A remote operation and maintenance control method and system with dual interactive modes

Abstract

The present invention discloses a remote operation and maintenance control method and system in a dual interactive mode, which relates to the field of remote operation and maintenance control technology, including: establishing a remote two-way communication connection, collecting substation data for pre-processing; judging the equipment status to determine the execution strategy; comparing the result with the preset standard configuration according to the result, triggering the corresponding rollback or repair operation; analyzing the abnormal state of the equipment, generating a fault diagnosis report; establishing an early warning mechanism based on the diagnosis report, and dynamically adjusting the early warning response by classification. The present invention adopts a multi-level control mechanism to dynamically adjust the execution strategy during the task execution process. When multiple tasks are concurrent, resources are automatically allocated through task scheduling and priority control. Effectively improve the response speed and processing efficiency of the operation and maintenance system. After each task or fault processing, the system automatically archives the operation results to the log database, and adjusts future strategies according to the task execution effect.

Description

Technical Field

[0001] The present invention relates to the technical field of remote operation and maintenance control, and specifically to a remote operation and maintenance control method and system in a dual interactive mode. Background Art

[0002] The traditional operation and maintenance of substation equipment mainly relies on manual inspection and on-site maintenance. This model not only faces severe challenges in power systems with heavy tasks and wide distribution, but also has problems such as delayed response and untimely fault handling due to frequent manual intervention. As the distribution area of ​​equipment continues to expand, especially in substations in remote areas, inspection and maintenance require a lot of manpower and material resources, which is not only costly, but also difficult to achieve high-frequency, real-time monitoring and response. When a problem occurs with the equipment, it can often only be discovered by on-site personnel, resulting in delayed location and repair of the problem, affecting the stability of the power grid and the quality of power supply.

[0003] Although the existing automated operation and maintenance system has introduced some remote monitoring and data collection technologies, its capabilities are still limited and mainly remain at the level of simple status alarm and monitoring. The traditional system lacks refined data analysis and feedback control mechanisms, and cannot implement multi-level equipment status judgment and dynamic control strategies. Especially in the case of multi-task concurrency, the system lacks effective priority management and resource scheduling capabilities, resulting in the inability to respond to high-priority tasks in a timely manner. At the same time, because the traditional model mainly relies on static threshold comparison and preset alarms, it shows obvious lag and passivity when facing emergencies and complex faults. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to realize intelligent and automated remote operation and maintenance control of substation equipment in a complex multi-tasking environment and unstable network conditions, improve the stability and operation and maintenance efficiency of the system, reduce manual intervention, and achieve efficient fault warning and resource scheduling.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a remote operation and maintenance control method in a dual interactive mode, which comprises the following steps:

[0007] Establish a remote two-way communication connection and collect substation data in real time for pre-processing;

[0008] Determine the execution strategy based on the device status and perform remote operations based on the judgment results;

[0009] According to the command execution results, the results are compared with the preset standard configuration, and the corresponding rollback or repair operation is triggered according to the comparison results;

[0010] Perform fault diagnosis, classify and analyze abnormal conditions of equipment, and generate fault diagnosis reports;

[0011] A multi-level early warning mechanism is established based on diagnostic reports to classify warning events and dynamically adjust warning responses.

[0012] As a preferred solution of a remote operation and maintenance control method with a dual interactive mode described in the present invention, the remote two-way communication connection is established through the MQTT protocol, and the heartbeat packet mechanism is enabled to monitor the network connection status. If the connection establishment fails or the heartbeat packet times out, the fallback strategy is triggered and the connection failure information is recorded.

[0013] As a preferred solution of the dual-interaction mode remote operation and maintenance control method described in the present invention, wherein: the substation data includes operation data, network status and historical data;

[0014] The preprocessing is to perform signal denoising on the substation data, detect preliminary anomalies through the Z-Score algorithm, convert data formats from different sources into a unified format, standardize the data, aggregate data within a time period, extract feature information and cache it, and use a timestamp alignment algorithm to ensure that the data is synchronized to the historical database.

