually decreases with the deviation.

[0105] Weight Item Used to dynamically adjust the contribution of each environmental variable to the suitability score; parameter ∈ j is the fixed importance coefficient of the jth environmental variable, which is usually set by experience or prior knowledge. For example, in a scenario where humidity has a significant impact on the sensor, the humidity variable ∈ H will be assigned a higher value; R j It is the volatility or rate of change of the current environmental variables. For example, the amplitude of change is quantified by calculating the time derivative of humidity or terrain characteristics to reflect the dynamic impact of the variables on the current operating conditions.

[0106] The entire scoring mechanism uses a Gaussian distribution function to model the suitability of each environmental variable, combined with a dynamic weighting strategy to comprehensively consider the theoretical adaptability of the sensor and the complexity of the real-time environment. This can effectively avoid scoring bias dominated by a single variable, and flexibly adjust the adaptability evaluation results when multiple variables change at the same time, providing a scientific basis for further optimization of sensor weights. This method can significantly improve the adaptability of sensors to changing environments when applied to complex agricultural scenarios, thereby supporting the high-precision and high-stability requirements of speed monitoring and control.

[0107] The above step S202 uses the Kalman filter algorithm to fuse the three speed data to obtain the second predicted speed V2, including the following steps S401-S405:

[0108] Step S401, define the state variable of the system as the current speed prediction value x k , the measurement vector is the three speed data z provided by the sensor k =[a1,a2,a3] T , the state transfer matrix A k =1, control variable matrix B k = 0, the transformation matrix H from the state vector to the measurement vector k =[1,1,1] T / 3, system interference covariance matrix Q, and measurement interference covariance matrix R;

[0109] The state equation (prediction equation) of the system is: k =A k x k-1 +B k u k +w k ;

[0110] The measurement equation is: k =H k x k +v k ;

[0111] Where: u k is the state control vector; w k is the control system interference factor, which obeys Gaussian distribution w k ~N(0,Q);v k is the measured interference factor, which obeys the Gaussian distribution v k ~N(0,R);

[0112] Step S402: Initialize the initial value of the state variable is the weighted average of the historical speed data or the first predicted speed V1, and the initialization state covariance matrix P0 is the estimated uncertainty;

[0113] Step S403, at each time step k, first predict the speed according to the state transfer equation and calculate the prior state estimate at the current moment:

[0114]

[0115] And calculate the prior state covariance:

[0116]

[0117] Step S404, calculating the Kalman gain according to the measurement equation:

[0118]

[0119] Use the Kalman gain to update the posterior state estimate of the current velocity:

[0120]

[0121] And update the posterior state covariance:

[0122]

[0123] in: and The posterior state estimates at time k-1 and time k are one of the filtering results, that is, the updated result, also called the best estimate; represents the prior state estimate at time k, and predicts the state at time k based on the optimal estimate at time k-1; P k-1 and P k , respectively, the posterior estimated covariance at time k-1 and time k, which corresponds to and The covariance of represents the uncertainty of the state and is one of the results of filtering; is the prior estimated covariance at time k, which corresponds to The covariance of K k is the Kalman gain;

[0124] Step S405: Estimate the posterior state of each time step As the second predicted speed V2 after fusion.

[0125] Kalman filtering is used to fuse multi-sensor speed data. Through the iterative process of state prediction and observation update, the influence of sensor noise and system interference is effectively reduced, and the smoothness and stability of speed prediction are enhanced. This method makes full use of the complementarity of encoder, GPS, and Doppler radar data, realizes accurate estimation of dynamic speed changes, improves the accuracy and real-time performance of speed monitoring, and is suitable for complex agricultural machinery operation environments.

[0126] In the above step S203, the first predicted speed V1 and the second predicted speed V2 are fused by the DS evidence theory algorithm, and the fused speed prediction result V is used as the operating speed of the agricultural machinery, including the following steps S501-S505:

[0127] Step S501, define the recognition framework Θ = {V1, V2}, and construct the power set 2 Θ ={Φ,{V1},{V2},{V1,V2}}; the power set contains all possible subsets, including the empty set Φ, V1 alone, V2 alone, and the union of V1 and V2;

[0128] Step S502, define basic trust allocation functions m1 and m2 for each subset in the identification framework Θ, where m1({V1}) and m2({V2}) represent direct trust values ​​for V1 and V2 respectively, and m1({V1, V2}) and m2({V1, V2}) represent trust values ​​for both, reflecting the uncertainty part. The basic trust allocation function satisfies the following conditions:

[0129]

[0130] in, That is to say and m(A) is the basic trust allocation function corresponding to subset A, A is a non-empty subset, A can be {V1}, {V2}, {V1, V2}. Based on the above conditions, it is not clear how the trust values ​​of each non-empty subset are allocated. If the value of m(A) is greater than 0, then A can be called a focal element;

[0131] Step S503, calculate the conflict coefficient K to measure the degree of conflict between V1 and V2, that is, V1 and V2 are two pieces of evidence in the DS evidence theory algorithm; the formula is:

[0132]

[0133] Among them: A i and B j Represent the focal element of basic trust allocation functions m1 and m2 respectively;

[0134] Step S504, using the Dempster combination rule, calculate the value of the fused basic trust allocation function m(A) for each non-empty subset A:

[0135]

[0136] Step S505, calculate the final speed prediction result V according to the fused basic trust allocation function m(A):

[0137] - If the trust value of {V1} or {V2} is the largest, the corresponding predicted speed is selected as the speed prediction result V;

[0138] -If the trust value of {V1, V2} is the largest, V1 and V2 are further weighted and their weighted average is taken as the final speed prediction result V.

