A power distribution cabinet insulation resistance measuring robot intelligent control method and system

Abstract

This invention discloses an intelligent control method and system for measuring the insulation resistance of a distribution cabinet. The method includes: acquiring the measurement task and parsing the cabinet structure code of the target distribution cabinet; retrieving the corresponding busbar spatial layout diagram based on the cabinet structure code and generating an obstacle avoidance movement path for the robot's end effector between each measurement point; controlling the robot to move along the obstacle avoidance movement path and performing probe docking with the busbar terminals at each measurement point; injecting a high-voltage DC test signal and acquiring the raw insulation resistance signal after docking; performing frequency domain separation processing on the raw insulation resistance signal, extracting and suppressing the power frequency interference component to obtain pure insulation resistance data; and storing and uploading the pure insulation resistance data in association with the three-dimensional coordinates of the measurement points. This method can automatically generate the measurement path according to the distribution cabinet structure and effectively suppress power frequency interference, improving the automation and accuracy of insulation resistance measurement.

Description

Technical Field

[0001] This invention relates to the field of insulation resistance measurement and robot control technology for power distribution cabinets, specifically to an intelligent control method and system for measuring the insulation resistance of power distribution cabinets. Background Technology

[0002] As a core node in power transmission and distribution systems, switchgear's busbars and insulation components operate under high voltage and high load conditions for extended periods. Periodic measurement of insulation resistance is essential for assessing the equipment's insulation status and preventing phase-to-phase short circuits and grounding faults. Early insulation resistance measurements relied primarily on operators manually connecting and testing each phase and point by phase using a megohmmeter or insulation resistance tester within the distribution area. This method required close contact with live busbars, posing a significant risk of electric shock. Furthermore, the consistency of manual connection force and contact time was difficult to guarantee, resulting in poor repeatability and low data reliability. With the increasing compactness and density of power distribution equipment, the narrow space within cabinets and complex busbar layouts have made manual operation even more challenging, significantly reducing measurement efficiency. To reduce manual intervention, some solutions have introduced fixed automatic testing devices. These devices use pre-embedded test leads and relay arrays to switch measurement circuits, enabling sequential testing of a limited number of measurement points. However, these devices require pre-planning of cables and access nodes during the switchgear design or renovation phase. If the cabinet structure or busbar configuration changes, they cannot be easily adapted, exhibiting extremely poor scalability. Another approach is to use a robotic arm equipped with a test probe for automatic measurement, but this mostly relies on teaching programming. For each type of distribution cabinet, the measurement points and movement trajectories need to be manually pre-calibrated. When faced with inspection tasks involving multiple models and batches of distribution cabinets, the deployment and conversion work is extremely cumbersome.

[0003] At the signal acquisition level, the operating environment of the distribution cabinet contains a strong power frequency electromagnetic field. The leakage current under high voltage DC testing is usually extremely small and easily overwhelmed by spatially coupled power frequency and harmonic interference components. Most existing measurement methods only suppress interference through simple hardware filtering or averaging, which makes it difficult to effectively distinguish between weak DC leakage signals and large-amplitude power frequency induced signals. This leads to significant deviations in the insulation resistance calculation values ​​and reduces the accuracy of condition determination.

[0004] Therefore, how to enable robots to automatically generate measurement motion paths and achieve reliable obstacle avoidance based on the specific structure of different power distribution cabinets, and how to effectively separate and suppress power frequency interference in the real-time processing of leakage current signals, have become urgent problems to be solved. Summary of the Invention

[0005] This paper provides an intelligent control method and system for measuring the insulation resistance of a power distribution cabinet. The robot can autonomously generate obstacle avoidance measurement paths based on the structural code of the power distribution cabinet without the need for manual teaching for different cabinets. At the same time, by performing frequency domain separation processing on the original insulation resistance signal, power frequency interference is effectively suppressed, and pure insulation resistance measurement data is obtained, thereby improving the automation adaptability and data accuracy of the measurement.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides an intelligent control method and system for measuring the insulation resistance of a distribution cabinet. The method includes: acquiring the measurement task and parsing the cabinet structure code of the target distribution cabinet; retrieving the corresponding busbar spatial layout diagram based on the cabinet structure code, and generating an obstacle avoidance movement path for the robot's end effector between each measurement point; controlling the robot to move along the obstacle avoidance movement path, and performing a docking action between the probe and the busbar terminal at each measurement point; after the docking action is completed, injecting a high-voltage DC test signal and acquiring the original insulation resistance signals of each phase busbar to ground and between phases; performing frequency domain separation processing on the original insulation resistance signals, extracting and suppressing the power frequency interference component to obtain pure insulation resistance data; associating and storing the pure insulation resistance data with the three-dimensional coordinates of the corresponding measurement points, and uploading it to a remote monitoring platform.

[0007] When acquiring measurement tasks and parsing the cabinet structure code, the robot receives measurement task data packets from the host computer, extracts the cabinet number and measurement mode identifier of the target power distribution cabinet, retrieves the corresponding power distribution cabinet model information based on the cabinet number, determines the internal busbar arrangement and spatial coordinate data of the terminals, and combines the arrangement, spatial coordinate data, and measurement mode identifier to generate the cabinet structure code. This allows the robot to adapt to the differences between different power distribution cabinet models, providing consistent structured information for subsequent motion planning.

[0008] In the process of retrieving the busbar spatial layout diagram and generating an obstacle avoidance path based on the cabinet structure code, the busbar spatial layout diagram is retrieved from a pre-stored 3D model library of distribution cabinets using the cabinet structure code as an index. This diagram includes the 3D coordinates and external dimensions of each phase busbar terminal and grounding terminal. The robot's current base coordinates and joint angle values ​​are obtained, and the current spatial pose of the end effector is calculated. Starting from the current spatial position, the 3D coordinates of each measurement point are used as waypoints, and the external dimensions are used as the obstacle expansion radius. A search algorithm is used to plan the obstacle avoidance path for the end effector to move between each measurement point. By introducing the actual obstacle size expansion radius for path search, live parts can be reliably avoided in different cabinet structures, ensuring movement safety.

[0009] When controlling the robot to move along the obstacle avoidance path and perform docking actions, the obstacle avoidance path is discretized into several path interpolation points. The robot's joints are driven sequentially to the joint angle corresponding to each interpolation point in chronological order. After the end effector reaches the current measurement point, a vision positioning device is activated to acquire real-time images of the bus terminal. Based on the real-time images, the offset of the terminal's center relative to the probe axis is calculated. This offset drives a fine-tuning platform to compensate for the probe's displacement in both the horizontal and vertical directions. After the probe axis is aligned with the terminal's center, the probe extends axially and presses against the terminal surface. This vision-based fine-tuning compensation eliminates accumulated positioning errors, achieving accurate and stable docking between the probe and the terminal.

