A grounding switching device and method based on flexible tightening

Abstract

The present invention relates to the technical field of power supply equipment, and specifically discloses a grounding switching device and method based on flexible tightening, including a data acquisition module, a path planning module, a height adaptive module, a vibration suppression module, and a collaborative decision-making module. The data acquisition module generates three-dimensional point cloud data of obstacles and the positioning coordinates of high-voltage switch cabinets through the fusion of lidar and visual sensors; the path planning module generates an obstacle avoidance path based on the genetic algorithm and encodes the target coordinates to drive the mobile robot chassis to avoid obstacles autonomously; the height adaptive module optimizes the hydraulic parameters of the lifting platform and combines the closed-loop feedback of the screw-driven slide module to achieve precise positioning of the grounding switch height. Through the optimization of the multi-module genetic algorithm and closed-loop control, the present invention solves the problems of low manual operation efficiency, insufficient vibration suppression of equipment, and poor positioning accuracy, and realizes high-efficiency positioning, low vibration, and high safety of the grounding switch opening and closing operation in a complex power distribution environment.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution equipment, and particularly to a grounding switching device and method based on flexible tightening. Background Art

[0002] In the power supply maintenance of high-voltage switch cabinets, the opening and closing operations of grounding switches usually rely on manual labor, which has problems of low operation efficiency and high safety risks. Due to the differences in the positions and heights of grounding switches in different high-voltage switch cabinets in the power distribution system, manual operation requires frequent adjustment of the tool position and angle, making it difficult to achieve rapid and accurate docking, resulting in extended operation time and increased risk of electric shock to personnel. Existing automated equipment lacks effective vibration suppression and alignment fault tolerance mechanisms during the docking process, resulting in vibration being transmitted to the equipment structure during operation, affecting the positioning accuracy and equipment stability, and making it difficult to meet the high-efficiency and reliable grounding switching requirements in complex power distribution environments. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the present invention provides a grounding switching device and method based on flexible tightening, which are used to solve the technical problems of low operation efficiency, high safety risks, and difficulty in adapting to complex power distribution environments caused by low manual efficiency, insufficient vibration suppression of existing automated equipment, and poor positioning accuracy in the opening and closing operations of grounding switches of high-voltage switch cabinets in the power distribution system.

[0004] To solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0005] In the first aspect, the grounding switching device based on flexible tightening provided by the present invention includes:

[0006] A data acquisition module, which is used to acquire environmental perception data, equipment status data, and damping parameters of polyurethane vibration damping blocks. The environmental perception data includes three-dimensional point cloud data of obstacles and visual coordinate data. The equipment status data includes hydraulic control parameters of the lifting platform, grounding switch height data, and screw drive slide module displacement data;

[0007] A path planning module, which generates an obstacle avoidance path for the mobile robot chassis based on the three-dimensional point cloud data of obstacles and visual coordinate data, encodes the obstacle avoidance path of the mobile robot chassis through a genetic algorithm, and generates the coordinates of the target high-voltage switch cabinet;

[0008] A height adaptive module, which receives the coordinates of the target high-voltage switch cabinet, combines the hydraulic control parameters of the lifting platform and the grounding switch height data of the data acquisition module, optimizes the hydraulic control parameters of the lifting platform through a genetic algorithm, drives the screw drive slide module to move vertically to the target height. The slide module linearly displaces through screw transmission, and the displacement state of the slide module is real-time feedback by the screw drive slide module displacement data. After positioning, a height adjustment signal is sent to the vibration suppression module;

[0009] A vibration suppression module, according to the height adjustment signal, collects the vibration spectrum data at the end of the grounding switch wrenching tooling, dynamically optimizes the damping parameters of the polyurethane vibration damping block through a genetic algorithm, and collects the pose data of the end of the tooling through an attitude sensor;

[0010] A collaborative decision-making module integrates the path error of the path planning module, the height deviation of the height adaptive module, the spectrum data of the vibration suppression module, and the angle deviation of the alignment control module, constructs an objective optimization model, generates an instruction set through the objective optimization model, and distributes the instructions to drive the mobile robot chassis, the slide table module, and the servo motor to cooperate to complete the opening and closing operation of the grounding switch.

[0011] Further, for the grounding switch opening and closing device based on flexible tightening of the present invention, the path planning module is further used for:

[0012] Based on the three-dimensional point cloud data and visual coordinate data of the obstacles collected by the data acquisition module, generates the obstacle contour information through a lidar, and matches it with the high-voltage switchgear positioning identifier in the visual feature identification data to construct a path planning fitness function to evaluate the feasibility of the obstacle avoidance path;

[0013] Generates path nodes based on the obstacle avoidance path, and performs chromosome crossover and mutation operations on the steering angle, moving speed, and safe obstacle avoidance distance between path nodes through a genetic algorithm to generate a candidate path set;

[0014] Outputs the candidate path solution set that meets the preset positioning accuracy threshold to the collaborative decision-making module as the path input parameter of the multi-objective optimization model in the collaborative decision-making module.

[0015] Further, for the grounding switch opening and closing device based on flexible tightening of the present invention, the height adaptive module is further used for:

[0016] Based on the hydraulic control parameters of the lifting platform and the grounding switch height data, constructs a multi-dimensional optimization objective including the height error of the lifting platform, the displacement speed of the slide table module, and the load current of the servo motor through a genetic algorithm, and calculates the height adaptive fitness value;

[0017] According to the preset height adaptive fitness value threshold, screens the candidate solution set of the hydraulic control parameters, and adopts the elitist retention strategy to retain the candidate parameters with the top 10% of the fitness values;

[0018] Synchronizes the optimized target height value to the alignment control module through the genetic algorithm as the height constraint condition for the alignment control module to generate the rotation angle sequence of the grounding switch wrenching tooling.

[0019] Further, for the earthing switching device based on flexible tightening according to the present invention, the vibration suppression module is further configured to:

[0020] Based on the end - pose data of the tooling collected by the pose sensor and the damping parameters of the data acquisition module, collect the vibration spectrum data of the end of the earthing switch wrenching tooling through the acceleration sensor, and extract the vibration energy distribution characteristics in the 0 - 1 kHz frequency band as the population initialization parameters of the genetic algorithm;

[0021] Through the genetic algorithm, with the compression deformation amount and vibration attenuation rate of the polyurethane damping block as the vibration suppression fitness evaluation index, screen the candidate solutions of the damping parameters;

[0022] Feed back the optimized damping parameter data that meets the vibration suppression fitness evaluation index to the collaborative decision - making module, and trigger the collaborative decision - making module to dynamically correct the switching operation instruction set based on the multi - objective optimization model.

[0023] Further, for the earthing switching device based on flexible tightening according to the present invention, the collaborative decision - making module is further configured to:

[0024] Based on the positioning error of the path planning module, the height deviation of the height adaptive module, the spectrum data of the vibration suppression module, and the angle deviation of the alignment control module, construct a multi - objective optimization model, and perform normalized weighted processing on the positioning error, height deviation, spectrum data, and angle deviation;

[0025] Through the non - dominated sorting genetic algorithm of the collaborative decision - making module, generate a globally optimal operation instruction set including the rotation speed instruction of the high - torque servo motor, the displacement instruction of the sliding table module, and the hydraulic control instruction of the lifting platform based on the multi - objective optimization model;

[0026] Distribute the globally optimal operation instruction set to the mobile robot chassis, the screw - driven sliding table module, and the high - torque servo motor, and drive the mobile robot chassis, the screw - driven sliding table module, and the high - torque servo motor to cooperate to complete the earthing switch opening and closing operation.

[0027] In a second aspect, the earthing switching method based on flexible tightening provided by the present invention is applied to the earthing switching device based on flexible tightening, and includes:

[0028] Step S1, collect environmental perception data, equipment status data, and the damping parameters of the polyurethane damping block. The environmental perception data includes obstacle three - dimensional point cloud data and visual coordinate data, and the equipment status data includes the hydraulic control parameters of the lifting platform, the earthing switch height data, and the displacement data of the screw - driven sliding table module;

[0029] Step S2: Generate an obstacle avoidance path for the mobile robot chassis based on the three-dimensional point cloud data of the obstacles and the visual coordinate data. Encode the obstacle avoidance path of the mobile robot chassis through a genetic algorithm to generate the coordinates of the target high-voltage switchgear cabinet.

