Intelligent agricultural machine performance improvement method and system

Abstract

The invention discloses an intelligent agricultural machine performance improvement method and system. The method comprises the steps of sensing data processing, agricultural machine virtual debugging, agricultural machine operation optimization and agricultural machine performance improvement. The invention relates to the technical field of agricultural machinery intelligent management, in particular to an intelligent agricultural machinery performance improvement method and system, and the scheme adopts the sequential optimization of two dimensions of virtual agricultural machinery debugging and agricultural machinery operation optimization, and carries out the layer-by-layer depth of digital modeling from virtuality to reality. The problem of adaptability of agricultural machinery in a complex farmland environment is systematically solved, the operation precision and efficiency are improved, meanwhile, resource saving and soil protection are both considered, and the actual requirements of precision agriculture are met; performing agricultural machine virtual debugging by adopting a dynamic digital twin mirror image optimization method, and constructing a high-precision virtual farmland model through a dynamic weight factor; and agricultural machinery operation optimization is carried out by adopting a multi-target hierarchical adaptive reinforcement learning method.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management of agricultural machinery, and specifically to an intelligent agricultural machinery performance improvement method and system. Background Art

[0002] The intelligent agricultural machinery performance improvement method and system is a comprehensive solution based on advanced sensing technology, digital twins, artificial intelligence, and the Internet of Things. It aims to significantly improve the operating efficiency, accuracy, and intelligence of agricultural machinery through real-time data collection, virtual debugging, operation optimization, and performance improvement. The system constructs a high-precision virtual farmland model and agricultural machinery operating condition model, combines multi-source data fusion and reinforcement learning algorithms, and realizes autonomous decision-making and optimized operation of agricultural machinery in complex farmland environments, while taking into account resource conservation and soil protection, promoting the development of precision agriculture, reducing agricultural production costs, improving the quality and output of agricultural products, and promoting agricultural modernization and sustainable development.

[0003] However, in the existing methods for improving the performance of agricultural machinery, the environment of intelligent farmland operation is complex and changeable, which leads to the problem of insufficient precision in the intelligent and automated process of agricultural machinery operation. At the same time, the existing agricultural machinery debugging is often out of touch with the actual operating conditions, which leads to the technical problem that the existing methods for improving the performance of agricultural machinery intelligently are difficult to balance efficiency and sustainability.

[0004] However, in the existing agricultural machinery virtual debugging methods, there are technical problems such as insufficient accuracy of virtual farmland modeling and large deviation between agricultural machinery working condition modeling and actual conditions, which leads to a lack of feedback mechanism in the interaction process between the virtual model and actual use, and difficulty in applying the virtual environment to the debugging process of actual farmland operations. In the existing agricultural machinery operation optimization methods, the goal of agricultural machinery operation is usually single in previous intelligent systems, which further aggravates the problem of difficulty in balancing efficiency and sustainability, and also leads to low training efficiency in complex farmland environments and difficulty in migrating from virtual environments to actual farmlands. Summary of the invention

[0005] In view of the above situation, in order to overcome the defects of the prior art, the technical solution adopted by the present invention is as follows: The present invention provides a method for improving the performance of intelligent agricultural machinery, which includes the following steps:

[0006] Step S1: sensor data processing;

[0007] Step S2: virtual commissioning of agricultural machinery;

[0008] Step S3: agricultural machinery operation optimization;

[0009] Step S4: Improving agricultural machinery performance.

[0010] Further, in step S1, the sensor data processing is used to collect sensor data and integrate data resources, specifically to integrate sensor combinations, collect real-time agricultural machinery data information, and obtain an original data set for agricultural machinery performance improvement through data optimization and integrated storage;

[0011] The integrated sensor combination includes a positioning attitude sensor, an environmental perception sensor, a soil and crop sensor, and an operation power monitoring sensor;

[0012] The agricultural machinery performance improvement original data set includes agricultural machinery operation status data, operation environment data, crop growth data, soil status data and agricultural machinery operation efficiency record data.

[0013] Further, in step S2, the agricultural machinery virtual debugging is used to build a digital twin model of agricultural machinery and debug and optimize it in a virtual environment. Specifically, based on the original data set of agricultural machinery performance improvement, virtual farmland modeling and virtual agricultural machinery working condition modeling are performed, and a dynamic digital twin image optimization method is introduced to perform virtual debugging of agricultural machinery to obtain agricultural machinery virtual environment simulation data, including the following steps:

[0014] Step S21: virtual farmland modeling, which is used to construct a virtual environment for farmland operations, specifically, based on the operating environment data, crop growth data and soil state data in the original data set for improving the performance of agricultural machinery, farmland soil moisture distribution modeling is performed to obtain virtual farmland environment dynamic change modeling data;

[0015] Step S22: virtual agricultural machinery working condition modeling, which is used to perform virtual modeling of agricultural machinery working conditions in combination with agricultural machinery operation data, specifically, based on the agricultural machinery operation status data and agricultural machinery operation efficiency record data in the original data set of agricultural machinery performance improvement, a digital system modeling method combining physical modeling and data-driven modeling is used to perform virtual agricultural machinery working condition modeling to obtain virtual agricultural machinery working condition modeling data;

