Overheat and insulation fault risk assessment and early warning system under multiple sensing parameters

Abstract

The invention relates to the technical field of power system risk monitoring, and discloses an overheating and insulation fault risk assessment and early warning system under multiple sensing parameters, and the system comprises a data collection module which is used for collecting the operation parameter information and key information of power equipment; the defect evolution rate analysis module is used for carrying out defect analysis on the operation parameter information by adopting a machine learning method and extracting change characteristics of defects along with time; the risk rating module is used for storing division standards of different risk grades and carrying out risk grade division on the change characteristics of the defects along with time to obtain the risk grades of the power equipment; the comprehensive evaluation module is used for identifying the operation state of the power equipment according to the risk level of the power equipment according to a set first fuzzy logic rule and a set second fuzzy logic rule to obtain a risk evaluation result; the early warning module is used for judging whether an early warning mechanism is started or not according to the risk assessment result; according to the invention, the accuracy and reliability of electric power risk early warning and evaluation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system risk monitoring, and in particular to an overheating and insulation failure risk assessment and early warning system under multiple sensing parameters. Background Art

[0002] In the field of power systems, especially in the operation and maintenance of high-voltage power equipment, overheating and insulation failure are one of the main causes of equipment performance degradation and failure. These failures not only affect the stability and reliability of power supply, but may also cause safety accidents, resulting in huge economic losses and social impacts. Therefore, early warning and risk assessment of overheating and insulation failure of power equipment are crucial.

[0003] Existing early warning and assessment technologies for overheating and insulation failure of power equipment mainly rely on the monitoring of a single or a few parameters, such as temperature, partial discharge, etc. These methods are often difficult to fully reflect the operating status and potential risks of the equipment. In addition, due to the lack of comprehensive analysis of the evolution rate of equipment defects, existing technologies are difficult to accurately predict the time and severity of failures. In terms of risk assessment, the existing rating system is often based on simple threshold judgments and lacks comprehensive consideration of historical equipment failure data and expert experience, resulting in insufficient accuracy and reliability of the assessment results. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides an overheating and insulation failure risk assessment and early warning system under multi-sensory parameters, which is used to solve the problem that the early warning and risk assessment of overheating and insulation failure of power equipment in the prior art rely on the monitoring of a single or a few parameters, resulting in difficulty in fully reflecting the operating status and potential risks of the equipment, low accuracy and insufficient reliability.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] An overheating and insulation failure risk assessment and early warning system based on multiple sensing parameters, including:

[0007] Data collection module, used to monitor and collect operating parameter information and key information of power equipment including temperature, humidity, voltage and partial discharge in real time;

[0008] Among them, key information includes expert experience, historical statistical data of defect evolution, and relevant specifications and standards information for power equipment management.

[0009] The defect evolution rate analysis module is used to receive the operating parameter information and key information transmitted by the data collection module, and use machine learning methods to perform defect analysis on the operating parameter information to extract the characteristics of defect changes over time;

[0010] The risk rating module is used to receive the defect variation characteristics over time transmitted by the defect evolution rate analysis module, store the classification standards of different risk levels, and classify the defect variation characteristics over time into risk levels to obtain the risk level of the power equipment;

[0011] A comprehensive evaluation module is used to identify the operating status of the power equipment according to the risk level of the power equipment and obtain the risk assessment result according to the set first fuzzy logic rule and second fuzzy logic rule;

[0012] The early warning module is used to determine whether the risk assessment result reaches the preset high-risk threshold. If so, the early warning mechanism is activated; otherwise, no action is taken.

[0013] Furthermore, the risk levels of power equipment are:

[0014] R=f(D,E,S)

[0015] Among them, R represents the risk level, that is, the risk status of the power equipment, f represents the risk level function, D represents the defect evolution rate, E represents the historical failure rate of the power equipment, and S represents the current operating status of the power equipment.

[0016] Furthermore, the first fuzzy logic rule is that if the defect evolution rate is high and the historical failure rate of the power equipment is high, the operating state of the power equipment is classified as a high-level risk state.

[0017] Furthermore, the second fuzzy logic rule is that if the current operating state of the power equipment is poor, the operating state of the power equipment is classified as a high-level risk state.

[0018] Furthermore, it also includes a user interface module for displaying risk assessment results, and performing graphical display, alarm prompts and fault diagnosis on the risk assessment results.

[0019] Furthermore, it also includes an API interface and a database synchronization module;

[0020] API interface, used to realize data exchange and function call with power system management software;

[0021] The database synchronization module is used to directly access and update the data information stored in the power system management software.

