Intelligent agricultural machine control optimization method and system based on mathematical decoupling and deep learning

Abstract

The invention discloses an intelligent agricultural machine control optimization method and system based on mathematical decoupling and deep learning. The method comprises the following steps: constructing a simulation model of an intelligent agricultural machine; collecting field operation data of a real vehicle; based on the simulation model, limiting the excitation source to participate in simulation analysis by changing parameters to obtain simulation data; performing qualitative analysis on the influence of each excitation source on the operation quality based on the simulation data to obtain a strong unfavorable excitation source; performing quantitative analysis by using real vehicle field operation data, and establishing a nonlinear mapping model; based on the nonlinear mapping model, obtaining system characteristics corresponding to the agricultural implement in a real working environment; and optimizing control parameters of the intelligent agricultural machine based on the system characteristics and the determined strong unfavorable excitation source. According to the method, a strategy making method combining theoretical simulation and processing of real vehicle collection data is adopted, and the rationality of intelligent agricultural machine control optimization can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic operation of intelligent agricultural machinery, and in particular to an intelligent agricultural machinery control optimization method and system based on mathematical decoupling and deep learning. Background Art

[0002] As smart agricultural machinery integrates more and more functions, such as rotary tillage, sowing, fertilization, etc., these devices usually use a variety of drive components, including traditional mechanical transmission systems and modern motors and hydraulic systems. Although this multifunctional integration improves work efficiency, it also brings new challenges. In actual operation, when the tractor needs to switch from one working state to another, due to the differences in response speed and characteristics between different actuators, it is easy to have operational inconsistencies, such as sudden acceleration or deceleration of the vehicle, and impact when shifting gears. These problems directly affect the driver's comfort experience. Especially in complex and changeable field conditions, this effect is particularly obvious when the speed and direction are frequently adjusted to adapt to different soil conditions or crop requirements. In addition, being in such a working environment for a long time not only increases the driver's physical fatigue, but may also reduce overall work efficiency. Therefore, in the design of modern smart agricultural machinery, how to improve the tractor's operating smoothness and reduce the discomfort caused by function switching by optimizing the control system has become the key to improving user experience.

[0003] In the prior art, the applicant's prior application CN 116476842A discloses a startup control method for intelligent agricultural machinery to improve driving comfort, but the method of the patent relies on a specially designed transmission system and has a high cost. In other prior arts, control strategies are generally formulated by establishing a simulation model of agricultural machinery for simulation. For example, patent CN103048148A discloses a semi-physical simulation test platform for a high-power tractor electro-hydraulic suspension system, which is used for the research and development, production and performance testing of key components of high-power tractors through simulation. This method lacks comprehensive consideration of the complex factors that may be encountered in actual use. Summary of the invention

[0004] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides an intelligent agricultural machinery control optimization method and system based on mathematical decoupling and deep learning.

[0005] Technical solution: To achieve the above purpose, the intelligent agricultural machinery control optimization method based on mathematical decoupling and deep learning of the present invention comprises:

[0006] Construct a simulation model of intelligent agricultural machinery to describe the relationship between various excitation sources and transmission systems in intelligent agricultural machinery;

[0007] Collect real vehicle field operation data;

[0008] Based on the simulation model, limiting the excitation source from participating in the simulation analysis by changing parameters to obtain simulation data;

[0009] Based on the simulation data, a qualitative analysis is performed on the influence of each of the excitation sources on the operation quality to obtain a strong adverse excitation source;

[0010] Use the actual vehicle field operation data for quantitative analysis and establish a nonlinear mapping model;

[0011] Based on the nonlinear mapping model, the system characteristics corresponding to the agricultural implement in the real working environment are obtained; the system characteristics include: the dynamic torque response speed of each working system and the influence relationship between them, and the mapping relationship between the control and delayed response characteristics and the power output and the engine working point;

[0012] Based on the system characteristics and the determined strong adverse excitation source, the control parameters of the intelligent agricultural machinery are optimized.