[0010] After the docking operation is completed, a high-voltage DC test signal is injected and the original insulation resistance signal is acquired. After the probe is docked with the current bus terminal and the grounding terminal, the high-voltage generation module is activated to apply a DC test signal with the rated voltage between the bus terminal and the grounding terminal. After the DC test signal reaches a preset stable duration, the sampling circuit is activated to acquire the original leakage current signal flowing through the bus terminal and the ambient temperature signal in the test circuit. The original leakage current signal of multiple cycles is continuously acquired according to the set sampling frequency, and the original leakage current signal of each sampling moment is associated with the corresponding ambient temperature signal and stored as the original insulation resistance signal.

[0011] As a preferred embodiment of the present invention, before activating the sampling circuit to acquire the original leakage current signal, a continuity check of the test circuit is performed: after the probe is connected to the bus terminal and grounding terminal, a low-voltage verification signal is injected into the test circuit, and the response current value of the verification circuit is acquired; the resistance value of the verification circuit is calculated based on the voltage value of the low-voltage verification signal and the response current value, and compared with a preset continuity resistance threshold; if the resistance value of the verification circuit is less than the preset continuity resistance threshold, the test circuit is determined to be normally conductive and the sampling circuit is allowed to be activated; if the resistance value of the verification circuit is not less than the preset continuity resistance threshold, the test circuit is determined to be abnormally conductive and the probe is reconnected to the bus terminal. Preferably, the voltage value of the low-voltage verification signal is less than the voltage value of the DC test signal, and the preset continuity resistance threshold is dynamically set according to the contact material type of the probe and the terminal. This solution confirms the circuit connection quality before applying pressure, which can avoid test errors or high-voltage safety hazards caused by poor contact, and at the same time, it adaptively adjusts the threshold according to the material characteristics, improving the applicability in different scenarios.

[0012] When performing frequency domain separation processing on the original insulation resistance signal, the original leakage current signal is segmented according to time sequence. A time-to-frequency domain transformation is performed on each signal segment to obtain the corresponding spectral distribution. The peak amplitude at the power frequency and its harmonics is located in the spectral distribution, and the spectral component corresponding to the peak amplitude is marked as the power frequency interference component. The power frequency interference component is subtracted from the spectral distribution, and then an inverse frequency-to-time domain transformation is performed on the resulting spectral distribution to obtain the power frequency interference-suppressed leakage current signal. The pure insulation resistance data is calculated based on the power frequency interference-suppressed leakage current signal and the ambient temperature signal. Through frequency domain separation and interference component removal, power frequency and harmonic interference introduced by the strong electrical environment can be effectively suppressed, significantly improving the accuracy and repeatability of insulation resistance measurement.

[0013] When storing and uploading pure insulation resistance data associated with the corresponding three-dimensional coordinates of the measurement points, the bus phase identifier and three-dimensional coordinates of the current measurement point are obtained. The pure insulation resistance data is then written into the local temporary storage area according to the association relationship between the measurement timestamp, bus phase identifier, and three-dimensional coordinates. After completing the measurement of all measurement points in the target distribution cabinet, all data in the local temporary storage area is packaged into a data message and sent to the remote monitoring platform via the wireless communication module. After transmission, the transmitted data in the local temporary storage area is cleared. This method achieves automatic binding and orderly transmission of measurement data with spatial location and phase, facilitating remote trend analysis and status management.

[0014] This invention also provides an intelligent control system for a robot measuring the insulation resistance of a power distribution cabinet, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the above-described method. This control system can automatically complete the intelligent measurement of the insulation resistance of the power distribution cabinet, improving the efficiency and reliability of the measurement task.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: The system acquires the measurement task and parses the cabinet structure code of the target power distribution cabinet. Based on the cabinet structure code, it retrieves the corresponding busbar spatial layout diagram and generates an obstacle avoidance path for the robot's end effector between each measurement point. The process of parsing the cabinet structure code involves extracting the cabinet number and measurement mode identifier from the measurement task, retrieving the spatial coordinate data of the busbar arrangement and terminals, and combining this with the measurement mode identifier to form a structure code. Using this structure code as an index, the system directly retrieves the busbar spatial layout diagram containing the 3D coordinates and outer dimensions of each phase busbar terminal and grounding terminal from a pre-stored power distribution cabinet 3D model library. Based on the robot's current base coordinates and joint angles, the initial spatial pose of the end effector is obtained. Then, using this pose as the starting point, the 3D coordinates of each measurement point as the waypoints, and the terminal outer dimensions as the obstacle expansion radius, a search algorithm is used to plan the obstacle avoidance path. This solution enables robots to autonomously adapt to different models and busbar arrangements of distribution cabinets without manual teaching or on-site calibration. It eliminates the need for repetitive programming and adjustments due to differences in cabinet structure, significantly improving the conversion efficiency of measurement tasks and the flexibility of on-site deployment. Simultaneously, obstacle avoidance planning via expansion radius ensures the safety of the end effector's movement within confined cabinet spaces. The original insulation resistance signal undergoes frequency domain separation processing to extract and suppress power frequency interference components, yielding clean insulation resistance data. After the probe is connected to the busbar terminals and grounding terminals and the rated DC test voltage is applied, the original leakage current signal and ambient temperature signal are acquired. The leakage current signal is segmented in time sequence and subjected to time-frequency transformation to obtain the spectral distribution. The power frequency and its harmonics are located in this spectrum, and the corresponding spectral components are marked as power frequency interference components. After subtracting these power frequency interference components from the original spectrum, an inverse frequency-to-time transformation is performed to obtain the power frequency interference-suppressed leakage current signal. The insulation resistance value is then calculated in conjunction with the ambient temperature signal. This processing method acts directly on the measurement signal itself, without relying on additional hardware filtering circuits, to achieve accurate separation and filtering of the power frequency fundamental wave and its harmonic interference. It prevents the large-amplitude induction signal from masking the weak DC leakage signal, thereby significantly reducing the fluctuation of the insulation resistance value. The accuracy and repeatability of the measurement results are enhanced, providing more reliable raw data for the objective judgment of the insulation status. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a flowchart of the intelligent control method for measuring the insulation resistance of a power distribution cabinet. Figure 2 This is a flowchart of the frequency domain separation and processing of the original signal of insulation resistance; Figure 3 It is a curve showing the change in offset of the probe's visual positioning image; Figure 4 This is the time-domain waveform of the original leakage current signal; Figure 5 This is a comparison chart of the leakage current spectrum of insulation resistance and the results of power frequency interference suppression. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 This invention provides an intelligent control method for a robot measuring the insulation resistance of a power distribution cabinet, comprising: acquiring the measurement task and parsing the cabinet structure code of the target power distribution cabinet to obtain structured information related to the arrangement of busbars, spatial coordinates of terminals, and measurement mode within the cabinet; retrieving the busbar spatial layout diagram using the cabinet structure code as an index, and generating an obstacle avoidance path that considers the external dimensions of obstacles based on the current pose of the robot's end effector and each measurement point; controlling the robot to move along the obstacle avoidance path, and performing a docking action between the probe and the busbar terminals after reaching each measurement point; injecting a high-voltage DC test signal into the test circuit after the docking action is completed, and simultaneously acquiring the original insulation resistance signals of each phase busbar to ground and between phases; performing frequency domain separation processing on the original insulation resistance signals, extracting and suppressing power frequency interference components to obtain pure insulation resistance data; associating and storing the pure insulation resistance data with the three-dimensional coordinates of the corresponding measurement points, and uploading it to a remote monitoring platform via wireless communication.

