Electric agricultural machine operation path planning optimization method and device, equipment and medium

Abstract

The invention relates to an electric agricultural machine operation path planning optimization method and device, equipment and a medium, and the method comprises the steps: obtaining a current position point, a to-be-operated point set and a charging point set of an electric agricultural machine, the to-be-operated point set comprises a plurality of to-be-operated points, and the charging point set comprises a plurality of target charging points; calling a preset path planning optimization algorithm, and generating a first optimal task path and a first optimal charging path corresponding to the first optimal task path according to the current position point, the to-be-operated point set, the charging point set and the charge state constraint condition; generating a second optimal task path and a second optimal charging path of the electric agricultural machine based on a path planning optimization algorithm if it is detected that the residual charge state of the battery of the electric agricultural machine is lower than a charging charge state threshold value in the operation process along the first optimal task path; and repeatedly executing until all the to-be-worked points in the to-be-worked point set are executed. The problems of operation interruption, retention and the like of the electric agricultural machine can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of path planning, in particular to an electric agricultural machine operation path planning optimization method, a corresponding device, an electronic device and a computer readable storage medium. BACKGROUND

[0002] With the popularization of electric agricultural machinery, pure electric drive tractors, orchard operation robots and electric transport vehicles are gradually applied to field operation, orchard harvesting and agricultural product transportation.

[0003] Unlike traditional fuel agricultural machinery, the endurance of electric agricultural machinery is limited by battery capacity and state of charge (SOC). Currently, the path planning of electric agricultural machinery does not fully consider the return or mid-way charging demand, which easily leads to task interruption or vehicle retention, affecting operation safety and reliability.

[0004] In summary, the path planning of electric agricultural machinery in the prior art does not fully consider the return or mid-way charging demand, which easily leads to task interruption or vehicle retention, affecting operation safety and reliability, and other problems. The present applicant made corresponding explorations to solve this problem. SUMMARY

[0005] The present application aims to solve the above problems and provide an electric agricultural machine operation path planning optimization method, a corresponding device, an electronic device and a computer readable storage medium.

[0006] To achieve the various purposes of the present application, the present application adopts the following technical solutions: An electric agricultural machine operation path planning optimization method is proposed to adapt to one of the purposes of the present application, comprising: obtaining a current position point of an electric agricultural machine, a set of to-be-operated points and a set of charging points, wherein the set of to-be-operated points contains a plurality of to-be-operated points, and the set of charging points contains a plurality of target charging points; calling a preset path planning optimization algorithm, and generating a first optimal task path and its corresponding first optimal charging path according to the current position point, the set of to-be-operated points, the set of charging points and the charge state constraint condition, wherein the charge state constraint condition is determined by the total electric energy consumption of the task path and the charging path, the remaining state of charge of the battery, the nominal capacity of the battery, the electric energy consumption and the state of charge conversion coefficient, and the safe reserved state of charge; If, during operation along the first optimal task path, it is detected that the remaining charge state of the electric agricultural machine's battery is lower than the charging state threshold, then based on the path planning optimization algorithm, according to the set of remaining task points, the set of charging points, and the charge state constraints, a second optimal task path and a second optimal charging path are generated for the electric agricultural machine, and operation continues along the second optimal task path. The charging state threshold is determined by the safe retention of charge state, the conversion factor between energy consumption and charge state, the minimum energy consumption from the current location to the target charging point, and the nominal battery capacity. Repeat the process until all work points in the set of work points have been completed, in order to optimize the path planning for electric agricultural machinery operations.

[0007] Optionally, the step of constructing the charge state constraint conditions includes: Obtain the total power consumption of the task path and the power replenishment path, the remaining state of charge of the battery corresponding to the electric agricultural machinery, the nominal capacity of the battery, the conversion factor between power consumption and state of charge, and the safe reserved state of charge. Calculate and determine a first ratio between the total power consumption and the nominal capacity of the battery, and calculate and determine a first product between the power consumption and the state of charge conversion factor and the first ratio; Calculate and determine a first difference between the remaining state of charge of the battery and the first product, and use the first difference being greater than or equal to the safe reserved state of charge as the charge state constraint condition.

[0008] Optionally, the step of invoking a preset path planning optimization algorithm to generate a first optimal task path and its corresponding first optimal recharging path based on the current location point, the set of work points, the set of charging points, and the charge state constraints includes: The preset genetic algorithm is used as the path planning optimization algorithm. The population size, crossover probability, mutation probability and maximum number of iterations of the genetic algorithm are set. The current position of the electric agricultural machine, the access sequence of the work points, and the target charging point are used as chromosome coding rules. The first M positions of the chromosome are the access order codes of the M work points, and the last position is the index code of the target charging point in the charging point set. Based on the preset fitness function and the charge state constraint, the fitness value of each individual in the initial population is calculated. Several individuals with the highest fitness values ​​are selected as the parent population. Gene recombination of the parent population is carried out through single-point crossover. Gene mutation is carried out by randomly exchanging the access order code of any two work points or replacing the target charging point index code to generate the offspring population. After merging the parent population and the offspring population, several individuals with the highest fitness values ​​are retained to form a new population. Repeat the iteration until the maximum number of iterations is reached. Decode the chromosome with the highest fitness value at the end of the iteration into the order of visiting the work points and the target charging point. Generate the first optimal task path from the current position point according to the order of visiting the work points, and the first optimal charging path from the last work point to the target charging point.