[0013] Furthermore, the construction of the simulation model of the intelligent agricultural machinery specifically includes:

[0014] Establish engine model, hydraulic system model and PTO load model respectively;

[0015] The engine model, the hydraulic system model and the PTO load model are coupled according to the torque balance equation based on the transmission system to form a comprehensive power transmission system model of mechanical-electrical-hydraulic multi-field coupling;

[0016] An environmental factor model applied to the transmission system model is established to obtain the complete simulation model.

[0017] Furthermore, based on the simulation model, the simulation data is obtained by changing parameters to limit one or more excitations to participate in the simulation analysis, including:

[0018] Based on the simulation model, simulation analysis is performed on each of the excitation sources acting alone in turn, and the dynamic response characteristics of each of the excitation sources acting independently on the transmission system under different working conditions are evaluated to obtain decoupling data;

[0019] Carry out data acquisition of multi-channel excitation coupling to obtain coupling data.

[0020] Furthermore, the analysis of the influence of each of the excitation sources on the work quality based on the simulation data to obtain a strong unfavorable excitation source includes the following steps:

[0021] Based on the decoupling data and the coupling data, a sensitivity analysis is performed on the influence of each excitation source on the dynamic characteristics of the output torque of the transmission system;

[0022] Based on the sensitivity analysis results, calculate the impact factor of each excitation source;

[0023] Arrange all motivation sources in descending order of their impact factors to form a priority list;

[0024] Constructing a multidimensional feature vector corresponding to the excitation source according to the influencing factors corresponding to the excitation source and specific torque fluctuation and response delay time indicators;

[0025] Clustering is performed based on the multi-dimensional feature vector, and the strong adverse incentive source is determined by analyzing the clustering result.

[0026] Furthermore, the quantitative analysis using the actual vehicle field operation data to establish a nonlinear mapping model includes:

[0027] Use variable parameter One-Class-SVM algorithm to clean the field operation data of real vehicles and remove unreasonable outliers;

[0028] Organize the cleaned data into a time series database;

[0029] Use LSTM algorithm to train time series data and establish nonlinear mapping model;

[0030] Tune model parameters using the validation set.

[0031] Furthermore, the control parameters of the intelligent agricultural machinery are optimized based on the system characteristics and the determined strong adverse incentive source, including:

[0032] For each identified strong adverse excitation source, the control parameters are adjusted using a feedforward compensation algorithm according to its impact under different operating conditions;

[0033] Combined with the dynamic torque response speed in the system characteristics, the model predictive control algorithm is used to optimize the throttle opening θ;

[0034] According to the influence relationship between the dynamic torques of various working systems in the system characteristics, an adaptive shifting logic algorithm is used to adjust the gearbox gear g.

[0035] A delay compensation mechanism is used to offset the known response delay through the time lag term τ;

[0036] Through real-time monitoring and iterative learning control algorithms, control parameters are continuously adjusted to improve operating efficiency and driving comfort.

[0037] The present invention also provides an intelligent agricultural machinery control optimization system based on mathematical decoupling and deep learning, which includes:

[0038] A construction module, which is used to construct a simulation model of the intelligent agricultural machinery and to describe the relationship between each excitation source and the transmission system in the intelligent agricultural machinery;

[0039] A collection module, which is used to collect field operation data of real vehicles;

[0040] A simulation module, which is used to obtain simulation data by changing parameters to limit the excitation source from participating in simulation analysis based on the simulation model;

[0041] A first analysis module, which is used to perform a qualitative analysis on the influence of each of the excitation sources on the operation quality based on the simulation data to obtain a strong adverse excitation source;

[0042] The second analysis module is used to perform quantitative analysis using actual vehicle field operation data and establish a nonlinear mapping model;

[0043] An application module, which is used to obtain system characteristics corresponding to agricultural machinery in a real working environment based on the nonlinear mapping model; the system characteristics include: dynamic torque response speed of each working system and the influence relationship between them, and the mapping relationship between control and delayed response characteristics and power output and engine working point;

[0044] An optimization control module is used to optimize the control parameters of the intelligent agricultural machinery based on the system characteristics and the determined strong adverse excitation source.

