New energy agricultural machine mobile charging and replacing energy complementing method and new energy agricultural machine mobile charging and replacing energy complementing system

Abstract

The invention relates to the technical field of agricultural mechanization and automation, in particular to a new energy agricultural machine mobile charging and replacing energy supplementing method and system. The method comprises the following steps: obtaining an operation concentration area of the new energy agricultural machinery, and deploying a mobile charging and battery replacing vehicle in the operation concentration area; establishing an energy transaction platform based on the block chain technology; obtaining charging demand information of the new energy agricultural machine, wherein the charging demand information comprises work amount and energy demand, charging efficiency, geographic factors and environmental factor information; determining the optimal cost between the mobile charging and battery swapping vehicle and the new energy agricultural machinery; and mobile charging and energy supplement of the new energy agricultural machinery are realized. According to the invention, by fusing the dynamic deployment of the mobile charging vehicle, the block chain-driven energy transaction platform and the multi-dimensional charging demand analysis model, the energy complementing efficiency is greatly improved, the operation cost is reduced, the major innovation of the energy complementing system is realized, and subversive technical support is provided for the green and intelligent development of agriculture.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural mechanization and automation, and in particular to a mobile charging, battery replacement and energy replenishment method and system for new energy agricultural machinery. Background Art

[0002] As the global agricultural green transformation accelerates, new energy agricultural machinery, with its zero-emission and low-energy consumption characteristics, has become a core focus of modern agricultural equipment upgrades. However, the large-scale application of new energy agricultural machinery is constrained by its range and energy supply model: while traditional fuel-powered agricultural machinery can be quickly recharged at decentralized gas stations, new energy agricultural machinery relies on fixed charging facilities, which face insufficient coverage and delayed response times in wide-area farmland operations. This is especially true during the busy farming season, when the need for centralized agricultural machinery operations and energy supply efficiency become even more pronounced.

[0003] In existing technologies, there are two main types of energy replenishment solutions for new energy agricultural machinery: one is a static energy replenishment mode based on fixed charging piles, which requires agricultural machinery to leave the operating area and travel back and forth to charge, resulting in operational interruptions and time loss; the other is the use of centralized battery swap stations, but these are limited by high infrastructure costs and insufficient geographical coverage density, making them difficult to adapt to the decentralized and seasonally fluctuating operating characteristics of farmland. In addition, existing solutions lack the ability to dynamically perceive the real-time operating status and environmental factors of agricultural machinery, and energy replenishment scheduling relies on manual experience, which can easily lead to resource mismatches or energy waste. More importantly, traditional energy replenishment systems fail to address the trust and coordination issues between agricultural machinery, charging vehicles, and power grid entities. Energy trading transparency is low, which restricts the large-scale application of distributed energy replenishment networks.

[0004] At present, there is not much research on mobile charging and battery replacement for new energy agricultural machinery. There is no specific method for charging and battery replacement for new energy agricultural machinery by integrating the dynamic deployment of mobile charging and battery replacement vehicles, a blockchain-driven energy trading platform and a multi-dimensional demand optimization model. Summary of the Invention

[0005] In response to the deficiencies in the prior art, the present invention provides a method and system for mobile charging, battery replacement and energy replenishment of new energy agricultural machinery.

[0006] In the first aspect, the present invention provides a mobile charging, swapping and energy replenishment method for new energy agricultural machinery, comprising the following steps: obtaining a concentrated operation area of new energy agricultural machinery, and deploying mobile charging and swapping vehicles in the concentrated operation area; establishing an energy trading platform based on blockchain technology based on the mobile charging and swapping vehicles; obtaining charging and swapping demand information of new energy agricultural machinery based on the energy trading platform, the charging and swapping demand information including operation volume and energy demand, charging and swapping efficiency, geographical factors and environmental factors; determining the optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery based on the charging and swapping demand information and obtaining a determination result; and realizing mobile charging, swapping and energy replenishment of new energy agricultural machinery through the determination result. The present invention achieves precise delivery and efficient coverage of energy replenishment resources by obtaining concentrated operating areas of new energy agricultural machinery and dynamically deploying mobile charging and swapping vehicles, solving the problems of insufficient coverage of traditional fixed charging piles and low efficiency of round-trip charging of agricultural machinery; by establishing an energy trading platform based on blockchain technology, decentralized and transparent energy transactions are realized between agricultural machinery, mobile charging and swapping vehicles and energy suppliers, solving the problems of difficult multi-subject collaboration and high trust costs in traditional energy replenishment systems; by integrating information on workload, energy demand, geographical factors and environmental factors, a charging and swapping demand analysis model and a cost optimization model are constructed, and an optimal energy replenishment plan is generated, solving the problems of traditional energy replenishment scheduling relying on manual experience and serious resource mismatch, and realizing dynamic response, intelligent decision-making and trusted collaborative closed loop of the new energy agricultural machinery energy replenishment system.

[0007] Optionally, the method of obtaining the concentrated operation area of new energy agricultural machinery and deploying mobile charging and swapping vehicles in the concentrated operation area includes: obtaining information on the number, location, and operation status of new energy agricultural machinery; determining the concentrated operation area of the new energy agricultural machinery based on the information; and deploying mobile charging and swapping vehicles in the concentrated operation area, wherein the mobile charging and swapping vehicles include mobile charging and swapping vehicles controlled by a small program and unmanned mobile charging and swapping vehicles equipped with a high-precision navigation system and obstacle avoidance sensors. The present invention achieves accurate drawing and dynamic updating of the heat map of agricultural machinery operations by obtaining information on the number, location, and operation status of new energy agricultural machinery and analyzing and determining the concentrated operation area, thereby solving the problems of blind resource allocation and uneven coverage in traditional energy replenishment solutions; by deploying mobile charging and swapping vehicles controlled by a small program in the concentrated operation area, users can make remote reservations, conduct real-time monitoring, and intelligent scheduling, thereby solving the problems of low efficiency and slow response of manual scheduling in traditional energy replenishment modes; by deploying unmanned mobile charging and swapping vehicles equipped with a high-precision navigation system and obstacle avoidance sensors, autonomous path planning and safe driving in complex farmland environments are achieved, thereby solving the problems of traditional mobile energy replenishment vehicles relying on manual driving and having poor adaptability.