[0016] The physical modeling uses the ground friction coefficient and crop resistance as the physical model, and calculates the energy consumption and operation quality during the operation;

[0017] The data-driven modeling uses historical operation data as a data source to train the operation optimization model to improve the operation accuracy prediction capability;

[0018] Step S23: Dynamic digital twin image optimization, specifically optimizing the operating parameters in the dynamic change modeling data of the virtual farmland environment and the virtual agricultural machinery working condition modeling data through a dynamic multimodal data fusion method, and performing dynamic digital twin image optimization through a virtual-real interactive feedback mechanism;

[0019] The virtual-reality interactive feedback mechanism simulates agricultural machinery operations in a virtual environment and calculates the operation indicators of agricultural machinery operations for comparison with actual agricultural machinery operation data, and calculates virtual-reality difference parameters. By setting a difference threshold and comparing it with the virtual-reality difference parameters, the parameters of the digital twin model are automatically adjusted and dynamically optimized. The calculation formula of the virtual-reality difference parameters is:

[0020] ;

[0021] Where D is the virtual-real difference parameter, N is the total number of agricultural machinery, i is the index of agricultural machinery, V i is the virtual environment agricultural machinery operation index of the i-th agricultural machinery, P i is the actual agricultural machinery operation index of the i-th agricultural machinery;

[0022] Step S24: virtual debugging of agricultural machinery, specifically, performing virtual debugging of agricultural machinery through the virtual farmland modeling, the virtual agricultural machinery working condition modeling and the dynamic digital twin image optimization to obtain virtual environment simulation data of agricultural machinery;

[0023] The agricultural machinery virtual environment simulation data includes agricultural machinery operation efficiency prediction data, agricultural machinery operation quality assessment data, agricultural machinery operation path optimization data and virtual environment adjustment parameters;

[0024] The agricultural machinery operation efficiency prediction data includes the farmland operation area and energy consumption efficiency per unit time.

[0025] Further, in step S3, the agricultural machinery operation optimization is used to control the agricultural machinery to autonomously learn the optimization strategy during the operation process, specifically, based on the original data set of agricultural machinery performance improvement, a multi-objective hierarchical adaptive improved enhanced agricultural machinery operation learning method is used to optimize the autonomous operation of the agricultural machinery, and obtain the reference data for the autonomous operation optimization of the agricultural machinery, including the following steps:

[0026] Step S31: constructing a hierarchical decision-making architecture, including a global reinforcement learning layer and a local reinforcement learning layer; the global reinforcement learning layer is used for long-term operation task planning, and the local reinforcement learning layer is used for real-time agricultural machinery action adjustment;

[0027] The global reinforcement learning layer specifically uses Q-value learning to plan long-term operation tasks of agricultural machinery and generates a sequence of agricultural machinery operation sub-goals;

[0028] The agricultural machinery operation sub-goal sequence includes path segmentation and operation mode switching;

[0029] The local reinforcement learning layer specifically uses a proximal strategy optimization algorithm to adjust the real-time operation of the agricultural machinery and generate a real-time adjustment plan for the agricultural machinery;

[0030] The real-time adjustment scheme of the agricultural machinery includes a speed control scheme, a steering angle scheme and a seeding amount adjustment scheme;

[0031] Step S32: constructing a multi-objective reward, specifically designing a multi-objective reward function and applying it to the hierarchical decision-making framework to perform reinforcement learning training; the multi-objective reward function decomposes the agricultural machinery performance indicators into the operating efficiency dimension, the resource consumption dimension and the soil protection dimension, and constructs the multi-objective reward function by dynamically adjusting the dimension weights. The calculation formula is:

[0032] ;

[0033] Where R(t) is the multi-objective reward function, w1 is the operation efficiency weight, S(·) is the operation area calculation function, S MAX is the maximum theoretical operating area, The whole is the work efficiency reward item, w2 is the resource consumption weight, FC(·) is the fuel consumption calculation function, and FC p is the fuel consumption benchmark value, The whole is the resource consumption reward term, w3 is the soil protection weight, and FP(·) is the soil protection penalty term;

[0034] Step S33: construct a soil protection penalty term to balance the performance of agricultural machinery and prevent excessive compaction of the land by agricultural machinery. Specifically, the soil compaction degree parameter is calculated and a soil protection penalty term is constructed to construct a multi-objective reward. The calculation formula is:

[0035] ;

[0036] Where FP(·) is the soil protection penalty term, F L (·) is the calculation function of the pressure applied by the tire of the agricultural machinery to the soil, F r is the critical pressure for soil compaction;

[0037] Step S34: virtual environment migration, specifically, by constructing a virtual migration loss function, pre-training the hierarchical decision architecture in a digital twin virtual environment according to the virtual environment in the agricultural machinery virtual environment simulation data to obtain virtual strategy parameters, and migrating the virtual strategy parameters to the actual agricultural machinery operation environment for verification and fine-tuning to obtain virtual migration agricultural machinery operation optimization data;

[0038] Step S35: Strengthening the optimization of agricultural machinery operations, specifically, performing reinforcement learning training of virtual environment migration and agricultural machinery operation optimization according to the multi-objective reward and the soil protection penalty item through the hierarchical decision-making framework, and obtaining reference data for autonomous optimization of agricultural machinery;

[0039] The agricultural machinery autonomous optimization reference data includes operation efficiency optimization data, resource consumption optimization data, soil protection reference data and agricultural machinery equipment status control optimization data.