[0045] Beneficial effects: The intelligent agricultural machinery control optimization method and system based on mathematical decoupling and deep learning of the present invention adopts a strategy formulation method that combines theoretical simulation with processing of data collected from actual vehicles. On the one hand, it simulates based on the theoretical structural composition of agricultural machinery and qualitatively analyzes to determine the strong adverse excitation sources that have a greater impact on driving comfort. On the other hand, it performs quantitative analysis based on the data collected from actual vehicle operations to obtain the system characteristics under the real operating environment, which can make up for the limitations of the simulation model. Targeted control of intelligent agricultural machinery based on strong adverse excitation sources and system characteristics can effectively improve the rationality of the control of intelligent agricultural machinery during operation and improve driving comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of the intelligent agricultural machinery control optimization method based on mathematical decoupling and deep learning;

[0047] Figure 2 This is a schematic diagram of the intelligent agricultural machinery data collection system;

[0048] Figure 3 Schematic diagram of the structure of the intelligent agricultural machinery control optimization device based on mathematical decoupling and deep learning. DETAILED DESCRIPTION

[0049] The present invention will be further described below in conjunction with the accompanying drawings.

[0050] like Figure 1 As shown, the intelligent agricultural machinery control optimization method based on mathematical decoupling and deep learning of the present invention includes the following steps S101-S107:

[0051] Step S101, constructing a simulation model of an intelligent agricultural machine to describe the relationship between various excitation sources and a transmission system in the intelligent agricultural machine;

[0052] In this step, the simulation model is constructed based on the mechanical structure of the intelligent agricultural machinery. The simulation model describes the connection between each component of the intelligent agricultural machinery and the transmission system. Each component can generate an excitation that acts on the transmission system, that is, it serves as an excitation source. The connection between each part and the transmission system includes a mechanical transmission relationship, a hydraulic coupling relationship, an electrical connection relationship, and a traction / suspension connection relationship (the component is towed on the rear side of the tractor, or can be lifted and lowered); the transmission system includes an engine and a gearbox. The excitation source includes components that can affect the engine harmonic torque, hydraulic working dynamic torque and PTO working torque, such as: a sowing mechanism, a fertilizing mechanism, a workbench hydraulic lifting mechanism, a rotary tillage mechanism, a fan mechanism, etc. The state switching of some components can instantly increase the load on the transmission system and have a significant impact on driving comfort, while some components have little impact on driving comfort. The parameter data range corresponding to the above components can be obtained by analyzing the field operation data of the actual vehicle. For example, the hydraulic working dynamic torque can be measured when the workbench is in the lifting state and the lowering state.

[0053] Step S102, collecting actual vehicle field operation data;

[0054] In this step, if Figure 2 As shown in the figure, torque sensors are installed on the front and rear drive shafts and PTO shafts of the tractor in the intelligent agricultural machinery, and a sound level meter is installed on the gearbox housing. A multi-channel data acquisition system is used to collect torque and noise data; D2P is used to quickly develop a prototype to read the real-time parameters of the engine, throttle position, gear position, and hydraulic system in the agricultural machinery CAN bus; all data are aggregated and unified in time scale through D2P, and uploaded to the host computer through Kvaser for recording and storage. Among them, a sound level meter is used to collect noise spectrum for auxiliary analysis.

[0055] Step S103, based on the simulation model, limiting the excitation source from participating in simulation analysis by changing parameters to obtain simulation data;

[0056] In this step, simulation data is collected from the simulation model. Simulation data is output data obtained from a specific position of the simulation model after inputting the input data corresponding to the excitation source involved in the simulation. These output data can be used as indicators to measure the quality of intelligent agricultural machinery operations. In this embodiment, the torque fluctuation and response delay time of the PST output shaft are used as the above indicators. The torque fluctuation can effectively reflect the indicators that affect driving comfort such as jitter, setbacks, and noise during the operation of intelligent agricultural machinery. The response delay can reflect the time delay between the input data and the response of the indicator;

[0057] Step S104, based on the simulation data, a qualitative analysis is performed on the influence of each of the excitation sources on the work quality to obtain a strong adverse excitation source; here, a strong adverse excitation source refers to an excitation source that can have a significant influence on driving comfort.