[0022] Furthermore, it also includes an adaptive learning module for obtaining historical operating parameter information and current operating parameter information of power equipment, and optimizing system risk assessment results through online learning and offline learning.

[0023] Furthermore, it also includes a data security module for data encryption, access control and audit tracking to ensure the security, confidentiality and availability of system data.

[0024] The present invention has the following beneficial effects:

[0025] 1. The overheating and insulation failure risk assessment and early warning system proposed in the present invention under multi-sensory parameters, in the data collection module, integrates multiple sensors to monitor multiple operating parameters of power equipment in real time, such as temperature, humidity, voltage, current, partial discharge, etc. Compared with the monitoring method relying on a single or a few parameters, it can more comprehensively reflect the operating status and potential risks of the equipment, thereby improving the accuracy of early warning and assessment;

[0026] 2. In the defect evolution rate analysis module, the defect evolution rate is analyzed using machine learning methods, which can predict the time point and severity of the fault, overcoming the problem of lack of comprehensive analysis of the defect evolution rate of power equipment in the existing technology and enhancing the prediction ability;

[0027] 3. In the risk rating module, based on the characteristics of defects changing over time, a risk rating model based on the historical failure rate of the equipment, the defect evolution rate and the current status of the equipment is established, which provides a more accurate basis for risk level classification and improves the accuracy and reliability of risk assessment;

[0028] 4. In the comprehensive evaluation module, the fuzzy logic controller is used to deal with uncertainty and complexity problems, which can better deal with the fuzziness and uncertainty in the evaluation process and improve the robustness of the evaluation results;

[0029] 5. In the user interface module, a user interface is provided to display risk assessment results and operation instructions, including graphical display, alarm prompts and fault diagnosis suggestions, which optimizes the user experience and enables non-professional users to quickly understand and respond to assessment results;

[0030] 6. In the early warning module, when a high-risk state is detected, the early warning signal can be automatically triggered, such as sound and light alarm, SMS notification or automatic email sending, which improves the response speed to emergency situations;

[0031] 7. In the synchronization of API interface and database, it can be integrated with the existing power system management software to realize data sharing and interoperability, which is realized through API interface or database synchronization, thus enhancing the compatibility and practicality of the system;

[0032] 8. In the adaptive learning module, the evaluation model is continuously optimized based on new data and feedback, including online learning and offline learning modes, so that the system can continue to evolve and improve the adaptability and accuracy of the evaluation model;

[0033] 9. In the data security module, the collected and analyzed data are protected from unauthorized access, including data encryption, access control and audit tracking functions, which enhances data security and privacy protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a structural schematic diagram of an overheating and insulation failure risk assessment and early warning system under multiple sensing parameters proposed by the present invention. DETAILED DESCRIPTION

[0035] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0036] like Figure 1 As shown, a risk assessment and early warning system for overheating and insulation failure under multiple sensing parameters includes:

[0037] The data collection module is used to monitor and collect operating parameter information and key information of power equipment including temperature, humidity, voltage and partial discharge in real time.

[0038] In this embodiment, the data collection module plays a vital role. It is composed of multiple sensors, which are deployed at key locations of power equipment to monitor the operating status of the equipment in real time. These sensors can capture operating parameters including temperature, humidity, voltage, current and partial discharge, which are important indicators for evaluating the health status of power equipment. By continuously collecting these data, the system can obtain a real-time snapshot of the operation of the power equipment, providing detailed information for subsequent analysis and evaluation. This real-time monitoring method not only improves the response speed to changes in equipment status, but also enhances the ability to detect potential problems early. For example, the temperature sensor can detect signs of overheating of the equipment in time, and the partial discharge sensor can detect tiny discharge phenomena in the insulating material, which are early signs of potential failures of the power equipment. In this way, the system can provide timely feedback to operators, enabling them to take preventive measures before the problem evolves into a serious failure, thereby improving the reliability and safety of the equipment, reducing the risk of unexpected power outages, and extending the service life of the equipment. In addition, the data collection module also stores key information, including expert experience, historical statistical data of defect evolution, and relevant specifications and standards for power equipment management, so as to provide a solid data foundation for subsequent analysis.

[0039] The defect evolution rate analysis module is used to receive the operating parameter information and key information transmitted by the data collection module, and use machine learning methods to perform defect analysis on the operating parameter information to extract the characteristics of defect changes over time.

