Agricultural machinery scheduling method and equipment based on dynamic fault emergency and load balancing

Abstract

The invention discloses a cross-regional agricultural machinery scheduling method and device based on dynamic fault emergency and load balancing, and the method comprises the steps: building a dynamic fault emergency response mechanism and a load balancing scheduling model through monitoring the operation state of cross-regional operation agricultural machinery, regional farmland operation load and operation demands in real time; when the agricultural machinery breaks down, the agricultural machinery is quickly matched and replaced, an optimal scheduling path is planned, and meanwhile, an agricultural machinery distribution scheme is dynamically adjusted based on regional load rate differences, so that efficient utilization of cross-regional agricultural machinery resources is realized. According to the method, the problems of slow fault emergency response, no maintenance condition in a fault occurrence area, non-uniform load distribution and low cross-regional cooperation efficiency in existing cross-regional agricultural machinery scheduling are solved, the agricultural machinery operation efficiency is effectively improved, the agricultural operation progress is guaranteed, and the agricultural production loss is reduced.

Description

Technical Field

[0001] This invention relates to the field of agricultural machinery scheduling technology, specifically to an agricultural machinery scheduling method and equipment based on dynamic fault emergency response and load balancing. Background Technology

[0002] Shared agricultural machinery is an application of the sharing economy in the agricultural field. Cross-regional agricultural machinery mainly refers to the nationwide agricultural machinery fleet harvesting crops such as wheat and corn across regions according to the season. During cross-regional operations, the long-term heavy use of agricultural machinery increases the possibility of malfunctions. In the event of a sudden malfunction, the repair requires the allocation of corresponding spare parts from other regions for replacement and repair. The time cost involved needs to be considered. Therefore, cross-regional agricultural machinery dispatching based on dynamic fault emergency response and load balancing can use backup agricultural machinery to make up for the time cost of repairing malfunctioning agricultural machinery. Through load balancing, damage to agricultural machinery caused by excessive load can be reduced, operating efficiency can be improved, and harvesting time can be guaranteed.

[0003] While existing cross-regional agricultural machinery dispatching technologies can achieve basic dispatching through initial dispatch plan generation and weather forecast interval optimization, they lack emergency handling mechanisms for sudden malfunctions during cross-regional operations. When dispatched machinery malfunctions, replacement machinery must be selected, otherwise harvesting delays are likely. Existing cross-regional dispatching methods for agricultural machinery maintenance, while considering potential machinery damage during cross-regional operations and collecting historical data on vulnerable parts for preliminary prediction, fail to account for the time cost of repairs during cross-regional operations, as well as the time cost of repairs due to the lack of adequate repair facilities in damaged areas. Furthermore, when multiple machines operate together, selection is based solely on cost priority, without balancing the operating area and duration. Some machines operate at high loads for extended periods, increasing the risk of malfunction, while others operate at low loads, resulting in low resource utilization. In addition, existing technologies lack a systematic solution for transferring tasks after machinery malfunctions, failing to quickly fill gaps to ensure harvesting progress and failing to meet the dual requirements of "efficiency and stability" in cross-regional operations.

[0004] To address the aforementioned issues, there is an urgent need for a cross-regional agricultural machinery dispatching solution that integrates dynamic fault emergency response and load balancing, in order to improve the flexibility, reliability, and resource utilization of agricultural machinery dispatching. Summary of the Invention

[0005] This invention aims to solve the technical problems of slow emergency response to faults, uneven load distribution, and low efficiency of cross-regional coordination in existing cross-regional agricultural machinery scheduling. It proposes an agricultural machinery scheduling method and equipment based on dynamic emergency response to faults and load balancing, so as to achieve rapid response to agricultural machinery faults, load balancing between regions, dynamic balance of load within regions, and efficient coordination of cross-regional resources.

[0006] To achieve the above objectives, the present invention provides an agricultural machinery scheduling method based on dynamic fault emergency response and load balancing, comprising: The system uses IoT sensors mounted on agricultural machinery to collect agricultural machinery operation information and regional operation terminals to collect farmland information in each operation area to calculate real-time load data. The collected data is cleaned and standardized. A dynamic emergency database is built based on the cleaned information, and a relationship between "faulty agricultural machinery and replacement agricultural machinery" is established. Set a reasonable regional load rate to conduct regional load assessment, and carry out regional load balancing scheduling based on the load rate of each work area; A load balancing model within the region is established based on the objective function of linear programming, enabling real-time dynamic fault response and load balancing scheduling within the region.

