Method, device and medium for assisting robot operation and maintenance based on power operation and inspection scene multifunctional exoskeleton

Abstract

The application discloses a power operation and inspection scene multifunctional exoskeleton assisted robot operation and maintenance method, system, device and medium, and belongs to the technical field of power operation and maintenance, which comprises the following steps: collecting preset state quantities to generate review tasks; calculating electric field gradient parameters and magnetic field gradient parameters, and constructing risk indexes; determining action levels, and controlling active insulation execution units and action constraint execution units; collecting review data, and continuously controlling the active insulation execution units and the action constraint execution units according to the action levels during the execution of the mobile collection; and recording the review data and the data collected by the fixed inspection terminal to the same operation and maintenance record. The application improves the intrinsic safety level, abnormal disposal efficiency and operation and maintenance data reliability of wind power operation and inspection work, and realizes the intelligent transformation and upgrading of new energy station.

Description

Technical Field

[0001] This invention relates to the field of power operation and maintenance technology, specifically to operation and maintenance methods, equipment, and media for a multifunctional exoskeleton-assisted robot based on power operation and maintenance scenarios. Background Technology

[0002] In the daily operation and maintenance of wind turbine generators, the nacelle and tower are located in confined spaces with complex electromagnetic environments. High-voltage electrical equipment such as converter cabinets, control cabinets, and ring main units are densely distributed, requiring maintenance personnel to frequently approach energized components for status checks and troubleshooting. While existing fixed online monitoring systems can collect real-time data on cabinet temperature, humidity, door status, and energized indicators and trigger alarms, they cannot confirm the cause of alarms on-site. This still relies on maintenance personnel carrying handheld instruments to manually verify the data in high-risk areas, which is not only inefficient but also poses serious safety hazards such as electric shock and accidental entry into energized areas. In recent years, exoskeleton robot technology has been gradually applied in industrial handling and assembly assistance. However, existing solutions generally adopt fixed speed, torque limits or single distance threshold control, which cannot adapt to the special working conditions of wind power operation and maintenance scenarios, such as diverse equipment types, dynamic changes in electromagnetic fields, and susceptibility of optical ranging sensors to metal reflection and dust interference. At the same time, data is fragmented between fixed monitoring and mobile inspection, lacking a closed-loop collaborative mechanism from "alarm triggering" to "on-site verification" and then to "data comparison". This results in scattered operation and maintenance records, insufficient reliability of anomaly diagnosis, and difficulty in meeting the urgent needs of intelligent operation and maintenance for inherent safety and data fusion. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention provides an operation and maintenance method for a multifunctional exoskeleton-assisted robot based on power operation and maintenance scenarios.

[0004] Therefore, the technical problem solved by this invention is: from the perspective of overall system collaboration, this invention aims to solve the problem of safety control and intelligent verification of exoskeleton robots under the coupling conditions of multiple devices, multiple parameters, and multiple risk sources in the operation and maintenance scenario of wind turbine units.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a maintenance method based on a multifunctional exoskeleton-assisted robot for power operation and maintenance scenarios, comprising, Before the operation and maintenance begins, fixed inspection terminals are deployed at multiple power operation and maintenance targets and preset status quantities are continuously collected. When the preset status quantities meet the triggering rules, a review task is generated. During the operation and maintenance process, the exoskeleton-assisted robot collects electric field strength data, magnetic field data, and proximity distance data between the end of the exoskeleton-assisted robot and the corresponding charged body of the operation and maintenance target. The electric field gradient parameter and magnetic field gradient parameter, which characterize the rate of change of field strength, are calculated based on the electric field strength data and the magnetic field data, respectively. A risk index is constructed based on the electric field strength, electric field gradient parameter, magnetic field strength, magnetic field gradient parameter, and proximity distance. The risk index is compared with a preset threshold to determine the action level, and the active insulation execution unit and the action constraint execution unit are controlled according to the action level. After the exoskeleton-assisted robot reaches the inspection object corresponding to the verification task, it binds the verification task based on the device tag number and performs mobile acquisition to obtain verification data. During the execution of the mobile acquisition, it continuously controls the active insulation execution unit and the action constraint execution unit according to the action level, and associates and records the verification data with the data collected by the fixed inspection terminal into the same maintenance record.

[0006] As a preferred embodiment of the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario described in this invention, the electric field gradient parameter is calculated by dividing the difference in electric field intensity between two adjacent sampling times by the sampling period to characterize the rate of change of electric field intensity with time, and the magnetic field gradient parameter is calculated by dividing the difference in magnetic field intensity between two adjacent sampling times by the sampling period to characterize the rate of change of magnetic field intensity with time.

[0007] As a preferred embodiment of the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario described in this invention, the risk index construction includes converting electric field strength, electric field gradient parameters, magnetic field strength, magnetic field gradient parameters, and proximity distance into risk scores according to a segmented threshold mapping rule. Each risk score is segmented by a warning threshold and a danger threshold. When the corresponding quantity is less than the warning threshold, the risk score takes the first threshold; when the corresponding quantity is greater than or equal to the danger threshold, the risk score takes the second threshold; when the corresponding quantity is between the warning threshold and the danger threshold, the risk score is determined by linear interpolation. The risk index is obtained by weighted summation of each risk score according to non-negative weights, and the non-negative weights are stored in the controller of the exoskeleton-assisted robot.