[0058] In this step, mathematical modeling and simulation analysis methods are used to qualitatively find the dynamic response law of the transmission system torque under coupled excitation. Because it is based on a mathematical model, many system parameters are assumed and it is difficult to be accurate, so the results cannot be completely consistent with reality, but the regular trend can be used as a reference, so it is a qualitative analysis, and the strong adverse excitation source determined based on this qualitative analysis is of great reference significance.

[0059] Step S105, using the actual vehicle field operation data to perform quantitative analysis and establish a nonlinear mapping model;

[0060] Step S106, based on the nonlinear mapping model, obtain the system characteristics corresponding to the agricultural implement under the real working environment; the system characteristics include: the dynamic torque response speed of each working system and the influence relationship between them, and the mapping relationship between the control and delayed response characteristics and the power output and the engine working point; the above-mentioned working system dynamic torque includes PST output shaft torque, PTO torque, engine torque, hydraulic pump shaft torque;

[0061] Step S107, based on the system characteristics and the determined strong adverse excitation source, the control parameters of the intelligent agricultural machinery are optimized, and the control parameters include throttle opening, gearbox gear and delay compensation.

[0062] The above-mentioned intelligent agricultural machinery automatic operation strategy optimization method adopts a strategy formulation method that combines theoretical simulation with processing of data collected from real vehicles. On the one hand, it simulates based on the theoretical structure of agricultural machinery and qualitatively analyzes to determine the strong adverse excitation sources that have a greater impact on driving comfort. On the other hand, it performs quantitative analysis based on the data collected from real vehicle operations to obtain the system characteristics under the real operating environment, which can make up for the limitations of the simulation model. Targeted control of intelligent agricultural machinery based on strong adverse excitation sources and system characteristics can effectively improve the rationality of intelligent agricultural machinery control optimization and improve driving comfort.

[0063] The construction of the simulation model of the intelligent agricultural machinery in the above step S101 specifically includes the following steps S201-S202:

[0064] Step S201, respectively establishing an engine model, a hydraulic system model and a PTO load model;

[0065] Step S202, coupling the engine model, the hydraulic system model and the PTO load model according to the torque balance equation based on the transmission system to form a comprehensive power transmission system model of mechanical-electrical-hydraulic multi-field coupling;

[0066] Step S203, establish an environmental factor model applied to the transmission system model to obtain the complete simulation model. The environmental factor model includes soil resistance, wind resistance, ground inclination and slope, etc.

[0067] In the above simulation system, the harmonic torque output by the engine model is transmitted to the input shaft of the transmission system through coupling, simulating the excitation effect of the engine on the transmission system; the hydraulic pump in the hydraulic system model is connected to the transmission system through a hydraulic pipeline, forming a dynamic coupling relationship between the hydraulic pump torque and the transmission system; the output torque of the PTO model is directly transmitted to the PTO shaft of the transmission system, and the PTO shaft in the coupling model rotates synchronously with other output shafts of the transmission system, simulating the working conditions of various working tools under the action of PTO.

[0068] In one embodiment, the simulation data includes decoupling data obtained by each of the excitation sources participating in the simulation individually and coupling data obtained by multiple excitation sources participating in the simulation; the above step S103, based on the simulation model, obtains the simulation data by changing parameters to limit one or more excitations to participate in the simulation analysis, including the following steps S301-S302:

[0069] Step S301, performing simulation analysis on each of the excitation sources acting alone based on the simulation model, evaluating the dynamic response characteristics of each of the excitation sources acting independently on the transmission system under different working conditions, and obtaining decoupling data;

[0070] Step S302, collect data of multi-channel excitation coupling to obtain coupling data.

[0071] In another embodiment, the simulation data includes decoupling data obtained by each of the excitation sources participating in the simulation individually; in addition, coupling data is obtained by analyzing actual vehicle operation data.

