Combine harvester operating speed control method, system and electronic equipment

Abstract

The present invention discloses a combine harvester operating speed control method, system, and electronic device, relating to the technical field of agricultural device control. The present invention filters the acquired header drive shaft torque signal, constructs a univariate linear regression prediction model for feed rate using the least squares method, and uses a Kalman filter to re-estimate the feed rate in combination with the driving speed to obtain an optimal feed rate estimate. Subsequently, the combine harvester is repeatedly controlled based on a feed rate deviation value determined between the optimal feed rate estimate and a rated feed rate value to obtain an optimal combine harvester driving speed. Finally, a fuzzy PID controller is used to control the combine harvester's operating speed based on a speed deviation and a speed deviation change rate determined between the combine harvester's current driving speed and the optimal combine harvester driving speed, thereby achieving real-time and efficient adjustment of the combine harvester's driving speed in the field.

Description

Technical Field

[0001] The present invention relates to the field of agricultural device control, and in particular to a method, system and electronic equipment for controlling the operating speed of a combine harvester. Background Art

[0002] Feed rate refers to the mass of grain entering the threshing drum of a combine harvester per unit time. Measured in kg / s, it is a key indicator of combine harvester operating efficiency. Feed rate is primarily related to the harvester's speed, swath width, and crop density. Therefore, the feed rate of a harvester in the field is not a fixed value. Currently available combine harvesters, whether from Chinese or international manufacturers, are unable to adjust their speed in real time to maximize harvesting efficiency based on changes in feed rate. Under these technical conditions, operators must either reduce harvest quality by harvesting at a constant speed or increase their workload by manually adjusting the speed. In either case, the combine harvester cannot efficiently adjust its speed in real time during field operations. Summary of the Invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides a method, system and electronic equipment for controlling the operating speed of a combine harvester.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for controlling the operating speed of a combine harvester, comprising:

[0006] Obtaining the torque signal of the header driving shaft of the combine harvester and the driving speed of the combine harvester;

[0007] Filtering the cutter drive shaft torque signal, and constructing a univariate linear regression prediction model of feed amount based on the filtered cutter drive shaft torque signal by least square method;

[0008] Based on the univariate linear regression prediction model and the driving speed, a Kalman filter is used to estimate the feeding amount to obtain an optimal estimated value of the feeding amount;

[0009] Determining a feed amount deviation value according to the feed amount optimal estimate value and the feed amount rated value;

[0010] Repeatedly controlling the combine harvester based on the feed amount deviation value to obtain an optimal value of the combine harvester's travel speed;

[0011] Obtaining a speed deviation and a speed deviation change rate based on a current driving speed of the combine harvester and an optimal value of the driving speed of the combine harvester;

[0012] A fuzzy PID controller is used to control the operating speed of the combine harvester based on the speed deviation and the speed deviation change rate.

[0013] Optionally, a low-pass filter is used to filter the header driving shaft torque signal.

[0014] Optionally, the univariate linear regression prediction model is:

[0015] Y=KT'+b;

[0016] Where Y is the predicted value of the feed amount, T' is the filtered torque signal of the header drive shaft, K is the proportional coefficient, and b is the deviation compensation.

[0017] Optionally, the optimal estimated value of the feeding amount is:

[0018] y k =Cx k ;

[0019] Where y k is the optimal estimated value of feeding amount, C is the product of cutting width and planting density, x k is the driving speed.

[0020] Optionally, a repetitive control controller is used to repeatedly control the combine harvester based on the feed amount deviation value to obtain an optimal value of the travel speed of the combine harvester;

[0021] Among them, the repetitive control controller transfer function is:

[0022]

[0023] Where H(z) is the transfer function value, Q(z) is 0.95, Z is the complex variable of the discrete-time signal, and C(z) = k r ·Z k , N is the number of samples in one cycle, K r is the number of phase compensations, and k is the number of phase compensations.

[0024] Optionally, a fuzzy PID controller is used to control the operating speed of the combine harvester based on the speed deviation and the speed deviation change rate, specifically including:

[0025] determining a degree of membership according to the speed deviation and the speed deviation change rate;

[0026] After fuzzy rule reasoning based on the membership degree, a PWM driving signal is obtained;

[0027] The PWM drive signal is used as a stepper motor drive signal of the combine harvester to control the accelerator pedal opening of the combine harvester and realize the control of the operating speed.