[0008] Optionally, the energy trading platform established based on the mobile charging and swapping vehicle includes: establishing an energy trading platform based on blockchain technology based on the mobile charging and swapping vehicle, the energy trading platform being used to record, verify and store detailed information of all charging and swapping transactions, the detailed information including the identity information of both parties to the transaction, transaction timestamp, transaction power and transaction price. The present invention realizes the decentralized recording and tamper-proof storage of all charging and swapping transactions by establishing an energy trading platform based on blockchain technology, solving the problems of low data transparency and high trust cost in traditional energy transactions; by recording the detailed information of the identity information, transaction timestamp, transaction power and transaction price of both parties to the transaction, the whole process of energy trading is traceable and auditable, solving the problems of information asymmetry and difficulty of supervision in the traditional transaction model; automatically executing transaction verification and settlement through blockchain smart contracts, realizing the immediacy and efficiency of charging and swapping transactions, solving the problems of cumbersome traditional transaction processes and long settlement cycles.

[0009] Optionally, determining the optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery based on the charging and swapping demand information and obtaining a determined result includes: determining the priority order of charging, swapping and energy replenishment of the new energy agricultural machinery based on the charging and swapping demand information; based on the priority order, determining the nearest idle mobile charging and swapping vehicle near each new energy agricultural machinery, and constructing a cost optimization model; based on the cost optimization model, determining the optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery and obtaining a determined result. The present invention determines the charging and energy replenishment priority order of new energy agricultural machinery according to the charging and energy replenishment demand information, realizes priority energy replenishment scheduling for agricultural machinery with high workload and high energy consumption, and solves the problems of uneven resource allocation and interruption of key operations in the traditional energy replenishment mode; by determining the nearest idle mobile charging and energy replenishment vehicle based on the priority order and constructing a cost optimization model, the optimal configuration of energy replenishment path and resources is achieved, and the problems of unreasonable path planning and high energy replenishment cost in the traditional scheduling method are solved; by determining the optimal cost result between mobile charging and energy replenishment vehicles and new energy agricultural machinery according to the cost optimization model, the precision and efficiency of energy replenishment scheduling are achieved, the problem of traditional energy replenishment scheme relying on manual experience and low efficiency is solved, and the intelligent decision-making and global resource optimization of the new energy agricultural machinery energy replenishment system are realized.

[0010] Optionally, determining the priority order of charging, battery swapping and energy replenishment of new energy agricultural machinery based on the charging and battery swapping demand information includes: constructing a charging demand analysis model for judging the degree of charging demand of new energy agricultural machinery based on the charging demand information; constructing a battery swapping demand analysis model for judging the degree of battery swapping demand of new energy agricultural machinery based on the battery swapping demand information; constructing a charging and battery swapping demand comprehensive analysis model for comprehensively analyzing the degree of charging and battery swapping demand of new energy agricultural machinery using the charging demand analysis model and the battery swapping demand analysis model; obtaining the charging and battery swapping demand value of new energy agricultural machinery through the charging and battery swapping demand comprehensive analysis model; determining the priority order of charging, battery swapping and energy replenishment of new energy agricultural machinery based on the charging and battery swapping demand value, the higher the charging and battery swapping demand value, the higher the priority of charging, battery swapping and energy replenishment of the new energy agricultural machinery. The present invention realizes accurate quantitative evaluation of the charging and battery swapping demand of new energy agricultural machinery by constructing a charging demand analysis model and a battery swapping demand analysis model, and solves the problem of relying on subjective judgment and inaccurate demand assessment in traditional energy replenishment scheduling; by combining the charging demand analysis model and the battery swapping demand analysis model to construct a comprehensive charging and battery swapping demand analysis model, it realizes multi-dimensional dynamic evaluation of the charging and battery swapping demand of agricultural machinery, and solves the problem of high one-sidedness and poor adaptability of single model evaluation; by calculating the charging and battery swapping demand degree value and determining the priority order through the comprehensive charging and battery swapping demand analysis model, it realizes the scientific allocation and efficient utilization of energy replenishment resources, solves the problems of serious resource waste and delayed response to key demands in the traditional energy replenishment mode, and realizes the precise, intelligent and global optimization of new energy agricultural machinery energy replenishment scheduling.

[0011] Optionally, the charging demand analysis model satisfies the following expression: in, is the charging demand value of new energy agricultural machinery, To estimate the remaining workload, The energy required to complete a unit of work, is the current remaining power, The efficiency of converting electrical energy into battery storage energy during charging. is the output power of the charging device, Navigation route from mobile charging and battery swapping vehicles to new energy agricultural machinery, is the path attenuation coefficient, is the quantitative value of weather influencing factors, is the quantitative value of the time influencing factor, 、 is the weight coefficient; the battery swap demand analysis model satisfies the following expression: in, is the demand value for battery replacement of new energy agricultural machinery, To estimate the remaining workload, The energy required to complete a unit of work, is the current remaining power, is the minimum power threshold for battery replacement, is the rated capacity of the new energy agricultural machinery battery, Navigation route from mobile charging and battery swapping vehicles to new energy agricultural machinery, is the attenuation coefficient of the switching circuit, is the quantitative value of weather influencing factors, is the quantitative value of the time influencing factor, 、 is the weight coefficient; the comprehensive analysis model of charging and swapping demand satisfies the following expression: in, is the demand value for charging and battery replacement of new energy agricultural machinery, The charging demand for new energy agricultural machinery, The demand for battery replacement for new energy agricultural machinery, 、 is the weight coefficient. By predicting the charging and battery swapping needs of new energy agricultural machinery, the present invention can optimize the layout and capacity design of mobile charging and battery swapping vehicles, ensuring an efficient match between energy supply and demand. Through a comprehensive analysis model of charging and battery swapping needs, it can integrate charging and battery swapping data, providing a scientific basis for energy management and scheduling, helping to balance grid loads, optimize energy allocation, reduce operating costs, and promote the establishment of a battery recycling and utilization system to achieve sustainable development.

