Intelligent operation method and system for programmed control of transformer substation

Abstract

The invention provides an intelligent operation method and system for programmed control of a transformer substation, and relates to the technical field of power grids. After the programmed control instruction is detected, the operation order corresponding to the target equipment and the target operation type is matched, the robot is operated to replace manual operation, all the operation steps are executed in sequence, double safety verification is carried out according to the sequential control operation result of each operation step, and the state sensing accuracy and the system reliability are remarkably improved. Through the cooperative effect of robot execution, multi-sensor fusion and double safety verification, the problems that the operation and maintenance efficiency of the transformer substation is low, the safety risk is high, and misoperation is prone to occurring are solved, and the operation and maintenance efficiency and safety of the transformer substation are improved.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to a method and system for programmed control of intelligent operation of substations. Background Technology

[0002] Switching operations for substation equipment such as circuit breakers, disconnectors, and grounding switches are among the most common and fundamental operation and maintenance tasks in power systems. The core task of switching operations is to change the operating status of power equipment according to dispatch instructions, such as switching from "operation" to "maintenance".

[0003] Switching operations within substations are generally performed manually on-site or remotely in single-step control. Operators must carry paper operation tickets, verify the equipment name and status item by item in the high-voltage equipment area, and manually execute the operation, or frequently click on the monitoring system in the main control room to remotely control individual devices. A complex switching task (such as busbar rerouting) often involves dozens or even hundreds of single-step operations, with the entire process taking tens of minutes to several hours. This mode is not only labor-intensive but also severely restricts the response speed of grid switching and fault recovery operations, limiting grid operating efficiency.

[0004] Furthermore, operator fatigue, stress, lack of skills, or verification errors can lead to serious operational accidents such as accidentally entering the wrong compartment or accidentally opening or closing switches, which can easily cause equipment damage or power grid failures. Traditional methods of checking equipment status, such as whether disconnectors are properly opened or closed and whether grounding wires are reliably connected, are greatly affected by lighting and angle, resulting in blind spots and the possibility of misjudgment.

[0005] Currently, the operation of substations is highly dependent on manual labor, resulting in low maintenance efficiency, high safety risks, and a high risk of misoperation. Summary of the Invention

[0006] This invention provides a programmed control intelligent operation method and system for substations, which solves the problems of low operation and maintenance efficiency, high safety risks, and easy misoperation in substations, thereby improving the operation and maintenance efficiency and safety of substations.

[0007] In a first aspect, the present invention provides a method for intelligent operation of programmed control in a substation. The method includes: detecting programmed control instructions from substation maintenance personnel to execute a target operation type on a target device; responding to the programmed control instructions, matching and obtaining an operation ticket corresponding to the target device and the target operation type; the operation ticket includes a sequence of sequentially executed operation steps; acquiring multi-sensor data from the target device and the operating robot in real time, and based on the multi-sensor data and the operation ticket, controlling the operating robot to perform sequential control operations on the target device step by step to obtain the sequential control operation result of the current operation step; performing dual safety checks based on the multi-sensor data and the sequential control operation result of the current operation step to determine whether the current operation step has passed the check; if the check passes, executing the next operation step until the programmed control instructions are completed.

[0008] In one possible implementation, the programmed control instruction includes a unique identifier for the target device and an operation code for the target operation type. The step of matching the operation ticket corresponding to the target device and the target operation type in response to the programmed control instruction includes: performing a matching query in a pre-generated standardized operation ticket database based on the unique identifier of the target device and the operation code of the target operation type to determine a pre-verified operation ticket; the pre-verified operation ticket includes a sequence of sequentially executed operation steps, as well as device state transition logic, five-prevention interlocking logic conditions, and device state sequences in each operation step; based on the pre-verified operation ticket, rehearsing the device state and operation ticket requirements before and after each operation step, and performing consistency verification to determine the verification result of the pre-verified operation ticket; if the verification result is successful, the pre-verified operation ticket is determined as the operation ticket corresponding to the target device and the target operation type.

[0009] In one possible implementation, controlling the operating robot to perform sequential control operations on the target device step by step based on the multi-sensor data and the operation ticket to obtain the sequential control operation result of the current operation step includes: parsing the operation content of the current operation step in the operation ticket and generating robot drive instructions for the current operation step; the operation content includes mechanical motion trajectory, operation target pose, and expected physical parameters; the robot drive instructions include various actions to be executed sequentially, wherein the types of actions include opening and closing, turning, plugging and unplugging, and positioning; sending the robot drive instructions to the operating robot to control the robotic arm and end effector of the operating robot to execute the current operation step on the target device; determining whether the current operation step has been completed based on the multi-sensor data in the current operation step; the multi-sensor data includes force sensor data, torque sensor data, vision data, high-precision positioning data, and device electrical status data; if the current operation step has been completed, determining the sequential control operation result of the current operation step based on the multi-sensor data in the current operation step, the sequential control operation result including the operation status of the current operation step, the status information of the target device, and the process data of the current operation step.

[0010] In one possible implementation, the step of performing dual safety verification based on the multi-sensor data and the sequential control operation result of the current operation step to determine whether the current operation step has passed verification includes: extracting electrical-related data and visual-related data based on the multi-sensor data and the sequential control operation result of the current operation step; performing safety verification based on the electrical-related data to obtain an electrical verification result; performing safety verification based on the visual-related data to obtain a visual verification result; and determining whether the current operation step has passed verification based on the electrical verification result and the visual verification result.

[0011] In one possible implementation, the step of performing dual safety verification based on the multi-sensor data and the sequential control operation result of the current operation step to determine whether the current operation step has passed verification further includes: extracting mechanical sensing data based on the multi-sensor data and the sequential control operation result of the current operation step; performing safety verification based on the mechanical sensing data to obtain mechanical verification results; and determining whether the current operation step has passed verification based on the electrical verification results, the visual verification results, and the mechanical verification results.

[0012] In one possible implementation, the step of performing safety verification based on the electrical-related data to obtain electrical verification results includes: extracting equipment status signals from the electrical-related data, the equipment status signals including microswitch status signals, auxiliary contact signals, and magnetic induction sensor signals; extracting the expected state requirements of the current operation step in the operation ticket; the expected state requirements including expected switch state, expected contact position, and expected sensor reading range; and performing comparative analysis based on the equipment status signals and the expected state of the current operation step to determine the electrical verification results.

