An electrical control system for an extended-range orchard operation robot

Abstract

The present invention discloses an electrical control system for an extended-range orchard operation robot, relating to the technical field of robot control systems. The control system comprises: a functional control module for identifying and managing the various functional components of the operation robot; an environmental perception module for identifying the current working scenario of the operation robot and, based on the various functional components of the operation robot, dividing the working scenario into corresponding operation sub-scenarios; and a processing module for calculating the energy consumption demand data of the corresponding functional component based on the operation type, operating priority, and operating environment of the corresponding functional component after receiving the operation sub-scenarios. The present invention uses the environmental perception module to collect multidimensional perception data, generate standard operation sub-scenario labels, and adapt the operation sub-scenarios to the functional components by utilizing a component-scenario matching rule library, thereby identifying the corresponding sub-operation sub-sub-scenario for each functional component.

Description

Technical Field

[0001] The present 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, orchard robots are increasingly being used in a variety of operational scenarios, including spraying, weeding, picking, and transporting. Due to the diversity of these tasks and the complexity of the orchard's terrain, the power requirements of different functional components are time-varying and scenario-dependent. Therefore, the system must comprehensively consider task characteristics, environmental conditions, and functional component capabilities to implement coordinated control and energy scheduling.

[0003] Existing robotic orchard systems typically employ a structured task execution approach based on functional modules, with a certain degree of parallel collaboration between functional components. Some systems already possess preliminary task recognition and environmental perception capabilities, enabling them to manage energy resource allocation according to pre-set strategies, providing fundamental support for automated orchard operations.

[0004] After searching, a Chinese patent (publication number: CN111421544B) discloses an intelligent logistics robot and its electrical control system. The patent 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 sudden changes in scenarios, how to achieve dynamic energy consumption perception and timely energy distribution control of various functional components has become an important direction for improving system stability and energy efficiency. Especially when multifunctional components operate 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 extended-range orchard operation robot electrical control system. 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 through 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;

[0008] 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;

[0009] The chassis component is responsible for the movement of the entire robot. Its power consumption is strongly related to the terrain, slope, and speed, and is a source of continuous power consumption.

[0010] 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);

[0011] The working tool components include short-term high-power consumption components such as working pumps, fans, and compressors, which have sudden energy peaks;

[0012] Auxiliary working components include cooling components, hydraulic pumps, supercapacitor charge and discharge management components and other long-term low-power consumption components;

[0013] The handling components include handling arms, conveying components, unloading pushers and other components with irregular task occurrence frequency but high single energy consumption;

[0014] The environment perception module is used to identify the work scene, which is composed of multiple operation sub-scenes. The environment perception module matches the work scene with each functional component based on the various functional components of the operation robot, and the operation sub-scene that successfully matches the functional component is used as the operation sub-scene for subsequent calculations.

[0015] 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;

[0016] 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;

[0017] 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.

[0018] A further technical improvement of the present invention is that the method of dividing the operation sub-scenes of each functional component by the environment perception module includes the following steps:

[0019] A1. The environmental perception module collects raw data based on the information sensor 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.

[0020] A2. The environmental perception module matches the set of environmental feature parameters with the built-in scene recognition model, classifies and judges the feature vector, and outputs a predefined standard work scene label consisting of multiple sub-labels, thereby obtaining the current work scene of the working robot;

[0021] The scene recognition model can adopt a rule-based classification tree structure, decision table, support vector machine model, or lightweight neural network algorithm;

[0022] A3. The environment perception module obtains the list of functional components currently activated or mounted on the working robot through the functional control module;

[0023] A4. The environment perception module matches each functional component in the functional component list with the standard work scenario label using a preset component-scenario matching rule library to obtain the sub-label corresponding to each functional component in the functional component list;

[0024] The part represented by the sub-label in the work scenario is the work sub-scenario of the corresponding functional component;

[0025] The component-scenario matching rule library is formed based on the operational behavior requirements, power consumption models, or capability differences of each functional component in different environments. It is used to store the mapping relationship between standard work scenario labels and the corresponding operational sub-scenarios of each functional component of the working robot.

[0026] A5. After the matching is completed, the environmental perception module unifies and structures each functional component and its corresponding sub-label to form a demand set, and the environmental perception module outputs the demand set to the processing module as the input basis for subsequent energy budgeting and task scheduling.