[0009] Optionally, the step of constructing the fitness function includes: The path length and turning complexity of the task path of the electric agricultural machine, the path cost of the recharging path, the total power consumption of the task path and the recharging path, the remaining battery charge state and the safe retained charge state after the task path and the recharging path are completed. A penalty term is constructed based on the difference between the safe retained state of charge and the remaining charge state of the battery. The fitness function is constructed by weighting and summing the path length of the task path, the turning complexity of the task path, the path cost of the recharging path, the total energy consumption of the task path and the recharging path, and the penalty term.

[0010] Optionally, the step of determining the charge state threshold includes: Obtain the corresponding safe retained state of charge, energy consumption and state of charge conversion factor, minimum energy consumption from the current location to the target charging point, and battery nominal capacity for electric agricultural machinery. Calculate and determine a second ratio between the minimum energy consumption from the current location point to the target charging point and the nominal capacity of the battery; calculate and determine a second product between the energy consumption and the state of charge conversion factor and the second ratio. A first sum is calculated to determine the safe retained state of charge and the second product, and the first sum is used as the threshold of the replenished charge state.

[0011] Optionally, the remaining state of charge of the battery represents the ratio between the remaining battery charge and the nominal battery capacity.

[0012] Optionally, the path planning optimization algorithm includes a genetic algorithm or an A* algorithm.

[0013] An electric agricultural machinery operation path planning and optimization device provided for another purpose of this application includes: The module for acquiring work points is configured to acquire the current location of the electric agricultural machinery, a set of work points, and a set of charging points, wherein the set of work points contains multiple work points and the set of charging points contains multiple target charging points; The first path planning module is configured to call a preset path planning optimization algorithm to generate a first optimal task path and its corresponding first optimal recharging path based on the current location point, the set of work points, the set of charging points, and the charge state constraints. The charge state constraints are determined by the total energy consumption of the task path and the recharging path, the remaining state of charge of the battery, the nominal capacity of the battery, the energy consumption and state of charge conversion factor, and the safe reserved state of charge. The second path planning module is configured to detect that the remaining charge state of the electric agricultural machine's battery is lower than the charging state threshold during operation along the first optimal task path. Then, based on the path planning optimization algorithm, it generates a second optimal task path and a second optimal charging path for the electric agricultural machine according to the set of remaining task points, the set of charging points, and the charge state constraints. The operation continues along the second optimal task path. The charging state threshold is determined by the safe retention of charge state, the conversion factor between energy consumption and charge state, the minimum energy consumption from the current location to the target charging point, and the nominal battery capacity. The electric agricultural machinery operation module is set to execute repeatedly until all the work points in the set of work points have been completed, so as to complete the path planning optimization of the electric agricultural machinery operation.

[0014] An electronic device provided for another purpose of this application includes a central processing unit and a memory, the central processing unit being used to invoke and run a computer program stored in the memory to perform the steps of the electric agricultural machinery operation path planning optimization method of this application.

[0015] A computer-readable storage medium is provided for another purpose of this application, which stores, in the form of computer-readable instructions, a computer program implemented according to the electric agricultural machinery operation path planning optimization method, which, when called by a computer, executes the steps included in the corresponding method.

[0016] Compared to existing technologies, this application addresses the problems in existing electric agricultural machinery path planning that fail to adequately consider return trips or mid-journey recharging needs, which can easily lead to task interruptions or vehicle delays, affecting operational safety and reliability. This application includes, but is not limited to, the following beneficial effects: Firstly, existing electric agricultural machinery path planning methods mostly focus only on optimizing path length or travel time, without fully considering battery energy constraints. This often leads to agricultural machinery being forced to interrupt its tasks and remain stranded in the field due to battery depletion. This application achieves closed-loop coordination between battery remaining power estimation and path planning by unifying the task path and recharging path and embedding charge state constraints throughout the planning process. This completely avoids the problem of optimizing path performance while ignoring battery safety, and fundamentally solves the problems of electric agricultural machinery operation interruption and stranding.

[0017] Secondly, this application constructs an energy safety guarantee system for the entire operation cycle. In the initial planning stage, the energy consumption of the task path, the energy consumption of the recharge path, and the safety reserve charge state are incorporated into the charge state constraints to ensure that the agricultural machinery has sufficient energy reserved before departure to complete the operation and recharge safely. During the operation, the system will monitor the remaining battery power in real time. When the power is lower than the recharge charge state threshold, the task path and recharge path will be replanned immediately to ensure that the agricultural machinery can continue to perform the operation task while still safely traveling to the nearest target charging point. This ensures that the electric agricultural machinery can not only complete the operation task but also recharge safely, significantly improving the operational reliability.

