Harvester automatic control method and system and harvester

Abstract

This invention discloses an automatic control method, system, and harvester for a harvester, relating to the field of agricultural machinery technology. The method includes: acquiring the rotational speeds of multiple working devices in the harvester in real time using multiple speed sensors, and calculating the speed change rate corresponding to each working device; determining the current load value of the harvester based on regression calculations using all monitored speeds and speed change rates; determining the load deviation based on the current load value; and controlling the drive device of the harvester according to the load deviation. This invention employs real-time monitoring of the rotational speed change rates of each major working device and the error between the target speed signal and the actual speed signal from the engine's CAN communication, using multivariate regression analysis to establish a grey relational prediction relationship for the engine load, which is used to calculate the current load condition of the harvester in real time. Therefore, based on the load condition, the harvester is controlled, achieving precise control.

Description

Technical Field

[0001] This invention relates to the field of agricultural machinery technology, and in particular to an automatic control method, system and harvester for a harvester. Background Technology

[0002] In modern agricultural production, combine harvesters require monitoring and real-time adjustment of nearly 50 parameters during high-speed operation. In the field environment, different terrains, soils, and climates significantly impact crop production. Even within the same plot, varying planting conditions result in different plant heights and densities. These factors directly affect the combine harvester's performance. In grain harvesting, 75% of grain loss during mechanized harvesting is caused by improper adjustment of the harvester's operating conditions. Excessive feed leads to excessive load on the harvester, reducing harvesting efficiency and causing a surge in losses. Conversely, insufficient feed prevents the harvester from performing at its optimal level, resulting in energy waste. With the increasing harvesting speed of grain combine harvesters, the traditional method of adjusting harvester parameters based on driver experience to achieve optimal harvesting conditions is no longer sufficient to meet the current demands for improved harvesting efficiency, reduced driver workload, and lower failure rates of key components. Summary of the Invention

[0003] The technical problem to be solved by this invention is to address the shortcomings of existing technologies, specifically the problem of low harvesting efficiency. Specifically, this invention provides an automatic control method, system, and harvester for a harvester, as detailed below:

[0004] 1) In a first aspect, the present invention provides an automatic control method for a harvester, the specific technical solution of which is as follows:

[0005] S1, the rotation speed of multiple working devices in the harvester is acquired in real time through multiple speed sensors, and the rotation speed change rate of each working device is calculated.

[0006] S2, determine the current load value of the harvester based on all rotational speeds and rotational speed change rates;

[0007] S3, determine the load deviation based on the current load value, and control the drive device of the harvester according to the load deviation.

[0008] The beneficial effects of the automatic control method for harvesters provided by this invention are as follows:

[0009] Without fundamentally altering the hardware structure of existing commercial vehicles, a grey relational analysis method is employed to establish an engine load prediction relationship using multiple regression analysis. This relationship is derived from real-time monitoring of the rotational speed changes of each major working device and the target rotational speed signal sent to the engine via CAN communication, and the error between the target rotational speed signal and the actual engine rotational speed signal. This information is used to calculate the current load condition of the harvester in real time. Based on this load condition, precise control of the harvester can be achieved. Furthermore, in addition to monitoring the current rotational speed, the rotational speed change rate is incorporated into the program calculation. Within the same data acquisition cycle, at the same rotational speed, different rotational speed change rates will lead to significant differences in the changes of the two sets of signals. The trend of the rotational speed change rate of the working device can, to some extent, represent the current load condition of the working device; therefore, in a sense, monitoring the rotational speed signal change rate can replace the function of a torque sensor.

[0010] Based on the above solution, the present invention can be further improved as follows.

[0011] Furthermore, the multiple working devices include at least two of the following: harvester engine speed, harvester operating speed, harvester axial flow drum, harvester cleaning fan, harvester cleaning screen box, and harvester grain elevator.

[0012] Furthermore, the process of determining the current load value of the harvester includes:

[0013] The current load value of the harvester is determined by a multi-component regression prediction grey relational method.

