Electric power reliability management platform based on defect integration and hierarchical classification

Abstract

The invention relates to the technical field of power system reliability management, and discloses a power reliability management platform based on defect integration and grading classification, which comprises a defect acquisition module, a grading classification module, a reliability evaluation module, a dynamic monitoring module, a mode switching module, an emergency response module and a risk early warning module. The platform provides reliability evaluation in a conventional mode and an emergency response mode by integrating defect information of power equipment and performing grading and classification processing, dynamically adjusts an evaluation process in combination with real-time operation state data, and predicts a potential risk level through a risk early warning module. The method can improve the reliability management level of the power system, is suitable for a complex and changeable operation environment, and achieves the precise evaluation and dynamic optimization.

Description

Technical Field

[0001] This invention relates to the field of power system reliability management technology, specifically a power reliability management platform based on defect integration and hierarchical classification. Background Technology

[0002] In the operation and management of power systems, reliability is a core indicator for ensuring power supply quality and service levels. With the continuous expansion and increasing complexity of power systems, the management and handling of equipment defects have become crucial factors affecting power reliability. Currently, defect management in power systems typically relies on manual recording, decentralized management systems, or simple information tools. These methods show significant shortcomings when faced with massive amounts of data and diverse defect types. For example, existing technologies struggle to comprehensively integrate and efficiently classify defect information, leading to low defect handling efficiency and potentially causing serious safety incidents due to information delays or omissions. Furthermore, existing defect management methods lack a scientific grading mechanism, failing to accurately assess and prioritize defects based on their severity and impact, thus affecting the overall reliability and operational efficiency of the power system. Therefore, there is an urgent need for a power reliability management platform capable of integrating defect information, implementing grading and classification, and improving management efficiency to address the problems in existing technologies and meet the demands of modern power systems for high reliability and intelligent management. Summary of the Invention

[0003] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a power reliability management platform based on defect integration and hierarchical classification. This platform can improve the reliability management level of the power system and effectively cope with complex and ever-changing power operating environments by integrating defect information from power equipment operation and classifying it hierarchically.

[0004] One embodiment of the present invention provides a power reliability management platform based on defect integration and hierarchical classification, comprising: a defect acquisition module for real-time acquisition of defect data generated during the operation of power equipment; a hierarchical classification module for classifying defects according to their severity and impact range; a reliability assessment module for performing reliability assessment of the power system in a normal mode; wherein the reliability assessment of the power system in the normal mode includes: initiating the assessment process and initializing a timer, generating reliability indicators based on the hierarchical classification results, predicting future reliability trends based on historical data, and stopping the assessment process when the assessment time reaches a preset threshold; a dynamic monitoring module for acquiring real-time operating status data collected by sensors installed on the power equipment; and a mode switching module for determining whether the current power system is in an abnormal state based on the real-time operating status data, and if so, switching the normal mode to an emergency response mode; and an emergency response module. The module is used to perform reliability assessment of the power system in emergency response mode. This reliability assessment includes: dynamically adjusting reliability indicators based on real-time operating status data, continuously monitoring the data flow of the dynamic monitoring module, and stopping the assessment process after the operating status data returns to normal. A risk warning module is used to start timing after the assessment is completed. When the power system is in a stable operating state, it acquires operating status images at the first, second, and third warning intervals, respectively, and determines the potential risk level of the power system based on these images. If the operating status image shows an anomaly in the first warning interval, the risk level is determined to be high; if the operating status image shows an anomaly in the second warning interval, the risk level is determined to be medium; if the operating status image shows an anomaly in the third warning interval, the risk level is determined to be low. The first warning interval is shorter than the second warning interval, and the second warning interval is shorter than the third warning interval.

[0005] According to some embodiments of the present invention, the dynamic monitoring module is used to acquire real-time operating status data within a unit time after the sensor starts collecting data; the mode switching module is used to determine whether the operating status data exceeds a set threshold, and if so, to determine that the current power system is in an abnormal state.

[0006] According to some embodiments of the present invention, the platform further includes: an adaptive calibration module, used to acquire a first assessment time required for reliability assessment of the power system under normal operating conditions and to acquire operating status data; acquire a second assessment time required for reliability assessment of the power system under abnormal operating conditions, and when the difference between the second assessment time and the first assessment time exceeds a threshold, use the operating status data corresponding to the second assessment time as the set threshold; the operating status data is the data acquired per unit time after the sensor starts collecting data.

[0007] According to some embodiments of the present invention, the platform further includes: a remote collaboration module, configured to connect to an external terminal device via a communication component, and to perform parameter configuration and control platform reliability assessment based on remote operation by the user via the terminal device; and to send notification information to the terminal device after the assessment is completed.