[0008] As a preferred embodiment of the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario described in this invention, the action level includes L0 level, L1 level, and L2 level. The action level is determined based on the comparison relationship between the risk index and a first threshold and a second threshold, wherein the first threshold is less than the second threshold, and when the risk index is less than the first threshold, it is determined to be L0 level; when the risk index is greater than or equal to the first threshold and less than the second threshold, it is determined to be L1 level; and when the risk index is greater than or equal to the second threshold, it is determined to be L2 level.

[0009] As a preferred embodiment of the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario described in this invention, wherein: when the action level is determined to be L1 level, the action constraint execution unit implements an upper limit limit on the end-effector motion speed and an upper limit limit on the joint output torque of the exoskeleton-assisted robot. The upper limit limit on the end-effector motion speed is achieved by reducing the default upper limit on the end-effector motion speed by a speed limiting coefficient to obtain the current upper limit on the end-effector motion speed. The upper limit limit on the joint output torque is achieved by reducing the default upper limit on the joint output torque by a torque limiting coefficient to obtain the current upper limit on the joint output torque. Both the speed limiting coefficient and the torque limiting coefficient are preset parameters that are greater than zero and less than one.

[0010] When the action level is determined to be L2, the active insulation execution unit is controlled to perform an insulation deployment operation, and the action constraint execution unit is controlled to generate a hard restricted space and perform boundary projection processing on the end-effector pose command so that the end-effector pose command is restricted outside the hard restricted space. The hard restricted space is constructed according to a distance threshold as a restricted area extending outward from the outer surface of the charged body, and the boundary projection processing replaces the target point of the end-effector pose command within the restricted area with the nearest point on the boundary of the restricted area. At L2 level, the action of extending into the cabinet is locked and the high torque output action is locked.

[0011] As a preferred embodiment of the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario described in this invention, when the proximity distance data output by the ranging sensor meets the invalidation flag and reaches a preset number of consecutive times, the proximity distance data is determined to be low-confidence data and is not used for the risk score conversion. Instead, an alternative distance value obtained by back-calculation from the electric field strength is used to participate in the risk score conversion of the proximity distance. The alternative distance value establishes an inverse proportional relationship between the electric field strength and the distance through calibration parameters and introduces a zero-prevention parameter for numerical stabilization.

[0012] As a preferred embodiment of the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario described in this invention, the verification task generated by the fixed inspection terminal includes the device tag number, trigger time, abnormal quantity name, and abnormal type. The abnormal type includes limit-over-threshold trigger type, slope trigger type, and state change trigger type. The limit-over-threshold trigger type is when the abnormal quantity continuously exceeds a preset upper limit threshold and continues to reach a preset duration. The slope trigger type is when the change in the abnormal quantity within a unit time continuously exceeds a preset slope threshold and continues to reach a preset duration. The state change trigger type is when a discrete state quantity switches from a first preset state to a second preset state and remains there for a preset duration.

[0013] As a preferred embodiment of the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario described in this invention, the exoskeleton-assisted robot, after arriving at the operation and maintenance object corresponding to the review task, binds the review task with the mobile acquisition result based on the device tag number, collects mobile side review data and generates a mobile side record, and simultaneously reads fixed side data corresponding to the review task from the fixed inspection terminal and generates a fixed side record. Based on the mobile side review data and the fixed side data, the difference is calculated and the absolute value of the difference is compared with a preset consistency threshold to complete the consistency check. The device tag number, the trigger time, the anomaly name, the anomaly type, the mobile side record, the fixed side record, the difference, and the consistency check result are stored as fields of the same operation and maintenance record.

[0014] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario.

[0015] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario.

[0016] The beneficial effects of this invention are as follows: Compared with existing technologies, this invention achieves multi-dimensional technological leaps and efficiency improvements. In terms of safety protection, through a five-dimensional physical field fusion and hierarchical decision-making mechanism, the robot can make real-time, continuous, and quantifiable risk assessments of the dynamically changing electromagnetic environment. When the ranging sensor fails, it automatically switches to an electric field-backward fault-tolerant mode, completely eliminating the protection vacuum caused by the failure of a single sensor. Simultaneously, based on the triangular mesh equidistant expansion of the restricted area boundary projection method, it physically prevents the end effector from intruding into the danger zone at the pose command level, integrating the concept of inherent safety into the control layer. In terms of operational efficiency, the parameter indexing and task binding mechanism with the device tag number as the primary key allows the exoskeleton robot to adapt to the differentiated protection requirements of different cabinets without human intervention, significantly shortening the operation preparation time. The collaborative operation mode of fixed terminal trigger judgment and exoskeleton movement verification reduces the time of a single anomaly verification to the minute level. Furthermore, through precise mapping of multiple types of sensors, such as point temperature, door magnetic sensors, and photoelectric readers, to anomaly names, it ensures that mobile and fixed-side data originate from the same physical measurement point. The introduction of difference calculation and consistency verification thresholds gives the verification conclusions quantifiable credibility, completely changing the previous situation where alarm records and on-site conditions were separate entities. In terms of data value, this invention encapsulates dozens of fields, including device tag number, trigger time, fixed-side data, mobile-side data, difference quantity, consistency result, action level timestamp, sensor confidence flag, fault tolerance mode activation flag, and prohibited boundary parameters, into a complete operation and maintenance record and stores it uniformly in the database. This provides high-confidence comparison samples for equipment status trend analysis and lays a solid data foundation for subsequent operation and maintenance strategy optimization and even exoskeleton control parameter self-learning, effectively supporting the transformation and upgrading of new energy power plants towards a less-manned, intelligent operation and maintenance model. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the overall operation and maintenance method of a multifunctional exoskeleton-assisted robot for power operation and maintenance scenarios, as provided in one embodiment of the present invention. Detailed Implementation