[0014] Furthermore, the process for determining the load deviation includes:

[0015] The load deviation is determined based on the current load value and the preset load value.

[0016] 2) In a second aspect, the present invention also provides an automatic control system for a harvester, the specific technical solution of which is as follows:

[0017] The calculation module is used to: acquire the rotational speeds of multiple working devices in the harvester in real time through multiple speed sensors, and calculate the rate of change of rotational speed for each working device;

[0018] The determination module is used to: determine the current load value of the harvester based on all speed change rates;

[0019] The control module is used to: determine the load deviation based on the current load value, and control the drive device of the harvester according to the load deviation.

[0020] Based on the above solution, the present invention can be further improved as follows.

[0021] Furthermore, the multiple working devices include at least two of the harvester's axial flow drum, the harvester's cleaning fan, the harvester's cleaning screen box, and the harvester's grain elevator.

[0022] Furthermore, the process of determining the current load value of the harvester includes:

[0023] The current load value of the harvester is determined by a preset grey relational analysis method.

[0024] Furthermore, the process for determining the load deviation includes:

[0025] The load deviation is determined based on the current load value and the preset load value.

[0026] 3) In a third aspect, the present invention also provides a harvester, including any of the systems described above.

[0027] It should be noted that the beneficial effects of the technical solutions and corresponding possible implementations of the second and third aspects of the present invention can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0028] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0029] Figure 1 This is a flowchart illustrating an automatic control method for a harvester according to an embodiment of the present invention;

[0030] Figure 2 This is a structural framework diagram of an automatic control system for a harvester according to an embodiment of the present invention;

[0031] Figure 3 This is a schematic diagram of the structural principle;

[0032] Figure 4 A schematic diagram illustrating the calculation principle of the KP correction value;

[0033] Figure 5 This is one of the schematic diagrams of PID correction rules;

[0034] Figure 6 This is the second diagram illustrating the PID correction rule.

[0035] Figure 7 This is the third diagram illustrating the PID correction rules;

[0036] Figure 8 This is the fourth diagram illustrating the PID correction rules;

[0037] Figure 9 This is the fifth diagram illustrating the PID correction rules;

[0038] Figure 10 This is the sixth diagram illustrating the PID correction rule. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0040] like Figure 1 As shown, an automatic control method for a harvester according to an embodiment of the present invention includes the following steps:

[0041] S1, the rotation speed of multiple working devices in the harvester is acquired in real time through multiple speed sensors, and the rotation speed change rate of each working device is calculated.

[0042] S2, determine the current load value of the harvester based on all rotational speeds and rotational speed change rates;

[0043] S3, determine the load deviation based on the current load value, and control the drive device of the harvester according to the load deviation.

[0044] The beneficial effects of the automatic control method for harvesters provided by this invention are as follows:

[0045] Without fundamentally altering the hardware structure of existing commercial models, a grey relational prediction relationship for engine load is established using multiple regression analysis. This is achieved by real-time monitoring of the speed changes of each major working device and the error between the target speed signal and the actual engine speed signal via engine CAN communication. This relationship is then used to calculate the current load status of the harvester in real time. Based on this load status, precise control of the harvester can be achieved.

[0046] S1 acquires the rotational speeds of multiple working devices in the harvester in real time through multiple speed sensors and calculates the rate of change of rotational speed for each working device. Wherein:

[0047] like Figure 3 As shown, the working device includes: the axial flow drum of the harvester, the cleaning fan of the harvester, the cleaning screen box of the harvester, and the grain elevator of the harvester.

[0048] Each working device is equipped with at least one speed sensor.

[0049] The process of calculating the rate of change of rotational speed for each working device is as follows:

[0050] The rate of change of rotational speed for any working device is calculated using the first formula, which is:

[0051]

[0052] Among them, a k a represents the signal value collected by the speed sensor of the working device at time k. k-N This represents the sensor signal value up to N, where N represents the phase difference period.

[0053] Since the controller uses a cyclic sampling method per program cycle, the requirement for continuous differentiation is approximated by subtracting the signal value from N cycles ago from the current cycle signal value.

