Electric control system for extended-range orchard operation robot

Abstract

The invention discloses an extended-range orchard operation robot electrical control system, and relates to the technical field of robot control systems, and the system comprises a function control module which is used for recognizing and managing all function assemblies of an operation robot; the environment sensing module is used for recognizing a current working scene of the operation robot, and the environment sensing module divides the working scene into operation sub-scenes corresponding to all functional components based on all the functional components of the operation robot; and the processing module is used for calculating energy consumption demand data of the corresponding functional component based on the operation type, the operation priority and the operation environment of the corresponding functional component after receiving each operation sub-scene. According to the invention, multi-dimensional sensing data is collected through the environment sensing module, a standard operation sub-scene label is generated, and an operation sub-scene is matched with a functional component by using a component-scene matching rule base, so that a corresponding sub-operation sub-scene is identified for each functional component.

Description

Technical Field

[0001] The invention relates to the technical field of robot control systems, and in particular to an extended-range orchard operation robot electrical control system. Background Art

[0002] With the development of intelligent agricultural machinery and equipment, orchard operation robots are gradually being used in various operation sub-scenarios such as spraying, weeding, picking, and carrying. Due to the diversity of operation tasks and the complexity of the orchard terrain environment, the power requirements of different functional components are time-varying and scenario-dependent. The system needs to comprehensively consider the task characteristics, environmental status, and functional component capabilities for coordinated control and energy scheduling.

[0003] In existing orchard operation robot systems, a task execution method based on functional module structure is usually adopted, and there is a certain degree of parallel collaboration between functional components. Some systems already have preliminary task recognition and environmental perception capabilities, and can manage the allocation of energy resources according to preset strategies, providing basic support for orchard automation operations.

[0004] After searching, a Chinese patent (publication number: CN111421544B) discloses an intelligent logistics robot and its electrical control system, which includes: a robot body; at least one target component located on the robot body; an image acquisition module for acquiring an environmental image around the at least one target component; and a control module for controlling the at least one target component to avoid collision when determining that there is a possible collision based on the environmental image around the at least one target component.

[0005] During orchard operations, limited by the influence of battery capacity, operation rhythm and scene mutations, how to achieve dynamic energy consumption perception and timely energy distribution regulation of various functional components has become an important direction for improving system stability and energy efficiency. Especially when multifunctional components are operating in coordination, the need for accurate modeling, reasonable prediction and dynamic compensation of energy supply and demand status has gradually become prominent. Therefore, the present invention proposes an electrical control system for an extended-range orchard operation robot. Summary of the invention

[0006] The purpose of the present invention is to provide an extended-range orchard operation robot electrical control system to solve the problems mentioned in the above background technology.

[0007] The present invention can be implemented by the following technical solutions: an electrical control system for an extended-range orchard operation robot, comprising a processing module, an environment perception module, an energy management module and a function control module; The function control module is used to identify and manage various functional components of the working robot, including a chassis component, a functional arm component, a working tool component, an auxiliary working component and a handling component; Among them, the chassis component is responsible for the movement of the entire operation robot. Its power consumption is strongly related to the terrain, slope, and speed, and is the source of continuous power consumption. The functional arm components include a plant protection spraying component (short-term high power consumption), a weeding component (continuous medium power consumption) and a fruit picking component (intermittent high precision); The working tool components include short-term high-power consumption components such as working pumps, fans, and compressors, which have sudden energy peaks; Auxiliary working components include cooling components, hydraulic pumps, supercapacitor charge and discharge management components and other long-term low-power consumption components; The handling components include handling arms, conveying components, unloading pushers and other components with irregular task occurrence frequency but high single energy consumption; The environment perception module is used to identify the working scene, which is composed of multiple working sub-scenes; and the environment perception module matches the working scene with each functional component based on each functional component of the working robot, and the working sub-scene that successfully matches the functional component is used as the working sub-scene for subsequent calculation; After receiving each operation sub-scenario, the processing module calculates the energy consumption demand data of the corresponding functional component based on the operation type, operation priority and operation environment of the corresponding functional component, and sends the energy consumption demand data to the energy management module; The energy management module is used to identify the real-time energy consumption data of each functional component under the current operating state, and after receiving each energy consumption demand data, the energy management module compares and analyzes the energy consumption demand data of the functional component with its corresponding energy consumption demand data; The energy management module performs dynamic energy allocation based on the comparison results to ensure stable energy supply for each functional component in its corresponding operating sub-scenario.

