A power transformation equipment monitoring method and system

Abstract

This invention discloses a method and system for monitoring substation equipment, relating to the field of power equipment monitoring technology. The method includes: receiving multi-source heterogeneous monitoring data from different monitoring devices on the substation equipment via a standardized interface protocol; performing standardized fusion processing on the multi-source heterogeneous monitoring data to obtain standardized monitoring data; performing real-time analysis on the standardized monitoring data based on preset dynamic threshold rules to generate early warning events; sending the early warning events to a monitoring terminal and receiving feedback results from the monitoring terminal; and updating the health status of the substation equipment and / or the operating status of the monitoring devices based on the feedback results. This invention can achieve accurate early warning and closed-loop management of equipment status through standardized fusion and real-time intelligent analysis of multi-source heterogeneous data, effectively improving operation and maintenance efficiency and safety levels.

Description

Technical Field

[0001] This invention relates to the field of power equipment monitoring technology, and in particular to a method and system for monitoring substation equipment. Background Technology

[0002] As a critical component of power grid engineering, the operational reliability of substation equipment directly impacts the safety and stability of the entire power system. With the rapid development of the national economy, the scale of substation construction is continuously expanding, and the types and scope of monitoring data are becoming increasingly diverse, including multi-source information such as infrared images, video data, ultrasonic data, and operational data from various devices. Currently, monitoring data from substation equipment in power systems is typically scattered across different independent systems, such as online monitoring devices, Supervisory Control and Data Acquisition (SCADA) systems, video surveillance, and robotic inspections. These systems collect data to reflect the operating status of equipment; however, in practical applications, the lack of effective data fusion mechanisms and shared analytical capabilities leads to equipment status assessments relying heavily on human experience, which can result in delayed early warnings and untimely detection of device anomalies.

[0003] In domestic and international research, smart substation and power Internet of Things (IoT) technologies have made some progress. For example, my country is promoting the construction of a new generation of lean equipment asset management system (PMS3.0) and power IoT (EIoT), and some regions have piloted multi-source data fusion technology. However, most of these are still in the laboratory or small-scale pilot stage. Countries in Europe and the United States have also developed smart grid systems (such as iSM&D system, MS2000 system, SMMIS system, etc.), but monitoring data is often analyzed in isolation according to equipment type, lacking a comprehensive evaluation mechanism.

[0004] Existing technologies generally suffer from data silos. Because monitoring devices are developed by different manufacturers, data formats and communication protocols vary significantly, making data integration difficult and requiring manual conversion or customized interfaces, increasing system complexity and cost. Furthermore, existing monitoring systems have significant shortcomings in real-time performance. Data acquisition cycles are long, and transmission requires passing through multiple system layers, making immediate response impossible. Simultaneously, due to network latency or asynchronous transmission, timestamps from different data sources are difficult to synchronize, further affecting the time-series consistency during comprehensive analysis. In actual operation, the diagnosis and handling of equipment faults still heavily rely on on-site expert assessment. A significant time delay exists between anomaly detection and diagnosis, severely impacting equipment availability and the safety level of power grid operation.

[0005] Therefore, how to achieve effective fusion of multi-source data and real-time early warning has become a pressing technical problem to be solved in the field of power equipment monitoring. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a method and system for monitoring power equipment. Through standardized fusion and real-time intelligent analysis of multi-source heterogeneous data, it can achieve accurate early warning and closed-loop management of equipment status, effectively improving operation and maintenance efficiency and safety level.

[0007] To achieve the above objectives, the present invention provides a method for monitoring power equipment, comprising: Receive multi-source heterogeneous monitoring data from different monitoring devices of power equipment through a standardized interface protocol; The multi-source heterogeneous monitoring data are standardized and fused to obtain standardized monitoring data; The standardized monitoring data is analyzed in real time based on preset dynamic threshold rules to generate early warning events; the early warning events include at least one of equipment warning, equipment alarm, device offline and data anomaly; The warning event is sent to the monitoring terminal, and feedback results are received from the monitoring terminal. Based on the feedback results, update the health status of the power equipment and / or the operating status of the monitoring device.

