Collaborative operation management method and system for agricultural machinery group

Abstract

The invention relates to the technical field of agricultural machinery group management, and particularly discloses a collaborative operation management method and system for an agricultural machinery group, and the method comprises the following steps: S1, agricultural machinery registration and grouping, S2, farmland digital map construction, S3, collaborative operation planning, S4, state real-time synchronous monitoring, S5, operation deviation dynamic detection and analysis, and S6, task dynamic redistribution and system self-adjustment. The agricultural machine group cooperative management system integrates intelligent planning, cooperative execution, quality control and flexible fault tolerance to perform cooperative management on the agricultural machine group, and can realize autonomous cooperation of the agricultural machine group, fine management of the operation process, intelligent fault response and adaptive adjustment of the system, thereby comprehensively improving the efficiency and intelligent level of agricultural production.

Description

Technical Field

[0001] This invention relates to the field of agricultural machinery group management technology, and more specifically to a collaborative operation management method and system for agricultural machinery groups. Background Technology

[0002] Agricultural mechanization is the core pillar of modern agricultural development, and clustered agricultural machinery operations have become the mainstream mode of large-scale farmland operations. With the development of the BeiDou Navigation Satellite System, mobile communication technology, and sensor technology, agricultural machinery operation management has evolved from single-machine manual operation to intelligent and networked operations. Currently, existing agricultural machinery management systems generally adopt BeiDou positioning technology to achieve real-time location monitoring of agricultural machinery; some agricultural machinery is equipped with sensors to monitor its own operating status, such as fuel level and engine temperature, and displays this information visually at the monitoring center, thus initially realizing remote management functions for agricultural machinery.

[0003] However, existing technological solutions still have significant limitations and are insufficient to meet the higher requirements of modern agriculture for refined, intelligent, and collaborative cluster management. Most existing management systems operate on a monitoring model, primarily focusing on displaying the location and basic status of agricultural machinery and issuing alarms for anomalies. They lack scientific planning of operational tasks and collaborative linkage mechanisms within the cluster. Agricultural machinery operation paths often rely on presets or driver experience, failing to fully integrate high-precision geographic information, real-time meteorological data, and soil moisture conditions for system optimization. This can lead to duplicate or missed operation paths, impacting overall operational efficiency.

[0004] Secondly, when a single piece of agricultural machinery malfunctions or its operating efficiency is low, the existing system can usually only issue an alarm, relying on management personnel to make manual decisions, contact repair services, and then manually adjust the work assignments. This process is time-consuming, causing the work in the area where the malfunctioning machinery is located to come to a standstill, seriously affecting the overall work progress. In the complex environment of farmland, problems such as communication interruptions and dust contamination of monitoring cameras occur frequently, leading to missing or distorted data, making it impossible for management terminals to obtain accurate information, thus causing misjudgments or management blind spots.

[0005] Therefore, how to provide a collaborative operation management method and system for agricultural machinery fleets is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a collaborative operation management method and system for agricultural machinery fleets to solve the problems mentioned in the background section. The present invention integrates intelligent planning, collaborative execution, quality control, and resilience fault tolerance for collaborative management of agricultural machinery fleets. The present invention can realize autonomous collaboration of agricultural machinery fleets, refined management of operation process, intelligent fault response and system adaptive adjustment, thereby comprehensively improving the efficiency and intelligence level of agricultural production.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for collaborative operation management of agricultural machinery fleets includes the following steps: S1. Agricultural machinery registration and grouping: The basic information, performance parameters and current health status of each agricultural machine in the agricultural machinery group are entered into the control and management terminal. The agricultural machines are divided into several functional coordination groups according to the type of operation task, and some agricultural machines are designated as dynamic standby groups. S2. Construction of digital farmland map: The control and management terminal obtains high-precision farmland geographic information through the Beidou positioning system, and then integrates farmland boundary, elevation difference, soil moisture distribution and obstacle location data to generate a layered electronic map of the operation area containing multiple geographic attributes. The precise location of each agricultural machine is obtained in real time through the Beidou positioning system and dynamically projected onto the layered electronic map of the operation area. S3. Collaborative Operation Planning: Based on the hierarchical operation area electronic map generated in step S2 and the agricultural machinery performance parameters entered in step S1, the control and management terminal plans the optimal collaborative operation path for each functional collaborative group and estimates the operation completion time for each area. S4. Real-time synchronous monitoring of status: Sensors are installed on each agricultural machine to monitor engine load, fuel level, hydraulic system pressure, travel speed, implement lifting status and tire pressure. Sensor data is transmitted to the control and management terminal through the 5G network to form a real-time data stream of agricultural machine health and operation status. S5. Dynamic detection and analysis of operational deviations: During the operation of the machine group, the control and management terminal compares the real-time Beidou positioning information of each agricultural machine with the optimal collaborative operation path planned in step S3, and automatically detects whether the agricultural machine has deviated from the path, lagged behind in progress, or interrupted the operation; if a deviation is found, the real-time status data stream of the agricultural machine in step S4 is automatically linked and called for analysis, and the cause of the deviation is automatically analyzed. S6. Dynamic task reallocation and system self-adjustment: For agricultural machinery that needs to be withdrawn due to failure or low efficiency, the control and management terminal automatically isolates it from the current work group and immediately dispatches the nearest agricultural machinery from the dynamic standby machine group set in step S1 to access it. At the same time, it recalculates and allocates the remaining work tasks in real time based on the current work progress and generates new collaborative work instructions to be sent to the newly accessed agricultural machinery.