[0019] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0020] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a maintenance method based on a multifunctional exoskeleton-assisted robot for power operation and maintenance scenarios, including: S100: Before the operation and maintenance begins, fixed inspection terminals are deployed at multiple power operation and maintenance targets and preset status quantities are continuously collected. When the preset status quantities meet the triggering rules, a review task is generated. During the operation and maintenance process, the exoskeleton-assisted robot collects electric field strength data, magnetic field data, and proximity distance data between the end of the exoskeleton-assisted robot and the corresponding charged body of the object being inspected.

[0021] S200: Calculate the electric field gradient parameter and magnetic field gradient parameter to characterize the rate of change of field strength based on the electric field strength data and the magnetic field data, respectively, and construct a risk index based on the electric field strength, electric field gradient parameter, magnetic field strength, magnetic field gradient parameter, and proximity distance.

[0022] S300: The risk index is compared with a preset threshold to determine the action level, and the active insulation execution unit and the action constraint execution unit are controlled according to the action level.

[0023] S400: After the exoskeleton-assisted robot reaches the inspection object corresponding to the verification task, it binds the verification task based on the device tag number and performs mobile acquisition to obtain verification data. During the execution of the mobile acquisition, it continuously controls the active insulation execution unit and the action constraint execution unit according to the action level, and associates and records the verification data with the data collected by the fixed inspection terminal into the same maintenance record.

[0024] Example 2, refer to Figure 1 As one embodiment of the present invention, based on the previous embodiment, a maintenance method for a multifunctional exoskeleton-assisted robot for power operation and maintenance scenarios is provided, including: S100: Before the operation and maintenance begins, fixed inspection terminals are deployed at multiple power operation and maintenance targets and preset status quantities are continuously collected. When the preset status quantities meet the triggering rules, a review task is generated. During the operation and maintenance process, the exoskeleton-assisted robot collects electric field strength data, magnetic field data, and proximity distance data between the end of the exoskeleton-assisted robot and the corresponding charged body of the object being inspected.

[0025] It should be noted that in this embodiment, the preset state quantities are defined as 6 types of fields, including: cabinet internal temperature value, cabinet internal relative humidity value, cabinet door open / closed state value, equipment operating vibration root mean square value, live indication state value, and fan or electrical equipment alarm state value.

[0026] The rules for the fixed inspection terminal to judge preset status quantities are used to determine whether to generate a review task.

[0027] It should also be noted that in this embodiment, the triggering rule consists of three types of rules, and a continuous judgment window is specified for each type of rule, including the limit-crossing triggering rule, the slope triggering rule, and the state change triggering rule; The over-limit triggering rule is that a certain continuous state variable is always greater than the upper limit threshold during the continuous duration. The slope triggering rule is that the change of a certain continuous state variable within a unit time is always greater than the slope threshold during the continuous duration. The state change trigger rule is that a certain discrete state variable switches from a first state to a second state and maintains the second state for a continuous duration.

[0028] The duration, slope threshold, and upper limit threshold are preset by the parameter table of the corresponding operation and maintenance object and are not specifically limited here.

[0029] S200: Calculate the electric field gradient parameter and magnetic field gradient parameter to characterize the rate of change of field strength based on the electric field strength data and the magnetic field data, respectively, and construct a risk index based on the electric field strength, electric field gradient parameter, magnetic field strength, magnetic field gradient parameter, and proximity distance.

[0030] S201: The electric field strength data is output by an electric field sensor installed at the end of the exoskeleton assistive robot according to a preset sampling period. The magnetic field data is output by a three-axis magnetic field sensor installed at the end of the exoskeleton assistive robot, which outputs magnetic field components in three directions and converts them into magnetic field strength. The approach distance data is output by a ranging sensor installed at the end of the exoskeleton assistive robot, and the approach distance is the closest distance from the end of the exoskeleton assistive robot to the outer surface of the charged body.

[0031] S202: The electric field gradient parameter is calculated by dividing the difference in electric field intensity between two adjacent sampling times by the sampling period to characterize the rate of change of electric field intensity with time. The magnetic field gradient parameter is calculated by dividing the difference in magnetic field intensity between two adjacent sampling times by the sampling period to characterize the rate of change of magnetic field intensity with time.

[0032] The electric field gradient parameters are determined according to the following formula: in, For a moment The electric field change rate parameter is used to characterize how fast the electric field strength changes with time. For a moment The electric field strength obtained by the electric field sensor. For a moment The electric field strength. The sampling period for the exoskeleton end sensor is 0.05s in this embodiment. This is an absolute value operator used to take the absolute value of the difference, avoiding the influence of positive or negative directions on the determination of the rate of change amplitude. This is a time variable, representing the current sampling time.

