Power grid equipment operation monitoring auxiliary system based on artificial intelligence

Abstract

The invention relates to the technical field of power grid monitoring, and discloses a power grid equipment operation monitoring auxiliary system based on artificial intelligence, and the system comprises an equipment collection module which obtains all equipment data in a power grid monitoring area to generate equipment sets of different levels; the equipment data processing module determines a connection relation between the equipment sets, sets monitoring indexes and determines monitoring time of the equipment monitoring indexes; the monitoring module performs step-by-step monitoring according to the hierarchical sorting of the equipment set and formulates a monitoring strategy; and the early warning module carries out early warning reporting on the abnormal equipment. According to the invention, through comprehensive collection and classification of equipment data, all-directional and emphasized monitoring is realized, ordered monitoring is carried out according to levels, a result is output, through a preset threshold value and an anomaly detection algorithm, abnormal equipment is early warned and reported, distribution of monitoring resources is optimized, the occurrence probability of power failure accidents is reduced, and the power failure rate is improved. The monitoring capability, the early warning capability and the resource optimization capability of the system are improved, and the safety and the reliability of the overall operation of the power grid are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and in particular to an artificial intelligence-based power grid equipment operation monitoring auxiliary system. Background Art

[0002] With the continuous expansion of the scale and complexity of the power system, the operation monitoring of power grid equipment has become increasingly important. Traditional power grid equipment monitoring systems mainly rely on manual inspections and simple automation systems. They can only collect data from some equipment and cannot fully cover the entire power grid area, resulting in the existence of monitoring blind spots. They also lack advanced data processing capabilities and cannot perform real-time analysis and processing of large amounts of data. They have insufficient anomaly detection capabilities, low monitoring efficiency, and cannot be dynamically adjusted according to the actual operation of the equipment. They lack comprehensive early warnings for related equipment and are prone to missing fault propagation paths. At the same time, they lack optimized management of monitoring resources, resulting in unnecessary monitoring and maintenance operations, increased operation and maintenance costs, and low resource utilization. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides an artificial intelligence-based power grid equipment operation monitoring auxiliary system to solve the above problems.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides an artificial intelligence-based power grid equipment operation monitoring auxiliary system, including: an equipment acquisition module, for acquiring all equipment data in a power grid monitoring area, defining a division rule to classify equipment, and generating equipment sets of different levels; A device data processing module, used to receive device data from the device acquisition module, determine the connection relationship between the device sets, set monitoring indicators according to the device sets, divide the monitoring indicators into monitoring levels, and determine the monitoring time of the device monitoring indicators according to the monitoring levels; A monitoring module, used to receive data from the device acquisition module and the device data processing module, perform level-by-level monitoring according to the hierarchical sorting of the device set, formulate monitoring strategies, and output device monitoring results; An early warning module is used to receive the equipment monitoring results of the monitoring module, make equipment abnormality judgments, issue early warning reports for abnormal equipment, and record abnormal data; The communication module is used to establish real-time communication between the equipment acquisition module, the equipment data processing module, the monitoring module and the early warning module.

[0006] As a preferred solution of the power grid equipment operation monitoring auxiliary system based on artificial intelligence described in the present invention, the working process of the equipment acquisition module includes: Acquire equipment data according to the power grid monitoring area, wherein the equipment data includes equipment number, equipment type, equipment name, equipment location, connected equipment, and maintenance records; The division rule includes equipment maintenance records and equipment importance, calculating comprehensive hierarchical indicators of equipment, defining hierarchical range intervals, comparing the comprehensive hierarchical indicators with the hierarchical range intervals to obtain corresponding hierarchical range intervals, extracting equipment corresponding to the comprehensive hierarchical indicators in each hierarchical range interval, generating equipment sets in each hierarchical range, and regularly updating the equipment sets, wherein the equipment in the equipment set is arranged in descending order according to the value of the comprehensive hierarchical indicator; Comprehensive level indicators of the computing equipment It is expressed as: ; in, Indicates the number of maintenance times. Indicates the maintenance cycle, Indicates the area affected when equipment fails. Indicates the total area covered by the power grid monitoring area. Indicates the equipment failure recovery time. Indicates the difficulty of device recovery. , Indicates the maximum value corresponding to the preset restoration time and restoration difficulty of the power grid. Indicates the absolute value of the characteristic deviation of the equipment parameter, , Indicates the maximum and minimum values ​​allowed by the device parameter characteristics. , They represent maintenance frequency and equipment importance weight coefficient respectively.

