Combine harvester threshing system multi-target dynamic balance optimization method, device, equipment and medium

Abstract

The invention discloses a multi-target dynamic balance optimization method, device, equipment and medium for a threshing system of a combine harvester, and relates to the field of grain harvesting, the method comprises the following steps: constructing a multi-target optimization model by taking a rotor speed and a concave clearance as control variables and taking the combined minimum of threshing loss, separation loss and grain breakage rate as a target; based on the multi-objective optimization model, carrying out optimization solution on the rotor rotating speed and the concave plate gap to obtain an optimal rotor rotating speed and an optimal concave plate gap; and according to the optimal rotor rotating speed and the optimal concave plate gap, the rotor rotating speed and the concave plate gap in the working process of the threshing system of the combine harvester are adjusted. According to the method, accurate solution and dynamic adjustment of the operation parameters of the threshing system are realized, and the grain recovery rate and the grain quality are greatly improved.

Description

Technical Field

[0001] This application relates to the field of grain harvesting, and in particular to a multi-objective dynamic balance optimization method, apparatus, equipment and medium for a combine harvester threshing system. Background Technology

[0002] The threshing system is the core working unit of a combine harvester, and its performance directly determines the yield and quality of the harvested grain. Existing threshing systems suffer from the following technical shortcomings in their control methods: The contradiction between multiple objectives is prominent: the core conflict of the threshing system is "minimizing separation loss" and "minimizing grain breakage". Increasing the rotor speed can reduce separation loss, but it will aggravate grain breakage; increasing the gap between the concave plates can reduce grain breakage, but it may lead to an increase in separation loss. Existing technologies make it difficult to achieve a dynamic and precise balance between the two.

[0003] Poor adaptability to working conditions: The dynamic fluctuations and spatial heterogeneity of crop density and moisture content in the field make it impossible for fixed actuator parameters (such as rotor speed) to adapt to complex working conditions, which can easily lead to a sudden increase in separation loss or excessive grain breakage.

[0004] Inappropriate selection of control variables: The existing algorithm does not fully quantify the impact weight of the actuator on performance, blindly adjusting the rotor speed and the concave plate clearance at the same time, resulting in high control complexity and slow response speed, making it difficult to meet the real-time requirements of field operations.

[0005] Therefore, developing a threshing system optimization technology that can accurately quantify multi-objective conflicts and dynamically adapt to working conditions has become the key to improving the intelligence level of combine harvesters. Summary of the Invention

[0006] The purpose of this application is to provide a multi-objective dynamic balance optimization method, device, equipment and medium for a combine harvester threshing system, which can improve the control accuracy of the combine harvester threshing system, thereby improving the grain recovery rate and grain quality.

[0007] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a multi-objective dynamic equilibrium optimization method for a combine harvester threshing system, including: A multi-objective optimization model is constructed with rotor speed and concave plate clearance as control variables and minimizing the combined threshing loss, separation loss and grain breakage rate as the objective. Based on the multi-objective optimization model, the rotor speed and concave plate clearance are optimized and solved to obtain the optimal rotor speed and optimal concave plate clearance. Based on the optimal rotor speed and the optimal concave plate clearance, adjust the rotor speed and concave plate clearance during the operation of the combine harvester threshing system.

[0008] Secondly, this application provides a multi-objective dynamic balance optimization device for a combine harvester threshing system, comprising: The model building module is used to construct a multi-objective optimization model with rotor speed and concave plate gap as control variables and the goal of minimizing the combined threshing loss, separation loss and grain breakage rate. The multi-objective optimization module is used to optimize the rotor speed and concave plate clearance based on the multi-objective optimization model to obtain the optimal rotor speed and optimal concave plate clearance. A multi-target adjustment module is used to adjust the rotor speed and the concave plate gap during the operation of the combine harvester threshing system based on the optimal rotor speed and the optimal concave plate gap.

[0009] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described multi-objective dynamic balance optimization method for a combine harvester threshing system.

[0010] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described multi-objective dynamic balance optimization method for a combine harvester threshing system.