[0013] In one possible implementation, the step of performing security verification based on the visually relevant data to obtain a visual verification result includes: extracting visual state features from the visually relevant data, the visual state features including equipment state features, indicator features, and instrument features; extracting the expected visual state of the current operation step in the operation ticket; the expected visual state including the expected mechanical position, the expected status indicator display state, and the expected instrument reading range; and performing comparative analysis based on the visual state features and the expected visual state of the current operation step to determine the visual verification result.

[0014] In one possible implementation, the method further includes: recording the electrical verification results, visual verification results, and mechanical verification results of each operation step; based on the electrical verification results, visual verification results, and mechanical verification results of each operation step, plotting the timing change curves of various verification results during the execution of programmed control instructions; based on the timing change curves of various verification results and the standard timing feature templates corresponding to various verification results, performing feature matching and similarity calculation to determine the similarity corresponding to various verification results; if the similarity corresponding to any verification result is less than a preset threshold, generating abnormal alarm information; the abnormal alarm information is used to indicate that the execution of the programmed control instructions is abnormal.

[0015] In one possible implementation, the method further includes: if the current operation step fails verification, an adaptive retry mechanism is initiated, the adaptive retry mechanism including adjusting the force of the operating robot, fine-tuning the operation pose, and a short delay; after the adaptive retry mechanism is executed, the current operation step is re-executed and a double safety check is performed to obtain the retry check result; if the retry check result is a successful check, it is recorded as a successful retry and the process continues; if the retry check result is a failed check, it is determined as a permanent fault and the programmed control instructions are terminated.

[0016] Secondly, embodiments of the present invention provide a substation programmed control intelligent operation device, which includes: a communication module and a processing module. The communication module is used to detect programmed control instructions executed by substation operation and maintenance personnel on target equipment for a target operation type. The processing module is used to respond to the programmed control instructions and match an operation ticket corresponding to the target equipment and the target operation type. The operation ticket includes a sequence of sequentially executed operation steps. The device acquires multi-sensor data of the target equipment and the operation robot in real time, and controls the operation robot to perform sequential control operations on the target equipment step by step based on the multi-sensor data and the operation ticket to obtain the sequential control operation result of the current operation step. Based on the multi-sensor data and the sequential control operation result of the current operation step, a dual safety verification is performed to determine whether the current operation step has passed the verification. If the verification passes, the next operation step is executed until the programmed control instructions are completed.

[0017] Thirdly, embodiments of the present invention provide an electronic device including a memory and a processor. The memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to perform the steps of the method as described in the first aspect and any possible implementation thereof.

[0018] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the method as described in the first aspect and any possible implementation thereof.

[0019] This invention provides a method and system for intelligent operation of programmed control in substations. Upon detecting programmed control commands, the invention matches operation tickets corresponding to the target equipment and operation type. A robot then replaces manual operation, sequentially executing each step. Based on the sequential control results of each step, dual safety checks are performed, significantly improving the accuracy of state perception and system reliability. This invention, through the synergistic effect of robot execution, multi-sensor fusion, and dual safety checks, solves the problems of low substation operation and maintenance efficiency, high safety risks, and susceptibility to misoperation, thereby improving the efficiency and safety of substation operation and maintenance. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0021] Figure 1This is a flowchart illustrating a substation programmed control intelligent operation method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a substation programmed control intelligent operation device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0023] In the description of this invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" and "more than one" refer to two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0024] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0025] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include other steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or device.

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0027] like Figure 1As shown, this embodiment of the invention provides a method for intelligent operation of a substation with programmed control. The method includes steps S101-S106.

[0028] S101. Detect the programmed control instructions executed by substation operation and maintenance personnel on the target equipment for the target operation type.

[0029] In some embodiments, the programmed control instructions include a unique identifier for the target device and an operation code for the target operation type.

[0030] In some embodiments, the target operation type includes single device commissioning / decommissioning, single device start / stop, main transformer commissioning / decommissioning, protection setting switching, protection soft pressure plate commissioning / decommissioning, distribution network load transfer, or post-fault reconfiguration.

[0031] For example, embodiments of the present invention confirm operation permissions through an operation and maintenance personnel authentication system (such as biometrics + digital certificates), and ensure the legality of the instruction structure by combining instruction format verification (such as XML / JSON specifications). For example, the instruction must include a unique identifier for the target device (such as circuit breaker number), an operation type code (such as "opening-001"), and a timestamp to prevent invalid or tampered instructions from triggering operations. Real-time detection: A message queue (such as Kafka) is used to implement asynchronous processing of instructions, and a priority queue ensures that high-priority instructions (such as emergency opening) are executed first. At the same time, the instruction reception time and device status snapshot are recorded to provide a benchmark for subsequent operations.

[0032] S102. In response to the programmed control command, match and obtain the operation ticket corresponding to the target device and the target operation type.

[0033] In some embodiments, an operation ticket includes a sequence of operation steps to be performed sequentially.

[0034] As one possible implementation, step S102 can be specifically implemented as steps S1021-S1023.

[0035] S1021. Based on the unique identifier of the target device and the operation code of the target operation type, perform a matching query in the pre-generated standardized operation ticket database to determine the pre-verified operation ticket.

[0036] In some embodiments, the pre-verification operation ticket includes a sequence of sequentially executed operation steps, as well as device state transition logic, five-prevention interlocking logic conditions, and device state sequence in each operation step.

[0037] For example, the standardized operation ticket database uses distributed database technology to store the operation step sequence, equipment state transition logic, five-prevention interlocking logic conditions, and equipment state sequence. For instance, an expert system transforms substation wiring methods, equipment types, and five-prevention rules into a knowledge base, forming a rule base and a fact base. A state machine model is used to design equipment bay status registers, recording the real-time status of equipment such as circuit breakers and disconnectors. The operation step sequence is automatically generated by comparing the initial state with the target state. Standardized coding: The operation ticket includes a unique identifier, operation code, equipment state transition logic (such as circuit breaker opening / closing state transition conditions), five-prevention interlocking logic (such as rules to prevent disconnectors from being opened under load), and equipment state sequence (such as bay state arrays).

[0038] For example, embodiments of the present invention achieve precise matching in a standardized database using a hash table or a deep learning model (such as a BP neural network for word segmentation + a recurrent neural network for identifying the operation object) based on the unique identifier of the device (such as the circuit breaker number) and the operation code (such as "opening-001"). For example, a two-layer neural network model is used: the first layer determines the group of the operation ticket and the instruction (such as "interval level" or "device level"), and the second layer verifies the specific device information (such as the consistency between "50121 switch" and the device number in the instruction). Dynamic verification: After matching, the completeness of the operation ticket needs to be verified, including whether the sequence of operation steps covers all necessary steps (such as opening, voltage testing, and grounding), whether the device state transition logic conforms to the five-prevention rules (such as "the isolating switch can only be opened after the circuit breaker is opened"), and whether the state sequence is consistent with the real-time device state.