[0007] As a preferred solution of the artificial intelligence-based power grid equipment operation monitoring auxiliary system of the present invention, wherein: determining the connection relationship between the device sets in the device data processing module includes: Based on the device sets at each level, a first connection relationship and a second connection relationship of the devices are established, and a first relationship matrix and a second relationship matrix are generated according to the first connection relationship and the second connection relationship, and the first relationship matrix and the second relationship matrix are combined to generate a comprehensive relationship matrix; The establishment of the first connection relationship includes: modeling the physical connection of the power grid equipment and constructing a power grid topology map. , where Equipment collection, Representing an edge set, each device is a node in a graph data structure, and the connection lines between devices are edges, generating a first relationship matrix; Establishing the second connection relationship includes: determining the logical connection relationship of the devices according to the functional association between the devices, and generating a second relationship matrix; The comprehensive relationship matrix is ​​obtained by performing weighted calculation on the first relationship matrix and the second relationship matrix.

[0008] As a preferred solution of the power grid equipment operation monitoring auxiliary system based on artificial intelligence of the present invention, wherein: setting monitoring indicators according to the equipment set, dividing the monitoring indicators into monitoring levels, and determining the monitoring time series according to the monitoring levels include: Based on the devices in the device set, determining a monitoring indicator for each device according to the device type; According to the degree and scope of influence of the equipment indicator on the equipment, the priority of the equipment indicator is defined, and the equipment indicator is divided into different monitoring levels according to the priority of the equipment indicator; According to the monitoring cycle corresponding to the preset indicator monitoring level, a monitoring time series within the power grid monitoring range is generated, and the monitoring time of the monitoring indicator of each device is determined.

[0009] As a preferred solution of the artificial intelligence-based power grid equipment operation monitoring auxiliary system of the present invention, the working process of the monitoring module includes: According to the grid monitoring time series, a device inspection queue is established at each grid monitoring time point; According to the hierarchical sorting of the device collection, the device collection is monitored level by level, and the devices are added to the queue one by one according to the sorting of the devices in the device collections at each level to generate a device inspection queue; Based on the device inspection queue, obtain the real-time value corresponding to the monitoring indicator of each device in the current monitoring time series to obtain the device monitoring result; If the real-time value of the equipment monitoring indicator is not within the first-level abnormal range, or is within the first-level abnormal range and the first-level abnormal duration is less than the preset abnormal time threshold, the current equipment abnormal event is recorded; If the real-time value of the equipment monitoring indicator is within the first-level abnormal range, and the first-level abnormality duration is greater than the preset abnormal time threshold, the corresponding abnormal equipment is obtained and an equipment abnormality queue is established.

[0010] As a preferred solution of the power grid equipment operation monitoring auxiliary system based on artificial intelligence described in the present invention, wherein: the establishment of the equipment abnormality queue includes: Based on the abnormal device, determine whether the operating state of the abnormal device has changed, and automatically adjust the monitoring parameter range of the monitoring indicator according to the real-time monitoring data and the preset monitoring model to generate a secondary abnormal range; If the real-time value of the device monitoring indicator is greater than the secondary abnormal range, the current corresponding device is added to a new queue in the order of the inspection queue to generate a first abnormal queue; According to the comprehensive relationship matrix, associated devices of the devices in the first abnormal queue are obtained in queue order, and added to a new queue to generate a second abnormal queue; If the real-time monitoring value of the device monitoring indicator in the second abnormal queue is not within the first-level abnormal range, or is within the first-level abnormal range and the first-level abnormal duration is less than the preset abnormal time threshold, remove the device from the second abnormal queue and update the second abnormal queue; The first exception queue and the second exception queue constitute a device exception queue.

[0011] As a preferred solution of the power grid equipment operation monitoring auxiliary system based on artificial intelligence described in the present invention, the workflow of the early warning module includes: De-duplication and update the device abnormality queue, and divide the warning level according to the degree of device abnormality; The information of the equipment in the equipment abnormality queue, the real-time monitoring index value, the warning level and the correlation between the equipment are reported as warning information, the current warning data and maintenance data are recorded, and the power grid equipment data is updated.

[0012] In a second aspect, the present invention provides an artificial intelligence-based power grid equipment operation monitoring auxiliary method, comprising: Obtain all equipment data within the power grid monitoring area, define classification rules to classify equipment, and generate equipment sets at different levels; Receive device data, determine the connection relationship between the device sets, set monitoring indicators according to the device sets, divide the monitoring indicators into monitoring levels, and determine the monitoring time of the device monitoring indicators according to the monitoring levels; Performing level-by-level monitoring according to the hierarchical sorting of the device set, formulating monitoring strategies, and outputting device monitoring results; Receive equipment monitoring results, make equipment abnormality judgments, issue early warning reports for abnormal equipment, and record abnormal data.

[0013] In a third aspect, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the workflow of the artificial intelligence-based power grid equipment operation monitoring auxiliary system is implemented.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the workflow of the artificial intelligence-based power grid equipment operation monitoring auxiliary system.