[0033] The magnetic field gradient parameter is calculated by dividing the difference in magnetic field strength between two adjacent sampling times by the sampling period to characterize the rate of change of magnetic field strength over time.

[0034] S203: The construction of the risk index includes converting electric field strength, electric field gradient parameters, magnetic field strength, magnetic field gradient parameters, and proximity distance into risk scores according to a segmented threshold mapping rule. Each risk score is segmented by a warning threshold and a danger threshold. When the corresponding quantity is less than the warning threshold, the risk score takes the first threshold. When the corresponding quantity is greater than or equal to the danger threshold, the risk score takes the second threshold. When the corresponding quantity is between the warning threshold and the danger threshold, the risk score is determined by linear interpolation. The risk index is obtained by weighting and summing each risk score according to non-negative weights, and the non-negative weights are stored in the controller of the exoskeleton-assisted robot.

[0035] S204: When the proximity distance data output by the ranging sensor meets the invalidation flag and reaches the preset number of times, the proximity distance data is determined to be low confidence data and is not used for the risk score conversion. Instead, an alternative distance value obtained by back-calculation of the electric field strength is used to participate in the risk score conversion of the proximity distance. The alternative distance value establishes an inverse proportional relationship between the electric field strength and the distance through calibration parameters and introduces a zero-prevention parameter for numerical stabilization.

[0036] The low-confidence data includes measurement data whose validity cannot meet the requirements due to sensor malfunction, signal obstruction, or environmental interference during the acquisition process.

[0037] It should be noted that the ranging sensor outputs the distance value and a valid flag bit each time it takes a sample.

[0038] When the valid flag is invalid multiple times consecutively, and the number of consecutive invalidations reaches a preset threshold, the controller classifies the current distance data as low-confidence data. Data classified as low-confidence does not participate in the risk score mapping calculation; instead, an alternative distance value is generated by the alternative distance model and used in subsequent risk assessments.

[0039] In addition, the preset threshold for the number of attempts is determined based on historical data and expert experience, and no specific limit is set here.

[0040] It should be noted that the alternative distance value is determined according to the following formula: in, For a moment The alternative distance value is derived from the electric field strength when the ranging sensor determines that the confidence level is low, and is used to participate in the segmented threshold mapping of the approach distance. The calibration parameters (scale coefficients) are obtained from calibration experiments and written into the parameter table to match the order of magnitude of different sensors and different charged body environments. To prevent zero parameters, a positive constant is used to avoid... A value that is too small will cause the denominator to approach zero, resulting in numerical instability. In this example, a value of 10 is used. . is an exponential parameter, a positive constant, used to describe the degree of nonlinearity in the inverse proportional relationship between electric field strength and distance; in this example, it is taken as 1.2. For exponentiation, it represents the quantity within the parentheses. Power of 1. This is a time variable, representing the current sampling time.

[0041] S300: The risk index is compared with a preset threshold to determine the action level, and the active insulation execution unit and the action constraint execution unit are controlled according to the action level.

[0042] S301: The action level includes L0 level, L1 level, and L2 level. The action level is determined based on the comparison relationship between the risk indicator and a first threshold and a second threshold. The first threshold is less than the second threshold. When the risk indicator is less than the first threshold, it is determined to be L0 level. When the risk indicator is greater than or equal to the first threshold and less than the second threshold, it is determined to be L1 level. When the risk indicator is greater than or equal to the second threshold, it is determined to be L2 level.

[0043] S302: When the level is determined to be L1, the motion constraint execution unit implements an upper limit limit on the end-effector motion speed and an upper limit limit on the joint output torque of the exoskeleton-assisted robot. The upper limit limit on the end-effector motion speed is achieved by reducing the default upper limit on the end-effector motion speed by a speed limiting coefficient and using it as the current upper limit on the end-effector motion speed. The upper limit limit on the joint output torque is achieved by reducing the default upper limit on the joint output torque by a torque limiting coefficient and using it as the current upper limit on the joint output torque. Both the speed limiting coefficient and the torque limiting coefficient are preset parameters that are greater than zero and less than one.

[0044] When the level is determined to be L2, the active insulation execution unit is controlled to perform an insulation deployment operation, and the motion constraint execution unit is controlled to generate a hard restricted space and perform boundary projection processing on the end-effector pose command so that the end-effector pose command is restricted outside the hard restricted space. The hard restricted space is constructed according to a distance threshold as a restricted area extending outward from the outer surface of the charged body, and the boundary projection processing replaces the target point of the end-effector pose command within the restricted area with the nearest point on the boundary of the restricted area. At the L2 level, the action of extending into the cabinet is locked and the high torque output action is locked.

[0045] S400: After the exoskeleton-assisted robot reaches the inspection object corresponding to the verification task, it binds the verification task based on the device tag number and performs mobile acquisition to obtain verification data. During the execution of the mobile acquisition, it continuously controls the active insulation execution unit and the action constraint execution unit according to the action level, and associates and records the verification data with the data collected by the fixed inspection terminal into the same maintenance record.

[0046] S401: The review task generated by the fixed inspection terminal includes the device tag number, trigger time, abnormal quantity name, and abnormal type. The abnormal type includes limit-over trigger type, slope trigger type, and state change trigger type. The limit-over trigger type is when the abnormal quantity continuously exceeds the preset upper limit threshold and continues to reach the preset duration. The slope trigger type is when the change of the abnormal quantity in a unit of time continuously exceeds the preset slope threshold and continues to reach the preset duration. The state change trigger type is when the discrete state quantity switches from the first preset state to the second preset state and continues to remain there for the preset duration.