[0018] Third, the path planning aims to minimize the total path cost, comprehensively considering indicators related to operational efficiency such as the length of the task path, turning complexity, and travel time, to avoid ineffective paths that prolong the operation cycle. At the same time, total energy consumption is used as a core evaluation item, and the importance of energy consumption is highlighted through weight adjustment, guiding the path planning algorithm to select low-energy-consumption paths, reducing the energy consumption cost of electric agricultural machinery, thus ensuring both operational efficiency and energy economy, and meeting the actual needs of agricultural production for operational timeliness and cost control. Attached Figure Description

[0019] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating the electric agricultural machinery operation path planning and optimization method in the embodiments of this application; Figure 2 This is a flowchart of the electric agricultural machinery operation path planning and optimization method in the embodiments of this application; Figure 3 This is a schematic diagram of the electric agricultural machinery operation path planning and optimization device in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the computer device in the embodiments of this application. Detailed Implementation

[0020] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0021] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0022] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0023] Those skilled in the art will understand that the terms "client," "terminal," and "terminal device" as used herein include both devices that receive wireless signals, devices that only possess wireless signal receiver capabilities without transmission capabilities, and devices with receiving and transmitting hardware, devices that have receiving and transmitting hardware capable of bidirectional communication over a bidirectional communication link. Such devices may include: cellular or other communication devices such as personal computers or tablets, having single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service) that can combine voice, data processing, fax, and / or data communication capabilities; PDAs (Personal Digital Assistants) that may include radio frequency receivers, pagers, internet / intranet access, web browsers, notebooks, calendars, and / or GPS (Global Positioning System) receivers; and conventional laptops and / or handheld computers or other devices that have and / or include radio frequency receivers. As used herein, "client," "terminal," and "terminal device" can be portable, transportable, installed in a means of transportation (air, sea, and / or land), or suitable and / or configured to operate locally and / or in a distributed manner, operating in any other location on Earth and / or in space. "Client," "terminal," and "terminal device" as used herein can also be a communication terminal, an internet access terminal, or a music / video playback terminal, such as a PDA, a MID (Mobile Internet Device), and / or a mobile phone with music / video playback capabilities, or a smart TV, set-top box, etc.

[0024] The hardware referred to by the names "server," "client," and "service node" in this application is essentially an electronic device with the equivalent capabilities of a personal computer. It is a hardware device with the necessary components revealed by the von Neumann architecture, such as a central processing unit (including an arithmetic logic unit and a control unit), memory, input devices, and output devices. The computer program is stored in its memory, and the central processing unit loads the program stored in the secondary storage into the main memory to run it, execute the instructions in the program, and interact with the input and output devices to complete specific functions.

[0025] It should be noted that the concept of "server" used in this application can also be extended to the case of server clusters. Based on the network deployment principles understood by those skilled in the art, the servers should be logically divided. Physically, these servers can be independent of each other but accessible through interfaces, or they can be integrated into a single physical computer or a computer cluster. Those skilled in the art should understand this flexibility and should not use it to constrain the implementation of the network deployment method in this application.

[0026] One or more of the technical features of this application, unless explicitly specified herein, can be deployed on a server and accessed by a client remotely calling the online service interface provided by the server, or can be directly deployed and run on a client for access.

[0027] Unless otherwise specified, the neural network models referenced or potentially referenced in this application may be deployed on a remote server and invoked remotely on the client, or deployed on a client with the capability to invoke directly. In some embodiments, when running on the client, the corresponding intelligence may be acquired through transfer learning in order to reduce the requirements on the client's hardware resources and avoid excessive consumption of the client's hardware resources.

[0028] Unless otherwise specified, all data involved in this application may be stored remotely on a server or on a local terminal device, as long as it is suitable for use by the technical solution of this application.

[0029] Those skilled in the art will understand that although the various methods in this application are described based on the same concept and thus present commonality among them, they can be performed independently unless otherwise specified. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept; therefore, concepts expressed in the same way, as well as concepts that are appropriately changed for convenience but are expressed differently, should be understood equivalently.

[0030] Unless otherwise expressly stated, the various embodiments disclosed in this application can be combined in a cross-cutting manner to flexibly construct new embodiments, as long as such combination does not depart from the inventive spirit of this application and can meet the needs of the prior art or solve a certain deficiency in the prior art. Those skilled in the art should be aware of such modifications.

[0031] Please see Figure 1 In one embodiment of the electric agricultural machinery operation path planning optimization method of this application, the method includes: Step S10: Obtain the current location of the electric agricultural machinery, the set of work points to be completed, and the set of charging points, wherein the set of work points to be completed contains multiple work points to be completed, and the set of charging points contains multiple target charging points; The electric agricultural machinery operation path planning system in the terminal device can obtain the current location point of the electric agricultural machinery, the set of operation points to be completed, and the set of charging points. The set of operation points to be completed includes multiple operation points, and the set of charging points includes multiple target charging points. Specifically, a working space for the electric agricultural machinery can be established, wherein the working space is represented as... ,in, Represents a set of nodes. The edge set is represented as; the current position of the electric agricultural machinery is represented as... wherein, The current position of the electric agricultural machinery can be its starting point; the set of work points to be completed is represented as... , represents the set of nodes that agricultural machinery needs to complete its tasks; where, The set of charging points is represented as follows: , which represents the set constructed to indicate the location of the target charging point.