[0028] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0029] The combine harvester operating speed control method provided by the present invention filters the obtained header driving shaft torque signal, constructs a unary linear regression prediction model of the feed amount by the least squares method, and uses a Kalman filter to estimate the feed amount again in combination with the driving speed to obtain the optimal estimated value of the feed amount; then, the combine harvester is repeatedly controlled based on the feed amount deviation value determined by the optimal estimated value of the feed amount and the rated value of the feed amount to obtain the optimal value of the combined harvester driving speed; then, a fuzzy PID controller is used to control the operating speed of the combine harvester based on the speed deviation and the speed deviation change rate determined by the current driving speed of the combine harvester and the optimal value of the combined harvester driving speed, thereby achieving the purpose of real-time and efficient adjustment of the combined harvester's driving speed in the field.

[0030] Furthermore, the present invention provides a combine harvester operating speed control system, which is applied to the above-mentioned combine harvester operating speed control method; the system includes:

[0031] A data acquisition module is used to obtain the torque signal of the header driving shaft of the combine harvester and the driving speed of the combine harvester;

[0032] A model building module is used to filter the cutter drive shaft torque signal and build a univariate linear regression prediction model of feed amount based on the filtered cutter drive shaft torque signal by least square method;

[0033] A feeding amount estimation module is used to estimate the feeding amount using a Kalman filter based on the univariate linear regression prediction model and the driving speed to obtain an optimal estimated value of the feeding amount;

[0034] a first deviation determining module, configured to determine a feed amount deviation value according to the feed amount optimal estimate value and the feed amount rated value;

[0035] a repetitive control module, configured to repeatedly control the combine harvester based on the feed amount deviation value to obtain an optimal value of the combine harvester's travel speed;

[0036] a second deviation determining module, configured to obtain a speed deviation and a speed deviation change rate based on a current travel speed of the combine harvester and an optimal value of the travel speed of the combine harvester;

[0037] An operating speed control module is used to implement operating speed control of the combine harvester based on the speed deviation and the speed deviation change rate using a fuzzy PID controller.

[0038] Furthermore, the present invention also provides an electronic device, comprising:

[0039] Memory for storing computer programs;

[0040] A processor is connected to the memory and is used to retrieve and execute the computer program to implement the above-mentioned combine harvester operating speed control method.

[0041] Since the technical effects achieved by the two implementation structures provided above in the present invention are the same as the technical effects achieved by the combine harvester operating speed control method provided in the present invention, they will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 A flow chart of the combine harvester operating speed control method provided by the present invention;

[0044] Figure 2 A three-dimensional diagram of the distributed patch method provided by an embodiment of the present invention;

[0045] Figure 3 A planar expansion diagram of the distributed patch method provided by an embodiment of the present invention;

[0046] Figure 4 A structural diagram of a Wheatstone bridge provided in an embodiment of the present invention;

[0047] Figure 5 A schematic diagram of the installation of magnetic steel provided in an embodiment of the present invention;

[0048] Figure 6 A schematic diagram of the changing number of pulses provided by an embodiment of the present invention;

[0049] Figure 7 A structural diagram of a repetitive control controller provided by an embodiment of the present invention;

[0050] Figure 8 A block diagram of a fuzzy PID controller according to an embodiment of the present invention;

[0051] Figure 9This is a diagram illustrating an implementation architecture of a combine harvester operating speed control method provided by an embodiment of the present invention;

[0052] Figure 10 The embodiment of the present invention provides a speed control architecture diagram of a combine harvester in practical application. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] The object of the present invention is to provide a method, system and electronic equipment for controlling the operating speed of a combine harvester, which can effectively adjust the operating speed of the combine harvester in the field in real time.

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] like Figure 1 As shown, the combine harvester operation speed control method provided by the present invention includes:

[0057] Step 100: Obtain the torque signal of the combine harvester's header drive shaft and the combine harvester's travel speed. A resistance strain gauge is a component used to measure strain. It can convert changes in strain on mechanical components into changes in resistance. Based on this, the resistance strain gauge can be used to collect the torque signal of the header drive shaft. For example:

[0058] When the header is in operation, its active shaft will experience stress changes and slight surface deformation, so four resistance strain gauges are glued to the header active shaft at a 45° angle (such as Figure 2 and Figure 3 As shown), the resistance change of each strain gauge is

[0059]

[0060] Where K is the sensitivity coefficient of the resistance strain. ε is the longitudinal strain of the conductor, which is generally very small and often measured in microstrain. ΔR is the change in resistance of the resistance strain gauge. R is the resistance of the resistance strain gauge.