[0040] In this embodiment, the defect evolution rate analysis module conducts an in-depth analysis of the collected data, paying special attention to the rate of change of defects over time. This step is crucial for predicting the occurrence of potential faults. That is, the defect evolution rate analysis module is a key link. It improves the accuracy of the analysis of the defect evolution rate of power equipment by adopting advanced machine learning methods. These algorithms include but are not limited to decision trees, random forests, support vector machines or neural networks. Each algorithm has its unique advantages and applicable scenarios. Among them: decision trees can clearly show the path of defect evolution with their intuitive decision-making process and easy-to-understand characteristics; random forests improve the stability and accuracy of predictions by integrating multiple decision trees; support vector machines can find the best segmentation plane in high-dimensional space. The ability to handle complex nonlinear problems; neural networks, especially deep learning models, can simulate complex function mapping relationships and capture deep-level features in the data; these machine learning algorithms work together to conduct in-depth analysis of a large amount of operating parameter information and key information obtained from the data collection module, and identify the patterns and trends of defects over time. Through the intelligent analysis of machine learning algorithms, the system can predict the development speed and possible impact of potential faults, thereby providing a scientific basis for risk rating and early warning; this method not only improves the accuracy of defect analysis, but also enhances the system's ability to adapt to new situations, enabling it to continuously optimize over time and with the accumulation of data, providing an efficient and intelligent analysis tool for the health management of power equipment. Among them, the characteristic of defect changes over time is the defect evolution rate.

[0041] The risk rating module is used to receive the defect change characteristics over time transmitted by the defect evolution rate analysis module, store the classification standards of different risk levels, and classify the defect change characteristics over time into risk levels to obtain the risk level of the power equipment.

[0042] Specifically, the risk levels of power equipment are:

[0043] R=f(D,E,S)

[0044] Among them, R represents the risk level, that is, the risk status of the power equipment, f represents the risk level function, D represents the defect evolution rate, E represents the historical failure rate of the power equipment, and S represents the current operating status of the power equipment.

[0045] In this embodiment, the risk rating module is the core component of the evaluation system, which includes a specially designed risk level classification database, which is responsible for storing the classification standards of different risk levels, that is, these standards are not set arbitrarily, but are determined by an accurate mathematical formula R = f (D, E, S), to ensure the scientificity and objectivity of the risk rating, where R represents the risk level, a comprehensive indicator that reflects the overall risk status of the power equipment, f represents the risk level function, D represents the defect evolution rate, which reveals the development trend of the defects of the power equipment over time, E represents the historical failure rate of the power equipment, which provides a reference for the past performance of the power equipment, and S represents the current operating status of the power equipment, which reflects the equipment The operating status at the current moment; therefore, through the combined effect of these three parameters and the comprehensive calculation of the risk level function, an accurate risk level is obtained; in addition, the application of R=f(D,E,S) enables the risk rating module to not only evaluate based on historical data and current operating status, but also predict possible risk changes in the future, thereby providing forward-looking guidance for the maintenance and operation of power equipment. In this way, the system can dynamically adjust the risk control strategy, respond to changes in equipment status in a timely manner, optimize the allocation of maintenance resources, and improve the stability and safety of the power system; this data-driven risk assessment method provides a new, more accurate and efficient solution for risk management in the power industry.

[0046] The comprehensive evaluation module is used to identify the operating status of the power equipment according to the risk level of the power equipment according to the set first fuzzy logic rule and the second fuzzy logic rule, and obtain the risk assessment result.

[0047] Specifically, the first fuzzy logic rule is that if the defect evolution rate is high and the historical failure rate of the power equipment is high, the operating state of the power equipment is classified as a high-level risk state.

[0048] Specifically, the second fuzzy logic rule is that if the current operating state of the power equipment is poor, the operating state of the power equipment is classified as a high-level risk state.