[0030] Step S3: Receive the coordinates of the target high-voltage switchgear cabinet. Combine the hydraulic control parameters of the lifting platform of the data acquisition module and the earthing switch height data. Optimize the hydraulic control parameters of the lifting platform through a genetic algorithm, and drive the screw-driven sliding table module to move vertically to the target height. The sliding table module linearly displaces through screw drive, and the displacement state of the sliding table module is real-time feedback by the screw-driven sliding table module displacement data. After positioning, send a height adjustment signal to the vibration suppression module.

[0031] Step S4: According to the height adjustment signal, collect the vibration spectrum data at the end of the earthing switch tightening tooling. Dynamically optimize the damping parameters of the polyurethane vibration damping block through a genetic algorithm, and collect the pose data of the tooling end through the attitude sensor.

[0032] Step S5: Integrate the path error of the path planning module, the height deviation of the height adaptive module, the spectrum data of the vibration suppression module, and the angle deviation of the alignment control module to construct a target optimization model. Generate an instruction set through the target optimization model, and distribute the instructions to drive the mobile robot chassis, the sliding table module, and the servo motor to cooperate to complete the opening and closing operation of the earthing switch.

[0033] Further, in the earthing switch opening and closing method based on flexible tightening of the present invention, the step S2 includes:

[0034] Based on the three-dimensional point cloud data of the obstacles and the visual coordinate data collected by the data acquisition module, generate obstacle contour information through a lidar, and match it with the high-voltage switchgear cabinet positioning identifier in the visual feature identification data to construct a path planning fitness function to evaluate the feasibility of the obstacle avoidance path.

[0035] Generate path nodes based on the obstacle avoidance path. Perform chromosome crossover and mutation operations on the steering angle, moving speed, and safe obstacle avoidance distance between path nodes through a genetic algorithm to generate a candidate path set.

[0036] Output the candidate path solution set that meets the preset positioning accuracy threshold to the collaborative decision-making module as the path input parameter of the multi-objective optimization model in the collaborative decision-making module.

[0037] Further, in the earthing switch opening and closing method based on flexible tightening of the present invention, the step S3 includes:

[0038] Based on the hydraulic control parameters of the lifting platform and the height data of the grounding switch, a multi-dimensional optimization objective including the height error of the lifting platform, the displacement speed of the sliding table module, and the load current of the servo motor is constructed through a genetic algorithm, and the height adaptive fitness value is calculated;

[0039] According to the preset height adaptive fitness value threshold, a candidate solution set of hydraulic control parameters is screened, and the elite retention strategy is used to retain the top 10% of the candidate parameters in terms of fitness value;

[0040] The optimized target height value is synchronized to the alignment control module through the genetic algorithm as the height constraint condition for the alignment control module to generate the rotation angle sequence of the grounding switch tightening tooling.

[0041] Furthermore, for the grounding switch opening and closing method based on flexible tightening of the present invention, the step S4 includes:

[0042] Based on the pose data of the end of the tooling collected by the pose sensor and the damping parameters of the data acquisition module, the vibration spectrum data at the end of the grounding switch tightening tooling is collected through an acceleration sensor, and the vibration energy distribution characteristics in the 0-1 kHz frequency band are extracted as the population initialization parameters of the genetic algorithm;

[0043] Through the genetic algorithm, with the compression deformation amount and vibration attenuation rate of the polyurethane damping block as the vibration suppression fitness evaluation index, the candidate solutions of damping parameters are screened;

[0044] The optimized damping parameter data that meets the vibration suppression fitness evaluation index is fed back to the collaborative decision-making module, triggering the collaborative decision-making module to dynamically correct the opening and closing operation instruction set based on the multi-objective optimization model.

[0045] Furthermore, for the grounding switch opening and closing method based on flexible tightening of the present invention, the step S5 includes:

[0046] Based on the positioning error of the path planning module, the height deviation of the height adaptive module, the spectrum data of the vibration suppression module, and the angle deviation of the alignment control module, a multi-objective optimization model is constructed, and the positioning error, height deviation, spectrum data, and angle deviation are normalized and weighted;

[0047] Through the non-dominated sorting genetic algorithm of the collaborative decision-making module, a global optimal operation instruction set including the rotation speed instruction of the high-torque servo motor, the displacement instruction of the sliding table module, and the hydraulic control instruction of the lifting platform is generated based on the multi-objective optimization model;

[0048] Distribute the global optimal operation instruction set to the mobile robot chassis, the screw-driven sliding table module, and the high-torque servo motor, and drive the mobile robot chassis, the screw-driven sliding table module, and the high-torque servo motor to cooperate to complete the opening and closing operation of the grounding switch.

[0049] Advantages of the present invention:

[0050] Through the combination of multi-module cooperative control and genetic algorithm optimization, the present invention effectively solves the technical problems of low manual efficiency, insufficient vibration suppression of automated equipment, and poor positioning accuracy in the opening and closing operation of the grounding switch of high-voltage switchgear. The data acquisition module fuses the environmental perception data of lidar and vision sensors to construct an accurate three-dimensional environmental map, providing reliable input for the path planning module to generate obstacle avoidance paths, and improving the autonomous navigation efficiency of the mobile robot chassis in complex power distribution environments; the height adaptive module optimizes the hydraulic control parameters based on the genetic algorithm, combined with the closed-loop feedback mechanism of the screw-driven sliding table module, to achieve precise positioning and dynamic calibration of the grounding switch height, reducing the impact of vibration transmission on vertical positioning; the vibration suppression module dynamically adjusts the damping parameters of the vibration damping block through spectrum feature extraction and genetic algorithm to suppress mechanical vibration interference and improve the pose stability of the end of the tooling; the cooperative decision-making module integrates multi-source data to construct a multi-objective optimization model, generates a global optimal instruction set to drive the execution unit to cooperate, and corrects the motion deviation through real-time feedback, realizing high-precision and low-vibration automated opening and closing operations under vibration interference and complex space constraints, significantly improving the operation safety and system reliability. Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can be obtained according to the drawings without creative efforts.

[0052] Figure 1 It is a flowchart of the grounding switch opening and closing method based on flexible tightening provided by the embodiment of the present invention. Detailed Embodiments

[0053] To make the purpose, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below with reference to the drawings.

[0054] To better understand the object of the present invention, the present invention will be further described in detail below.

[0055] In a first aspect, the grounding switching device based on flexible tightening provided by the present invention includes:

[0056] A data acquisition module, configured to acquire environmental perception data, device status data, and damping parameters of polyurethane vibration damping blocks. The environmental perception data includes obstacle three-dimensional point cloud data and visual coordinate data, and the device status data includes hydraulic control parameters of a lifting platform, grounding switch height data, and screw drive slide module displacement data;

[0057] The data acquisition module acquires environmental perception data and device status data in real time through multi-sensor fusion technology, providing basic input parameters for subsequent modules. Specifically, the obstacle three-dimensional point cloud data in the environmental perception data is generated by a lidar scanning the working environment three-dimensionally. The scanning frequency and resolution are set according to the positioning accuracy requirements of the high-voltage switchgear. The point cloud data contains the spatial coordinates, contour dimensions, and distribution density of the obstacles. The visual coordinate data is acquired by a visual sensor installed on the mobile robot chassis. The positioning identification features of the high-voltage switchgear, including the identification shape, color, and spatial pose, are extracted through image processing algorithms, and are spatio-temporally aligned with the lidar point cloud data to eliminate the calibration error between sensors and construct an accurate three-dimensional environment map.

[0058] The hydraulic control parameters of the lifting platform in the device status data are monitored in real time by a pressure sensor and a flowmeter, including the hydraulic cylinder pressure, oil circuit flow rate, and valve opening, which are used to quantify the power output state of the lifting mechanism. The grounding switch height data is measured by a laser ranging sensor or an encoder, and the dynamic deviation between the actual height and the target height is calculated based on the installation reference plane of the grounding switch. The screw drive slide module displacement data is acquired by a linear encoder, and the linear displacement and movement speed of the slide module are fed back in real time, providing a feedback signal for the closed-loop control of the height adaptive module.

[0059] The damping parameters of the polyurethane vibration damping blocks are acquired in real time by an embedded sensor group, including a strain sensor and an acceleration sensor. The strain sensor measures the compression deformation of the vibration damping block under the action of a load, and the acceleration sensor monitors the spectral characteristics of the vibration transmitted to the end of the tooling. The dynamic change of the damping coefficient is calculated after fusing the two sets of data. The damping parameters are associated with the spectral analysis results of the vibration suppression module, which are used to optimize the dynamic response characteristics of the vibration damping block and suppress the mechanical vibration during the switching operation.