[0040] Furthermore, in step S4, the agricultural machinery performance improvement is used to optimize and improve the agricultural machinery performance in combination with the virtual environment and the optimization strategy. Specifically, based on the agricultural machinery virtual environment simulation data and the agricultural machinery autonomous operation optimization reference data, the simulation data and the actual operation data are compared and weighted evaluated, and by combining the agricultural machinery control decisions of the simulation data and the agricultural machinery autonomous optimization data, the simulation level and the actual level of the agricultural machinery performance are collaboratively optimized to obtain the comprehensive performance reference data of the agricultural machinery.

[0041] The present invention provides an intelligent agricultural machinery performance improvement system, comprising a data perception module, a network transmission module, an intelligent application module and a performance improvement module;

[0042] The data perception module is used for sensor data processing, and obtains an original data set for improving agricultural machinery performance through sensor data processing, and sends the original data set for improving agricultural machinery performance to the network transmission module and the intelligent application module;

[0043] The network transmission module is used for virtual debugging of agricultural machinery, obtains agricultural machinery virtual environment simulation data through virtual debugging of agricultural machinery, and sends the agricultural machinery virtual environment simulation data to the intelligent application module and the performance improvement module;

[0044] The intelligent application module is used for optimizing agricultural machinery operations, obtaining agricultural machinery autonomous operation optimization reference data through agricultural machinery operation optimization, and sending the agricultural machinery autonomous operation optimization reference data to the performance improvement module;

[0045] The performance improvement module is used for improving the performance of agricultural machinery, and through improving the performance of agricultural machinery, comprehensive performance reference data of agricultural machinery is obtained.

[0046] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0047] (1) In the existing methods for improving the performance of agricultural machinery, the environment of intelligent farmland operations is complex and changeable, which leads to the problem of insufficient precision in the intelligent and automated process of agricultural machinery operations. At the same time, the existing agricultural machinery debugging is often out of touch with the actual operating conditions, which leads to the technical problem that the existing methods for improving the performance of agricultural machinery intelligently cannot balance efficiency and sustainability. This solution creatively adopts the optimization of virtual agricultural machinery debugging and agricultural machinery operation optimization in two dimensions, and conducts digital modeling from virtual to actual, systematically solving the adaptability problem of agricultural machinery in complex farmland environments, improving operation accuracy and efficiency, while taking into account resource conservation and soil protection, and meeting the actual needs of precision agriculture.

[0048] (2) In view of the technical problems in the existing virtual debugging methods of agricultural machinery, such as insufficient accuracy of virtual farmland modeling and large deviation between the modeling of agricultural machinery working conditions and the actual operation, which leads to a lack of feedback mechanism in the interaction process between the virtual model and the actual use, and difficulty in applying the virtual environment to the debugging process of actual farmland operations, this scheme creatively adopts a dynamic digital twin image optimization method for virtual debugging of agricultural machinery, constructs a high-precision virtual farmland model through dynamic weight factors, and designs a virtual-real difference feedback mechanism to achieve a high degree of consistency between the virtual debugging results and the actual operation, shorten the debugging cycle, and improve the debugging reliability and the overall availability of the virtual digital model;

[0049] (3) In the existing agricultural machinery operation optimization methods, the goal of agricultural machinery operation is usually single in previous intelligent systems, which further aggravates the problem of difficulty in balancing efficiency and sustainability. At the same time, it also leads to low training efficiency in complex farmland environments and difficult technical problems in migration from virtual environments to actual farmlands. This scheme creatively adopts a multi-objective hierarchical adaptive reinforcement learning method to optimize agricultural machinery operations. Through a hierarchical decision-making architecture and a multi-objective reward function, multi-objective collaborative optimization of operation efficiency, resource consumption, and soil protection is achieved. A virtual migration loss function is introduced to accelerate the strategy migration from the virtual environment to the actual farmland, significantly improve the comprehensive performance of autonomous agricultural machinery operations, and better meet the actual needs of precision agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of a flow chart of an intelligent agricultural machinery performance improvement method provided by the present invention;

[0051] Figure 2 A schematic diagram of an intelligent agricultural machinery performance improvement system provided by the present invention;

[0052] Figure 3 This is a flow chart of virtual commissioning of agricultural machinery in step S2;

[0053] Figure 4 This is a schematic diagram of the process of optimizing agricultural machinery operations in step S3.

[0054] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0057] Example 1, see Figure 1 The present invention provides a method for improving the performance of intelligent agricultural machinery, which comprises the following steps:

[0058] Step S1: sensor data processing;

[0059] Step S2: virtual commissioning of agricultural machinery;

[0060] Step S3: agricultural machinery operation optimization;

[0061] Step S4: Improving agricultural machinery performance.