[0054] In addition to obtaining the rotational speed of the working device, it is also necessary to determine the harvester's speed and the rate of change of speed. The method of determination is the same as that for determining the rotational speed and the rate of change of speed of the working device mentioned above, and will not be repeated here.

[0055] It is also necessary to monitor the difference and rate of change between the "engine speed command target value" issued by the vehicle controller and the "current actual engine speed value". Engine speed is also acquired through at least one speed sensor. The engine speed command target value is a preset value, which can be adaptively adjusted based on actual conditions.

[0056] Modern mainstream engines are equipped with an engine ECU that receives a "target command speed" signal and then adjusts relevant engine hardware (such as throttle opening) to achieve the actual speed adjustment of the engine.

[0057] Therefore, in current domestically produced mid-to-high-end harvester models in my country, the foot or hand throttle signals are generally input to the onboard controller. After comprehensively judging the vehicle's condition, the onboard controller outputs a more precise "target command speed" signal, which is sent to the ECU via CAN communication to achieve precise engine adjustment.

[0058] Assuming the optimal rated engine speed is 2200 rpm during normal harvesting operations, the actual engine speed fluctuates within ±50 rpm and ±150 rpm of the rated speed. It can be assumed that the latter bears a higher load than the former. Assuming the engine speed is increased from 1900 rpm to 2200 rpm by adjusting the throttle, the former can complete the adjustment process in 5 program sampling cycles of the onboard controller, while the latter requires 10 program cycles. Therefore, it can also be assumed that the latter bears a higher load than the former. Thus, this patented technology uses the difference between the target and actual engine speeds and the rate of change of that difference in program calculations.

[0059] S2, determine the current load value of the harvester based on all rotational speed change rates. Wherein:

[0060] The current load value is determined by using a multi-component regression prediction grey relational method.

[0061] The grey relational regression method for multi-component regression prediction is as follows:

[0062] The vehicle controller simultaneously monitors multiple signal changes and performs grey relational regression calculations based on the rate of change of those signals. The structure of the regression equation is as follows:

[0063]

[0064] Where: Y Pre_Load The predicted load calculated by the regression equation is: x1 is the engine speed difference, x2 is the current engine speed, x3 is the harvester speed during harvesting, x4 is the axial flow drum speed, x5 is the cleaning fan speed, x6 is the cleaning screen box speed, and x7 is the harvest grain elevator drive shaft speed.

[0065] β1 is the coefficient for the engine speed difference, β2 is the coefficient for the current engine speed, β3 is the coefficient for the harvester operating speed, β4 is the coefficient for the axial flow drum speed, β5 is the coefficient for the cleaning fan speed, β6 is the coefficient for the cleaning screen box speed, and β7 is the coefficient for the grain lifting speed.

[0066] μ1 is the coefficient of the rate of change of engine speed difference, μ2 is the coefficient of the rate of change of current engine speed, μ3 is the coefficient of the rate of change of harvester operating speed, μ4 is the coefficient of the rate of change of axial flow drum speed, μ5 is the coefficient of the rate of change of cleaning fan speed, μ6 is the coefficient of the rate of change of cleaning screen box speed, μ7 is the coefficient of the rate of change of grain lifting speed; k is a constant term.

[0067] The grey relational regression equation was obtained by designing a field harvesting test for a specific model of commercial vehicle. Engine load values ​​were collected and recorded in real time using an engine diagnostic instrument, and the parameters and rate of change of various working components of the harvester were collected and recorded in real time using an on-board controller. After data cleaning and filtering, the two sets of data were correlated and matched using time stamps, and then multiple regression calculations were performed. The data structure is shown in the following formula.

[0068]

[0069] Among them, a 11 To a 114 This indicates that after data clearing, time at moment 1 represents the rotational speed of each component and its rate of change (i.e., x1 in the formula above). Up to x7 ), a n1 To a n14 The time interval at moment 1 represents the rotational speed of each component and its rate of change, y1 to y2. n This represents the predicted load calculated by the regression equation from time 1 to time n.