[0060] The acquisition, alignment, and fusion of the above data are achieved through the central processing unit. The environmental perception data is used for generating the obstacle avoidance path of the path planning module. The device status data drives the optimization of the hydraulic parameters of the height adaptive module. The damping parameters provide the initial population for the genetic algorithm of the vibration suppression module. Each data is transmitted to the corresponding module through the bus, forming a closed-loop control chain of "perception - decision - execution" to provide high-precision and low-latency data support for the opening and closing operation of the grounding switch. Through the collaborative acquisition and processing of multi-source heterogeneous data, the present invention solves the problem of control lag caused by data islands in the prior art.

[0061] The path planning module generates an obstacle avoidance path for the mobile robot chassis based on the three-dimensional point cloud data of obstacles and the visual coordinate data, encodes the obstacle avoidance path of the mobile robot chassis through the genetic algorithm, and generates the coordinates of the target high-voltage switchgear.

[0062] Based on the three-dimensional point cloud data of obstacles and the visual coordinate data provided by the data acquisition module, the path planning module performs obstacle avoidance path generation and optimization operations. Specifically, the lidar performs three-dimensional scanning on the working environment to generate point cloud data containing obstacle contour information. At the same time, the visual sensor captures the positioning identification features of the high-voltage switchgear. The lidar point cloud and the visual coordinate data are fused through the spatio-temporal calibration algorithm to eliminate the pose deviation between sensors, construct an accurate three-dimensional environment map, and identify the spatial coordinates of the target high-voltage switchgear and the distribution of surrounding obstacles.

[0063] Based on the three-dimensional environment map, the path planning module generates an initial obstacle avoidance path for the mobile robot chassis. The path ends at the coordinates of the target high-voltage switchgear, and the path nodes include the steering angle, moving speed, and safe obstacle avoidance distance parameters of the mobile robot. The path nodes are encoded through the genetic algorithm, and a path planning fitness function is constructed. Using the total path length, the smoothness of the steering angle, and the minimum safe distance from obstacles as evaluation indicators, the feasibility of each candidate path is quantified. Chromosome crossover and mutation operations are performed on the encoded path nodes to generate a candidate path set containing multiple groups of path schemes, covering different motion trajectories and obstacle avoidance strategies.

[0064] In the candidate path set, a path solution set that meets the preset positioning accuracy threshold is screened. The threshold is set based on the geometric dimensions of the high-voltage switchgear and the motion accuracy of the mobile robot chassis, and path schemes that deviate from the target coordinates or have insufficient safety distance are excluded. The screened candidate path solution set is output to the collaborative decision-making module as the path input parameter of the multi-objective optimization model for subsequent modules to globally optimize the motion trajectory of the mobile robot chassis. The above steps form a closed-loop control chain: environmental perception data drives path generation, the genetic algorithm optimizes path parameters, the screened solution set provides input for collaborative decision-making, and finally realizes the precise obstacle avoidance and target positioning of the mobile robot chassis.

[0065] The height adaptive module receives the coordinates of the target high-voltage switchgear cabinet, combines the hydraulic control parameters of the lifting platform of the data acquisition module and the earthing switch height data, optimizes the hydraulic control parameters of the lifting platform through a genetic algorithm, and drives the screw-driven slide module to move vertically to the target height. The slide module linearly displaces through screw transmission, and the displacement state of the slide module is real-time feedback by the displacement data of the screw-driven slide module. After positioning, a height adjustment signal is sent to the vibration suppression module;

[0066] The height adaptive module receives the coordinates of the target high-voltage switchgear cabinet provided by the path planning module, combines the hydraulic control parameters of the lifting platform collected by the data acquisition module and the earthing switch height data, and performs hydraulic parameter optimization and height positioning operations. Specifically, a multi-dimensional optimization objective is constructed through a genetic algorithm, including the height error of the lifting platform, the displacement speed of the slide module, and the load current of the servo motor. The height error is calculated from the difference between the real-time measured earthing switch height and the target height. The displacement speed of the slide module reflects the movement efficiency in the vertical direction, and the load current of the servo motor characterizes the real-time load state of the lifting mechanism. After weighting and fusing the above parameters, a height adaptive fitness value is generated to quantify the optimization degree of the hydraulic control parameters. The higher the fitness value, the better the height positioning accuracy and movement stability.

[0067] The candidate solution set of hydraulic control parameters is screened according to a preset height adaptive fitness value threshold. The threshold is set based on the installation accuracy requirements of the earthing switch of the high-voltage switchgear cabinet to exclude parameter combinations that may cause excessive height deviation or unstable movement. The elite retention strategy is used to iteratively optimize the candidate solution set, retaining the top 10% of the candidate parameters in terms of fitness value, and generating a new generation of parameter sets through the crossover and mutation operations of the genetic algorithm, gradually approaching the optimal solution. The optimized target height value is accurately positioned through the closed-loop control of the screw-driven slide module, and the displacement data of the slide module is real-time feedback to the data acquisition module to form a dynamic calibration mechanism for height adjustment.

[0068] The optimized target height value is synchronized to the alignment control module through the data bus as the height constraint condition for generating the rotation angle sequence of the earthing switch wrenching tool. The height constraint condition is combined with the hexagonal structure parameters of the earthing switch, and the minimum rotation angle of the end of the tool is calculated through a geometric matching algorithm to ensure the precise alignment of the geometric features of the wrenching tool and the earthing switch. The height adaptive module and the alignment control module form a collaborative control chain through real-time data interaction to realize the linkage adjustment of height positioning and angle alignment. After completing the height positioning, the height adaptive module sends a height adjustment signal to the vibration suppression module to trigger the vibration spectrum acquisition and damping parameter optimization process. The above steps solve the technical problems of low height adjustment efficiency and cumulative positioning deviation in the existing methods through genetic algorithm optimization and multi-module data linkage.

[0069] The vibration suppression module collects the vibration spectrum data at the end of the grounding switch wrenching tooling according to the height adjustment signal, dynamically optimizes the damping parameters of the polyurethane vibration damping block through the genetic algorithm, and collects the pose data of the tooling end through the attitude sensor;

[0070] After receiving the height adjustment signal sent by the height adaptation module, the vibration suppression module starts the vibration data acquisition and parameter optimization process. Specifically, it collects the vibration signal at the end of the grounding switch wrenching tooling in real time through the acceleration sensor, converts the time-domain vibration signal into the frequency-domain energy distribution by using the Fourier transform, and extracts the vibration energy characteristics in the 0-1 kHz frequency band as the input parameters of the genetic algorithm. This frequency band covers the main vibration energy caused by mechanical transmission and load fluctuations during the opening and closing operations, quantifies the amplitude distribution at different frequencies, and provides a data basis for optimizing the damping parameters of the vibration damping block.

[0071] Based on the extracted vibration energy characteristics, the vibration suppression module dynamically optimizes the damping parameters of the polyurethane vibration damping block through the genetic algorithm. The fitness function takes the compression deformation amount and vibration attenuation rate of the vibration damping block as evaluation indicators. The compression deformation amount reflects the elastic deformation ability of the damping material, and the vibration attenuation rate characterizes the energy dissipation efficiency. The two are weighted and fused to generate the fitness value, which is used to screen the optimal damping parameter combination. Perform chromosome crossover and mutation operations on the initial population parameters to generate multiple groups of candidate solutions, retain the parameter solutions whose fitness values meet the preset threshold, and gradually approach the optimal solution of the vibration damping effect.

[0072] The optimized damping parameters are fed back to the collaborative decision-making module through the data bus, triggering the dynamic correction of the opening and closing operation instruction set. The collaborative decision-making module combines the vibration spectrum data, path planning error, and height deviation, recalculates the servo motor torque output curve and the displacement instruction of the sliding table module, and adjusts the motion parameters in real time to suppress the vibration interference. At the same time, the attitude sensor collects the real-time pose data of the tooling end, including the position offset and rotation angle, which is used to evaluate the impact of vibration on the positioning accuracy and is fed back to the alignment control module for pose compensation.

[0073] The above process forms a closed-loop vibration suppression mechanism through vibration data acquisition, genetic algorithm optimization, and multi-module collaborative control. The dynamic parameter adjustment and pose feedback mechanism of the vibration suppression module effectively reduce the interference of mechanical vibration on the positioning of the tooling end, and improve the stability and accuracy of the grounding switch opening and closing operations.

[0074] The collaborative decision-making module integrates the path error of the path planning module, the height deviation of the height adaptation module, the spectrum data of the vibration suppression module, and the angle deviation of the alignment control module, constructs an objective optimization model, generates an instruction set through the objective optimization model, and distributes the instructions to drive the mobile robot chassis, sliding table module, and servo motor to cooperate to complete the opening and closing operations of the grounding switch.