[0008] A further technical improvement of the present invention is that the method for dividing the operation sub-scenes of each functional component by the environment perception module comprises the following steps: A1. The environmental perception module collects the corresponding raw data based on the information sensing unit installed on the working robot, performs data preprocessing and feature extraction, and constructs a set of environmental feature parameters for the current working scene; A2. The environment perception module matches the environment feature parameter set with the built-in scene recognition model, and then classifies and judges the feature vector, outputs a predefined standard work scene label including multiple sub-labels, and thus obtains the work scene of the current working robot; The scene recognition model can adopt a rule-based classification tree structure, a decision table, a support vector machine model, or a lightweight neural network algorithm; A3. The environment perception module obtains the list of functional components currently activated or mounted on the working robot through the functional control module; A4. The environment perception module matches each functional component in the functional component list with the standard working scene label through the preset component-scene matching rule library to obtain the sub-label corresponding to each functional component in the functional component list; The part represented by the sub-label in the work scenario is the work sub-scenario of the corresponding functional component; Among them, the component-scenario matching rule library is formed based on the operating behavior requirements, power consumption models or capability differences of each functional component in different environments, and is used to store the mapping relationship between the standard working scenario labels and the corresponding working sub-scenario of each functional component of the working robot; A5. After the matching is completed, the environment perception module unifies the structure of each functional component and its corresponding sub-label to form a demand set, and the environment perception module outputs the demand set to the processing module as the input basis for subsequent energy budgeting and task scheduling.

[0009] A further technical improvement of the present invention is that: the processing module is preset with an energy consumption mapping rule library; The energy consumption mapping rule base records the energy consumption demand data of each functional component in different operation sub-scenario, that is, each demand set including the functional component and the corresponding operation sub-scenario is associated with a set of corresponding energy consumption demand data; After receiving the requirement set transmitted by the environment perception module, the processing module matches the requirement set with the energy consumption mapping rule library to obtain the energy consumption requirement data required by the corresponding functional components.

[0010] A further technical improvement of the present invention is that: the energy management module is preset with a difference threshold value corresponding to each functional component, which is used to determine the acceptable deviation range between the energy consumption demand data and the real-time energy consumption data; After receiving the energy consumption demand data of the functional component, the energy management module compares the energy consumption demand data of the functional component with its corresponding real-time energy consumption data; When the difference between the real-time energy consumption data of a functional component and its energy consumption demand data is not greater than the corresponding difference threshold, the energy management module maintains the existing energy output state of the functional component without making any adjustments; When the difference between the real-time energy consumption data of a functional component and its energy consumption demand data is greater than the corresponding difference threshold, the energy management module marks the functional component as a basis for subsequent dynamic energy allocation or priority adjustment.

[0011] A further technical improvement of the present invention is that: the energy management module calculates the relationship between the energy demand data of the marked functional components and the real-time energy consumption data; If the energy consumption demand data is greater than the real-time energy consumption data, the difference between the energy consumption demand data and the real-time energy consumption data is calculated to generate energy replenishment data; If the energy demand data is less than the real-time energy consumption data, the difference between the energy demand data and the real-time energy consumption data is calculated as the recovered energy, and the energy management module temporarily stores the recovered energy as the highest priority reallocatable energy in the system.

[0012] A further technical improvement of the present invention is that the energy management module preferentially uses the recycled energy to meet the energy replenishment data based on the energy replenishment data of the marked functional components.

[0013] A further technical improvement of the present invention is that after the corresponding functional component uses the recycled energy for energy replenishment, if the energy consumption demand data is balanced with the real-time energy consumption data, the energy replenishment is completed; If the energy demand data is still less than the real-time energy consumption data, the remaining energy replenishment data will be marked as , and set functional components with different priorities, the number of functional components is n; The energy management module sets the energy adjustment coefficient of the i-th functional component according to the priority order of different functional components: , ; In the formula, i is the priority number of the current functional component, 1 is the highest and n is the lowest; Then the energy adjustment data that the i-th functional component should bear is , ; Then, the lower the priority of the functional component (the later the number), the higher the adjustment data is, so as to form a trapezoidal burden distribution; The energy management module then retrieves energy adjustment data starting from the functional component i with the lowest priority. ; If the maximum adjustable energy of the functional component i ≥Energy adjustment data , the allocation is completed and the energy replenishment is completed; among them, the maximum energy that can be allocated is is the difference between the energy demand data and the real-time energy consumption data in functional component i; If the maximum available energy <Energy adjustment data , then the difference Push it upward to the functional component i-1 of the previous priority; The new energy adjustment requirement for functional component i-1 is ; The energy management module then continues to verify and deploy the remaining functional components, moving forward in order of priority of each functional component.