[0020] In practice, the host computer system generates a measurement task data packet based on the job scheduling instructions. This data packet contains at least the cabinet number of the target distribution cabinet and a measurement mode identifier. The measurement mode identifier distinguishes whether the measurement task involves measuring the insulation resistance to ground of each phase busbar, measuring the phase-to-phase insulation resistance of each phase busbar, or simultaneously measuring both the busbar-to-ground and phase-to-phase insulation resistance. The robot control device's built-in wireless communication module receives the measurement task data packet from the host computer and, after verification at the data link layer, submits it to the parsing module. The parsing module extracts the cabinet number and measurement mode identifier of the target distribution cabinet from the measurement task data packet. This extraction is performed by reading the cabinet number field at a fixed offset position after the frame header identifier, and then reading the measurement mode identifier from the measurement mode field, based on a preset message frame structure.

[0021] In practical implementation, the robot control device retrieves the corresponding distribution cabinet model information from the local database based on the cabinet number of the target distribution cabinet obtained through parsing. The local database is implemented using an embedded relational database, pre-stored with a distribution cabinet model information table. This table includes fields for cabinet number, distribution cabinet model, busbar arrangement, and terminal space coordinates. The retrieval process is as follows: using the cabinet number of the target distribution cabinet as the query key, the system searches the distribution cabinet model information table for records matching the query key and reads the distribution cabinet model information from the matching records. The busbar arrangement defined in the distribution cabinet model information includes horizontal, vertical, and triangular arrangements, identified by enumeration values. The terminal space coordinates refer to the coordinate values ​​of each terminal in a three-dimensional coordinate system with the distribution cabinet reference point as the origin. The terminals include A-phase busbar terminals, B-phase busbar terminals, C-phase busbar terminals, and grounding terminals.

[0022] In practical implementation, the busbar arrangement, spatial coordinate data of the terminals, and measurement mode identifier are combined and encoded to generate the cabinet structure code. During the combination encoding, each information unit is converted into a fixed-length or variable-length data block according to a pre-agreed order. The arrangement is represented by a 1-byte enumeration value, the spatial coordinate data is represented by 9 single-precision floating-point numbers representing the (x, y, z) coordinates of the four terminals (A-phase, B-phase, C-phase, and grounding terminal) in sequence, and the measurement mode identifier is represented by a 1-byte identifier code. The arrangement byte, the spatial coordinate data floating-point array, and the measurement mode identifier byte are sequentially concatenated to form the cabinet structure code. The cabinet structure code serves as a structured index identifier used when retrieving the busbar spatial layout diagram and generating obstacle avoidance movement paths.

[0023] In specific implementation, please refer to Figure 2Using the cabinet structure code as an index, the system retrieves the corresponding busbar space layout diagram from a pre-stored 3D model library of distribution cabinets. The distribution cabinet 3D model library is built within the solid-state storage area of ​​the robot control device, employing a key-value pair storage structure. The key is the cabinet structure code, and the value is a serialized busbar space layout diagram data block. The process of retrieving the busbar space layout diagram is as follows: the cabinet structure code is passed as the search key to the query interface of the distribution cabinet 3D model library. The query interface matches the search key in the key-value index tree, reads the busbar space layout diagram data block corresponding to the search key, and performs a deserialization operation on the busbar space layout diagram data block to reconstruct the busbar space layout diagram. The busbar space layout diagram includes the 3D coordinates of the A-phase busbar terminals. Three-dimensional coordinates of the B-phase busbar terminals Three-dimensional coordinates of C-phase busbar terminals Three-dimensional coordinates of the grounding terminal The dimensions of each phase busbar terminal and the grounding terminal are described in the form of a cuboid box, including the length, width and height.

[0024] In practical implementation, the current base coordinates and joint angle values ​​of the robot are obtained. The current base coordinates refer to the three-dimensional coordinates of the robot base reference point in the workshop coordinate system or global coordinate system, which are obtained through the calibration data of the robot base installation position, and are denoted as... Joint angle values ​​refer to the current position readings of each joint of the robot, obtained from feedback by the encoders of the servo motors of each joint. Taking a six-DOF articulated robot as an example, the joint angle value is denoted as a six-dimensional vector. ,in This indicates the rotation angle of the robot's waist joint. Indicates the pitch angle of the upper arm. Indicates the forearm pitch angle. Indicates the wrist rotation angle. Indicates the wrist's tilt angle. Indicates the end rotation angle.

[0025] In practical implementation, the current spatial pose is used as the starting point of the path, the three-dimensional coordinates of each measurement point are used as waypoints, and the outer dimensions of the busbar terminals and structural components are used as the obstacle expansion radius. A search algorithm is used to plan a collision-free obstacle avoidance path for the end effector to move between the measurement points. The measurement points include the A-phase busbar terminal docking points, B-phase busbar terminal docking points, C-phase busbar terminal docking points, and grounding terminal docking points. Each docking point corresponds to the three-dimensional coordinates of the corresponding terminal in the busbar spatial layout diagram. The outer dimensions of each phase busbar terminal and grounding terminal in the busbar spatial layout diagram, as well as the outer dimensions of the structural components inside the cabinet, are extracted. The length, width, and height dimensions included in the outer dimensions are converted into convex polyhedral obstacle regions. The convex polyhedral obstacle regions are expanded along the safety envelope radius of the robot's end effector tool center point to form expanded obstacle regions. The search algorithm employs a sampling-based fast expanding random tree algorithm, which expands nodes within a joint configuration space containing the starting point and waypoint configurations. During the expansion process, the collision detection step uses the minimum distance between the expanding obstacle region and the center point of the end effector tool to determine the collision. Only nodes that do not interfere with the expanding obstacle region are retained, generating a collision-free obstacle avoidance movement path that starts from the starting point and passes through each measurement point in sequence. It also ensures that the kinematic constraints of the robot joints between adjacent nodes in the path are satisfied, that is, the change in joint angle between adjacent nodes does not exceed the threshold set by the ratio of the maximum movement speed of each joint to the path step length.