[0027] A further technical improvement of the present invention is that: the processing module is preset with an energy consumption mapping rule library;

[0028] The energy consumption mapping rule base records the energy consumption demand data of each functional component in different operation sub-scenarios. That is, each demand set including a functional component and a corresponding operation sub-scenarios is associated with a set of corresponding energy consumption demand data.

[0029] After receiving the demand set transmitted by the environment perception module, the processing module matches the demand set with the energy consumption mapping rule library to obtain the energy consumption demand data required by the corresponding functional components.

[0030] 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 demand data and the real-time energy consumption data;

[0031] 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;

[0032] 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;

[0033] When the difference between a functional component's real-time energy consumption data 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.

[0034] 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;

[0035] 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;

[0036] 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, and the energy management module temporarily stores the recovered energy as the highest priority reallocatable energy in the system.

[0037] A further technical improvement of the present invention is that the energy management module preferentially uses recycled energy to meet the energy replenishment data based on the energy replenishment data of the marked functional components.

[0038] A further technical improvement of the present invention is that after the corresponding functional component uses the recycled energy to replenish energy, if the energy consumption demand data is balanced with the real-time energy consumption data, the energy replenishment is completed;

[0039] 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 of different priorities, the number of functional components is n;

[0040] The energy management module sets the energy adjustment coefficient of the i-th functional component according to the priority order of different functional components: , ;Where i is the priority number of the current functional component, 1 is the highest and n is the lowest;

[0041] Then the energy adjustment data that the i-th functional component should bear is , ;The lower the priority of the functional component (the later the number), the higher the adjustment data, so as to form a trapezoidal burden distribution;

[0042] The energy management module then retrieves energy adjustment data starting from the functional component i with the lowest priority. ;

[0043] 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 The difference between the energy demand data and the real-time energy consumption data in functional component i;

[0044] If the maximum available energy Energy adjustment data , then the difference Push it up to the functional component i-1 of the previous priority;

[0045] The new energy adjustment requirement of functional component i-1 is ;

[0046] The energy management module then continues to verify and deploy the remaining functional components, moving forward in order of priority of each functional component.

[0047] 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 , ;

[0048] The energy management module will It is included in the reserve energy set and can be used by subsequent higher priority components;

[0049] The maximum available energy of the subsequent higher priority functional components Energy adjustment data When the energy is adjusted, priority is given to the backup energy source according to the priority of the corresponding functional components from low to high until the energy adjustment data is met. deployment needs;

[0050] 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;

[0051] The energy management module will Pass it to the higher-priority functional component i-1 for continuing energy compensation.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 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 components by utilizing 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 system's task adaptability and deployment intelligence.

[0054] At the same time, the present invention introduces an interval budget model into the energy consumption forecast of functional components and establishes a tolerance judgment mechanism by comparing real-time energy consumption monitoring data with budget data. After identifying energy consumption deviations, the system can perform differential classification processing, including maintaining current energy supply, recovering excess energy, and recording energy replenishment needs. This makes energy management more flexible and data-driven, helping to improve operational stability and response accuracy.

[0055] 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 gradient distribution, upward transmission of differences, priority call of backup energy and backup energy guarantee, it can achieve energy supply guarantee for high-priority tasks and optimization of overall energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0057] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0058] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, 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.

[0059] 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;

[0060] The function control module is used to identify and manage the various functional components of the working robot, including the chassis component, functional arm component, working tool component, auxiliary working component and handling component;

[0061] The chassis component is responsible for the movement of the entire robot. Its power consumption is strongly related to the terrain, slope, and speed, and is a source of continuous power consumption.

[0062] 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);

[0063] The working tool components include short-term high-power consumption components such as working pumps, fans, and compressors, which have sudden energy peaks;

[0064] Auxiliary working components include cooling components, hydraulic pumps, supercapacitor charge and discharge management components and other long-term low-power consumption components;

[0065] The handling components include handling arms, conveying components, unloading pushers and other components with irregular task occurrence frequency but high single energy consumption;

[0066] The environment perception module is used to identify the work scene, which is composed of multiple work sub-scenes. The environment perception module divides the work scene into work sub-scenes corresponding to each functional component of the work robot based on the various functional components.

[0067] The method for dividing the operation sub-scenes of each functional component of the environmental perception module includes the following steps:

[0068] A1. The environmental perception module collects raw data based on the information sensor 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.