[0032] Step S20: Call the preset path planning optimization algorithm to generate a first optimal task path and its corresponding first optimal recharging path based on the current location point, the set of work points, the set of charging points, and the charge state constraints. The charge state constraints are determined by the total energy consumption of the task path and the recharging path, the remaining state of charge of the battery, the nominal capacity of the battery, the energy consumption and state of charge conversion factor, and the safe reserved state of charge. After obtaining the current location of the electric agricultural machinery, the set of work points, and the set of charging points, a preset path planning optimization algorithm is invoked. Based on the current location, the set of work points, the set of charging points, and the charge state constraints, a first optimal task path and its corresponding first optimal recharging path are generated. The charge state constraints are determined by the total energy consumption of the task path and the recharging path, the remaining state of charge of the battery, the nominal battery capacity, the energy consumption to state of charge conversion factor, and the safe reserved state of charge. The remaining state of charge represents the ratio between the remaining battery capacity and the nominal battery capacity. The path planning optimization algorithm includes genetic algorithms or A* algorithms, etc. Both the task path and the recharging path contain multiple drivable road segments.

[0033] Let the task path be The power replenishment path is Target charging point A certain drivable road section The energy consumption value is expressed as Let θ represent the energy consumption model parameters. Then, the total energy consumption of the task path and the recharging path can be expressed as: , in, This indicates the total power consumption of the task path and the power replenishment path; Indicates the power consumption of the task path; This indicates the amount of electrical energy consumed by the power replenishment path.

[0034] In some embodiments, the step of constructing the charge state constraint conditions includes: Step S21: Obtain the total power consumption of the task path and the power replenishment path, the remaining state of charge of the battery corresponding to the electric agricultural machinery, the nominal capacity of the battery, the conversion factor between power consumption and state of charge, and the safe reserved state of charge. Step S22: Calculate and determine the first ratio between the total power consumption and the nominal capacity of the battery, and calculate and determine the first product between the power consumption and the state of charge conversion factor and the first ratio; Step S23: Calculate and determine the first difference between the remaining state of charge of the battery and the first product, and use the first difference being greater than or equal to the safe retained state of charge as the charge state constraint condition.

[0035] Specifically, to ensure that the electric agricultural machinery still has enough energy to return to the target charging point for recharging after completing its task path, charge state constraints are established, wherein the charge state constraints are expressed as follows: , in, This indicates the remaining state of charge of the battery in the electric agricultural machinery. This represents the conversion factor between electrical energy consumption and state of charge. Indicates the battery's nominal capacity; Indicates that the state of charge is safely maintained; This indicates the total power consumption of the task path and the power replenishment path.

[0036] As can be seen from the above charge state constraints, the path is only considered valid when the remaining state of charge of the battery after the path is executed is not lower than the safe reserve state of charge. This completely avoids the risk of electric agricultural machinery breaking down midway due to insufficient power, provides the greatest guarantee for energy safety throughout the entire process of electric agricultural machinery operation and recharging, and reflects the energy priority principle in electric agricultural machinery path planning.

[0037] In a further embodiment, the step of constructing the fitness function includes: Step S201: Obtain the path length and turning complexity of the task path of the electric agricultural machine, the path cost of the recharging path, the total power consumption of the task path and the recharging path, the remaining battery charge state and the safe retained charge state after completing the task path and the recharging path. Step S202: Construct a penalty term based on the difference between the safe retained state of charge and the remaining charge state of the battery. Then, perform a weighted summation of the path length of the task path, the turning complexity of the task path, the path cost of the recharging path, the total energy consumption of the task path and the recharging path, and the penalty term to construct the fitness function.

[0038] Specifically, the formula for calculating the fitness function is as follows: , in, This represents the fitness function value; A weighting coefficient representing the path length of the task path; Indicates the task path. Indicates the power supply path; Indicates the path length of the task path; Weighting coefficients representing the turning complexity of the task path; Indicates the turning complexity of the task path; The weighting coefficients represent the path cost of the power replenishment path; Represents the path cost of the power replenishment path; Weighting coefficients representing the total energy consumption of the task path and the power replenishment path; This indicates the total power consumption of the task path and the power replenishment path; Indicates that the state of charge is safely maintained; This indicates the remaining battery charge status after completing the task path and the recharging path; This represents the coefficient for the energy penalty term.

[0039] As can be seen from the above embodiments, the fitness function covers path performance including path length, turning complexity, and the cost of recharging paths, as well as total power consumption and remaining charge state, ensuring that the evaluation dimensions fully match the needs of efficient operation and energy safety of electric agricultural machinery. By constructing a penalty term based on the difference between the safe retention state of charge and the battery's remaining charge state, the energy constraint and path performance are integrated and optimized. The introduction of the penalty term ensures that energy-unsafe paths are eliminated by the algorithm due to a sharp increase in fitness value, thus guaranteeing energy safety. The weighted summation of path length, turning complexity, etc., takes into account both path efficiency and smoothness under the premise of energy safety. The finally constructed fitness function can guide the genetic algorithm to find the optimal balance between energy safety, path length, turning complexity, and recharging path cost, providing a quantitative basis for outputting the optimal operation path that is both efficient and smooth without running out of power midway.

