Seeding speed compensation control method and system based on seeding operation speed prediction

Abstract

The invention discloses a seeding rotation speed compensation control method and system based on seeding operation speed prediction. The method comprises the following steps: acquiring a final speed prediction result V fed back by a speed monitoring and predicting system; based on the final speed prediction result V, calculating a corresponding seed-metering plate rotating speed X1; acquiring an actual seed-metering disc rotating speed X2 measured by a seed-metering rotating speed measuring device; performing information fusion on the predicted seed-metering disc rotating speed X1 and the actual seed-metering disc rotating speed X2, and estimating a seed-metering rotating speed error interval caused by speed change in the next time period; and when the estimated seed metering rotation speed error interval exceeds a given reliable interval, carrying out rotation speed advanced compensation on the seed metering motor. According to the method, whether rotating speed compensation is needed or not is determined by predicting the working speed in advance and predicting the rotating speed error interval of the seed-metering disc in advance, and the phenomenon that the seed-metering rotating speed is lagged or advanced can be effectively reduced in the process that speed changes such as acceleration and deceleration of a vehicle are obvious, so that the seed drop point is more uniform.

Description

Technical Field

[0001] The present invention relates to the technical field of sowing control of sowing machinery, and in particular to a sowing speed compensation control method and system based on sowing operation speed prediction. Background Art

[0002] With the continuous improvement of agricultural mechanization, intelligent control technology of seeding machinery has become an important research direction to improve operation quality and efficiency. In modern precision agriculture, the consistency of seeding spacing has a significant impact on the uniformity of crop growth and yield, and the consistency of seeding spacing is directly affected by the change of machine speed and the response performance of the seeding system.

[0003] The seeding control of traditional seeding machinery is mostly based on the assumption of constant speed. When the vehicle accelerates, decelerates or the driving speed changes dynamically, the rotation speed of the seeding disc is difficult to respond to the speed change in time, resulting in a lag or advance in the seed landing point, which in turn affects the consistency of plant spacing and seeding accuracy. Especially in the case of complex working environment and frequent speed fluctuations, traditional control methods cannot fully meet the needs of efficient and high-precision seeding.

[0004] In recent years, some studies have proposed methods for dynamically compensating the rotation speed of the seeding disc through feedback control or closed-loop regulation. For example, the solution of patent CN111083968A discloses that the control system calculates the theoretical grain distance and the delay distance of the sowing amount compensation at the time of sowing based on the rotation speed information of the ground wheel and the sowing amount information output by the precision seeding device, obtains the sowing amount compensation difference, and further calculates the speed update value corresponding to the initial value of the rotation speed of the seeding disc at the time of sowing, so as to adjust the rotation state of the seeding motor accordingly. These methods in the prior art are mainly based on actual measurement values, lack the ability to predict the speed change trend, and are difficult to adjust in time in rapidly changing working conditions. In addition, the existing methods do not take into account the mechanical delay of the seeding system and the dynamic characteristics of the vehicle, resulting in limited compensation effect and inability to achieve precise control of the seed landing position. Summary of the invention

[0005] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a method and system for controlling the seeding speed compensation based on the prediction of the seeding operation speed, which can compensate the seeding speed in advance based on the predicted driving speed to ensure the seeding spacing.

[0006] Technical solution: To achieve the above-mentioned purpose, the present invention provides a seeding speed compensation control method based on seeding operation speed prediction, the method comprising:

[0007] Obtain the final speed prediction result V fed back by the speed monitoring and prediction system;

[0008] Based on the final speed prediction result V, the corresponding seed disc speed X1 is calculated;

[0009] Obtain the actual seeding disc speed X2 measured by the seeding speed measuring device;

[0010] The predicted seeding disc speed X1 and the actual seeding disc speed X2 are integrated to estimate the seeding disc speed error range caused by speed changes in the next time period;

[0011] When the estimated seeding speed error interval exceeds a given reliable interval, the seeding motor speed is compensated in advance.

[0012] Furthermore, the reliability interval is determined based on the following mathematical model:

[0013]

[0014] Where: R(v) is the reliability interval; v(t) is the speed of the seeder; is the speed change rate; a and b are coefficients related to equipment and operating conditions; is the influence function of the speed change rate; C is the benchmark reliability interval.