[0069] Through data preprocessing, the raw data collected by each information sensing unit is cleaned, structured and standardized, including:

[0070] Time synchronization processing: Use timestamps or ROS time synchronization mechanism to align data from different sources at the frame level to ensure data consistency within the same time window;

[0071] Denoising and filtering processing;

[0072] Coordinate transformation and space reconstruction:

[0073] The data collected by different information sensing units are uniformly projected into the coordinate system of the working robot to build a three-dimensional space environment perception model;

[0074] Data structure standardization: convert the data of each information sensing unit into a unified format for subsequent calls;

[0075] 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. These environmental characteristic parameters are recorded in the form of feature vectors and serve as the input basis for scene recognition;

[0076] In this embodiment, feature extraction includes:

[0077] Terrain feature extraction:

[0078] Slope recognition: Using the IMU's acceleration and gyroscope angular velocity data, the current slope angle is obtained through attitude calculation;

[0079] Uphill and downhill judgment: judge the slope type (uphill, downhill, flat ground) based on the speed vector and slope angle direction;

[0080] Surface type extraction:

[0081] Texture recognition: Surface texture analysis is performed based on camera images, using the gray-level co-occurrence matrix (GLCM) to extract surface roughness and uniformity, and determine whether the surface is grass, mud, or hard ground.

[0082] Crop structure feature extraction:

[0083] Crown density: Calculate the point cloud occlusion rate within a unit area using a depth camera or LiDAR;

[0084] Fruit tree spacing: Calculate the distance between adjacent vertical structures in the point cloud and infer the row spacing;

[0085] Occlusion rate: calculates the proportion of visible area in the visible image and identifies the degree of occlusion of the target object;

[0086] Light and climate parameter extraction:

[0087] Light level: The Lux value is obtained through the ambient light sensor and classified into strong light, medium light, and weak light;

[0088] Temperature and humidity level: Determined by meteorological sensors as dry, humid, high temperature, etc.

[0089] 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;

[0090] A2. The environmental perception module matches the set of environmental feature parameters with the built-in scene recognition model, classifies and judges the feature vector, and outputs a predefined standard work scene label consisting of multiple sub-labels, thereby obtaining the current work scene of the working robot;

[0091] The scene recognition model can adopt a rule-based classification tree structure, decision table, support vector machine model, or lightweight neural network algorithm;

[0092] In this embodiment, the current working scene is a slope of 16°, the ground is wet mud, the tree crown density is 0.85, and the light intensity is weak. The final standard working scene label is: "slippery slope + high-density fruit trees + weak-light working area", and its sub-labels are "slippery slope", "slippery slope" and "weak-light working area";

[0093] A3. The environment perception module obtains the list of functional components currently activated or mounted on the working robot through the functional control module;

[0094] For example: chassis assembly, functional arm assembly, spray tool assembly, handling assembly, auxiliary lighting assembly, etc.;

[0095] A4. The environment perception module matches each functional component in the functional component list with the standard work scenario label using a preset component-scenario matching rule library to obtain the sub-label corresponding to each functional component in the functional component list;

[0096] The part represented by the sub-label in the work scenario is the work sub-scenario of the corresponding functional component;

[0097] The component-scenario matching rule library is formed based on the operational behavior requirements, power consumption models, or capability differences of each functional component in different environments. It is used to store the mapping relationship between standard work scenario labels and the corresponding operational sub-scenarios of each functional component of the working robot.

[0098] When the environmental perception module identifies the current work scenario label, the system calls the component-scenario matching rule library and, based on the currently enabled functional component types, matches the corresponding operating sub-scenario of each component under the work scenario, thereby providing a basis for subsequent energy budgeting and task control. The component-scenario matching rule library can be constructed using a preset logic table or rule model to ensure rapid matching and accurate response of component operating sub-scenarios;

[0099] For example, the environmental perception module can divide the operation sub-scenarios according to terrain and road conditions, including:

[0100] Terrain dimensions: uphill, downhill, flat;

[0101] Surface dimensions: hard ground, grass, mud, orchard and forest paths, etc.

[0102] According to the task objectives of the operation robot, it can identify the target density and target range when spraying, weeding or picking fruits;

[0103] A5. After the matching is completed, the environmental perception module unifies and structures each functional component and its corresponding sub-label to form a demand set, and the environmental perception module outputs the demand set to the processing module as the input basis for subsequent energy budgeting and task scheduling.

[0104] 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;

[0105] 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;

[0106] 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.