[0075] The collaborative decision-making module integrates the path error of the path planning module, the height deviation of the height adaptive module, the spectral data of the vibration suppression module, and the angle deviation of the alignment control module to construct a multi-objective optimization model to achieve global instruction optimization. Specifically, the path error is calculated from the deviation between the actual motion trajectory of the mobile robot chassis and the planned path, reflecting the execution accuracy of the obstacle avoidance path; the height deviation is the difference between the actual displacement of the screw-driven slide module and the target height, characterizing the accuracy of vertical positioning; the spectral data includes the vibration energy distribution characteristics in the 0-1 kHz frequency band extracted by the vibration suppression module, quantifying the interference degree of mechanical vibration on the pose of the tooling end; the angle deviation is collected by the pose sensor of the alignment control module, reflecting the alignment offset between the tooling end and the hexagon structure of the grounding switch. Through normalization and weighting processing, the above parameters are mapped to a unified dimension, eliminating the influence of magnitude differences on the optimization results, and the weighting coefficients are dynamically adjusted according to the key importance of each parameter to the opening and closing operations.

[0076] Based on the multi-objective optimization model, the non-dominated sorting genetic algorithm is used to perform multi-objective optimization on the parameter solution set. The algorithm aims at minimizing the positioning error, converging the height deviation, suppressing the vibration energy, and the angle alignment accuracy. The Pareto optimal solution set is screened through non-dominated sorting and crowding degree calculation. During the iteration process, the individual with the optimal fitness value is retained, and a new generation of candidate solutions is generated through crossover and mutation operations, gradually approaching the global optimal operation instruction set. The instruction set includes the rotation speed instruction of the high-torque servo motor, the displacement instruction of the slide module, and the hydraulic control instruction of the lifting platform. The parameter of each instruction is dynamically matched to ensure the motion coordination of the execution unit.

[0077] The global optimal operation instruction set is distributed to the mobile robot chassis, the screw-driven slide module, and the high-torque servo motor through the industrial bus. The mobile robot chassis adjusts the traveling speed and steering angle according to the rotation speed instruction, the slide module executes the displacement instruction to achieve precise vertical positioning, and the lifting platform dynamically adjusts the height according to the hydraulic control instruction. Each execution unit forms a closed-loop control with the collaborative decision-making module through real-time data feedback, dynamically correcting the motion deviation in the opening and closing operations. For example, when the vibration spectral data exceeds the preset threshold, the collaborative decision-making module recalculates the torque output curve of the servo motor and reduces the motion speed to reduce the vibration energy; if the angle deviation increases, the rotation angle sequence of the tooling end is adjusted to compensate for the pose offset. The above technical solution solves the technical problems of poor system coordination and low operation efficiency caused by local optimization in the existing methods through multi-source data fusion, multi-objective optimization algorithm, and distributed instruction coordination.

[0078] The grounding switch opening and closing device based on flexible tightening provided by the present invention realizes the automatic opening and closing operation of the grounding switch of the high-voltage switch cabinet through multi-module collaborative control. The functions and logical relationships of each module in the technical solution are described in detail below:

[0079] The data acquisition module is responsible for obtaining environmental perception data and device status data in real time. The environmental perception data includes the three-dimensional point cloud data of obstacles generated by lidar and the visual coordinate data collected by visual sensors, which are used to identify the obstacle distribution in the working environment and the positioning marks of high-voltage switchgear. The device status data includes the hydraulic control parameters of the lifting platform, the height data of the grounding switch, and the real-time displacement data of the screw-driven slide table module, providing input parameters for path planning, height adjustment, and vibration suppression of subsequent modules.

[0080] Based on the three-dimensional point cloud of obstacles and visual coordinate data in the environmental perception data, the path planning module generates an obstacle avoidance path for the mobile robot chassis through a genetic algorithm. Specifically, after the lidar point cloud data is matched with the visual feature marks, a path planning fitness function is constructed to evaluate the feasibility of the steering angle, moving speed, and obstacle avoidance distance of the path; a candidate path set is generated through the chromosome crossover and mutation operations of the genetic algorithm, and the solution set that meets the preset positioning accuracy threshold is screened and output to the collaborative decision-making module as the path input parameters for multi-objective optimization.

[0081] The height adaptive module receives the coordinates of the target high-voltage switchgear generated by the path planning module, combines the hydraulic control parameters and the grounding switch height data of the data acquisition module, and optimizes the hydraulic control parameters of the lifting platform through a genetic algorithm. The optimization objectives include the height error of the lifting platform, the displacement speed of the slide table module, and the load current of the servo motor. The height adaptive fitness value is calculated by weighted summation, and the candidate parameters with the top 10% fitness rankings are screened and retained. The optimized target height value drives the screw-driven slide table module to move vertically to the target position, and the positioning is confirmed through the real-time feedback displacement data, triggering the vibration suppression module to start.

[0082] After receiving the height adjustment signal, the vibration suppression module collects the vibration spectrum data at the end of the grounding switch tightening tooling through an acceleration sensor, and extracts the vibration energy distribution characteristics in the 0-1 kHz frequency band as the population initialization parameters of the genetic algorithm. Taking the compression deformation amount and vibration attenuation rate of the polyurethane vibration damping block as the fitness evaluation indexes, the candidate solutions of the damping parameters are screened, and the optimized damping parameters are fed back to the collaborative decision-making module for dynamically correcting the switching operation instruction set to reduce the influence of vibration on the positioning accuracy.

[0083] The collaborative decision-making module integrates the path error of the path planning module, the height deviation of the height adaptive module, the spectral data of the vibration suppression module, and the angle deviation of the alignment control module to construct a multi-objective optimization model. After normalizing and weighting each parameter, a non-dominated sorting genetic algorithm is used to generate a globally optimal operation instruction set, including the rotation speed instruction of the high-torque servo motor, the displacement instruction of the slide table module, and the hydraulic control instruction of the lifting platform. The optimized instruction set is distributed to the mobile robot chassis, the screw-driven slide table module, and the servo motor to drive each execution unit to cooperate to complete the opening and closing operations of the grounding switch, realizing high-precision positioning and stable operation in a complex power distribution environment.

[0084] Each module forms a complete operation chain through the closed-loop transmission of data flow and control signals: the environmental perception data drives path planning, the target coordinates trigger height adjustment, the positioning completion signal starts vibration suppression, and finally the collaborative decision-making module generates global instructions overall. This technical solution solves the problems of low manual operation efficiency, insufficient vibration suppression of equipment, and poor positioning accuracy through the multi-level optimization of the genetic algorithm and modular collaborative control.

[0085] Specifically, for the grounding switch opening and closing device based on flexible tightening described in the present invention, the path planning module is further used for:

[0086] Based on the three-dimensional point cloud data of obstacles and visual coordinate data collected by the data acquisition module, the obstacle contour information is generated by lidar and matched with the high-voltage switch cabinet positioning identifier in the visual feature identifier data to construct a path planning fitness function to evaluate the feasibility of the obstacle avoidance path;

[0087] Based on the obstacle avoidance path, path nodes are generated, and chromosome crossover and mutation operations are performed on the steering angle, moving speed, and safe obstacle avoidance distance between path nodes through a genetic algorithm to generate a candidate path set;

[0088] The candidate path solution set that meets the preset positioning accuracy threshold is output to the collaborative decision-making module as the path input parameter of the multi-objective optimization model in the collaborative decision-making module.

[0089] The path planning module performs obstacle avoidance path generation and optimization based on the three-dimensional point cloud data of obstacles and visual coordinate data provided by the data acquisition module. Specifically, the operation environment is scanned three-dimensionally by lidar to generate point cloud data containing obstacle contour information, and at the same time, the positioning identifier data of the high-voltage switch cabinet is obtained through a visual sensor. The obstacle contour information in the lidar point cloud is matched with the high-voltage switch cabinet positioning identifier in the visual feature identifier data to identify the spatial coordinates of the target high-voltage switch cabinet and the surrounding obstacle distribution, thereby constructing a three-dimensional environment map.

[0090] Based on the three-dimensional environmental map, the path planning module generates an initial obstacle avoidance path for the mobile robot chassis and constructs a path planning fitness function. The fitness function uses path length, steering angle smoothness, and safety distance from obstacles as evaluation indicators to quantify the feasibility of each candidate path. The path nodes of the initial obstacle avoidance path are encoded through a genetic algorithm. The path nodes include the steering angle, moving speed, and safety obstacle avoidance distance parameters of the mobile robot. Chromosome crossover and mutation operations are performed on the encoded path nodes to generate a candidate path set containing multiple path schemes.