[0040] In a further embodiment, the step of invoking a preset path planning optimization algorithm to generate a first optimal task path and its corresponding first optimal recharging path based on the current location point, the set of work points, the set of charging points, and the charge state constraints includes: Step S2001: Use the preset genetic algorithm as the path planning optimization algorithm, set the population size, crossover probability, mutation probability and maximum number of iterations of the genetic algorithm, and use the current position of the electric agricultural machine, the access sequence of the work points, and the target charging point as the chromosome coding rules, wherein the first M positions of the chromosome are the access order codes of the M work points, and the last position is the index code of the target charging point in the charging point set. Step S2002: Calculate the fitness value of each individual in the initial population based on the preset fitness function and the charge state constraint conditions. Select the individuals with the highest fitness values ​​as the parent population. Perform gene recombination of the parent population through single-point crossover. Perform gene mutation by randomly exchanging the access order code of any two work points or replacing the target charging point index code to generate the offspring population. After merging the parent population and the offspring population, retain the individuals with the highest fitness values ​​to form a new population. Step S2003: Repeat the iteration until the maximum number of iterations is reached. Decode the chromosome with the highest fitness value at the end of the iteration into the order of access to the work points and the target charging point. Generate the first optimal task path from the current position point according to the order of access to the work points, and the first optimal charging path from the last work point to the target charging point.

[0041] As can be seen from steps S2001 to S2003 above, the selection, crossover, and mutation operations based on the genetic algorithm achieve efficient optimization of multi-objective paths. The fitness value calculation based on the fitness function and charge state constraints takes into account performance indicators such as path length and energy consumption, while filtering out energy-insecure and invalid paths through charge state constraints. The parent recombination and offspring mutation operations introduce new search directions while preserving high-quality path genes, ensuring population diversity and the depth of optimal solution exploration. The chromosome with the highest fitness value is decoded into a specific path, ultimately generating a complete link from the current location to the work point and then to the charging point. This ensures full coverage of the work tasks and guarantees energy replenishment through the charging path, achieving the dual-objective optimization of efficient operation and energy safety for electric agricultural machinery.

[0042] Step S30: During the operation along the first optimal task path, if it is detected that the remaining charge state of the battery of the electric agricultural machine is lower than the charging state threshold, then based on the path planning optimization algorithm, according to the set of remaining task points, the set of charging points, and the charge state constraints, a second optimal task path and a second optimal charging path for the electric agricultural machine are generated, and the operation continues along the second optimal task path. The charging state threshold is determined by the safe retention of charge state, the conversion factor between energy consumption and charge state, the minimum energy consumption from the current location point to the target charging point, and the nominal battery capacity. After calling a preset path planning optimization algorithm to generate a first optimal task path and its corresponding first optimal recharging path based on the current location point, the set of work points, the set of charging points, and the charge state constraints, if the remaining charge state of the electric agricultural machine's battery is detected to be lower than the recharging charge state threshold during the operation along the first optimal task path, then based on the path planning optimization algorithm according to the set of remaining task points, the set of charging points, and the charge state constraints, a second optimal task path and a second optimal recharging path for the electric agricultural machine are generated, and the operation continues along the second optimal task path. The recharging charge state threshold is determined by the safe retention of charge state, the conversion factor between energy consumption and charge state, the minimum energy consumption from the current location point to the target charging point, and the nominal battery capacity. Specifically, during operation along the first optimal task path, the system obtains the remaining state of charge of the battery in real time through a battery state estimation module. This battery state estimation module can be implemented based on various estimation models and algorithms, such as equivalent circuit models, data-driven models, or estimation methods based on empirical features. Its core function is to estimate the current remaining state of charge of the electric agricultural machinery's battery based on real-time operating parameters such as battery voltage, battery current, and battery temperature, for subsequent path energy constraint judgment and dynamic replanning decisions. The remaining state of charge of the battery is represented as... , in, Indicates the next moment The remaining state of charge of the battery; The prediction function representing the remaining state of charge of a battery can be a mathematical function based on the battery equivalent circuit model, electrochemical model, or data-driven model, used to describe the mapping relationship from the current state to the next state. Indicates the current time The remaining state of charge of the battery; Indicates the current time Battery voltage; Indicates the current time The battery temperature; Indicates the current time The set of battery model parameters; Indicates the current time The battery current.