[0008] This invention achieves a leap from passive monitoring to proactive collaborative management of agricultural machinery clusters through a refined closed-loop management system encompassing agricultural machinery grouping, digital map construction, collaborative planning, real-time monitoring, and dynamic adjustment. It solves the problem of overall operation interruption caused by the failure or inefficiency of a single agricultural machine. By dynamically scheduling backup machines and reallocating tasks, it significantly improves the overall operational efficiency, anti-interference capability, and task completion reliability of agricultural machinery clusters.

[0009] To further optimize the above technical solution, in step S4, an operation quality sensor is also installed on the agricultural machinery to collect data on tillage depth, sowing density, or fertilizer application rate in real time. The control and management terminal compares the operation quality data with preset agronomic standards. If a quality deviation is found, a quality correction command is generated and sent to the corresponding agricultural machinery via the network to control it to adjust the machinery parameters. By adding an operation quality sensor to the machinery and providing feedback to the preset standards, not only can the working status of the agricultural machinery be monitored, but the work quality can also be further supervised. This effectively avoids operation quality problems such as missed sowing, double sowing, and uneven fertilization caused by improper machinery adjustment or poor condition. The management granularity is deepened from the mechanical level to the agronomic level, ensuring the high-quality completion of agricultural production operations.

[0010] To further optimize the above technical solution, in step S2, the control and management terminal, while generating a hierarchical electronic map of the work area, also integrates and receives precise meteorological data for a specific future time period. In the collaborative work planning of step S3, the work plan is dynamically adjusted based on the meteorological data. If adverse weather is predicted, agricultural machinery is dispatched to prioritize completing the work tasks in the most affected areas. By incorporating precise meteorological data into the work planning decision factors, the system possesses the ability to foresee and respond to weather risks, proactively avoiding the risks of decreased work quality, machinery damage, or complete shutdown caused by sudden severe weather, thus realizing a transformation from static planning to dynamic predictive scheduling.

[0011] To further optimize the above technical solution, the control and management terminal is equipped with a human-machine interface. Managers can click on any agricultural machinery icon on the electronic map to view a comprehensive, integrated data view of that machinery in real time. This integrated view combines location information from step S2 and sensor data from step S4. Managers can make remote manual intervention decisions based on this integrated view and manually issue emergency commands to specific agricultural machinery. This invention, by providing a visualized manual intervention channel that integrates multi-source data, retains human supervision and decision-making power within a highly automated system. This not only leverages the efficiency and precision of automated systems but also allows humans to handle unpredictable and extremely complex situations based on experience, enhancing the system's flexibility and emergency response capabilities.

[0012] To further optimize the above technical solution, in step S4, each agricultural machine is also equipped with front and rear dual-channel high-definition cameras to collect real-time video streams of the working environment in front of the machine and the working status of the implements behind it. The video streams are transmitted to the control and management terminal via a 5G network to assist in judging whether the sensor data is erroneous and to remotely evaluate the quality of the operation. This invention adopts dual-channel video monitoring, and the video streams are used to remotely determine the cause of faults (such as visible mechanical damage), verify the operation effect (such as seedling emergence), and provide video data for manual intervention, enhancing the dimension of system status perception.