[0012] Optionally, the cost optimization model satisfies the following expression: in, is the objective function value of optimizing cost, is a set of path point sequences corresponding to the priority order of new energy agricultural machinery, A binary variable for charging or replacing new energy agricultural machinery. From the waypoint To waypoint The path length, is the path length from the mobile charging and swapping vehicle to the highest priority new energy agricultural machinery path point, For mobile charging and swapping vehicles from the path point To waypoint The average driving speed, is the average speed of the mobile charging and swapping vehicle from its initial position to the highest priority new energy agricultural machinery path point, A collection of charging and swapping points. For charging and swapping points The time required to charge, For charging and swapping points The charging rate, 、 Respectively indicate the charging and swapping points A binary variable indicating whether to perform charging and battery swapping operations. For charging and swapping points The cost of battery replacement, 、 、 is the weight coefficient, A collection of new energy agricultural machinery. is the task weight, For the task The waiting time, Factors influencing path selection, For additional cost, This is a collection of factors related to path selection. Through a cost optimization model, this invention achieves a precise match between energy replenishment scheduling and the actual needs of agricultural machinery, resolving the issues of traditional cost optimization models that ignore demand differences and irrational resource allocation. This model also enables optimal planning and real-time adjustment of energy replenishment paths, resolving the issues of fixed paths and poor adaptability in traditional models. By incorporating geographical and environmental factors into the cost optimization model, it achieves precise calculation and global optimization of energy replenishment costs in complex farmland scenarios, resolving the issues of traditional models that ignore external environmental influences and have large deviations in optimization results.

[0013] Optionally, determining the optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery based on the cost optimization model and obtaining a determination result includes: designing a comprehensive objective function for optimizing the total driving time of the mobile charging and swapping vehicle, the total task waiting time of the new energy agricultural machinery, and the total charging and swapping time based on the cost optimization model, and the comprehensive objective function is as follows: in, is the comprehensive objective function, is the optimal cost objective function for the total driving time of mobile charging and swapping vehicles, is the optimal cost objective function for the total waiting time of new energy agricultural machinery tasks, is the optimal cost objective function of the total charging and swapping time, 、 、 is the target weight coefficient; according to the comprehensive objective function, the hierarchical optimization method is used to obtain the optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery. The present invention realizes multi-objective collaborative optimization in energy replenishment scheduling by designing a comprehensive objective function that includes the total driving time of the mobile charging and swapping vehicle, the total waiting time of the new energy agricultural machinery tasks, and the total charging and swapping time, and solves the problem of the traditional single-objective optimization model ignoring the global efficiency; by introducing the target weight coefficient, the dynamic weight allocation and flexible adjustment of different optimization objectives are realized, and the problem of fixed weights and poor adaptability of the traditional model is solved; by adopting the hierarchical optimization method to analyze the comprehensive objective function, the rapid calculation and accurate decision-making of the optimal cost under complex constraints are realized, and the problems of low calculation efficiency and large result deviation of the traditional optimization method are solved.

[0014] Optionally, the method of obtaining the optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery based on the comprehensive objective function using a hierarchical optimization method includes: according to the comprehensive objective function, finding a first cost that satisfies the minimum total driving time of the charging and swapping vehicle, and the total driving time includes the additional driving time caused by traffic congestion and road restrictions; according to the minimum first cost, finding a second cost that satisfies the minimum total task waiting time of the new energy agricultural machinery; according to the minimum second cost, finding a third cost that satisfies the minimum total charging and swapping time; based on the first cost, the second cost and the third cost, obtaining the optimal weight coefficient of the comprehensive objective function using a hierarchical optimization method; and determining the optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery through the first cost, the second cost, the third cost and the optimal weight coefficient. The present invention makes the scheduling of mobile charging and swapping vehicles more accurate and efficient by comprehensively considering the actual factors of traffic congestion and road restrictions, which not only reduces unnecessary driving time, but also reduces energy consumption and operating costs, providing strong support for energy supply for agricultural production; by minimizing the task waiting time cost, it ensures that new energy agricultural machinery can quickly obtain the required power supply, thereby effectively avoiding production delays caused by waiting for charging; by integrating the total time cost of charging and swapping, it realizes the comprehensive optimization of the collaborative work between mobile charging and swapping vehicles and new energy agricultural machinery, which not only reduces the overall operating cost, but also improves the overall efficiency of the system.

[0015] In the second aspect, the present invention provides a new energy agricultural machinery mobile charging, swapping and energy replenishment system, including an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the system uses the new energy agricultural machinery mobile charging, swapping and energy replenishment method. The present invention integrates the new energy agricultural machinery operation heat map, dynamically deploys mobile charging and swapping vehicles and blockchain energy trading platforms, realizes the precise allocation of energy replenishment resources and the coordination of decentralized trusted transactions, and solves the problems of insufficient coverage and low efficiency of multi-agent coordination of traditional energy replenishment systems; through the comprehensive analysis model of charging and swapping demand and the cost optimization model, the workload, energy demand and geographical environment parameters are incorporated into the dynamic optimization algorithm, and the global optimal resource configuration of energy replenishment scheduling is realized, solving the problem that the traditional solution relies on manual experience and has serious resource mismatch; by utilizing multi-objective comprehensive functions and hierarchical optimization methods, combined with dynamic weight coefficient adjustment and complex scenario adaptation mechanism, high-precision and high-efficiency energy replenishment decision-making is achieved, solving the problems of poor adaptability and low computational efficiency of single-objective optimization models. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a method for mobile charging, swapping and energy replenishment of new energy agricultural machinery according to an embodiment of the present invention; Figure 2 This is a structural diagram of a mobile charging, swapping and energy replenishment system for new energy agricultural machinery according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0018] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0019] See Figure 1 An embodiment of the present invention provides a method for mobile charging, swapping and energy replenishment of new energy agricultural machinery, the method comprising the following steps: S1. Obtain concentrated operation areas for new energy agricultural machinery and deploy mobile charging and swapping vehicles in the concentrated operation areas.

[0020] Among them, S1 includes the following steps: S11. Obtain information on the quantity, location, and operating status of new energy agricultural machinery.

[0021] In one embodiment, information on the number, location, and operating status of new energy agricultural machinery is obtained through field investigation.

[0022] In another example, a national agricultural machinery operation command and dispatch platform was used to obtain information on the number, location, and operating status of new energy agricultural machinery. The platform leverages advanced Beidou, 5G, and IoT technologies to enable real-time monitoring and dispatch of agricultural machinery nationwide.

[0023] S12. Determine the concentrated operation area of the new energy agricultural machinery based on the information.

[0024] In one embodiment, geographic information system (GIS) technology is first used to mark the location information of new energy agricultural machinery on a map.

[0025] Furthermore, through the spatial cluster analysis method, areas with a large number of new energy agricultural machinery and dense distribution were identified.