[0015] Furthermore, the predicted seeding disc speed X1 and the actual seeding disc speed X2 are fused to estimate the seeding disc speed error range caused by speed change in the next time period, specifically including:

[0016] The error range of seeding speed is estimated based on the following model:

[0017] E n+1 =W1·(X1-X2)+W2·ΔX+W3·G(v);

[0018] Where: E n+1 is the seeding speed error range for the next time period; W1, W2, W3 are weight parameters; ΔX=X 2,n -X 2,n-1 Indicates the rate of change of actual speed;

[0019] G(v) is the offset function, specifically:

[0020]

[0021] γ, β are the adjustment coefficients of acceleration and deceleration.

[0022] Furthermore, when the estimated seeding speed error interval exceeds a given reliable interval, the seeding motor is compensated for the speed in advance, including:

[0023] The compensation amount is determined based on the following method:

[0024]

[0025] Where: ΔC is the compensation amount; λ is the compensation coefficient, which is set according to the dynamic characteristics of the seeder;

[0026] The speed of the seeding motor is determined based on the following companies:

[0027] X = X2 + ΔC;

[0028] Where: X is the target seed disc speed after compensation.

[0029] Furthermore, the obtaining of the final speed prediction result V fed back by the speed monitoring and prediction system includes:

[0030] The speed data of the seed drill is collected respectively through various speed measuring sensors;

[0031] The speed data collected by each speed sensor is fused to obtain the final speed prediction result V.

[0032] Furthermore, the seed drill is equipped with a ground wheel speed measurement module, a GPS module and a Doppler speed measurement module.

[0033] A seeding speed compensation control system based on seeding operation speed prediction, the system comprises:

[0034] A speed prediction module is used to obtain a final speed prediction result V fed back by a speed monitoring and prediction system;

[0035] A speed prediction module, which is used to calculate the corresponding seed disc speed X1 based on the final speed prediction result V;

[0036] An actual rotation speed measurement module, which is used to obtain the actual rotation speed X2 of the seeding disc measured by the seeding rotation speed measurement device;

[0037] A fusion module is used to fuse the predicted seeding disc speed X1 with the actual seeding disc speed X2, and estimate the seeding disc speed error range caused by speed changes in the next time period;

[0038] The compensation module performs speed compensation on the seeding motor in advance when the estimated seeding speed error interval exceeds a given reliable interval.

[0039] Beneficial effects: The seeding speed compensation control method and system based on seeding operation speed prediction of the present invention have the following beneficial effects:

[0040] (1) By predicting the operating speed in advance and the speed error range of the seeding disc in advance, it is determined whether speed compensation is needed. The present invention can effectively reduce the lag or advance phenomenon of the seeding speed during the process of significant speed changes such as vehicle acceleration and deceleration, which makes the seed landing point more uniform, thereby significantly improving the consistency of plant spacing and overall seeding accuracy.

[0041] (2) The reliable interval fully considers the impact of the machine's operating speed and dynamic changes. The reliable interval is more in line with the operating conditions and can adapt to different operating speeds and acceleration and deceleration conditions. It improves the accuracy of dynamic judgment of seeding speed errors and contributes to more accurate speed compensation control.

[0042] (3) The information fusion model can comprehensively capture the dynamic change trend of the seed disc speed by fusing multi-source data and provide more accurate error prediction for the next time period. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a seeding speed compensation control method based on seeding operation speed prediction;

[0044] Figure 2 The schematic diagram is a composition diagram of a seeding speed compensation control system based on seeding operation speed prediction. DETAILED DESCRIPTION

[0045] The present invention will be further described below in conjunction with the accompanying drawings.

[0046] The seeding speed compensation control method based on seeding operation speed prediction is applied to agricultural seeding machinery. The agricultural seeding machinery has a speed monitoring and prediction system and an electric-driven seeding mechanism. The electric-driven seeding mechanism includes a seeding disc and a seeding motor. The control system controls the operation of the seeding motor according to the speed of the seeding machinery. Figure 1 As shown, the method includes the following steps S101-S105:

[0047] Step S101, obtaining the final speed prediction result V fed back by the speed monitoring and prediction system;

[0048] Step S102, based on the final speed prediction result V, calculating the corresponding seed disc speed X1;

[0049] Step S103, obtaining the actual seeding disc speed X2 measured by the seeding speed measuring device;

[0050] Step S104, fusing the predicted seeding disk speed X1 with the actual seeding disk speed X2 to estimate the seeding disk speed error range caused by speed changes in the next time period;

[0051] Step S105, when the estimated seeding speed error interval exceeds the given reliable interval, the seeding motor is compensated for the speed in advance to reduce the seeding delay or seeding advance caused by speed changes, thereby improving the consistency of sowing spacing.