[0011] According to the specific embodiments provided in this application, this application achieves the following technical effects: By constructing a multi-objective optimization model that jointly minimizes threshing loss, separation loss, and grain breakage rate using rotor speed and concave plate clearance as key control variables, it realizes accurate solution and dynamic adjustment of the operating parameters of the threshing system. This method breaks through the problem of neglecting one aspect for another caused by traditional single-objective optimization, and can quickly match the optimal rotor speed and concave plate clearance during operation, effectively achieving a synergistic reduction of the three core loss indicators, and significantly improving grain recovery rate and grain quality. At the same time, the dynamic parameter adjustment mode can adapt to changes in different crop varieties, moisture content, and field conditions, enhancing the operational stability and adaptability of the threshing system, reducing mechanical wear and energy consumption caused by unreasonable parameters, and ultimately improving the overall operating efficiency of the combine harvester. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1This is a flowchart illustrating a multi-objective dynamic balance optimization method for a combine harvester threshing system, provided as an embodiment of this application.

[0014] Figure 2 This is a schematic diagram of the functional modules of a multi-objective dynamic balance optimization device for a combine harvester threshing system, provided in an embodiment of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0016] This application achieves real-time observation of key parameters of the threshing system (such as separation loss and grain breakage rate) through state estimation, and adjusts the actuator (rotor speed and concave plate clearance) by combining optimization algorithms to balance the conflicting objectives of "minimizing separation loss" and "controlling grain breakage rate".

[0017] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] In one exemplary embodiment, such as Figure 1 As shown, a multi-objective dynamic balance optimization method for a combine harvester threshing system is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the multi-objective dynamic balance optimization method for a combine harvester threshing system includes the following steps 101 to 103.

[0019] Step 101: Using rotor speed and concave plate gap as control variables, and aiming to minimize the combined threshing loss, separation loss and grain breakage rate, construct a multi-objective optimization model.

[0020] Among them, threshing loss refers to the flow rate of unthreshed grains discharged with straw, in ton / h, and separation loss refers to the amount of grains remaining with straw after threshing, in ton / h.

[0021] The optimization objective of this application is to minimize separation loss while ensuring that the grain breakage rate is ≤ a threshold (e.g., wheat ≤ 2%), and to adapt to dynamic disturbances in the field. It adopts a material flow steady-state module + dynamic prediction module to take into account the material flow relationship and dynamic delay characteristics (e.g., material transport delay) of the threshing system.

[0022] Steady-state characteristics can be fitted using low-order polynomials to describe the nonlinear relationship between inputs such as rotor speed and concave clearance, and are used to predict steady-state indicators such as threshing loss and grain breakage. Through multi-layer perceptron training, a complex nonlinear mapping between input and output is fitted. The input layer consists of threshing parameters (operating speed, drum speed, etc.), and the output layer is the dynamic response or breakage rate. Optimization is achieved by balancing performance parameters through an objective function and combining experimental data for modeling. The optimization objectives for rotor speed and concave clearance are to balance separation loss and grain breakage, while also considering the impact of total feed rate, ultimately maximizing harvest benefits.

[0023] The objective function of the multi-objective optimization model is: .

[0024] in, To optimize the objective function value of the multi-objective optimization model, The objective function is dominated by rotor speed. The objective function, adapted by the gap between the concave plates, varies with the rotor speed. Let λ be the weighting coefficient for the gap between the concave plates, λ∈[0.2,0.3]. This is the direct coupling term between rotor speed and concave plate clearance, where γ is the coupling coefficient, calibrated offline. For example, for wheat, γ = 10−4% / (RPM·mm). The rotor speed is d p The distance between the concave plates is in mm.

[0025] The gradient distribution of the multi-objective optimization model reflects "rotation speed as the primary factor and clearance as the secondary factor": Rotational speed gradient magnitude: (Significant impact).

[0026] Gap gradient magnitude: (The impact is slight and depends on the rotational speed.)

[0027] Cross gradient: This indicates that the gradient direction of the clearance changes with the rotational speed; for example, increasing the clearance at higher rotational speeds yields greater benefits.

[0028] In a specific application example, the objective function dominated by rotor speed is: .

[0029] in, The objective function is dominated by rotor speed. The optimized weighting for grain crushing is set by the operator via an interface. The optimization weights for separating losses are set by the operator through the interface. The model showing the relationship between rotor speed and grain crushing was obtained by fitting experimental data. The model showing the relationship between rotor speed and separation loss was obtained by fitting experimental data and exhibits a nonlinear negative correlation.

[0030] Specifically, based on laboratory and field test data, a multinomial regression model was used to fit the relationship between rotor speed and separation loss: For the high-speed range, the relationship between rotor speed and separation loss is expressed in exponential form: .in, , , , These are the coefficients of the polynomial regression. n The number of polynomials, a , b , c It is the exponential coefficient.