[0049] In this embodiment, the comprehensive evaluation module includes a fuzzy logic controller for dealing with uncertainty and complexity problems. The controller is the core of the system's intelligent decision-making. It uses fuzzy logic rules to solve fuzziness and uncertainty problems that are difficult to handle with traditional logic. Specifically, the controller adopts the following two core fuzzy logic rules: The first fuzzy logic rule: If the defect evolution rate is high and the historical failure rate of the power equipment is also high, then the risk level is judged to be high. This rule reflects that when the power equipment shows rapid defect development and frequent historical failures, the overall risk level of the system will increase significantly; The second fuzzy logic rule: If the current state of the equipment is not good, then the risk level is also judged to be high. This rule emphasizes the importance of the equipment's immediate state to risk assessment. Even if the historical data is good, the deterioration of the current state should immediately attract attention. Therefore, these rules enable the comprehensive evaluation module to flexibly handle various complex situations, rather than simply relying on strict thresholds or binary logic. Through the application of fuzzy logic, the system can more accurately capture subtle changes in the risk status of equipment and make a more reasonable assessment of risks. This method not only improves the accuracy of risk assessment, but also enhances the adaptability and flexibility of the system, enabling it to better cope with the uncertainty and complexity in the operation of power equipment. In this way, the comprehensive evaluation module provides a more accurate and reliable assessment method for the health management of power equipment, which helps to timely discover and respond to potential risks, thereby improving the stability and safety of the power system.

[0050] The early warning module is used to determine whether the risk assessment result reaches the preset high-risk threshold. If so, the early warning mechanism is activated; otherwise, no action is taken.

[0051] In this embodiment, by integrating an early warning module, the early warning capability of the system is significantly enhanced. The module is specially designed to automatically trigger an early warning signal when the system detects a high-level risk state. The addition of this function greatly improves the system's response capability to emergencies and early warning efficiency. The working principle of the early warning module is that after comprehensive evaluation by the system, once it identifies that the operating status of the equipment has reached a preset high-risk threshold, it will immediately start the early warning mechanism. The early warning signal is in various forms, including but not limited to sound and light alarms, text message notifications or automatic emails to ensure that the information can be quickly conveyed to the relevant responsible persons. As an immediate warning method, the sound and light alarm can directly attract the attention of operators at the control center or equipment site, prompting them to immediately check the system status and take necessary emergency measures. Text message notifications and automatic emails provide a remote early warning method to ensure that even if the operator is not present, they can receive the early warning information in time and respond quickly. This multi-channel early warning mechanism not only improves the coverage of early warning, but also enhances the reliability of the system. No matter what the circumstances, the system can ensure the timely transmission of early warning information, thereby providing a valuable time window for preventing equipment failures and accidents. In this way, the system not only improves the automatic response capability to high-risk conditions, but also strengthens the safety and effectiveness of the entire power equipment management system, providing strong technical support for the stable operation of the power system.

[0052] Specifically, it also includes a user interface module for displaying risk assessment results, and performing graphical display, alarm prompts and fault diagnosis on the risk assessment results.

[0053] In this embodiment, the system further expands its functions by introducing a user interface to enhance user experience and ease of operation. The user interface is a bridge between the system and the operator. It not only displays the results of risk assessment, but also provides necessary operation guidance. The design of the interface focuses on intuitiveness and ease of use to ensure that the operator can quickly understand the assessment results and take corresponding measures. One of the core functions of the interface is graphical display, which intuitively displays the operating status and risk assessment results of power equipment in the form of charts, curves and dashboards, making complex data information clear at a glance. This visual information presentation method greatly improves the readability and understanding speed of information, helping operators to quickly grasp the overall status of the equipment. In addition, the user interface also includes an alarm prompt function. When the system detects a high-level risk state or a potential fault, the interface will immediately notify the operator through sound, light or other visual prompts to ensure timely response. This real-time alarm mechanism is the key to preventing faults and accidents. It can reduce reaction time and improve the efficiency of handling emergencies. At the same time, the user interface also provides fault diagnosis suggestions to help operators understand the cause of the fault and guide them to take correct maintenance measures. These suggestions are based on the comprehensive evaluation results of the system and provide targeted solutions, thereby improving the accuracy and effectiveness of fault handling.

[0054] Specifically, it also includes an API interface and a database synchronization module; the API interface is used to realize data exchange and function call with the power system management software; the database synchronization module is used to directly access and update the data information stored in the power system management software.

[0055] In this embodiment, by adding integration modules, namely, API interface and database synchronization module, the compatibility and scalability of the system are significantly improved, because it can be integrated with the existing power system management software. This integration capability enables the system to not only serve as an independent evaluation tool, but also to be seamlessly integrated into the existing workflow and management framework to achieve data sharing and interoperability; wherein, the integration process is mainly implemented in two ways: API interface and database synchronization. The API interface provides a flexible communication method, which enables the system to exchange data and call functions with the power system management software. This interface method supports real-time data interaction between systems, allowing the two systems to share key information, such as power equipment status, risk assessment results and early warning signals, without human intervention; at the same time, database synchronization is another integration means, which allows the system to directly access and update the information stored in the power system management software database. Through database synchronization, the system can ensure the consistency and update of the data, avoid the problem of data islands, and improve the utilization efficiency and accuracy of the data. The addition of this integrated module not only improves the practicality of the system, but also enhances its application value in the power system. Operators can directly access and operate the risk assessment system in the familiar management software interface without switching multiple platforms, thereby improving work efficiency. At the same time, the integration of the system also reduces the risk of data redundancy and inconsistency, ensuring the accuracy and reliability of information. In this way, the system not only improves its own functions, but also provides strong support for the intelligent and information management of the power system.