[0014] A further technical improvement of the present invention is that after the energy management module completes the energy allocation of the corresponding functional component i, if the functional component i still has an energy consumption greater than the energy consumption demand data , that is, there is redundant energy , ; The energy management module will It is included in the reserve energy set and can be used by subsequent components with higher priority; The maximum available energy of the subsequent higher priority functional components <Energy adjustment data When the energy adjustment data is met, the backup energy source is selected according to the priority of the corresponding functional components from low to high. The deployment needs; If after allocating the backup energy set, there is still a difference in the corresponding functional component i , , where The portion of the nth functional component that exceeds the energy adjustment requirement; The energy management module will Pass it to the higher priority functional component i-1 for continuing to perform energy compensation.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention collects multi-dimensional perception data through the environmental perception module, generates standard operation sub-scenario labels, and adapts the operation sub-scenario to the functional component by using the component-scenario matching rule library, thereby identifying the corresponding sub-operation sub-scenario for each functional component, realizing scenario-driven functional behavior prediction and energy planning, and improving the task adaptability and deployment intelligence of the system; At the same time, the present invention introduces an interval budget model in the energy consumption prediction of functional components, and establishes a tolerance judgment mechanism by comparing real-time energy consumption monitoring data with budget data. The system can perform differential classification processing after identifying energy consumption deviations, including maintaining current energy supply, recovering excess energy, and recording energy replenishment needs, making energy management more flexible and data-driven, and helping to improve operational stability and response accuracy; In addition, the present invention constructs an energy ladder distribution allocation method based on priority perception, which combines energy recovery and backup energy set management to achieve dynamic balance adjustment of energy among multiple functional components. By introducing mechanisms such as energy compensation gradient distribution, upward transmission of differences, priority calling of backup energy and backup energy guarantee, energy supply guarantee for high-priority tasks and optimization of overall energy efficiency can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0019] See also Figure 1 As shown, the present invention provides an electrical control system for an extended-range orchard operation robot, including a processing module, an environment perception module, an energy management module and a function control module; The function control module is used to identify and manage the various functional components of the working robot, including the chassis component, the functional arm component, the working tool component, the auxiliary working component and the handling component; Among them, the chassis component is responsible for the movement of the entire operation robot. Its power consumption is strongly related to the terrain, slope, and speed, and is the source of continuous power consumption. The functional arm components include a plant protection spraying component (short-term high power consumption), a weeding component (continuous medium power consumption) and a fruit picking component (intermittent high precision); The working tool components include short-term high-power consumption components such as working pumps, fans, and compressors, which have sudden energy peaks; Auxiliary working components include cooling components, hydraulic pumps, supercapacitor charge and discharge management components and other long-term low-power consumption components; The handling components include handling arms, conveying components, unloading pushers and other components with irregular task occurrence frequency but high single energy consumption; The environment perception module is used to identify the working scene, which is composed of multiple working sub-scenes; and the environment perception module divides the working scene into working sub-scenes corresponding to each functional component based on each functional component of the working robot; The method for dividing each functional component operation sub-scene by the environment perception module includes the following steps: A1. The environmental perception module collects the corresponding raw data based on the information sensing unit installed on the working robot, performs data preprocessing and feature extraction, and constructs a set of environmental feature parameters for the current working scene; Through data preprocessing, the raw data collected by each information sensor unit is cleaned, structured and standardized, including: Time synchronization processing: Use timestamps or ROS time synchronization mechanism to align data from different sources at the frame level to ensure data uniformity within the same time window; De-noising and filtering; Coordinate transformation and space reconstruction: The