[0039] S1022. Based on the pre-verification operation ticket, rehearse the equipment status and operation ticket requirements before and after each operation step, and perform consistency verification to determine the verification result of the pre-verification operation ticket.

[0040] For example, the consistency verification mechanism includes: Equipment status verification: Equipment status signals are collected through sensors (such as microswitches and auxiliary contacts) and compared with the expected status in the operation ticket (such as "circuit breaker tripping signal") to ensure that the state transition is logical (such as "current drops to zero after tripping"). Operation step rationality verification: Based on the state transition method, it is verified whether the operation steps cover all state transition paths (such as "operation state → maintenance state" requires the steps of "tripping - voltage testing - grounding wire connection"), and whether there are invalid transitions (such as "pulling the disconnecting switch under load"). Five-prevention logic interlock verification: Combining logic interlocks (such as "disconnecting switch must not be pulled when the circuit breaker is not tripped") and physical interlocks (such as mechanical interlocks to prevent erroneous operations), intelligent locks (such as coded locks) and sensors (such as current transformers to monitor load current) are used to ensure that the operation sequence complies with safety regulations.

[0041] S1023. If the verification result is successful, the pre-verification operation ticket is determined as the operation ticket corresponding to the target device and the target operation type.

[0042] For example, exception handling and optimization include handling verification failures: If the pre-verification operation ticket fails (e.g., the device status does not match expectations), the system triggers an exception alarm and stops the operation, while recording error information for subsequent analysis. For instance, the operation log can be used to trace the operation trajectory and locate the cause of the erroneous operation (e.g., sensor failure or rule configuration error). Dynamic adjustment mechanism: In emergency scenarios (e.g., black start), the operation steps can be temporarily adjusted through permission hierarchy and emergency unlock functions, but the operation ticket must be completed and re-verified afterward to ensure the safety of subsequent steps.

[0043] S103. Real-time acquisition of multi-sensor data from the target device and the operating robot.

[0044] S104. Based on multi-sensor data and operation tickets, control the operation robot to perform sequential control operations on the target equipment step by step, and obtain the sequential control operation result of the current operation step.

[0045] As one possible implementation, step S104 can be specifically implemented as steps S1041-S1044.

[0046] S1041. Parse the operation content of the current operation step in the operation ticket and generate the robot drive instruction for the current operation step.

[0047] In some embodiments, the operation content includes mechanical motion trajectory, operation target pose and expected physical parameters; the robot drive instructions include various actions to be executed sequentially, wherein the types of actions include opening and closing, turning knobs, plugging and unplugging, and positioning.

[0048] For example, embodiments of the present invention can employ micro-applications to parse the operation content in the operation ticket using natural language processing technology. For instance, given the input task "a 10kV circuit is switched from operation to maintenance," the system automatically breaks it down into a sequence of steps such as "opening the circuit breaker - voltage testing - grounding the circuit breaker" based on a knowledge graph and rule engine. It also extracts the mechanical motion trajectory (e.g., the linear interpolation path of the robotic arm when the circuit breaker is opened), the target pose (e.g., the angle deviation after the disconnector is opened ≤0.5°), and the expected physical parameters (e.g., the current after opening ≤5A). Drive command generation: A Cartesian space trajectory planning algorithm (e.g., spline interpolation) is used to generate the robotic arm's motion trajectory. Combined with a joint space optimization algorithm (e.g., genetic algorithm), the angles of each joint are adjusted to ensure the end effector accurately reaches the target pose. Action types include opening and closing (based on PID control torque accuracy ±5%), knob (using impedance control to avoid excessive force), insertion and removal (based on visual guidance positioning), and positioning (high-precision lidar calibration). Drive commands are encapsulated in XML format, including timestamps, action sequences, and fault-tolerant parameters.

[0049] S1042. Send robot drive instructions to the operating robot to control the robotic arm and end effector of the operating robot to perform the current operation steps on the target device.

[0050] For example, embodiments of the present invention can employ flexible robotic arm control algorithms (such as LQR optimal control + input shaping method) to achieve torque-position dual closed loop through impedance control, suppressing residual vibration. For instance, in opening and closing operations, the force sensor at the end of the robotic arm provides real-time feedback on the contact force, dynamically adjusting the motion acceleration to ensure that the torque during opening does not exceed 120% of the rated value, avoiding mechanical damage. End-effector operation: Based on the design principles of large-scale spatial end-effectors (such as "large load, large tolerance, soft capture"), a flexible steel wire rope capture element achieves a ±20mm operational tolerance, a ball screw drive mechanism eliminates posture deviations, and a form-locking four-bar linkage completes high-rigidity locking. During operation, a multi-sensor system (torque sensor, positioning sensor) monitors the drag contact force (controlled within the system's allowable range ≤100N) and locking status in real time, ensuring the reliability of the electrical connection.

[0051] S1043. Based on the multi-sensor data in the current operation step, determine whether the current operation step has been completed.

[0052] In some embodiments, the multi-sensor data includes force sensor data, torque sensor data, visual data, high-precision positioning data, and device electrical status data.

[0053] For example, embodiments of the present invention can integrate a force sensor (monitoring operating force), a vision sensor (YOLOv5 identifying equipment status characteristics, such as the open / closed state of a disconnector switch), a high-precision positioning sensor (LiDAR acquiring equipment pose with an accuracy ≤0.1mm), and an electrical sensor (current transformer monitoring load current). Multi-source data is fused using Kalman filtering, and the operation is deemed complete when the following conditions are simultaneously met: Force sensor data: the tripping operation torque reaches the rated value and stabilizes within ±5% error; Vision data: the SSIM similarity between the equipment status characteristics and the expected visual state on the operation ticket (e.g., "disconnector switch in place") is ≥0.9; Positioning data: equipment pose deviation ≤0.5mm; Electrical data: the current drops to zero (≤5A) after tripping and the auxiliary contact signal is correctly triggered.

[0054] S1044. If the current operation step is completed, determine the sequential control operation result of the current operation step based on the multi-sensor data in the current operation step.

[0055] In some embodiments, the sequential control operation result includes the operation status of the current operation step, the status information of the target device, and the process data of the current operation step.