[0070] When calculating the regression equation, the number of data sets used must satisfy n>10000, and the calculated regression equation R0 2 The coefficients must be greater than 0.8. The resulting regression equation needs to be tested on a real vehicle. At the same time, the Pearson correlation coefficient between the engine load fitted value predicted by the controller based on the regression equation and the actual engine load value monitored by the engine diagnostic instrument must be greater than 0.8 for the harvester load regression equation to meet the practical application conditions.

[0071] S3, determine the load deviation based on the current load value, and control the drive unit of the harvester according to the load deviation. Wherein:

[0072] Load deviation is determined by the difference between the load target setpoint and the current load value.

[0073] The load target setting can be entered by the driver on the in-vehicle display terminal or preset in the processor.

[0074] The process of controlling the harvester's drive unit based on load deviation is as follows:

[0075] The obtained deviation e(k) is used for PID calculation. The proportional coefficient P, integral coefficient I, and derivative coefficient D required for PID calculation are set in the early stage of the program. The controller then makes appropriate adjustments and corrections based on the deviation e(k), the rate of change of the deviation e(k), and the 49 correlation rules set in the controller program.

[0076] The method for obtaining the rate of change of deviation e(k) is the same as the method for determining the rate of change of rotational speed, and will not be elaborated here.

[0077] The 49 association rules refer to: dividing the deviation range and the deviation change rate range into 7 different activity intervals, and arranging and combining the two intervals to form 49 working conditions. For each condition, a parameter correction rule for the rotational speed is set, resulting in a total of 49 association rules.

[0078] The determination process for the proportional coefficient P, integral coefficient I, and derivative coefficient D is as follows: At any given time, the onboard controller's internal program monitors in real-time the predicted load value of the harvester calculated through regression and the rate of change of that predicted load value. Based on a set interval division, it identifies which of the 49 possible operating conditions the harvester's load condition falls under within the current monitoring cycle. Then, combining this with the association rules for PID parameter correction under that condition, it corrects the proportional coefficient P, integral coefficient I, and derivative coefficient D respectively. Within this monitoring cycle, the system performs PID calculations based on the corrected proportional coefficient P, integral coefficient I, and derivative coefficient D, and outputs the adjustment amount for the harvester in the current cycle. In the next monitoring cycle, this process is repeated to determine the harvester's operating condition, correct the corresponding PID parameters, and the program outputs the corrected PID adjustment amount.

[0079] PID control, based on the deviation e(k) between the current signal and the target signal, is widely used in the field of automatic control. However, PID control strategies are prone to integral saturation, resulting in a slow and sluggish response. Furthermore, the fixed three sets of PID parameters are insufficient to achieve accurate control in complex control environments. For example, when the monitored signal is far from the target value, the control system needs to output a larger value per cycle, i.e., a larger proportional coefficient P, to reduce the response time when approaching the target value. When the monitored signal is near the target value, it is necessary to avoid overshoot caused by excessive output from the control system; therefore, the proportional coefficient P should be smaller.

[0080] Based on the above problems, this patented technology adds seven thresholds (-3, -2, -1, 0, 1, 2, 3) to the range of the harvester load deviation e(k) and the range of the deviation change rate, respectively, to divide the machine into six intervals: positive, negative, high, medium, and low. Each threshold is combined to form 49 deviation states. A corresponding PID parameter correction rule is set for each deviation state. The rules corresponding to the three sets of PID parameters are shown in Tables 1 to 3. Table 1 is the proportional coefficient KP correction rule table, Table 2 is the integral coefficient KI correction rule table, and Table 3 is the derivative coefficient KD correction rule table.

[0081] Table 1

[0082]

[0083]

[0084] Table 2

[0085]

[0086] Table 3

[0087]

[0088] The driver sets the base values ​​and correction ranges for the three PID parameters through the human-machine interface. The program then divides the threshold interval into [-3, +3] based on the set correction range. Each program cycle, the controller searches for the correlation rules corresponding to the three PID parameters based on the intervals corresponding to the deviation and the rate of change of deviation. It then corrects the parameters according to these rules, and finally inputs the corrected PID parameters into the program for calculation and outputs the corresponding adjustment signal.