data collected by different information sensing units are uniformly projected into the coordinate system of the working robot body to build a three-dimensional space environment perception model; Data structure standardization: convert the data of each information sensing unit into a unified format for subsequent calls; Environmental characteristic parameters include terrain type (flat, uphill, downhill), surface type (grass, mud, hard ground), slope angle, tree crown density, fruit tree spacing, occlusion rate, light intensity level, current temperature, humidity, wind speed, etc., and the above environmental characteristic parameters are recorded in the form of feature vectors and serve as the input basis for scene recognition; In this embodiment, feature extraction includes: Terrain feature extraction: Slope recognition: Using the acceleration and gyroscope angular velocity data of the IMU, the current slope angle is obtained through attitude calculation; Uphill and downhill judgment: judge the slope type (uphill, downhill, flat ground) according to the speed vector and slope angle direction; Surface type extraction: Texture recognition: Surface texture analysis is performed based on camera images, and the gray-level co-occurrence matrix (GLCM) is used to extract the surface roughness and uniformity to determine whether it is grass, mud, hard ground, etc. Crop structure feature extraction: Crown density: Calculate the point cloud occlusion rate within a unit area through a depth camera or LiDAR; Fruit tree spacing: Count the distances between adjacent vertical structures in the point cloud and calculate the row spacing; Occlusion rate: calculate the proportion of visible area in the visible image and identify the degree of occlusion of the target object; Light and climate parameter extraction: Light level: The Lux value is obtained through the ambient light sensor and is graded into strong light, medium light, and weak light; Temperature and humidity level: Determined by meteorological sensors as dry, humid, high temperature and other environments; Wind speed estimation: If there is a wind speed sensor, it can be measured directly; if not, it can be estimated indirectly based on the amplitude of plant swaying; A2. The environment perception module matches the environment feature parameter set with the built-in scene recognition model, and then classifies and judges the feature vector, outputs a predefined standard work scene label including multiple sub-labels, and thus obtains the work scene of the current working robot; The scene recognition model can adopt a rule-based classification tree structure, a decision table, a support vector machine model, or a lightweight neural network algorithm; In this embodiment, the current working scene is a slope of 16°, the ground surface is wet mud, the tree crown density is 0.85, and the light intensity is weak light, then the final standard working scene label is: "slippery slope + high-density fruit trees + weak light operation area", and its sub-labels are "slippery slope", "slippery slope" and "weak light operation area"; A3. The environment perception module obtains the list of functional components currently activated or mounted on the working robot through the functional control module; For example: chassis assembly, functional arm assembly, spray tool assembly, handling assembly, auxiliary lighting assembly, etc.; A4. The environment perception module matches each functional component in the functional component list with the standard working scene label through the preset component-scene matching rule library to obtain the sub-label corresponding to each functional component in the functional component list; The part represented by the sub-label in the work scenario is the work sub-scenario of the corresponding functional component; Among them, the component-scenario matching rule library is formed based on the operating behavior requirements, power consumption models or capability differences of each functional component in different environments, and is used to store the mapping relationship between the standard working scenario labels and the corresponding working sub-scenario of each functional component of the working robot; When the environment perception module recognizes the current work scene label, the system calls the component-scene matching rule library, combines the currently enabled functional component types, and matches the corresponding operation sub-scenes of each component in the work scene, thereby providing a basic basis for subsequent energy budget and task control. The component-scene matching rule library can be constructed using a preset logic table or rule model to ensure fast matching and accurate response of component operation sub-scenes; For example, the environmental perception module can divide the operation sub-scenarios according to terrain and road conditions, including: Terrain dimensions: uphill, downhill, flat; Surface dimensions: hard ground, grass, mud, orchard and forest paths, etc. According to the task objectives of the operating robot, identify the target density and target range during plant protection spraying, weeding or fruit picking; A5. After the matching is completed, the environment perception module unifies the structure of each functional component and its corresponding sub-label to form a demand set, and the environment perception module outputs the demand set to the processing module as the input basis for subsequent energy budgeting and task scheduling.