[0026] Furthermore, the operation status information of the new energy agricultural machinery is analyzed, where the operation status information includes operation time, operation type and operation efficiency.

[0027] Furthermore, areas where agricultural machinery with high operating frequency, long operating time and stable operating efficiency are located are identified.

[0028] Furthermore, combining the results of spatial analysis and operation status analysis, a comprehensive assessment was conducted on the concentrated operation areas of new energy agricultural machinery; among them, the number, distribution density, operation efficiency, and the influence of terrain and climatic conditions of agricultural machinery were taken into consideration.

[0029] Furthermore, the final work concentration area is determined based on the comprehensive evaluation results.

[0030] S13. Deploy mobile charging and swapping vehicles in the concentrated operation area, wherein the mobile charging and swapping vehicles include mobile charging and swapping vehicles that use mini-program navigation and unmanned mobile charging and swapping vehicles equipped with high-precision navigation systems and obstacle avoidance sensors.

[0031] In one embodiment, a mobile charging and swapping vehicle that uses a mobile phone applet for navigation is deployed in the concentrated operation area. The mobile charging and swapping vehicle is equipped with a mobile charging and swapping platform, which mainly includes a generator set, an energy storage battery pack, a modular battery pack, a battery transport mechanism and a monitoring and scheduling system.

[0032] In this embodiment, the generator set is based on the needs of 10 new energy agricultural machinery, with an average of 4 batteries (capacity of 27Ah) per unit, an average of 2 battery replacements, and 10 hours of operation. The power required for each unit is about 160Kwh, and the minimum power generation required per hour is 16Kw. Taking into account the factors of silent noise reduction and long-term continuous operation, and reserving a part of the expansion capacity, the generator set selected is the Isuzu GF-20WSL diesel generator set with a rated power of 20Kw. The modular battery pack is arranged with 18 rechargeable battery compartments, each of which can be quickly inserted and removed, and 15 battery packs are pre-installed. The battery transfer mechanism realizes battery transfer through linear guide traction, and a battery pick-up and placement mechanism is designed to realize intelligent battery pick-up and placement. The energy storage battery pack is equipped with a 100Ah energy storage battery pack, which is superimposed with modular battery packs to achieve a battery energy storage of more than 500Ah in the valley section at night. The specific functions of this applet are as follows: (1) Query online agricultural machinery list and equipment details.

[0033] (2) Query the distribution location and navigation status of new energy agricultural machinery.

[0034] (3) The agricultural machinery is sorted according to the power level or distance, and the estimated remaining operation time is displayed; when the power level of the agricultural machinery is low, the monitoring and scheduling system pushes a reminder message through the mini program to remind the user to discover and deal with it in time.

[0035] (4) Connect to the computer side and use the computer side's task creation, assignment, mini-program acceptance, and completion functions to help users view task details and execution status information on the mini-program, and cancel or complete tasks according to actual conditions.

[0036] (5) Provide user login, registration and personal information management functions.

[0037] In another embodiment, an unmanned mobile charging and swapping vehicle equipped with a high-precision navigation system and obstacle avoidance sensors is deployed in the concentrated operation area.

[0038] In this embodiment, the high-precision navigation system, based on high-precision maps and real-time positioning technology, can accurately plan the vehicle's route and ensure that the vehicle can accurately reach the designated charging and swapping location. The obstacle avoidance sensors include lidar, cameras, and infrared obstacle avoidance sensors, which can sense obstacles and agricultural machinery dynamics around the vehicle in real time to ensure the safety of the vehicle during driving. The unmanned mobile charging and swapping vehicle is also equipped with efficient charging and swapping equipment, which can provide fast and stable charging and swapping services according to the needs of new energy agricultural machinery.

[0039] S2. Based on the mobile charging and swapping vehicles, establish an energy trading platform based on blockchain technology.

[0040] In one embodiment, based on the mobile charging and swapping vehicle, an energy trading platform is established to record, verify and store all charging and swapping transaction details, including the identity information of both parties to the transaction, transaction timestamp, transaction power and transaction price.

[0041] Specifically, the functional requirements of the platform must first be clarified, including recording, verifying and storing detailed information on charging and swapping transactions, as well as providing interface operations for user registration, login and transaction inquiries.

[0042] Furthermore, the overall architecture of the platform is designed, including the front-end user interface, back-end services, blockchain layer, and data storage.

[0043] Furthermore, we need to select a suitable blockchain platform and customize smart contracts according to our needs. At the same time, we need to choose a stable and reliable back-end development framework and database technology.

[0044] Furthermore, based on the demand analysis, write smart contract code to define the transaction data structure, transaction process, and verification rules; perform unit testing and integration testing on the smart contract in a test environment to ensure the correctness and security of the contract functions; deploy the tested smart contract to the blockchain network and configure the necessary permissions and parameters.

[0045] Furthermore, based on the design draft and user experience requirements, a user-friendly front-end interface will be developed to support user registration, login and transaction query functions; back-end services will be developed, including user management, transaction processing, and blockchain interaction modules to ensure that the back-end services can efficiently handle user requests and seamlessly integrate with smart contracts; a data storage solution will be designed to ensure that transaction data can be stored securely and reliably in the database, and at the same time, a synchronization mechanism with the blockchain network will be implemented to ensure the consistency and integrity of transaction data.

[0046] Furthermore, the platform will be subject to comprehensive functional testing, including testing of user registration, login, transaction initiation, transaction confirmation and transaction query functions; the platform's concurrent processing capabilities and response time performance indicators will be tested to ensure that the platform can meet actual usage needs; the platform will be subject to security testing, including vulnerability scanning and penetration testing, to ensure the security of the platform; based on the test results, the platform will be optimized and adjusted as necessary to improve its stability and performance.

[0047] It's important to note that blockchain technology ensures the credibility of energy trading platforms through the following mechanisms: First, data is tamper-proof. All charging and swapping transaction information is packaged into blocks and linked into a chain using a hash algorithm. Once the data is on the chain, any tampering will cause the hash value of subsequent blocks to change, thereby being detected and rejected by the system. Second, it is transparent and traceable. All transaction records are open and transparent to authorized nodes, and any participant can trace transaction history, ensuring an open and fair transaction process. Third, smart contracts are automatically executed. Transaction rules are encoded into smart contracts and automatically executed when pre-set conditions are met, preventing human intervention or fraud.