[0007] As a further optimization of the agricultural machinery scheduling method based on dynamic fault emergency response and load balancing of the present invention, the agricultural machinery scheduling method specifically includes the following steps: S1: Collect agricultural machinery operation information using IoT sensors mounted on agricultural machinery; use regional operation terminals to obtain in real time the regional location, area, number of agricultural machinery in each operation area, regional crop harvesting information of the crop name in the area, agricultural machinery identification, operation type, operation efficiency and regional standby attributes, and perform real-time load data calculation. S2: Perform unified coordinate format, fault code mapping, and data cleaning and standardization processing on the data collected by agricultural machinery in step S1 and the information obtained in real time from regional terminals; construct a dynamic fault emergency database to store agricultural machinery information of multiple cross-regional agricultural machinery with dispatch authority, and establish the "faulty agricultural machinery-replacement agricultural machinery" relationship. S3: Based on the data obtained from the cleaning and standardization process in step S2 and the load rate calculated in step S1, set a reasonable area load rate to conduct an area load assessment, and adjust the area load according to the load rate of each work area: When an area is identified as a high-load area, idle agricultural machinery is transferred from low-load areas to high-load areas; When an area is identified as a low-load area, if there is an overload demand in other areas, the idle agricultural machinery in this area will be dispatched first. If there is no dispatch demand, the operation rhythm of agricultural machinery in this area will be adjusted: the duration of a single operation will be extended and the operation sequence of plots will be optimized. S4: Based on the crop harvesting information from step S1 and the information of multiple agricultural machines in the region, establish a multi-agricultural-machine load balancing model; The system receives real-time data on the operating status and load status of agricultural machinery. If a fault signal is detected or the operation of agricultural machinery exceeds the linear constraint objective function of the load balancing model, an emergency dispatch algorithm is used to match a compatible standby agricultural machine from the dynamic fault emergency database that is closest to the fault location. A dispatch instruction is generated to transfer the unfinished sub-area of ​​the machine to low-load agricultural machines and substitute agricultural machines, thereby dynamically adjusting the operation allocation. By combining the substitute dispatch instructions with the dynamically adjusted job allocation, a final agricultural machinery dispatch plan is generated and sent to the regional operation terminal and the substitute agricultural machinery. During fault repair, a task redistribution model is used to add the repaired faulty agricultural machinery to the task.

[0008] As a further optimization of the agricultural machinery scheduling method based on dynamic fault emergency response and load balancing of the present invention: the calculation formula for real-time load data in step S1 is as follows: ; in, For real-time load rate, This represents the current area awaiting work. The total number of agricultural machines in the region. This represents the maximum daily operating area for a single agricultural machine.

[0009] As a further optimization of the agricultural machinery scheduling method based on dynamic fault emergency response and load balancing of the present invention: in step S2, the correlation between faulty agricultural machinery and substitute agricultural machinery is as follows: Agricultural machinery is considered to be operating normally if no serious abnormal operating status alarm is triggered after data cleaning. When a fault occurs, the "faulty agricultural machinery-backup agricultural machinery association" refers to calling upon agricultural machinery of the same or similar model (but with the same operation type, i.e., the same harvesting type, and a harvesting efficiency difference within a certain range) when an abnormal operating status alarm is triggered. The backup agricultural machinery is mainly determined by the data acquisition in S1 and the data cleaning in S2. Specifically, it involves selecting agricultural machinery with the same operation type and an efficiency difference within a certain range. Agricultural machinery is identified as a compatible replacement; regional reserve status is indicated by the list of reserve agricultural machinery in the region to which the agricultural machinery belongs, and the proportion of reserve agricultural machinery is not less than 20% of the total number of agricultural machinery with dispatch authority; The constraints are: .

[0010] As a further optimization of the agricultural machinery scheduling method based on dynamic fault emergency response and load balancing of the present invention: the reasonable regional load rate in step S3 is: ; in, For a reasonable regional load factor, then The area is considered a low-load area. The area is considered a high-load area.