[0056] For example, the sequential control operation results include the operation status (success / failure), equipment status information (such as the status of the circuit breaker after tripping, and the position of the disconnector switch), and process data (such as operation time, torque curve, and visual image sequence). For instance, the tripping operation results record the tripping time, maximum torque value, current change curve, and visual comparison chart, supporting subsequent time-series analysis. Data recording and analysis: The results are stored in a database through an equipment data acquisition system (sensors + signal collectors + data processors), supporting real-time monitoring, fault diagnosis, and predictive maintenance. For example, based on historical operation data, machine learning models (such as LSTM) are used to predict the probability of equipment failure and trigger maintenance alarms in advance; by comparing the time-series change curve with a standard template, early warning of anomalies is achieved (such as triggering an alarm when the torque-time curve deviates from the standard threshold by ≥10%).

[0057] S105. Based on multi-sensor data and the sequential control operation results of the current operation step, perform dual safety verification to determine whether the current operation step has passed the verification.

[0058] As one possible implementation, step S105 can be specifically implemented as steps S1051-S1054.

[0059] S1051. Based on multi-sensor data and the sequential control operation results of the current operation steps, extract electrical-related data and visual-related data.

[0060] For example, embodiments of the present invention can use high-precision sensors (such as microswitches, auxiliary contacts, and magnetic induction sensors) to collect equipment status signals, combined with a dual confirmation mechanism of remote signaling + pressure sensing / image recognition. For instance, the position of the disconnecting switch is dually verified by auxiliary contact signals and pressure sensor data, meeting the requirement of non-same-source dual confirmation. Signal acquisition uses Nyquist's theorem to set the sampling rate (e.g., 1000Hz), and uses a Butterworth low-pass filter to suppress noise and ensure signal quality. Verification logic: Based on the expected state requirements in the operation ticket (e.g., circuit breaker tripping signal, current threshold ≤ 5A), it is compared with the real-time collected electrical data. For example, after the tripping operation, it is necessary to verify whether the load current monitored by the current transformer has dropped to zero, whether the auxiliary contact signal has been triggered, and to perform logical verification in conjunction with the five-prevention interlocking logic (e.g., "the disconnecting switch must not be pulled when the circuit breaker is not tripped") to ensure that the state transition complies with safety regulations.

[0061] For example, embodiments of the present invention can deploy high-definition cameras or infrared thermal imagers to capture equipment status images, and perform image analysis using grayscale, binarization, feature extraction (such as edge detection), and BP neural network models. For instance, the YOLOv5 algorithm can be used to identify the open / closed status of disconnect switches, instrument readings (such as SSIM similarity ≥ 0.9), and indicator light color changes, and compare them with the expected visual status in the operation ticket (such as "disconnect switch in position"). Behavioral recognition technology: Deep learning models (such as convolutional neural networks) are combined to analyze personnel behavior, such as not wearing a safety helmet or illegally crossing a safety fence, to achieve automatic warnings of violations. Visual data is linked with electrical signals to form a non-homogeneous dual confirmation; for example, the position of the disconnect switch is verified by both remote signaling signals and image recognition to avoid misjudgment based on a single signal.

[0062] S1052. Based on electrical-related data, perform safety verification to obtain electrical verification results.

[0063] For example, step S1052 can be specifically implemented as steps A1-A3.

[0064] A1. Extract equipment status signals from electrical-related data.

[0065] In some embodiments, the device status signal includes a microswitch status signal, an auxiliary contact signal, and a magnetic induction sensor signal.

[0066] For example, embodiments of the present invention can employ high-precision sensors (such as microswitches, auxiliary contacts, and magnetic induction sensors) to acquire equipment status signals, combined with a dual confirmation mechanism of remote signaling + pressure sensing / image recognition. For instance, the position of the disconnector switch is dually verified through auxiliary contact signals and pressure sensor data, meeting the requirement of non-homogeneous dual confirmation. Signal acquisition uses Nyquist's theorem to set the sampling rate (e.g., a 500Hz signal requires a 1000Hz sampling rate, with a sampling period of 1ms), and uses a Butterworth low-pass filter (cutoff frequency 200Hz) to suppress noise and ensure signal quality. Data preprocessing: The original signal is amplified, filtered (e.g., sliding window mean filtering), and denoised. Multi-sensor data is fused using a Kalman filter algorithm to eliminate instantaneous impact interference and improve data accuracy.

[0067] A2. Extract the expected status requirements of the current operation step in the operation ticket.

[0068] In some embodiments, the expected state requirements include the expected switch state, the expected contact position, and the expected sensor reading range.

[0069] For example, the operation ticket clearly specifies the expected switch state (e.g., circuit breaker tripped), the expected contact position (e.g., disconnector tripping angle deviation ≤ 0.5°), and the expected sensor reading range (e.g., current ≤ 5A after tripping). For instance, the operation ticket might specify "the current drops to zero and the auxiliary contact signal is triggered after the circuit breaker trips," combined with five-prevention interlocking logic (e.g., "the disconnector must not be pulled when the circuit breaker is not tripped") for logical verification. State transition verification: Based on the state transition method, verify whether the operation steps cover all state transition paths (e.g., "operation state → maintenance state" requires the steps of "tripping - voltage testing - grounding wire connection"), and check for invalid transitions (e.g., "pulling the disconnector under load").

[0070] A3. Based on the equipment status signals and the expected status of the current operation steps, conduct comparative analysis to determine the electrical verification results.

[0071] For example, embodiments of the present invention can compare real-time collected electrical data (such as current, voltage, and switch status) with the expected state on the operation ticket, and verify whether the state transition complies with safety regulations by combining the five-prevention interlocking logic. For instance, after a tripping operation, it is necessary to verify whether the load current monitored by the current transformer has dropped to zero and whether the auxiliary contact signal has been triggered. Anomaly determination: If the comparison result does not meet expectations (such as the current not dropping to zero or the auxiliary contact not being triggered), the electrical verification is determined to have failed, triggering an adaptive retry mechanism or terminating the operation and generating an anomaly alarm message.

[0072] S1053. Based on visually relevant data, perform security verification to obtain visual verification results.

[0073] For example, step S1053 can be specifically implemented as steps B1-B3.

[0074] B1. Extract visual state features from visually related data.

[0075] In some embodiments, visual status features include device status features, indicator features, and meter features.

[0076] For example, embodiments of the present invention can deploy high-definition cameras or infrared thermal imagers to capture equipment status images, and perform image analysis using grayscale, binarization, feature extraction (such as edge detection, texture analysis, shape recognition), and BP neural network / CNN models. For instance, the YOLOv5 algorithm can be used to identify the open / closed status of disconnect switches, instrument readings (such as SSIM similarity ≥ 0.9), and indicator light color changes, and compare them with the expected visual status in the operation ticket. Multimodal fusion: Combining data from visual sensors (such as high-definition cameras) and electrical sensors (such as current transformers) forms non-homogeneous dual confirmation. For example, the position of the disconnect switch is verified by both remote signaling signals and image recognition, avoiding misjudgment based on a single signal.