[0043] In some embodiments, the circuit model of the first-order RC equivalent circuit, combined with the extended Kalman filter (EKF), is used to realize real-time estimation of the remaining battery capacity (SOC). First, the dynamic characteristics of the battery are modeled using a first-order RC circuit. The circuit model of the first-order RC equivalent circuit consists of the open-circuit voltage... Ohmic internal resistance Polarization resistance and polarization capacitor composition, , , and All of these are related to the battery's remaining state of charge (SOC). In the embodiments, the variation of SOC with the battery's SOC can be obtained through experimental calibration or table lookup. This application does not limit the specific function form. The terminal voltage of the drive battery of the electric agricultural machinery can be modeled and expressed as: , In the formula, The current battery current is at the current moment. The current battery voltage. This refers to the battery polarization voltage. Indicates open-circuit voltage; Indicates the internal resistance of the ohm; Indicates polarization resistance; Indicates polarization capacitor In implementation, a state vector is selected. The input quantity is The output is By iteratively performing prediction and correction steps using the Extended Kalman Filter (EKF) algorithm, a relatively accurate estimate of the battery's remaining state of charge (SOC) can be obtained even in the presence of measurement noise and model uncertainty. The SOC output by this battery state of charge estimation module is provided in real-time to the route planning module to determine the energy feasibility of the route.

[0044] In a further embodiment, the step of determining the charge state threshold includes: Step S301: Obtain the safe retained state of charge, energy consumption and state of charge conversion factor, minimum energy consumption from the current location to the target charging point, and nominal battery capacity corresponding to the electric agricultural machinery. Step S302: Calculate and determine the second ratio between the minimum energy consumption from the current location point to the target charging point and the nominal capacity of the battery; calculate and determine the second product between the energy consumption and the state of charge conversion factor and the second ratio. Step S303: Calculate and determine the first sum between the safe retained state of charge and the second product, and use the first sum as the threshold of the replenished charge state.

[0045] Specifically, the formula for calculating the charge state threshold is as follows: , in, Indicates the threshold value for the charge state of the supplementary charge; This indicates the safe reserved charge state corresponding to the electric agricultural machinery; Indicates the current position point The minimum energy consumption to reach the target charging point c; where, The energy consumption can be calculated by traversing the candidate paths from the current location to each charging point, and then taking the minimum value. The energy consumption calculation can be obtained by accumulating the energy consumption of each segment on each side of the path, which is expressed as: , In the formula, This represents the energy consumption of a certain drivable road segment. θ These are the model parameters for electricity consumption.

[0046] If the battery's remaining state of charge (SOC) is sufficient, the electric farm machinery continues to perform the remaining tasks; if the battery's remaining state of charge (SOC) is low, the genetic algorithm uses the current location as a new starting point and the set of charging points as the target to quickly generate a feasible charging path. It then prioritizes driving to the nearest target charging point for charging and then resumes the task execution path.

[0047] Step S40: Repeat the process until all work points in the set of work points have been completed, in order to complete the path planning optimization for electric agricultural machinery operations.

[0048] Repeat step S30 until all work points in the set of work points have been completed, in order to complete the path planning optimization for electric agricultural machinery operations.

[0049] Specifically, battery power is dynamically consumed during operation. Each repeated execution recalculates the optimal task path and optimal recharging path based on the current real-time remaining battery charge and remaining task points. This dynamic adjustment accurately adapts to changes in battery power, avoiding wasted operation time due to overly conservative premature recharging or forced operation interruption due to insufficient power. Each generation of a new optimal task path and optimal recharging path is a local optimum search for the remaining task scenario. After multiple iterations, global path optimization is ultimately achieved for the entire operation cycle. For example, in the early stages, low-energy paths may be prioritized to complete core task points, while later adjustments are made flexibly based on remaining battery power to ensure all tasks are completed and the user safely reaches the charging point before the battery runs out, maximizing operational efficiency and energy utilization.

[0050] As can be seen from the above embodiments, compared with the prior art, this application addresses the problems in the prior art where the path planning of electric agricultural machinery fails to fully consider the return trip or mid-journey recharging needs, which can easily lead to task interruption or vehicle delays, affecting operational safety and reliability. This application has, but is not limited to, the following beneficial effects: Firstly, existing electric agricultural machinery path planning methods mostly focus only on optimizing path length or travel time, without fully considering battery energy constraints. This often leads to agricultural machinery being forced to interrupt its tasks and remain stranded in the field due to battery depletion. This application achieves closed-loop coordination between battery remaining power estimation and path planning by unifying the task path and recharging path and embedding charge state constraints throughout the planning process. This completely avoids the problem of optimizing path performance while ignoring battery safety, and fundamentally solves the problems of electric agricultural machinery operation interruption and stranding.

[0051] Secondly, this application constructs an energy safety guarantee system for the entire operation cycle. In the initial planning stage, the energy consumption of the task path, the energy consumption of the recharge path, and the safety reserve charge state are incorporated into the charge state constraints to ensure that the agricultural machinery has sufficient energy reserved before departure to complete the operation and recharge safely. During the operation, the system will monitor the remaining battery power in real time. When the power is lower than the recharge charge state threshold, the task path and recharge path will be replanned immediately to ensure that the agricultural machinery can continue to perform the operation task while still safely traveling to the nearest target charging point. This ensures that the electric agricultural machinery can not only complete the operation task but also recharge safely, significantly improving the operational reliability.