[0011] As a further optimization of the agricultural machinery scheduling method based on dynamic fault emergency response and load balancing of the present invention: the formula for calculating the number of idle agricultural machines transferred from low-load areas to high-load areas in step S3 is as follows: ; in, To allocate the number of agricultural machines.

[0012] As a further optimization of the agricultural machinery scheduling method based on dynamic fault emergency response and load balancing of the present invention: the load balancing model in step S4 is as follows: ; The constraints are: , in, For load balance, For agricultural machinery Homework time, The average operating time for all agricultural machinery. For the first The operating area allocated to agricultural machinery in Taiwan. The average area covered by all agricultural machinery.

[0013] As a further optimization of the agricultural machinery scheduling method based on dynamic fault emergency response and load balancing of the present invention: the emergency dispatch algorithm in step S4 is as follows: ; in, For the fitness function, Total operation time , , These are the weighting coefficients. The total scheduling cost is expressed as follows: ; in, For the amount of farmland, For agricultural machinery to farmland distance, The variable is 0-1, where 1 represents the allocation of substitute agricultural machinery to farmland, and 0 otherwise.

[0014] As a further optimization of the agricultural machinery scheduling method based on dynamic fault emergency response and load balancing of the present invention: the task redistribution model in step S4 is as follows: ; in, The number of fault scenarios, For fault scenarios Agricultural machinery Distributed to farmland The cost, The variable is 0-1, where 1 represents the allocation of substitute agricultural machinery to farmland, and 0 otherwise. For agricultural machinery The cost of troubleshooting These are 0-1 variables, where 1 represents a fault scenario. Agricultural machinery Needs repair, otherwise 0; Constraints: ; in, For agricultural machinery Repair time.

[0015] The present invention also provides an agricultural machinery scheduling device based on dynamic fault emergency response and load balancing, the device comprising: at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Based on the IoT sensors mounted on the agricultural machinery, the system collects the operation information of the agricultural machinery. Based on the regional operation terminal, it obtains the regional information such as the location, area, and number of agricultural machinery in each operation area, as well as the regional crop harvesting information, the agricultural machinery identification, operation type, operation efficiency, and regional standby attributes, and performs real-time load data calculation. Data cleaning and standardization are performed based on the collected data, including unified coordinate format and fault code mapping. Construct a dynamic emergency database: store agricultural machinery information for multiple cross-regional agricultural machines with dispatching authority, and establish a "faulty agricultural machine - replacement agricultural machine" relationship; Regional load adjustments are made based on the load rate of each work area. When an area is identified as a high-load area, idle agricultural machinery is transferred from low-load areas to high-load areas. When an area is identified as a low-load area, if there is overload demand in other areas, idle agricultural machinery in that area is prioritized for dispatch. A multi-machine load balancing model is established. Based on real-time received data on the operating and load status of the machines, if a machine fault signal is detected or the machine's workload exceeds the linear constraint objective function of the load balancing model, an emergency dispatch algorithm is used to match a compatible backup machine closest to the fault location from a dynamic fault emergency database. A dispatch instruction is generated to transfer the machine's unfinished work sub-area to low-load machines and substitute machines, dynamically adjusting the work allocation. Combining the substitute dispatch instruction with the dynamically adjusted work allocation, a final machine dispatch plan is generated and sent to the regional work terminal and the substitute machines. During fault repair, a task redistribution model is used to add the repaired faulty machine to the task.

[0016] This invention offers the following advantages: By monitoring the real-time operating status of agricultural machinery operating across regions, the regional farmland workload, and operational demands, it constructs a dynamic fault emergency response mechanism and a load balancing scheduling model. When agricultural machinery malfunctions, it quickly matches replacement machinery and plans the optimal scheduling path. Simultaneously, based on regional load rate differences, it dynamically adjusts the agricultural machinery allocation scheme, achieving efficient utilization of agricultural machinery resources across regions. This solves the problems of slow fault emergency response, lack of repair conditions in the fault location, uneven load distribution, and low cross-regional coordination efficiency in existing cross-regional agricultural machinery scheduling. It effectively improves agricultural machinery operation efficiency, ensures the progress of agricultural operations, and reduces agricultural production losses. Attached Figure Description

[0017] Figure 1 This is a flowchart of the cross-regional agricultural machinery scheduling method based on dynamic fault emergency response and load balancing of the present invention; Figure 2 This is a logic block diagram of the cross-regional agricultural machinery scheduling method based on dynamic fault emergency response and load balancing of the present invention. Figure 3 This is a schematic diagram of the cross-regional agricultural machinery dispatching equipment based on dynamic fault emergency response and load balancing of the present invention.