[0048] Blockchain technology also decentralizes energy trading platforms through the following methods: First, a distributed ledger allows transaction data to be stored across multiple nodes, rather than relying on a single central server. Any node can verify and store transaction data, ensuring the system has no single point of failure. Second, a consensus mechanism uses a consensus algorithm to ensure that all nodes agree on transaction records, eliminating the need for centralized arbitration and reducing trust costs. Third, peer-to-peer transactions allow agricultural machinery owners and charging and swapping service providers to conduct direct peer-to-peer transactions through the blockchain network, eliminating the need for a third-party intermediary, reducing transaction costs and improving efficiency.

[0049] S3. Based on the energy trading platform, obtain the demand information for charging and battery replacement of new energy agricultural machinery.

[0050] In one embodiment, based on the energy trading platform, information on the workload and energy demand, charging and swapping efficiency, geographical factors and environmental factors of new energy agricultural machinery is obtained.

[0051] Specifically, information on the workload and energy requirements of new energy agricultural machinery was first collected. Each of these new energy agricultural machinery is equipped with appropriate data collection and transmission equipment, including sensors and data loggers. These devices transmit data on the machinery's workload, energy requirements, and power status in real time to the energy trading platform. The platform verifies and integrates the received data to ensure its accuracy and completeness.

[0052] Furthermore, information on charging and swapping efficiency was obtained by querying the charging and swapping records of new energy agricultural machinery, including charging and swapping time, power consumption, and location information.

[0053] Furthermore, geographical factor information is obtained. The energy trading platform provides relevant geographical factor information, including topography, climate, and traffic conditions, based on the location information of the agricultural machinery.

[0054] Furthermore, environmental factor information is obtained. The energy trading platform accesses external environmental monitoring data, including weather forecasts and air quality index data; this data is obtained from relevant institutions through API interfaces or data sharing agreements. The energy trading platform will associate the obtained environmental factor information with the operating location and time of new energy agricultural machinery to provide real-time environmental factor information.

[0055] S4. Based on the demand information, perform optimal cost planning and obtain planning results.

[0056] Among them, S4 includes the following steps: S41. Determine the priority order of charging, battery replacement and energy replenishment of new energy agricultural machinery based on the charging and battery replacement demand information.

[0057] Wherein, S41 further includes the following steps: S411. Based on the charging demand information, a charging demand analysis model is constructed to determine the degree of charging demand of new energy agricultural machinery.

[0058] In one embodiment, a charging demand analysis model for determining the degree of charging demand for new energy agricultural machinery is constructed by comprehensively considering the navigation distance from the mobile charging and swapping vehicle to the new energy agricultural machinery, the remaining power of the new energy agricultural machinery, and other relevant information. The charging demand analysis model satisfies the following expression: in, is the charging demand value of new energy agricultural machinery, To estimate the remaining workload, The energy required to complete a unit of work, is the current remaining power of the new energy agricultural machinery, The efficiency of converting electrical energy into battery storage energy during charging. is the output power of the charging device, Navigation route from mobile charging and battery swapping vehicles to new energy agricultural machinery, is the path attenuation coefficient, is the quantitative value of weather influencing factors, is the quantitative value of the time influencing factor, 、 is the weight coefficient.

[0059] It's worth emphasizing that the shorter the distance between a mobile charging and swapping vehicle and a new energy agricultural machine, the more work the machine can complete without recharging, which means the charging demand for the new energy agricultural machine is smaller under this factor alone. Therefore, it's reasonable to consider this factor in the charging demand analysis model.

[0060] It should be noted that It indicates that as the distance traveled by the agricultural machinery increases, the degree of attenuation of charging demand can be determined based on the relationship between power level and distance traveled; Obtain weather data, such as temperature, humidity, and wind speed, and then quantify the impact of these factors on charging efficiency; The impact of the usage time or aging of the charging equipment on the charging efficiency is quantified and determined.

[0061] S412. Based on the battery replacement demand information, a battery replacement demand analysis model is constructed to determine the degree of battery replacement demand for new energy agricultural machinery.

[0062] In one embodiment, a battery swap demand analysis model for determining the battery swap demand of new energy agricultural machinery is constructed by comprehensively considering the navigation distance from the mobile charging and swapping vehicle to the new energy agricultural machinery, the remaining power of the new energy agricultural machinery, and other relevant information. The battery swap demand analysis model satisfies the following expression: in, is the demand value for battery replacement of new energy agricultural machinery, To estimate the remaining workload, The energy required to complete a unit of work, The current remaining power of new energy agricultural machinery, is the minimum power threshold for battery replacement, is the rated capacity of the new energy agricultural machinery battery, Navigation route from mobile charging and battery swapping vehicles to new energy agricultural machinery, is the path attenuation coefficient, is the quantitative value of weather influencing factors, is the quantitative value of the time influencing factor, 、 is the weight coefficient.

[0063] It should be noted that It indicates that as the distance traveled by agricultural machinery increases, the degree of attenuation of the demand for battery replacement can be determined based on actual conditions; Obtain weather data, such as temperature, humidity, and wind speed, and then quantify the impact of these factors on battery swap efficiency; The impact of the battery swapping equipment’s usage time or aging on the battery swapping efficiency is quantified and determined.

[0064] S413. Utilize the charging demand analysis model and the battery swapping demand analysis model to construct a comprehensive charging and battery swapping demand analysis model.

[0065] In one embodiment, by integrating the charging demand analysis model and the battery swapping demand analysis model, a charging demand analysis model for comprehensively analyzing the charging and battery swapping demand of new energy agricultural machinery is constructed. The charging demand analysis model satisfies the following expression: in, is the comprehensive demand value for charging and battery replacement of new energy agricultural machinery, The charging demand for new energy agricultural machinery, The demand for battery replacement for new energy agricultural machinery, 、 is the weight coefficient.

[0066] It should be noted that 、 The weight coefficient can be determined by combining qualitative methods and quantitative methods. The qualitative methods include the Delphi method and the analytic hierarchy process; the quantitative methods include the principal component analysis method and the regression analysis method.

[0067] Specifically, the Delphi method collects and organizes experts' judgments on the relative importance of charging demand and battery swapping demand in the demand level of new energy agricultural machinery through expert surveys; and then forms a relatively consistent weight coefficient through multiple rounds of feedback and corrections.

[0068] The hierarchical analysis method decomposes the degree of demand for charging and battery swapping of new energy agricultural machinery into multiple levels, including usage scenarios, user needs and technical feasibility; at each level, the charging demand and battery swapping demand are compared and judged to form a judgment matrix; and the weight coefficient is obtained by calculating the eigenvalues and eigenvectors of the judgment matrix.