[0072] The above step S104 analyzes the influence of each excitation source on the work quality based on the simulation data to obtain a strong unfavorable excitation source, including the following steps S401-S405:

[0073] Step S401, based on the decoupling data and the coupling data, a sensitivity analysis is performed on the influence of each excitation source on the dynamic characteristics of the output torque of the transmission system to evaluate the influence of each excitation source on the torque fluctuation and response delay time of the PST output shaft under different working conditions; this process identifies which excitation sources have a significant influence on the torque fluctuation and response delay time under different working conditions;

[0074] Step S402, based on the sensitivity analysis result, calculating the influence factor of each excitation source, where the influence factor reflects the contribution of the excitation source to the change of the output characteristic of the transmission system;

[0075] Step S403, arranging all the excitation sources in descending order according to their influence factors to form a priority list, wherein the excitation source with a larger influence factor indicates a greater influence on the transmission system;

[0076] Step S404, constructing a multidimensional feature vector corresponding to the excitation source according to the influencing factors corresponding to the excitation source and the specific torque fluctuation and response delay time indicators; the feature vector includes key attribute information of the excitation source;

[0077] Step S405: clustering is performed based on the multi-dimensional feature vector, and the strong adverse incentive source is determined by analyzing the clustering result.

[0078] In the above steps, the purpose of clustering is to find out the groups of incentive sources with similar behavior patterns, so as to better understand their common influence on the operation quality of intelligent agricultural machinery. By analyzing the clustering results, especially those categories containing high-impact factor incentive sources, we can further confirm which incentive sources are the main factors causing gear shifting jitter, frustration and other phenomena, and define them as strong unfavorable incentive sources.

[0079] The quantitative analysis of the actual vehicle field operation data in the above step S105 to establish a nonlinear mapping model includes the following steps S501-S504:

[0080] Step S501, using variable parameter One-Class-SVM algorithm to clean the real vehicle field operation data and remove unreasonable outliers;

[0081] Step S502, organizing the cleaned data into a time series database;

[0082] Step S503, using LSTM algorithm to train the time series data and establish a nonlinear mapping model;

[0083] Step S504, adjusting the model parameters through the validation set to ensure that the model has good generalization ability.

[0084] The step S106 described above, based on the nonlinear mapping model, obtains the system characteristics corresponding to the agricultural machine in the real working environment, including the following steps S601-S603:

[0085] Step S601, using a nonlinear mapping model to predict the dynamic torque response speed of each working system under a real working environment;

[0086] Step S602, analyzing the mutual influence relationship of dynamic torques between different working systems;

[0087] Step S603, determining the mapping relationship between the control and delayed response characteristics and the power output and the engine operating point.

[0088] The above step S107, based on the system characteristics and the determined strong adverse incentive source, optimizes the control parameters of the intelligent agricultural machinery, including the following steps S701-S705:

[0089] Step S701, for each identified strong adverse excitation source, a feedforward compensation algorithm is used to adjust the control parameters according to the degree of its influence under different working conditions;

[0090] In this step, assuming that the influence of a strong adverse excitation source I can be expressed as a function f(t), the feedforward compensation amount C f It can be calculated by the following formula: C f (t) = -K f f(t) where K f is the feedforward gain coefficient, which is determined by experiment or simulation tuning.

[0091] Step S702, combining the dynamic torque response speed in the system characteristics, and optimizing the throttle opening θ using a model predictive control algorithm;

[0092] In this step, the objective function J of the model predictive control algorithm can be defined as: Where e(k) is the tracking error, u(k) is the control input, w1 and w2 are weight coefficients, and N is the prediction time domain length;

[0093] Step S703, using an adaptive shifting logic algorithm to adjust the gear position g of the transmission according to the influence relationship between the dynamic torques of the various working systems in the system characteristics.

[0094] In this step, the adaptive shift logic algorithm determines the optimal gear based on the current engine speed n, vehicle speed v and load torque TL. The decision rule can be expressed as: * =argmin g (∣nn + ∣+α·∣vv + ∣+β·∣T, -T ,+ ∣) where n + , v + , and T ,+ They are target engine speed, target vehicle speed and target load torque, and α and β are adjustment factors.