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0019] The structural and working principles of the present invention will be described in detail below with reference to the accompanying drawings: <Example 1> like Figure 1 and 2 As shown, a method for cross-regional agricultural machinery scheduling based on dynamic fault emergency response and load balancing specifically includes the following steps: S1: Calculate real-time load data based on agricultural machinery operation information and regional information; First, IoT sensors are used to obtain real-time operating information such as agricultural machinery speed, load, and fault signals. Then, regional operation terminals are used to obtain regional information such as location, area, and number of agricultural machinery. At the same time, crop information such as crop name and maturity level are obtained, as well as agricultural machinery attributes such as identification, operation type, operation efficiency, and standby attributes, which facilitates load calculation and subsequent data processing.

[0020] Then, based on the agricultural machinery information collected by the aforementioned IoT platform, and by obtaining real-time information on each work area from the regional operation terminal, real-time load calculation is performed.

[0021] For real-time load calculation, the method is as follows: ; in, For real-time load rate, This represents the current area awaiting work. The total number of agricultural machines in the region. This represents the maximum daily operating area for a single agricultural machine.

[0022] S2: Clean and standardize the data collected in S1; construct a dynamic emergency database to establish a "faulty agricultural machinery - replacement agricultural machinery" relationship; First, the information collected in S1 is cleaned and standardized. The coordinate format of the regional information is unified and the agricultural machinery fault codes are uniformly identified and mapped. Faults that require emergency response, such as engine failure and blade damage, are identified as emergency faults.

[0023] Secondly, a dynamic emergency response database is constructed to establish a "faulty agricultural machinery - replacement agricultural machinery" association. The specific association is as follows: Agricultural machinery is considered to be operating normally if no serious abnormal operating status alarm is triggered after data cleaning. When a fault occurs, the "faulty agricultural machinery-reserve agricultural machinery association" refers to calling upon an agricultural machinery of the same or similar model (but with the same operation type, i.e., the same harvesting type, and harvesting efficiency difference within a certain range) when an abnormal operating status alarm is triggered. The substitute agricultural machinery is mainly determined by data acquisition in S1 and data cleaning in S2. Model compatibility is determined by the agricultural machinery's operation type and the range of operation efficiency deviation.

[0024] Specifically, this refers to tasks with the same type but different efficiency levels. Agricultural machinery is identified as a compatible replacement; regional standby status is indicated by the list of standby agricultural machinery in the region to which the agricultural machinery belongs, and the proportion of standby agricultural machinery is not less than 20% of the total number of agricultural machinery with dispatch authority.

[0025] The constraints are: .

[0026] S3: Based on the data obtained from cleaning in S2 and the load rate calculated in S1, set a reasonable regional load rate to conduct regional load assessment and implement regional load balancing scheduling.

[0027] First, based on the data obtained after cleaning and standardization in S2, and the load factor calculated in S1, a reasonable regional load factor is set for regional load assessment. The reasonable regional load is: , in, For a reasonable regional load factor, then The area is considered a low-load area. The area is considered a high-load area.

[0028] Secondly, regional load assessment is conducted based on a set reasonable load rate. Then, based on the regional load rate threshold, the agricultural machinery operation area and operation time are evenly allocated. High-load areas are subject to regional load scheduling, while low-load areas are assessed to achieve balanced allocation. Specifically: Area 1 is identified as a high-load area, and its adjacent area, Area 2, is identified as a low-load area. For high-load areas: idle agricultural machinery is transferred from low-load areas to Area 1 to ensure that Area 1, after transfer, remains within a reasonable regional load rate threshold. For low-load areas: first, it is determined whether there is a need for high-load areas. If Area 1 does have a scheduling requirement, idle agricultural machinery is preferentially transferred from Area 2 to Area 1. If there is no scheduling requirement, the work rhythm in Area 2 is adjusted, working hours are extended, and the order of plots is optimized to optimize local operations, ultimately achieving balanced regional load scheduling.