[0052] In the above method, by predicting the operating speed in advance and the speed error range of the seeding disk based on this, it is determined whether speed compensation is needed. The present invention can effectively reduce the lag or advance phenomenon of the seeding speed during the process of obvious speed changes such as vehicle acceleration and deceleration, which makes the seed landing point more uniform, thereby significantly improving the consistency of plant spacing and overall sowing accuracy.

[0053] Specifically, the reliable interval in the above step S105 is determined based on the following mathematical model:

[0054]

[0055] Where: R(v) is the reliability interval; v(t) is the speed of the seeder, which reflects the change of speed over time over a period of time; is the speed change rate; a and b are coefficients related to equipment and operating conditions; is the influence function of the velocity change rate, which is usually in linear or exponential form. The influence function in exponential form is as follows: α is the index; C is the reference reliability interval.

[0056] The above-mentioned reliable range fully considers the influence of the machine's operating speed and dynamic changes. The reliable range is more in line with the operating conditions, can adapt to different operating speeds and acceleration and deceleration conditions, improves the accuracy of dynamic judgment of seeding speed errors, and facilitates more precise speed compensation control.

[0057] Specifically, the predicted seeding disc speed X1 and the actual seeding disc speed X2 are fused in the above step S104 to estimate the seeding disc speed error range caused by speed change in the next time period, specifically including:

[0058] The error range of seeding speed is estimated based on the following model:

[0059] E n+1 =W1·(X1-X2)+W2·ΔX+W3·G(v);

[0060] Where: E n+1 is the seeding speed error range for the next time period; W1, W2, W3 are weight parameters; ΔX=X 2,n -X 2,n-1 Indicates the rate of change of actual speed;

[0061] G(v) is the offset function based on vehicle acceleration and deceleration, specifically:

[0062]

[0063] γ, β are the adjustment coefficients of acceleration and deceleration.

[0064] The information fusion model combines the predicted seed disc speed X1 and the actual seed disc speed X2, and further considers the change rate of the actual speed ΔX and the dynamic impact of vehicle acceleration and deceleration G(v). Through the fusion of multi-source data, it can fully capture the dynamic change trend of the seed disc speed and provide a more accurate error prediction for the next time period.

[0065] The offset function G(v) in the information fusion model dynamically adjusts the speed error according to the acceleration and deceleration status of the vehicle: during acceleration, the offset is predicted forward to prevent the landing point from being advanced due to acceleration; during deceleration, the offset is predicted backward to compensate for the landing point lag caused by deceleration. This dynamic adjustment mechanism enables the model to adapt to complex speed changes under different operating conditions and has a high degree of environmental adaptability.

[0066] The information fusion model uses real-time speed measurement value X2 and speed change rate In conjunction with the predicted speed X1, it can quickly respond to changes in the operating status of the machine. This real-time calculation capability is particularly suitable for scenarios where vehicles frequently accelerate and decelerate in agricultural operations, and helps to correct seeding deviations in a timely manner. In addition, the importance of different data sources is allocated in the model through weight coefficients W1, W2, and W3: W1 emphasizes the reliability of the predicted value X1; W2 enhances the sensitivity to changes in actual speed; and W3 dynamically adapts to the acceleration and deceleration status of the vehicle. The flexible allocation of weights allows the model to be adjusted for different equipment or working conditions, making it more adaptable.

[0067] When the estimated seeding speed error interval exceeds the given reliable interval in the above step S105, the seeding motor is compensated for the speed in advance, including the following steps S201-S202:

[0068] Step S201, determining the compensation amount based on the following method:

[0069]

[0070] Where: ΔC is the compensation amount; λ is the compensation coefficient, which is set according to the dynamic characteristics of the seeder;

[0071] Step S202, determining the rotation speed of the seeding motor based on the following:

[0072] X = X2 + ΔC;

[0073] Where: X is the target seed disc speed after compensation.

[0074] Through the above compensation mechanism, the delay or advance of seeding caused by vehicle acceleration and deceleration can be effectively reduced, ensuring uniform seed placement and improving plant spacing consistency and sowing accuracy.

[0075] Preferably, the step S101 described above of obtaining the final speed prediction result V fed back by the speed monitoring and prediction system includes the following steps S301-S302:

[0076] Step S301, collecting speed data of the seed drill respectively through a plurality of speed measuring sensors;

[0077] Step S302: fusing the speed data collected by each of the speed sensors to obtain the final speed prediction result V.