[0069] The principal component analysis method extracts the main components that affect the degree of charging and battery swapping demand by statistically analyzing the usage data of new energy agricultural machinery; determines the weight coefficient based on the contribution rate of the main components to the charging demand and battery swapping demand; this method is suitable for situations with a large amount of data support and can objectively reflect the actual situation of charging and battery swapping demand.

[0070] The regression analysis method establishes a regression model between the degree of demand for charging and battery swapping of new energy agricultural machinery and the demand for charging and battery swapping, and then performs regression analysis to derive estimated weight coefficients. This method requires the collection of a large amount of historical data, as well as data preprocessing and model validation.

[0071] S414. Determine the priority order of charging, battery swapping and energy replenishment for new energy agricultural machinery through the comprehensive analysis model of charging and battery swapping demand.

[0072] In one embodiment, the number of new energy agricultural machinery is first set to Taiwan; then Comprehensive demand value of charging and battery replacement for new energy agricultural machinery Calculated by the following expression: in, For the The charging demand value of new energy agricultural machinery, For the The demand level for battery replacement of new energy agricultural machinery is 、 is the weight coefficient.

[0073] Furthermore, for all Arrange in descending order to get the sequence: It should be noted that the descending order above represents the priority order for charging, swapping, and recharging new energy agricultural machinery. In other words, the lower the overall demand for charging, swapping, and recharging, the lower the priority.

[0074] S42. Based on the priority order, determine the nearest mobile charging and swapping vehicle near each new energy agricultural machinery, and build a cost optimization model.

[0075] In one embodiment, based on the priority order in step S41, the nearest mobile charging and battery swapping vehicle near each new energy agricultural machine is obtained.

[0076] Specifically, the number of mobile charging and swapping vehicles is set to Taiwan, No. The position coordinates of the vehicle are , the status is , the mobile charging and swapping vehicle only serves one new energy agricultural machine at a time, and the coordinates of the new energy agricultural machine are Distance from mobile charging and swapping vehicles to new energy agricultural machinery Using Euclidean distance: Furthermore, the new energy agricultural machinery is processed in descending order of priority, and the nearest idle mobile charging and swapping vehicle is selected for each new energy agricultural machinery: It should be noted that when one of the mobile charging and swapping vehicles is assigned to a high-priority new energy agricultural machinery for energy replenishment, the status of the mobile charging and swapping vehicle Low-priority new energy agricultural machinery will not be considered for the allocation of mobile charging and swapping vehicles for the time being.

[0077] Therefore, the above method is used to determine the nearest idle mobile charging and swapping vehicle near each new energy agricultural machinery.

[0078] In a specific embodiment, only one mobile charging and swapping vehicle is considered, which means that the process of determining the shortest distance is omitted, and the distance from the mobile charging and swapping vehicle to the new energy vehicle with the highest priority is directly obtained.

[0079] Furthermore, a cost optimization model is constructed, which satisfies the following expression: in, is the objective function value of optimizing cost, is a set of path point sequences corresponding to the priority order of new energy agricultural machinery, A binary variable for charging or replacing new energy agricultural machinery. From the waypoint To waypoint The path length, is the path length from the mobile charging and swapping vehicle to the highest priority new energy agricultural machinery path point, For mobile charging and swapping vehicles from the path point To waypoint The average driving speed, is the average speed of the mobile charging and swapping vehicle from its initial position to the highest priority new energy agricultural machinery path point, A collection of charging and swapping points. For charging and swapping points The time required to charge, For charging and swapping points The charging rate, 、 Respectively indicate the charging and swapping points A binary variable indicating whether to perform charging and battery swapping operations. For charging and swapping points The cost of battery replacement, 、 、 is the weight coefficient, A collection of new energy agricultural machinery. is the task weight, For the task The waiting time, Factors influencing path selection, For additional cost, A collection of relevant factors for path selection.

[0080] S43. Determine the optimal cost between mobile charging and swapping vehicles and new energy agricultural machinery based on the cost optimization model.

[0081] In one embodiment, a hierarchical optimization method is used to optimize the objectives in sequence to determine the optimal cost between mobile charging and swapping vehicles and new energy agricultural machinery.

[0082] Specifically, a comprehensive objective function is first designed, which is as follows: in, is the comprehensive objective function, is the optimal cost objective function for the total driving time of mobile charging and swapping vehicles, is the optimal cost objective function for the total waiting time of new energy agricultural machinery tasks, is the optimal cost objective function of the total charging and swapping time, 、 、 is the target weight coefficient.

[0083] Furthermore, the optimal solution of each sub-objective function is determined.

[0084] Specifically, according to the comprehensive objective function, the first cost that satisfies the minimum total driving time of the charging and swapping vehicle is found, that is, The total driving time includes the additional driving time caused by traffic congestion and road restrictions; according to the comprehensive objective function, find the second cost that satisfies the task of new energy agricultural machinery with the minimum total waiting time, that is, According to the comprehensive objective function, find the third cost that satisfies the minimum total charging and swapping time, that is, The value of .

[0085] Furthermore, hierarchical optimization is performed, the core idea of which is to optimize the upper layer objectives while ensuring the optimality of a certain layer objective.

[0086] Specifically, the first layer is optimized to As the main optimization goal, fixed and The weight coefficient and , in guarantee Under the premise of optimality, adjust The value of , observe the comprehensive objective function The impact of and the corresponding value; the second layer is optimized to As the main optimization goal, fix the optimized and The weight coefficient , in ensuring Under the premise of optimality, adjust The value of , observe the comprehensive objective function The impact of and the corresponding value; the third layer is optimized to As the main optimization goal, fix the optimal and ,Adjustment The value of , observe the comprehensive objective function The impact of and the corresponding value.

[0087] Furthermore, according to the above optimal 、 、 and the best 、 、 , determine the comprehensive objective function The value of is the optimal cost between mobile charging and swapping vehicles and new energy agricultural machinery. The optimal cost mainly reflects the time cost.