[0008] According to some embodiments of the present invention, the platform further includes: an automatic optimization module, used to determine whether there are potential risks in the current power system through a state detection component after the evaluation is completed, and if so, to optimize the performance of the power system through an optimization strategy.

[0009] According to some embodiments of the present invention, the risk warning module is used to acquire normal operation status images and abnormal operation status images and input them into the risk identification model for training to obtain a trained risk identification model. The risk identification model is used to determine the potential risk level of the power system based on the operation status images.

[0010] According to some embodiments of the present invention, the platform further includes: a parameter dynamic adjustment module, used to dynamically adjust the assessment cycle or assessment strategy when the risk level is medium or high.

[0011] According to some embodiments of the present invention, the equipment type adaptation module is used to match the corresponding evaluation cycle and evaluation strategy according to the type of power equipment selected by the user.

[0012] This invention's platform provides users with comprehensive defect integration and classification functions by setting up a defect acquisition module and a hierarchical classification module, enabling accurate reliability assessments of power systems based on actual user needs. It also offers two operating modes: a conventional mode for assessment when the power system is in normal operation, and a mode for dynamically adjusting the assessment process based on real-time acquisition of operational status data when the power system is in an abnormal state, to cope with complex operating environments. This embodiment, by setting up a conventional mode and an emergency response mode, facilitates users in quickly completing assessment tasks when the power system is in normal operation, saving assessment time and lowering the technical threshold. Simultaneously, for power systems in abnormal states, it eliminates the need for pre-setting a fixed assessment cycle, instead determining the assessment end time based on changes in operational status data. Thus, in the conventional mode, the system controls the assessment process, reducing the operation frequency of the dynamic monitoring module and meeting user needs. In the emergency response mode, the dynamic monitoring module leads the assessment process, ensuring accurate and reliable assessment results and overcoming the difficulties in accurate assessment and dynamic adjustment under complex operating environments. Attached Figure Description

[0013] Figure 1 This is an architectural block diagram of the platform of the present invention; Figure 2 This is a flowchart of the hierarchical classification module in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the mode switching logic in an embodiment of the present invention; Figure 4 This is a flowchart of the risk warning module in an embodiment of the present invention.

[0014] The modules include: 100 Defect Acquisition Module; 200 Classification and Grading Module; 300 Reliability Assessment Module; 400 Dynamic Monitoring Module; 500 Mode Switching Module; 600 Emergency Response Module; and 700 Risk Warning Module. Detailed Implementation

[0015] The technical solution of the present invention will now be clearly and completely described 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.

[0016] This invention provides a power reliability management platform based on defect integration and hierarchical classification. Through the collaborative work of multiple functional modules, it achieves comprehensive collection, hierarchical classification processing, and dynamic evaluation of defect information during power equipment operation, thereby significantly improving the reliability management level of the power system. The following is in conjunction with the appendix... Figure 1 To be continued Figure 4 The technical solution of the present invention will be described in detail below with reference to specific embodiments.

[0017] First, such as Figure 1 As shown, the power reliability management platform of this embodiment includes a defect acquisition module 100, a hierarchical classification module 200, a reliability assessment module 300, a dynamic monitoring module 400, a mode switching module 500, an emergency response module 600, and a risk warning module 700. These modules together constitute a complete system architecture, which can flexibly adjust the assessment strategy according to the actual operating status of power equipment, ensuring accurate reliability assessment in both normal and emergency response modes.

[0018] The defect acquisition module 100 is the foundational module of the entire platform, used to acquire defect data generated during the operation of power equipment in real time. This defect data can originate from various sources, such as abnormal equipment operating parameter records collected by sensors, problem reports discovered during manual inspections, and relevant information stored in historical databases. To ensure the comprehensiveness and accuracy of the data, the defect acquisition module 100 employs multi-source data fusion technology to integrate data from different sources and removes noise and redundant information through preprocessing algorithms. Specifically, the defect data preprocessing process includes data cleaning, format standardization, and timestamp alignment steps to ensure that the subsequent hierarchical classification module 200 can efficiently process this data.

[0019] The workflow of the hierarchical classification module 200 is as follows: Figure 2 As shown, its core task is to classify and categorize defect data. The classification criteria are primarily based on the severity and scope of the defect. Severity can be quantified by the potential equipment downtime, economic losses, or security risks, while the scope of impact involves whether the defect will cause a chain reaction on other equipment or systems. To achieve this goal, the classification module 200 employs a weighted comprehensive scoring algorithm, with the following formula: S=w1 D+w2 I+w3 R represents the overall defect score, S represents the defect severity score, D represents the defect impact range score, R represents the defect occurrence frequency score, and w1, w2, and w3 are the corresponding weighting coefficients. After calculating the overall score for each defect using this formula, the classification module 200 will divide the defects into high, medium, and low levels according to preset thresholds and generate corresponding classification results for use by subsequent modules.