[0026] In practice, the obstacle avoidance path is discretized into several path interpolation points. The obstacle avoidance path consists of a series of ordered pose nodes in Cartesian space representing the center point of the robot's end effector, as output by the path planning algorithm. Each pose node contains position coordinates and Euler angles. The path discretization operation employs an equal-arc-length interpolation strategy, setting the arc-length step parameter. , The value is set to 2 mm, based on the balance between position control resolution and path tracking accuracy of the robot's end effector in low-speed precision motion mode. When the interpolation point spacing is 2 mm, it ensures path smoothness without exceeding the interpolation buffer capacity of the joint servo driver. The arc length along the obstacle avoidance path is set... The tool is divided into equal parts. At each division point, the pose value of the end effector tool center point is calculated using the spline curve parametric equation. The calculated pose value is used as the path interpolation point. The arc length distance between adjacent path interpolation points is equal to... .

[0027] In practice, the robot's joints are driven sequentially to the joint angle corresponding to each path interpolation point according to time sequence. For each path interpolation point, the target joint angle of each robot joint is calculated using an inverse kinematics algorithm based on the pose value of the end effector tool center point at the path interpolation point. The inverse kinematics algorithm uses a numerical iterative method based on the Jacobian matrix. Taking the current joint angle as the initial value of the iteration, it calculates the pose error vector between the current pose of the end effector tool center point and the target pose of the path interpolation point. The pose error vector is mapped to a joint angle correction vector using the robot's Jacobian pseudo-inverse matrix. The joint angles are iteratively updated until the magnitude of the pose error vector is less than a preset convergence threshold. The calculated target joint angles of each joint are sent to the joint servo driver via a fieldbus. The joint servo driver uses a three-loop cascaded control structure of position loop, velocity loop, and current loop to drive each joint of the robot to move to the target joint angle. The target joint angles of each path interpolation point are sent and executed one by one according to the path sequence. The robot's end effector moves along the obstacle avoidance path and arrives in the vicinity of each measurement point in sequence.

[0028] In practice, after the end effector reaches the current measurement point, it activates the vision positioning device to acquire real-time images of the bus terminal. The vision positioning device consists of an industrial camera fixed to the side of the end effector and a ring light source. The optical axis of the industrial camera is arranged parallel to the probe axis, and the relative pose between them is obtained through prior hand-eye calibration and stored in the hand-eye calibration matrix. In the middle. When the vision positioning device is activated, the ring light source is lit at a preset brightness, and the industrial camera is triggered to capture a real-time image of the bus terminal. The real-time image is transmitted to the image processing module of the robot control device through the GigE interface.

[0029] In practice, the offset of the terminal center relative to the probe axis is calculated based on the real-time image. The image processing module performs preprocessing operations on the real-time image, including Gaussian filtering for noise reduction and adaptive histogram equalization to enhance contrast. The preprocessed real-time image is then used with a template matching algorithm based on gradient orientation histograms to detect the area of ​​the bus terminal. Within the matched terminal area, the Canny edge detection operator is used to extract the terminal edge contour. Ellipse fitting is then performed on the terminal edge contour, using the least squares method to minimize the sum of squared algebraic distances from the edge points to the ellipse equation. The ellipse fitting yields the center coordinates of the terminal in the image coordinate system. ,in, Let be the pixel coordinates of the terminal center in the horizontal direction of the image coordinate system. Let be the pixel coordinates of the terminal center in the vertical direction of the image coordinate system. The projection point of the probe axis onto the image plane is determined by hand-eye calibration and denoted as . ,in These are the pixel coordinates of the probe axis projection point in the horizontal direction of the image coordinate system. This represents the pixel coordinates of the probe axis projection point in the vertical direction of the image coordinate system. The horizontal component of the image offset of the terminal center relative to the probe axis. and the component in the vertical direction Calculate using the following formula: Combined with the pixel equivalent coefficient of industrial cameras , The unit is millimeters per pixel, obtained through camera calibration, which converts the image offset into the actual offset in physical space.

[0030] In practice, the offset-driven fine-tuning platform compensates for the probe's displacement in both the horizontal and vertical directions. The fine-tuning platform is a two-degree-of-freedom precision displacement platform, installed between the robot's end effector flange and the probe holder. The adjustment directions of the platform are perpendicular to the probe axis and correspond to the probe's radial direction in both the horizontal and vertical directions. The robot control unit converts the actual horizontal and vertical offsets into motion pulses for the two drive motors of the fine-tuning platform. Pulse commands are then sent through the platform's drive controller to move the platform a corresponding distance in both the horizontal and vertical directions, aligning the probe axis with the center of the terminal.

[0031] In practice, after the probe axis is aligned with the center of the terminal, the probe is driven to extend axially and press against the surface of the terminal. The probe extension mechanism is driven by a cylinder or lead screw, and the probe extension action is controlled by a solenoid valve or servo motor. The robot control device sends an extension command to the drive unit of the probe extension mechanism, driving the probe to move axially towards the terminal until the contact force detected by the pressure sensor mounted on the probe seat reaches the preset clamping force threshold. At this point, the probe extension action stops, and the conductive contact at the tip of the probe presses against the surface of the busbar terminal, completing the docking action between the probe and the busbar terminal.

[0032] See Figure 3 In the graph, the horizontal axis represents time in seconds, ranging from 0 to 10 seconds; the vertical axis represents offset in pixels, with values ​​ranging from positive to negative, and the zero point located at the dashed line in the middle of the vertical axis. In the legend, the blue solid line represents the horizontal offset Δu, and the red dashed line represents the vertical offset Δv.

[0033] Analysis of the curve trends shows that within the time interval from 0 seconds to approximately 8 seconds, the horizontal offset Δu exhibits a significant monotonically decreasing trend, starting at approximately 17 pixels and gradually decreasing to near 0 pixels. This indicates that the error of the probe in the horizontal direction is gradually decreasing, tending to align with the center of the bus terminal. The vertical offset Δv shows a monotonically increasing trend in the same interval, starting at approximately -12 pixels and gradually increasing to near 0 pixels. This also indicates that the vertical error is continuously decreasing, ultimately achieving precise alignment between the probe and the terminal.