[0088] It should be noted that the objective function The value of step S42 is replaced The value of can find an optimal path for a mobile charging and swapping vehicle to charge and swap the new energy agricultural machinery. However, this optimal path is not necessarily the shortest path, but it must be the path with the lowest comprehensive cost. In addition, the optimal cost described in the present invention is mainly reflected in the time cost. The additional costs involved in the cost optimization model also include battery costs. The battery cost refers to the comprehensive cost related to the use, loss and management of batteries during the charging and swapping of new energy agricultural machinery. Combined with step S13, it can be seen that the battery cost can be effectively reduced by modularizing and standardizing the battery pack design.

[0089] S5. Through the above determination results, mobile charging, battery replacement and energy replenishment of new energy agricultural machinery are realized.

[0090] In one embodiment, the determination result is used to carry out efficient, safe, and intelligent mobile charging, swapping, and energy replenishment of new energy agricultural machinery; after the mobile charging, swapping, and energy replenishment are completed, the energy trading platform automatically records the transaction information and calculates the fees.

[0091] It should be noted that the present invention does not embody the movement of new energy agricultural machinery as in the prior art, but the movement of charging and swapping vehicles, which is conducive to the continuous and efficient operation of new energy agricultural machinery.

[0092] See Figure 2 , Figure 2 This is a schematic diagram of the structure of a mobile charging, swapping, and energy replenishment system for new energy agricultural machinery in an embodiment of the present invention. The system includes an input device, a processor, an output device, and a memory, wherein the input device, the processor, the output device, and the memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The system uses the described mobile charging, swapping, and energy replenishment method for new energy agricultural machinery.

[0093] In this embodiment, the input device includes a data entry terminal, a sensor module, and a third-party map API data interface. The data entry terminal is used to enter basic data such as the mobile charging and swapping platform number, the new energy agricultural machinery number, and the machine operation scenario; the sensor module is used to obtain real-time information on the agricultural machinery's location, battery level, charging and swapping platform energy storage status, and battery compartment usage status; the third-party map API data interface integrates third-party map API input to input road network data, supporting navigation path calculation and geographic environment parameter collection, including terrain and distance parameters.

[0094] The processor includes a central server cluster, blockchain nodes, an optimization algorithm engine, a GIS data processing unit and a database management system. The optimization algorithm engine contains a cost optimization model and a charging and swapping demand analysis model. The processor is used for data integration, dynamic analysis and decision-making, and blockchain transaction processing.

[0095] The output device includes a visualization terminal, a navigation instruction transmission module, a task execution instruction interface, and a data report generation module. The visualization terminal is used to dynamically display the distribution of agricultural machinery, power ranking, and charging and swapping platform trajectories on a GIS map. The navigation instruction transmission module is used to send navigation routes to mobile charging and swapping vehicles. The data report generation module is used to generate mileage statistics, energy transaction audit reports, and energy replenishment cost optimization analysis reports.

[0096] The memory adopts a high-speed solid-state hard disk, which has the characteristics of fast reading and writing speed, large capacity and high reliability. It can meet the needs of large data storage and is mainly used to store data input by the input device and data processed by the processor.

[0097] In summary, the present invention provides a mobile charging and swapping energy replenishment method for new energy agricultural machinery. By obtaining the concentrated operation area of new energy agricultural machinery and dynamically deploying mobile charging and swapping vehicles, it realizes the precise delivery and efficient coverage of energy replenishment resources, and solves the problems of insufficient coverage of traditional fixed charging piles and low efficiency of round-trip charging of agricultural machinery; by establishing an energy trading platform based on blockchain technology, it realizes decentralized and transparent energy transactions between agricultural machinery, mobile charging and swapping vehicles and energy suppliers, solves the problems of difficult multi-subject collaboration and high trust costs in the traditional energy replenishment system, and greatly reduces the energy transaction costs; by integrating information on operation volume, energy demand, geographical factors and environmental factors, constructs a charging and swapping demand optimization model and generates the optimal energy replenishment plan, solves the problems of traditional energy replenishment scheduling relying on manual experience and serious resource mismatch, and realizes the dynamic response, intelligent decision-making and trusted collaborative closed loop of the new energy agricultural machinery energy replenishment system.

[0098] The present invention provides a mobile charging and swapping energy replenishment system for new energy agricultural machinery. By integrating the agricultural machinery operation heat map to dynamically deploy mobile charging and swapping vehicles and the blockchain energy trading platform, it realizes the precise allocation of energy replenishment resources and the coordination of decentralized trusted transactions, and solves the problems of insufficient coverage of traditional energy replenishment systems and low efficiency of multi-agent coordination; by constructing a comprehensive analysis model for charging and swapping demand and a cost optimization model, the workload, energy demand and geographical environment parameters are incorporated into the dynamic optimization algorithm, and the global optimal resource configuration of energy replenishment scheduling is achieved, which solves the problems of traditional solutions relying on manual experience and serious resource mismatch; by designing a multi-objective comprehensive function and a hierarchical optimization method, combined with dynamic weight coefficient adjustment and a complex scenario adaptation mechanism, high-precision and high-efficiency energy replenishment decision-making is achieved, which solves the problems of poor adaptability and low computational efficiency of single-objective optimization models, and constructs a full-link energy replenishment ecosystem of "dynamic perception-intelligent decision-making-trusted execution" in the field of new energy agricultural machinery.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A mobile charging and battery-recharging method for new energy agricultural machinery, characterized in that: The method comprises the following steps: Identify concentrated operating areas for new energy agricultural machinery and deploy mobile charging and swapping vehicles in these concentrated operating areas; Based on the mobile charging and swapping vehicles, an energy trading platform based on blockchain technology is established; Obtaining charging and swapping demand information for new energy agricultural machinery based on the energy trading platform, wherein the charging and swapping demand information includes information on workload and energy demand, charging and swapping efficiency, geographical factors, and environmental factors; Determine the optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery based on the charging and swapping demand information and obtain a determination result; Through the determination results, mobile charging, battery replacement and energy replenishment of new energy agricultural machinery can be realized.

2. A mobile charging and recharging method for new energy agricultural machinery according to claim 1, characterized in that: The obtaining of a concentrated operation area for new energy agricultural machinery and the deployment of mobile charging and battery swapping vehicles in the concentrated operation area include: Obtain information on the number, location, and operating status of new energy agricultural machinery; Determining a concentrated operation area of the new energy agricultural machinery based on the information; Mobile charging and swapping vehicles are deployed in the concentrated operation area, and the mobile charging and swapping vehicles include mobile charging and swapping vehicles controlled by mini-programs and unmanned mobile charging and swapping vehicles equipped with high-precision navigation systems and obstacle avoidance sensors.