[0015] Compared with the prior art, the present invention has the following beneficial effects: the present invention can accurately focus on key equipment and potential risk points by comprehensively collecting and grading equipment data, determining equipment connection relationships and setting monitoring indicators, achieving all-round and focused monitoring, and monitoring and outputting results in an orderly manner by level. Through preset thresholds and anomaly detection algorithms, the abnormal status of equipment can be discovered in time, and early warning reports can be issued for abnormal equipment. The allocation of monitoring resources can be optimized, and hidden dangers of equipment failures can be discovered quickly, effectively avoiding the expansion of failures, maximizing the guarantee of continuous and stable power supply to the power grid, reducing the probability of power outages, improving the system's monitoring capabilities, early warning capabilities, and resource optimization capabilities, and enhancing the safety and reliability of the overall operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0017] Figure 1 This is a schematic diagram of the structure of an artificial intelligence-based power grid equipment operation monitoring auxiliary system according to an embodiment of the present invention; Figure 2 The present invention is a schematic diagram of the workflow of an artificial intelligence-based power grid equipment operation monitoring auxiliary system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0022] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0023] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] Reference Figure 1-Figure 2 , as an embodiment of the present invention, provides an artificial intelligence-based power grid equipment operation monitoring auxiliary system, comprising: The equipment collection module is used to obtain all equipment data in the power grid monitoring area, define classification rules to classify equipment, and generate equipment sets of different levels; The device data processing module is used to receive the device data from the device acquisition module, determine the connection relationship between the device sets, set monitoring indicators according to the device sets, divide the monitoring indicators into monitoring levels, and determine the monitoring time of the device monitoring indicators according to the monitoring levels; The monitoring module is used to receive data from the device acquisition module and the device data processing module, monitor the device level by level according to the hierarchical sorting of the device set, formulate monitoring strategies, and output device monitoring results; The early warning module is used to receive the equipment monitoring results of the monitoring module, make equipment abnormality judgments, issue early warning reports for abnormal equipment, and record abnormal data; The communication module is used to establish real-time communication between the equipment acquisition module, the equipment data processing module, the monitoring module and the early warning module.

[0025] In a preferred embodiment, the workflow of the device acquisition module includes: Acquire equipment data according to the power grid monitoring area, including equipment number, equipment type, equipment name, equipment location, connected equipment and maintenance records; The classification rules include equipment maintenance records and equipment importance, calculating the comprehensive hierarchical index of the equipment, defining the hierarchical range interval, comparing the comprehensive hierarchical index with the hierarchical range interval to obtain the corresponding hierarchical range interval, extracting the equipment corresponding to the comprehensive hierarchical index of each hierarchical range interval, generating the equipment set of each hierarchical range, and regularly updating the equipment set, wherein the equipment in the equipment set is arranged in descending order according to the value of the comprehensive hierarchical index; Comprehensive level indicators of computing equipment It is expressed as: ; in, Indicates the number of maintenance times. Indicates the maintenance cycle, Indicates the area affected when equipment fails. Indicates the total area covered by the power grid monitoring area. Indicates the equipment failure recovery time. Indicates the difficulty of device recovery. , Indicates the maximum value corresponding to the preset restoration time and restoration difficulty of the power grid. Indicates the absolute value of the characteristic deviation of the equipment parameter, , Indicates the maximum and minimum values ​​allowed by the device parameter characteristics. , They represent the maintenance frequency and equipment importance weight coefficients respectively, and can be set according to the actual application scenario. The preferred values ​​are 0.4 and 0.6 respectively.

[0026] Specifically, the comprehensive level index of equipment comprehensively considers the maintenance frequency of equipment and the importance of equipment. The importance of equipment can be considered from aspects such as the scope of impact, degree of impact and difficulty of recovery. If a device failure will cause power outage in a large area, the scope of impact is large and the importance is high. For example, the failure of a trunk transmission line may affect the power supply of multiple cities, while the failure of non-critical auxiliary equipment in a small substation may only affect the operation of some equipment in the substation; consider the impact of equipment failure on power quality (such as voltage, frequency, etc.) and power supply continuity. For example, generator failure will directly lead to power supply interruption, while some monitoring instrument failures may only temporarily affect data monitoring and will not directly interrupt power supply, so the importance of generators is higher; recovery difficulty includes the time required for equipment maintenance, technical difficulty, etc.

[0027] The level range interval can be set according to actual needs, and is specifically divided according to the value of the comprehensive level index. In this embodiment, three levels of range can be defined: ,like The device is the first level. The device is the second level. The equipment is at the third level, among which the first level, second level and third level of equipment can be divided according to functionality. The first level: high-importance, high-maintenance frequency equipment. This level includes the equipment that is most critical to the operation of the power grid. Any failure may cause a large-scale power outage or seriously affect the stability of the power grid. For example, large-scale generator sets, core equipment of major substations, important transmission lines, etc., require regular and frequent maintenance inspections; the second level: medium-importance, medium-maintenance frequency equipment. This level includes those equipment that have a certain impact on the operation of the power grid but are not critical. Failures will affect local power supply but will not cause the entire power grid to collapse. For example, medium-sized substations, medium-voltage distribution lines, etc. have relatively long maintenance cycles, but still require regular inspection and maintenance; the third level: low-importance, low-maintenance frequency equipment. This level includes those equipment that have a smaller impact on the operation of the power grid. Failures usually only affect individual users or small areas.