[0091] In the candidate path set, a path solution set that meets the preset positioning accuracy threshold is screened. The positioning accuracy threshold is set based on the positioning mark size of the high-voltage switchgear and the motion accuracy of the mobile robot chassis, and is used to eliminate path schemes that deviate from the target coordinates or have insufficient safety distance from obstacles. The screened candidate path solution set is output to the collaborative decision-making module as the path input parameters of the multi-objective optimization model for subsequent modules to globally optimize the motion trajectory of the mobile robot chassis. The above steps form a closed-loop control chain: environmental perception data drives path generation, the genetic algorithm optimizes path parameters, the screened solution set provides input for collaborative decision-making, and finally realizes precise obstacle avoidance and target positioning of the mobile robot chassis.

[0092] Specifically, for the earthing closing and opening device based on flexible tightening of the present invention, the height adaptive module is further used for:

[0093] Based on the hydraulic control parameters of the lifting platform and the earthing switch height data, a multi-dimensional optimization objective including the height error of the lifting platform, the displacement speed of the slide table module, and the load current of the servo motor is constructed through a genetic algorithm, and the height adaptive fitness value is calculated;

[0094] According to the preset height adaptive fitness value threshold, a candidate solution set of hydraulic control parameters is screened, and an elitist retention strategy is used to retain the top 10% of the candidate parameters in terms of fitness value;

[0095] The optimized target height value is synchronized to the alignment control module through the genetic algorithm as the height constraint condition for the alignment control module to generate the rotation angle sequence of the earthing switch tightening tooling.

[0096] The height adaptive module performs hydraulic control parameter optimization and height positioning operations based on the hydraulic control parameters of the lifting platform and the height data of the grounding switch. Specifically, a multi-dimensional optimization objective including the height error of the lifting platform, the displacement speed of the sliding table module, and the load current of the servo motor is constructed through a genetic algorithm. The height error is calculated from the difference between the height data of the grounding switch measured in real time and the target height. The displacement speed of the sliding table module and the load current of the servo motor respectively reflect the motion efficiency and load state of the lifting mechanism. After weighted fusion of the above parameters, the height adaptive fitness value is calculated to quantify the optimization degree of the hydraulic control parameters. The higher the fitness value, the better the height positioning accuracy and motion stability.

[0097] According to the preset height adaptive fitness value threshold, the candidate solution set of hydraulic control parameters is screened. The threshold is set based on the installation accuracy requirements of the grounding switch of the high-voltage switchgear to exclude parameter combinations that may cause excessive height deviation or unstable motion. The elite retention strategy is used to iteratively optimize the candidate solution set, retaining the top 10% of the candidate parameters in terms of fitness value, and generating a new generation of parameter sets through the crossover and mutation operations of the genetic algorithm to gradually approach the optimal solution. The optimized target height value is accurately positioned through the closed-loop control of the screw-driven sliding table module, and the displacement data of the sliding table module is fed back to the data acquisition module in real time to form dynamic calibration of height adjustment.

[0098] The optimized target height value is synchronized to the alignment control module as a constraint condition for generating the rotation angle sequence of the grounding switch tightening tool. The height constraint condition is combined with the hexagonal structure parameters of the grounding switch to calculate the minimum rotation angle at the end of the tool, realizing the geometric feature matching between the tightening tool and the grounding switch. The height adaptive module and the alignment control module perform parameter interaction through the data bus to form a collaborative control chain for height positioning and angle alignment, ultimately realizing high-precision automated operation of the grounding switch opening and closing operation. The above steps optimize through the genetic algorithm and multi-module data linkage, solving the technical problems of low height adjustment efficiency and cumulative positioning deviation in the existing methods.

[0099] Specifically, for the grounding switch opening and closing device based on flexible tightening of the present invention, the vibration suppression module is further used for:

[0100] Based on the pose data of the end of the tool collected by the pose sensor and the damping parameters of the data acquisition module, the vibration spectrum data of the end of the grounding switch tightening tool is collected through an acceleration sensor, and the vibration energy distribution characteristics in the 0-1 kHz frequency band are extracted as the population initialization parameters of the genetic algorithm;

[0101] Through the genetic algorithm, with the compression deformation amount and vibration attenuation rate of the polyurethane damping block as the vibration suppression fitness evaluation index, the candidate solutions of the damping parameters are screened;

[0102] Feed the optimized damping parameter data that meets the vibration suppression fitness evaluation index back to the collaborative decision-making module, triggering the collaborative decision-making module to dynamically correct the opening and closing operation instruction set based on the multi-objective optimization model.

[0103] The vibration suppression module performs vibration spectrum analysis and damping parameter optimization operations based on the end-effector pose data of the tooling collected by the attitude sensor and the damping parameters of the data acquisition module. Specifically, the vibration spectrum data of the end of the grounding switch wrenching tooling is collected in real time by the acceleration sensor, and the vibration energy distribution characteristics in the 0-1 kHz frequency band are extracted as the population initialization parameters of the genetic algorithm. This frequency band covers the main vibration energy caused by mechanical transmission and load changes during the opening and closing operations of the grounding switch. The time-domain vibration signal is converted into a frequency-domain energy distribution through Fourier transform to quantify the vibration amplitude at different frequencies, providing an initial optimization space for the genetic algorithm.

[0104] Model the dynamic relationship between the compression deformation of the polyurethane damping block and the vibration attenuation rate through the genetic algorithm, and construct a vibration suppression fitness evaluation index. The compression deformation reflects the elastic deformation ability of the damping block, and the vibration attenuation rate characterizes the vibration energy dissipation efficiency. The two are weighted and fused to generate a fitness value, which is used to evaluate the damping effect of different damping parameter combinations. Perform chromosome crossover and mutation operations on the initial population parameters to generate a candidate solution set containing multiple groups of damping parameter solutions, screen the candidate solutions whose fitness values meet the preset threshold, and retain the parameter combination with the greatest optimization potential.

[0105] Feed the optimized damping parameter data back to the collaborative decision-making module through the data bus, triggering dynamic instruction correction based on the multi-objective optimization model. The collaborative decision-making module integrates the spectrum data of the vibration suppression module, the positioning error of the path planning module, and the height deviation of the height adaptive module, recalculates the torque output curve of the servo motor and the displacement instruction of the slide table module, and adjusts the motion parameters during the opening and closing operations in real time. Through the closed-loop feedback mechanism, the interference of vibration on the end-effector pose is suppressed, and the positioning accuracy and equipment stability of the grounding switch tightening operation are improved. The above steps solve the technical problems of lagging vibration suppression and poor parameter adaptability in the existing methods through spectrum feature extraction, genetic algorithm optimization, and multi-module collaborative control.

[0106] Specifically, for the grounding switch opening and closing device based on flexible tightening of the present invention, the collaborative decision-making module is further configured to:

[0107] Construct a multi-objective optimization model based on the positioning error of the path planning module, the height deviation of the height adaptive module, the spectrum data of the vibration suppression module, and the angle deviation of the alignment control module, and perform normalized weighted processing on the positioning error, height deviation, spectrum data, and angle deviation;

[0108] Based on the non-dominated sorting genetic algorithm of the collaborative decision-making module, a globally optimal operation instruction set including the rotational speed instruction of the high-torque servo motor, the displacement instruction of the screw-driven slide module, and the hydraulic control instruction of the lifting platform is generated based on the multi-objective optimization model;

[0109] The globally optimal operation instruction set is distributed to the mobile robot chassis, the screw-driven slide module, and the high-torque servo motor to drive the mobile robot chassis, the screw-driven slide module, and the high-torque servo motor to cooperate to complete the opening and closing operation of the grounding switch.

[0110] The collaborative decision-making module integrates the positioning error of the path planning module, the height deviation of the height adaptive module, the spectral data of the vibration suppression module, and the angle deviation of the alignment control module to construct a multi-objective optimization model to achieve global instruction optimization. Specifically, the positioning error is calculated from the deviation between the actual path and the planned path of the mobile robot chassis, the height deviation is the difference between the actual displacement of the screw-driven slide module and the target height, the spectral data includes the vibration energy distribution characteristics in the 0-1 kHz frequency band extracted by the vibration suppression module, and the angle deviation reflects the alignment offset between the end pose of the tooling and the hexagonal structure of the grounding switch. By normalizing and weighting, each parameter is mapped to a unified dimension to eliminate the influence of magnitude differences on the optimization results, and the weighting coefficients are dynamically adjusted according to the influence weights of each parameter on the opening and closing operations.