[0008] Optionally, the multi-source heterogeneous monitoring data undergoes standardized fusion processing, including: Convert multi-source heterogeneous monitoring data from different monitoring devices into a unified structured data format; The converted multi-source heterogeneous monitoring data is supplemented and time-series synchronized.

[0009] Optionally, the standardized monitoring data is analyzed in real time based on preset dynamic threshold rules, including: Based on the equipment type and voltage level of the substation, configure differentiated threshold rules containing multi-level thresholds for the monitoring items; The standardized monitoring data acquired in real time is compared with the corresponding differentiated threshold rules; When the standardized monitoring data exceeds any level of the differentiated threshold rule, a corresponding early warning event is generated.

[0010] Optionally, the differentiated threshold rule includes setting two levels of thresholds for the same monitoring item: a attention value and an alarm value; the pre-alarm event includes equipment warnings and equipment alarms; the method further includes: When the standardized monitoring data exceeds the attention value but does not exceed the alarm value, a device early warning event is generated. A device alarm event is generated again when the monitoring data reaches the alarm value.

[0011] Optionally, after receiving the feedback result from the monitoring terminal, the method further includes: If the feedback result is a confirmed alarm, then a corresponding troubleshooting task is generated.

[0012] Optionally, the method further includes: After the status update, if the relevant standardized monitoring data is detected to have returned to normal within a preset time, a status recovery event is generated and sent to the monitoring terminal.

[0013] Optionally, the multi-source heterogeneous monitoring data includes at least one of electrical characteristic data, mechanical characteristic data, chemical characteristic data, physical image data, and environmental parameter data.

[0014] The present invention also provides a power equipment monitoring system, comprising: The data acquisition unit is used to receive multi-source heterogeneous monitoring data from different monitoring devices of the power equipment through a standardized interface protocol. The data fusion unit is used to perform standardized fusion processing on the multi-source heterogeneous monitoring data to obtain standardized monitoring data. The analysis unit is used to perform real-time analysis on the standardized monitoring data based on preset dynamic threshold rules and generate early warning events; the early warning events include at least one of equipment warning, equipment alarm, device offline and data anomaly; An information transmission unit is used to send the pre-alarm event to the monitoring terminal and receive feedback results from the monitoring terminal; The status management unit is used to update the health status of the substation equipment and / or the operating status of the monitoring device based on the feedback results.

[0015] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: The power equipment monitoring method provided by this invention receives multi-source heterogeneous monitoring data from different monitoring devices by adopting a standardized interface protocol, and performs standardized fusion processing to convert heterogeneous data into a unified format. This effectively breaks down data barriers between different sources and protocols, providing a consistent and reliable data foundation for subsequent analysis, thereby solving the data silo problem caused by differences in data formats and protocols in the prior art.

[0016] Building upon this foundation, real-time analysis of standardized monitoring data based on preset dynamic threshold rules enables timely detection of data anomalies and generation of pre-alarm events. This significantly improves the real-time nature of status monitoring and the proactiveness of early warnings, overcoming the problems of long data collection cycles and delayed alarms inherent in existing monitoring systems. Furthermore, by sending pre-alarm events to the monitoring terminal and updating equipment status based on feedback results, a closed-loop management mechanism involving human and machine collaboration is established. This reduces the risk of missed or false alarms caused by relying solely on manual judgment and achieves full-process traceability management from anomaly detection to handling, thereby improving operational efficiency and equipment safety. Attached Figure Description

[0017] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0018] Figure 1 This is a schematic flowchart of a method for monitoring power equipment according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the integration and processing of monitoring data for power equipment according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the real-time early warning processing flow according to an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the statistical effects of the operation in an embodiment of the present invention; Figure 5 This is a schematic diagram of the module structure of a power equipment monitoring system according to an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 and Figure 2 , Figure 1 This is a flowchart illustrating the method for monitoring power equipment. Figure 2 This is a schematic diagram illustrating the integration and processing of monitoring data for power equipment according to an embodiment of the present invention.