[0013] To further optimize the above technical solution, the high-definition camera is equipped with a protective cover made of transparent high-strength material. The protective cover is equipped with a rinsing device and a cleaning scraper. The control and management terminal can trigger a cleaning command at regular intervals or automatically according to the clarity of the camera image. The micro water pump of the rinsing device sprays water to remove dust from the lens surface and the cleaning scraper removes water stains and dirt, thus solving the problem that high-definition cameras are easily contaminated and damaged in harsh agricultural operating environments.

[0014] To further optimize the above technical solution, in step S6, the control and management terminal plans the optimal takeover path for the standby agricultural machinery to reach the location of the malfunctioning machinery. This path planning comprehensively considers obstacles such as field ridges and ditches, as well as existing work tracks. This invention plans the optimal takeover path for standby agricultural machinery by comprehensively considering geographical obstacles and existing work tracks, minimizing ineffective travel, energy waste, and damage to already worked areas during the standby machine's relocation process, and achieving accurate task handover between units.

[0015] To further optimize the above technical solution, the control and management terminal automatically summarizes and analyzes global data after each operation, generating a comprehensive performance report that includes total operating area, total fuel consumption, average operating efficiency, agricultural machinery utilization rate, abnormal event records, and operation quality assessment results. This comprehensive performance report, automatically generated upon completion of the operation, allows for data mining and analysis throughout the entire process. The report provides managers with analytical data, helping to evaluate agricultural machinery performance, optimize team configuration, improve work plans, and calculate operating costs, thereby achieving the management goal of cost reduction and efficiency improvement.

[0016] To further optimize the above technical solution, the control and management terminal uses a heartbeat detection method to monitor the equipment with each agricultural machine, periodically checking the communication connection status. If the communication heartbeat signal of the agricultural machine is continuously lost, the machine is immediately marked as disconnected, an early warning is automatically triggered, and contact is restored through redundant communication channels. Simultaneously, its responsible area is included in the dynamic redistribution plan in step S6. In equipment monitoring, heartbeat detection refers to the technology of periodically sending "heartbeat packets" to confirm the online status of the equipment. If no response is received within a timeout, an alarm is triggered. Establishing a heartbeat monitoring mode adds a self-diagnostic capability to the communication link status of the system. It can promptly detect disconnections caused by signal obstruction, equipment failure, etc., and trigger corresponding early warnings and task redistribution processes, effectively avoiding agricultural machine breakdowns and task blind spots caused by communication interruptions.

[0017] A collaborative operation management system for agricultural machinery fleets includes a central processing unit, a high-precision BeiDou positioning module, an agricultural machinery status sensor array, a dual-channel visual monitoring module, and a 5G communication gateway. The central processing unit, deployed in the control center, is used to execute all the logical functions of the control and management terminal, and to integrate and coordinate the work of the following modules and the dynamic task scheduling module; The system includes a high-precision BeiDou positioning module integrated into each agricultural machine for real-time positioning and farmland geographic information collection, providing location data input to the central processing unit. Distributed agricultural machine status sensor arrays are installed on key parts of each machine to collect machinery and operational status data, which is then uploaded via the communication module described below. A dual-channel visual monitoring module is installed on each machine to collect environmental and operational video streams, complementing the status sensor data. A 5G communication gateway is deployed between each machine and the control center for low-latency, high-reliability bidirectional transmission of all data, forming the system's information transmission neural network. A dynamic task scheduling module is built into the central processing unit, responsible for task allocation, path replanning, and backup machine scheduling. The central processing unit integrates and processes all input data, driving the collaborative operation management of the entire agricultural machine fleet through the dynamic task scheduling module.

[0018] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a method and system for collaborative operation management of agricultural machinery groups, realizing the transformation of agricultural machinery group operation management mode from decentralized and passive to active and collaborative. The present invention deeply integrates high-precision Beidou positioning, multi-source status sensing, 5G communication, visual monitoring and central intelligent processing unit to realize collaborative operation management of agricultural machinery groups.