[0078] In this embodiment, the speed monitoring and prediction system includes a ground wheel speed measurement module, a GPS module and a Doppler speed measurement module.

[0079] The speed data measured by the ground wheel speed measurement module, the GPS module and the Doppler speed measurement module are respectively the first speed data a1, the second speed data a2 and the third speed data a3; the running speeds collected by the speed measurement sensors are fused in the above step S302 to obtain the final speed prediction result V, including the following steps S401-S403:

[0080] Step S401, using a weighted average filtering algorithm to process the speed data obtained by each speed sensor, and fusing the processed three speed data to obtain a first predicted speed V1;

[0081] Step S402, using a Kalman filter algorithm to fuse the three speed data to obtain a second predicted speed V2;

[0082] Step S403: 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.

[0083] The above method uses encoders, GPS and Doppler radar to obtain speed data, and adopts algorithms such as weighted average filtering, Kalman filtering and DS evidence theory for data fusion, which significantly improves the accuracy and reliability of speed prediction, enhances the robustness and adaptability of the system, effectively copes with complex and changeable working environments, and improves the operating accuracy and efficiency of the seeder.

[0084] Specifically, the method in step S401 specifically includes:

[0085] Obtain the speed data a corresponding to each of the i The time series of (t) are respectively denoted as

[0086] A i ={a i (t1),a i (t2),…,ai (t n )};

[0087] Among them, t1, t2, …, t n is the time point; i=1,2,3;

[0088] The definition corresponds to the time series A i The weight of each element in ω(j);

[0089] Among them, ω(j) represents the value corresponding to a j is the weight of ; σ is the standard deviation.

[0090] The processed speed data corresponding to the current moment is obtained using the following formula:

[0091]

[0092] 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;

[0093] The current first predicted speed V1 is obtained by weighted calculation based on the processed three speed data:

[0094]

[0095] Among them, α, β, and γ are weight coefficients used to indicate the importance of each sensor speed data.

[0096] In order to ensure the calculation accuracy of the first predicted speed V1, it is necessary to dynamically adjust the weight distribution of each sensor according to the real-time environment to avoid the global measurement error caused by the failure of a single sensor. The specific measures are to comprehensively score each sensor according to the operating environment parameters and dynamically re-determine the weight based on the comprehensive score, specifically:

[0097] The comprehensive score W of each sensor is determined based on the following formula: i :

[0098]

[0099] Among them, λ1, λ2, λ3 are the proportional factors for adjusting the contribution of the three weights; σ i is the standard deviation, P i is the credibility, S i Score suitability;

[0100] Based on the above comprehensive score W i Update the weights α, β, and γ of each sensor, respectively: The updated weights α, β, and γ are then used to calculate the subsequent first predicted speed V1.

[0101] The above standard deviation 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;

[0102] Credibility 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;

[0103] Suitability Rating Among them, X j is the measured value of the jth environmental variable, which includes humidity, terrain characteristics, temperature, and vibration intensity; Represents the effect of sensor i on environmental variable X j The best fitness value of 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 gradually decreases with the degree of deviation; the weight term 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. 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.

[0104] Step S403 specifically includes:

[0105] Define the recognition framework Θ = {V1, V2} and construct the power set 2 Θ ={Φ,{V1},{V2},{V1,V2}};

[0106] 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:

[0107]

[0108] 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;

[0109] Calculate the conflict coefficient K to measure the degree of conflict between V1 and V2; the formula is:

[0110]

[0111] Among them: A o and B j Represent the focal element of basic trust allocation functions m1 and m2 respectively;

[0112] 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:

[0113]

[0114] According to the fused basic trust allocation function m(A), the final speed prediction result V is calculated: if the trust value of {V1} or {V2} is the largest, the corresponding predicted speed is selected as the speed prediction result V; 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.