[0077] B2. Extract the expected visual state of the current operation step in the operation ticket.

[0078] In some embodiments, the expected visual state includes the expected mechanical position, the expected state indicator display state, and the expected instrument reading range.

[0079] For example, the operation ticket clearly specifies the expected mechanical position (e.g., the deviation of the disconnector switch opening angle ≤ 0.5°), the expected status indicator display status (e.g., the indicator light color is green), and the expected instrument reading range (e.g., the ammeter reading ≤ 5A). For instance, the operation ticket might specify that "after the disconnector switch is opened, the opening position indicator light should illuminate and the angle deviation ≤ 0.5°." Process action recognition: Deep vision action recognition technology is used to identify a series of process actions performed by substation site personnel (e.g., whether the substation maintenance operation process conforms to the specifications). For example, when personnel perform substation maintenance operations, the system automatically identifies whether they are following the process of "cleaning indoor and outdoor switchgear → tightening → insulation inspection → oil leakage treatment." If the operation is not performed according to the process, the system will automatically identify it and issue a real-time alarm.

[0080] B3. Based on the visual state characteristics and the expected visual state of the current operation step, conduct comparative analysis to determine the visual verification result.

[0081] For example, embodiments of the present invention can compare real-time image features (such as equipment status features, indicator features, and instrument features) with the expected visual state on the operation ticket, and use SSIM similarity ≥ 0.9 to determine the consistency of the visual state. For example, the opening and closing status of the disconnector switch is analyzed by an image recognition algorithm and compared with the expected status (such as "opening in place"). If the similarity is ≥ 0.9, the visual verification is deemed successful. Anomaly detection: If the comparison result does not meet expectations (such as the disconnector switch not being fully opened or the instrument reading being out of range), the visual verification is deemed to have failed, triggering an adaptive retry mechanism or terminating the operation and generating an anomaly alarm message. At the same time, combined with personnel behavior recognition technology (such as not wearing a safety helmet or crossing a safety fence), automatic early warning of violations is achieved, improving the safety management level of the substation.

[0082] S1054. Based on the electrical verification results and visual verification results, determine whether the current operation step has passed verification.

[0083] For example, electrical verification results and visual verification results must simultaneously meet certain conditions, or be determined through a weighted scoring system. For instance, if any verification fails, an adaptive retry mechanism is triggered (adjusting the robot's force, fine-tuning its pose, or a brief delay). If the retry fails again, it is considered a permanent fault, operation is terminated, and an abnormal alarm is generated. Timing monitoring and anomaly warning: The electrical and visual verification results of each step are recorded, and timing change curves are plotted. Similarity matching (e.g., the torque-time curve of a normal tripping operation) is performed with standard timing feature templates (e.g., beta distribution corrected reliability values) to achieve early anomaly warning. For example, an alarm is triggered when the torque-time curve deviates from the standard threshold by ≥10%, indicating a risk of equipment failure.

[0084] Optionally, step S105 may further include steps S1055-S1057.

[0085] S1055. Based on multi-sensor data and the sequential control operation results of the current operation steps, extract mechanical sensing data.

[0086] For example, embodiments of the present invention may employ high-precision force sensors (such as piezoelectric force sensors, with a range of 0-500N and an accuracy of ±0.5%), torque sensors (such as strain gauge torque sensors, with a range of 0-20N·m and an accuracy of ±1%), vibration sensors (such as triaxial accelerometers, with a sampling rate of 2kHz), and tactile sensors (such as flexible array tactile sensors, with a resolution of 1 square millimeter). For instance, sensors are deployed at the end effector of the robotic arm and at the contact points of the operating mechanism to collect real-time data on contact force, torque, vibration frequency, and pressure distribution during operation. The raw sensor data is amplified, filtered (e.g., using a Butterworth low-pass filter, with a cutoff frequency of 500Hz), and denoised using a signal conditioning module. Multi-sensor data is then fused using a Kalman filter algorithm to eliminate noise interference and improve data accuracy. For example, torque data is processed using a sliding window mean filter to suppress instantaneous impact interference and ensure data stability.

[0087] S1056. Based on the mechanical sensing data, perform safety verification to obtain the mechanical verification results.

[0088] For example, embodiments of the present invention can set mechanical parameter thresholds based on equipment operation specifications. For instance, during circuit breaker opening operations, the maximum permissible torque is 120% of the rated value (e.g., 12 N·m); exceeding this triggers an over-limit alarm. During disconnector switch opening and closing operations, the contact force must be maintained within the range of 50-100 N; values ​​below or above the threshold are considered abnormal. Machine learning algorithms (such as Support Vector Machines, SVM) are used to classify and identify mechanical data patterns. For example, by training a model using historical operation data, the model identifies characteristic patterns of normal operations (e.g., the torque curve smoothly rises to its peak and then stabilizes during opening) and abnormal operations (e.g., sudden torque changes, abnormal vibration frequencies). The model then uses a Dynamic Time Warping (DTW) algorithm to compare real-time data with a standard template to determine whether the operation process complies with safety regulations.

[0089] S1057. Based on the electrical verification results, visual verification results, and mechanical verification results, determine whether the current operation step has passed verification.

[0090] For example, embodiments of the present invention may require that electrical verification results (such as the consistency of equipment status signals), visual verification results (such as equipment status image recognition), and mechanical verification results (such as torque and contact force data) simultaneously meet certain conditions, or may use a weighted scoring method for comprehensive determination. For example, using AND logic: the current operation step is determined to be safe only when all three verifications pass; if any verification fails, an adaptive retry mechanism is triggered. Weighted scoring system: electrical, visual, and mechanical verification results are assigned weights (e.g., electrical 40%, visual 30%, mechanical 30%), and a comprehensive score is determined based on fuzzy logic. For example, if the mechanical verification score is 85 points (out of 100), the electrical score is 90 points, and the visual score is 95 points, then the comprehensive score is 85×0.3 + 90×0.4 + 95×0.3 = 89.5 points, which exceeds a preset threshold (e.g., 80 points), thus determining that the operation has passed.

[0091] S106. If the verification passes, proceed to the next operation step until the programmed control instructions are completed.