[0047] It should be noted that the warning threshold, danger threshold, action classification threshold, duration, slope threshold, and consistency threshold are all stored in the controller's parameter table. The parameter table is loaded according to the device tag number.

[0048] It should also be noted that the parameter table is determined by the operation and maintenance specifications, the equipment's rated parameters, and the on-site calibration values.

[0049] S402: After the exoskeleton-assisted robot reaches the inspection object corresponding to the device tag number, it binds the verification task with the mobile acquisition result using the device tag number, collects mobile side verification data and generates a mobile side record, and simultaneously reads the fixed side data corresponding to the verification task from the fixed inspection terminal and generates a fixed side record. Based on the mobile side verification data and the fixed side data, it calculates the difference and compares the absolute value of the difference with a preset consistency threshold to complete the consistency check. The device tag number, the trigger time, the anomaly name, the anomaly type, the mobile side record, the fixed side record, the difference, and the consistency check result are stored as fields of the same maintenance record.

[0050] Example 3 is an embodiment of the present invention, which provides an operation and maintenance method for a multifunctional exoskeleton-assisted robot based on power operation and maintenance scenarios. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0051] This embodiment discloses a maintenance method based on a multifunctional exoskeleton-assisted robot for power operation and maintenance scenarios, applicable to the operation and maintenance of power equipment inside the nacelle and tower of wind turbines. The maintenance objects include converter cabinets, control cabinets, and ring main unit cabinets inside the nacelle, with equipment tag numbers of converter cabinet ID=FC-01, control cabinet ID=CC-01, and ring main unit ID=RMU-01, respectively. The maintenance system consists of a fixed inspection terminal, an exoskeleton-assisted robot, and a maintenance record storage device. Fixed inspection terminals are installed at preset positions on the outside of cabinets FC-01, CC-01, and RMU-01 and maintain communication with the maintenance record storage device. The exoskeleton-assisted robot includes an end effector sensor assembly, an active insulating actuator, a motion constraint actuator, and a controller. The end effector sensor assembly includes an electric field sensor, a triaxial magnetic field sensor, a ranging sensor, a point temperature sensor, a door magnetic reading module, and an indicator light photoelectric reading module. The active insulating actuator is a deployable insulating sleeve. The motion constraint actuator is used to limit the upper limit of the end effector's movement speed and the upper limit of the joint output torque, and is used to perform boundary projection processing of the end effector's pose command based on the restricted area.

[0052] Before operation and maintenance begins, the fixed terminal trigger parameter table and the exoskeleton safety control parameter table are stored in the exoskeleton-assisted robot controller. The parameter tables are managed using the device tag number as an index. Before the exoskeleton-assisted robot enters the working range corresponding to a certain device tag number, the parameter table corresponding to that device tag number is loaded and remains effective within that working range. The fixed terminal trigger parameter table includes fields such as state variable name, upper limit threshold, slope threshold, duration of exceeding the limit, duration of slope, and duration of state retention. The exoskeleton safety control parameter table includes fields such as exoskeleton sampling period, electric field warning threshold, electric field danger threshold, electric field change rate warning threshold, electric field change rate danger threshold, magnetic field warning threshold, magnetic field danger threshold, magnetic field change rate warning threshold, magnetic field change rate danger threshold, distance warning threshold, distance danger threshold, weight set, action classification threshold set, L1 velocity limiting coefficient, L1 torque limiting coefficient, threshold for consecutive invalid ranging times, reverse distance calibration parameters, zero-prevention parameters, and consistency verification threshold set.

[0053] In this embodiment, the fixed terminal collects preset status quantities at a 1-second sampling period and caches the most recent 15-minute time series of status quantities locally. FC-01 and CC-01 enable cabinet temperature, cabinet humidity, cabinet door open / close status, and equipment alarm status; RMU-01 enables cabinet temperature, cabinet door open / close status, and power-on indication status.

[0054] It should be noted that the corresponding thresholds are set as follows: FC-01: upper temperature limit 85℃, temperature rise slope threshold 2℃ / min, upper humidity limit 85%RH, duration of exceeding the limit 120 s, duration of slope 60 s, duration of door open 30 s, and duration of alarm 5 s; CC-01: upper temperature limit 75℃, temperature rise slope threshold 2℃ / min, upper humidity limit 85%RH, duration of exceeding the limit 120 s, duration of slope 60 s, duration of door open 30 s, and duration of alarm 5 s; RMU-01: upper temperature limit 70℃, temperature rise slope threshold 1℃ / min, duration of exceeding the limit 120 s, duration of slope 60 s, duration of door open 30 s, and duration of power indicator light 10 s.

[0055] Furthermore, the fixed terminal performs limit-over-limit trigger judgment and slope trigger judgment on continuous state variables, and performs state change trigger judgment on discrete state variables.

[0056] The over-limit trigger judgment is that the continuous state variable is greater than the corresponding upper limit threshold in each sample during the over-limit duration.

[0057] The slope trigger judgment is that the change of a continuous state quantity, calculated per unit time, during the slope duration is greater than the corresponding slope threshold each time.