[0062] By executing the above operations, in the existing methods for improving the performance of agricultural machinery, the environment of intelligent farmland operations is complex and changeable, which leads to the problem of insufficient precision in the process of intelligent and automated agricultural machinery operations. At the same time, the existing agricultural machinery debugging is often easily disconnected from the actual operating conditions, which leads to the technical problem that the existing methods for improving the performance of agricultural machinery intelligently are difficult to balance efficiency and sustainability. This solution creatively adopts the sequential optimization of two dimensions: virtual agricultural machinery debugging and agricultural machinery operation optimization, and conducts in-depth digital modeling from virtual to actual, systematically solving the adaptability problems of agricultural machinery in complex farmland environments, improving operation accuracy and efficiency, while taking into account resource conservation and soil protection, to meet the actual needs of precision agriculture.

[0063] Example 2, see Figure 1 and Figure 2 , in step S1, the sensor data processing is used to collect sensor data and integrate data resources, specifically integrating sensor combinations, to collect real-time agricultural machinery data information, and to obtain an original data set for agricultural machinery performance improvement through data optimization and integrated storage;

[0064] The integrated sensor combination includes a positioning attitude sensor, an environmental perception sensor, a soil and crop sensor, and an operation power monitoring sensor;

[0065] The agricultural machinery performance improvement original data set includes agricultural machinery operation status data, operation environment data, crop growth data, soil status data and agricultural machinery operation efficiency record data;

[0066] The positioning attitude sensor is used for agricultural machinery positioning, attitude detection and trajectory tracking, including a GNSS sensor and an IMU sensor;

[0067] The environmental perception sensor is used to identify farmland terrain and farmland obstacles, including a lidar sensor, a camera, and an air temperature and humidity sensor;

[0068] The soil crop sensor is used to monitor soil moisture, temperature and nutrition, including a soil moisture sensor, a temperature and humidity sensor and a conductivity sensor;

[0069] The operating power monitoring sensor is used to monitor the power system status, fuel consumption status and mechanical load status of the agricultural machinery, including an engine sensor, a fuel consumption sensor and a torque sensor;

[0070] The agricultural machinery operation status data includes the agricultural machinery position, speed, acceleration, steering angle, engine speed and torque parameter data;

[0071] The working environment data includes terrain type, obstacle distribution type, weather type, and air temperature and humidity data;

[0072] The crop growth data include farmland crop growth cycle and health status type data;

[0073] The soil state data includes soil moisture, temperature, pH value and conductivity data;

[0074] The agricultural machinery operation efficiency record data includes operation area, operation speed and fuel consumption reference data.

[0075] Example 3, see Figure 1 , Figure 2 and Figure 3 This embodiment is based on the above embodiment. In step S2, the agricultural machinery virtual debugging is used to build a digital twin model of agricultural machinery and debug and optimize it in a virtual environment. Specifically, the agricultural machinery virtual debugging is performed based on the original data set of agricultural machinery performance improvement, through virtual farmland modeling and virtual agricultural machinery working condition modeling, and the dynamic digital twin image optimization method is introduced to obtain the agricultural machinery virtual environment simulation data, including the following steps:

[0076] Step S21: virtual farmland modeling is used to construct a virtual environment for farmland operations. Specifically, farmland soil moisture distribution modeling is performed based on the operating environment data, crop growth data and soil state data in the original data set for improving the performance of agricultural machinery, so as to obtain dynamic change modeling data of the virtual farmland environment. The calculation formula is:

[0077] ;

[0078] Where SoilMap(·) is the modeling function of farmland soil moisture distribution, x is the horizontal index of the sensor location, y is the vertical index of the sensor location, t is the current time index, m is the total number of sensors, k is the sensor index, is the sensor weight, Sensor k (·) is the value function of the kth sensor, exp(·) is the natural base function, is the time decay factor, used to control the timeliness of soil moisture, t k is the time of sensor data collection of the kth sensor;

[0079] Step S22: virtual agricultural machinery working condition modeling, which is used to perform virtual modeling of agricultural machinery working conditions in combination with agricultural machinery operation data, specifically, based on the agricultural machinery operation status data and agricultural machinery operation efficiency record data in the original data set of agricultural machinery performance improvement, a digital system modeling method combining physical modeling and data-driven modeling is used to perform virtual agricultural machinery working condition modeling to obtain virtual agricultural machinery working condition modeling data;

[0080] The physical modeling uses the ground friction coefficient and crop resistance as the physical model, and calculates the energy consumption and operation quality during the operation;

[0081] The data-driven modeling uses historical operation data as a data source to train the operation optimization model to improve the operation accuracy prediction capability;

[0082] Step S23: Dynamic digital twin image optimization, specifically optimizing the operating parameters in the dynamic change modeling data of the virtual farmland environment and the virtual agricultural machinery working condition modeling data through a dynamic multimodal data fusion method, and performing dynamic digital twin image optimization through a virtual-real interactive feedback mechanism;

[0083] The calculation formula of the dynamic multimodal data fusion is:

[0084] ;