[0056] Specifically, it also includes an adaptive learning module for obtaining historical operating parameter information and current operating parameter information of power equipment, and optimizing system risk assessment results through online learning and offline learning.

[0057] In this embodiment, the intelligence level of the system is further improved by introducing an adaptive learning module. The design purpose of this module is to continuously optimize the assessment model according to new data (current operating parameter information) and feedback information (historical operating parameter information and risk assessment results) to improve the accuracy and adaptability of risk assessment. The addition of this module enables the system to not only perform static assessments, but also evolve itself over time and with the accumulation of data. Among them, the adaptive learning module includes two learning modes: online learning and offline learning. The online learning mode allows the system to learn and adjust in real-time operation and respond to new data and changes immediately. This mode is crucial for quickly adapting to dynamic changes in equipment status. It can capture immediate trends and anomalies in equipment operation and quickly update the assessment model to ensure the timeliness and accuracy of the assessment results. The offline learning mode focuses on in-depth analysis of historical data and accumulated feedback information when the system is not in operation. This mode helps the system learn rules from long-term data and identify potential patterns and trends, thereby periodically optimizing and adjusting the assessment model. The introduction of the offline learning mode enables the system to more comprehensively understand and predict the risk status of the equipment and improve the depth and breadth of the assessment. Through the combination of these two learning modes, the adaptive learning module gives the system powerful learning ability and adaptability. The system can not only respond to new data instantly, but also learn from historical data and continuously optimize the evaluation system. This continuous learning and optimization process enables the system to better adapt to the complexity and variability of power equipment operation, and improve the reliability and effectiveness of risk assessment. In this way, the system not only improves its own performance, but also provides a more intelligent and dynamic solution for the health management of power equipment.

[0058] Specifically, it also includes a data security module for data encryption, access control, and audit tracking to ensure the security, confidentiality, and availability of system data.

[0059] In this embodiment, the data security of the system is particularly enhanced by integrating a data security module to ensure that the collected and analyzed data are protected from the risk of unauthorized access. This module is an important part of the system, and its core task is to protect the integrity, confidentiality and availability of the data to prevent data leakage, tampering or loss; among them, the functions of the data security module include data encryption, access control and audit tracking; the data encryption function ensures that the stored and transmitted data are encrypted, and even if the data is illegally intercepted, it cannot be interpreted, thereby protecting the confidentiality of the data; the access control function strictly manages the access rights to the data, and only authorized users can access sensitive data, which can prevent unauthorized data access and operation; the audit tracking function records all access and operation behaviors to the data, providing a post-audit capability for data security. Once a security incident occurs, the problem can be tracked and located through the audit log. The combination of these functions provides all-round protection for the system's data security. Data encryption ensures the security of data during transmission and storage. Access control ensures that only authorized personnel can access sensitive information, and audit tracking provides a means of monitoring and post-analysis for data security. In this way, the system can not only ensure data security, but also meet compliance and audit requirements, thereby enhancing users' trust in the system. This comprehensive data security measure makes the system more reliable when processing sensitive data, and provides a solid security guarantee for the stable operation of the power system.