[0034] Within a time interval of approximately 8 to 10 seconds, the offset curves in both directions showed a clear trend towards stabilization, with values ​​close to zero and minimal fluctuations. This indicates that the fine-tuning platform has completed the displacement compensation for the probe, and the probe axis has achieved effective alignment with the center of the terminal, which conforms to the technical solution described in this embodiment for achieving precise probe alignment through visual positioning and fine-tuning platform drive.

[0035] In practice, after the probe is connected to the current busbar terminal and the grounding terminal, the test circuit forms the following electrical path: from the output of the high-voltage generator module, through the switching relay matrix, probe wires, the conductive contact at the probe tip, the busbar terminal, the busbar under test, the insulation support, the grounding wire, the grounding terminal, and the grounding probe wire back to the return end of the high-voltage generator module. Before injecting the high-voltage DC test signal, the continuity of the test circuit is checked. The purpose of this check is to prevent the test circuit from being open-circuited or having excessive contact resistance due to poor probe connection or oxidation of the terminal surface, which would affect the accuracy of the insulation resistance measurement results.

[0036] In practice, the continuity verification process for the test circuit is as follows: the robot control device sends a low-voltage verification signal output command to the high-voltage generation module, the low-voltage power supply unit integrated inside the high-voltage generation module starts, and injects the low-voltage verification signal into the test circuit. The voltage value of the low-voltage verification signal is denoted as... , The value is 5 volts. The 5-volt value is chosen because it is below the rated voltage of the DC test signal and within the safe voltage range for the human body, providing sufficient current response to detect circuit continuity while ensuring safety. The low-voltage verification signal is a DC voltage with the same polarity as the subsequently applied DC test signal.

[0037] In practice, after a low-voltage verification signal is injected, the current detection channel in the sampling circuit activates, acquiring the response current value of the verification circuit. The response current value is denoted as... The unit is amperes, calculated from the voltage difference across a precision shunt resistor connected in series in the test circuit. The precision shunt resistor has a resistance of 100 ohms, an accuracy of 0.1%, and a temperature coefficient of 25 ppm / ℃. The sampling circuit conditions this voltage difference using an instrumentation amplifier and performs analog-to-digital conversion. The robot control device reads the analog-to-digital conversion result and divides it by the resistance of the precision shunt resistor to obtain the response current value. .

[0038] In practical implementation, the voltage value of the low voltage verification signal is used as the basis. and response current value Calculate the resistance value of the verification loop. The resistance value of the verification loop is denoted as... The calculation method uses the following formula:

[0039] in, To verify the circuit resistance value, the unit is ohms; The voltage value of the low voltage verification signal, in volts; The response current value of the verification circuit is expressed in amperes. If the response current value... If the noise level is zero or below the sampling circuit noise floor, the test circuit is directly determined to be open.

[0040] In practice, the resistance value of the verification circuit will be checked. With preset on-resistance threshold Compare the preset on-resistance threshold. The contact material type is dynamically set based on the contact material type between the probe and the terminal. This contact material type is obtained by reading the bus terminal material field stored in the distribution cabinet model information. The material field values ​​include "copper," "aluminum," "silver plating," and "tin plating." The dynamic setting method is as follows: before continuity verification, the robot control device looks up the corresponding threshold in a preset continuity resistance threshold mapping table based on the obtained contact material type. The preset continuity resistance threshold mapping table is constructed as follows: under standard laboratory conditions, for different contact material types, the contact resistance value between the probe and the terminal under a preset clamping force is measured using the four-wire method. The average contact resistance value from multiple measurements is added to a preset margin, and this is taken as the preset continuity resistance threshold for the corresponding material. For example, with copper, the average contact resistance is approximately 0.5 ohms, and the preset margin is 1.0 ohm. Therefore, the preset continuity resistance threshold for copper is... The preset on-resistance threshold is 1.5 ohms. The preset on-resistance threshold for the silver plating material is 2.0 ohms. The preset on-resistance threshold is 0.8 ohms, corresponding to the tin plating material. It is 1.2 ohms.

[0041] In practical implementation, if the resistance value of the verification circuit is... Less than the preset on-resistance threshold If the test circuit is found to be functioning correctly, the robot control device will allow the sampling circuit to be started for subsequent high-voltage signal acquisition. If the resistance value of the verification circuit is... Not less than the preset on-resistance threshold If the test circuit fails to connect, the robot control device records the continuity error and re-executes the probe-bus connection. During the re-execution, the robot control device first retracts the probe, shuts off the low-voltage verification signal, moves the end effector away, and then re-executes the entire connection process, including visual positioning, fine-tuning alignment, and probe extension and clamping. After connection, a continuity check is performed again until the check passes or the number of retries reaches the preset limit.

[0042] In practice, after the continuity test passes, the high-voltage generating module applies a DC test signal with the rated voltage between the bus terminal and the grounding terminal. The robot control device sends a high-voltage output command to the high-voltage generating module, which includes the rated voltage value and polarity information of the DC test signal. The rated voltage value of the DC test signal is selected according to the voltage level of the distribution cabinet under test; for low-voltage distribution cabinets, the rated voltage value is 500 volts, and for medium-voltage distribution cabinets, the rated voltage value is 2500 volts. The high-voltage DC power supply inside the high-voltage generating module outputs a stable DC test signal through PWM modulation and voltage doubler rectification, with a voltage ripple coefficient of less than 0.5%.

[0043] In practice, after the applied DC test signal reaches a preset stabilization time, the sampling circuit is activated to acquire the original leakage current signal flowing through the bus terminal. The preset stabilization time is determined based on the capacitance charging characteristics of the insulation system under test. When the DC test signal is applied between the bus and ground, there is a decay process in the polarization current and absorption current. The value is set to 60 seconds, based on the recommended measurement stabilization time in the insulation resistance measurement standard. After 60 seconds, the polarization current and absorption current have decayed to a state where leakage current dominates, and the leakage current value tends to stabilize. After reaching the preset stabilization time, the high-precision current detection front end in the sampling circuit acquires the original leakage current signal flowing through the bus terminal. The original leakage current signal is amplified by a programmable gain amplifier and then converted into a digital quantity by an analog-to-digital converter.

[0044] In practice, the ambient temperature signal in the test circuit is simultaneously acquired. This signal is provided by a PT100 platinum resistance temperature sensor installed near the test circuit, located inside the high-voltage generation module close to the test circuit terminals. The resistance change of the PT100 platinum resistance is converted into a 0-5 volt voltage signal via a bridge circuit and signal conditioning circuit, and then converted from analog to digital by the temperature acquisition channel of the sampling circuit. The ambient temperature signal is used for subsequent temperature correction of the insulation resistance value, reducing the measured value to the equivalent value at a standard reference temperature of 20°C.