[0015] As a preferred solution of the remote operation and maintenance control method of the dual interactive mode described in the present invention, wherein: the device status is judged to determine the execution strategy, and the remote operation is performed according to the judgment result, which is to compare the pre-processed real-time collected data with the preset safety threshold range. If all the collected parameters are within the safety threshold, the historical record analysis and risk assessment are entered. If it is detected that a parameter exceeds the threshold, the command execution is terminated and the process jumps to fault diagnosis;

[0016] The historical record analysis and risk assessment is based on the historical execution records and fault logs of the device to analyze the execution risk level of the current command. If the risk assessment result shows that the operation risk is controllable, the command is executed; if the risk is uncontrollable, a step-by-step execution strategy or delayed execution is adopted to wait until the network is stable.

[0017] During the command execution process, the feedback data and network connection status of the device are monitored in real time to ensure normal execution of the command. If the feedback data shows that the network is interrupted or the device is disconnected, the rollback operation is triggered immediately to restore the device to the previous stable version and record the abnormal situation. If the device does not respond as expected, jump to fault diagnosis and generate a preliminary diagnosis report.

[0018] As a preferred solution of the remote operation and maintenance control method of the dual interactive mode described in the present invention, wherein: the execution fault diagnosis is to compare the feedback data generated by the device during the command execution process with the safety threshold. If abnormal data is found in the comparison, it is marked as a soft fault or a hard fault, and the historical fault database is called to analyze whether the current fault matches the existing case;

[0019] If the fault matches the historical records, repair suggestions are automatically generated based on the solutions in the historical fault database. If the fault fails to match, the fault is marked as unresolved. The LSTM neural network model is used to perform correlation analysis on the historical data and logs of the device, predict potential risks, and generate a trend prediction report.

[0020] If the diagnosis result shows a problem that can be solved by remote commands, the corresponding repair operation will be directly triggered. If a problem that requires manual intervention is detected, the system will immediately notify the operation and maintenance personnel, suspend the automated operation, and archive all diagnostic information in the log database;

[0021] Soft failures are short-term fluctuations, while hard failures are equipment failures or network interruptions.

[0022] As a preferred solution of the remote operation and maintenance control method of the dual interactive mode described in the present invention, the multi-level warning mechanism includes classifying the warning events into three levels: high, medium and low according to the classification results of the fault diagnosis report, and adopting different processing strategies according to the risk level;

[0023] Monitor the frequency of the same type of warning events within a certain period of time, and determine whether the event priority needs to be adjusted based on trend analysis. If multiple similar events are detected continuously within the specified time, the corresponding priority will be automatically raised from low to medium or high. After the priority is raised, the corresponding emergency warning mechanism will be triggered, and the operation and maintenance team will be notified to intervene in advance;

[0024] The processing results of each warning event will be automatically archived in the log database;

[0025] If the warning is processed successfully, the future command strategy will be adjusted according to the current successful operation. If the warning is processed unsuccessfully, the current event will be marked as a special event and an analysis report will be generated.

[0026] As a preferred solution of the dual-interaction mode remote operation and maintenance control method described in the present invention, the classification of warning events and dynamic adjustment of warning responses include:

[0027] Dynamic task scheduling and priority control mechanisms are used during task execution to automatically allocate computing resources and execution order in a multi-task concurrent environment. When multiple tasks are triggered simultaneously, priorities are automatically allocated and task execution paths are selected based on task importance, network status, and device availability.

[0028] If a high-priority task needs to be executed immediately, a low-priority task will be paused or delayed, and the task status will be recorded. All task execution results will be fed back to the control end in real time, and the system control strategy will be adjusted according to the task priority and historical data.

[0029] Another object of the present invention is to provide a remote operation and maintenance control system with a dual interactive mode, which can solve the problems of lag and inefficiency in communication stability, task management and fault handling of traditional operation and maintenance systems through a closed-loop control mechanism that combines real-time data acquisition and preprocessing, dynamic task scheduling, fault diagnosis and trend prediction.