[0031] The data collection process for laboratory and field trials is shown in Table 1.

[0032] Table 1

[0033] The constraints for rotor speed optimization include: Nonnegativity constraint: .

[0034] Boundary constraints: ;in, This represents the minimum rotor speed, taken as 650 RPM. This represents the maximum rotor speed, which is 950 RPM.

[0035] Monotonicity constraint: .

[0036] In a specific application example, the objective function for optimizing the concave clearance focuses on minimizing the overall loss (separation loss + threshing loss + grain damage), while simultaneously constraining net grain flow rate and equipment load. By balancing performance priorities under different operating conditions through weighting, the goal is to ultimately achieve synergistic optimization of harvesting efficiency and grain quality. Specifically, the optimization objective of the concave clearance is to balance the three core indicators of threshing loss, separation loss, and grain damage within mechanical constraints, while ensuring safe equipment operation. Its objective function is: .

[0037] in, The objective function is the one that adapts to the gap between the concave plates. This refers to the threshing loss. For separation loss, For grain breakage rate, The operator sets the optimal weights for threshing loss. This refers to the total material feed rate, expressed in tons per hour (tons / hour). For clean grain quantity, For clean grain throughput.

[0038] The constraints for optimizing the concave plate clearance include: Mechanical safety constraints: This indicates the physical adjustment range of the gap between the concave plates, typically 4~25mm, depending on the crop type; among which, This represents the minimum value of the gap between the concave plates. This represents the maximum value of the gap between the concave plates.

[0039] Load constraints: This is to avoid crop blockage and excessive load caused by excessively small gaps. This is a model showing the relationship between the gap between the concave plates and the system load, in kW. This represents the maximum permissible load power (mechanical constraint) of the threshing system, in kW.

[0040] Nonnegativity constraint: .

[0041] Soft constraints: ;in, This is the upper limit for grain breakage rate, determined by crop type, such as ≤3% for soybeans.

[0042] Physical constraints (combining material flow conservation and experimental laws): ; ; .in, The model input vector (including total feed amount, etc.) This is a static relationship model between the gap between the concave plates and the threshing loss, in units of %.

[0043] threshing loss item Using the "theoretically threshed total amount of grain" as the benchmark, the separation loss item is based on "net grain + total separation loss" to eliminate the impact of feed fluctuations on the assessment. , , The operator sets the settings according to the intended use of the crop (e.g., for hard-threshing crops, the setting needs to be increased). To reduce threshing losses, edible grains need to improve... (Reduce damage). The core relationship between the concave plate gap and the indicators is "reduced gap → reduced threshing loss, but increased separation loss, grain damage, and equipment load". The objective function achieves a global optimal balance of conflicting indicators through convexity design.

[0044] This application sets different weights for different application scenarios: (1) High humidity crops (such as wheat after rain): Increase The gap between the concave plates should be appropriately widened to prevent straw from sticking together and causing a surge in separation losses.

[0045] (2) Low humidity can easily damage crops (such as soybeans): increase This increases the gap between the concave plates, reducing impact damage between the grains and the concave plates and rotor.

[0046] (3) High-yield, densely planted crops (such as corn): Increase To reduce the gap between the concave plates and ensure thorough threshing, while strictly adhering to load constraints to avoid clogging.

[0047] Furthermore, due to the coupling effect between rotor speed and concave plate clearance (e.g., rotor speed dominates separation loss and grain breakage, while concave plate clearance has a secondary effect), the optimization process must satisfy the following: .

[0048] In a specific application example, the constraints of the multi-objective optimization model include: principal variable priority constraints, coupling adaptation constraints, coupling loss constraints, and mechanical coupling constraints.

[0049] (1) The principal variable priority constraint is used to limit the optimization priority of the rotor speed to be higher than that of the concave plate clearance, and its expression is: This indicates that the overall loss reduction caused by speed adjustment is at least five times that of clearance adjustment. During optimization, the concave plate clearance is first locked as the initial value. After achieving the global optimal solution for the rotor speed, the concave plate clearance is then adjusted based on the optimal rotor speed. This is the adjustment step size for the rotor speed. The adjustment step size for the gap between the concave plates, The priority for optimizing rotor speed. The priority for optimizing the gap between the concave plates.