[0061] The circuit composed of these four resistance strain gauges is a Wheatstone bridge, such as Figure 4 As shown. When the shaft rotates, its output voltage will change with the shaft torque. The output voltage is:

[0062]

[0063] Where E is the input voltage and U is the output voltage of the Wheatstone bridge.

[0064] The purpose of gluing it to the header drive shaft at a 45° angle to the axis and in a Wheatstone full bridge manner is to eliminate the effects of axial force and bending moment. When the header drive shaft rotates, its torque is T':

[0065]

[0066] Where W p is the torsional modulus of the shaft section. v is the Poisson's ratio.

[0067] In actual application, Hall effect sensors can be used as measuring units for driving speed measurement. The speed measurement process is to install magnetic steel on the wheel hub of the combine harvester, such as Figure 5 As shown. The rotation of the wheel hub will cause the surrounding magnetic field to change periodically. According to the principle of the Hall effect, a voltage that changes periodically in the form of a certain pulse will be generated at the output end of the Hall sensor. The number of pulses that change will be recorded by the microcontroller, as shown in the figure. Figure 6 shown.

[0068] The actual speed of the combine harvester (i.e., driving speed) is calculated based on the number of pulses per unit time:

[0069]

[0070] Where V is the combine harvester's speed in m / s. n is the number of pulses generated by one wheel rotation. N is the number of pulses measured per unit time. T is the measurement time period. r is the wheel radius.

[0071] Step 101: filtering the header driving shaft torque signal, and constructing a univariate linear regression prediction model of the feed amount based on the filtered header driving shaft torque signal by the least square method.

[0072] In actual application, a low-pass filter can be used to filter the torque signal of the header drive shaft. The low-pass filter is G(s):

[0073]

[0074] Where s is the complex frequency and a is the cutoff angular frequency in rad / s, which is 95 rad / s here.

[0075] The univariate linear regression prediction model of feed amount constructed by the least squares method is:

[0076] Y=KT'+b.

[0077] Where Y is the feed rate prediction, T' is the filtered header drive shaft torque signal, K is the proportional coefficient, and b is the deviation compensation. Both the proportional coefficient K and the deviation compensation b can be determined through experiments with different feed rate gradients.

[0078] Step 102: Based on the univariate linear regression prediction model and the driving speed, a Kalman filter is used to estimate the feeding amount to obtain an optimal estimated value of the feeding amount.

[0079] Specifically, the optimization of feed quantity by Kalman filtering is based on the discrete mathematical model of feed quantity, which is:

[0080] x k =Ax k-1 +Ba k-1 +w (7)

[0081] y k =Cx k +v (8)

[0082] w~N(0,Q) (9)

[0083] v~N(0,P) (10)

[0084] Where A is the state transfer matrix, B is the input coefficient evidence, A = B = 1. C is the product of cutting width and planting density. k is the driving speed. k is the acceleration of the current period. k is the optimal estimate of the feed rate. w is the process noise, and v is the measurement noise, both of which follow a normal distribution. P is the measurement noise variance of the sensor.

[0085] The Kalman filter then makes an optimal estimate of the input quantity, which consists of two parts: the prediction part and the update part. The prediction part is:

[0086]

[0087]

[0088] Where, It is the speed value of the previous measurement cycle. The estimated speed value of the current measurement cycle is obtained based on the speed value and acceleration of the previous measurement cycle. is the covariance matrix. k-1 is the covariance matrix of the previous measurement period. Q is the variance of the process noise.

[0089] The updated part is:

[0090]

[0091]

[0092]

[0093] Where K k is the Kalman gain. is the optimal velocity estimate obtained after processing by the Kalman filter. I is the identity matrix.

[0094] Then, based on the above description, we can finally get the optimal estimated value of the feed amount:

[0095] y k =Cx k (16)

[0096] Step 103: Determine a feed quantity deviation value according to the optimal estimated feed quantity value and the feed quantity rated value.

