CN-121980958-A - Crop flow simulation regulation and control method, device, equipment and medium for combine harvester

CN121980958ACN 121980958 ACN121980958 ACN 121980958ACN-121980958-A

Abstract

The application discloses a crop flow simulation regulation and control method, a device, equipment and a medium of a combine harvester, and relates to the field of combine harvester simulation, the method comprises the steps of constructing a crop flow simulation model, constructing an MPC model for determining optimal operation input parameters based on a physical information ash box model, wherein the physical information ash box model is used for determining a prediction quality index, a prediction efficiency index and a prediction risk index based on the crop flow simulation model and according to the operation input parameters and the measurement disturbance parameters, and describing the state update of threshing load level and cleaning load level based on threshing effective processing capacity and cleaning effective processing capacity; and determining optimal operation input parameters through the MPC model according to the current operation input parameters and the current measurement disturbance parameters. The application can accurately couple the threshing and cleaning physical processes of the combine harvester so as to realize the accurate prediction of the prediction index in a wide load range, thereby improving the accuracy of optimizing the operation input parameters.

Inventors

QIAN ZHENJIE
YANG TENGXIANG
FENG YUGANG
YUAN WENSHENG
JIN CHENGQIAN
DAI DONG
CAI ZEYU
XU JINSHAN
NI YOULIANG
LIU ZHENG
CHEN MAN
ZHANG GUANGYUE

Assignees

农业农村部南京农业机械化研究所

Dates

Publication Date: 20260505
Application Date: 20260224

Claims (10)