[0089] like Figure 4 As shown, taking the calculation of KP parameters as an example (the calculation method for other parameters is the same), [m e m Δe The corresponding association rule is f(m) e m Δe The current deviation value e(k1) belongs to [m e n e The interval is given by de(k1) / dt, which belongs to [m]. Δe n Δe The principle of its correction parameters is as follows: Figure 4 As shown, the calculation formula is:

[0090]

[0091] Where, m e m is the lower limit of the deviation. Δe n is the lower limit of the rate of change of deviation. e n is the upper limit of the deviation. Δe f(n) is the lower limit of the rate of change of deviation. e n Δe This refers to the association rule when both the deviation and the rate of change of deviation are at the upper limit of the deviation.

[0092] Currently, the load rate of mainstream harvesters in my country is approximately 40-50% in no-load operation mode (all working parts are in operation but no harvesting is being carried out). Therefore, the target load rate of the harvester in normal operation mode can be set between 70% and 100%, with the deviation e(k) fluctuating in the range of [-30, +30], and the deviation change rate fluctuating in the range of [-10, +10].

[0093] Assuming the basic and correction ranges of the three sets of PID parameters for the harvester load control are shown in Table 4, the correction rules for the three sets of PID parameters will be as follows: Figure 5-10 As shown.

[0094] Table 4

[0095] KP KI KD base value +5 +0.5 +0.3 Maximum correction limit +3 +0.3 +0.1 Minimum Correction Lower Limit -3 -0.3 -0.1

[0096] Assuming the controller detects a deviation of +15 (corresponding to e(k) between +1 and +2) and a deviation change rate of +6 (corresponding to de(k) / dt between +1 and +2) in a certain program cycle, the calculation process for the KP coefficient correction value ΔKP at this time is as follows:

[0097]

[0098] The final result of the KP coefficient in the current program cycle is 5.54 (5 + 0.54), which means that the system needs to be accelerated to approach the target setpoint, so the proportional coefficient is increased. The parameters of KI and KD are calculated in the same way. The system performs PID program calculations based on the finally calculated PID parameters and outputs the adjustment amount corresponding to the current cycle.

[0099] The output value calculated by the PID controller is assigned to the drive current of the electromagnetic proportional valve coil at the harvester's walking hydraulic pump. The controller controls the harvester's speed by adjusting the drive current of the electromagnetic coil in real time to change the hydraulic flow rate that drives the hydraulic motor.

[0100] The effect that this solution can achieve is:

[0101] 1. The two core technical points of this patent are: ① Grey prediction of the current load of the harvester by monitoring the current signals and the rate of change of various working parts of the harvester and establishing a regression equation. ② Based on the traditional PID controller, 49 correlation rules are established based on the deviation and the rate of change of the deviation to correct the three sets of PID parameters in real time, thereby achieving optimized regulation.

[0102] 2. This patented technology incorporates the signal change rate into the monitoring range. By comparing the change rate of the working component's rotational speed with its rotational speed, the current torque load of the working component can be approximately measured.

[0103] 3. This patented technology incorporates the difference between the engine's target speed and actual speed, along with the rate of change of that difference, into the monitoring range. By monitoring the difference between the actual and target engine speeds and the rate of change of that difference, the current torque load at the engine's output can be approximated.

[0104] 4. Most current patented technologies related to load monitoring and control can reduce the harvester speed or stop it when the load is too high, but they do not have a function to increase the harvester's operating speed when the load is low. This patented technology can not only reduce the speed when the load increases, but also increase the speed when the load is too low, so that the overall load of the harvester is always maintained near the target set value, saving energy consumption of the harvester.

[0105] 5. This patented technology simultaneously monitors 7 signal changes and 7 signal change rates for regression calculation. Due to the transmission structure of the harvester, the working state of some working components is significantly correlated with the state of another working component (for example, the engine speed is significantly correlated with the cleaning blower speed). Therefore, by monitoring only a portion of the signal changes in the 7 signal changes and 7 signal change rates, a rough judgment of the harvester's load can be achieved.