[0097] Step 104: Repeatedly control the combine harvester based on the feed amount deviation value to obtain the optimal value of the combine harvester's travel speed. In actual application, a repeating control controller can be used to repeatedly control the combine harvester based on the feed amount deviation value. The structure diagram of the repeating control controller is shown in FIG. Figure 7 As shown, its transfer function is:

[0098]

[0099] Where H(z) is the transfer function value, Q(z) is 0.95, Z is the complex variable of the discrete-time signal, and C(z) = k r ·Z k , N is the number of samples in one cycle, K r is the number of phase compensations, and k is the number of phase compensations.

[0100] Step 105: Obtain a speed deviation and a speed deviation change rate based on the current driving speed of the combine harvester and the optimal value of the driving speed of the combine harvester.

[0101] Step 106: Use a fuzzy PID controller to control the operating speed of the combine harvester based on the speed deviation and the speed deviation change rate. The structure of the fuzzy PID controller is as follows: Figure 8 shown. Figure 8 In, X rarget represents input, X out Indicates output.

[0102] In the fuzzy reasoning process of actual application, according to the speed deviation and the speed deviation change rate de / dt, in K pRule table, K i Rule table, K d Calculate its membership in the rule table. K p Rule table, K i Rule table and K d The rule tables are shown in Table 1, Table 2 and Table 3 below respectively.

[0103] Table 1 K p Rules Table

[0104]

[0105] Table 2 K i Rules Table

[0106]

[0107] Table 3 K d Rules Table

[0108]

[0109] In Tables 1 to 3, e refers to the deviation between the actual system output value and the rated value. Deviations can be positive or negative. NB (Negative Big) indicates the maximum negative deviation, NM (Negative Medium) indicates the negative medium deviation, NS (Negative Small) indicates the minimum negative deviation, ZO (Zero) indicates no deviation, PS (Positive Small) indicates the minimum positive deviation, PM (Positive Medium) indicates the positive medium deviation, and PB (Positive Big) indicates the maximum positive deviation. ΔKp represents the PID coefficient K. p The adjustment amount, ΔKi represents the PID coefficient K i The adjustment amount, ΔKd represents the PID coefficient K d The adjustment amount.

[0110] After fuzzy rule reasoning, we can get ΔK p , ΔK i , ΔK d Adjust the amount, and finally the fuzzy PID controller outputs the PWM drive signal of the stepper motor. The value of the PWM drive signal is u:

[0111]

[0112] Where k p0 、k i0 、k d0 They are proportional coefficient, integral coefficient and differential coefficient respectively, all of which are fixed values ​​determined through long-term tests.

[0113] Finally, the stepper motor is driven based on the calculated PWM drive signal to adjust the accelerator pedal to achieve control of the working speed.

[0114] Based on the above description, we can use Figure 9 The architecture shown in FIG. 1 realizes the above-mentioned combine harvester operation speed control method. In actual application, the speed control architecture of the combine harvester is as follows: Figure 10 shown.

[0115] Furthermore, the present invention provides a combine harvester operating speed control system, which is applied to the above-mentioned combine harvester operating speed control method. The system includes:

[0116] The data acquisition module is used to obtain the torque signal of the header driving shaft of the combine harvester and the driving speed of the combine harvester.

[0117] The model building module is used to filter the torque signal of the header driving shaft and build a univariate linear regression prediction model of the feed amount based on the filtered header driving shaft torque signal through the least squares method.

[0118] The feeding amount estimation module is used to estimate the feeding amount based on the univariate linear regression prediction model combined with the driving speed and use the Kalman filter to obtain the optimal estimated value of the feeding amount.

[0119] The first deviation determination module is used to determine a feed quantity deviation value according to an optimal estimated value of the feed quantity and a rated value of the feed quantity.

[0120] The repetitive control module is used to repeatedly control the combine harvester based on the feed amount deviation value to obtain the optimal value of the combine harvester's travel speed.

[0121] The second deviation determining module is configured to obtain a speed deviation and a speed deviation change rate based on a current travel speed of the combine harvester and an optimal value of the travel speed of the combine harvester.

[0122] The operating speed control module is used to control the operating speed of the combine harvester based on the speed deviation and the speed deviation change rate using a fuzzy PID controller.