[0030] In order to solve the above technical problems, the present invention provides the following technical solutions: a remote operation and maintenance control system with dual interactive modes, comprising: a communication and data acquisition module, a state judgment and command execution module, a fault diagnosis and early warning module, and a task scheduling and feedback control module;

[0031] The communication and data acquisition module establishes remote communication through the MQTT protocol, enables the heartbeat packet mechanism to monitor the network status, and collects the operation data, network status and historical data of the substation;

[0032] The state judgment and command execution module judges the device state according to the collected data. If it meets the threshold, it continues to execute the command, otherwise it jumps to fault diagnosis. During the command execution process, it monitors the feedback data in real time. If an abnormality or loss of connection is detected, it executes a rollback operation;

[0033] The fault diagnosis and early warning module uses the LSTM model and historical fault database to conduct in-depth analysis and classification diagnosis, generate diagnostic reports, establish a multi-level early warning mechanism according to the fault type, monitor the frequency and trend of events, and dynamically adjust the early warning response;

[0034] The task scheduling and feedback control module dynamically allocates computing resources and execution order in a multi-task concurrent environment, so that high-priority tasks are executed first. The execution results of all tasks are fed back to the control end in real time, and the system strategy is adjusted through historical data and priority information.

[0035] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the remote operation and maintenance control method in a dual-interaction mode as described above are implemented.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a remote operation and maintenance control method in a dual-interaction mode as described above.

[0037] The beneficial effects of the present invention are as follows: the MQTT protocol and the heartbeat packet mechanism are used to ensure the stability of the two-way communication between the device and the control center, avoiding data loss and task failure caused by network fluctuations or interruptions. At the same time, the system introduces standardized processing and timestamp synchronization algorithms in data transmission, reducing transmission delays and bandwidth occupancy, and improving communication efficiency.

[0038] The present invention designs a dynamic task scheduling and priority control mechanism. In a multi-task concurrent environment, it can dynamically adjust resource allocation according to network status and task priority, realize priority execution of key tasks, and avoid task accumulation and resource waste. For fault diagnosis and risk management, this system realizes active monitoring and prediction through LSTM neural network and historical fault database, identifies potential risks in advance, and reduces the defects of delayed response and reliance on manual judgment in traditional models. A closed-loop feedback and log archiving mechanism is adopted to archive the operation results in real time after the task is completed, and continuously optimize future task strategies based on log information to improve the system's adaptability and self-learning ability. The present invention not only solves the pain points of unstable communication links, chaotic task scheduling, and delayed fault handling in traditional operation and maintenance systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0040] Figure 1 An overall flow chart of a remote operation and maintenance control method in a dual interactive mode provided by the first embodiment of the present invention;

[0041] Figure 2 A structural diagram of a remote operation and maintenance control system with dual interactive modes provided for the second embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0043] Example 1, reference Figure 1 According to an embodiment of the present invention, a remote operation and maintenance control method in a dual interactive mode is provided, comprising:

[0044] In order to better describe the implementation method of this embodiment, Figure 1 As shown:

[0045] Establish a remote two-way communication connection and collect substation data in real time for pre-processing.

[0046] Determine the execution strategy based on the device status and perform remote operations based on the judgment results.

[0047] According to the command execution result, the result is compared with the preset standard configuration, and the corresponding rollback or repair operation is triggered according to the comparison result.

[0048] Perform fault diagnosis, classify and analyze abnormal conditions of equipment, and generate fault diagnosis reports.

[0049] A multi-level early warning mechanism is established based on diagnostic reports to classify warning events and dynamically adjust warning responses.

[0050] The remote two-way communication connection is established through the MQTT protocol, and the heartbeat packet mechanism is enabled to monitor the network connection status. If the connection fails to be established or the heartbeat packet times out, the fallback strategy is triggered and the connection failure information is recorded.

[0051] Substation data includes operation data, network status and historical data;

[0052] The substation data is subjected to signal denoising and preliminary anomalies are detected through the Z-Score algorithm. The data formats from different sources are converted into a unified format and the data is standardized. The data within a time period is aggregated, feature information is extracted and cached, and a timestamp alignment algorithm is used to ensure that the data is synchronized to the historical database.

[0053] Compare the pre-processed real-time collected data with the preset safety threshold range. If all collected parameters are within the safety threshold, enter the historical record analysis and risk assessment. If it is detected that a parameter exceeds the threshold, terminate the command execution and jump to fault diagnosis;

[0054] Based on the historical execution records and fault logs of the device, the execution risk level of the current command is analyzed. If the risk assessment results show that the operation risk is controllable, the command is executed; if the risk is uncontrollable, a step-by-step execution strategy is adopted or execution is delayed until the network is stable.