[0019] This invention achieves a scientific layout for agricultural machinery cluster operations through digital map construction and collaborative path planning, preventing path overlap and omissions from the outset. During operation, multi-dimensional monitoring and intelligent analysis of real-time data streams enable immediate detection of operational deviations and potential faults, automatically tracing their root causes and elevating problem handling from reactive manual responses to proactive intelligent assessment. When individual units malfunction, the invention's dynamic reallocation and self-adjustment mechanisms can quickly mobilize backup resources to seamlessly take over tasks, ensuring the continuity of cluster operations and maintaining overall progress. Furthermore, through operational quality control and external risk warning, this invention ensures stable and reliable agronomic quality and adaptability to uncertain external environments while pursuing efficiency. In summary, this invention not only significantly improves the time utilization rate and task completion reliability of agricultural machinery clusters but also reduces reliance on human labor and enhances the overall level of intelligent operation through its high level of automation and intelligence. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0021] Figure 1 The attached figure is a flowchart of a collaborative operation management method for agricultural machinery groups according to the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention discloses a method for collaborative operation management of agricultural machinery fleets, comprising the following steps: S1. Agricultural machinery registration and grouping: The basic information, performance parameters and current health status of each agricultural machine in the agricultural machinery group are entered into the control and management terminal. The agricultural machines are divided into several functional coordination groups according to the type of operation task, and some agricultural machines are designated as dynamic standby groups. S2. Construction of digital farmland map: The control and management terminal obtains high-precision farmland geographic information through the Beidou positioning system, and then integrates farmland boundary, elevation difference, soil moisture distribution and obstacle location data to generate a layered electronic map of the operation area containing multiple geographic attributes. The precise location of each agricultural machine is obtained in real time through the Beidou positioning system and dynamically projected onto the layered electronic map of the operation area. S3. Collaborative Operation Planning: Based on the hierarchical electronic map of the operation area generated in step S2 and the agricultural machinery performance parameters entered in step S1, the control and management terminal plans the optimal collaborative operation path for each functional collaborative group and estimates the operation completion time for each area. S4. Real-time synchronous monitoring of status: Sensors are installed on each agricultural machine to monitor engine load, fuel level, hydraulic system pressure, travel speed, implement lifting status and tire pressure. Sensor data is transmitted to the control and management terminal through the 5G network to form a real-time data stream of agricultural machine health and operation status. S5. Dynamic detection and analysis of operational deviations: During the operation of the machine group, the control and management terminal compares the real-time Beidou positioning information of each agricultural machine with the optimal collaborative operation path planned in step S3, and automatically detects whether the agricultural machine has deviated from the path, lagged behind in progress, or interrupted the operation; if a deviation is found, the real-time status data stream of the agricultural machine in step S4 is automatically linked and called for analysis, and the cause of the deviation is automatically analyzed. S6. Dynamic task reallocation and system self-adjustment: For agricultural machinery that needs to be withdrawn due to failure or low efficiency, the control and management terminal automatically isolates it from the current work group and immediately dispatches the nearest agricultural machinery from the dynamic standby machine group set in step S1 to access it. At the same time, it recalculates and allocates the remaining work tasks in real time based on the current work progress and generates new collaborative work instructions to be sent to the newly accessed agricultural machinery.

[0024] This invention achieves a leap from passive monitoring to proactive collaborative management of agricultural machinery clusters through a refined closed-loop management system encompassing agricultural machinery grouping, digital map construction, collaborative planning, real-time monitoring, and dynamic adjustment. It solves the problem of overall operation interruption caused by the failure or inefficiency of a single agricultural machine. By dynamically scheduling backup machines and reallocating tasks, it significantly improves the overall operational efficiency, anti-interference capability, and task completion reliability of agricultural machinery clusters.

[0025] To further optimize the above technical solution, in step S4, an operation quality sensor is also installed on the agricultural machinery to collect data on tillage depth, sowing density, or fertilizer application rate in real time. The control and management terminal compares the operation quality data with preset agronomic standards. If a quality deviation is found, a quality correction command is generated and sent to the corresponding agricultural machinery via the network to control the adjustment of the machinery parameters. By adding an operation quality sensor to the machinery and providing feedback to the preset standards, not only can the working status of the agricultural machinery be monitored, but the work quality can also be further supervised. This effectively avoids operation quality problems such as missed sowing, double sowing, and uneven fertilization caused by improper machinery adjustment or poor condition. The management granularity is deepened from the mechanical level to the agronomic level, ensuring the high-quality completion of agricultural production operations.