[0021] Methods for monitoring power equipment include: S101: Receives multi-source heterogeneous monitoring data from different monitoring devices of power equipment through a standardized interface protocol.

[0022] In the application, multi-source heterogeneous monitoring data from different monitoring devices of the power equipment is received through a standardized interface protocol. During the operation of the power equipment, various monitoring devices are involved, which generate a wide variety of multi-source heterogeneous monitoring data, including but not limited to electrical characteristic data, mechanical characteristic data, chemical characteristic data, physical image data, and environmental parameter data.

[0023] Specifically, the multi-source heterogeneous monitoring data covers dissolved gases in oil, switchgear contact temperature, ambient temperature and humidity, GIS partial discharge, transformer partial discharge, switchgear partial discharge, circuit breaker mechanical characteristics, integrated testing device data, SF6 gas pressure, surge arrester insulation data, core grounding current, transformer mechanical vibration, micro-water monitoring, bushing online monitoring, current transformer oil temperature and pressure, transformer oil level, acoustic fingerprint monitoring, and video surveillance data. These data originate from various independent systems within the substation, including online monitoring devices, Supervisory Control and Data Acquisition (SCADA) systems, video surveillance systems, and robotic inspection systems. Because these systems are developed by different manufacturers, their data formats and communication protocols differ significantly, resulting in prominent data heterogeneity.

[0024] To address the data silo problem, this invention employs a standardized interface protocol to achieve unified access to multi-source data. This standardized interface protocol is compatible with the data formats and communication requirements of different monitoring devices, enabling automatic data acquisition and transmission. Simultaneously, it supports batch configuration and management of IoT node devices (such as aggregation nodes and access nodes), improving the efficiency and scalability of data access. Through the standardized interface protocol, monitoring data previously scattered across different systems can be centrally received, providing a unified and reliable data source for subsequent data fusion and real-time analysis. This effectively overcomes the difficulties in data integration and reliance on manual conversion in traditional monitoring systems, laying a solid foundation for comprehensive and real-time monitoring of substation equipment status.

[0025] S102: Standardize and fuse multi-source heterogeneous monitoring data to obtain standardized monitoring data.

[0026] The above-mentioned standardization and fusion processing of multi-source heterogeneous monitoring data includes: Convert multi-source heterogeneous monitoring data from different monitoring devices into a unified structured data format; The converted multi-source heterogeneous monitoring data is supplemented and time-series synchronized.

[0027] In this application, after receiving multi-source heterogeneous monitoring data, it undergoes standardized fusion processing to obtain standardized monitoring data that can be used for subsequent analysis. This process first transforms raw data from different monitoring devices into a unified structured data format, resolving the data silo problem caused by differences in data formats and protocols. For similar monitoring data with different units, unit standardization is performed to ensure data comparability in terms of dimensions.

[0028] To address potential data loss during data transmission, an intelligent completion strategy is employed: for data transmitted at a fixed frequency, if a single data loss occurs, it is filled using previous values; if data is missing for an extended period, the system determines whether the device is offline based on its voltage level and typical transmission frequency, and marks it accordingly. Furthermore, to resolve timing discrepancies caused by network latency, timestamp synchronization is performed on data reported by each monitoring device to ensure alignment of data from different sources on the timeline, providing an accurate foundation for subsequent comprehensive time-series analysis.

[0029] Building upon format conversion, unit standardization, data completion, and time-series synchronization, further analysis of historical trends and horizontal comparison with similar equipment are used to identify potential anomaly patterns. This series of standardized fusion processing operations integrates raw, heterogeneous, multi-source data into high-quality, consistent, and standardized monitoring data, effectively improving data availability and reliability.