[0055] During the command execution process, the feedback data and network connection status of the device are monitored in real time to ensure normal execution of the command. If the feedback data shows that the network is interrupted or the device is disconnected, the rollback operation is triggered immediately to restore the device to the previous stable version and record the abnormal situation. If the device does not respond as expected, jump to fault diagnosis and generate a preliminary diagnosis report.

[0056] It is further explained that:

[0057] The step-by-step execution strategy is suitable for high-risk tasks (such as device restarts and program upgrades) or when the system status is unstable (such as network fluctuations). Step-by-step execution breaks down the complete operation into multiple sub-steps. After each step is executed, the system will monitor the feedback results in real time to ensure the safety and controllability of the operation.

[0058] What needs to be further explained is the specific process of step-by-step execution:

[0059] Step 1: Confirm that the operating conditions meet preliminary requirements (such as network latency is within an acceptable range).

[0060] Step 2: Split complex tasks into several subtasks. For example, when upgrading a program, first back up the system and then conduct a small-scale version update test.

[0061] Step 3: After each subtask is completed, monitor the feedback results in real time. If there is no abnormality, proceed to the next step; otherwise, immediately trigger the rollback operation or enter the fault diagnosis.

[0062] For example, during the substation controller software upgrade process, the system first backs up the current configuration, and then updates each module in batches (such as network module, monitoring module, etc.). If the upgrade of a module fails, the system immediately rolls back to the previous version to avoid paralysis of the entire system.

[0063] The delayed execution strategy is mainly used for non-critical tasks or when the current environment does not meet the execution conditions, such as when the network delay is too high or the device status is unstable. The system will monitor changes in the network or device status and automatically resume the execution of the task when conditions are right.

[0064] When it is detected that the conditions for task execution are not met (such as network latency is higher than the threshold), the system marks the task as "pending" and records the current network and device status. The system regularly monitors the network connection status, device load, and other related parameters to determine whether the execution conditions are met. When it is detected that the network has returned to normal or the device status has returned to a safe range, the system will automatically resume the execution of the task.

[0065] For example, during the device restart process, if a network interruption is detected, the system will mark the restart task as pending, and automatically complete the restart after the network is restored. This process avoids device disconnection or task failure due to network instability.

[0066] Whether it is step-by-step execution or delayed execution, the system will ensure that the task execution is under monitoring throughout. Once an abnormality occurs in a step, the system will automatically trigger a rollback operation and restore the device to the last stable version. At the same time, the abnormal situation will be automatically recorded in the log database for subsequent analysis and optimization.

[0067] Compare the feedback data generated by the device during command execution with the safety threshold. If abnormal data is found, it is marked as a soft fault or hard fault, and the historical fault database is called to analyze whether the current fault matches the existing case.

[0068] If the fault matches the historical records, repair suggestions are automatically generated based on the solutions in the historical fault database. If the fault fails to match, the fault is marked as unresolved. The LSTM neural network model is used to perform correlation analysis on the historical data and logs of the device, predict potential risks, and generate a trend prediction report.

[0069] If the diagnosis result shows a problem that can be solved by remote commands, the corresponding repair operation will be directly triggered. If a problem that requires manual intervention is detected, the system will immediately notify the operation and maintenance personnel, suspend the automated operation, and archive all diagnostic information in the log database;

[0070] Soft failures are short-term fluctuations, while hard failures are equipment failures or network interruptions.

[0071] It is further explained that:

[0072] Feedback data analysis in traditional systems mainly relies on manual interpretation, which cannot compare a large amount of operating data and thresholds in real time, resulting in slow fault identification and delayed system processing. Some minor anomalies (such as short-term fluctuations) are easily ignored or misjudged, while serious faults may be discovered too late and cannot be responded to in time.

[0073] In traditional operation and maintenance, the system generally triggers alarms through simple threshold judgments, but it is difficult to further classify faults and determine their severity or causes. There is a lack of fault prediction and trend analysis capabilities based on historical data, and it is impossible to warn of potential risks.

[0074] When traditional systems encounter complex failures, they need to rely on operation and maintenance personnel to gradually analyze logs and past data. They are unable to automatically generate repair suggestions, which increases the frequency of manual intervention and operation and maintenance costs.