[0123] Furthermore, the present invention also provides an electronic device, comprising:

[0124] Memory for storing computer programs.

[0125] The processor is connected to the memory and is used to retrieve and execute the computer program to implement the above-mentioned combine harvester operating speed control method.

[0126] In addition, when the computer program in the above-mentioned memory is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.

[0127] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0128] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for controlling the operating speed of a combine harvester, characterized in that: include: Obtaining the torque signal of the header driving shaft of the combine harvester and the driving speed of the combine harvester; Filtering the cutter drive shaft torque signal, and constructing a univariate linear regression prediction model of feed amount based on the filtered cutter drive shaft torque signal by least square method; Based on the univariate linear regression prediction model and the driving speed, a Kalman filter is used to estimate the feeding amount to obtain an optimal estimated value of the feeding amount; Determining a feed amount deviation value according to the feed amount optimal estimate value and the feed amount rated value; Repeatedly controlling the combine harvester based on the feed amount deviation value to obtain an optimal value of the combine harvester's travel speed; Obtaining a speed deviation and a speed deviation change rate based on a current driving speed of the combine harvester and an optimal value of the driving speed of the combine harvester; A fuzzy PID controller is used to control the operating speed of the combine harvester based on the speed deviation and the speed deviation change rate.

2. The combine harvester operating speed control method according to claim 1, characterized in that: A low-pass filter is used to filter the torque signal of the header driving shaft.

3. The combine harvester operating speed control method according to claim 1, characterized in that: The univariate linear regression prediction model is: Y=KT'+b; Where Y is the predicted value of the feed amount, T' is the filtered torque signal of the header drive shaft, K is the proportional coefficient, and b is the deviation compensation.

4. The combine harvester operating speed control method according to claim 1, characterized in that: The optimal estimated value of the feeding amount is: y k =Cx k ; Where y k is the optimal estimated value of feeding amount, C is the product of cutting width and planting density, x k is the driving speed.

5. The combine harvester operating speed control method according to claim 1, characterized in that: A repetitive control controller is used to repeatedly control the combine harvester based on the feed amount deviation value to obtain an optimal value of the combine harvester's travel speed; Among them, the repetitive control controller transfer function is: Where H(z) is the transfer function value, Q(z) is 0.95, Z is the complex variable of the discrete-time signal, and C(z) = k r ·Z k , N is the number of samples in one cycle, K r is the number of phase compensations, and k is the number of phase compensations.

6. The combine harvester operating speed control method according to claim 1, characterized in that: The fuzzy PID controller is used to control the operating speed of the combine harvester based on the speed deviation and the speed deviation change rate, specifically including: determining a degree of membership according to the speed deviation and the speed deviation change rate; After fuzzy rule reasoning based on the membership degree, a PWM driving signal is obtained; The PWM drive signal is used as a stepper motor drive signal of the combine harvester to control the accelerator pedal opening of the combine harvester and realize the control of the operating speed.

7. A combine harvester operating speed control system, characterized in that: The method for controlling the operating speed of a combine harvester according to any one of claims 1 to 6 is applied; the system comprises: A data acquisition module is used to obtain the torque signal of the header driving shaft of the combine harvester and the driving speed of the combine harvester; A model building module is used to filter the cutter drive shaft torque signal and build a univariate linear regression prediction model of feed amount based on the filtered cutter drive shaft torque signal by least square method; A feeding amount estimation module is used to estimate the feeding amount using a Kalman filter based on the univariate linear regression prediction model and the driving speed to obtain an optimal estimated value of the feeding amount; a first deviation determining module, configured to determine a feed amount deviation value according to the feed amount optimal estimate value and the feed amount rated value; a repetitive control module, configured to repeatedly control the combine harvester based on the feed amount deviation value to obtain an optimal value of the combine harvester's travel speed; a second deviation determining module, configured to obtain a speed deviation and a speed deviation change rate based on a current travel speed of the combine harvester and an optimal value of the travel speed of the combine harvester; An operating speed control module is used to implement operating speed control of the combine harvester based on the speed deviation and the speed deviation change rate using a fuzzy PID controller.

8. An electronic device, characterized in that: include: memory for storing computer programs; A processor, connected to the memory, is used to call and execute the computer program to implement the combine harvester operating speed control method according to any one of claims 1 to 6.

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