[0050] (2) The coupling adaptation constraint is used to limit the adjustment range of the concave plate gap to dynamically shrink with the optimal rotor speed, so as to avoid the secondary variable from interfering with the effect of the main variable. Its expression is: .in, To achieve the optimal rotor speed, The reference value for the gap between the concave plates is determined offline by the coupled model, such as wheat. k =0.02mm / RPM b =2mm, , k The slope b The intercept is... To accommodate the adjustment range, Δd∈[1,3]mm.

[0051] (3) The coupling loss constraint is used to limit the combined adjustment of rotor speed and concave plate clearance to ensure that the comprehensive loss is lower than the loss of single rotor speed optimization. Its expression is: .in, To achieve the optimal concave plate clearance, This represents the coupling loss corresponding to the optimal rotor speed and the optimal concave plate clearance. The coupling loss corresponding to the optimal rotor speed and the initial concave plate clearance. This represents the initial gap between the concave plates.

[0052] (4) The mechanical coupling constraint is used to limit the combined adjustment of rotor speed and concave plate clearance to meet the upper limit of the load off the system and the coupled load is lower than the set threshold. Its expression is: .in, The load is adjusted by a combination of rotor speed and concave plate clearance. For loads where rotor speed is the primary factor, The load is adapted to the gap between the concave plates. The load coupling coefficient is... For coupled load terms, the smaller the gap between the concave plates and the higher the rotor speed, the more obvious the surge in load coupling.

[0053] Step 102: Based on the multi-objective optimization model, optimize the rotor speed and concave plate clearance to obtain the optimal rotor speed and optimal concave plate clearance.

[0054] In a specific application example, this application addresses the coupled characteristic of "rotor speed dominating grain loss / crushing, with concave plate gap having a secondary influence." Optimization must follow the principle of "primary variable priority + secondary variable adaptation + coupling constraints." Through a hierarchical optimization framework combined with a gradient decoupling strategy, the core dominant role of rotational speed is preserved, while allowing the gap to adapt to speed adjustments, ultimately minimizing the overall loss. Step 102 includes steps 21 to 22.

[0055] Step 21: Fix the gap between the concave plates to a reference value, and optimize the rotor speed based on the multi-objective optimization model to obtain the optimal rotor speed.

[0056] Specifically, a gradient-guided adaptive trust region method is adopted, locking the concave plate gap as the baseline value and optimizing only the rotor speed. First, the core gradient of the rotor speed is calculated. Then, the optimal rotor speed is solved iteratively. (Saving speed limits and load constraints); Record the core losses at the optimal speed. .

[0057] Step 22: Fix the rotor speed to the optimal rotor speed, and perform adaptation optimization on the concave plate gap based on the multi-objective optimization model to obtain the optimal concave plate gap.

[0058] Specifically, the rotor speed is fixed based on the optimal rotor speed, and the concave plate clearance is optimized only within the fit range. First, the coupling gradient is calculated. (Containing only the gap univariate gradient to eliminate speed interference), then using the gradient interpolation piecewise linear optimization method within the fitting interval. The gap between the concave plates can be solved quickly internally.

[0059] Verify coupling loss constraints: If Then take .

[0060] Further coupling verification and fine-tuning were performed. Cross gradients were calculated. If the absolute value of the cross gradient is greater than the threshold (e.g., 10), -5 If the value is % / (RPM·mm), then the rotor speed is slightly adjusted (±5RPM), the overall loss is recalculated, and the minimum value is taken as the final solution.

[0061] The optimization results under the coupling effect in the wheat harvesting scenario are shown in Table 2.

[0062] Table 2

[0063] Furthermore, the process of coupling verification and fine-tuning includes: (1) offline calibration through orthogonal experiments (different speed + clearance combinations). , , , Stored by crop type (wheat / barley / corn); (2) Calculate only the single-variable gradient online (first rotation speed, then gap), avoiding high-dimensional gradient calculation and reducing computing power consumption; (3) If the coupled gradient is abnormal (such as sensor failure), it automatically degenerates to only rotation speed optimization to ensure basic performance. The total time for two-step optimization is ≤200ms (rotation speed iteration ≤5 times + gap lookup table ≤1ms), which is suitable for the 1s control cycle of the harvester.

[0064] Step 103: Adjust the rotor speed and concave plate clearance during the operation of the combine harvester threshing system based on the optimal rotor speed and the optimal concave plate clearance.

[0065] In another exemplary embodiment, an online real-time optimization process is also provided, taking into account dynamic transmission delays in the threshing process (such as material transport delays), coping with complex field conditions (such as sudden changes in crop density), supporting high-precision threshing loss and grain breakage balance control, and handling disturbances such as grain moisture and crop type.