[0020] The reliability assessment module 300 is responsible for conducting reliability assessments of the power system under normal conditions. Its assessment process includes initiating the assessment process and initializing a timer, generating reliability indices based on the classification results generated by the classification module 200, predicting future reliability trends using historical data, and stopping the assessment process when the assessment time reaches a preset threshold. Specifically, the reliability index generation process is based on statistical analysis of the classification results and historical data, and its formula is: R=(1-Σ(Si) Pi)) 100%, where R represents the reliability index, Si represents the overall score of the i-th defect, and Pi represents the probability of the defect occurring. The reliability index calculated by this formula can intuitively reflect the overall reliability level of the current power system. In addition, the reliability assessment module 300 also uses time series analysis methods to model historical data to predict reliability trends over a future period, thereby providing users with forward-looking decision support.

[0021] The dynamic monitoring module 400 acquires real-time operating status data collected by sensors installed on the power equipment. This data includes various parameters such as voltage, current, temperature, and vibration, comprehensively reflecting the operating status of the power equipment. The dynamic monitoring module 400 acquires real-time operating status data within a unit of time after the sensors begin collecting data and transmits it to the mode switching module 500 for further processing. The core function of the mode switching module 500 is to determine whether the current power system is in an abnormal state, based on whether the real-time operating status data exceeds a set threshold. If the operating status data exceeds the set threshold, the mode switching module 500 will trigger mode switching logic, switching the system from normal mode to emergency response mode. Figure 3 As shown, the design of the mode switching logic fully considers the complex operating environment of the power system and can quickly respond and adjust the evaluation strategy under abnormal conditions.

[0022] The emergency response module 600 performs a reliability assessment of the power system in emergency response mode. Its assessment process includes dynamically adjusting reliability indicators based on real-time operating status data, continuously monitoring the data stream of the dynamic monitoring module 400, and stopping the assessment process once the operating status data returns to normal. To achieve the function of dynamically adjusting reliability indicators, the emergency response module 600 employs an adaptive adjustment algorithm, the formula of which is: Rt = Rt-1 + α (Dt-Rt-1), where Rt represents the reliability index at the current moment, Rt-1 represents the reliability index at the previous moment, Dt represents the real-time operating status data at the current moment, and α is an adjustment coefficient. Using this formula, the emergency response module 600 can quickly update the reliability index based on changes in real-time data, thereby ensuring the accuracy and timeliness of the assessment results.

[0023] The risk warning module 700 starts timing after the assessment is completed and acquires operating status images in the first, second, and third warning intervals when the power system is in a stable operating state. For example... Figure 4As shown, the risk warning module 700 determines the potential risk level of the power system based on the operating status image. Specifically, if the operating status image shows an anomaly in the first warning interval, the risk level is determined to be high; if the operating status image shows an anomaly in the second warning interval, the risk level is determined to be medium; and if the operating status image shows an anomaly in the third warning interval, the risk level is determined to be low. The first warning interval is shorter than the second warning interval, and the second warning interval is shorter than the third warning interval. To improve the accuracy of risk level determination, the risk warning module 700 also incorporates machine learning technology. It acquires normal operating status images and abnormal operating status images and inputs them into the risk identification model for training, resulting in a trained risk identification model. This model can automatically identify potential risk levels based on the operating status images, thereby providing users with a more intelligent risk warning service.

[0024] Furthermore, the platform in this embodiment of the invention also includes several auxiliary modules, such as an adaptive calibration module, a remote collaboration module, an automatic optimization module, a parameter dynamic adjustment module, and a device type adaptation module. The adaptive calibration module is used to obtain the first evaluation time required for reliability assessment of the power system under normal operating conditions and to obtain operating status data, and to obtain the second evaluation time required for reliability assessment of the power system under abnormal operating conditions. When the difference between the second evaluation time and the first evaluation time exceeds a threshold, the adaptive calibration module uses the operating status data corresponding to the second evaluation time as the set threshold, thereby achieving dynamic adjustment of the evaluation threshold. The remote collaboration module connects to external terminal devices through communication components. Users can configure platform parameters and control the platform to perform reliability assessments through the terminal devices, and receive notification information after the assessment is completed. After the assessment, the automatic optimization module determines whether there are potential risks in the current power system through a status detection component. If so, it optimizes the performance of the power system through optimization strategies. The parameter dynamic adjustment module dynamically adjusts the evaluation cycle or evaluation strategy when the risk level is medium or high to ensure the accuracy and reliability of the evaluation results. The device type adaptation module matches the corresponding evaluation cycle and evaluation strategy according to the power equipment type selected by the user, thereby meeting the personalized needs of different equipment types.