[0106] In summary, monitoring oil pressure using traditional hydraulic mechanisms and installing torque sensors at the working bearings can both achieve monitoring of the current load on the working components. However, this technology is expensive and requires significant modifications to the overall structure of the harvester. Monitoring the rotational speeds of multiple working components and using logical threshold signals can achieve approximate monitoring of the harvester load at a lower cost and with minimal changes to the overall structure, but it cannot achieve precise control over the harvester's status.

[0107] This patented technology requires minimal modification to the overall structure of the harvester, significantly reducing its application cost and making it widely applicable. Regarding monitoring methods and control strategies, it innovatively proposes a load rate grey prediction method based on regression analysis of multiple signals and signal change rates. It designs 49 correlation rules to adjust the PID correlation coefficient in real time based on different deviation values ​​and deviation correction rates, achieving optimal control.

[0108] Furthermore, the multiple working devices include at least two of the harvester's axial flow drum, the harvester's cleaning fan, the harvester's cleaning screen box, and the harvester's grain elevator.

[0109] Furthermore, the process of determining the current load value of the harvester includes:

[0110] The current load value of the harvester is determined by a preset grey relational analysis method.

[0111] Furthermore, the process for determining the load deviation includes:

[0112] The load deviation is determined based on the current load value and the preset load value.

[0113] like Figure 2 As shown, the present invention also provides an automatic control system 200 for a harvester, the specific technical solution of which is as follows:

[0114] The calculation module 210 is used to: acquire the rotational speeds of multiple working devices in the harvester in real time through multiple speed sensors, and calculate the rate of change of rotational speed for each working device;

[0115] The determining module 220 is used to: determine the current load value of the harvester based on all speed change rates;

[0116] The control module 230 is used to: determine the load deviation based on the current load value, and control the drive device of the harvester according to the load deviation.

[0117] Furthermore, the multiple working devices include at least two of the harvester's axial flow drum, the harvester's cleaning fan, the harvester's cleaning screen box, and the harvester's grain elevator.

[0118] Furthermore, the process of determining the current load value of the harvester includes:

[0119] The current load value of the harvester is determined by a preset grey relational analysis method.

[0120] Furthermore, the process for determining the load deviation includes:

[0121] The load deviation is determined based on the current load value and the preset load value.

[0122] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0123] It should be noted that the beneficial effects of the harvester automatic control system 200 provided in the above embodiments are the same as those of the harvester automatic control method described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0124] In an exemplary embodiment, a computer program product or computer program is also provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform any of the methods described above.

[0125] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0126] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product in one or more computer-readable media containing computer-readable program code.