[0028] It should be noted that the equipment acquisition module can effectively define and determine the upper and lower connections or mutual influence relationships between the first level, the second level and the third level, and establish an effective equipment-level monitoring and management system, thereby improving the monitoring and management level and fault response capabilities of the power grid system. It is not only conducive to the development of daily operation and maintenance work, but also can quickly locate the root problem when a fault occurs, reduce power outage time and losses, and help distinguish the importance and characteristics of equipment, so that subsequent monitoring, processing and other work can be more targeted, reasonably allocate resources, focus on key equipment, and improve the efficiency and accuracy of the entire power grid monitoring.

[0029] In a preferred embodiment, determining the connection relationship between the device sets in the device data processing module includes: Based on the device sets at all levels, a first connection relationship and a second connection relationship of the devices are established, and a first relationship matrix and a second relationship matrix are generated according to the first connection relationship and the second connection relationship, and the first relationship matrix and the second relationship matrix are combined to generate a comprehensive relationship matrix; The establishment of the first connection relationship includes: modeling the physical connection of the power grid equipment and constructing a power grid topology map. , where Equipment collection, Representing an edge set, each device is a node in a graph data structure, and the connection lines between devices are edges, generating a first relationship matrix; Establishing the second connection relationship includes: determining the logical connection relationship of the devices according to the functional association between the devices, and generating a second relationship matrix; A comprehensive relationship matrix is ​​obtained by performing weighted calculation on the first relationship matrix and the second relationship matrix.

[0030] Specifically, firstly, the physical connection of the power grid equipment is modeled, the power grid topology is constructed, the first connection relationship is established, and the depth-first search (DFS) or breadth-first search (BFS) algorithm is used to traverse the entire power grid topology to determine the connection path and connection sequence between different equipment sets. For example, starting from a main power generation equipment, the DFS algorithm is used to visit each substation, distribution room and other equipment along the transmission line in turn, and the equipment sequence and connection relationship passed are recorded, so as to clarify the upstream and downstream connection relationship of each equipment set in the power grid and the electrical connection between each other. For complex power grid structures, there may be multiple connection paths. The shortest path algorithm can be used to determine the key connection paths between different equipment sets so as to focus on them during monitoring and fault analysis. In addition to the physical connection, the functional collaboration relationship of the equipment must also be considered, the second connection relationship is determined, and the equipment functional connection matrix is ​​established. The rows and columns of the matrix represent the equipment of different equipment sets respectively. If the normal operation of equipment A has a direct or indirect functional support or protection effect on equipment B, the corresponding position in the matrix is ​​marked as 1, otherwise it is marked as 0. By analyzing the matrix, the function-based connection relationship network between different equipment sets can be clearly understood.

[0031] In a preferred embodiment, the device data processing module sets monitoring indicators according to the device set, divides the monitoring indicators into monitoring levels, and determines the monitoring time series according to the monitoring levels, including: Based on the devices in the device set, determine the monitoring indicators of each device according to the device type; Define the priority of equipment indicators according to the degree and scope of their impact on the equipment, and divide the equipment indicators into different monitoring levels according to their priority; According to the monitoring cycle corresponding to the preset indicator monitoring level, a monitoring time series within the power grid monitoring range is generated, and the monitoring time of the monitoring indicator of each device is determined.

[0032] Specifically, according to the device type to which the device belongs and the actual device characteristics, a group of monitoring indicators of the equipment are defined, and then a priority is defined for each monitoring indicator. According to the degree of influence of the monitoring indicators on the operation of the equipment, the parameters are divided into high priority, medium priority and low priority. According to the priority of the monitoring indicators, different monitoring cycles are set. The high-priority monitoring indicators can collect monitoring data every hour or more frequently, the medium-priority monitoring indicators can collect monitoring data every day or every week, and the low-priority monitoring indicators can collect monitoring data every month or every quarter. According to the actual operation status and historical data of the equipment, the priority of the parameters is dynamically adjusted. Then, according to the set monitoring cycle, the monitoring time of the monitoring indicators of each device is determined, the system's equipment monitoring time series value is generated, and the daily monitoring collection time point is determined. At this monitoring time point, the health indicator parameters corresponding to each device are collected to carry out monitoring and early warning.

[0033] It should be noted that this module sorts out the connection relationship between the equipment sets, which can clearly understand the overall architecture and mutual relationship of the power grid equipment. Through the equipment classification and connection relationship determination, it can realize the refined management of the equipment, and reasonably set the monitoring parameters, divide the levels and set the cycles. It can realize differentiated and refined monitoring of equipment with different importance and different operating characteristics, avoid excessive or insufficient monitoring, and can not only discover potential problems in time, but also reduce unnecessary resource consumption, improve the effectiveness and scientificity of monitoring, and improve the efficiency and effect of monitoring.