[0111] Based on the multi-objective optimization model, the non-dominated sorting genetic algorithm is used to perform multi-objective optimization on the parameter solution set. The algorithm aims to minimize the positioning error, converge the height deviation, suppress the vibration energy, and improve the angle alignment accuracy. The Pareto optimal solution set is screened through non-dominated sorting and crowding degree calculation. During the iteration process, the individual with the optimal fitness value is retained, and a new generation of candidate solutions is generated through crossover and mutation operations, gradually approaching the globally optimal operation instruction set. The instruction set includes the rotational speed instruction of the high-torque servo motor, the displacement instruction of the slide module, and the hydraulic control instruction of the lifting platform to ensure the coordinated matching of the motion parameters of each execution unit.

[0112] The globally optimal operation instruction set is distributed to the mobile robot chassis, the screw-driven slide module, and the high-torque servo motor through the industrial bus. The mobile robot chassis adjusts the traveling speed and steering angle according to the rotational speed instruction, the slide module executes the displacement instruction to achieve precise positioning in the vertical direction, and the lifting platform dynamically adjusts the height according to the hydraulic control instruction. Each execution unit forms a closed-loop control with the collaborative decision-making module through real-time data feedback, dynamically correcting the motion deviation in the opening and closing operations, and finally completing the high-precision tightening and opening / closing actions of the grounding switch. The above technical solution solves the technical problems of poor system coordination and low operation efficiency caused by local optimization in the existing methods through multi-source data fusion, multi-objective optimization algorithms, and distributed instruction coordination.

[0113] Second aspect, please refer to Figure 1 , the grounding switching method based on flexible tightening provided by the present invention is applied to the grounding switching device based on flexible tightening, and includes:

[0114] Step S1, collect environmental perception data, equipment status data, and damping parameters of the polyurethane shock absorber block. The environmental perception data includes obstacle three-dimensional point cloud data and visual coordinate data. The equipment status data includes hydraulic control parameters of the lifting platform, grounding switch height data, and screw drive slide module displacement data;

[0115] Step S2, generate an obstacle avoidance path for the mobile robot chassis based on the obstacle three-dimensional point cloud data and visual coordinate data, encode the obstacle avoidance path of the mobile robot chassis through a genetic algorithm, and generate the coordinates of the target high-voltage switchgear;

[0116] Step S3, receive the coordinates of the target high-voltage switchgear, combine the hydraulic control parameters of the lifting platform and the grounding switch height data of the data acquisition module, optimize the hydraulic control parameters of the lifting platform through a genetic algorithm, drive the screw drive slide module to move vertically to the target height. The slide module linearly displaces through screw transmission, and the displacement state of the slide module is real-time feedback by the screw drive slide module displacement data. After positioning, send a height adjustment signal to the vibration suppression module;

[0117] Step S4, according to the height adjustment signal, collect the vibration spectrum data at the end of the grounding switch tightening tooling, dynamically optimize the damping parameters of the polyurethane shock absorber block through a genetic algorithm, and collect the pose data of the tooling end through the attitude sensor;

[0118] Step S5, integrate the path error of the path planning module, the height deviation of the height adaptive module, the spectrum data of the vibration suppression module, and the angle deviation of the alignment control module, construct a target optimization model, generate an instruction set through the target optimization model, and distribute the instructions to drive the mobile robot chassis, slide module, and servo motor to cooperate to complete the grounding switch opening and closing operation.

[0119] Specifically, for the grounding switching method based on flexible tightening of the present invention, step S2 includes:

[0120] Based on the obstacle three-dimensional point cloud data and visual coordinate data collected by the data acquisition module, generate obstacle contour information through a lidar, and match it with the high-voltage switchgear positioning identifier in the visual feature identification data to construct a path planning fitness function to evaluate the feasibility of the obstacle avoidance path;

[0121] Generate path nodes based on the obstacle avoidance path, perform chromosome crossover and mutation operations on the steering angle, moving speed, and safe obstacle avoidance distance between path nodes through a genetic algorithm to generate a candidate path set;

[0122] Output the candidate path solution set that meets the preset positioning accuracy threshold to the collaborative decision-making module as the path input parameter of the multi-objective optimization model in the collaborative decision-making module.

[0123] Specifically, for the grounding switching-on and switching-off method based on flexible tightening according to the present invention, the step S3 includes:

[0124] Based on the hydraulic control parameters of the lifting platform and the grounding switch height data, construct a multi-dimensional optimization objective including the height error of the lifting platform, the displacement speed of the sliding table module, and the load current of the servo motor through a genetic algorithm, and calculate the height adaptive fitness value;

[0125] According to the preset height adaptive fitness value threshold, screen the candidate solution set of hydraulic control parameters, and adopt the elitist retention strategy to retain the candidate parameters with the top 10% fitness values;

[0126] Synchronize the optimized target height value to the alignment control module through the genetic algorithm as the height constraint condition for the alignment control module to generate the rotation angle sequence of the grounding switch tightening tooling.

[0127] Specifically, for the grounding switching-on and switching-off method based on flexible tightening according to the present invention, the step S4 includes:

[0128] Based on the pose data of the end of the tooling collected by the pose sensor and the damping parameters of the data acquisition module, collect the vibration spectrum data of the end of the grounding switch tightening tooling through an acceleration sensor, and extract the vibration energy distribution characteristics in the 0-1 kHz frequency band as the population initialization parameters of the genetic algorithm;

[0129] Through the genetic algorithm, use the compression deformation amount and vibration attenuation rate of the polyurethane damping block as the vibration suppression fitness evaluation index to screen the candidate solutions of the damping parameters;

[0130] Feed back the optimized damping parameter data that meets the vibration suppression fitness evaluation index to the collaborative decision-making module, and trigger the collaborative decision-making module to dynamically correct the switching-on and switching-off operation instruction set based on the multi-objective optimization model.

[0131] Specifically, for the grounding switching-on and switching-off method based on flexible tightening according to the present invention, the step S5 includes:

[0132] Based on the positioning error of the path planning module, the height deviation of the height adaptive module, the spectrum data of the vibration suppression module, and the angle deviation of the alignment control module, construct a multi-objective optimization model, and perform normalized weighted processing on the positioning error, height deviation, spectrum data, and angle deviation;

[0133] Through the non-dominated sorting genetic algorithm of the collaborative decision-making module, a globally optimal operation instruction set including the rotational speed instruction of the high-torque servo motor, the displacement instruction of the screw-driven slide module, and the hydraulic control instruction of the lifting platform is generated based on the multi-objective optimization model;

[0134] The globally optimal operation instruction set is distributed to the mobile robot chassis, the screw-driven slide module, and the high-torque servo motor, driving the mobile robot chassis, the screw-driven slide module, and the high-torque servo motor to cooperate to complete the opening and closing operation of the grounding switch.

[0135] The embodiment of the present invention combines the actual application scenario of the opening and closing operation of the grounding switch of the high-voltage switch cabinet, and solves the problems of low efficiency, insufficient vibration suppression, and poor positioning accuracy existing in the background technology through the technical solutions of different modules. The specific embodiments are as follows:

[0136] Embodiment 1 of the present invention:

[0137] In the complex power distribution environment of the substation, the mobile robot needs to cross multiple obstacles to approach the target high-voltage switch cabinet. The data acquisition module generates three-dimensional point cloud data of the obstacles through the lidar at a scanning frequency of 10Hz, and at the same time the vision sensor captures the positioning identification of the high-voltage switch cabinet with a resolution of 2 million pixels. After the path planning module fuses the point cloud data with the vision coordinates, a three-dimensional environment map is constructed to identify the target coordinates and the distribution of obstacles. An initial obstacle avoidance path is generated through the genetic algorithm, and the path node coding parameters include the steering angle of 0° to 180°, the moving speed of 0.1 to 0.5m / s, and the safe obstacle avoidance distance of ≥0.3m. The fitness function uses the path length, steering smoothness, and safety distance as evaluation indicators to screen out the candidate path solution set that meets the positioning accuracy of ±5mm. The mobile robot chassis autonomously avoids obstacles according to the optimized path instruction and completes the approach operation of the target high-voltage switch cabinet within 30 seconds, improving the efficiency compared with the existing manual operation.