[0092] For example, if the current step passes the verification, the next operation step is triggered; if the verification fails, an adaptive retry mechanism is initiated (such as adjusting the operation force, fine-tuning the posture, or a short delay). If the retry still fails, the operation is terminated and an abnormal alarm message is generated. Timing monitoring: Record the electrical, visual, and mechanical verification results of each step, plot the timing change curve, and perform similarity matching with standard timing feature templates (such as the torque-time curve of a normal tripping operation) to achieve early warning of abnormalities.

[0093] This invention provides a method for intelligent operation of programmed control in substations. Upon detecting a programmed control command, it matches an operation ticket corresponding to the target equipment and operation type. A robot then replaces manual operation, sequentially executing each step. Based on the sequential control result of each step, dual safety checks are performed, significantly improving the accuracy of state perception and system reliability. This invention, through the synergistic effect of robot execution, multi-sensor fusion, and dual safety checks, solves the problems of low substation operation and maintenance efficiency, high safety risks, and susceptibility to misoperation, thereby improving the efficiency and safety of substation operation and maintenance.

[0094] Furthermore, this invention completely replaces manual operation with a robot for on-site procedures, achieving physical isolation between maintenance personnel and high-voltage equipment, fundamentally eliminating direct personal safety risks such as electric shock and mechanical injury. It employs a dual (or even triple, including force sensing) non-homogeneous safety verification mechanism based on electrical signals and machine vision. The two verification methods are independent and complementary; even if a single sensor or verification method fails, the other method can still provide protection, greatly reducing the probability of misoperation due to signal distortion, contact corrosion, visual misjudgment, etc., achieving inherent safety at the technical level. The verification mechanism not only verifies the final state of the operation but also monitors the operation process (such as force and trajectory) through force sensing and other means, effectively avoiding secondary risks that may arise during mechanical collisions or improper operation.

[0095] This invention compresses the traditional manual switching operation process, which takes tens of minutes to several hours, into a fully automated process within minutes, increasing efficiency by more than tenfold. Through robotics, it successfully solves the automation challenge of equipment without electric operating mechanisms, such as 10kV medium-voltage switchgear, filling a gap in existing sequential control systems. For the first time, it achieves programmed control coverage across all voltage levels and equipment types in substations, significantly expanding the scope of automation applications. This improved operational efficiency directly translates into faster response times for grid fault recovery, operation mode adjustments, and equipment maintenance, significantly reducing power outage time for users and improving power supply reliability.

[0096] This invention overcomes the uncertainty and limitations of single information sources by fusing multi-dimensional sensor data, including force, vision, position, and electrical sensors. This results in a more comprehensive and accurate perception and judgment of equipment status, avoiding errors caused by subjective human judgment. It features an anti-disturbance retry mechanism that can distinguish between transient and permanent faults and automatically adjust strategies (such as fine-tuning posture and force) for retrying, improving the success rate and robustness of operations in complex field environments. By analyzing the time-series curves of various verification results during operation, it can compare them with standard templates to proactively detect potential fault signs such as mechanical jamming and mechanism fatigue, achieving a shift from "post-event processing" to "pre-event warning."

[0097] This invention liberates maintenance personnel from repetitive, tedious, and high-risk traditional operations, transforming their roles into "monitors, decision-makers, and managers" of the system. It optimizes human resource allocation and provides a solid technical foundation for unmanned and centralized monitoring of substations. All operational instructions, sensor data, and verification results are fully recorded throughout the entire process, forming a traceable, analyzable, and tamper-proof digital twin archive. This provides a valuable data foundation for accident tracing, performance analysis, condition assessment, and algorithm optimization, strongly supporting the digital transformation of the power grid.

[0098] This invention, through the organic synergy of three core technologies—robot execution, multi-sensor fusion, and dual security verification—not only effectively solves the two major pain points of "safety" and "efficiency" that have long constrained substation operation and maintenance, but also achieves significant breakthroughs in reliability, intelligence, and digitalization.

[0099] Optionally, the substation programmed control intelligent operation method provided in this embodiment of the invention further includes steps S201-S204.

[0100] S201. Record the electrical verification results, visual verification results, and mechanical verification results for each operation step.

[0101] For example, embodiments of the present invention can employ a structured log system to record the electrical, visual, and mechanical verification results of each operation step. Each record includes a timestamp (accurate to milliseconds), verification type (e.g., "electrical verification"), result value (0 = failure / 1 = pass), raw sensor data (e.g., current value, image hash value), and operation step ID. For instance, the record for the circuit breaker tripping step includes the tripping time, current curve hash value, visual image feature vector, and mechanical sensor peak data. Data is written to a time-series database (e.g., InfluxDB) in real time via a Kafka message queue, supporting high-concurrency writing and fast querying. Historical data is retained for one year, supporting audit traceability and trend analysis.

[0102] S202. Based on the electrical verification results, visual verification results, and mechanical verification results of each operation step, plot the timing change curves of various verification results during the execution of programmed control instructions.

[0103] S203. Based on the time-series change curves of various verification results and the standard time-series feature templates corresponding to various verification results, perform feature matching and similarity calculation to determine the similarity of various verification results.

[0104] For example, embodiments of the present invention can extract standard time-series feature templates for electrical, visual, and mechanical verification results based on historical normal operation data. For instance, the electrical verification template is a sequence of all 1s (ideally all passes), and the visual verification template is a feature vector of the device status image (e.g., SSIM ≥ 0.9). Similarity algorithm selection: Euclidean distance is used to calculate the similarity between real-time data and the standard template. For example, if the Euclidean distance between the real-time electrical verification data and the standard template is 8.72, the similarity calculation is 1 / (1+8.72) = 0.12. When the similarity is below the threshold of 0.8, it is determined to be abnormal.

[0105] S204. If the similarity of any verification result is less than the preset threshold, an abnormal alarm message is generated.

[0106] In some embodiments, abnormal alarm information is used to indicate abnormalities in the execution of programmed control instructions.

[0107] For example, when the similarity of any verification result falls below a threshold, a three-level alarm is triggered: Level 1 Alarm: A system pop-up notification is displayed, and an alarm log is recorded (including timestamp, operation steps, and similarity value). Level 2 Alarm: A push notification is sent to maintenance personnel via SMS / email, along with a screenshot of the real-time time-series curve. Level 3 Alarm: An audible and visual alarm is activated, and an emergency plan is initiated (e.g., automatically suspending operation or activating backup equipment). Root Cause Analysis Support: Alarm information is associated with the original sensor data of the operation steps, supporting subsequent root cause diagnosis. For example, when electrical verification fails, abnormal points in the current curve can be traced to pinpoint sensor drift or equipment failure.