[0058] The state change trigger is determined when a discrete state variable switches from the first state to the second state and remains in the second state for the duration specified in the state change trigger.

[0059] Furthermore, when any trigger condition is met, the fixed inspection terminal generates a review task and sends it to the exoskeleton-assisted robot. The review task fields include the device tag number ID, trigger time t0, anomaly name, anomaly type, and fixed-side data x. fixed Among them, the fixed-side data x fixed Defined as the last sampled value within the trigger window of a continuous state variable, and the second state value held within the trigger window of a discrete state variable.

[0060] In this embodiment, the fixed inspection terminal generates three verification tasks sequentially: First, FC-01 detects that the cabinet temperature is greater than 85°C in every sampling within 120 seconds at t0=10:12:30, forming verification task Q1. The abnormal quantity name is cabinet temperature, the abnormality type is over-limit trigger, and the fixed side data is x. fixed =86℃; Secondly, RMU-01 detected that the live indicator changed from 0 to 1 at t0=10:25:10 and remained at 1 for 10 s, forming review task Q2. The abnormal quantity name is live indicator state, the abnormality type is state change trigger, and the fixed side data x fixed=1; Thirdly, CC-01 detected that the cabinet door status changed from 0 to 1 at t0=10:40:00 and remained there for 30 seconds, forming review task Q3. The abnormal quantity name is cabinet door open / closed status, the abnormal type is status change triggered, and the fixed side data is x. fixed =1.

[0061] The exoskeleton-assisted robot continuously collects electric field strength E(t) and magnetic field data throughout the entire process of walking, approaching, and verifying data acquisition, and converts this data into magnetic field strength B(t) and approach distance d(t). The approach distance d(t) is defined as the Euclidean distance from the origin of the end-effector coordinate system to the nearest point on the outer surface of the charged body. The exoskeleton sampling period is set to 0.05 s, and the controller calculates the electric field change rate parameter according to this sampling period. Determined by the following formula: in, Let be the electric field strength at time t. The value is 0.05 s; the magnetic field change rate parameter is obtained by using the same adjacent sampling difference method as the electric field change rate. Calculated.

[0062] Furthermore, the controller performs segmented threshold mapping for electric field strength, electric field change rate, magnetic field strength, magnetic field change rate, and proximity distance. Even further, when the input is less than the corresponding warning threshold, it is mapped to 0; when the input is greater than or equal to the corresponding danger threshold, it is mapped to 1; and when the input is between the warning threshold and the danger threshold, it is mapped to a value between 0 and 1 using linear interpolation.

[0063] It should be noted that the thresholds corresponding to the five types of input quantities are uniformly configured as electric field early warning thresholds. Electric field danger threshold Electric field change rate warning threshold Electric field change rate danger threshold 800 Magnetic field warning threshold Magnetic field hazard threshold Magnetic field change rate warning threshold Danger threshold for the rate of change of magnetic field Distance warning threshold: 0.80 m; Distance danger threshold: 0.30 m.

[0064] It should also be noted that the controller weights the five risk scores together. , The risk index is obtained by weighted summation, and then the risk index is compared with the action classification threshold. The action level is determined by comparison: a risk index less than 0.35 is classified as L0, a risk index greater than or equal to 0.35 and less than 0.70 is classified as L1, and a risk index greater than or equal to 0.70 is classified as L2.

[0065] When the action level is L1, the action constraint execution unit will multiply the default end velocity limit by its preset weight. The current end-effector velocity limit is then multiplied by its preset weight, which is used as the current end-effector velocity limit. This serves as the current upper limit of joint torque. When the motion level is... At that time, the controller drives the unfoldable insulating sleeve to unfold, and the motion constraint execution unit constructs a restricted area and performs boundary projection processing on the end pose command. At the same time, it locks the action of extending into the cabinet and locks the high torque output action.

[0066] It should be noted that the restricted area is constructed with a distance of 0.30 m as the restricted distance threshold. The exoskeleton assists the robot in establishing the working coordinate system and reading the geometric model data of the charged outer surface. The geometric model data is a set of triangular facets obtained by triangulating the point cloud of the charged outer surface.

[0067] Simultaneously, the controller extends each triangular facet outward at equal intervals along the normal direction of the triangular facet to obtain the boundary mesh of the restricted area, and uses this boundary mesh as the boundary representation of the restricted area. When the target point corresponding to the end effector pose command is located within the restricted area, the controller searches for the boundary point with the smallest Euclidean distance to the target point on the restricted area boundary mesh, uses this boundary point to replace the target point, and outputs the replaced end effector pose command; when the target point is located outside the restricted area, the end effector pose command remains unchanged.

[0068] Furthermore, the ranging sensor outputs a distance value and a valid flag (valid) for each sample. A valid value of 1 indicates validity, and a valid value of 0 indicates invalidity. The controller continuously counts the valid values, and a check is performed when the valid value is consecutively 0. At this point, the ranging data is classified as low-confidence data, and this data is not used for proximity distance segmentation threshold mapping. Instead, electric field back-calculation is used to replace the distance value. The alternative distance value for proximity distance segmentation threshold mapping is determined by the following formula: in, K E For calibration parameters. During the calibration phase, keep the operating state of the charged body unchanged, and select a distance of 0.20 mm from the outer surface of the charged body. Five calibration points were established. The exoskeleton end was fixed at each calibration point, and the average electric field strength was collected at each point. Five sets of average electric field strength and corresponding distance data were obtained. The above data was input into the calibration module of the controller and... Pick Pick Under the condition of least squares fitting, K is obtained. E And write it into the exoskeleton safety control parameter table.