[0066] The delays in the threshing system include sensor data transmission delay, model calculation delay, and actuator response delay. These delays are compensated for through targeted design.

[0067] (1) Adopting an “edge computing + local caching” architecture: edge terminals process sensor data locally (filtering, filling) to reduce cloud transmission delay, and only upload the processed data to the fog server.

[0068] (2) Predictive prediction by time series model: The GRU-CNN model predicts the grain breakage rate in the next 5 seconds based on historical 30-second data. It captures the time dependency through the gating unit to offset the control lag caused by the perception delay.

[0069] (3) The model parameters are updated online based on the particle swarm algorithm with decreasing inertial weights to compensate for the model mismatch caused by the response delay of the actuator.

[0070] In addition, when field disturbance causes separation loss to exceed the threshold, the optimization algorithm is triggered to recalculate the rotor speed, with an adjustment range of ≤50 RPM / time (to avoid frequent actuator operation).

[0071] This application adopts a three-level architecture of "dynamic prediction + hierarchical optimization + robust correction". It uses a delay compensation prediction model to offset material transportation delays, a disturbance observer to suppress interference such as humidity / crop type, and gradient acceleration hierarchical optimization to achieve real-time parameter adjustment. It takes into account the dynamic operating condition response speed and optimization accuracy, and supports high-precision balance control of threshing loss and grain damage.

[0072] The model validation and correction process includes: using k-fold cross-validation (k=5) to evaluate the model's generalization ability and removing outlier samples (such as sensor anomalies caused by material agglomeration in the field). Model parameters are adjusted for different crops (such as corn and wheat). For example, corn's separation loss is more sensitive to the concave gap, requiring an increase in the weight of higher-order terms related to the concave gap.

[0073] In summary, this application constructs a multi-objective optimization model that jointly minimizes threshing loss, separation loss, and grain breakage rate, using rotor speed and concave plate clearance as key control variables. This model achieves precise solution and dynamic adjustment of the threshing system's operating parameters. This method overcomes the problem of neglecting one aspect for another caused by traditional single-objective optimization. It can quickly match the optimal rotor speed and concave plate clearance during operation, effectively reducing the three core loss indicators synergistically, and significantly improving grain recovery rate and grain quality. Simultaneously, the dynamic parameter adjustment mode can adapt to changes in different crop varieties, moisture content, and field conditions, enhancing the stability and adaptability of the threshing system, reducing mechanical wear and energy consumption caused by unreasonable parameters, and ultimately improving the overall operating efficiency of the combine harvester.

[0074] Based on the same inventive concept, this application also provides an apparatus for implementing the method described above. The solution provided by this apparatus is similar to the solution described in the above method; therefore, specific limitations in one or more apparatus embodiments provided below can be found in the limitations of the method described above, and will not be repeated here.

[0075] In one exemplary embodiment, such as Figure 2 As shown, a multi-objective dynamic balance optimization device for a combine harvester threshing system is provided, which includes the following functional modules.

[0076] The model building module 201 is used to construct a multi-objective optimization model with rotor speed and concave plate gap as control variables and the objective of minimizing the combined threshing loss, separation loss and grain breakage rate.

[0077] The multi-objective optimization module 202 is used to optimize the rotor speed and concave plate clearance based on the multi-objective optimization model to obtain the optimal rotor speed and optimal concave plate clearance.

[0078] The multi-target adjustment module 203 is used to adjust the rotor speed and the concave plate gap during the operation of the combine harvester threshing system according to the optimal rotor speed and the optimal concave plate gap.

[0079] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0080] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0081] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0082] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0083] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0084] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0085] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A multi-objective dynamic equilibrium optimization method for a combine harvester threshing system, characterized in that, The method includes: A multi-objective optimization model is constructed with rotor speed and concave plate clearance as control variables and minimizing the combined threshing loss, separation loss and grain breakage rate as the objective. Based on the multi-objective optimization model, the rotor speed and concave plate clearance are optimized and solved to obtain the optimal rotor speed and optimal concave plate clearance. Based on the optimal rotor speed and the optimal concave plate clearance, adjust the rotor speed and concave plate clearance during the operation of the combine harvester threshing system.

2. The multi-objective dynamic balance optimization method for a combine harvester threshing system according to claim 1, characterized in that, The objective function of the multi-objective optimization model is: ； in, To optimize the objective function value of the multi-objective optimization model, The objective function is dominated by rotor speed. The objective function, adapted by the gap between the concave plates, varies with the rotor speed. This is the weighting coefficient for the gap between the concave plates. This is the direct coupling term between rotor speed and concave plate clearance, where γ is the coupling coefficient. The rotor speed is d p This refers to the gap between the concave plates.