[0138] Embodiment 2 of the present invention:

[0139] For the positioning deviation problem caused by the height difference of the grounding switch, after receiving the target coordinates, the height adaptive module combines the hydraulic control parameters of 5-20 MPa and the grounding switch height data (measurement accuracy ±1 mm), and optimizes the motion parameters of the lifting platform through the genetic algorithm. The multi-dimensional optimization objectives include height error (weight 0.6), the displacement speed of the slide module (weight 0.3), and the load current of the servo motor (weight 0.1). The elite retention strategy is adopted to screen the candidate solutions of hydraulic parameters with the top 10% fitness values. The optimized parameters drive the screw to drive the slide module to move vertically to the target height at a speed of 0.2 m / s, and the displacement data is fed back in real time to form a closed-loop control, with a positioning accuracy of ±2 mm. After the height positioning is completed, the vibration suppression module collects the vibration spectrum at the end of the tooling through the acceleration sensor, extracts the energy characteristics in the 0-1 kHz frequency band (main peak frequency 500 Hz, amplitude 0.05 g), and takes the compression deformation threshold ≤3 mm and the vibration attenuation rate ≥20 dB / s as the fitness index to optimize the damping parameters of the polyurethane vibration damping block and reduce the vibration energy.

[0140] Embodiment 3 of the present invention:

[0141] In the precision docking scenario of the grounding switch hexagonal structure, the collaborative decision-making module integrates the path error of ±5 mm, the height deviation of ±2 mm, the energy ratio of the 0-1 kHz frequency band in the vibration spectrum data ≤15%, and the angle deviation of ±1°. A multi-objective optimization model is constructed through normalized weighted processing. The non-dominated sorting genetic algorithm is used to generate the global optimal instruction set, including the servo motor speed of 100-500 rpm, the displacement instruction accuracy of the slide module of ±0.5 mm, and the pressure fluctuation of the hydraulic control instruction ≤2 MPa. The instruction set is distributed to each execution unit through the CAN bus. The mobile robot chassis adjusts the traveling trajectory according to the speed instruction, the slide module performs vertical positioning, and at the same time, the vibration suppression module adjusts the damping parameters in real time. When the angle deviation exceeds ±1°, the alignment control module triggers the tooling to rotate by an angle compensation step angle of 5°, and completes the pose correction within 5 seconds. This embodiment improves the one-time success rate of the grounding switch opening and closing operation in a complex vibration environment.

[0142] In the specific implementation manner of the present invention, combined with the actual application scenario of the opening and closing operation of the grounding switch of the high-voltage switchgear, automatic operation is realized through multi-module collaborative control and genetic algorithm optimization. The data acquisition module uses lidar to scan the operation environment to generate three-dimensional point cloud data of obstacles. At the same time, it identifies the positioning identification features of the high-voltage switchgear through a vision sensor. The two are fused through a spatio-temporal calibration algorithm to construct a three-dimensional environment map, eliminating the sensor calibration error. The path planning module generates an initial obstacle avoidance path for the mobile robot chassis based on the fused environment data. The genetic algorithm is used to encode the steering angle, moving speed, and safe obstacle avoidance distance of the path nodes, construct a path planning fitness function to screen the candidate path solution set that meets the positioning accuracy threshold, and output it to the collaborative decision-making module as the input parameter for multi-objective optimization, driving the mobile robot chassis to autonomously avoid obstacles and accurately approach the target high-voltage switchgear.

[0143] After receiving the target coordinates output by the path planning module, the height adaptive module combines the hydraulic control parameters of the lifting platform and the grounding switch height data, constructs a multi-dimensional optimization target including height error, displacement speed, and load current through the genetic algorithm, and uses the elitist retention strategy to screen the top 10% of the hydraulic parameter candidate solutions in terms of fitness value. The optimized parameters drive the screw-driven sliding table module to move vertically to the target height, and the displacement data is fed back in real time to form a closed-loop control. At the same time, the height constraint condition is transmitted to the alignment control module. After the height positioning is completed, the vibration suppression module collects the vibration spectrum data at the end of the tooling through an acceleration sensor, extracts the energy distribution characteristics in the 0-1 kHz frequency band as the input of the genetic algorithm, optimizes the damping parameters of the polyurethane vibration damping block with the compression deformation amount and the vibration attenuation rate as the fitness indicators, and feeds the optimization result back to the collaborative decision-making module to trigger instruction correction.

[0144] The collaborative decision-making module integrates the path error, height deviation, vibration spectrum, and angle deviation data, constructs a multi-objective optimization model through normalized weighted processing, and uses the non-dominated sorting genetic algorithm to generate a global optimal instruction set. The instruction set is distributed to the mobile robot chassis, the sliding table module, and the servo motor through the industrial bus. The mobile robot chassis adjusts the travel trajectory according to the rotation speed instruction, the sliding table module executes the displacement instruction to achieve vertical positioning, the lifting platform dynamically adjusts the height according to the hydraulic instruction, and at the same time, the optimized parameters of the vibration suppression module can suppress the mechanical vibration interference in real time. Each execution unit dynamically corrects the motion deviation through a closed-loop feedback mechanism. For example, when the angle deviation exceeds the threshold, the rotation angle of the tooling is adjusted to compensate for the pose offset. The above implementation manner realizes the efficient positioning, vibration suppression, and safety control of the opening and closing operation of the grounding switch in a complex power distribution environment through modular collaboration and genetic algorithm optimization.

[0145] Through multi-module collaborative control and genetic algorithm optimization, the present invention solves the technical problems of low manual efficiency, insufficient vibration suppression, and poor positioning accuracy in the opening and closing operations of the grounding switch of high-voltage switchgear in the distribution system. First, the data acquisition module generates three-dimensional point cloud data of obstacles and the positioning coordinates of the high-voltage switchgear by fusing lidar and vision sensors. The path planning module generates an obstacle avoidance path based on the genetic algorithm and encodes the target coordinates, driving the mobile robot chassis to avoid obstacles autonomously and accurately position, eliminating the inefficiency and positioning deviation of manual operations, and improving the operation efficiency in complex distribution environments.

[0146] Secondly, the vibration suppression module collects the vibration spectrum data at the end of the tooling based on the acceleration sensor, extracts the vibration energy characteristics in the 0-1 kHz frequency band, and dynamically optimizes the damping parameters of the polyurethane vibration damping block through the genetic algorithm. Using the compression deformation amount and vibration attenuation rate as fitness evaluation indicators to screen the optimal solution, suppressing the interference of mechanical vibration on the tooling pose. At the same time, the height adaptive module optimizes the hydraulic parameters of the lifting platform through the genetic algorithm, combined with the closed-loop feedback control of the screw-driven slide module, to achieve dynamic calibration and stable positioning of the grounding switch height, reducing the impact of vibration transmission on the vertical direction positioning accuracy.

[0147] Finally, the collaborative decision-making module integrates path error, height deviation, vibration spectrum, and angle deviation data, constructs a multi-objective optimization model, and uses the non-dominated sorting genetic algorithm to generate a global optimal operation instruction set, which is distributed to the mobile robot chassis, slide module, and servo motor. Through the industrial bus, the collaborative control and real-time feedback correction of the execution unit are realized, and the motion parameters are dynamically adjusted to compensate for vibration and pose deviation, so as to achieve high precision, low vibration, and high safety in the opening and closing operations of the grounding switch in complex distribution environments.

Claims

1. A grounding opening and closing device based on flexible tightening, characterized in that: include: Data acquisition module, used to collect environmental perception data, equipment status data and damping parameters of polyurethane vibration damping blocks. Environmental perception data includes obstacle 3D point cloud data and visual coordinate data. Equipment status data includes hydraulic control parameters of the lifting platform, grounding gate height data and screw drive slide module displacement data. The path planning module generates the obstacle avoidance path of the mobile robot chassis based on the obstacle 3D point cloud data and visual coordinate data, encodes the obstacle avoidance path of the mobile robot chassis through the genetic algorithm, and generates the coordinates of the target high-voltage switch cabinet; The height adaptive module receives the coordinates of the target high-voltage switch cabinet, combines the hydraulic control parameters of the lifting platform and the height data of the grounding gate of the data acquisition module, optimizes the hydraulic control parameters of the lifting platform through the genetic algorithm, drives the screw to drive the slide module to move vertically to the target height, and the slide module is linearly displaced by the screw drive. The displacement state of the slide module is fed back in real time by the displacement data of the screw driving the slide module. After positioning is completed, a height adjustment signal is sent to the vibration suppression module; The vibration suppression module collects the vibration spectrum data of the end of the grounding switch according to the height adjustment signal, dynamically optimizes the damping parameters of the polyurethane vibration damping block through the genetic algorithm, and collects the end posture data of the tooling through the posture sensor; The collaborative decision-making module integrates the path error of the path planning module, the height deviation of the height adaptation module, the spectrum data of the vibration suppression module and the angle deviation of the alignment control module, builds a target optimization model, generates an instruction set through the target optimization model, and distributes the instructions to the chassis, slide module and servo motor of the driving mobile robot to collaboratively complete the grounding switch opening and closing operations.