[0085] In the formula, w k (t) is the dynamic weight factor of the kth sensor, which is used as the dynamic weight of multimodal data fusion, k is the sensor index, t is the current time index, is the sensor data confidence weight, Conf k (·) is the confidence value of the kth sensor, and the confidence value is specifically evaluated and calculated based on error and noise, is the weight of the degree of association of agricultural machinery operation conditions, Relev k (·) is the agricultural machinery operation condition association value of the kth sensor, which is used to indicate the correlation between the sensor data and the current agricultural machinery operation task, n is the total number of fused sensor data, which is used to indicate the total number of sensor data that need to be fused with multimodal data, and j is the fused sensor index;

[0086] The virtual-reality interactive feedback mechanism simulates agricultural machinery operations in a virtual environment and calculates the operation indicators of agricultural machinery operations for comparison with actual agricultural machinery operation data, and calculates virtual-reality difference parameters. By setting a difference threshold and comparing it with the virtual-reality difference parameters, the parameters of the digital twin model are automatically adjusted and dynamically optimized. The calculation formula of the virtual-reality difference parameters is:

[0087] ;

[0088] Where D is the virtual-real difference parameter, N is the total number of agricultural machinery, i is the index of agricultural machinery, V i is the virtual environment agricultural machinery operation index of the i-th agricultural machinery, P i is the actual agricultural machinery operation index of the i-th agricultural machinery;

[0089] Step S24: virtual debugging of agricultural machinery, specifically, performing virtual debugging of agricultural machinery through the virtual farmland modeling, the virtual agricultural machinery working condition modeling and the dynamic digital twin image optimization to obtain virtual environment simulation data of agricultural machinery;

[0090] The agricultural machinery virtual environment simulation data includes agricultural machinery operation efficiency prediction data, agricultural machinery operation quality assessment data, agricultural machinery operation path optimization data and virtual environment adjustment parameters;

[0091] The agricultural machinery operation efficiency prediction data includes the farmland operation area and energy consumption efficiency per unit time.

[0092] By executing the above operations, in order to address the technical problems in the existing virtual debugging methods of agricultural machinery, such as insufficient accuracy of virtual farmland modeling and large deviation between agricultural machinery working condition modeling and actual operation, which leads to a lack of feedback mechanism in the interaction process between the virtual model and actual use, and difficulty in applying the virtual environment to the debugging process of actual farmland operations, this solution creatively adopts a dynamic digital twin image optimization method for virtual debugging of agricultural machinery, constructs a high-precision virtual farmland model through dynamic weight factors, and designs a virtual-reality difference feedback mechanism to achieve a high degree of consistency between virtual debugging results and actual operations, shorten the debugging cycle, and improve the debugging reliability and the overall availability of the virtual digital model.

[0093] Example 4, see Figure 1 , Figure 2 and Figure 4 This embodiment is based on the above embodiment. In step S3, the agricultural machinery operation optimization is used to control the agricultural machinery to autonomously learn the optimization strategy during the operation process. Specifically, based on the original data set of agricultural machinery performance improvement, a multi-objective hierarchical adaptively improved enhanced agricultural machinery operation learning method is used to optimize the autonomous operation of agricultural machinery, and obtain the reference data for autonomous operation optimization of agricultural machinery, including the following steps:

[0094] Step S31: constructing a hierarchical decision-making architecture, including a global reinforcement learning layer and a local reinforcement learning layer; the global reinforcement learning layer is used for long-term operation task planning, and the local reinforcement learning layer is used for real-time agricultural machinery action adjustment;

[0095] The global reinforcement learning layer specifically uses Q-value learning to plan long-term operation tasks of agricultural machinery and generates a sequence of agricultural machinery operation sub-goals;

[0096] The agricultural machinery operation sub-goal sequence includes path segmentation and operation mode switching;

[0097] The local reinforcement learning layer specifically uses a proximal strategy optimization algorithm to adjust the real-time operation of the agricultural machinery and generate a real-time adjustment plan for the agricultural machinery;

[0098] The real-time adjustment scheme of the agricultural machinery includes a speed control scheme, a steering angle scheme and a seeding amount adjustment scheme;

[0099] Step S32: constructing a multi-objective reward, specifically designing a multi-objective reward function and applying it to the hierarchical decision-making framework to perform reinforcement learning training; the multi-objective reward function decomposes the agricultural machinery performance indicators into the operating efficiency dimension, the resource consumption dimension and the soil protection dimension, and constructs the multi-objective reward function by dynamically adjusting the dimension weights. The calculation formula is:

[0100] ;

[0101] Where R(t) is the multi-objective reward function, w1 is the operation efficiency weight, S(·) is the operation area calculation function, S MAX is the maximum theoretical operating area, The whole is the work efficiency reward item, w2 is the resource consumption weight, FC(·) is the fuel consumption calculation function, and FC p is the fuel consumption benchmark value, The whole is the resource consumption reward term, w3 is the soil protection weight, and FP(·) is the soil protection penalty term;

[0102] Step S33: construct a soil protection penalty term to balance the performance of agricultural machinery and prevent excessive compaction of the land by agricultural machinery. Specifically, the soil compaction degree parameter is calculated and a soil protection penalty term is constructed to construct a multi-objective reward. The calculation formula is:

[0103] ;