[0030] S103: Based on preset dynamic threshold rules, perform real-time analysis of standardized monitoring data and generate early warning events.

[0031] After obtaining standardized monitoring data, the data is analyzed in real time based on preset dynamic threshold rules, and corresponding early warning events are generated. These early warning events specifically include at least one of the following: equipment warning, equipment alarm, device offline, and data anomaly.

[0032] For example, the above-mentioned real-time analysis of standardized monitoring data based on preset dynamic threshold rules includes: Configure differentiated threshold rules with multi-level thresholds for monitoring items based on the equipment type and voltage level of the substation equipment; The standardized monitoring data acquired in real time is compared with the corresponding differentiated threshold rules; When standardized monitoring data exceeds any level of threshold in the differentiated threshold rules, a corresponding early warning event is generated.

[0033] In application, to achieve accurate analysis, differentiated threshold rules are first configured for various monitoring items based on key attributes such as equipment type and voltage level of the substation equipment. These threshold rules are multi-level; for example, two levels of thresholds, "attention value" and "alarm value," can be set for the same monitoring item. For equipment, a "shutdown value" can also be set, thus achieving gradient monitoring of equipment status evolution. For equipment pre-alarms, the monitoring types of different equipment at different voltage levels can be further subdivided. For example, switchgear contact temperature can be subdivided into switch contact temperature, cable joint temperature, and switchgear busbar temperature, making threshold settings more precise.

[0034] During real-time analysis, the continuously acquired standardized monitoring data is compared with the configured differentiated threshold rules. When the monitoring data exceeds a certain threshold level in the rules, a corresponding level of pre-alarm event is triggered. To optimize alarm management and avoid information redundancy, a repetitive alarm control mechanism can be designed: for example, when a monitoring data point first exceeds "Attention Value 1," a device warning event is generated; thereafter, if the data remains above "Attention Value 1" but does not reach "Attention Value 2" or the "Alarm Value," no further alarm events will be generated until the next level threshold is reached. This mechanism can be flexibly enabled or disabled via a switch to adapt to the monitoring priorities of different devices.

[0035] The judgment of monitoring device anomalies is also based on preset rules. Device offline is mainly determined by judging the offline time. Corresponding offline time thresholds are set according to the data transmission frequency of devices under different voltage levels. If a device fails to transmit data for all monitoring items within the set time, it is judged as offline. Data anomalies are mainly identified by setting upper and lower limits for abnormal values, while also incorporating a judgment on the number of consecutive zero-value transmissions. If the number of times the device transmits zero data reaches a preset threshold for consecutive zero-value transmissions, it is judged as a data anomaly, indicating a possible fault in the device itself. By classifying and judging equipment and devices under different abnormal conditions, the generated early warning events can comprehensively and accurately reflect the on-site situation.

[0036] S104: Send the pre-alarm event to the monitoring terminal and receive the feedback result from the monitoring terminal.

[0037] In the application, after a pre-alarm event is generated, it is sent to the monitoring terminal of the monitoring personnel. The monitoring terminal receives and displays these events, which can be presented as a list of events awaiting confirmation or as highlighted prompts, so that the monitoring personnel can detect them in a timely manner. The monitoring personnel analyze the events based on their professional knowledge and the on-site situation, and provide feedback results through the monitoring terminal. Feedback results include, but are not limited to, confirming alarms, confirming device malfunctions, modifying event types, or marking as false alarms.

[0038] Upon receiving feedback from the monitoring terminal, the system combines automated analysis and judgment with human decision-making. This step ensures that pre-alarm events are reviewed and confirmed by professionals, leveraging the efficiency advantages of automated monitoring while incorporating expert judgment. This improves the accuracy and reliability of status assessments, providing a basis for subsequent status updates and troubleshooting, and forming a human-machine collaborative, traceable closed-loop management process.

[0039] S105: Update the health status of the power equipment and / or the operating status of the monitoring devices based on the feedback results.