[0026] To further optimize the above technical solution, in step S2, the control and management terminal integrates and receives precise meteorological data for a specific future time period when generating the hierarchical electronic map of the work area; in the collaborative work planning of step S3, the work plan is dynamically adjusted based on the meteorological data. If adverse weather is predicted, agricultural machinery is dispatched to prioritize completing the work tasks in the most affected areas. By incorporating precise meteorological data into the work planning decision factors, the system possesses the ability to foresee and respond to weather risks, proactively avoiding the risks of decreased work quality, machinery damage, or complete shutdown caused by sudden severe weather, thus realizing the transformation from static planning to dynamic predictive scheduling.

[0027] To further optimize the above technical solution, the control and management terminal is equipped with a human-machine interface. Managers can click on any agricultural machinery icon on the electronic map to view a comprehensive, integrated data view of that machinery in real time. This integrated view combines location information from step S2 and sensor data from step S4. Managers can make remote manual intervention decisions based on this integrated view and manually issue emergency commands to specific agricultural machinery. This invention, by providing a visualized manual intervention channel that integrates multi-source data, retains human supervision and decision-making power within a highly automated system. This not only leverages the efficiency and precision of automated systems but also allows humans to handle unpredictable and extremely complex situations based on experience, enhancing the system's flexibility and emergency response capabilities.

[0028] To further optimize the above technical solution, in step S4, each agricultural machine is also equipped with front and rear dual-channel high-definition cameras to collect real-time video streams of the working environment in front of the machine and the working status of the implements behind it. The video streams are transmitted to the control and management terminal via a 5G network to assist in judging whether the sensor data is erroneous and to remotely evaluate the quality of the operation. This invention adopts dual-channel video monitoring, and the video streams are used to remotely determine the cause of faults (such as visible mechanical damage), verify the operation effect (such as seedling emergence), and provide video data for manual intervention, enhancing the dimension of system status perception.

[0029] To further optimize the above technical solution, the high-definition camera is equipped with a protective cover made of transparent high-strength material. The protective cover is equipped with a rinsing device and a cleaning scraper. The control and management terminal can trigger a cleaning command at regular intervals or automatically according to the clarity of the camera image. The micro water pump of the rinsing device sprays water to remove dust from the lens surface and the cleaning scraper removes water stains and dirt, thus solving the problem that high-definition cameras are easily contaminated and damaged in harsh agricultural operating environments.

[0030] To further optimize the above technical solution, in step S6, the control and management terminal plans the optimal takeover path for the standby agricultural machinery to reach the location of the faulty machinery. This path planning comprehensively considers obstacles such as field ridges and ditches, as well as existing work tracks. This invention plans the optimal takeover path for standby agricultural machinery by comprehensively considering geographical obstacles and existing work tracks, minimizing ineffective travel, energy waste, and damage to already worked areas during the standby machine's relocation process, thus achieving accurate task handover between units.

[0031] To further optimize the above technical solution, the control and management terminal automatically summarizes and analyzes global data after each operation, generating a comprehensive performance report that includes total operating area, total fuel consumption, average operating efficiency, agricultural machinery utilization rate, abnormal event records, and operation quality assessment results. This comprehensive performance report, automatically generated upon completion of the operation, allows for data mining and analysis throughout the entire process. The report provides managers with analytical data, helping to evaluate agricultural machinery performance, optimize team configuration, improve work plans, and calculate operating costs, thereby achieving the management goal of cost reduction and efficiency improvement.

[0032] To further optimize the above technical solution, the control and management terminal uses a heartbeat detection method to monitor the equipment and periodically check the communication connection status. If the communication heartbeat signal of the agricultural machine is continuously lost, the machine is immediately marked as disconnected, an early warning is automatically triggered, and contact is restored through redundant communication channels. Simultaneously, its responsible area is included in the dynamic redistribution plan in step S6. In equipment monitoring, heartbeat detection refers to the technology of periodically sending "heartbeat packets" to confirm the online status of the equipment. If no response is received within a timeout, an alarm is triggered. Establishing a heartbeat monitoring mode adds a self-diagnostic capability to the communication link status of the system. It can promptly detect disconnections caused by signal obstruction, equipment failure, etc., and trigger corresponding early warnings and task redistribution processes, effectively avoiding agricultural machine breakdowns and task blind spots caused by communication interruptions.