[0045] In practice, the raw leakage current signal for multiple cycles is continuously acquired according to a set sampling frequency. Sampling frequency The frequency was set to 1000 Hz, based on the fact that a 50 Hz power frequency signal can be sampled at 20 points per cycle, satisfying the Nyquist sampling theorem's requirement of sampling the highest frequency component at 400 Hz, and providing sufficient data resolution for subsequent frequency domain separation processing. The number of consecutive sampling cycles... The setting is 10 cycles, which corresponds to continuously collecting data for 10 power frequency cycles (0.2 seconds) after a preset stabilization period, to obtain the total... There are 10 sampling points. At each sampling moment, the original leakage current signal value obtained by analog-to-digital conversion is correlated with the ambient temperature signal value collected at the same time to form a data record. The record format is {timestamp, original leakage current signal amplitude, ambient temperature value}. All continuously collected data records are arranged in chronological order and stored as an original insulation resistance signal. The original insulation resistance signal is stored in the memory buffer of the robot control device in the form of a data structure for subsequent frequency domain separation processing.

[0046] See Figure 4 The figure shows the trend of the original leakage current signal collected in the above embodiment over time. The horizontal axis represents time in seconds, indicating a 1-second time interval for continuous sampling; the vertical axis represents the leakage current amplitude in microamperes (μA), reflecting the amplitude of the leakage current signal flowing through the bus terminal in the test circuit. The curve exhibits periodic oscillation characteristics, with the amplitude fluctuating between approximately 4.0 μA and 6.0 μA. The frequency of the periodic fluctuation is significantly close to the characteristic of the 50 Hz power frequency, which is consistent with the description in the above embodiment of the interference of power frequency AC signals and their harmonics on the original leakage current signal in the environment.

[0047] Observing the overall signal pattern, the original leakage current signal contains obvious interference components superimposed with power frequency and its harmonics, accompanied by certain random noise fluctuations. The signal baseline is basically maintained at around 5.0μA, showing good stability, indicating that the signal acquired after the preset stabilization time has reached a stable state after the polarization and absorption current decay have ended.

[0048] In specific implementation, please refer to Figure 2 The original insulation resistance signal is subjected to frequency domain separation processing to obtain pure insulation resistance data. The original insulation resistance signal contains a time-ordered sequence of original leakage current signals and a corresponding ambient temperature signal sequence. The original leakage current signal sequence is denoted as... ,in The sampling point number, The values ​​are integers from 0 to 199, with a total of 200 sampling points, corresponding to the sampling data of 10 power frequency cycles.

[0049] In practice, the original leakage current signal is segmented chronologically, with each segment containing sampling data from a complete power frequency cycle. The power frequency is 50 Hz, the power frequency cycle is 0.02 seconds, the sampling frequency is 1000 Hz, and a complete power frequency cycle contains 20 sampling points. (Leakage current original signal sequence) Divided into 10 signal segments, the first Each signal segment is denoted as ,in, Signal segment number, Integers with values ​​from 0 to 9 The sampling point number within the signal segment. Integers ranging from 0 to 19 .

[0050] In practice, a time-domain to frequency-domain transformation is performed on each signal segment. The time-domain to frequency-domain transformation uses the Discrete Fourier Transform (DFT) algorithm, performing a DFT on 20 sampling points within each signal segment. The discrete Fourier transform result of each signal segment is denoted as ,in For frequency domain index number, The value is an integer from 0 to 19. It is a complex number containing both amplitude and phase values. The Discrete Fourier Transform is calculated by multiplying the time-domain value of each sampling point within the signal segment by a rotation factor and then summing the results. The exponent of the rotation factor is determined by both the frequency domain index number and the sampling point number within the signal segment.

[0051] In practical implementation, the corresponding spectral distribution is obtained from the Discrete Fourier Transform (DFT) result of each signal segment. The spectral distribution represents the relationship between the amplitude of each frequency component within the signal segment and the frequency. The frequency resolution is determined by the sampling frequency and the number of sampling points within the signal segment; the frequency resolution is the sampling frequency of 1000 Hz divided by the number of sampling points in the signal segment, which is 20, i.e., 50 Hz. Frequency Domain Index Number The corresponding frequency value is Hertz. The spectral distribution of 10 signal segments is averaged by taking the arithmetic mean of the amplitude values ​​of the 10 signal segments at the same frequency domain index, resulting in the average spectral distribution. The average spectral distribution reduces the impact of random noise on spectral estimation.

[0052] In practice, the peak amplitude at the power frequency and its harmonics is located within the average spectral distribution. The power frequency is 50 Hz, corresponding to the frequency domain index number... The second harmonic of the power frequency is 100 Hz, corresponding to the frequency domain index number. The third harmonic of the power frequency is 150 Hz, corresponding to the frequency domain index number. The fourth harmonic of the power frequency is 200 Hz, corresponding to the frequency domain index number. In the average spectral distribution In the middle, check the amplitude values ​​at frequency domain index numbers 1, 2, 3, and 4 respectively. , , , ,in Representing complex numbers The modulus. The spectral components corresponding to these four amplitude values ​​are labeled as power frequency interference components. The source of the power frequency interference components is the interference signal generated by the 50 Hz AC power line and its harmonics entering the test circuit through spatial electromagnetic coupling in the environment around the distribution cabinet.

[0053] In practice, the power frequency interference component is subtracted from the spectral distribution of each signal segment. For the first... Spectral distribution of each signal segment In the frequency domain index number Values ​​1, 2, 3, and 4 will be used to... The complex amplitude is set to zero, meaning all spectral components at the power frequency and its harmonics are removed from the spectrum. The spectral distribution after subtracting the power frequency interference component is denoted as... , The value is determined as follows: when hour, ;when hour, .

[0054] In practice, the spectral distribution after subtracting the power frequency interference component undergoes an inverse frequency-to-time domain transformation. Perform the inverse Discrete Fourier transform. The inverse Discrete Fourier transform is calculated by multiplying the complex values ​​of each index in the frequency domain by the corresponding inverse rotation factor, summing them, and then normalizing. The time-domain signal obtained after performing an inverse discrete Fourier transform on each signal segment is denoted as . , This is the leakage current signal segment after power frequency interference suppression, in which The sampling point number within the signal segment. The values ​​are integers from 0 to 19. The 10 power frequency interference-suppressed leakage current signal segments are arranged according to their signal segment numbers. By sequentially splicing the signals, a complete leakage current signal sequence after power frequency interference suppression is obtained, denoted as... , The value is an integer from 0 to 199.