[0104] Where FP(·) is the soil protection penalty term, F L (·) is the calculation function of the pressure applied by the tire of the agricultural machinery to the soil, F r is the critical pressure for soil compaction;

[0105] Step S34: virtual environment migration, specifically, by constructing a virtual migration loss function, pre-training the hierarchical decision architecture in a digital twin virtual environment according to the virtual environment in the agricultural machinery virtual environment simulation data to obtain virtual strategy parameters, and migrating the virtual strategy parameters to the actual agricultural machinery operation environment for verification and fine-tuning to obtain virtual migration agricultural machinery operation optimization data;

[0106] The calculation formula of the virtual migration loss function is:

[0107] ;

[0108] Where, L adapt is the virtual transfer loss function, is the mean state of agricultural machinery in the virtual environment, is the mean value of the agricultural machinery status in the actual environment, is the divergence adjustment coefficient, KL(·) is the KL divergence calculation function, P V is the probability distribution of the state of agricultural machinery in the virtual environment, P R is the probability distribution of the state of agricultural machinery in the actual environment;

[0109] Step S35: Strengthening the optimization of agricultural machinery operations, specifically, performing reinforcement learning training of virtual environment migration and agricultural machinery operation optimization according to the multi-objective reward and the soil protection penalty item through the hierarchical decision-making framework, and obtaining reference data for autonomous optimization of agricultural machinery;

[0110] The agricultural machinery autonomous optimization reference data includes operation efficiency optimization data, resource consumption optimization data, soil protection reference data and agricultural machinery equipment status control optimization data.

[0111] By executing the above operations, in view of the fact that in the existing agricultural machinery operation optimization methods, the goal of agricultural machinery operation is usually single in previous intelligent systems, which further aggravates the problem of difficulty in balancing efficiency and sustainability, and also leads to low training efficiency in complex farmland environments and difficult migration from virtual environments to actual farmlands. This solution creatively adopts a multi-objective hierarchical adaptive reinforcement learning method to optimize agricultural machinery operations. Through a hierarchical decision-making architecture and a multi-objective reward function, multi-objective collaborative optimization of operation efficiency, resource consumption, and soil protection is achieved, and a virtual migration loss function is introduced to accelerate the strategy migration from virtual environments to actual farmlands, significantly improve the comprehensive performance of autonomous agricultural machinery operations, and better meet the actual needs of precision agriculture.

[0112] Example 5, see Figure 1 , Figure 2 and Figure 4 This embodiment is based on the above embodiment. In step S3, Table 1 is a reference example table of the content of the reference data for autonomous optimization of agricultural machinery, as shown in Table 1. The operation efficiency optimization data includes the optimized operation area, the optimized operation speed, the path planning deviation reference, the predicted time for completion of farmland operation and the predicted time for switching the operation mode; the resource consumption optimization data includes the predicted value of fuel consumption, the predicted value of battery current consumption, the predicted value of total operation energy consumption and the predicted value of agricultural material consumption; the soil protection reference data includes the reference value of soil compaction change, the reference value of tire contact pressure change, the reference value of soil moisture change and the reference value of soil organic matter content; the agricultural machinery equipment state control optimization data includes the engine load rate, the gearbox working state and the equipment vibration frequency.

[0113] Table 1 Reference data content of agricultural machinery autonomous optimization reference example table

[0114]

[0115] Example 6, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S4, the agricultural machinery performance improvement is used to optimize and improve the agricultural machinery performance in combination with the virtual environment and the optimization strategy. Specifically, based on the agricultural machinery virtual environment simulation data and the agricultural machinery autonomous operation optimization reference data, the simulation data and the actual operation data are compared and weighted evaluated. By combining the agricultural machinery control decision of the simulation data and the agricultural machinery autonomous optimization data, the simulation level and the actual level of the agricultural machinery performance are coordinated optimized to obtain the comprehensive performance reference data of the agricultural machinery.

[0116] Embodiment 7, see Figure 1 and Figure 2 , this embodiment is based on the above embodiment, and the present invention provides an intelligent agricultural machinery performance improvement system, including a data perception module, a network transmission module, an intelligent application module and a performance improvement module;

[0117] The data perception module is used for sensor data processing, and obtains an original data set for improving agricultural machinery performance through sensor data processing, and sends the original data set for improving agricultural machinery performance to the network transmission module and the intelligent application module;

[0118] The network transmission module is used for virtual debugging of agricultural machinery, obtains agricultural machinery virtual environment simulation data through virtual debugging of agricultural machinery, and sends the agricultural machinery virtual environment simulation data to the intelligent application module and the performance improvement module;

[0119] The intelligent application module is used for optimizing agricultural machinery operations, obtaining agricultural machinery autonomous operation optimization reference data through agricultural machinery operation optimization, and sending the agricultural machinery autonomous operation optimization reference data to the performance improvement module;

[0120] The performance improvement module is used for improving the performance of agricultural machinery, and through improving the performance of agricultural machinery, comprehensive performance reference data of agricultural machinery is obtained.