[0115] The present invention also provides a seeding speed compensation control system based on seeding operation speed prediction. The control system may include or be divided into one or more program modules. One or more program modules are stored in a storage medium and executed by one or more processors to complete the present invention and implement the above-mentioned electric drive seeding speed compensation control method. The program module referred to in the embodiment of the present invention refers to a series of computer program instruction segments that can perform specific functions, and is more suitable for describing the execution process of the electric drive seeding speed compensation control method in the storage medium than the program itself. The following description will specifically introduce the functions of each program module in this embodiment. Figure 2 As shown, the control system includes:

[0116] The speed prediction module 501 is used to obtain the final speed prediction result V fed back by the speed monitoring and prediction system;

[0117] A speed prediction module 502, which is used to calculate the corresponding seed disc speed X1 based on the final speed prediction result V;

[0118] An actual rotation speed measurement module 503, which is used to obtain the actual rotation speed X2 of the seeding disc measured by the seeding rotation speed measurement device;

[0119] A fusion module 504 is used to fuse the predicted seeding disk speed X1 with the actual seeding disk speed X2, and estimate the seeding disk speed error range caused by speed changes in the next time period;

[0120] The compensation module 505 performs speed advance compensation on the seeding motor when the estimated seeding speed error interval exceeds a given reliable interval.

[0121] Other contents of the electric drive seeding speed compensation control method based on the electric drive seeding speed compensation control system have been introduced in detail in the previous embodiments. Please refer to the corresponding contents in the previous embodiments and will not be repeated here.

[0122] 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 seeding speed compensation control method based on seeding operation speed prediction, characterized in that: The method comprises: Obtain the final speed prediction result V fed back by the speed monitoring and prediction system; Based on the final speed prediction result V, the corresponding seed disc speed X1 is calculated; Obtain the actual seeding disc speed X2 measured by the seeding speed measuring device; The predicted seeding disc speed X1 and the actual seeding disc speed X2 are integrated to estimate the seeding disc speed error range caused by speed changes in the next time period; When the estimated seeding speed error interval exceeds a given reliable interval, the seeding motor speed is compensated in advance.

2. The seeding speed compensation control method based on seeding operation speed prediction according to claim 1 is characterized in that: The reliability interval is determined based on the following mathematical model: Where: R(v) is the reliability interval; v(t) is the speed of the seeder; is the speed change rate; a and b are coefficients related to equipment and operating conditions; is the influence function of the speed change rate; C is the benchmark reliability interval.

3. The seeding speed compensation control method based on seeding operation speed prediction according to claim 2 is characterized in that: The predicted seeding disc speed X1 and the actual seeding disc speed X2 are integrated to estimate the seeding disc speed error range caused by speed change in the next time period, specifically including: The error range of seeding speed is estimated based on the following model: E n+1 =W1·(X1-X2)+W2·ΔX+W3·G(v); Where: E n+1 is the seeding speed error range for the next time period; W1, W2, W3 are weight parameters; ΔX=X 2,n -X 2,n-1 Indicates the rate of change of actual speed; G(v) is the offset function, specifically: γ, β are the adjustment coefficients of acceleration and deceleration.

4. The seeding speed compensation control method based on seeding operation speed prediction according to claim 2 is characterized in that: When the estimated seeding speed error interval exceeds a given reliable interval, the seeding motor is compensated for the speed in advance, including: The compensation amount is determined based on the following method: Where: ΔC is the compensation amount; λ is the compensation coefficient, which is set according to the dynamic characteristics of the seeder; The speed of the seeding motor is determined based on the following companies: X = X2 + ΔC; Where: X is the target seed disc speed after compensation.

5. The seeding speed compensation control method based on seeding operation speed prediction according to claim 1 is characterized in that: The obtaining of the final speed prediction result V fed back by the speed monitoring and prediction system includes: The speed data of the seed drill is collected respectively through various speed measuring sensors; The speed data collected by each speed sensor is fused to obtain the final speed prediction result V.

6. The seeding speed compensation control method based on seeding operation speed prediction according to claim 5 is characterized in that: The seed drill is equipped with a ground wheel speed measuring module, a GPS module and a Doppler speed measuring module.

7. A seeding speed compensation control system based on seeding operation speed prediction, characterized in that the system include: A speed prediction module is used to obtain a final speed prediction result V fed back by a speed monitoring and prediction system; A speed prediction module, which is used to calculate the corresponding seed disc speed X1 based on the final speed prediction result V; An actual rotation speed measurement module, which is used to obtain the actual rotation speed X2 of the seeding disc measured by the seeding rotation speed measurement device; A fusion module is used to fuse the predicted seeding disc speed X1 with the actual seeding disc speed X2, and estimate the seeding disc speed error range caused by speed changes in the next time period; The compensation module performs speed compensation on the seeding motor in advance when the estimated seeding speed error interval exceeds a given reliable interval.

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