[0034] In a preferred embodiment, the workflow of the monitoring module includes: According to the grid monitoring time series, a device inspection queue is established at each grid monitoring time point; According to the hierarchical sorting of the device collection, the device collection is monitored level by level, and the devices are added to the queue one by one according to the sorting of the devices in the device collections at each level to generate a device inspection queue; Based on the device inspection queue, obtain the real-time values ​​corresponding to the monitoring indicators of each device in the current monitoring time series to obtain the device monitoring results; If the real-time value of the equipment monitoring indicator is not within the first-level abnormal range, or is within the first-level abnormal range and the first-level abnormal duration is less than the preset abnormal time threshold, the current equipment abnormal event is recorded; If the real-time value of the equipment monitoring indicator is within the first-level abnormal range, and the first-level abnormality duration is greater than the preset abnormal time threshold, the corresponding abnormal equipment is obtained and an equipment abnormality queue is established.

[0035] Specifically, the inspection queue is an ordered list of devices. First, the queue is initialized and an empty inspection queue is created. The devices are added to the inspection queue in the order of hierarchical priority, that is, according to the order of the devices in the first layer, second layer, third layer and other device sets. Then, according to the inspection queue, through sensors and monitoring devices, the real-time operating values ​​of the monitoring indicators that the device currently needs to monitor are collected in real time. According to the first-level abnormal value range defined for each monitoring indicator, it is determined whether the device has an abnormality. The first-level abnormal range and first-level abnormal duration can be set according to the actual application scenario. Finally, if the abnormal condition is met, the corresponding device is extracted and a device abnormality queue is established.

[0036] In a preferred embodiment, establishing a device exception queue includes: Based on the abnormal equipment, determine whether the operating status of the abnormal equipment has changed, and automatically adjust the monitoring parameter range of the monitoring indicator according to the real-time monitoring data and the preset monitoring model to generate a secondary abnormal range; If the real-time value of the device monitoring indicator is greater than the secondary abnormal range, the current corresponding device is added to a new queue in the order of the inspection queue to generate a first abnormal queue; According to the comprehensive relationship matrix, the associated devices of the devices in the first abnormal queue are obtained in queue order, and added to a new queue to generate a second abnormal queue; If the real-time monitoring value of the device monitoring indicator in the second abnormal queue is not within the first-level abnormal range, or is within the first-level abnormal range and the first-level abnormal duration is less than the preset abnormal time threshold, the device is removed from the second abnormal queue and the second abnormal queue is updated; The first exception queue and the second exception queue constitute a device exception queue.

[0037] Specifically, the generation of the secondary abnormal range can use machine learning algorithms to analyze the historical operation data of the equipment and explore the potential relationships and rules between the equipment operation parameters. For example, the different operation states of the equipment are classified through cluster analysis, and a corresponding monitoring parameter model is established for each type of operation state. When the operation state of the equipment changes, the system automatically adjusts the monitoring parameters according to the real-time monitoring data and the pre-established model. For example, during the peak load period of the power grid, the operating current, temperature and other parameters of the equipment will increase accordingly. The system determines that it is currently in the peak load state by analyzing the real-time data, and automatically relaxes the threshold range of certain monitoring indicators (within the safe range) to avoid frequent false alarms; and during the low load period, the threshold range is tightened to improve the detection sensitivity of the hidden dangers of minor equipment failures. At the same time, the neural network can be used to predict the operation trend of the equipment, and the monitoring parameters can be adjusted in advance according to the prediction results. For example, if it is predicted that the temperature of a certain equipment will rise rapidly in the future, the temperature warning threshold of the equipment can be lowered in advance so that preventive measures can be taken in time. In addition, the various threshold ranges or hierarchical range definitions in this system support manual correction. If the value of the monitoring indicator of the equipment is still within the abnormal range based on the adjusted secondary abnormal range, it is further determined that the equipment has abnormal behavior and the equipment is added to the first abnormal queue.

[0038] After determining the abnormal device, according to the comprehensive relationship matrix, obtain the highly correlated row devices of the abnormal device, add them to the second abnormal queue, and further determine whether there is a real abnormality based on the real-time monitoring values. If there is an abnormality, continue to stay in the second abnormal queue. If there is no abnormality, delete it from the queue, and record the data of the abnormal device and its abnormal parameters.

[0039] It should be noted that through hierarchical sorting and monitoring strategy formulation, the logical order from critical to secondary equipment is followed, giving priority to ensuring the safe operation of core equipment, ensuring the orderliness and efficiency of monitoring, and helping to capture abnormal situations during equipment operation more efficiently and accurately. The output monitoring results provide a basis for judgment for subsequent early warning modules, thereby ensuring stable and reliable operation of the entire power grid system.

[0040] In a preferred embodiment, the workflow of the early warning module includes: De-duplicate and update the device abnormality queue, and divide the warning level according to the degree of device abnormality; The information of the equipment in the equipment abnormality queue, the real-time monitoring index value, the warning level and the relationship between the equipment are reported as warning information, the current warning data and maintenance data are recorded, and the power grid equipment data is updated.