[0055] In practical implementation, based on the leakage current signal sequence after power frequency interference suppression... The pure insulation resistance data is calculated using the ambient temperature signal sequence. The ambient temperature signal sequence is denoted as... , The values ​​are integers from 0 to 199, acquired synchronously with the leakage current signal. The leakage current signal sequence after power frequency interference suppression. Take the time average and calculate the arithmetic mean of 200 sampling points, denoted as . , The unit is ampere. For the ambient temperature signal sequence... Similarly, taking the time average, we calculate the arithmetic mean of the 200 sampling points, denoted as... , The unit is degrees Celsius. The following formula is used to calculate the pure insulation resistance data:

[0056] in, This is pure insulation resistance data, in ohms; This is the rated voltage value of the DC test signal, in volts. This is the time-averaged leakage current signal after power frequency interference suppression, in amperes; This is a dimensionless temperature correction factor. The method of obtaining is based on The temperature correction factor can be obtained by looking up and interpolating in the pre-stored temperature correction factor table. The temperature correction factor table stores correction factor values ​​in 0.5℃ increments within the range of -10℃ to 60℃. The temperature correction factor table is pre-determined through temperature characteristic tests of insulation materials. The correction factor value at the standard reference temperature of 20℃ is the base value of 1.0. The correction factor is approximately halved for every 10℃ increase in temperature and approximately doubled for every 10℃ decrease in temperature.

[0057] In practice, the pure insulation resistance data is associated with and stored in relation to the three-dimensional coordinates of the corresponding measurement point, and then uploaded. The method for obtaining the bus phase identifier and three-dimensional coordinates of the current measurement point is as follows: Before executing the docking action at the current measurement point, the robot control device reads the bus phase identifier of the current measurement point from the measurement task execution queue. The value of the bus phase identifier is one of "A phase", "B phase", or "C phase". The three-dimensional coordinates of the corresponding terminal are read from the bus space layout diagram. The coordinate values ​​of the three-dimensional coordinates are derived from the terminal space coordinate data in the bus space layout diagram data block. The association relationship is constructed by combining the measurement timestamp, bus phase identifier, X-axis component, Y-axis component, Z-axis component of the three-dimensional coordinates, and pure insulation resistance data into a single association record.

[0058] In practice, the pure insulation resistance data is written to a local temporary storage area according to the association between the measurement timestamp, bus phase identifier, and three-dimensional coordinates. The local temporary storage area is a circular buffer partitioned within the robot control device's memory, using a structured data storage format, with each associated record having a fixed storage length of 128 bytes. The measurement timestamp is a UNIX timestamp provided by the robot control device's real-time clock, with millisecond-level accuracy. The associated record for the current measurement point is written to the first free record position in the local temporary storage area, and the count of stored records in the local temporary storage area is updated.

[0059] In practice, after completing the insulation resistance measurement of all measurement points in the target distribution cabinet, all data in the local temporary storage area is packaged into a data message. All measurement points include the A-phase bus terminal to ground measurement point, the B-phase bus terminal to ground measurement point, the C-phase bus terminal to ground measurement point, and the phase-to-phase measurement points to be performed. The robot control device checks if the measurement task execution queue is empty; when the queue is empty, it indicates that all measurement points in the target distribution cabinet have been measured. At this point, all existing associated records in the local temporary storage area are read, sorted in ascending order by measurement timestamp, and a message header and length are added before each data record. A CRC checksum is added after the data record, and the data is packaged into a data message. The data message is encoded in JSON format.

[0060] In practice, data packets are sent to the remote monitoring platform via a wireless communication module. This module uses a 4G LTE communication module and supports the TCP / IP protocol stack. The robot control device calls the wireless communication module's Socket interface to establish a TCP connection using the pre-configured IP address and port number of the remote monitoring platform. Once the connection is established, data packets are sent to the remote monitoring platform via the TCP connection. Upon receiving the data packets, the remote monitoring platform performs a CRC check. If the check passes, it parses the associated records in the data packets and stores them in a remote database.

[0061] In practice, after transmission is completed, the transmitted data in the local temporary storage area is cleared. After receiving a reception confirmation response from the remote monitoring platform, the robot control device resets the count of stored records in the local temporary storage area to zero and releases all record positions in the local temporary storage area for subsequent measurement tasks.

[0062] In specific implementation, the intelligent control system for the distribution cabinet insulation resistance measurement robot corresponding to this embodiment includes a memory, a processor, and a computer program stored in the memory and running on the processor. The memory uses a combination of DDR4 SDRAM and non-volatile NAND Flash, and the processor uses an ARM Cortex-A series embedded processor. When the processor executes the computer program, it implements each step of the above-mentioned intelligent control method for the distribution cabinet insulation resistance measurement robot. The steps include: acquiring the measurement task and parsing the cabinet structure code of the target distribution cabinet; retrieving the corresponding busbar spatial layout diagram based on the cabinet structure code and generating an obstacle avoidance movement path for the robot's end effector between each measurement point; controlling the robot to move along the obstacle avoidance movement path and performing the docking action between the probe and the busbar terminal at each measurement point; injecting a high-voltage DC test signal after the docking action is completed and acquiring the original insulation resistance signals of each phase busbar to ground and between phases; performing frequency domain separation processing on the original insulation resistance signals, extracting and suppressing the power frequency interference component to obtain pure insulation resistance data; associating and storing the pure insulation resistance data with the three-dimensional coordinates of the corresponding measurement point and uploading it to the remote monitoring platform.

[0063] See Figure 5 In the graph, the horizontal axis represents frequency in Hertz, ranging from 0 to 500 Hertz; the vertical axis represents amplitude, reflecting the spectral intensity of the leakage current signal. The solid blue line in the graph represents the average spectral distribution of the original insulation resistance signal, while the dashed red line represents the pure spectral distribution obtained after power frequency interference suppression. The blue curve exhibits significant amplitude peaks at 50 Hertz, 100 Hertz, 150 Hertz, and 200 Hertz, corresponding to the power frequency and its second, third, and fourth harmonics, respectively. These peaks reflect the interference signal introduced by the 50 Hertz AC power supply in the environment surrounding the distribution cabinet.

[0064] After power frequency interference suppression, the peak amplitude of the red dashed line drops significantly at the aforementioned frequency positions, even approaching zero. This indicates that the spectral components of the power frequency and its harmonics have been effectively removed, resulting in a smoother, low-amplitude spectrum that eliminates the influence of power frequency interference. Removing the spectral components of the power frequency and its harmonics preserves random noise and other non-power frequency components in the signal, providing a reliable data foundation for subsequent insulation resistance calculations based on the clean signal.