[0040] In the application, upon receiving feedback from the monitoring terminal, the health status of the relevant substation equipment and / or the operating status of the monitoring devices are updated based on the results. If the feedback result is a confirmed alarm (e.g., confirmed equipment warning or alarm), the status of the substation equipment is updated to the corresponding abnormal status. Specifically, when an alarm is confirmed to require intervention, a corresponding troubleshooting task is generated, putting the equipment into a waiting-for-troubleshooting phase. During this phase, the data from the monitoring devices subordinate to the equipment is continuously monitored in real time. If the monitoring personnel modify the alarm type according to the actual situation, the equipment status will be updated according to the final confirmed type. For feedback results confirming device offline or data anomalies, the operating status of the corresponding monitoring device will be updated to abnormal, triggering a troubleshooting and remediation process for the monitoring device itself.

[0041] In one embodiment, the above method further includes: After the status update, if the relevant standardized monitoring data is found to have returned to normal within a preset time, a status recovery event is generated and sent to the monitoring terminal.

[0042] In practice, a status recovery mechanism can be further designed. Specifically, after updating the health status of the power equipment or the operating status of the monitoring device, the relevant standardized monitoring data is continuously monitored. If, within a preset time (e.g., 24 hours), all relevant monitoring data of the power equipment that triggered the original alarm returns to normal, or if the previously abnormal monitoring device resumes normal data transmission and the data values ​​stabilize within a reasonable range, a status recovery event will be automatically generated. This status recovery event is sent to the monitoring terminal to notify the monitoring personnel. After the monitoring personnel confirm the recovery event, the corresponding health status of the power equipment or the operating status of the monitoring device is updated to normal.

[0043] This mechanism ensures a closed loop throughout the entire process, from alarm generation and manual confirmation to status updates and automatic recovery confirmation. This transforms equipment status management from a one-way alarm triggering process into a traceable and archived cyclical management system. It not only reflects the recovery status of equipment and devices in a timely manner, reducing the burden of continuous monitoring for personnel, but also significantly improves the automation level and operational efficiency of status management, creating a virtuous cycle in the entire early warning and handling process.

[0044] In one embodiment, the differentiated threshold rule includes setting two levels of thresholds for the same monitoring item: a warning value and an alarm value; the pre-alarm events include device alerts and device alarms; the above method further includes: When standardized monitoring data exceeds the attention value but does not exceed the alarm value, a device early warning event is generated. A device alarm event is generated only when the monitoring data reaches the alarm value.

[0045] In the application, the specific configuration method of the differentiated threshold rule includes setting two levels of thresholds for the same monitoring item: a warning value and an alarm value. For devices, a shutdown value can also be further set. Correspondingly, the generated pre-alarm events include device warnings and device alarms. Based on this rule, when the standardized monitoring data acquired in real time exceeds the preset warning value but has not yet reached the alarm value, a device warning event will be generated. Afterward, if the monitoring data remains above the warning value but does not reach the alarm value, a warning event will not be generated again until the monitoring data changes further and reaches or exceeds the alarm value, at which point a new device alarm event will be generated.

[0046] This multi-threshold-based judgment mechanism effectively avoids alarm storms caused by minor data fluctuations near the threshold, significantly reducing the interference of redundant alarm information on monitoring personnel. Simultaneously, through tiered early warning prompts, this mechanism provides maintenance personnel with a longer response window and a clearer trend of status evolution, enabling them to adopt differentiated handling strategies based on the warning level. This allows for intervention before an alarm actually occurs, achieving a shift from passive response to proactive prevention and improving the refinement and foresight of equipment status management. Furthermore, corresponding on / off options can be set for each alarm rule, allowing monitoring personnel to flexibly enable or disable multi-level alarm functions for specific devices according to actual maintenance needs, thereby enhancing practicality and adaptability.