[0075] The system of the present invention realizes highly automated fault identification, prediction and processing through automatic classification of soft faults and hard faults and combination of a historical database and an LSTM model.

[0076] Soft faults: short-term fluctuations or slight parameter deviations (such as a short-term temperature rise). The system will mark these as "non-critical" faults and monitor the subsequent status. If soft faults continue to occur or are triggered frequently, their priority will be increased and their impact on the system will be further analyzed.

[0077] Hard failures: such as network interruption, device disconnection, or hardware failure. For hard failures, the system will directly enter the rollback operation or trigger manual notification to ensure timely processing.

[0078] The system calls the historical fault database and compares it with the current fault data. If a similar case is found, a repair suggestion is automatically generated based on the solution of the case. The automatically generated repair solution reduces the time of manual troubleshooting and reduces repeated faults caused by human judgment errors.

[0079] Traditional time series analysis models have limitations when processing complex data, while LSTM models can better capture long-term dependencies of data and identify potential trends and hidden dangers.

[0080] In the present invention, the system will regularly run the LSTM model, perform correlation analysis on historical data and current status, predict possible hidden faults, and generate trend reports in advance. During the fault diagnosis process, if the system determines that the fault can be solved by remote commands, the command will be executed immediately. If a problem requiring manual intervention (such as hardware damage) is detected, the system will suspend the automated operation and immediately notify the operation and maintenance personnel for on-site processing. All fault information and processing records will be archived in the log database for subsequent analysis and system optimization.

[0081] Table 1 Effect comparison

[0082] index Traditional Model System of the present invention Improvement ratio Fault classification error rate (%) 20 5 -75％ Average troubleshooting time (minutes) 60 15 300％ Predictive warning accuracy (%) none 90 90％ Weekly manual intervention times 10 2 -80％ Fault recovery rate (%) 50 95 45％

[0083] By comparing the data, it can be seen that the system of the present invention has significant advantages in fault classification, processing efficiency, prediction ability and manual intervention optimization. The traditional system relies on manual judgment, and the fault classification error rate is as high as 20%. However, this system reduces the error rate to 5% by combining the historical database with the LSTM model. At the same time, the fault processing time in the traditional system is an average of 60 minutes, while this system shortens the processing time to 15 minutes by automatically generating repair suggestions, which improves efficiency by 4 times.

[0084] In addition, the system achieved a 90% prediction accuracy through trend prediction and early warning mechanisms, effectively reducing the frequency of sudden failures. Due to the improvement of task automation, the frequency of manual intervention has been greatly reduced from 10 times per week to 2 times, reducing labor costs by 80%. Finally, the system's fault recovery rate has increased from 50% in the traditional mode to 95%, which shows that the system has higher reliability and robustness in complex network environments.

[0085] Early warning events are divided into three levels: high, medium, and low, and different handling strategies are adopted according to the risk level. The frequency of the same type of early warning events within a certain period of time is monitored, and based on trend analysis, it is determined whether the priority of the event needs to be adjusted. If multiple similar events are detected continuously within the specified time, the corresponding priority will be automatically raised from low to medium or high. After the priority is raised, the corresponding emergency early warning mechanism will be triggered, and the operation and maintenance team will be notified to intervene in advance.

[0086] The processing results of each warning event will be automatically archived in the log database.

[0087] If the warning is processed successfully, the future command strategy will be adjusted according to the current successful operation. If the warning is processed unsuccessfully, the current event will be marked as a special event and an analysis report will be generated.

[0088] A dynamic task scheduling and priority control mechanism is used during task execution to automatically allocate computing resources and execution order in a multi-task concurrent environment. When multiple tasks are triggered at the same time, priorities are automatically allocated and task execution paths are selected based on task importance, network status, and device availability.

[0089] If a high-priority task needs to be executed immediately, a low-priority task will be paused or delayed, and the task status will be recorded. All task execution results will be fed back to the control end in real time, and the system control strategy will be adjusted according to the task priority and historical data.