[0041] It should be noted that the early warning module can ensure that the power grid operation and maintenance personnel are aware of equipment anomalies at the first time through abnormal detection and early warning reporting, and promptly discover and handle equipment failures to minimize the impact of equipment failures on power grid operations. It not only detects abnormal equipment, but also detects related equipment to ensure the comprehensiveness of early warnings. Recording abnormal data provides data reference for subsequent fault analysis, power grid operation status evaluation, and system optimization, which helps to continuously improve the performance of the entire monitoring system. The present invention can accurately focus on key equipment and potential risk points by comprehensively collecting and grading equipment data, determining equipment connection relationships and setting monitoring indicators, achieving all-round and focused monitoring, and monitoring and outputting results in an orderly manner by level. Through preset thresholds and anomaly detection algorithms, the abnormal status of equipment can be discovered in time, and early warning reports can be issued for abnormal equipment. The allocation of monitoring resources can be optimized, and hidden dangers of equipment failures can be quickly discovered, effectively avoiding the expansion of failures, maximizing the guarantee of continuous and stable power supply to the power grid, reducing the probability of power outages, improving the system's monitoring capabilities, early warning capabilities, and resource optimization capabilities, and enhancing the safety and reliability of the overall operation of the power grid.

[0042] The above is a schematic scheme of an artificial intelligence-based power grid equipment operation monitoring auxiliary system of this embodiment. It should be noted that the technical scheme of the artificial intelligence-based power grid equipment operation monitoring auxiliary method and the technical scheme of the artificial intelligence-based power grid equipment operation monitoring auxiliary system belong to the same concept. The details of the technical scheme of the artificial intelligence-based power grid equipment operation monitoring auxiliary system in this embodiment that are not described in detail can be found in the description of the technical scheme of the artificial intelligence-based power grid equipment operation monitoring auxiliary system.

[0043] The power grid equipment operation monitoring auxiliary method based on artificial intelligence in this embodiment includes: Obtain all equipment data within the power grid monitoring area, define classification rules to classify equipment, and generate equipment sets at different levels; Receive device data, determine the connection relationship between device sets, set monitoring indicators according to the device sets, divide the monitoring indicators into monitoring levels, and determine the monitoring time of the device monitoring indicators according to the monitoring levels; Perform monitoring step by step according to the hierarchical sorting of the equipment set, formulate monitoring strategies, and output equipment monitoring results; Receive equipment monitoring results, make equipment abnormality judgments, issue early warning reports for abnormal equipment, and record abnormal data.

[0044] Among them, the device collections of different levels are generated: Acquire equipment data according to the power grid monitoring area, including equipment number, equipment type, equipment name, equipment location, connected equipment and maintenance records; The classification rules include equipment maintenance records and equipment importance, calculating the comprehensive hierarchical index of the equipment, defining the hierarchical range interval, comparing the comprehensive hierarchical index with the hierarchical range interval to obtain the corresponding hierarchical range interval, extracting the equipment corresponding to the comprehensive hierarchical index of each hierarchical range interval, generating the equipment set of each hierarchical range, and regularly updating the equipment set, wherein the equipment in the equipment set is arranged in descending order according to the value of the comprehensive hierarchical index; Comprehensive level indicators of computing equipment It is expressed as: ; in, Indicates the number of maintenance times. Indicates the maintenance cycle, Indicates the area affected when equipment fails. Indicates the total area covered by the power grid monitoring area. Indicates the equipment failure recovery time. Indicates the difficulty of device recovery. , Indicates the maximum value corresponding to the preset restoration time and restoration difficulty of the power grid. Indicates the absolute value of the characteristic deviation of the equipment parameter, , Indicates the maximum and minimum values ​​allowed by the device parameter characteristics. , They represent maintenance frequency and equipment importance weight coefficient respectively.

[0045] Determine the connection relationship between device sets, monitoring indicators, and monitoring time, including: Based on the device sets at all levels, a first connection relationship and a second connection relationship of the devices are established, and a first relationship matrix and a second relationship matrix are generated according to the first connection relationship and the second connection relationship, and the first relationship matrix and the second relationship matrix are combined to generate a comprehensive relationship matrix; The establishment of the first connection relationship includes: modeling the physical connection of the power grid equipment and constructing a power grid topology map. , where Equipment collection, Representing an edge set, each device is a node in a graph data structure, and the connection lines between devices are edges, generating a first relationship matrix; Establishing the second connection relationship includes: determining the logical connection relationship of the devices according to the functional association between the devices, and generating a second relationship matrix; By performing weighted calculation on the first relationship matrix and the second relationship matrix, a comprehensive relationship matrix is ​​obtained; Based on the devices in the device set, determine the monitoring indicators of each device according to the device type; Define the priority of equipment indicators according to the degree and scope of their impact on the equipment, and divide the equipment indicators into different monitoring levels according to their priority; According to the monitoring cycle corresponding to the preset indicator monitoring level, a monitoring time series within the power grid monitoring range is generated, and the monitoring time of the monitoring indicator of each device is determined.