[0025] In summary, the power reliability management platform of this invention, through the collaborative work of multiple functional modules, achieves comprehensive collection, hierarchical classification, and dynamic evaluation of defect information during the operation of power equipment. It not only completes evaluation tasks quickly in conventional mode, saving evaluation time and lowering the technical threshold, but also dynamically adjusts the evaluation process based on changes in real-time operating status data in emergency response mode, ensuring the accuracy and reliability of the evaluation results. Furthermore, the platform incorporates machine learning technology and adaptive adjustment algorithms, further improving risk warning and evaluation optimization capabilities, providing strong technical support for the reliability management of power systems.

[0026] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A power reliability management platform based on defect integration and hierarchical classification, characterized in that, include: The defect acquisition module (100) is used to acquire defect data generated during the operation of power equipment in real time. The classification module (200) is used to classify defects according to their severity and scope of impact. The reliability assessment module (300) is used to perform reliability assessments on the power system under normal conditions. The reliability assessment of the power system in the conventional mode includes: starting the assessment process and initializing the timer, generating reliability indicators based on the classification results, predicting future reliability trends by combining historical data, and stopping the assessment process when the assessment time reaches a preset threshold. The dynamic monitoring module (400) is used to acquire real-time operating status data collected by sensors installed on the power equipment; The mode switching module (500) is used to determine whether the current power system is in an abnormal state based on real-time operating status data. If so, it switches the normal mode to the emergency response mode. An emergency response module (600) is used to perform a reliability assessment of the power system in an emergency response mode. The reliability assessment of the power system in the emergency response mode includes: dynamically adjusting reliability indicators based on real-time operating status data, continuously monitoring the data flow of the dynamic monitoring module (400), and stopping the assessment process after the operating status data returns to the normal range. The risk warning module (700) is used to start timing after the assessment is completed. When the power system is in a stable operating state, it acquires operating status images at the first warning interval, the second warning interval, and the third warning interval, respectively, and determines the potential risk level of the power system based on the operating status images. If the operating status image shows an anomaly in the first warning interval, the risk level is determined to be high; if the operating status image shows an anomaly in the second warning interval, the risk level is determined to be medium; if the operating status image shows an anomaly in the third warning interval, the risk level is determined to be low. The first warning interval is shorter than the second warning interval, and the second warning interval is shorter than the third warning interval.

2. The power reliability management platform based on defect integration and hierarchical classification according to claim 1, characterized in that, The dynamic monitoring module (400) is used to acquire real-time operating status data within a unit time after the sensor starts collecting data; the mode switching module (500) is used to determine whether the operating status data exceeds a set threshold, and if so, to determine that the current power system is in an abnormal state.

3. The power reliability management platform based on defect integration and hierarchical classification according to claim 1, characterized in that, It also includes an adaptive calibration module, used to obtain the first evaluation time required for reliability assessment of the power system under normal operating conditions and to obtain operating status data; to obtain the second evaluation time required for reliability assessment of the power system under abnormal operating conditions; and when the difference between the second evaluation time and the first evaluation time exceeds a threshold, the operating status data corresponding to the second evaluation time is used as the set threshold; the operating status data is the data obtained per unit time after the sensor starts collecting data.

4. A power reliability management platform based on defect integration and hierarchical classification as described in claim 1, characterized in that, It also includes a remote collaboration module, which connects to external terminal devices via communication components, configures platform parameters and controls platform performance reliability assessment based on remote operations performed by users through the terminal devices, and sends notification information to the terminal devices after the assessment is completed.

5. A power reliability management platform based on defect integration and hierarchical classification as described in claim 1, characterized in that, It also includes an automatic optimization module, which is used to determine whether there are potential risks in the current power system after the evaluation is completed through the state detection component. If so, the power system performance is optimized through optimization strategies.

6. A power reliability management platform based on defect integration and hierarchical classification according to claim 1, characterized in that, The risk warning module (700) is used to acquire normal operation status images and abnormal operation status images and input them into the risk identification model for training to obtain a trained risk identification model. The risk identification model is used to determine the potential risk level of the power system based on the operation status images.

7. A power reliability management platform based on defect integration and hierarchical classification according to claim 1, characterized in that, It also includes a parameter dynamic adjustment module, which is used to dynamically adjust the assessment cycle or assessment strategy when the risk level is medium or high.

8. A power reliability management platform based on defect integration and hierarchical classification according to claim 1, characterized in that, It also includes an equipment type adaptation module, which is used to match the corresponding evaluation cycle and evaluation strategy according to the type of power equipment selected by the user.

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