[0108] For example, embodiments of the present invention can dynamically update standard feature templates based on machine learning algorithms (such as LSTM time series prediction). The system analyzes historical data quarterly and automatically adjusts template parameters to adapt to changes in equipment status (such as mechanical feature shifts caused by mechanical wear). New templates must undergo cross-validation (such as leave-one-out method) to ensure generalization ability and avoid overfitting. Template updates must be manually reviewed before taking effect to ensure security.

[0109] Thus, this invention integrates the specific technical implementations of verification result recording, timing analysis, feature matching, and anomaly alarms in the sequential control operation of intelligent substations. Combined with timing curves and similarity calculation examples from code execution results, a complete technical solution is formed. This solution supports real-time monitoring and anomaly warning of programmed control commands, improving the safety and reliability of substation operation and maintenance.

[0110] Optionally, the substation programmed control intelligent operation method provided in this embodiment of the invention further includes steps S301-S304.

[0111] S301. If the current operation step fails the verification, the adaptive retry mechanism is started.

[0112] In some embodiments, the adaptive retry mechanism includes adjusting the force of the robot operation, fine-tuning the operation pose, and a short delay.

[0113] For example, if any dimension of the electrical, visual, or mechanical verification fails (e.g., the electrical verification result is 0), the system automatically triggers an adaptive retry mechanism. For instance, during a circuit breaker tripping operation, if the current signal does not drop to the threshold (≤5A) or the visual recognition shows that the isolating switch has not fully tripped, the verification is considered a failure. The retry mechanism is initiated within 100ms after a verification failure, using a message queue to schedule the operating robot to execute the adjusted parameters in real time, ensuring operational continuity.

[0114] For example, force adjustment: An impedance control algorithm is used to reduce the output torque by 10% with each retry (exponential decay). For instance, the torque is adjusted to 90% of the original value on the first retry and to 81% on the second retry, ensuring that the operating force is within a safe threshold (e.g., not exceeding 120% of the rated value). Pose fine-tuning: The X / Y axis position of the robotic arm is fine-tuned (±2mm alternating adjustment) based on a visual feedback system (e.g., YOLOv5 + depth camera). For instance, the pose of the robotic arm end effector is dynamically adjusted by recognizing the edge of the device through image recognition, ensuring an operating accuracy of ≤0.5mm. Delay strategy: A 0.5-second operation delay is added with each retry to ensure that the device status is stable before proceeding with the operation. For instance, a delay is inserted after the circuit breaker tripping operation to wait for the arc to completely extinguish before proceeding to the next operation.

[0115] For example, the system is set to a maximum of 3 retries. After each retry, the electrical, visual, and mechanical triple safety checks are re-executed. Success criteria: If all three checks pass in any retry (electrical, visual, and mechanical check results are all 1), the retry is considered successful, and the next operation step continues. For example, if the code execution result shows that the triple checks passed after the first retry, the system continues the process. Permanent fault determination: If all three consecutive retries fail, it is considered a permanent fault, the programmed control instructions are aborted, a structured fault report is generated, and retry history data (adjustment parameters, verification results, timestamps, etc.) is recorded.

[0116] S302. After the adaptive retry mechanism is completed, the current operation steps are re-executed and double security checks are performed to obtain the retry check result.

[0117] S303. If the retry verification result is successful, record it as a successful retry and continue the process.

[0118] S304. If the retry verification result is that the verification fails, it is determined to be a permanent fault and the programmed control instructions are terminated.

[0119] For example, a fault report includes the number of retries, adjustment parameters (force attenuation rate, pose adjustment amount, delay time), verification results (electrical / visual / mechanical), timestamp, and a snapshot of the device status. For instance, the report might show that on the first retry, the torque was adjusted to 90%, the pose was adjusted to X-axis +2mm, the delay was 0.5 seconds, and the verification result was a full pass. Report storage and push: Reports are saved to a local database in CSV format and pushed to maintenance personnel via the system message queue.

[0120] For example, in anomaly alarm linkage: after a permanent fault is determined, the system triggers a three-level alarm mechanism: Level 1 alarm: A system pop-up notification is displayed, and an alarm log is recorded. Level 2 alarm: A notification is sent to maintenance personnel via SMS / email, along with a fault report and operation timing curve. Level 3 alarm: The system activates audible and visual alarm devices and initiates emergency plans (such as switching to manual operation mode).

[0121] For example, during the retry process, electrical, visual, and mechanical triple checks are re-executed to ensure operational safety after parameter adjustments. For instance, after the retry, it is verified again whether the current signal has dropped to the threshold, whether the visual recognition shows the device status is correct, and whether the mechanical data is within a safe range. If the retry is successful, the next operation step is executed, conforming to the overall flow control logic of the programmed control instructions. If the retry fails, the instructions are aborted to ensure operational safety.

[0122] Thus, this embodiment of the invention integrates the specific technical implementation of the adaptive retry mechanism in the sequential control operation of intelligent substations, dynamic parameter adjustment strategies, retry count limits, fault report generation, and abnormal alarm linkage mechanisms, forming a complete technical solution. This solution supports the efficient and secure execution of programmed control commands, improving the level of intelligence in substation operation and maintenance.

[0123] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0124] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0125] Figure 2 A schematic diagram of a substation programmed control intelligent operation device provided by an embodiment of the present invention is shown. The control device 400 includes a communication module 401 and a processing module 402.

[0126] The communication module 401 is used to detect the programmed control commands executed by substation operation and maintenance personnel on the target equipment for the target operation type.

[0127] The processing module 402 is used to respond to the programmed control instructions and match the operation ticket corresponding to the target device and the target operation type. The operation ticket includes a sequence of operation steps to be executed sequentially. The module acquires multi-sensor data of the target device and the operating robot in real time, and controls the operating robot to perform sequential control operations on the target device step by step based on the multi-sensor data and the operation ticket, so as to obtain the sequential control operation result of the current operation step. Based on the multi-sensor data and the sequential control operation result of the current operation step, a double safety check is performed to determine whether the current operation step has passed the check. If the check passes, the next operation step is executed until the programmed control instructions are completed.

[0128] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 500 includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program 503, it implements the steps in the above-described method embodiments. Alternatively, when the processor 501 executes the computer program 503, it implements the functions of each module / unit in the above-described device embodiments.

[0129] For example, the computer program 503 may be divided into one or more modules / units, which are stored in the memory 502 and executed by the processor 501 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 503 in the electronic device 500.