[0127] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media are not limited to—electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0128] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An automatic control method for a harvester, characterized in that, include: S1, the rotation speed of multiple working devices in the harvester is acquired in real time through multiple speed sensors, and the rotation speed change rate of each working device is calculated. S2, determine the current load value of the harvester by performing regression calculations based on all rotational speeds and rotational speed change rates; S3, determine the load deviation based on the current load value, and control the drive device of the harvester according to the load deviation; The working device includes: an axial flow drum of the harvester, a cleaning fan of the harvester, a cleaning sieve box of the harvester, and a grain elevator of the harvester; Each working device is equipped with at least one speed sensor; The process of calculating the rate of change of rotational speed for each working device is as follows: The rate of change of rotational speed for any working device is calculated using the first formula, which is: dadt≈ak-ak-N Where ak represents the signal value collected by the speed sensor of the working device at time k, ak−N represents the sensor signal value before N, and N represents the phase difference period; The system monitors the difference and rate of change between the target value of the engine speed command issued by the vehicle controller and the actual current engine speed. The engine speed is acquired through at least one speed sensor. The target value of the engine speed command is a preset value, which is adaptively adjusted based on the actual situation. The current load value is determined by using a multi-component regression prediction grey relational method. The grey relational regression method for multi-component regression prediction is as follows: The vehicle controller simultaneously monitors multiple signal changes and their rates of change, performing grey relational regression calculations. The structure of the regression equation in the grey relational regression calculation is as follows: in: The predicted load is calculated from the regression equation. For engine speed difference, Current engine speed The speed of the harvester during harvesting operations, For axial flow drum speed, To clean the fan speed, To adjust the rotation speed of the cleaning screen box, The rotational speed of the drive shaft of the grain harvester; For the coefficient of the engine speed difference term, For the current engine speed term coefficient, For the coefficient of the harvester's operating speed, For the axial flow drum speed coefficient, For the cleaning fan speed coefficient, For the rotational speed coefficient of the cleaning screen box, The coefficient for grain lifting speed; For the coefficient of the rate of change of engine speed difference, The coefficient of the rate of change of the engine's current speed. For the coefficient of the rate of change of the harvester's operating speed, For the coefficient of the rate of change of axial flow drum speed, For the coefficient of the rate of change of cleaning fan speed, For the coefficient of the rate of change of the rotation speed of the cleaning screen box, is the coefficient of the rate of change of grain lifting speed; k is a constant term; The grey relational regression equation was obtained by: designing a field harvesting test for a specific model of commercial vehicle; using engine diagnostic instruments to collect and record engine load values ​​in real time; and using an on-board controller to collect and record parameters and rate of change of various working components of the harvester in real time. Using time stamps, the two sets of data were cleaned, filtered, and then correlated and matched before performing multiple regression calculations. The data structure for the multiple regression calculation was as follows: in, to This indicates that after data clearing, the time at moment 1 represents the rotational speed of each component and its rate of change. to The time at moment 1 represents the rotational speed of each component and its rate of change. to This represents the predicted load calculated by the regression equation from time 1 to time n; When calculating the regression equation, the number of data sets used must satisfy n>10000, and the R2 coefficient of the calculated regression equation must satisfy greater than 0.

8. The obtained regression equation needs to be tested on a real vehicle. At the same time, the engine load fitting value predicted by the controller based on the regression equation and the actual engine load value monitored by the engine diagnostic instrument are collected. The Pearson correlation coefficient between the two must be greater than 0.

8.

2. The automatic control method for a harvester according to claim 1, characterized in that, The multiple working devices include at least two of the following: the axial flow drum of the harvester, the cleaning fan of the harvester, the cleaning screen of the harvester, and the grain elevator of the harvester.

3. The automatic control method for a harvester according to claim 1, characterized in that, The process of determining the current load value of the harvester includes: The current load value of the harvester is determined by a multi-component regression prediction grey relational method.

4. The automatic control method for a harvester according to claim 1, characterized in that, The process of determining the load deviation includes: The load deviation is determined based on the current load value and the target preset load value.

5. An automatic control system for a harvester, employing the automatic control method for a harvester as described in claim 1, characterized in that, include: The calculation module is used to: acquire the rotational speeds of multiple working devices in the harvester in real time through multiple speed sensors, and calculate the rate of change of rotational speed for each working device; The determination module is used to: determine the current load value of the harvester based on all rotational speeds and the rate of change of rotational speed; The control module is used to: determine the load deviation based on the current load value, and control the drive device of the harvester according to the load deviation.

6. The automatic control system for a harvester according to claim 5, characterized in that, The multiple working devices include at least two of the following: the harvester's axial flow drum, the harvester's cleaning fan, the harvester's cleaning screen, and the harvester's grain elevator.

7. The automatic control system for a harvester according to claim 5, characterized in that, The determination module specifically includes determining the current load value of the harvester, which includes: The current load value of the harvester is determined by a multi-component regression prediction grey relational method.

8. The automatic control system for a harvester according to claim 5, characterized in that, The process of determining the load deviation includes: The load deviation is determined based on the current load value and the target preset load value.

9. A harvester, characterized in that, Including an automatic control system for a harvester as described in any one of claims 5 to 8.

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