[0095] Step S704, using a delay compensation mechanism to offset the known response delay through a time lag term τ;

[0096] In this step, for a control system with delay, compensation can be performed using the following formula: G - (s) = G . (s) / (1+τ / );G . (S) is the transfer function of the controlled object, G - (s) is the controller transfer function including delay compensation;

[0097] Step S705, continuously adjust the control parameters through real-time monitoring and iterative learning control algorithm to improve operation efficiency and driving comfort. After each operation, the controller parameter ΔK is updated, and its update formula can be expressed as: K 012 =K 34+ +η·ΔK, where η is the learning rate, K new is the new control parameter, K 34+ is the old control parameter.

[0098] The present invention also discloses an intelligent agricultural machinery control optimization system based on mathematical decoupling and deep learning. The optimization system may include or be divided into one or more program modules. One or more program modules are stored in a storage medium and executed by one or more processors to complete the present invention and implement the above-mentioned optimization method. The program module referred to in the embodiment of the present invention refers to a series of computer program instruction segments that can perform specific functions, which is more suitable for describing the execution process of the optimization method in the storage medium than the program itself. The following description will specifically introduce the functions of each program module of this embodiment, such as Figure 3 As shown, it includes:

[0099] A construction module 801 is used to construct a simulation model of the intelligent agricultural machine, and is used to describe the relationship between each excitation source and the transmission system in the intelligent agricultural machine;

[0100] A collection module 802 is used to collect field operation data of real vehicles;

[0101] A simulation module 803 is used to obtain simulation data by changing parameters to limit the excitation source from participating in simulation analysis based on the simulation model;

[0102] A first analysis module 804, which is used to perform a qualitative analysis on each of the excitation source pairs based on the simulation data to obtain a strong adverse excitation source;

[0103] The second analysis module 805 is used to perform quantitative analysis using actual vehicle field operation data and establish a nonlinear mapping model;

[0104] An application module 806 is used to obtain system characteristics corresponding to the agricultural implement under a real working environment based on the nonlinear mapping model; the system characteristics include: the dynamic torque response speed of each working system and the influence relationship between them, and the mapping relationship between the control and delayed response characteristics and the power output and the engine working point;

[0105] The optimization control module 807 is used to optimize the control parameters of the intelligent agricultural machinery based on the system characteristics and the determined strong adverse excitation source.

[0106] Other contents of implementing the above-mentioned intelligent agricultural machinery control optimization method based on the intelligent agricultural machinery control optimization system have been introduced in detail in the previous embodiments. Please refer to the corresponding contents in the previous embodiments and will not be repeated here.

[0107] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent agricultural machinery control optimization method based on mathematical decoupling and deep learning, characterized in that: The method comprises: Construct a simulation model of intelligent agricultural machinery to describe the relationship between various excitation sources and transmission systems in intelligent agricultural machinery; Collect real vehicle field operation data; Based on the simulation model, limiting the excitation source from participating in the simulation analysis by changing parameters to obtain simulation data; Based on the simulation data, a qualitative analysis is performed on the influence of each of the excitation sources on the operation quality to obtain a strong adverse excitation source; Use the actual vehicle field operation data for quantitative analysis and establish a nonlinear mapping model; Based on the nonlinear mapping model, the system characteristics corresponding to the agricultural implement in the real working environment are obtained; the system characteristics include: the dynamic torque response speed of each working system and the influence relationship between them, and the mapping relationship between the control and delayed response characteristics and the power output and the engine working point; Based on the system characteristics and the determined strong adverse excitation source, the control parameters of the intelligent agricultural machinery are optimized.