[0052] Third, the path planning aims to minimize the total path cost, comprehensively considering indicators related to operational efficiency such as the length of the task path, turning complexity, and travel time, to avoid ineffective paths that prolong the operation cycle. At the same time, total energy consumption is used as a core evaluation item, and the importance of energy consumption is highlighted through weight adjustment, guiding the path planning algorithm to select low-energy-consumption paths, reducing the energy consumption cost of electric agricultural machinery, thus ensuring both operational efficiency and energy economy, and meeting the actual needs of agricultural production for operational timeliness and cost control.

[0053] Please see Figure 3 This application provides an electric agricultural machinery operation path planning and optimization device, comprising a task point acquisition module 1100, a first path planning module 1200, a second path planning module 1300, and an electric agricultural machinery operation module 1400. The task point acquisition module 1100 is configured to acquire the current location of the electric agricultural machinery, a set of task points, and a set of charging points. The set of task points includes multiple task points, and the set of charging points includes multiple target charging points. The first path planning module 1200 is configured to invoke a preset path planning optimization algorithm to generate a first optimal task path and its corresponding first optimal charging path based on the current location, the set of task points, the set of charging points, and charge state constraints. The charge state constraints are determined by the total energy consumption of the task path and the charging path, the remaining state of charge of the battery, the nominal battery capacity, the energy consumption and state of charge conversion factor, and the safe retained state of charge. The second path planning module 1300 is configured to... If, during the operation of the first optimal task path, it is detected that the remaining charge state of the electric agricultural machine's battery is lower than the charging state threshold, then based on the path planning optimization algorithm, according to the set of remaining task points, the set of charging points, and the charge state constraints, a second optimal task path and a second optimal charging path are generated for the electric agricultural machine. The operation continues along the second optimal task path. The charging state threshold is determined by the safe retention of charge state, the conversion factor between energy consumption and charge state, the minimum energy consumption from the current location to the target charging point, and the nominal battery capacity. The electric agricultural machine operation module 1400 is set to repeat the operation until all task points in the set of task points have been completed, thereby completing the path planning optimization for the electric agricultural machine operation.

[0054] Based on any embodiment of this application, please refer to Figure 4 Another embodiment of this application also provides an electronic device, which can be implemented by a computer device, such as... Figure 4The diagram shows the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. The computer-readable storage medium stores an operating system, a database, and computer-readable instructions. The database may store control information sequences. When the computer-readable instructions are executed by the processor, they enable the processor to implement an electric agricultural machinery operation path planning optimization method. The processor of the computer device provides computing and control capabilities, supporting the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When these computer-readable instructions are executed by the processor, they enable the processor to execute the electric agricultural machinery operation path planning optimization method of this application. The network interface of the computer device is used for communication with a terminal. Those skilled in the art will understand that… Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0055] In this embodiment, the processor is used to execute... Figure 3 The specific functions of each module are defined within the device, and the memory stores the program code and various data required to execute these modules. A network interface is used for data transmission between the user terminal and the server. In this embodiment, the memory stores the program code and data required to execute all modules in the electric agricultural machinery operation path planning and optimization device of this application. The server can call the server's program code and data to execute the functions of all modules.

[0056] This application also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the electric agricultural machinery operation path planning optimization method described in any embodiment of this application.

[0057] This application also provides a computer program product, including a computer program / instructions, which, when executed by one or more processors, implement the steps of the electric agricultural machinery operation path planning and optimization method described in any embodiment of this application.

[0058] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0059] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for optimizing the operation path of electric agricultural machinery, characterized in that, include: The current location, set of work points, and set of charging points of the electric agricultural machinery are obtained, wherein the set of work points contains multiple work points and the set of charging points contains multiple target charging points; A preset path planning optimization algorithm is invoked to generate a first optimal task path and its corresponding first optimal recharging path based on the current location point, the set of work points, the set of charging points, and the charge state constraints. The charge state constraints are determined by the total energy consumption of the task path and the recharging path, the remaining state of charge of the battery, the nominal capacity of the battery, the energy consumption and state of charge conversion factor, and the safe retention state of charge. If, during operation along the first optimal task path, it is detected that the remaining charge state of the electric agricultural machine's battery is lower than the charging state threshold, then based on the path planning optimization algorithm, according to the set of remaining task points, the set of charging points, and the charge state constraints, a second optimal task path and a second optimal charging path are generated for the electric agricultural machine, and operation continues along the second optimal task path. The charging state threshold is determined by the safe retention of charge state, the conversion factor between energy consumption and charge state, the minimum energy consumption from the current location to the target charging point, and the nominal battery capacity. Repeat the process until all work points in the set of work points have been completed, in order to optimize the path planning for electric agricultural machinery operations.