[0033] A collaborative operation management system for agricultural machinery fleets includes a central processing unit, a high-precision BeiDou positioning module, an agricultural machinery status sensor array, a dual-channel visual monitoring module, and a 5G communication gateway. The central processing unit, deployed in the control center, is used to execute all the logical functions of the control and management terminal, and integrates and coordinates the work of the following modules and the dynamic task scheduling module; The system includes a high-precision BeiDou positioning module integrated into each agricultural machine for real-time positioning and farmland geographic information collection, providing location data input to the central processing unit. Distributed agricultural machine status sensor arrays are installed on key parts of each machine to collect machinery and operational status data, which is then uploaded via the following communication module. A dual-channel visual monitoring module is installed on each machine to collect environmental and operational video streams, complementing the status sensor data. A 5G communication gateway is deployed between each machine and the control center for low-latency, high-reliability bidirectional transmission of all data, forming the system's information transmission neural network. A dynamic task scheduling module is built into the central processing unit, responsible for task allocation, path replanning, and backup machine scheduling. The central processing unit integrates and processes all input data, driving the collaborative operation management of the entire agricultural machine fleet through the dynamic task scheduling module.

[0034] Technical principle explanation: This invention first integrates BeiDou positioning with a geographic information system to generate a high-precision digital map, providing a global operational framework for agricultural machinery clusters. Based on this, multi-source sensors and vision modules deployed on agricultural machinery terminals collect real-time data on machinery status, operational quality, and the environment, transmitting this data back to the central processing unit via a high-speed communication network. The central unit, as the core processing unit, compares and analyzes the collected real-time data with preset operational plans, employing a dynamic task scheduling algorithm to optimize operational paths and intelligently diagnose anomalies. When deviations or malfunctions are detected, the system automatically triggers a dynamic reallocation mechanism, re-planning tasks and scheduling backup resources to ensure uninterrupted operational flow. This invention provides a collaborative operation management method and system for agricultural machinery clusters, offering a specific self-optimizing and autonomously collaborative intelligent operation management method and system, fundamentally improving the systematic nature, reliability, and refined management level of agricultural machinery cluster operations.

[0035] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0036] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cooperative work management method for a farm machine fleet, characterized by, The method comprises the following steps: S1, agricultural machinery registration and grouping: the basic information, performance parameters and current health status of each agricultural machine in the agricultural machine group are input into the control management terminal, and the agricultural machines are divided into several functional cooperative groups according to the types of operation tasks, and part of the agricultural machines are designated as dynamic standby machine groups; S2, digital farmland map construction: the control management terminal obtains high-precision farmland geographic information through the Beidou positioning system, then fuses farmland boundary, elevation difference, soil moisture distribution and obstacle location data to generate a layered operation area electronic map containing multiple geographic attributes, and dynamically projects the accurate position of each agricultural machine on the layered operation area electronic map through the Beidou positioning system; S3, cooperative operation planning: the control management terminal plans the optimal cooperative operation path for each functional cooperative group based on the layered operation area electronic map generated in step S2 and the performance parameters of the agricultural machines input in step S1, and estimates the operation completion time of each region; S4, real-time state synchronization monitoring: sensors for monitoring engine load, fuel level, hydraulic system pressure, travel speed, implement lifting state and tire pressure are installed on each agricultural machine, and the sensor data is transmitted to the control management terminal through the 5G network to form real-time data flow of the health and operation state of the agricultural machine; S5, operation deviation dynamic detection and analysis: during the operation of the machine group, the control management terminal compares the real-time Beidou positioning information of each agricultural machine with the optimal cooperative operation path planned in step S3 to automatically detect whether the agricultural machine deviates from the path, lags behind the schedule or interrupts the operation; if a deviation is found, the real-time state data flow of the agricultural machine in step S4 is automatically associated and called for analysis to automatically analyze the reason for the deviation; S6, task dynamic redistribution and system self-adjustment: for the agricultural machine that needs to be withdrawn due to failure or low efficiency, the control management terminal automatically isolates it from the current operation group and immediately schedules the nearest distance agricultural machine from the dynamic standby machine group set in step S1 to access, and real-time recalculates and distributes the remaining operation tasks based on the current operation progress to generate new cooperative operation instructions and issue them to the newly accessed agricultural machine.