[0047] The real-time early warning processing closed-loop flow implemented by this invention can be found in [reference needed]. Figure 3 .like Figure 3As shown, the process begins with real-time analysis of standardized monitoring data and generation of early warning events. These events are then pushed to the monitoring terminal for manual confirmation and feedback. Based on the feedback, the equipment or device status is updated, and troubleshooting tasks or status recovery events can be generated, ultimately completing the closed-loop archiving. This traceable closed-loop management mechanism ensures that the entire process, from automatic early warning to manual decision-making and status updates, is under control and manageable.

[0048] This invention, through a unified architecture and model, successfully integrates multi-source heterogeneous data, including system operation data and online monitoring devices, forming a unified, panoramic view of device status. The cross-verification of multi-source data significantly reduces the risk of missed alarms due to human error in judging monitoring data, thus significantly improving the accuracy of early warnings. This invention achieves closed-loop management of early warnings, with traceable processes, providing a complete closed-loop management process from early warning generation, event push, event processing, and result feedback, offering maintenance personnel a powerful decision support tool.

[0049] The results of the statistical analysis can be found in [link to statistical analysis]. Figure 4 ,like Figure 4 As shown, statistics on substation early warnings from January to August are presented. The statistics are divided into two dimensions. The first dimension still uses the previous method of manual judgment based on online monitoring data. This manual judgment involves preliminary anomaly assessment based on the current status of the equipment and trend analysis, followed by on-site verification by experts. The second dimension utilizes the monitoring method of this invention for early warning monitoring. This multi-source data fusion approach enables real-time monitoring of substation equipment and monitoring devices, including recording instances where thresholds are exceeded at certain times, resulting in more accurate monitoring.

[0050] As can be seen, this invention evolves from a traditional, isolated, and passive monitoring model to an integrated, collaborative, and proactive early warning management model. It not only achieves deep integration and intelligent analysis of multi-source data at the technical level, but also improves the safety operation level, maintenance efficiency, and economy of power grid equipment at the business level, providing strong technical support for building a modern equipment management system and promoting power grid digitalization.

[0051] Corresponding to the aforementioned application function implementation method embodiments, the present invention also provides a power equipment monitoring system and corresponding embodiments.

[0052] Please see Figure 5 , Figure 5 This is a schematic diagram of the module structure of a power equipment monitoring system.

[0053] The power equipment monitoring system includes: Data acquisition unit 51 is used to receive multi-source heterogeneous monitoring data from different monitoring devices of power equipment through a standardized interface protocol; The data fusion unit 52 is used to perform standardized fusion processing on multi-source heterogeneous monitoring data to obtain standardized monitoring data. Analysis unit 53 is used to perform real-time analysis on standardized monitoring data based on preset dynamic threshold rules and generate early warning events; the early warning events include at least one of equipment warning, equipment alarm, device offline and data anomaly; The information transmission unit 54 is used to send the early warning event to the monitoring terminal and receive the feedback result from the monitoring terminal. The status management unit 55 is used to update the health status of the power equipment and / or the operating status of the monitoring device based on the feedback results.

[0054] In one embodiment, in the standardization and fusion processing of multi-source heterogeneous monitoring data, the data fusion unit 52 is specifically used for: Convert multi-source heterogeneous monitoring data from different monitoring devices into a unified structured data format; The converted multi-source heterogeneous monitoring data is supplemented and time-series synchronized.

[0055] In one embodiment, in terms of real-time analysis of standardized monitoring data based on preset dynamic threshold rules, the analysis unit 53 is specifically used for: Configure differentiated threshold rules with multi-level thresholds for monitoring items based on the equipment type and voltage level of the substation equipment; The standardized monitoring data acquired in real time is compared with the corresponding differentiated threshold rules; When standardized monitoring data exceeds any level of threshold in the differentiated threshold rules, a corresponding early warning event is generated.