[0029] Finally, the number of scheduling operations was calculated, and the number of operations scheduled from region 2 to region 1 was: , in, To allocate the number of agricultural machines.

[0030] S4: Based on the information in S1, establish a multi-machine load balancing model. If there is a need for emergency fault response and load balancing, adopt the emergency dispatch algorithm, combine the substitute dispatch instructions with the dynamically adjusted job allocation, and generate the final machine dispatch plan. If the fault is recovered, adopt the task redistribution model, add the repaired faulty machine to the task, and complete the dynamic emergency fault response and load balancing machine dispatch.

[0031] First, based on normal load balancing, a multi-machine load balancing model is established. The load balancing model is designed as follows: ; The constraints are: , in, For load balance, For agricultural machinery Homework time, The average operating time for all agricultural machinery. For the first The operating area allocated to agricultural machinery in Taiwan. The average area covered by all agricultural machinery.

[0032] After the model is established, the duration and area of ​​agricultural machinery workload are monitored in real time to ensure that the load balance is close to 1.

[0033] Then, based on the agricultural machinery operation information collected by S1 and referring to the unified identifier mapping of fault codes in S2, emergency fault handling is carried out. In area 1, a fault of damaged blade of agricultural machinery 1 is detected. In the S1 data cleaning identifier, it is an emergency fault and fault repair must be carried out. At the same time, the emergency dispatch algorithm is executed.

[0034] The emergency allocation algorithm is designed as follows: ; in, For the fitness function, Total operation time , , These are the weighting coefficients. The total scheduling cost is expressed as follows: ; in, For the amount of farmland, For agricultural machinery to farmland distance, The variable is 0-1, where 1 represents the allocation of substitute agricultural machinery to farmland, and 0 otherwise.

[0035] Obviously, according to the algorithm, the fitness functions of each substitute and low-load agricultural machine in the dynamic emergency pool of regions 1 and 2 can be calculated. The agricultural machine with the largest fitness function can be dispatched to region 1, and the sub-regions where agricultural machine 1 has not completed its work can be transferred to the dispatched low-load or substitute agricultural machines, dynamically adjusting the work allocation in the sub-regions. The final agricultural machine dispatch plan is then generated and sent to the regional work terminals and the substitute agricultural machines.

[0036] Finally, a task redistribution model is used to determine whether the repaired faulty agricultural machinery should be added to the task. When agricultural machinery 1 is repaired, task redistribution calculation is performed.

[0037] The task redistribution model is designed as follows: ; in, The number of fault scenarios, For fault scenarios Agricultural machinery Distributed to farmland The cost, The variable is 0-1, where 1 represents the allocation of substitute agricultural machinery to farmland, and 0 otherwise. For agricultural machinery The cost of troubleshooting These are 0-1 variables, where 1 represents a fault scenario. Agricultural machinery Needs repair, otherwise 0.

[0038] Constraints: ; in, For agricultural machinery Repair time.

[0039] Based on the established task redistribution model, if agricultural machinery 1 meets the conditions, it will be added to the task in region 1 after repair. This approach fully considers the emergency handling mechanism for sudden failures during cross-regional agricultural machinery operations, enabling emergency dispatch and task redistribution of faulty machinery. It balances the allocation of agricultural machinery operating area and operating time, reducing repair time costs and the time lost due to incomplete repair conditions in damaged fields or other areas. This achieves dynamic fault emergency response and load balancing within the region, improving the flexibility, reliability, and resource utilization of agricultural machinery dispatch, increasing operational efficiency, and ensuring harvesting time. It meets the dual requirements of "high efficiency and stability" in cross-regional operations.

[0040] <Example 2> like Figure 3 As shown, a cross-regional agricultural machinery dispatching device based on dynamic fault emergency response and load balancing includes: a memory storing executable program code; A processor coupled to memory; The processor calls the executable program code stored in the memory to execute the steps in the intelligent control method of the air conditioning system described in Example 1.

[0041] <Example 3> A computer storage medium storing computer instructions, which, when invoked, are used to execute the steps in the cross-regional agricultural machinery scheduling method described in Example 1.