2. The cooperative operation management method for the agricultural machine group according to claim 1, characterized in that: In step S4, an operation quality sensor is also installed on the operation implement of the agricultural machine for real-time collection of tillage depth, seeding density or fertilizer amount data; the control management terminal compares the operation quality data with the preset agronomic standard, and if a quality deviation is found, generates a quality correction instruction and issues it to the corresponding agricultural machine through the network to control the adjustment of the implement parameters.

3. The cooperative operation management method for the agricultural machine group according to claim 2, characterized in that: In step S2, the control management terminal also integrates the reception of accurate weather data in a specific future time period when generating the layered operation area electronic map; in the cooperative operation planning of step S3, the operation plan is dynamically adjusted in combination with the weather data, and if adverse weather is predicted, the agricultural machines are scheduled to preferentially complete the operation tasks of the most affected regions.

4. The cooperative operation management method for the agricultural machine group according to claim 3, characterized in that: The control management terminal is provided with a human-computer interaction interface, and the management personnel can click on any agricultural machine icon on the electronic map to view the all-around data integrated view of the agricultural machine in real time, and the all-around data integrated view integrates the position information from step S2 and the sensor data from step S4; The management personnel can make remote manual intervention judgment based on the integrated view and manually issue emergency instructions to specific agricultural machines.

5. The method for cooperative operation management of a farm machine group according to claim 4, characterized by: In step S4, each agricultural machine is also equipped with front and rear dual high-definition cameras that collect real-time video streams of the working environment in front of the agricultural machine and the working state of the rear machine tool; the video streams are transmitted to the control management terminal through the 5G network for assisting in judging whether the sensor data is incorrect and remotely evaluating the working quality.

6. The method for cooperative operation management of a farm machine group according to claim 5, characterized by: The high-definition camera is externally provided with a protective cover made of transparent high-strength material, and the protective cover is provided with a washing device and a cleaning scraper; the control management terminal can automatically trigger a cleaning instruction according to the camera picture definition to control the micro water pump of the washing device to spray water flow to remove dust on the lens surface and remove water stains and stains through the cleaning scraper.

7. The method for cooperative operation management of a farm machine group according to claim 6, characterized by: In step S6, the control management terminal plans an optimal takeover path for the standby agricultural machine to the position of the failed agricultural machine, which comprehensively considers the obstacles such as ridges, ditches and existing working trajectories.

8. The method for cooperative operation management of a farm machine group according to claim 7, characterized by: The control management terminal automatically summarizes and analyzes global data after completing a single working task to generate a comprehensive performance report including total working area, total fuel consumption, average working efficiency, agricultural machine utilization rate, abnormal event record and working quality evaluation result.

9. The cooperative operation management method for the agricultural machine group according to any one of claims 1 to 8, characterized by: The control management terminal and each agricultural machine use heartbeat detection method for equipment monitoring, and periodically check the communication connection state; If the communication heartbeat signal of the agricultural machine is continuously lost, the agricultural machine is immediately marked as lost, an early warning is automatically triggered, the contact is restored through a redundant communication channel, and the responsible area is included in the dynamic redistribution plan of step S6.

10. A cooperative work management system for a fleet of agricultural machines for implementing the management method according to any one of claims 1 to 9, characterized in that, It includes a central processing unit, a high-precision Beidou positioning module, an agricultural machine state sensor array, a dual-channel visual monitoring module, and a 5G communication gateway. The central processing unit is deployed in the control center and is used to execute all logical functions of the control management terminal, and integrates and coordinates the work of the following modules and a dynamic task scheduling module. The high-precision Beidou positioning module is integrated in each agricultural machine for real-time positioning and farmland geographic information collection to provide position data input for the central processing unit; the agricultural machine state sensor array is distributedly installed at key positions of each agricultural machine for collecting mechanical and working state data and uploading through the following communication module; the dual-channel visual monitoring module is installed on each agricultural machine for collecting environmental and working video streams, which are complementary to the state sensor data; the 5G communication gateway is deployed in each agricultural machine and the control center for low-latency and high-reliability bidirectional transmission of all data, forming an information transmission neural network of the system; the dynamic task scheduling module is built-in in the central processing unit and is responsible for task allocation, path re-planning and standby machine scheduling of the agricultural machine; the central processing unit integrates and processes all input data and drives the whole agricultural machine group cooperative working management through the dynamic task scheduling module.

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