[0090] Embodiment 2 is an embodiment of the present invention, which provides a system for a remote operation and maintenance control method in a dual interactive mode, including: referring to Figure 2 The present invention involves four modules, a communication and data acquisition module 100, a state judgment and command execution module 200, a fault diagnosis and early warning module 300, and a task scheduling and feedback control module 400;

[0091] The communication and data acquisition module 100 establishes remote communication through the MQTT protocol, enables the heartbeat packet mechanism to monitor the network status, and collects the substation's operating data, network status, and historical data;

[0092] The status judgment and command execution module 200 judges the device status based on the collected data. If it meets the threshold, it continues to execute the command, otherwise it jumps to fault diagnosis. During the command execution process, it monitors the feedback data in real time. If an abnormality or loss of connection is detected, it executes a rollback operation;

[0093] The fault diagnosis and early warning module 300 uses the LSTM model and the historical fault database to perform in-depth analysis and classification diagnosis, generate a diagnosis report, establish a multi-level early warning mechanism according to the fault type, monitor the frequency and trend of events, and dynamically adjust the early warning response;

[0094] The task scheduling and feedback control module 400 dynamically allocates computing resources and execution order in a multi-task concurrent environment, so that high-priority tasks are executed first. The execution results of all tasks are fed back to the control end in real time, and the system strategy is adjusted through historical data and priority information.

[0095] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0096] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0097] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0098] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0099] Embodiment 3: In this embodiment, in order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. This embodiment experiments the existing traditional method and the method of this embodiment respectively.

[0100] In this experiment, we will simulate the remote monitoring and task management scenario of substation equipment, and build a virtual operation and maintenance system, covering the key technical steps of the invention such as remote communication, equipment status judgment, task scheduling, fault diagnosis, trend prediction and closed-loop control. The main objectives of the experiment are:

[0101] Verify the real-time response efficiency and resource scheduling capability of the system of the present invention in a multi-task concurrent environment.

[0102] Compare the performance of the new system and the traditional operation and maintenance model in terms of task execution time, fault response speed, and frequency of manual intervention.

[0103] Evaluate the performance of the new system in multi-level early warning and dynamic feedback, and verify its trend prediction and adaptive capabilities.

[0104] This experiment collects historical operation data, equipment status data, and network delay simulation data. It simulates the simultaneous triggering of more than 10 operation and maintenance tasks, simulates temperature and current exceeding the standard or equipment loss, simulates communication anomalies and triggers rollback operations during network interruption and recovery, and simulates historical data changes to verify fault prediction capabilities.

[0105] Use the MQTT protocol to establish a communication connection and simulate the device to upload real-time data such as temperature, current, and network status.

[0106] Enable heartbeat packet monitoring, trigger the fallback policy when communication is interrupted, and record the connection recovery time.

[0107] Compare the collected real-time data with the safety threshold and execute the task when the equipment status is normal.

[0108] If an abnormal device status is detected (such as excessive temperature), the task is immediately suspended and the task is redirected to the fault diagnosis module. Simulate multiple operation and maintenance tasks (such as remote upgrades, parameter adjustments, and device restarts) to be triggered simultaneously. Adjust the execution order according to the importance of the task and the network status to ensure that high-priority tasks are completed first. Simulate multiple abnormal states, such as device loss of connection and restart failure, and call the historical database and LSTM model for fault diagnosis and trend prediction. Based on the diagnosis results, the system automatically triggers remote repair or manual notification. After each task is executed, the results are fed back to the control end and archived in the log database. The system adjusts future task strategies based on historical data and feedback to form a closed-loop control.

[0109] Table 2 Data comparison table

[0110]

[0111] It can be seen from the experimental data in Table 2 that the remote operation and maintenance control system of the present invention has significantly improved the task response speed, fault location time and execution success rate of high-priority tasks compared with the traditional operation and maintenance mode. The average task response time in the traditional mode is 300 seconds, while this system reduces the response time to 50 seconds through remote communication and dynamic task scheduling mechanism, which is 5 times higher. In addition, because the system can identify problems in advance through multi-level judgment and trend prediction, the fault location time is shortened from 600 seconds to 100 seconds, which improves accuracy and efficiency.

[0112] In addition, the frequency of manual intervention has been greatly reduced, with only 2 manual interventions required for every 10 tasks, indicating that the system's automation level has been significantly improved. At the same time, the system's rollback operation has been triggered more frequently, indicating that it can quickly trigger a rollback and protect the device status when an anomaly is detected. These advantages make the system more stable and efficient when dealing with complex and changing operating environments.