[0046] Developing a monitoring strategy includes: According to the grid monitoring time series, a device inspection queue is established at each grid monitoring time point; According to the hierarchical sorting of the device collection, the device collection is monitored level by level, and the devices are added to the queue one by one according to the sorting of the devices in the device collections at each level to generate a device inspection queue; Based on the device inspection queue, obtain the real-time values ​​corresponding to the monitoring indicators of each device in the current monitoring time series to obtain the device monitoring results; If the real-time value of the equipment monitoring indicator is not within the first-level abnormal range, or is within the first-level abnormal range and the first-level abnormal duration is less than the preset abnormal time threshold, the current equipment abnormal event is recorded; If the real-time value of the device monitoring indicator is within the first-level abnormal range, and the first-level abnormal duration is greater than the preset abnormal time threshold, the corresponding abnormal device is obtained and a device abnormal queue is established; Based on the abnormal equipment, determine whether the operating status of the abnormal equipment has changed, and automatically adjust the monitoring parameter range of the monitoring indicator according to the real-time monitoring data and the preset monitoring model to generate a secondary abnormal range; If the real-time value of the device monitoring indicator is greater than the secondary abnormal range, the current corresponding device is added to a new queue in the order of the inspection queue to generate a first abnormal queue; According to the comprehensive relationship matrix, the associated devices of the devices in the first abnormal queue are obtained in queue order, and added to a new queue to generate a second abnormal queue; If the real-time monitoring value of the device monitoring indicator in the second abnormal queue is not within the first-level abnormal range, or is within the first-level abnormal range and the first-level abnormal duration is less than the preset abnormal time threshold, the device is removed from the second abnormal queue and the second abnormal queue is updated; The first exception queue and the second exception queue constitute a device exception queue.

[0047] This embodiment also provides an electronic device, which is applicable to the case of power grid equipment operation monitoring assistance based on artificial intelligence, including: Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the artificial intelligence-based power grid equipment operation monitoring auxiliary system proposed in the above embodiment.

[0048] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the power grid equipment operation monitoring auxiliary system based on artificial intelligence as proposed in the above embodiment is implemented.

[0049] The storage medium proposed in this embodiment and the power grid equipment operation monitoring auxiliary system based on artificial intelligence proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0050] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0051] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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. An artificial intelligence-based power grid equipment operation monitoring auxiliary system, characterized in that: include: The equipment collection module is used to obtain all equipment data in the power grid monitoring area, define classification rules to classify equipment, and generate equipment sets of different levels; A device data processing module, used to receive device data from the device acquisition module, determine the connection relationship between the device sets, set monitoring indicators according to the device sets, divide the monitoring indicators into monitoring levels, and determine the monitoring time of the device monitoring indicators according to the monitoring levels; A monitoring module, used to receive data from the device acquisition module and the device data processing module, perform level-by-level monitoring according to the hierarchical sorting of the device set, formulate monitoring strategies, and output device monitoring results; An early warning module is used to receive the equipment monitoring results of the monitoring module, make equipment abnormality judgments, issue early warning reports for abnormal equipment, and record abnormal data; The communication module is used to establish real-time communication between the equipment acquisition module, the equipment data processing module, the monitoring module and the early warning module.

2. The power grid equipment operation monitoring auxiliary system based on artificial intelligence according to claim 1 is characterized in that: The workflow of the equipment acquisition module includes: Acquire equipment data according to the power grid monitoring area, wherein the equipment data includes equipment number, equipment type, equipment name, equipment location, connected equipment, and maintenance records; The division rule includes equipment maintenance records and equipment importance, calculating comprehensive hierarchical indicators of equipment, defining hierarchical range intervals, comparing the comprehensive hierarchical indicators with the hierarchical range intervals to obtain corresponding hierarchical range intervals, extracting equipment corresponding to the comprehensive hierarchical indicators in each hierarchical range interval, generating equipment sets in each hierarchical range, and regularly updating the equipment sets, wherein the equipment in the equipment set is arranged in descending order according to the value of the comprehensive hierarchical indicator; Comprehensive level indicators of the computing equipment It is expressed as: ； in, Indicates the number of maintenance times. Indicates the maintenance cycle, Indicates the area affected when equipment fails. Indicates the total area covered by the power grid monitoring area. Indicates the device failure recovery time. Indicates the difficulty of device recovery. , Indicates the maximum value corresponding to the preset restoration time and restoration difficulty of the power grid. Indicates the absolute value of the characteristic deviation of the equipment parameter, , Indicates the maximum and minimum values ​​allowed by the device parameter characteristics. , They represent maintenance frequency and equipment importance weight coefficient respectively.