[0107] The processing module is preset with an energy consumption mapping rule base;

[0108] The energy consumption mapping rule base records the energy consumption demand data of each functional component in different operation sub-scenarios. That is, each demand set including a functional component and a corresponding operation sub-scenarios is associated with a set of corresponding energy consumption demand data.

[0109] After receiving the demand set transmitted by the environment perception module, the processing module matches the demand set with the energy consumption mapping rule library to obtain the energy consumption demand data required by the corresponding functional components.

[0110] The energy management module has preset difference thresholds corresponding to each functional component, which are used to determine the acceptable deviation range between energy demand data and real-time energy consumption data;

[0111] 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;

[0112] 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;

[0113] When the difference between a functional component's real-time energy consumption data 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.

[0114] The energy management module calculates the relationship between the energy consumption demand data of the marked functional components and the real-time energy consumption data;

[0115] 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 the energy replenishment data, i.e., the additional energy currently required by the functional component. The energy management module uses the energy replenishment data as the basic input for energy allocation.

[0116] 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, and the energy management module temporarily stores the recovered energy as the highest priority reallocatable energy in the system.

[0117] The energy management module prioritizes the use of recycled energy to meet the energy replenishment data based on the energy replenishment data of the marked functional components.

[0118] After the corresponding functional component uses recycled energy to recharge, if the energy demand data is balanced with the real-time energy consumption data, the recharging is completed;

[0119] 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 of different priorities, the number of functional components is n;

[0120] The energy management module sets the energy adjustment coefficient of the i-th functional component according to the priority order of different functional components: , ;Where i is the priority number of the current functional component, 1 is the highest and n is the lowest;

[0121] 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, to form a trapezoidal burden distribution;

[0122] The energy management module then retrieves energy adjustment data starting from the functional component i with the lowest priority. ;

[0123] 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 The difference between the energy demand data and the real-time energy consumption data in functional component i;

[0124] If the maximum available energy Energy adjustment data , then the difference Push it up to the functional component i-1 of the previous priority;

[0125] The new energy adjustment requirement of functional component i-1 is ;

[0126] The energy management module then continues to verify and deploy the remaining functional components, moving forward in order of priority of each functional component.

[0127] 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 , ;

[0128] The energy management module will It is included in the reserve energy set and can be used by subsequent higher priority components;

[0129] The maximum available energy of the subsequent higher priority functional components Energy adjustment data When the energy is adjusted, priority is given to the backup energy source according to the priority of the corresponding functional components from low to high until the energy adjustment data is met. deployment needs;

[0130] 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;

[0131] The energy management module will Pass it 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 .

[0132] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. An electrical control system for an extended-range orchard operation robot, characterized in that: include: Function control module, used to identify and manage various functional components of the working robot; The environment perception module is used to identify the current working scene of the working robot. The environment perception module divides the working scene into working sub-scenes corresponding to each functional component based on the various functional components 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; 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 demand set transmitted by the environment perception module, the processing module matches the demand set with the energy consumption mapping rule library to obtain the energy consumption demand data required by the corresponding functional components; The energy management module is preset with a difference threshold 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 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; 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; The energy management module calculates the relationship between the energy consumption 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; The energy management module prioritizes the use of recycled energy for energy replenishment based on the energy replenishment data of the marked functional components to meet the energy replenishment data requirements; After the corresponding functional component uses recycled energy to recharge, if the energy demand data is balanced with the real-time energy consumption data, the recharging 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: , ;Where 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 up to the functional component i-1 of the previous priority; The new energy adjustment requirement of 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.

2. The electrical control system of the extended-range orchard operation robot according to claim 1, characterized in that: The method for dividing the operation sub-scenes of each functional component by the environment perception module includes the following steps: A1. The environmental perception module collects raw data based on the information sensor 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 environmental perception module matches the set of environmental feature parameters with the built-in scene recognition model, classifies and judges the feature vector, and outputs a predefined standard work scene label consisting of multiple sub-labels, thereby obtaining the current work scene of the 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 work scenario label using a preset component-scenario 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; 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 1, 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 that is 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 higher priority components; The maximum available energy of subsequent high-priority functional components Energy adjustment data When the redundant energy is transferred from the backup energy source, priority is given to , and call on redundant energy The order is to call from low to high priority of the corresponding functional components until the energy adjustment data of the corresponding functional components is met. 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 , , where 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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