3. The multi-objective dynamic balance optimization method for a combine harvester threshing system according to claim 2, characterized in that, The objective function dominated by rotor speed is: ； in, The objective function is dominated by rotor speed. Optimize the weights for grain crushing. To optimize the weights for separation loss, A model showing the relationship between rotor speed and grain crushing. This is a model showing the relationship between rotor speed and separation loss.

4. The multi-objective dynamic balance optimization method for a combine harvester threshing system according to claim 2, characterized in that, The objective function for adapting the gap between the concave plates is: ； in, The objective function is the one that adapts to the gap between the concave plates. This refers to the threshing loss. For separation loss, For grain breakage rate, For the optimization weight of threshing loss, This represents the total amount of material fed in. For clean grain quantity, For clean grain throughput.

5. The multi-objective dynamic balance optimization method for a combine harvester threshing system according to claim 1, characterized in that, The constraints of the multi-objective optimization model include: principal variable priority constraints, coupling adaptation constraints, coupling loss constraints, and mechanical coupling constraints. The main variable priority constraint is used to limit the optimization priority of the rotor speed to be higher than that of the concave plate gap; The coupling adaptation constraint is used to limit the adjustment range of the concave plate gap to dynamically shrink as the optimal rotor speed increases. The coupling loss constraint is used to limit the combined adjustment of rotor speed and concave plate clearance to ensure that the overall loss is lower than the loss of single rotor speed optimization. The mechanical coupling constraint is used to limit the combined adjustment of rotor speed and concave plate clearance to meet the upper limit of the load detached from the system, and the coupled load is lower than the set threshold.

6. The multi-objective dynamic equilibrium optimization method for a combine harvester threshing system according to claim 5, characterized in that, The priority constraint of the main variable is: ; The coupling adaptation constraint is: ; The coupling loss constraint is: ; The mechanical coupling constraint is: ; in, This is the adjustment step size for the rotor speed. The adjustment step size for the gap between the concave plates, The priority for optimizing rotor speed. Priority for optimizing the gap between the concave plates. To achieve the optimal rotor speed, To achieve the optimal concave plate clearance, This is the reference value for the gap between the concave plates. , k The slope b The intercept is... To accommodate the adjustment range, This represents the coupling loss corresponding to the optimal rotor speed and the optimal concave plate clearance. The coupling loss corresponding to the optimal rotor speed and the initial concave plate clearance. The initial gap between the concave plates. The load is adjusted by a combination of rotor speed and concave plate clearance. For loads where rotor speed is the primary factor, The load is adapted to the gap between the concave plates. The load coupling coefficient is... This represents the maximum allowable load power of the threshing system. The rotor speed is d p This refers to the gap between the concave plates.

7. The multi-objective dynamic equilibrium optimization method for a combine harvester threshing system according to claim 1, characterized in that, Based on the aforementioned multi-objective optimization model, the rotor speed and concave plate clearance are optimized to obtain the optimal rotor speed and optimal concave plate clearance, including: The gap between the concave plates is fixed as a reference value, and the rotor speed is optimized based on the multi-objective optimization model to obtain the optimal rotor speed. The rotor speed is fixed at the optimal rotor speed, and the concave plate gap is adapted and optimized based on the multi-objective optimization model to obtain the optimal concave plate gap.

8. A multi-objective dynamic balance optimization device for a combine harvester threshing system, characterized in that, The apparatus performs the multi-objective dynamic balance optimization method for the combine harvester threshing system according to any one of claims 1-7, and the apparatus comprises: The model building module is used to construct a multi-objective optimization model with rotor speed and concave plate gap as control variables and the goal of minimizing the combined threshing loss, separation loss and grain breakage rate. The multi-objective optimization module is used to optimize the rotor speed and concave plate clearance based on the multi-objective optimization model to obtain the optimal rotor speed and optimal concave plate clearance. A multi-target adjustment module is used to adjust the rotor speed and the concave plate gap during the operation of the combine harvester threshing system based on the optimal rotor speed and the optimal concave plate gap.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the multi-objective dynamic equilibrium optimization method for a combine harvester threshing system according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the multi-objective dynamic equilibrium optimization method for the combine harvester threshing system as described in any one of claims 1-7.

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