[0113] In summary, the remote operation and maintenance management method of the present invention demonstrates powerful intelligent advantages in all aspects of task execution and system management through dynamic scheduling, fault prediction and closed-loop feedback control, effectively solving the pain points of response lag, chaotic task management and slow fault handling in the traditional mode, and greatly improving the safety and operation efficiency of the power system.

[0114] Table 3 Effect comparison table

[0115]

[0116] It can be clearly seen from the data in Table 3 that the remote operation and maintenance control system of the dual interactive mode of the present invention comprehensively surpasses the traditional operation and maintenance mode in multiple dimensions. First, the traditional mode lacks fault prediction capabilities and can only deal with faults passively after they occur, resulting in 0 seconds of advance warning. However, this system uses the LSTM model and trend analysis to achieve a 300-second fault warning, providing the operation and maintenance team with valuable response time and effectively avoiding the occurrence of serious faults. Secondly, the data collection coverage of the traditional system is only 65%, mainly because it is difficult for on-site manual inspections to obtain data from various types of equipment in a comprehensive and real-time manner. This system increases the coverage rate to 99% through standardized processing and multi-source synchronous acquisition, ensuring the integrity and accuracy of the data.

[0117] In terms of task recovery rate after network interruption, the task recovery rate in the traditional mode is only 50%, and some tasks cannot be automatically recovered after interruption and require manual intervention. The rollback and recovery mechanism of the system of the present invention has achieved a recovery rate of 95%, greatly reducing the frequency of manual intervention. In addition, the average energy consumption of each task of this system has dropped by 60%, from 5W to 2W, thanks to its intelligent task scheduling and resource optimization mechanism, showing a significant energy-saving effect.

[0118] Finally, the error rate of log analysis in traditional systems is as high as 15%, mainly due to reliance on manual analysis, which is prone to errors. The system of the present invention reduces the error rate to 2% through automated log analysis and closed-loop feedback control, greatly improving the accuracy and efficiency of operation and maintenance. These data show that the present invention not only improves the speed and accuracy of task execution and fault handling, but also improves the stability of the system, reduces energy consumption, and realizes more intelligent and efficient operation and maintenance control.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A remote operation and maintenance control method in dual interactive mode, characterized in that: include: Establish a remote two-way communication connection and collect substation data in real time for pre-processing; Determine the execution strategy based on the device status and perform remote operations based on the judgment results; According to the command execution results, the results are compared with the preset standard configuration, and the corresponding rollback or repair operation is triggered according to the comparison results; Perform fault diagnosis, classify and analyze abnormal conditions of equipment, and generate fault diagnosis reports; A multi-level early warning mechanism is established based on diagnostic reports to classify warning events and dynamically adjust warning responses.

2. A dual-interaction mode remote operation and maintenance control method according to claim 1, characterized in that: The remote two-way communication connection is established through the MQTT protocol, and the heartbeat packet mechanism is enabled to monitor the network connection status. If the connection fails to be established or the heartbeat packet times out, the fallback strategy is triggered and the connection failure information is recorded.

3. A dual-interaction mode remote operation and maintenance control method as claimed in claim 2, characterized in that: The substation data includes operation data, network status and historical data; The preprocessing is to perform signal denoising on the substation data, detect preliminary anomalies through the Z-Score algorithm, convert data formats from different sources into a unified format, standardize the data, aggregate data within a time period, extract feature information and cache it, and use a timestamp alignment algorithm to ensure that the data is synchronized to the historical database.

4. A dual-interaction mode remote operation and maintenance control method as claimed in claim 3, characterized in that: The device status is judged to determine the execution strategy, and the remote operation is performed according to the judgment result. The pre-processed real-time collected data is compared with the preset safety threshold range. If all the collected parameters are within the safety threshold, the historical record analysis and risk assessment are entered. If it is detected that a parameter exceeds the threshold, the command execution is terminated and the process jumps to fault diagnosis. The historical record analysis and risk assessment is based on the historical execution records and fault logs of the device to analyze the execution risk level of the current command. If the risk assessment result shows that the operation risk is controllable, the command is executed; If the risk is uncontrollable, adopt a step-by-step execution strategy or delay execution until the network is stable; During the command execution process, the feedback data and network connection status of the device are monitored in real time to ensure normal execution of the command. If the feedback data shows that the network is interrupted or the device is disconnected, the rollback operation is triggered immediately to restore the device to the previous stable version and record the abnormal situation. If the device does not respond as expected, jump to fault diagnosis and generate a preliminary diagnosis report.