2. The intelligent agricultural machinery control optimization method based on mathematical decoupling and deep learning according to claim 1 is characterized in that: The construction of the simulation model of intelligent agricultural machinery specifically includes: Establish engine model, hydraulic system model and PTO load model respectively; The engine model, the hydraulic system model and the PTO load model are coupled according to the torque balance equation based on the transmission system to form a comprehensive power transmission system model of mechanical-electrical-hydraulic multi-field coupling; An environmental factor model applied to the transmission system model is established to obtain the complete simulation model.

3. The intelligent agricultural machinery control optimization method based on mathematical decoupling and deep learning according to claim 1 is characterized in that: The simulation model is based on which one or more excitations are restricted from participating in the simulation analysis by changing parameters to obtain simulation data, including: Based on the simulation model, simulation analysis is performed on each of the excitation sources acting alone in turn, and the dynamic response characteristics of each of the excitation sources acting independently on the transmission system under different working conditions are evaluated to obtain decoupling data; Carry out data acquisition of multi-channel excitation coupling to obtain coupling data.

4. The intelligent agricultural machinery control optimization method based on mathematical decoupling and deep learning according to claim 3 is characterized in that: The method of analyzing the influence of each of the excitation sources on the operation quality based on the simulation data to obtain a strong unfavorable excitation source comprises the following steps: Based on the decoupling data and the coupling data, a sensitivity analysis is performed on the influence of each excitation source on the dynamic characteristics of the output torque of the transmission system; Based on the sensitivity analysis results, calculate the impact factor of each excitation source; Arrange all motivation sources in descending order of their impact factors to form a priority list; Constructing a multidimensional feature vector corresponding to the excitation source according to the influencing factors corresponding to the excitation source and specific torque fluctuation and response delay time indicators; Clustering is performed based on the multi-dimensional feature vector, and the strong adverse incentive source is determined by analyzing the clustering result.

5. The intelligent agricultural machinery control optimization method based on mathematical decoupling and deep learning according to claim 1 is characterized in that: The method of using the actual vehicle field operation data for quantitative analysis and establishing a nonlinear mapping model includes: Use variable parameter One-Class-SVM algorithm to clean the field operation data of real vehicles and remove unreasonable outliers; Organize the cleaned data into a time series database; Use LSTM algorithm to train time series data and establish nonlinear mapping model; Tune model parameters using the validation set.

6. The intelligent agricultural machinery control optimization method based on mathematical decoupling and deep learning according to claim 1 is characterized in that: The optimizing the control parameters of the intelligent agricultural machinery based on the system characteristics and the determined strong adverse excitation source includes: For each identified strong adverse excitation source, the control parameters are adjusted using a feedforward compensation algorithm according to its impact under different operating conditions; Combined with the dynamic torque response speed in the system characteristics, the model predictive control algorithm is used to optimize the throttle opening θ; According to the influence relationship between the dynamic torques of various working systems in the system characteristics, an adaptive shifting logic algorithm is used to adjust the gearbox gear g. A delay compensation mechanism is used to offset the known response delay through the time lag term τ; Through real-time monitoring and iterative learning control algorithms, control parameters are continuously adjusted to improve operating efficiency and driving comfort.

7. An intelligent agricultural machinery control optimization system based on mathematical decoupling and deep learning, characterized in that: It includes: A construction module, which is used to construct a simulation model of the intelligent agricultural machinery and to describe the relationship between each excitation source and the transmission system in the intelligent agricultural machinery; A collection module, which is used to collect field operation data of real vehicles; A simulation module, which is used to obtain simulation data by changing parameters to limit the excitation source from participating in simulation analysis based on the simulation model; A first analysis module, which is used to analyze the influence of each of the excitation sources on the operation quality based on the simulation data to obtain a strong adverse excitation source; The second analysis module is used to perform quantitative analysis using actual vehicle field operation data and establish a nonlinear mapping model; An application module, which is used to obtain system characteristics corresponding to agricultural machinery in a real working environment based on the nonlinear mapping model; the system characteristics include: dynamic torque response speed of each working system and the influence relationship between them, and the mapping relationship between control and delayed response characteristics and power output and engine working point; An optimization control module is used to optimize the control parameters of the intelligent agricultural machinery based on the system characteristics and the determined strong adverse excitation source.

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