[0069] After receiving the review task, the exoskeleton-assisted robot arrives at the corresponding device tag number's work area according to the task queue order and completes the task binding. The binding method is to compare the device tag number ID in the review task field with the current work object ID to confirm that the binding is successful.

[0070] During mobile data acquisition, the exoskeleton-assisted robot continuously controls the active insulation actuator and motion constraint actuator based on the motion level.

[0071] During the process of reaching RMU-01 to perform the Q2 verification task, when the terminal approached the area near the door of the ring network cabinet, the approach distance entered the range of 0.80 m to 0.30 m and the electric field intensity entered the range of 0.80 m to 0.30 m. By 2000 If the risk index in the zone reaches 0.35 or higher but less than 0.70, the action level will be switched to L1.

[0072] The end continues to approach until the approach distance is less than 0.30 m and the electric field strength reaches or exceeds [a certain value]. When the risk index reaches or exceeds 0.70, the action level is switched to L2 and insulation deployment and restricted area boundary projection processing are performed.

[0073] The data fields acquired by the mobile acquisition system must be consistent with the anomaly names in the review task, and the mobile acquisition measurement points must be consistent with the fixed terminal measurement points: when the anomaly name is cabinet temperature, the mobile acquisition system uses a point temperature sensor to read the temperature at the same measurement point location as the fixed terminal and uses it as the mobile side data. mobile .

[0074] When the abnormal quantity name is cabinet door open / closed status, the mobile acquisition uses the door magnetic reader module to read the cabinet door status and use it as x. mobile .

[0075] When the abnormal quantity name is "energized indication state", the mobile acquisition uses the indicator light photoelectric reading module to read the indication state and use it as x. mobile The controller processes the mobile side data x. mobile Compared with fixed-side data x fixed Calculate the difference mobile fixed The absolute value of the difference is compared with the consistency verification threshold to complete the consistency verification, wherein the cabinet temperature is used as... The cabinet door opening and closing status is determined by door The live indication status adopts live When the absolute value of the difference is less than or equal to the corresponding consistency verification threshold, the consistency verification result is recorded as 1; when the absolute value of the difference is greater than the corresponding consistency verification threshold, the consistency verification result is recorded as 0.

[0076] In this embodiment, the temperature of the moving side of Q1 is... mobile Fixed side temperature fixed , The consistency check result is 1. The Q2 mobile side energization indicator x... mobile Fixed-side live indicator x fixed The consistency check result is 1. The status of the Q3 moving side cabinet door is x. mobile Fixed side cabinet door status x fixed The consistency check result is 1.

[0077] Finally, the controller will input the device tag number ID, trigger time t0, abnormal quantity name, abnormal type, and fixed side data x. fixed Mobile side data x mobile Difference The consistency verification result, exoskeleton sampling cycle, action classification thresholds R1 and R2, action level timestamp sequence during mobile acquisition, continuous statistical value of valid distance measurement flag, alternative distance activation flag, and prohibited distance threshold are stored together as the same maintenance record field, and the maintenance record is written into the maintenance record storage device.

[0078] This embodiment also provides an electronic device applicable to the operation and maintenance method of a multifunctional exoskeleton-assisted robot based on a power operation and maintenance scenario, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the operation and maintenance method of a multifunctional exoskeleton-assisted robot based on a power operation and maintenance scenario as proposed in the above embodiment.

[0079] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario proposed in the above embodiment.

[0080] The storage medium proposed in this embodiment and the operation and maintenance method for a multifunctional exoskeleton-assisted robot based on a power operation and maintenance scenario proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0081] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A maintenance method based on a multifunctional exoskeleton-assisted robot for power operation and maintenance scenarios, characterized by: include, Before the operation and maintenance begins, fixed inspection terminals are deployed at multiple power operation and maintenance targets and preset status quantities are continuously collected. When the preset status quantities meet the triggering rules, a review task is generated. During the operation and maintenance process, the exoskeleton-assisted robot collects electric field strength data, magnetic field data, and proximity distance data between the end of the exoskeleton-assisted robot and the corresponding charged body of the operation and maintenance target. The electric field gradient parameter and magnetic field gradient parameter, which characterize the rate of change of field strength, are calculated based on the electric field strength data and the magnetic field data, respectively. A risk index is constructed based on the electric field strength, electric field gradient parameter, magnetic field strength, magnetic field gradient parameter, and proximity distance. The risk index is compared with a preset threshold to determine the action level, and the active insulation execution unit and the action constraint execution unit are controlled according to the action level. After the exoskeleton-assisted robot reaches the inspection object corresponding to the verification task, it binds the verification task based on the device tag number and performs mobile acquisition to obtain verification data. During the execution of the mobile acquisition, it continuously controls the active insulation execution unit and the action constraint execution unit according to the action level, and associates and records the verification data with the data collected by the fixed inspection terminal into the same maintenance record.