[0042] <Example 4> A computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform the steps in the cross-regional agricultural machinery scheduling method described in Example 1.

[0043] Those skilled in the art will recognize that the embodiments of this specification can be provided in the form of a method, apparatus, or computer program product. Therefore, this specification may include hardware-only embodiments, software-only embodiments, and hybrid embodiments incorporating both software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, hard disk drives (HDDs), floppy disks, and other disk storage devices, as well as optical storage devices such as CD-ROMs and DVD-ROMs, and semiconductor storage media such as solid-state drives (SSDs), USB flash drives, and memory cards) containing computer-usable program code.

[0044] This specification is described with reference to flowchart illustrations and block diagrams of methods, apparatus, and computer program products according to embodiments of this specification. It should be understood that each step and block in the flowchart or block diagram, and even combinations of steps in the flowchart or block diagram, can be implemented by computer program instructions. These program instructions can be provided to a processor of a programmable data processing device such as a computer, PLC, or embedded processor to generate a machine capable of implementing the method, such that the instructions, executable by the processor of the computer or other programmable data processing device, generate means for implementing the functions specified in one or more steps in the flowchart and one or more blocks in the block diagram.

[0045] The aforementioned program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to function in a particular manner, such that the program instructions stored in the computer-readable storage medium produce an article of manufacture including a program instruction means, the article of manufacture being able to perform the functions specified in one or more flowcharts and one or more blocks in a block diagram.

[0046] The aforementioned program instructions may also be loaded onto a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to enable the computer to perform processing, thereby providing steps for implementing the functions specified in one or more processes in the flowchart and one or more boxes in the block diagram.

[0047] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0048] Memory may include volatile memory in computer-readable media, random access memory (RAM), and non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0049] Computer-readable media include permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The stored information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, hard disk drives (HDDs), floppy disks, magnetic tapes, read-only optical discs (CD-ROMs, DVD-ROMs), write-once optical discs (CD-Rs, DVD-Rs), rewritable optical discs (CD-RWs, DVD-RWs, etc.), solid-state drives (SSDs), USB flash drives, memory cards, read-only memory (ROMs), phase-change memory (PCMs), ferroelectric random access memory (FRAMs), resistive random access memory (PRAMs), and any other non-transfer media that can be used to store information accessible by computing devices.

[0050] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0051] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for agricultural machinery scheduling based on dynamic fault emergency response and load balancing, characterized in that: The agricultural machinery scheduling method includes: The system uses IoT sensors mounted on agricultural machinery to collect agricultural machinery operation information and regional operation terminals to collect farmland information in each operation area to calculate real-time load data. The collected data is cleaned and standardized. A dynamic emergency database is built based on the cleaned information, and a relationship between "faulty agricultural machinery and replacement agricultural machinery" is established. Set a reasonable regional load rate to conduct regional load assessment, and carry out regional load balancing scheduling based on the load rate of each work area; A load balancing model within the region is established based on the objective function of linear programming, enabling real-time dynamic fault response and load balancing scheduling within the region.