[0121] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0122] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0123] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A method for improving the performance of intelligent agricultural machinery, characterized in that: The method comprises the following steps: Step S1: sensor data processing to obtain the original data set for agricultural machinery performance improvement; Step S2: virtual debugging of agricultural machinery, through virtual farmland modeling and virtual agricultural machinery working condition modeling, and introducing dynamic digital twin image optimization method, virtual debugging of agricultural machinery is carried out to obtain agricultural machinery virtual environment simulation data, including the following steps: step S21: virtual farmland modeling; step S22: virtual agricultural machinery working condition modeling; step S23: dynamic digital twin image optimization; step S24: virtual debugging of agricultural machinery; Step S3: agricultural machinery operation optimization, adopting a multi-objective hierarchical adaptively improved enhanced agricultural machinery operation learning method to optimize the autonomous operation of agricultural machinery and obtain reference data for the optimization of autonomous operation of agricultural machinery, including the following steps: step S31: constructing a hierarchical decision-making framework; step S32: constructing a multi-objective reward; step S33: constructing a soil protection penalty item; step S34: virtual environment migration; step S35: strengthening the optimization of agricultural machinery operations; Step S4: Improve the performance of agricultural machinery by coordinating the simulation and reality levels of agricultural machinery performance to obtain reference data on the comprehensive performance of agricultural machinery.

2. The method for improving the performance of intelligent agricultural machinery according to claim 1, characterized in that: In step S1, the sensor data processing is used to collect sensor data and integrate data resources, specifically integrating sensor combinations to collect real-time agricultural machinery data information, and obtain the original data set for agricultural machinery performance improvement through data optimization and integrated storage; The integrated sensor combination includes a positioning attitude sensor, an environmental perception sensor, a soil and crop sensor, and an operation power monitoring sensor; The agricultural machinery performance improvement original data set includes agricultural machinery operation status data, operation environment data, crop growth data, soil status data and agricultural machinery operation efficiency record data.

3. The method for improving the performance of intelligent agricultural machinery according to claim 2, characterized in that: In step S2, the agricultural machinery virtual debugging is used to build a digital twin model of agricultural machinery and debug and optimize it in a virtual environment. Specifically, based on the original data set of agricultural machinery performance improvement, virtual farmland modeling and virtual agricultural machinery working condition modeling are performed, and a dynamic digital twin image optimization method is introduced to perform virtual debugging of agricultural machinery to obtain agricultural machinery virtual environment simulation data, including the following steps: Step S21: virtual farmland modeling, which is used to construct a virtual environment for farmland operations, specifically, based on the operating environment data, crop growth data and soil state data in the original data set for improving the performance of agricultural machinery, farmland soil moisture distribution modeling is performed to obtain virtual farmland environment dynamic change modeling data; Step S22: virtual agricultural machinery working condition modeling, which is used to perform virtual modeling of agricultural machinery working conditions in combination with agricultural machinery operation data, specifically, based on the agricultural machinery operation status data and agricultural machinery operation efficiency record data in the original data set of agricultural machinery performance improvement, a digital system modeling method combining physical modeling and data-driven modeling is used to perform virtual agricultural machinery working condition modeling to obtain virtual agricultural machinery working condition modeling data; The physical modeling uses the ground friction coefficient and crop resistance as the physical model, and calculates the energy consumption and operation quality during the operation; The data-driven modeling uses historical operation data as a data source to train the operation optimization model to improve the operation accuracy prediction capability; Step S23: Dynamic digital twin image optimization, specifically optimizing the operating parameters in the dynamic change modeling data of the virtual farmland environment and the virtual agricultural machinery working condition modeling data through a dynamic multimodal data fusion method, and performing dynamic digital twin image optimization through a virtual-real interactive feedback mechanism; The virtual-reality interactive feedback mechanism simulates agricultural machinery operations in a virtual environment and calculates the operation indicators of agricultural machinery operations for comparison with actual agricultural machinery operation data, and calculates virtual-reality difference parameters. By setting a difference threshold and comparing it with the virtual-reality difference parameters, the parameters of the digital twin model are automatically adjusted and dynamically optimized. The calculation formula of the virtual-reality difference parameters is: ； Where D is the virtual-real difference parameter, N is the total number of agricultural machinery, i is the index of agricultural machinery, V i is the virtual environment agricultural machinery operation index of the i-th agricultural machinery, P i is the actual agricultural machinery operation index of the i-th agricultural machinery; Step S24: virtual debugging of agricultural machinery, specifically, performing virtual debugging of agricultural machinery through the virtual farmland modeling, the virtual agricultural machinery working condition modeling and the dynamic digital twin image optimization to obtain agricultural machinery virtual environment simulation data.

4. The method for improving the performance of intelligent agricultural machinery according to claim 3, characterized in that: In step S2, the agricultural machinery virtual environment simulation data includes agricultural machinery operation efficiency prediction data, agricultural machinery operation quality assessment data, agricultural machinery operation path optimization data and virtual environment adjustment parameters; The agricultural machinery operation efficiency prediction data includes the farmland operation area and energy consumption efficiency per unit time.