5. A dual-interaction mode remote operation and maintenance control method according to claim 4, characterized in that: The execution fault diagnosis is to compare the feedback data generated by the device during the command execution process with the safety threshold. If abnormal data is found in the comparison, it is marked as a soft fault or a hard fault, and the historical fault database is called to analyze whether the current fault matches the existing case; If the fault matches the historical records, repair suggestions are automatically generated based on the solutions in the historical fault database. If the fault fails to match, the fault is marked as unresolved. The LSTM neural network model is used to perform correlation analysis on the historical data and logs of the device, predict potential risks, and generate a trend prediction report. If the diagnosis result shows a problem that can be solved by remote commands, the corresponding repair operation will be directly triggered. If a problem that requires manual intervention is detected, the system will immediately notify the operation and maintenance personnel, suspend the automated operation, and archive all diagnostic information in the log database; Soft failures are short-term fluctuations, while hard failures are equipment failures or network interruptions.

6. A dual-interaction mode remote operation and maintenance control method according to claim 5, characterized in that: The multi-level early warning mechanism includes classifying early warning events into three levels: high, medium and low according to the classification results of the fault diagnosis report, and adopting different processing strategies according to the risk level; Monitor the frequency of the same type of warning events within a certain period of time, and determine whether the event priority needs to be adjusted based on trend analysis. If multiple similar events are detected continuously within the specified time, the corresponding priority will be automatically raised from low to medium or high. After the priority is raised, the corresponding emergency warning mechanism will be triggered, and the operation and maintenance team will be notified to intervene in advance; The processing results of each warning event will be automatically archived in the log database; If the warning is processed successfully, the future command strategy will be adjusted according to the current successful operation. If the warning is processed unsuccessfully, the current event will be marked as a special event and an analysis report will be generated.

7. A remote operation and maintenance control method in a dual interactive mode as claimed in claim 6, characterized in that: The classification of warning events and dynamic adjustment of warning responses include: Dynamic task scheduling and priority control mechanisms are used during task execution to automatically allocate computing resources and execution order in a multi-task concurrent environment. When multiple tasks are triggered simultaneously, priorities are automatically allocated and task execution paths are selected based on task importance, network status, and device availability. If a high-priority task needs to be executed immediately, a low-priority task will be paused or delayed, and the task status will be recorded. All task execution results will be fed back to the control end in real time, and the system control strategy will be adjusted according to the task priority and historical data.

8. A system adopting a remote operation and maintenance control method of a dual interactive mode as claimed in any one of claims 1 to 7, characterized in that: It comprises a communication and data acquisition module (100), a state judgment and command execution module (200), a fault diagnosis and early warning module (300), and a task scheduling and feedback control module (400); The communication and data acquisition module (100) establishes remote communication through the MQTT protocol, and enables a heartbeat packet mechanism to monitor the network status, and collects the operation data, network status and historical data of the substation; The state judgment and command execution module (200) judges the device state according to the collected data, and continues to execute the command if it meets the threshold, otherwise jumps to fault diagnosis. During the command execution process, the feedback data is monitored in real time, and a rollback operation is performed if an abnormality or loss of connection is detected; The fault diagnosis and early warning module (300) performs in-depth analysis and classification diagnosis through the LSTM model and the historical fault database, generates a diagnosis report, establishes a multi-level early warning mechanism according to the fault type, monitors the frequency and trend of events, and dynamically adjusts the early warning response; The task scheduling and feedback control module (400) dynamically allocates computing resources and execution order in a multi-task concurrent environment, so that high-priority tasks are executed first, the execution results of all tasks are fed back to the control end in real time, and the system strategy is adjusted through historical data and priority information.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a remote operation and maintenance control method in a dual interactive mode as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a remote operation and maintenance control method in a dual interactive mode as described in any one of claims 1 to 7 are implemented.

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