3. The power grid equipment operation monitoring auxiliary system based on artificial intelligence according to claim 1 or 2, characterized in that: Determining the connection relationship between the device sets in the device data processing module includes: Based on the device sets at each level, a first connection relationship and a second connection relationship of the devices are established, and a first relationship matrix and a second relationship matrix are generated according to the first connection relationship and the second connection relationship, and the first relationship matrix and the second relationship matrix are combined to generate a comprehensive relationship matrix; The establishment of the first connection relationship includes: modeling the physical connection of the power grid equipment and constructing a power grid topology map. , where Equipment collection, Representing an edge set, each device is a node in a graph data structure, and the connection lines between devices are edges, generating a first relationship matrix; Establishing the second connection relationship includes: determining the logical connection relationship of the devices according to the functional association between the devices, and generating a second relationship matrix; The comprehensive relationship matrix is ​​obtained by performing weighted calculation on the first relationship matrix and the second relationship matrix.

4. The power grid equipment operation monitoring auxiliary system based on artificial intelligence according to claim 3 is characterized in that: Setting monitoring indicators according to the device set, dividing the monitoring indicators into monitoring levels, and determining monitoring time series according to the monitoring levels include: Based on the devices in the device set, determining a monitoring indicator for each device according to the device type; According to the degree and scope of influence of the equipment indicator on the equipment, the priority of the equipment indicator is defined, and the equipment indicator is divided into different monitoring levels according to the priority of the equipment indicator; According to the monitoring cycle corresponding to the preset indicator monitoring level, a monitoring time series within the power grid monitoring range is generated, and the monitoring time of the monitoring indicator of each device is determined.

5. The artificial intelligence-based power grid equipment operation monitoring auxiliary system according to claim 4, characterized in that: The workflow of the monitoring module includes: According to the grid monitoring time series, a device inspection queue is established at each grid monitoring time point; According to the hierarchical sorting of the device collection, the device collection is monitored level by level, and the devices are added to the queue one by one according to the sorting of the devices in the device collections at each level to generate a device inspection queue; Based on the device inspection queue, obtain the real-time value corresponding to the monitoring indicator of each device in the current monitoring time series to obtain the device monitoring result; If the real-time value of the equipment monitoring indicator is not within the first-level abnormal range, or is within the first-level abnormal range and the first-level abnormal duration is less than the preset abnormal time threshold, the current equipment abnormal event is recorded; If the real-time value of the equipment monitoring indicator is within the first-level abnormal range, and the first-level abnormality duration is greater than the preset abnormal time threshold, the corresponding abnormal equipment is obtained and an equipment abnormality queue is established.

6. The artificial intelligence-based power grid equipment operation monitoring auxiliary system according to claim 5, characterized in that: The establishing of the device exception queue comprises: Based on the abnormal device, determine whether the operating state of the abnormal device has changed, and automatically adjust the monitoring parameter range of the monitoring indicator according to the real-time monitoring data and the preset monitoring model to generate a secondary abnormal range; If the real-time value of the device monitoring indicator is greater than the secondary abnormal range, the current corresponding device is added to a new queue in the order of the inspection queue to generate a first abnormal queue; According to the comprehensive relationship matrix, associated devices of the devices in the first abnormal queue are obtained in queue order, and added to a new queue to generate a second abnormal queue; If the real-time monitoring value of the device monitoring indicator in the second abnormal queue is not within the first-level abnormal range, or is within the first-level abnormal range and the first-level abnormal duration is less than the preset abnormal time threshold, remove the device from the second abnormal queue and update the second abnormal queue; The first exception queue and the second exception queue constitute a device exception queue.

7. The power grid equipment operation monitoring auxiliary system based on artificial intelligence according to claim 1 or 6, characterized in that: The workflow of the early warning module includes: De-duplication and update the device abnormality queue, and divide the warning level according to the degree of device abnormality; The information of the equipment in the equipment abnormality queue, the real-time monitoring index value, the warning level and the correlation between the equipment are reported as warning information, the current warning data and maintenance data are recorded, and the power grid equipment data is updated.

8. An artificial intelligence-based power grid equipment operation monitoring auxiliary method, used in the artificial intelligence-based power grid equipment operation monitoring auxiliary system as described in claims 1-7, characterized in that: include, Obtain all equipment data within the power grid monitoring area, define classification rules to classify equipment, and generate equipment sets at different levels; Receive device data, determine the connection relationship between the device sets, set monitoring indicators according to the device sets, divide the monitoring indicators into monitoring levels, and determine the monitoring time of the device monitoring indicators according to the monitoring levels; Performing level-by-level monitoring according to the hierarchical sorting of the device set, formulating monitoring strategies, and outputting device monitoring results; Receive equipment monitoring results, make equipment abnormality judgments, issue early warning reports for abnormal equipment, and record abnormal data.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the workflow of the artificial intelligence-based power grid equipment operation monitoring auxiliary system described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, implement the workflow of the artificial intelligence-based power grid equipment operation monitoring auxiliary system as described in any one of claims 1 to 7.

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