[0139] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for monitoring the operation speed of intelligent agricultural machinery based on multi-sensor information fusion, the method comprising: Acquire three kinds of speed data, namely, first speed data a1 measured by an encoder speed measuring device, second speed data a2 measured by a GPS speed measuring device, and third speed data a3 measured by a Doppler radar speed measuring device; The operating speed of agricultural machinery is obtained based on the three speed data; It is characterized in that: the agricultural machinery operation speed obtained based on the three speed data includes: The speed data are processed by using a weighted average filtering algorithm, and the three speed data are fused to obtain a first predicted speed V1; The three speed data are fused using the Kalman filter algorithm to obtain the second predicted speed V2; The first predicted speed V1 and the second predicted speed V2 are fused with data through the DS evidence theory algorithm, and the fused speed prediction result V is used as the operating speed of the agricultural machinery.

2. The intelligent agricultural machinery operation speed monitoring method based on multi-sensor information fusion according to claim 1 is characterized in that: The weighted average filtering algorithm is used to process each speed data, and the processed three speed data are fused to obtain the first predicted speed V1, which includes: Obtain the speed data a corresponding to each of the i The time series of (t) are respectively denoted as A i ={a i (t1),a i (t2),…,a i (t n )}; Among them, t1, t2, …, t n is the time point; i=1,2,3; The definition corresponds to the time series A i The weight of each element in ω(j); The processed speed data corresponding to the current moment is obtained using the following formula: Where c is the current time point, j is the index of the historical data point in the weighted moving sliding window; The current first predicted speed V1 is obtained by weighted calculation based on the processed three speed data: Among them, α, β, and γ are weight coefficients.

3. The intelligent agricultural machinery operation speed monitoring method based on multi-sensor information fusion according to claim 2 is characterized in that: Corresponding to time series A i The weight ω(j) of each element in is specifically: Among them, ω(j) represents the value corresponding to a j is the weight of ; σ is the standard deviation.

4. The intelligent agricultural machinery operation speed monitoring method based on multi-sensor information fusion according to claim 1 is characterized in that: The method of fusing the three speed data using the Kalman filter algorithm to obtain the second predicted speed V2 includes: Define the state variable of the system as the current speed prediction value x k , the measurement vector is the three speed data z provided by the sensor k =[a1,a2,a3] T , the state transfer matrix A k =1, control variable matrix B2 = 0, conversion matrix H from state vector to measurement vector k =[1,1,1] T / 3, system interference covariance matrix Q, and measurement interference covariance matrix R; Initialize the initial value of the state variable Initialize the state covariance matrix P0; At each time step k, the velocity is first predicted according to the state transfer equation, and the prior state estimate at the current moment is calculated: And calculate the prior state covariance: The Kalman gain is calculated from the measurement equation: Use the Kalman gain to update the posterior state estimate of the current velocity: And update the posterior state covariance: in: and The posterior state estimates P at time k-1 and time k are k-1 and P k , respectively, the posterior estimated covariance at time k-1 and time k; is the prior estimated covariance at time k; K k is the Kalman gain; The posterior state estimate for each time step As the second predicted speed V2 after fusion.

5. The intelligent agricultural machinery operation speed monitoring method based on multi-sensor information fusion according to claim 1 is characterized in that: The method of fusing the first predicted speed V1 and the second predicted speed V2 by using the DS evidence theory algorithm and using the fused speed prediction result V as the operating speed of the agricultural machinery includes: Define the recognition framework Θ = {V1, V2} and construct the power set 2 Θ ={Φ,{V1},{V2},{V1,V2}}; Define basic trust assignment functions m1 and m2 for each subset in the identification framework Θ, where m1({V1}) and m2({V2}) represent the direct trust values ​​of V1 and V2 respectively, and m1({V1, V2}) and m2({V1, V2}) represent the trust values ​​of both, reflecting the uncertainty part. The basic trust allocation function satisfies the following conditions: in, That is to say and m(A) is the basic trust allocation function corresponding to subset A, where A is a non-empty subset; Calculate the conflict coefficient K to measure the degree of conflict between V1 and V2; the formula is: Among them: A i and B j Represent the focal element of basic trust allocation functions m1 and m2 respectively; The Dempster combination rule is used to calculate the value of the fused basic trust allocation function m(A) for each non-empty subset A: According to the fused basic trust allocation function m(A), the final speed prediction result V is calculated.

6. Intelligent agricultural machinery operation speed monitoring system based on multi-sensor information fusion, characterized in that: It includes an encoder speed measuring device, a GPS speed measuring device and a Doppler radar speed measuring device, and also includes a controller connected to the three speed measuring devices, and the controller can implement the intelligent agricultural machinery operation speed monitoring method based on multi-sensor information fusion as described in any one of claims 1-5.

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