1. The simulation regulation and control method for the crop flow of the combine harvester is characterized by comprising the following steps of: constructing a crop flow simulation model, wherein the crop flow simulation model comprises crop particles, a header module, a threshing module and a cleaning module; constructing an MPC model based on a physical information ash box model, wherein the MPC model is used for determining optimal operation input parameters based on the physical information ash box model according to threshing load level, cleaning load level and measured disturbance parameters and combining preset target optimization functions, prediction index constraint conditions and parameter constraint conditions to be optimized, and the prediction index constraint conditions comprise constraint conditions corresponding to a prediction quality index, a prediction efficiency index and a prediction risk index; The physical information ash box model is used for determining a prediction quality index, a prediction efficiency index and a prediction risk index according to operation input parameters and measurement disturbance parameters based on the crop flow simulation model, wherein the operation input parameters are used for controlling the crop flow simulation model; the physical information ash box model is also used for determining threshing load level of the threshing module and cleaning load level of the cleaning module according to operation input parameters and measurement disturbance parameters, aggregating the threshing load level of the threshing module and the cleaning load level of the cleaning module into a two-state system, performing cascade coupling on the threshing module and the cleaning module based on mass conservation of crop particles, and describing state update of the threshing load level and the cleaning load level through a first-order inertia link of discrete time based on threshing effective processing capacity of the threshing module and cleaning effective processing capacity of the cleaning module; And determining optimal operation input parameters through the MPC model according to the current operation input parameters and the current measured disturbance parameters.
2. The method of claim 1, wherein the state update equation of the physical information ash box model comprises: Wherein, the , , , , In the above-mentioned method, the step of, Representation of The threshing load level at the moment, Representation of The purge load level at the moment in time, Representation of The threshing load level at the moment, Representation of The purge load level at the moment in time, Representing the duration of the interval between two adjacent moments, Indicating the effective time constant for threshing, Indicating the time constant of the cleaning effective time, Representation of The threshing load ratio at the moment, Representation of The cleaning load ratio at the moment; Representation of The crop feeding amount of the threshing module at any moment, Representation of The threshing effective processing capacity at the moment, The standard threshing processing capacity is indicated, Indicating the correction factor of the threshing ability, Representation of The rotational speed of the drum at the moment, Representation of The time of day is the effective throughput of the cleaning, Indicating the standard cleaning processing capacity of the device, Representing the cleaning ability correction factor, Representation of The fan speed at the moment in time, Representation of The opening degree of the upper screen at the moment, Representation of The opening degree of the lower sieve at the moment, Representation of The crop feeding amount of the cleaning module at any moment, Representing the threshing loss of the previous step.
3. The method for regulating and controlling crop flow simulation of a combine harvester according to claim 2, wherein the predicted quality index comprises a total loss rate, a breakage rate and an impurity rate; the determination formula of the total loss rate comprises the following steps: Wherein, the , , , In the above-mentioned method, the step of, The total loss rate is indicated as being indicative of the total loss rate, Indicating the threshing loss, Indicating a purge loss; represents the basic threshing loss of the threshing machine, The indication function is represented by a representation of the indication function, The threshing correction weight is indicated, The threshing load ratio is indicated by the ratio of the threshing load, The threshing super-linear correction index is indicated, Represents the basic threshing loss rate under the standard working condition, The ratio coefficient of threshing overload threshing loss is represented, Indicating the threshing load level, Represents the threshing loss penalty coefficient of the rotating speed deviation of the roller, Indicating the rotation speed of the roller, Representing an optimal drum rotation speed calibration value; Indicating a loss of basic cleaning and sorting, Representing the cleaning correction weight of the object, Indicating the ratio of the purge load, Indicating that the cleaning exceeds the linear correction index, Represents the basic cleaning loss rate under the standard working condition, Represents the cleaning overload cleaning loss proportion coefficient, Indicating the level of the purge load, Represents a fan speed deviation purge loss penalty coefficient, Indicating the rotational speed of the fan, Indicating an optimal fan speed calibration value, Represents the upper screen opening deviation cleaning loss penalty coefficient, The opening degree of the upper screen is represented, Represents the optimal upper sieve opening calibration value, Represents a lower screen opening deviation cleaning loss penalty coefficient, The opening degree of the lower sieve is represented, Representing an optimal lower sieve opening calibration value; the determination formula of the crushing rate comprises the following steps: , In the above-mentioned method, the step of, The crushing rate is indicated by the expression of the crushing rate, Represents the basic breaking rate under the standard working condition, The ratio coefficient of threshing overload crushing is represented, Representing a roller rotating speed deviation crushing punishment coefficient; the determination formula of the impurity rate comprises the following steps: In the above-mentioned method, the step of, The impurity rate is represented by the number of the impurities, Represents the basic impurity rate under the standard working condition, The ratio coefficient of the threshing overload impurities is expressed, Represents the impurity punishment coefficient of the deviation of the rotating speed of the fan, Represents the impurity punishment coefficient of the deviation of the opening degree of the upper screen, Representing a lower sieve opening deviation impurity punishment coefficient; the determination formula of the prediction risk index comprises the following steps: Wherein, the , In the above-mentioned method, the step of, Represents a predicted risk indicator of the risk of the person, Indicating the risk of threshing, Indicating the risk of threshing, A non-linear function of the type softplus is represented, Indicating the threshing load level, Indicating the threshing load level.
4. The method of claim 1, wherein determining optimal operational input parameters from the MPC model based on current operational input parameters and current measured disturbance parameters comprises: Determining an optimal MPC super-parameter combination from a strategy library according to the actual load level and the actual risk preference; The strategy library is obtained by acquiring MPC super-parameter candidate sets, wherein the MPC super-parameter candidate sets comprise a plurality of groups of MPC super-parameter combinations, simulating each group of MPC super-parameter combinations in a plurality of simulation scenes, and recording a prediction quality index, a prediction efficiency index and a prediction risk index corresponding to each group of MPC super-parameter combinations in each simulation scene; And determining the optimal operation input parameters according to the current operation input parameters and the current measurement disturbance parameters through an MPC model based on the optimal MPC super-parameter combination.
5. The simulated control method of combine crop flow as claimed in claim 1, further comprising: Obtaining an actual measurement quality index, determining that the quality index deviation does not meet the preset index deviation threshold according to the actual measurement quality index, the predicted quality index and the preset index deviation threshold, and updating model parameters of the physical information ash box model until the quality index deviation meets the preset index deviation threshold.
6. The method for simulating regulation of crop flow of a combine harvester according to claim 1, wherein the preset objective optimization function comprises a stage cost and a speed smoothing term; The stage cost is shown in the following formula: In the above-mentioned method, the step of, Representation of The cost of the phase at the moment in time, Representation of The total loss rate at the moment in time, For the rate of loss to be a target, Representation of The rate of breakage at the moment of time, Representation of The impurity rate at the moment of time is, Representation of The running speed at the moment of time is, 、、、 The weight coefficients of the total loss rate, the crushing rate, the impurity rate and the running speed are respectively larger than 0; The velocity smoothing term is shown as follows: In the above-mentioned method, the step of, Representation of A time-of-day speed smoothing term, Is that The running speed at the moment of time is, Is that The actual running speed at the moment in time, Is a smoothing weight and is greater than 0.
7. A simulated regulation device for crop flow of a combine harvester, characterized in that the simulated regulation device for crop flow of the combine harvester comprises: the simulation construction module is used for constructing a crop flow simulation model, and the crop flow simulation model comprises crop particles, a header module, a threshing module and a cleaning module; The regulation and control model construction module is used for constructing an MPC model based on a physical information ash box model, wherein the MPC model is used for determining optimal operation input parameters according to threshing load level, cleaning load level, measured disturbance parameters, a preset target optimization function, a prediction index constraint condition and a parameter constraint condition to be optimized, and the prediction index constraint condition comprises constraint conditions corresponding to a prediction quality index, a prediction efficiency index and a prediction risk index; The physical information ash box model is used for determining a prediction quality index, a prediction efficiency index and a prediction risk index according to operation input parameters and measurement disturbance parameters based on the crop flow simulation model, wherein the operation input parameters are used for controlling the crop flow simulation model; the physical information ash box model is also used for determining threshing load level of the threshing module and cleaning load level of the cleaning module according to operation input parameters and measurement disturbance parameters, aggregating the threshing load level of the threshing module and the cleaning load level of the cleaning module into a two-state system, performing cascade coupling on the threshing module and the cleaning module based on mass conservation of crop particles, and describing state update of the threshing load level and the cleaning load level through a first-order inertia link of discrete time based on threshing effective processing capacity of the threshing module and cleaning effective processing capacity of the cleaning module; And the processing module is used for determining the optimal operation input parameters through the MPC model according to the current operation input parameters and the current measurement disturbance parameters.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the combine crop flow simulation regulation method of any one of claims 1-6.
9. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor realizes the steps of the combine crop flow simulation regulation method of any of claims 1-6.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the combine crop flow simulation regulation method of any of claims 1-6.