[0060] In summary, the overheating and insulation failure risk assessment and early warning system under multi-sensory parameters proposed in the present invention integrates multiple sensors to monitor multiple operating parameters of power equipment (such as temperature, humidity, voltage, current, partial discharge, etc.) in real time. Compared with the monitoring method that relies on a single or a few parameters, it can more comprehensively reflect the operating status and potential risks of the equipment, thereby improving the accuracy of early warning and assessment; secondly, the defect evolution rate is analyzed by machine learning method, which can predict the time point and severity of the fault, overcome the problem of lack of comprehensive analysis of the defect evolution rate of power equipment in the prior art, and enhance the prediction ability; at the same time, by establishing a risk rating model based on the historical failure rate of the equipment, the defect evolution rate and the current status of the equipment (i.e., the risk assessment module), a more accurate basis for risk level division is provided, which improves the accuracy and reliability of risk assessment; the fuzzy logic controller is used to deal with uncertainty and complexity problems (i.e., the comprehensive evaluation module), which can better deal with the ambiguity and uncertainty in the evaluation process and improve the robustness of the evaluation results; in addition, a user interface is provided to display the risk assessment Results and operation instructions, including graphical display, alarm prompts and fault diagnosis suggestions, optimize the user experience, so that non-professional users can also quickly understand and respond to the evaluation results; and an integrated early warning module can automatically trigger an early warning signal when a high-risk state is detected, such as an audible and visual alarm, a text message notification or an automatic email, thereby improving the response speed to emergency situations; it can also be integrated with the existing power system management software to achieve data sharing and interoperability, which is achieved through an API interface or database synchronization, thereby enhancing the compatibility and practicality of the system; it also includes an adaptive learning module, which continuously optimizes the evaluation model according to new data and feedback, including online learning and offline learning modes, so that the system can continue to evolve and improve the adaptability and accuracy of the evaluation model; finally, a data security module is integrated to protect the collected and analyzed data from unauthorized access, including data encryption, access control and audit tracking functions, thereby enhancing data security and privacy protection; therefore, the present invention provides scientific decision-making support for the maintenance and operation of power equipment, helps to reduce the economic losses and social impacts caused by failures, and improves the stability and reliability of the power system.

[0061] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0062] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A risk assessment and early warning system for overheating and insulation failure based on multiple sensing parameters, characterized in that: include: Data collection module, used to monitor and collect operating parameter information and key information of power equipment including temperature, humidity, voltage and partial discharge in real time; Among them, key information includes expert experience, historical statistical data of defect evolution, and relevant specifications and standards information for power equipment management. The defect evolution rate analysis module is used to receive the operating parameter information and key information transmitted by the data collection module, and use machine learning methods to perform defect analysis on the operating parameter information to extract the characteristics of defect changes over time; The risk rating module is used to receive the defect variation characteristics over time transmitted by the defect evolution rate analysis module, store the classification standards of different risk levels, and classify the defect variation characteristics over time into risk levels to obtain the risk level of the power equipment; A comprehensive evaluation module is used to identify the operating status of the power equipment according to the risk level of the power equipment and obtain the risk assessment result according to the set first fuzzy logic rule and second fuzzy logic rule; The early warning module is used to determine whether the risk assessment result reaches the preset high-risk threshold. If so, the early warning mechanism is activated; otherwise, no action is taken.

2. The overheating and insulation failure risk assessment and early warning system based on multiple sensing parameters according to claim 1 is characterized in that: The risk levels of electrical equipment are: R=f(D,E,S) Among them, R represents the risk level, that is, the risk status of the power equipment, f represents the risk level function, D represents the defect evolution rate, E represents the historical failure rate of the power equipment, and S represents the current operating status of the power equipment.

3. The overheating and insulation failure risk assessment and early warning system based on multiple sensing parameters according to claim 2 is characterized in that: The first fuzzy logic rule is that if the defect evolution rate is high and the historical failure rate of the power equipment is high, the operating state of the power equipment is classified as a high-level risk state.

4. The overheating and insulation failure risk assessment and early warning system based on multiple sensing parameters according to claim 3 is characterized in that: The second fuzzy logic rule is that if the current operating state of the power equipment is poor, the operating state of the power equipment is classified as a high-level risk state.

5. The overheating and insulation failure risk assessment and early warning system based on multiple sensing parameters according to claim 4 is characterized in that: It also includes a user interface module for displaying risk assessment results, and performing graphical display, alarm prompts and fault diagnosis on the risk assessment results.

6. The overheating and insulation failure risk assessment and early warning system based on multiple sensing parameters according to claim 5 is characterized in that: It also includes API interface and database synchronization module; API interface, used to realize data exchange and function call with power system management software; The database synchronization module is used to directly access and update the data information stored in the power system management software.

7. The overheating and insulation failure risk assessment and early warning system based on multiple sensing parameters according to claim 6 is characterized in that: It also includes an adaptive learning module for obtaining historical operating parameter information and current operating parameter information of power equipment, and optimizing system risk assessment results through online learning and offline learning.

8. The overheating and insulation failure risk assessment and early warning system based on multiple sensing parameters according to claim 7 is characterized in that: It also includes a data security module for data encryption, access control, and audit tracking to ensure the security, confidentiality, and availability of system data.

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