5. The method for improving the performance of intelligent agricultural machinery according to claim 4, characterized in that: In step S3, the agricultural machinery operation optimization is used to control the agricultural machinery to autonomously learn the optimization strategy during the operation process, specifically, based on the original data set of agricultural machinery performance improvement, a multi-objective hierarchical adaptive improved enhanced agricultural machinery operation learning method is used to optimize the autonomous operation of the agricultural machinery, and obtain the reference data for the autonomous operation optimization of the agricultural machinery, including the following steps: Step S31: constructing a hierarchical decision-making architecture, including a global reinforcement learning layer and a local reinforcement learning layer; the global reinforcement learning layer is used for long-term operation task planning, and the local reinforcement learning layer is used for real-time agricultural machinery action adjustment; The global reinforcement learning layer specifically uses Q-value learning to plan long-term operation tasks of agricultural machinery and generates a sequence of agricultural machinery operation sub-goals; The agricultural machinery operation sub-goal sequence includes path segmentation and operation mode switching; The local reinforcement learning layer specifically uses a proximal strategy optimization algorithm to adjust the real-time operation of the agricultural machinery and generate a real-time adjustment plan for the agricultural machinery; The real-time adjustment scheme of the agricultural machinery includes a speed control scheme, a steering angle scheme and a seeding amount adjustment scheme; Step S32: constructing a multi-objective reward, specifically designing a multi-objective reward function and applying it to the hierarchical decision-making framework to perform reinforcement learning training; the multi-objective reward function decomposes the agricultural machinery performance indicators into the operating efficiency dimension, the resource consumption dimension and the soil protection dimension, and constructs the multi-objective reward function by dynamically adjusting the dimension weights. The calculation formula is: ； Where R(t) is the multi-objective reward function, w1 is the operation efficiency weight, S(·) is the operation area calculation function, S MAX is the maximum theoretical operating area, The whole is the work efficiency reward item, w2 is the resource consumption weight, FC(·) is the fuel consumption calculation function, and FC p is the fuel consumption benchmark value, The whole is the resource consumption reward term, w3 is the soil protection weight, and FP(·) is the soil protection penalty term; Step S33: construct a soil protection penalty term to balance the performance of agricultural machinery and prevent excessive compaction of the land by agricultural machinery. Specifically, the soil compaction degree parameter is calculated and a soil protection penalty term is constructed to construct a multi-objective reward. The calculation formula is: ； Where FP(·) is the soil protection penalty term, F L (·) is the calculation function of the pressure applied by the tire of the agricultural machinery to the soil, F r is the critical pressure for soil compaction; Step S34: virtual environment migration, specifically, by constructing a virtual migration loss function, pre-training the hierarchical decision architecture in a digital twin virtual environment according to the virtual environment in the agricultural machinery virtual environment simulation data to obtain virtual strategy parameters, and migrating the virtual strategy parameters to the actual agricultural machinery operation environment for verification and fine-tuning to obtain virtual migration agricultural machinery operation optimization data; Step S35: Strengthening the optimization of agricultural machinery operations, specifically, through the hierarchical decision-making framework, based on the multi-objective rewards and the soil protection penalty items, performing reinforcement learning training for virtual environment migration and agricultural machinery operation optimization, and obtaining reference data for autonomous optimization of agricultural machinery.

6. The method for improving the performance of intelligent agricultural machinery according to claim 5, characterized in that: In step S3, the agricultural machinery autonomous optimization reference data includes operation efficiency optimization data, resource consumption optimization data, soil protection reference data and agricultural machinery equipment status control optimization data.

7. The method for improving the performance of intelligent agricultural machinery according to claim 6, characterized in that: In step S4, the agricultural machinery performance improvement is used to optimize and improve the agricultural machinery performance in combination with the virtual environment and the optimization strategy. Specifically, based on the agricultural machinery virtual environment simulation data and the agricultural machinery autonomous operation optimization reference data, the simulation data and the actual operation data are compared and weighted evaluated. By combining the agricultural machinery control decisions of the simulation data and the agricultural machinery autonomous optimization data, the simulation level and the actual level of the agricultural machinery performance are coordinated optimized to obtain the comprehensive performance reference data of the agricultural machinery.

8. An intelligent agricultural machinery performance improvement system, used to implement an intelligent agricultural machinery performance improvement method as described in any one of claims 1 to 7, characterized in that: It includes data perception module, network transmission module, intelligent application module and performance improvement module.

9. The intelligent agricultural machinery performance improvement system according to claim 8, characterized in that: The data perception module is used for sensor data processing, and obtains an original data set for improving agricultural machinery performance through sensor data processing, and sends the original data set for improving agricultural machinery performance to the network transmission module and the intelligent application module; The network transmission module is used for virtual debugging of agricultural machinery, obtains agricultural machinery virtual environment simulation data through virtual debugging of agricultural machinery, and sends the agricultural machinery virtual environment simulation data to the intelligent application module and the performance improvement module; The intelligent application module is used for optimizing agricultural machinery operations, and obtains reference data for optimizing autonomous agricultural machinery operations through optimizing agricultural machinery operations, and sends the reference data for optimizing autonomous agricultural machinery operations to the performance improvement module; The performance improvement module is used for improving the performance of agricultural machinery, and through improving the performance of agricultural machinery, comprehensive performance reference data of agricultural machinery is obtained.

Images