2. The grounding opening and closing device based on flexible tightening according to claim 1 is characterized in that: The path planning module is also used for: Based on the three-dimensional point cloud data and visual coordinate data of the obstacle collected by the data acquisition module, the obstacle contour information is generated by the laser radar, and matched with the high-voltage switchgear positioning mark in the visual feature identification data, and the path planning fitness function is constructed to evaluate the feasibility of the obstacle avoidance path; Generate path nodes based on the obstacle avoidance path, perform chromosome crossover and mutation operations on the steering angles, moving speeds, and safe obstacle avoidance distances between the path nodes through a genetic algorithm, and generate a candidate path set; The candidate path solution set that meets the preset positioning accuracy threshold is output to the collaborative decision-making module as the path input parameter of the multi-objective optimization model in the collaborative decision-making module.

3. The grounding opening and closing device based on flexible tightening according to claim 2 is characterized in that: The highly adaptive module is further used for: Based on the hydraulic control parameters of the lifting platform and the grounding gate height data, a multi-dimensional optimization target including the height error of the lifting platform, the displacement speed of the sliding table module and the load current of the servo motor is constructed by a genetic algorithm to calculate the height adaptive fitness value; According to the preset highly adaptive fitness value threshold, the candidate solution set of hydraulic control parameters is screened, and the candidate parameters with the top 10% fitness values ​​are retained using an elite retention strategy; The optimized target height value is synchronized to the alignment control module through the genetic algorithm, and is used as a height constraint condition for the alignment control module to generate a rotation angle sequence of the grounding gate screwing tooling.

4. The grounding opening and closing device based on flexible tightening according to claim 3 is characterized in that: The vibration suppression module is further used for: Based on the posture data of the tooling end collected by the posture sensor and the damping parameters of the data acquisition module, the vibration spectrum data of the grounding switch twisting tooling end is collected by the acceleration sensor, and the vibration energy distribution characteristics in the 0-1kHz frequency band are extracted as the population initialization parameters of the genetic algorithm; By using the genetic algorithm, the compression deformation and vibration attenuation rate of the polyurethane vibration damping block are used as vibration suppression fitness evaluation indicators to screen candidate solutions for damping parameters; The damping parameter optimization data that meets the vibration suppression fitness evaluation index is fed back to the collaborative decision-making module, triggering the collaborative decision-making module to dynamically correct the opening and closing operation instruction set based on the multi-objective optimization model.

5. The grounding opening and closing device based on flexible tightening according to claim 4 is characterized in that: The collaborative decision-making module is also used to: Based on the positioning error of the path planning module, the height deviation of the height adaptive module, the spectrum data of the vibration suppression module and the angle deviation of the alignment control module, a multi-objective optimization model is constructed to perform normalized weighted processing on the positioning error, height deviation, spectrum data and angle deviation; Generate a global optimal operation instruction set including a high-torque servo motor speed instruction, a slide module displacement instruction and a lifting platform hydraulic control instruction based on the multi-objective optimization model through the non-dominated sorting genetic algorithm of the collaborative decision-making module; The global optimal operation instruction set is distributed to the mobile robot chassis, the screw-driven slide module and the high-torque servo motor, so as to drive the mobile robot chassis, the screw-driven slide module and the high-torque servo motor to collaboratively complete the grounding switch opening and closing operations.

6. A grounding opening and closing method based on flexible tightening, applied to the grounding opening and closing device based on flexible tightening as claimed in any one of claims 1 to 5, characterized in that: include: Step S1, collecting environmental perception data, equipment status data and damping parameters of polyurethane vibration damping blocks, the environmental perception data includes obstacle three-dimensional point cloud data and visual coordinate data, and the equipment status data includes hydraulic control parameters of the lifting platform, grounding gate height data and screw drive slide module displacement data; Step S2, generating an obstacle avoidance path for the mobile robot chassis based on the obstacle three-dimensional point cloud data and the visual coordinate data, encoding the obstacle avoidance path for the mobile robot chassis through a genetic algorithm, and generating the coordinates of the target high-voltage switch cabinet; Step S3, receiving the coordinates of the target high-voltage switch cabinet, combining the hydraulic control parameters of the lifting platform of the data acquisition module and the height data of the grounding switch, optimizing the hydraulic control parameters of the lifting platform through the genetic algorithm, driving the screw to drive the slide module to move vertically to the target height, the slide module is linearly displaced by the screw drive, and the displacement state of the slide module is fed back in real time by the displacement data of the screw driving the slide module, and sending a height adjustment signal to the vibration suppression module after positioning is completed; Step S4, collecting vibration spectrum data of the end of the grounding switch turning tooling according to the height adjustment signal, dynamically optimizing the damping parameters of the polyurethane vibration damping block through a genetic algorithm, and collecting the posture data of the end of the tooling through a posture sensor; Step S5, integrating the path error of the path planning module, the height deviation of the height adaptation module, the spectrum data of the vibration suppression module and the angle deviation of the alignment control module, constructing a target optimization model, generating an instruction set through the target optimization model, and distributing the instructions to the chassis, slide module and servo motor of the driving mobile robot to collaboratively complete the grounding switch opening and closing operation.

7. The grounding opening and closing method based on flexible tightening according to claim 6 is characterized in that: The step S2 comprises: Based on the three-dimensional point cloud data and visual coordinate data of the obstacle collected by the data acquisition module, the obstacle contour information is generated by the laser radar, and matched with the high-voltage switchgear positioning mark in the visual feature identification data, and the path planning fitness function is constructed to evaluate the feasibility of the obstacle avoidance path; Generate path nodes based on the obstacle avoidance path, perform chromosome crossover and mutation operations on the steering angles, moving speeds, and safe obstacle avoidance distances between the path nodes through a genetic algorithm, and generate a candidate path set; The candidate path solution set that meets the preset positioning accuracy threshold is output to the collaborative decision-making module as the path input parameter of the multi-objective optimization model in the collaborative decision-making module.

8. The grounding opening and closing method based on flexible tightening according to claim 7 is characterized in that: The step S3 comprises: Based on the hydraulic control parameters of the lifting platform and the grounding gate height data, a multi-dimensional optimization target including the height error of the lifting platform, the displacement speed of the sliding table module and the load current of the servo motor is constructed by a genetic algorithm to calculate the height adaptive fitness value; According to the preset highly adaptive fitness value threshold, the candidate solution set of hydraulic control parameters is screened, and the candidate parameters with the top 10% fitness values ​​are retained using an elite retention strategy; The optimized target height value is synchronized to the alignment control module through the genetic algorithm, and is used as a height constraint condition for the alignment control module to generate a rotation angle sequence of the grounding gate screwing tooling.

9. The grounding opening and closing method based on flexible tightening according to claim 8 is characterized in that: The step S4 comprises: Based on the posture data of the tooling end collected by the posture sensor and the damping parameters of the data acquisition module, the vibration spectrum data of the grounding switch twisting tooling end is collected by the acceleration sensor, and the vibration energy distribution characteristics in the 0-1kHz frequency band are extracted as the population initialization parameters of the genetic algorithm; By using the genetic algorithm, the compression deformation and vibration attenuation rate of the polyurethane vibration damping block are used as vibration suppression fitness evaluation indicators to screen candidate solutions for damping parameters; The damping parameter optimization data that meets the vibration suppression fitness evaluation index is fed back to the collaborative decision-making module, triggering the collaborative decision-making module to dynamically correct the opening and closing operation instruction set based on the multi-objective optimization model.

10. The grounding opening and closing method based on flexible tightening according to claim 9, characterized in that: The step S5 comprises: Based on the positioning error of the path planning module, the height deviation of the height adaptive module, the spectrum data of the vibration suppression module and the angle deviation of the alignment control module, a multi-objective optimization model is constructed to perform normalized weighted processing on the positioning error, height deviation, spectrum data and angle deviation; Generate a global optimal operation instruction set including a high-torque servo motor speed instruction, a slide module displacement instruction and a lifting platform hydraulic control instruction based on the multi-objective optimization model through the non-dominated sorting genetic algorithm of the collaborative decision-making module; The global optimal operation instruction set is distributed to the mobile robot chassis, the screw-driven slide module and the high-torque servo motor, so as to drive the mobile robot chassis, the screw-driven slide module and the high-torque servo motor to collaboratively complete the grounding switch opening and closing operations.

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