2. The maintenance method for a multifunctional exoskeleton-assisted robot based on power operation and maintenance scenarios as described in claim 1, characterized in that: The electric field gradient parameter is calculated by dividing the difference in electric field intensity between two adjacent sampling times by the sampling period to characterize the rate of change of electric field intensity with time. The magnetic field gradient parameter is calculated by dividing the difference in magnetic field intensity between two adjacent sampling times by the sampling period to characterize the rate of change of magnetic field intensity with time.

3. The maintenance method for a multifunctional exoskeleton-assisted robot based on power operation and maintenance scenarios as described in claim 2, characterized in that: The construction of the risk index includes converting electric field strength, electric field gradient parameters, magnetic field strength, magnetic field gradient parameters, and proximity distance into risk scores according to a segmented threshold mapping rule. Each risk score is segmented by a warning threshold and a danger threshold. When the corresponding quantity is less than the warning threshold, the risk score is taken as the first threshold. When the corresponding quantity is greater than or equal to the danger threshold, the risk score is taken as the second threshold. When the corresponding quantity is between the warning threshold and the danger threshold, the risk score is determined by linear interpolation. The risk index is obtained by weighting and summing each risk score according to non-negative weights, and the non-negative weights are stored in the controller of the exoskeleton-assisted robot.

4. The maintenance method for a multifunctional exoskeleton-assisted robot based on power operation and maintenance scenarios as described in claim 3, characterized in that: The action level includes L0 level, L1 level, and L2 level. The action level is determined based on the comparison relationship between the risk indicator and a first threshold and a second threshold. The first threshold is less than the second threshold. When the risk indicator is less than the first threshold, it is determined to be L0 level. When the risk indicator is greater than or equal to the first threshold and less than the second threshold, it is determined to be L1 level. When the risk indicator is greater than or equal to the second threshold, it is determined to be L2 level.

5. The maintenance method for a multifunctional exoskeleton-assisted robot based on power operation and maintenance scenarios as described in claim 4, characterized in that: The determination of the action level includes, when determined to be L1 level, the action constraint execution unit implements an upper limit limit on the end-effector motion speed and an upper limit limit on the joint output torque of the exoskeleton-assisted robot. The upper limit limit on the end-effector motion speed is achieved by reducing the default upper limit on the end-effector motion speed by a speed limiting coefficient and using it as the current upper limit on the end-effector motion speed. The upper limit limit on the joint output torque is achieved by reducing the default upper limit on the joint output torque by a torque limiting coefficient and using it as the current upper limit on the joint output torque. Both the speed limiting coefficient and the torque limiting coefficient are preset parameters that are greater than zero and less than one. When the level is determined to be L2, the active insulation execution unit is controlled to perform an insulation deployment operation, and the motion constraint execution unit is controlled to generate a hard forbidden space and perform boundary projection processing on the end pose command so that the end pose command is restricted outside the hard forbidden space. The hard forbidden space is constructed as a forbidden area extending outward from the outer surface of the charged body according to a distance threshold, and the boundary projection processing is to replace the target point of the end pose command in the forbidden area with the nearest point on the boundary of the forbidden area. At L2 level, the action of extending into the cabinet is locked and the high torque output action is locked.

6. The maintenance method for a multifunctional exoskeleton-assisted robot based on power operation and maintenance scenarios as described in claim 5, characterized in that: When the proximity distance data output by the ranging sensor meets the invalidation flag and reaches the preset number of times, the proximity distance data is determined to be low confidence data and is not used for the risk score conversion. Instead, an alternative distance value obtained by back-deriving the electric field strength is used to participate in the risk score conversion of the proximity distance. The alternative distance value establishes an inverse proportional relationship between the electric field strength and the distance through calibration parameters and introduces a zero-prevention parameter for numerical stabilization.

7. The maintenance method for a multifunctional exoskeleton-assisted robot based on power operation and maintenance scenarios as described in claim 6, characterized in that: The verification task generated by the fixed inspection terminal includes the device tag number, trigger time, abnormal quantity name, and abnormal type. The abnormal type includes limit-over trigger type, slope trigger type, and state change trigger type. The limit-over trigger type is when the abnormal quantity continuously exceeds the preset upper limit threshold and continues to reach the preset duration. The slope trigger type is when the change of the abnormal quantity in a unit of time continuously exceeds the preset slope threshold and continues to reach the preset duration. The state change trigger type is when the discrete state quantity switches from the first preset state to the second preset state and continues to remain there for the preset duration.

8. The maintenance method for a multifunctional exoskeleton-assisted robot based on power operation and maintenance scenarios as described in claim 7, characterized in that: After the exoskeleton-assisted robot reaches the inspection object corresponding to the verification task, it binds the verification task with the mobile acquisition result based on the device tag number, collects mobile verification data and generates a mobile record, and at the same time reads the fixed-side data corresponding to the verification task from the fixed inspection terminal and generates a fixed-side record. Based on the mobile verification data and the fixed-side data, it calculates the difference and compares the absolute value of the difference with a preset consistency threshold to complete the consistency check. The device tag number, the trigger time, the anomaly name, the anomaly type, the mobile record, the fixed record, the difference, and the consistency check result are stored as fields of the same maintenance record.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the operation and maintenance method of the multifunctional exoskeleton-assisted robot based on the power operation and maintenance scenario as described in any one of claims 1 to 8.

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