[0020] After receiving each operation sub-scenario, the processing module calculates the energy consumption demand data of the corresponding functional component based on the operation type, operation priority and operation environment of the corresponding functional component, and sends the energy consumption demand data to the energy management module; The energy management module is used to identify the real-time energy consumption data of each functional component under the current operating state, and after receiving each energy consumption demand data, the energy management module compares and analyzes the energy consumption demand data of the functional component with its corresponding energy consumption demand data; The energy management module performs dynamic energy allocation based on the comparison results to ensure stable energy supply for each functional component in its corresponding operating sub-scenario.

[0021] The processing module is preset with an energy consumption mapping rule base; The energy consumption mapping rule base records the energy consumption demand data of each functional component in different operation sub-scenario, that is, each demand set including the functional component and the corresponding operation sub-scenario is associated with a set of corresponding energy consumption demand data; After receiving the requirement set transmitted by the environment perception module, the processing module matches the requirement set with the energy consumption mapping rule library to obtain the energy consumption requirement data required by the corresponding functional components.

[0022] The energy management module is preset with a difference threshold corresponding to each functional component, which is used to determine the acceptable deviation range between energy demand data and real-time energy consumption data; After receiving the energy consumption demand data of the functional component, the energy management module compares the energy consumption demand data of the functional component with its corresponding real-time energy consumption data; When the difference between the real-time energy consumption data of a functional component and its energy consumption demand data is not greater than the corresponding difference threshold, the energy management module maintains the existing energy output state of the functional component without making any adjustments; When the difference between the real-time energy consumption data of a functional component and its energy consumption demand data is greater than the corresponding difference threshold, the energy management module marks the functional component as a basis for subsequent dynamic energy allocation or priority adjustment.

[0023] The energy management module calculates the relationship between the energy demand data of the marked functional components and the real-time energy consumption data; If the energy demand data is greater than the real-time energy consumption data, the difference between the energy demand data and the real-time energy consumption data is calculated to generate energy replenishment data, i.e., the additional energy currently required by the functional component, and the energy management module uses the energy replenishment data as the basic input for energy allocation; If the energy demand data is less than the real-time energy consumption data, the difference between the energy demand data and the real-time energy consumption data is calculated as the recovered energy, and the energy management module temporarily stores the recovered energy as the highest priority reallocatable energy in the system.

[0024] The energy management module gives priority to using recycled energy to meet the energy replenishment data based on the energy replenishment data of the marked functional components.

[0025] After the corresponding functional component uses recycled energy for energy replenishment, if the energy demand data is balanced with the real-time energy consumption data, the energy replenishment is completed; If the energy demand data is still less than the real-time energy consumption data, the remaining energy replenishment data will be marked as , and set functional components with different priorities, the number of functional components is n; The energy management module sets the energy adjustment coefficient of the i-th functional component according to the priority order of different functional components: , ; In the formula, i is the priority number of the current functional component, 1 is the highest and n is the lowest; Then the energy adjustment data that the i-th functional component should bear is , , and then the lower the priority of the functional component (numbered later), the higher the adjustment data is, so as to form a trapezoidal burden distribution; The energy management module then retrieves energy adjustment data starting from the functional component i with the lowest priority. ; If the maximum adjustable energy of the functional component i ≥Energy adjustment data , the allocation is completed and the energy replenishment is completed; among them, the maximum energy that can be allocated is is the difference between the energy demand data and the real-time energy consumption data in functional component i; If the maximum available energy <Energy adjustment data , then the difference Push it upward to the functional component i-1 of the previous priority; The new energy adjustment requirement for functional component i-1 is ; The energy management module then continues to verify and deploy the remaining functional components, moving forward in order of priority of each functional component.

[0026] After the energy management module completes the energy allocation of the corresponding functional component i, if the functional component i still has energy consumption greater than the energy consumption demand data , that is, there is redundant energy , ; The energy management module will It is included in the reserve energy set and can be used by subsequent components with higher priority; The maximum available energy of the subsequent higher priority functional components <Energy adjustment data When the energy adjustment data is met, the backup energy source is selected according to the priority of the corresponding functional components from low to high. The deployment needs; If after allocating the backup energy set, there is still a difference in the corresponding functional component i , In the formula, The portion of the nth functional component that exceeds the energy adjustment requirement; The energy management module will It is passed to the higher priority functional component i-1 to continue to perform energy compensation. The new energy adjustment requirement of functional component i-1 is .

[0027] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An extended-range orchard operation robot electrical control system, characterized in that: include: Function control module, used to identify and manage various functional components of the working robot; An environment perception module is used to identify the current working scene of the working robot, and the environment perception module divides the working scene into working sub-scenes corresponding to each functional component based on each functional component of the working robot; The processing module, after receiving each operation sub-scenario, calculates the energy consumption demand data of the corresponding functional component based on the operation type, operation priority and operation environment of the corresponding functional component, and sends the energy consumption demand data to the energy management module; The energy management module is used to identify the real-time energy consumption data of each functional component under the current operating state, and after receiving each energy consumption demand data, the energy management module compares and analyzes the energy consumption demand data of the functional component with its corresponding energy consumption demand data; The energy management module performs dynamic energy allocation based on the comparison results.