Description

Crop flow simulation regulation and control method, device, equipment and medium for combine harvester Technical Field The application relates to the field of simulation of combine harvesters, in particular to a method, a device, equipment and a medium for regulating and controlling crop flow simulation of a combine harvester. Background The combine harvester is used as the core equipment for grain harvesting, and the operation performance of the combine harvester directly affects the harvesting quality and efficiency. With the development of agriculture intellectualization, the long research and development period of the traditional combine harvester can not meet the agricultural requirements. The virtual simulation technology provides a new path for the research and development of agricultural machinery equipment. However, the existing simulation platform has the defects of loose connection between the model and the physical verification and the like, and can not effectively support iterative optimization of the intelligent regulation algorithm. Therefore, a set of high-precision and high-adaptability simulation regulation and control method for crop flow of the combine harvester is needed to be constructed, and the intelligent regulation and control of the crop flow and the depth integration of digital simulation are realized. Disclosure of Invention The application aims to provide a crop flow simulation regulation and control method, device, equipment and medium for a combine harvester, which can accurately couple the physical processes of a threshing module and a cleaning module of the combine harvester so as to realize accurate prediction of a prediction index in a wide load range, thereby improving the accuracy of optimizing operation input parameters according to the accurate prediction index. In order to achieve the above object, the present application provides the following solutions: In a first aspect, the present application provides a method for simulating and controlling crop flow of a combine harvester, comprising: the method comprises the steps of constructing a crop flow simulation model, constructing an MPC model based on a physical information ash box model, determining optimal operation input parameters according to threshing load levels and cleaning load levels of the threshing module and measuring disturbance parameters based on the physical information ash box model, combining a preset target optimization function, a prediction index constraint condition and a parameter constraint condition to be optimized, wherein the prediction index constraint condition comprises constraint conditions corresponding to a prediction quality index, a prediction efficiency index and a prediction risk index, the physical information ash box model is used for determining the prediction quality index, the prediction efficiency index and the prediction risk index according to the operation input parameters and the measuring disturbance parameters based on the crop flow simulation model, the operation input parameters are used for controlling the crop flow simulation model, the physical information ash box model is further used for determining the threshing load levels of the threshing module and the cleaning load levels of the cleaning module according to the operation input parameters and the measuring disturbance parameters, aggregating the threshing load levels of the threshing module and the cleaning load levels of the cleaning module into a two-state system, cascading the threshing module and the cleaning module based on mass conservation of the crop particles, coupling the cleaning module and the cleaning module based on the mass conservation of the crop particles, and determining the optimal operation input parameters according to the operation input parameters and the current operation disturbance parameters, wherein the current operation input parameters and the current state parameters are updated according to the operation input parameters and the operation disturbance parameters. Optionally, the state update equation of the physical information gray box model includes: Wherein, the ,, , , In the above-mentioned method, the step of,Representation ofThe threshing load level at the moment,Representation ofThe purge load level at the moment in time,Representation ofThe threshing load level at the moment,Representation ofThe purge load level at the moment in time,Representing the duration of the interval between two adjacent moments,Indicating the effective time constant for threshing,Indicating the time constant of the cleaning effective time,Representation ofThe threshing load ratio at the moment,Representation ofThe cleaning load ratio at the moment; Representation of The crop feeding amount of the threshing module at any moment,Representation ofThe threshing effective processing capacity at the moment,The standard threshing processing capacity is indicated,Indicating the correction factor of t