3. A mobile charging and energy replenishment method for new energy agricultural machinery according to claim 1, characterized in that: The energy trading platform established based on the mobile charging and swapping vehicle includes: Based on the mobile charging and swapping vehicle, an energy trading platform based on blockchain technology is established. The energy trading platform is used to record, verify and store detailed information of all charging and swapping transactions, including the identity information of both parties to the transaction, transaction timestamp, transaction power and transaction price.

4. A mobile charging and recharging method for new energy agricultural machinery according to claim 1, characterized in that: The determining the optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery based on the charging and swapping demand information and obtaining a determination result includes: Determining the priority order of charging, swapping and recharging of new energy agricultural machinery based on the charging and swapping demand information; Based on the priority order, determine the nearest idle mobile charging and swapping vehicle near each new energy agricultural machinery, and build a cost optimization model; Based on the cost optimization model, the optimal cost between mobile charging and swapping vehicles and new energy agricultural machinery is determined and a definite result is obtained.

5. A mobile charging and recharging method for new energy agricultural machinery according to claim 4, characterized in that: Determining the priority order of charging, swapping and energy replenishment of new energy agricultural machinery based on the charging and swapping demand information includes: Based on the charging demand information, a charging demand analysis model is constructed for determining the degree of charging demand of new energy agricultural machinery; Based on the battery replacement demand information, a battery replacement demand analysis model is constructed to determine the degree of battery replacement demand of new energy agricultural machinery; Using the charging demand analysis model and the battery swapping demand analysis model, a comprehensive charging and battery swapping demand analysis model is constructed for comprehensively analyzing the degree of charging and battery swapping demand for new energy agricultural machinery; Obtaining the charging and swapping demand level value of new energy agricultural machinery through the comprehensive analysis model of charging and swapping demand; According to the charging and swapping demand value, the priority order of charging and swapping energy replenishment of new energy agricultural machinery is determined. The higher the charging and swapping demand value, the higher the priority of charging and swapping energy replenishment of the new energy agricultural machinery.

6. A mobile charging and battery-replacing method for new energy agricultural machinery according to claim 5, characterized in that: The charging demand analysis model satisfies the following expression: in, is the charging demand value of new energy agricultural machinery, To estimate the remaining workload, The energy required to complete a unit of work, is the current remaining power, The efficiency of converting electrical energy into battery storage energy during charging. is the output power of the charging device, Navigation route from mobile charging and battery swapping vehicles to new energy agricultural machinery, is the path attenuation coefficient, is the quantitative value of weather influencing factors, is the quantitative value of the time influencing factor, 、 is the weight coefficient; the battery swap demand analysis model satisfies the following expression: in, is the demand value for battery replacement of new energy agricultural machinery, To estimate the remaining workload, The energy required to complete a unit of work, is the current remaining power, is the minimum power threshold for battery replacement, is the rated capacity of the new energy agricultural machinery battery, Navigation route from mobile charging and battery swapping vehicles to new energy agricultural machinery, is the attenuation coefficient of the switching circuit, is the quantitative value of weather influencing factors, is the quantitative value of the time influencing factor, 、 is the weight coefficient; the comprehensive analysis model of charging and swapping demand satisfies the following expression: in, is the demand value for charging and replacing batteries for new energy agricultural machinery. The charging demand for new energy agricultural machinery, The demand for battery replacement for new energy agricultural machinery, 、 is the weight coefficient.

7. A mobile charging and recharging method for new energy agricultural machinery according to claim 4, characterized in that: The cost optimization model satisfies the following expression: in, is the objective function value of optimizing cost, is a set of path point sequences corresponding to the priority order of new energy agricultural machinery, A binary variable for charging or replacing new energy agricultural machinery. From the waypoint To waypoint The path length, is the path length from the mobile charging and swapping vehicle to the highest priority new energy agricultural machinery path point, For mobile charging and swapping vehicles from the path point To waypoint The average driving speed, is the average speed of the mobile charging and swapping vehicle from its initial position to the highest priority new energy agricultural machinery path point, A collection of charging and swapping points. For charging and swapping points The time required to charge, For charging and swapping points The charging rate, 、 Respectively indicate the charging and swapping points A binary variable indicating whether to perform charging and battery swapping operations. For charging and swapping points The cost of battery replacement, 、 、 is the weight coefficient, A collection of new energy agricultural machinery. is the task weight, For the task The waiting time, Factors influencing path selection, For additional cost, A collection of relevant factors for path selection.

8. A mobile charging and energy replenishment method for new energy agricultural machinery according to claim 4, characterized in that: Determining the optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery based on the cost optimization model and obtaining the determination result includes: Based on the cost optimization model, a comprehensive objective function is designed to optimize the total driving time of mobile charging and swapping vehicles, the total waiting time of new energy agricultural machinery, and the total charging and swapping time. The comprehensive objective function is as follows: in, is the comprehensive objective function, is the optimal cost objective function for the total driving time of mobile charging and swapping vehicles, is the optimal cost objective function for the total waiting time of new energy agricultural machinery tasks, is the optimal cost objective function of the total charging and swapping time, 、 、 is the target weight coefficient; According to the comprehensive objective function, the hierarchical optimization method is used to obtain the optimal cost between mobile charging and swapping vehicles and new energy agricultural machinery.

9. A mobile charging and energy replenishment method for new energy agricultural machinery according to claim 8, characterized in that: According to the comprehensive objective function, the optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery is obtained by using the hierarchical optimization method, which includes: According to the comprehensive objective function, a first cost is found that minimizes the total driving time of the charging and swapping vehicle, where the total driving time includes additional driving time caused by traffic congestion and road restrictions; According to the minimum first cost, finding a second cost that satisfies the task of minimizing the total waiting time of the new energy agricultural machinery; Based on the minimum second cost, find a third cost that minimizes the total charging and swapping time; Based on the first cost, the second cost and the third cost, obtaining an optimal weight coefficient of the comprehensive objective function by using a hierarchical optimization method; The optimal cost between the mobile charging and swapping vehicle and the new energy agricultural machinery is determined through the first cost, the second cost, the third cost and the optimal weight coefficient.

10. A mobile charging, swapping and energy replenishment system for new energy agricultural machinery, the system using a mobile charging, swapping and energy replenishment method for new energy agricultural machinery according to any one of claims 1 to 9, characterized in that: The system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions.

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