2. The method for optimizing the operation path of electric agricultural machinery according to claim 1, characterized in that, The steps for constructing the charge state constraints include: Obtain the total power consumption of the task path and the power replenishment path, the remaining state of charge of the battery corresponding to the electric agricultural machinery, the nominal capacity of the battery, the conversion factor between power consumption and state of charge, and the safe reserved state of charge. Calculate and determine a first ratio between the total power consumption and the nominal capacity of the battery, and calculate and determine a first product between the power consumption and the state of charge conversion factor and the first ratio; Calculate and determine a first difference between the remaining state of charge of the battery and the first product, and use the first difference being greater than or equal to the safe reserved state of charge as the charge state constraint condition.

3. The method for optimizing the operation path of electric agricultural machinery according to claim 1, characterized in that, The steps of invoking a preset path planning optimization algorithm to generate a first optimal task path and its corresponding first optimal recharging path based on the current location point, the set of work points, the set of charging points, and charge state constraints include: The preset genetic algorithm is used as the path planning optimization algorithm. The population size, crossover probability, mutation probability and maximum number of iterations of the genetic algorithm are set. The current position of the electric agricultural machine, the access sequence of the work points, and the target charging point are used as chromosome coding rules. The first M positions of the chromosome are the access order codes of the M work points, and the last position is the index code of the target charging point in the charging point set. Based on the preset fitness function and the charge state constraint, the fitness value of each individual in the initial population is calculated. Several individuals with the highest fitness values ​​are selected as the parent population. Gene recombination of the parent population is carried out through single-point crossover. Gene mutation is carried out by randomly exchanging the access order code of any two work points or replacing the target charging point index code to generate the offspring population. After merging the parent population and the offspring population, several individuals with the highest fitness values ​​are retained to form a new population. Repeat the iteration until the maximum number of iterations is reached. Decode the chromosome with the highest fitness value at the end of the iteration into the order of visiting the work points and the target charging point. Generate the first optimal task path from the current position point according to the order of visiting the work points, and the first optimal charging path from the last work point to the target charging point.

4. The method for optimizing the operation path of electric agricultural machinery according to claim 3, characterized in that, The steps for constructing the fitness function include: The path length and turning complexity of the task path of the electric agricultural machine, the path cost of the recharging path, the total power consumption of the task path and the recharging path, the remaining battery charge state and the safe retained charge state after the task path and the recharging path are completed. A penalty term is constructed based on the difference between the safe retained state of charge and the remaining charge state of the battery. The fitness function is constructed by weighting and summing the path length of the task path, the turning complexity of the task path, the path cost of the recharging path, the total energy consumption of the task path and the recharging path, and the penalty term.

5. The method for optimizing the operation path of electric agricultural machinery according to claim 1, characterized in that, The steps for determining the charge state threshold include: Obtain the safe retained state of charge, energy consumption and state of charge conversion factor, minimum energy consumption from the current location to the target charging point, and battery nominal capacity for electric agricultural machinery. Calculate and determine a second ratio between the minimum energy consumption from the current location point to the target charging point and the nominal capacity of the battery; calculate and determine a second product between the energy consumption and the state of charge conversion factor and the second ratio. A first sum is calculated and determined between the safe retained state of charge and the second product, and the first sum is used as the threshold of the replenished charge state.

6. The method for optimizing the operation path of electric agricultural machinery according to claim 1, characterized in that, The remaining state of charge of the battery represents the ratio between the remaining charge of the battery and the nominal capacity of the battery.

7. The method for optimizing the operation path of electric agricultural machinery according to any one of claims 1 to 5, characterized in that, The path planning optimization algorithm includes either a genetic algorithm or an A* algorithm.

8. A device for optimizing the operation path of electric agricultural machinery, characterized in that, include: The module for acquiring work points is configured to acquire the current location of the electric agricultural machinery, a set of work points, and a set of charging points, wherein the set of work points contains multiple work points and the set of charging points contains multiple target charging points; The first path planning module is configured to call a preset path planning optimization algorithm to generate a first optimal task path and its corresponding first optimal recharging path based on the current location point, the set of work points, the set of charging points, and the charge state constraints. The charge state constraints are determined by the total energy consumption of the task path and the recharging path, the remaining state of charge of the battery, the nominal capacity of the battery, the energy consumption and state of charge conversion factor, and the safe reserved state of charge. The second path planning module is configured to detect that the remaining charge state of the electric agricultural machine's battery is lower than the charging state threshold during operation along the first optimal task path. Then, based on the path planning optimization algorithm, it generates a second optimal task path and a second optimal charging path for the electric agricultural machine according to the set of remaining task points, the set of charging points, and the charge state constraints. The operation continues along the second optimal task path. The charging state threshold is determined by the safe retention of charge state, the conversion factor between energy consumption and charge state, the minimum energy consumption from the current location to the target charging point, and the nominal battery capacity. The electric agricultural machinery operation module is set to execute repeatedly until all the work points in the set of work points have been completed, so as to complete the path planning optimization of the electric agricultural machinery operation.

9. An electronic device comprising a central processing unit and a memory, characterized in that, The central processing unit is used to invoke and run a computer program stored in the memory to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores, in the form of computer-readable instructions, a computer program implemented according to any one of claims 1 to 7, which, when invoked by a computer, executes the steps included in the corresponding method.

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