2. The agricultural machinery scheduling method based on dynamic fault emergency response and load balancing according to claim 1, characterized in that, The agricultural machinery scheduling method specifically includes the following steps: S1: Collect agricultural machinery operation information using IoT sensors mounted on agricultural machinery; use regional operation terminals to obtain in real time the regional location, area, number of agricultural machinery in each operation area, regional crop harvesting information of the crop name in the area, agricultural machinery identification, operation type, operation efficiency and regional standby attributes, and perform real-time load data calculation. S2: Perform unified coordinate format, fault code mapping, and data cleaning and standardization processing on the agricultural machinery operation information collected in step S1 and the information obtained in real time from regional terminals; construct a dynamic fault emergency database to store agricultural machinery information of multiple cross-regional agricultural machinery with dispatch authority, and establish the "faulty agricultural machinery-replacement agricultural machinery" association relationship. S3: Based on the data obtained from the cleaning and standardization process in step S2 and the load rate calculated in step S1, set a reasonable area load rate to conduct an area load assessment, and adjust the area load according to the load rate of each work area: When an area is identified as a high-load area, idle agricultural machinery is transferred from low-load areas to high-load areas; When an area is identified as a low-load area, if there is an overload demand in other areas, the idle agricultural machinery in this area will be dispatched first. If there is no dispatch demand, the operation rhythm of agricultural machinery in this area will be adjusted: the duration of a single operation will be extended and the operation sequence of plots will be optimized. S4: Based on the crop harvesting information from step S1 and the information of multiple agricultural machines in the region, establish a multi-agricultural-machine load balancing model; The system receives real-time data on the operating status and load status of agricultural machinery. If a fault signal is detected or the operation of agricultural machinery exceeds the linear constraint objective function of the load balancing model, an emergency dispatch algorithm is used to match a compatible standby agricultural machine from the dynamic fault emergency database that is closest to the fault location. A dispatch instruction is generated to transfer the unfinished sub-area of ​​the machine to low-load agricultural machines and substitute agricultural machines, thereby dynamically adjusting the operation allocation. By combining the substitute dispatch instructions with the dynamically adjusted job allocation, a final agricultural machinery dispatch plan is generated and sent to the regional operation terminal and the substitute agricultural machinery. During fault repair, a task redistribution model is used to add the repaired faulty agricultural machinery to the task.

3. The agricultural machinery scheduling method based on dynamic fault emergency response and load balancing according to claim 2, characterized in that: The formula for calculating the real-time load data in step S1 is as follows: ； in, For real-time load rate, This represents the current area awaiting work. The total number of agricultural machines in the region. This represents the maximum daily operating area of ​​a single agricultural machine.

4. The agricultural machinery scheduling method based on dynamic fault emergency response and load balancing according to claim 2, characterized in that: In step S2, the relationship between the faulty agricultural machinery and the replacement agricultural machinery is as follows: The types of tasks are the same and the efficiency deviation is . Agricultural machinery is identified as a compatible replacement; regional reserve status is indicated by the list of reserve agricultural machinery in the region to which the agricultural machinery belongs, and the proportion of reserve agricultural machinery is not less than 20% of the total number of agricultural machinery with dispatch authority; The constraints are: .

5. The agricultural machinery scheduling method based on dynamic fault emergency response and load balancing according to claim 3, characterized in that: The reasonable regional load factor in step S3 is: ； in, For a reasonable regional load factor, then The area is considered a low-load area. The area is considered a high-load area.

6. The agricultural machinery scheduling method based on dynamic fault emergency response and load balancing according to claim 2, characterized in that: The formula for calculating the number of idle agricultural machines transferred from low-load areas to high-load areas in step S3 is as follows: ， in, To allocate the number of agricultural machines.

7. The agricultural machinery scheduling method based on dynamic fault emergency response and load balancing according to claim 2, characterized in that: The load balancing model in step S4 is as follows: ， The constraints are: ， in, For load balance, For agricultural machinery Homework time, The average operating time for all agricultural machinery. For the first The operating area allocated to agricultural machinery in Taiwan. The average area covered by all agricultural machinery.

8. The agricultural machinery scheduling method based on dynamic fault emergency response and load balancing according to claim 2, characterized in that: The emergency allocation algorithm in step S4 is as follows: ， in, For the fitness function, Total operation time , , These are the weighting coefficients. The total scheduling cost is expressed as follows: ， in, For the amount of farmland, For agricultural machinery to farmland distance, The variable is 0-1, where 1 represents the allocation of substitute agricultural machinery to farmland, and 0 otherwise.

9. The agricultural machinery scheduling method based on dynamic fault emergency response and load balancing according to claim 2, characterized in that: The task redistribution model in step S4 is as follows: ， in, The number of fault scenarios, For fault scenarios Agricultural machinery Distributed to farmland The cost, The variable is 0-1, where 1 represents the allocation of substitute agricultural machinery to farmland, and 0 otherwise. For agricultural machinery The cost of troubleshooting These are 0-1 variables, where 1 represents a fault scenario. Agricultural machinery Needs repair, otherwise 0; Constraints: ； in, For agricultural machinery Repair time.

10. An agricultural machinery scheduling device based on dynamic fault emergency response and load balancing, characterized in that, The device includes: A memory storing executable program code and a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the agricultural machinery scheduling method as described in any one of claims 1-9.

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