[0065] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent control of a robot for measuring the insulation resistance of a power distribution cabinet, characterized in that, The method includes: Acquire the measurement task and parse the cabinet structure code of the target power distribution cabinet; Based on the cabinet structure code, retrieve the corresponding busbar space layout diagram and generate the obstacle avoidance movement path of the robot end effector between each measurement point; Control the robot to move along the obstacle avoidance path and perform the docking action between the probe and the bus terminal at each measurement point; After the docking operation is completed, a high-voltage DC test signal is injected and the original insulation resistance signals of each phase bus to ground and between phases are collected. The original insulation resistance signal is subjected to frequency domain separation processing to extract and suppress the power frequency interference component, thereby obtaining pure insulation resistance data. The pure insulation resistance data is associated with and stored in relation to the three-dimensional coordinates of the corresponding measurement points, and then uploaded to the remote monitoring platform.

2. The intelligent control method for a distribution cabinet insulation resistance measurement robot according to claim 1, characterized in that, The measurement task is acquired and the cabinet structure code of the target power distribution cabinet is parsed, specifically as follows: Receive the measurement task data packet sent by the host computer, and extract the cabinet number and measurement mode identifier of the target power distribution cabinet from the measurement task data packet; Based on the cabinet number, the corresponding power distribution cabinet model information is retrieved from the local database, and the arrangement of its internal busbars and the spatial coordinate data of the wiring terminals are determined based on the power distribution cabinet model information. The arrangement method, spatial coordinate data, and measurement mode identifier are combined and encoded to generate the cabinet structure code.

3. The intelligent control method for measuring the insulation resistance of a distribution cabinet according to claim 2, characterized in that, Based on the cabinet structure code, the corresponding busbar space layout diagram is retrieved, and the obstacle avoidance movement path of the robot end effector between each measurement point is generated, specifically as follows: Using the cabinet structure code as an index, the corresponding busbar space layout diagram is retrieved from the pre-stored 3D model library of distribution cabinets. The busbar space layout diagram includes the 3D coordinates and outer dimensions of each phase busbar terminal and grounding terminal. Obtain the robot's current base coordinates and joint angle values, and calculate the current spatial pose of the end effector based on the joint angle values; Starting from the current spatial pose, using the three-dimensional coordinates of each measurement point as the waypoints, and using the outer dimension as the obstacle expansion radius, a search algorithm is used to plan the obstacle avoidance path for the end effector to move between each measurement point.

4. The intelligent control method for a distribution cabinet insulation resistance measuring robot according to claim 3, characterized in that, The robot is controlled to move along the obstacle avoidance path and to perform the docking action between the probe and the busbar terminal at each measurement point, specifically: The obstacle avoidance path is discretized into several path interpolation points, and the robot's joints are driven to move to the joint angle corresponding to each path interpolation point in chronological order. After the end effector reaches the current measurement point, the vision positioning device is activated to acquire a real-time image of the bus terminal, and the offset of the terminal center relative to the probe axis is calculated based on the real-time image. According to the offset, the fine-tuning platform drives the probe to perform displacement compensation in the horizontal and vertical directions, so that the probe axis is aligned with the center of the terminal, and then the probe is driven to extend axially and press against the surface of the terminal.

5. The intelligent control method for a distribution cabinet insulation resistance measuring robot according to claim 4, characterized in that, After the docking operation is completed, a high-voltage DC test signal is injected and the original insulation resistance signals of each phase bus to ground and between phases are collected, specifically: After the probe has been connected to the current bus terminal and the ground terminal, the high voltage generation module is activated to apply a DC test signal with the rated voltage between the bus terminal and the ground terminal. After the DC test signal is applied and reaches a preset stable duration, the sampling circuit is started to collect the original signal of the leakage current flowing through the bus terminal, and at the same time, the ambient temperature signal in the test circuit is collected. The original leakage current signal is continuously acquired for multiple cycles according to the set sampling frequency, and the original leakage current signal at each sampling moment is associated with the corresponding ambient temperature signal and stored as the original insulation resistance signal.

6. The intelligent control method for a distribution cabinet insulation resistance measuring robot according to claim 5, characterized in that, The original insulation resistance signal is subjected to frequency domain separation processing to extract and suppress the power frequency interference component, resulting in pure insulation resistance data. Specifically: The original leakage current signal in the original insulation resistance signal is segmented in time sequence, and a time-domain to frequency-domain transformation operation is performed on each signal segment to obtain the corresponding spectrum distribution. Locate the peak amplitude at the power frequency and its harmonics in the spectrum distribution, and mark the spectral component corresponding to the peak amplitude as the power frequency interference component; The power frequency interference component is subtracted from the spectrum distribution, and then the spectrum distribution after subtracting the power frequency interference component is subjected to an inverse frequency domain to time domain transformation operation to obtain the leakage current signal after power frequency interference suppression. The pure insulation resistance data is calculated based on the leakage current signal after power frequency interference suppression and the ambient temperature signal.

7. The intelligent control method for a distribution cabinet insulation resistance measuring robot according to claim 6, characterized in that, The pure insulation resistance data is associated with and stored in relation to the three-dimensional coordinates of the corresponding measurement points, and then uploaded to the remote monitoring platform, specifically as follows: Obtain the bus phase identifier and three-dimensional coordinates of the current measurement point, and write the pure insulation resistance data into the local temporary storage area according to the correlation between the measurement timestamp, bus phase identifier, and three-dimensional coordinates; After completing the insulation resistance measurement at all measurement points of the target distribution cabinet, package all the data in the local temporary storage area into a data message; The data message is sent to the remote monitoring platform via the wireless communication module, and the sent data is cleared from the local temporary storage area after the transmission is completed.

8. The intelligent control method for a distribution cabinet insulation resistance measurement robot according to claim 5, characterized in that, Before starting the sampling circuit to acquire the original signal of the leakage current flowing through the bus terminal, a continuity check of the test circuit is also included, specifically: After the probe is connected to the bus terminal and grounding terminal, a low-voltage verification signal is injected into the test circuit, and the response current value of the verification circuit is collected. The resistance value of the verification loop is calculated based on the voltage value of the low voltage verification signal and the response current value, and the resistance value of the verification loop is compared with a preset on-resistance threshold. If the resistance value of the verification circuit is less than the preset on-resistance threshold, the test circuit is determined to be conducting normally and the sampling circuit is allowed to start. If the resistance value of the verification circuit is not less than the preset conduction resistance threshold, the test circuit is determined to be abnormal and the probe is re-connected to the bus terminal.

9. The intelligent control method for a distribution cabinet insulation resistance measuring robot according to claim 8, characterized in that, The voltage value of the low voltage verification signal is less than the voltage value of the DC test signal, and the preset on-resistance threshold is dynamically set according to the contact material type between the probe and the terminal.

10. An intelligent control system for a robot measuring the insulation resistance of a power distribution cabinet, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent control method for measuring the insulation resistance of a distribution cabinet as described in any one of claims 1 to 9.

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