2. The electrical control system of an extended-range orchard operation robot according to claim 1, characterized in that: The method for dividing each functional component operation sub-scene by the environment perception module comprises the following steps: A1. The environmental perception module collects the corresponding raw data based on the information sensing unit installed on the working robot, performs data preprocessing and feature extraction, and constructs a set of environmental feature parameters for the current working scene; A2. The environment perception module matches the environment feature parameter set with the built-in scene recognition model, and then classifies and judges the feature vector, outputs a predefined standard work scene label including multiple sub-labels, and thus obtains the work scene of the current working robot; A3. The environment perception module obtains the list of functional components currently activated or mounted on the working robot through the functional control module; A4. The environment perception module matches each functional component in the functional component list with the standard working scene label through a preset component-scene matching rule library to obtain a sub-label corresponding to each functional component in the functional component list; The part represented by the sub-label in the work scenario is the work sub-scenario of the corresponding functional component; A5. After the matching is completed, the environment perception module uniformly structures each functional component and its corresponding sub-label to form a requirement set, and the environment perception module outputs the requirement set to the processing module.

3. The electrical control system of the extended-range orchard operation robot according to claim 2, characterized in that: The processing module is preset with an energy consumption mapping rule library; The energy consumption mapping rule base records the energy consumption demand data of each functional component in different operation sub-scenarios; After receiving the requirement set transmitted by the environment perception module, the processing module matches the requirement set with the energy consumption mapping rule library to obtain the energy consumption requirement data required by the corresponding functional components.

4. The electrical control system of the extended-range orchard operation robot according to claim 3, characterized in that: The energy management module is preset with a difference threshold value corresponding to each functional component; After receiving the energy consumption demand data of the functional component, the energy management module compares the energy consumption demand data of the functional component with its corresponding real-time energy consumption data; When the difference between the real-time energy consumption data of the functional component and its energy consumption demand data is not greater than the corresponding difference threshold, the energy management module maintains the existing energy output state of the functional component; When the difference between the real-time energy consumption data of a functional component and its energy consumption demand data is greater than the corresponding difference threshold, the energy management module marks the functional component.

5. The electrical control system of the extended-range orchard operation robot according to claim 4, characterized in that: The energy management module calculates the relationship between the energy demand data of the marked functional components and the real-time energy consumption data; If the energy consumption demand data is greater than the real-time energy consumption data, the difference between the energy consumption demand data and the real-time energy consumption data is calculated to generate energy replenishment data; If the energy consumption demand data is less than the real-time energy consumption data, the difference between the energy consumption demand data and the real-time energy consumption data is calculated as the recovered energy.

6. The electrical control system of the extended-range orchard operation robot according to claim 5, characterized in that: Based on the energy replenishment data of the marked functional components, the energy management module gives priority to using recycled energy for energy replenishment to meet the requirements of the energy replenishment data.

7. The electrical control system of the extended-range orchard operation robot according to claim 6, characterized in that: After the corresponding functional component uses recycled energy for energy replenishment, if the energy demand data is balanced with the real-time energy consumption data, the energy replenishment is completed; If the energy demand data is still less than the real-time energy consumption data, the remaining energy replenishment data will be marked as , and set n functional components with different priorities; The energy management module sets the energy adjustment coefficient of the i-th functional component according to the priority order of different functional components: , ; In the formula, i is the priority number of the current functional component, 1 is the highest and n is the lowest; Then the energy adjustment data that the i-th functional component should bear is , ; The energy management module then retrieves energy adjustment data starting from the functional component i with the lowest priority. ; If the maximum adjustable energy of the functional component i ≥Energy adjustment data , then the deployment is completed and the energy replenishment is completed; If the maximum available energy <Energy adjustment data , then the difference Push it upward to the functional component i-1 of the previous priority; The new energy adjustment requirement for functional component i-1 is ; The energy management module then continues to verify and deploy the remaining functional components, moving forward in order of priority of each functional component.

8. The electrical control system of the extended-range orchard operation robot according to claim 7, characterized in that: After the energy management module completes the energy allocation of the corresponding functional component i, if the functional component i still has redundant energy greater than the energy consumption demand data , the energy management module will redundant energy It is included in the reserve energy set and can be used by subsequent components with higher priority; The maximum available energy of the subsequent high-priority functional components <Energy adjustment data When the redundant energy is transferred from the backup energy center, priority is given to , and call on redundant energy The order is to call from low to high according to the priority of the corresponding functional components until the energy adjustment data of the corresponding functional components is met. The deployment needs; If the redundant energy of the backup energy center is deployed After that, there is still a difference in the corresponding functional component i , In the formula, is the redundant energy source of the nth functional component; The energy management module will Passed to the higher priority functional component i-1.

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