[0056] In one embodiment, the differentiated threshold rule includes setting two levels of thresholds for the same monitoring item: a warning value and an alarm value; the pre-alarm events include equipment warnings and equipment alarms; the analysis unit 53 is also used for: When standardized monitoring data exceeds the attention value but does not exceed the alarm value, a device early warning event is generated. A device alarm event is generated only when the monitoring data reaches the alarm value.

[0057] In one embodiment, after receiving feedback from the monitoring terminal, the status management unit 55 is further configured to: If the feedback result is a confirmed alarm, a corresponding troubleshooting task will be generated.

[0058] In one embodiment, the state management unit 55 is further configured to: After the status update, if the relevant standardized monitoring data is found to have returned to normal within a preset time, a status recovery event is generated and sent to the monitoring terminal.

[0059] Regarding the system in the above embodiments, the specific manner in which each unit module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0060] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for monitoring power equipment, characterized in that, include: Receive multi-source heterogeneous monitoring data from different monitoring devices of power equipment through a standardized interface protocol; The multi-source heterogeneous monitoring data are standardized and fused to obtain standardized monitoring data; The standardized monitoring data is analyzed in real time based on preset dynamic threshold rules to generate early warning events; The warning events include at least one of the following: device alert, device alarm, device offline, and data anomaly; The warning event is sent to the monitoring terminal, and feedback results are received from the monitoring terminal. Based on the feedback results, update the health status of the power equipment and / or the operating status of the monitoring device.

2. The method for monitoring power equipment according to claim 1, characterized in that, The standardized fusion processing of the multi-source heterogeneous monitoring data includes: Convert multi-source heterogeneous monitoring data from different monitoring devices into a unified structured data format; The converted multi-source heterogeneous monitoring data is supplemented and time-series synchronized.

3. The method for monitoring power equipment according to claim 1, characterized in that, The standardized monitoring data is analyzed in real time based on preset dynamic threshold rules, including: Based on the equipment type and voltage level of the substation, configure differentiated threshold rules containing multi-level thresholds for the monitoring items; The standardized monitoring data acquired in real time is compared with the corresponding differentiated threshold rules; When the standardized monitoring data exceeds any level of the differentiated threshold rule, a corresponding early warning event is generated.

4. The method for monitoring power equipment according to claim 3, characterized in that, The differentiated threshold rules include setting two levels of thresholds for the same monitoring item: attention value and alarm value. The warning events include equipment alerts and equipment alarms; the method further includes: When the standardized monitoring data exceeds the attention value but does not exceed the alarm value, a device early warning event is generated. A device alarm event is generated again when the monitoring data reaches the alarm value.

5. The method for monitoring power equipment according to claim 1, characterized in that, After receiving the feedback result from the monitoring terminal, the method further includes: If the feedback result is a confirmed alarm, then a corresponding troubleshooting task is generated.

6. The method for monitoring power equipment according to claim 1, characterized in that, The method further includes: After the status update, if the relevant standardized monitoring data is detected to have returned to normal within a preset time, a status recovery event is generated and sent to the monitoring terminal.

7. The method for monitoring power equipment according to claim 1, characterized in that, The multi-source heterogeneous monitoring data includes at least one of electrical characteristic data, mechanical characteristic data, chemical characteristic data, physical image data, and environmental parameter data.

8. A power equipment monitoring system, characterized in that, include: The data acquisition unit is used to receive multi-source heterogeneous monitoring data from different monitoring devices of the power equipment through a standardized interface protocol. The data fusion unit is used to perform standardized fusion processing on the multi-source heterogeneous monitoring data to obtain standardized monitoring data. The analysis unit is used to perform real-time analysis on the standardized monitoring data based on preset dynamic threshold rules and generate early warning events. The warning events include at least one of the following: device alert, device alarm, device offline, and data anomaly; An information transmission unit is used to send the pre-alarm event to the monitoring terminal and receive feedback results from the monitoring terminal; The status management unit is used to update the health status of the substation equipment and / or the operating status of the monitoring device based on the feedback results.

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