[0130] The processor 501 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0131] The memory 502 can be an internal storage unit of the electronic device 500, such as a hard disk or memory of the electronic device 500. The memory 502 can also be an external storage device of the electronic device 500, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 500. Furthermore, the memory 502 can include both internal and external storage units of the electronic device 500. The memory 502 is used to store the computer program and other programs and data required by the terminal. The memory 502 can also be used to temporarily store data that has been output or will be output.

[0132] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for intelligent operation of programmed control in a substation, characterized in that, include: Detects the programmed control commands executed by substation operation and maintenance personnel on target equipment for the target operation type; In response to the programmed control command, an operation ticket corresponding to the target device and the target operation type is obtained; the operation ticket includes a sequence of operation steps to be executed sequentially. The system acquires multi-sensor data from the target device and the operating robot in real time, and controls the operating robot to perform sequential control operations on the target device step by step based on the multi-sensor data and the operation ticket, thereby obtaining the sequential control operation result of the current operation step. Based on the multi-sensor data and the sequential control operation result of the current operation step, a dual safety verification is performed to determine whether the current operation step has passed the verification. If the verification passes, proceed to the next step until the programmed control instructions are completed.

2. The intelligent operation method for programmed control of substations according to claim 1, characterized in that, The programmed control instructions include a unique identifier for the target device and an operation code for the target operation type; The step of responding to the programmed control command and matching the operation ticket corresponding to the target device and the target operation type includes: Based on the unique identifier of the target device and the operation code of the target operation type, a matching query is performed in the pre-generated standardized operation ticket database to determine the pre-verified operation ticket; the pre-verified operation ticket includes a sequence of sequentially executed operation steps, as well as the device state transition logic, five-prevention interlocking logic conditions, and device state sequence in each operation step; Based on the pre-verification operation ticket, the equipment status and operation ticket requirements before and after each operation step are rehearsed, and consistency verification is performed to determine the verification result of the pre-verification operation ticket. If the verification result is successful, the pre-verification operation ticket is determined as the operation ticket corresponding to the target device and the target operation type.

3. The substation programmed control intelligent operation method according to claim 1, characterized in that, The process of controlling the operating robot to perform sequential control operations on the target device step by step, based on the multi-sensor data and the operation ticket, to obtain the sequential control operation result of the current operation step, includes: The operation content of the current operation step in the operation ticket is analyzed to generate the robot drive instruction for the current operation step; the operation content includes mechanical motion trajectory, operation target pose and expected physical parameters; the robot drive instruction includes various actions to be executed in sequence, wherein the types of actions include opening and closing the circuit breaker, turning the knob, plugging and unplugging and positioning. The robot drive command is sent to the operating robot to control the robotic arm and end effector of the operating robot to perform the current operation steps on the target device; Based on the multi-sensor data in the current operation step, determine whether the current operation step has been completed; the multi-sensor data includes force sensor data, torque sensor data, vision data, high-precision positioning data, and equipment electrical status data; If the current operation step is completed, the sequential control operation result of the current operation step is determined based on the multi-sensor data in the current operation step. The sequential control operation result includes the operation status of the current operation step, the status information of the target device, and the process data of the current operation step.

4. The intelligent operation method for programmed control of substations according to claim 1, characterized in that, The method of performing dual security checks based on the multi-sensor data and the sequential control operation result of the current operation step to determine whether the current operation step has passed the check includes: Based on the multi-sensor data and the sequential control operation results of the current operation step, electrical-related data and visual-related data are extracted; Based on the aforementioned electrical-related data, a safety verification is performed to obtain the electrical verification results; Based on the aforementioned visual data, a security verification is performed to obtain the visual verification result; Based on the electrical verification results and the visual verification results, determine whether the current operation step has passed verification.

5. The intelligent operation method for programmed control of substations according to claim 4, characterized in that, The method of performing dual security checks based on the multi-sensor data and the sequential control operation result of the current operation step to determine whether the current operation step has passed the check also includes: Based on the multi-sensor data and the sequential control operation results of the current operation step, extract mechanical sensing data; Based on the aforementioned mechanical sensing data, a safety verification is performed to obtain the mechanical verification results; Based on the electrical verification results, the visual verification results, and the mechanical verification results, determine whether the current operation step has passed verification.

6. The substation programmed control intelligent operation method according to claim 4, characterized in that, The step of performing safety verification based on the electrical-related data to obtain electrical verification results includes: Extract the equipment status signals from the electrical-related data, including microswitch status signals, auxiliary contact signals, and magnetic induction sensor signals; Extract the expected state requirements of the current operation step in the operation ticket; the expected state requirements include the expected switch state, the expected contact position, and the expected sensor reading range; Based on the equipment status signals and the expected status of the current operation step, a comparative analysis is performed to determine the electrical verification results.

7. The substation programmed control intelligent operation method according to claim 4, characterized in that, The process of performing security verification based on the visually relevant data to obtain a visual verification result includes: Extract visual state features from the visually related data, including device state features, indicator features, and instrument features; Extract the expected visual state of the current operation step in the operation ticket; the expected visual state includes the expected mechanical position, the expected status indicator display status, and the expected instrument reading range; Based on the visual state characteristics and the expected visual state of the current operation step, a comparative analysis is performed to determine the visual verification result.

8. The substation programmed control intelligent operation method according to any one of claims 1 to 7, characterized in that, The method further includes: Record the electrical verification results, visual verification results, and mechanical verification results for each operation step; Based on the electrical verification results, visual verification results, and mechanical verification results of each operation step, plot the time-series change curves of various verification results during the execution of programmed control commands; Based on the time-series change curves of various verification results and the standard time-series feature templates corresponding to various verification results, feature matching and similarity calculation are performed to determine the similarity of various verification results. If the similarity of any verification result is less than a preset threshold, an abnormal alarm message is generated; the abnormal alarm message is used to indicate that the programmed control instruction is executed abnormally.

9. The substation programmed control intelligent operation method according to any one of claims 1 to 7, characterized in that, The method further includes: If the current operation step fails the verification, an adaptive retry mechanism is activated. The adaptive retry mechanism includes adjusting the force of the operating robot, fine-tuning the operation posture, and a short delay. After the adaptive retry mechanism is completed, the current operation steps are re-executed and double security checks are performed to obtain the retry check result; If the retry verification result is successful, then record it as a successful retry and continue the process; If the retry verification fails, it is determined to be a permanent fault and the programmed control instructions are terminated.

10. A substation programmed control intelligent operating system, the control system comprising electronic equipment, the electronic equipment comprising a memory and a processor, the memory storing a computer program, the processor being configured to call and run the computer program stored in the memory to perform the method as described in any one of claims 1 to 9.

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