A power distribution network operation monitoring method and system and a storage medium

Abstract

The application discloses a power distribution network operation monitoring method and system and a storage medium. The method comprises the following steps: based on a self-learning feedback mechanism, dynamically optimizing a device health monitoring process, a power grid load abnormality monitoring process and a power grid operation monitoring process respectively, obtaining a device health index of each device, a power grid load abnormality index and an operation risk abnormality index of a target power distribution network; inputting time sequence correlation analysis results of the device health index, the power grid load abnormality index and the operation risk abnormality index, the device health index, the power grid load abnormality index and the operation risk abnormality index into a pre-trained deep learning model to obtain a risk monitoring result; and controlling a function component matched with the target power distribution network to execute a risk control strategy corresponding to the risk monitoring result. The power distribution network operation monitoring method and system and the storage medium provided by the application improve the accuracy of power distribution network monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a power distribution network operation monitoring method and system and a storage medium. BACKGROUND

[0002] As an important part of the power system, the safe operation of the power distribution network ensures uninterrupted power supply and user safety, which is of great significance to the stability, economy and normal order of the entire power system. By monitoring the power distribution network, the operation state of the power grid can be monitored in real time. Once an abnormality or potential failure is found, an early warning can be issued immediately, so that the operation and maintenance personnel can take prompt measures to prevent the failure from expanding and ensure the safe operation of the power grid.

[0003] However, the existing technology for monitoring the power distribution network is single and static analysis, which cannot cope with the complex operation of the power grid and equipment and real-time changes, resulting in low accuracy of monitoring the power distribution network.

[0004] Therefore, how to improve the accuracy of monitoring the power distribution network has become a technical problem to be solved by those skilled in the art. SUMMARY

[0005] The present application provides a power distribution network operation monitoring method and system and a storage medium to solve the technical problem of low accuracy of monitoring the power distribution network due to single and static analysis of the existing technology for monitoring the power distribution network.

[0006] To solve the above technical problems, the present application provides a power distribution network operation monitoring method.

[0007] The device health monitoring process is optimized based on a self-learning feedback mechanism to obtain a device health index of each device, wherein the device health monitoring process is designed to dynamically weight each device state parameter obtained;

[0008] The power grid load abnormality monitoring process is optimized based on a self-learning feedback mechanism to obtain a power grid load abnormality index of the target power distribution network, wherein the power grid load abnormality monitoring process reflects the change analysis result of the power grid load rate in different time dimensions;

[0009] The power grid operation monitoring process is optimized based on a self-learning feedback mechanism to obtain an operation risk abnormality index of the target power distribution network, wherein the power grid operation monitoring process reflects the abnormal analysis result of the predicted operation parameter;

[0010] The time sequence correlation analysis result of the device health index, the power grid load abnormality index and the operation risk abnormality index, the device health index, the power grid load abnormality index and the operation risk abnormality index are input into a pre-trained deep learning model to obtain a risk monitoring result.

[0011] The function component matched with the target power distribution network is controlled to execute a risk control strategy corresponding to the risk monitoring result.

[0012] As one of the preferred solutions, the device health monitoring process is optimized based on the self-learning feedback mechanism to obtain the device health index of each device, including:

[0013] The device health index is calculated by dynamically weighting the collected device state parameters.

[0014] The dynamic weighting processing is designed to adjust the weight of each device state parameter in real time based on the deviation between the first real-time data of each device state parameter and the corresponding first historical data, and the updated weight obtained by real-time adjustment is used to weight and sum the first real-time data as the device health index.

[0015] As one of the preferred solutions, the power grid load abnormality monitoring process is optimized based on the self-learning feedback mechanism to obtain the power grid load abnormality index of the target power distribution network, including:

[0016] The power load rate is calculated according to the linear relationship between the rated power and the operating power of the target power distribution network collected.

[0017] The second real-time data of the power load rate is dynamically threshold compared and analyzed based on the distribution characteristics of the second historical data of the power load rate, and the power grid load abnormality index is calculated.

[0018] As one of the preferred solutions, the power grid operation monitoring process is optimized based on the self-learning feedback mechanism to obtain the operation risk abnormality index of the target power distribution network, including:

[0019] The third historical data of each operation parameter collected is predicted and analyzed by using a long short-term memory network model to obtain prediction data of each operation parameter, and the operation risk abnormality index is obtained by performing abnormality detection on all the prediction data.

[0020] As one of the preferred solutions, the power grid operation monitoring process is optimized based on the self-learning feedback mechanism to obtain the operation risk abnormality index of the target power distribution network, further including:

[0021] The weight parameters of the long short-term memory network model are adjusted in feedback based on deviation between the predicted data of each operation parameter and third real-time data of a corresponding moment.

[0022] As one of the preferred solutions, the weight of each device state parameter is adjusted in real time based on deviation between the first real-time data of each device state parameter and corresponding first historical data, including:

[0023] The average value of all the first historical data of each device state parameter is taken as a state reference value of each device state parameter.

[0024] The absolute value of the difference between the first real-time data of each device state parameter and the corresponding state reference value is taken as a state deviation degree of each device state parameter.

[0025] The result obtained by normalizing all the state deviation degrees is taken as the updated weight of each device state parameter.

[0026] As one of the preferred solutions, the second real-time data of the grid load rate is dynamically threshold-comparison analyzed based on the distribution characteristics of the second historical data of the grid load rate, and a grid load abnormality index is calculated, including:

[0027] The sum of the average value of all the second historical data and the corresponding standard deviation is taken as an early warning upper limit, and the absolute value of the difference between the second real-time data and the early warning upper limit is taken as an upper limit deviation degree.

[0028] The difference between the average value of all the second historical data and the corresponding standard deviation is taken as an early warning lower limit, and the absolute value of the difference between the second real-time data and the early warning lower limit is taken as a lower limit deviation degree.

[0029] The minimum value of the upper limit deviation degree and the lower limit deviation degree is taken as a grid load abnormality index.

[0030] As one of the preferred solutions, the risk monitoring result includes a mild risk, a moderate risk and a severe risk.

[0031] The risk control strategy includes a preventive reminder strategy corresponding to the mild risk, a forced reminder strategy corresponding to the moderate risk, and a forced intervention strategy corresponding to the severe risk.

[0032] Another embodiment of the present application provides a power distribution network operation monitoring system, including:

[0033] The device monitoring module is used for optimizing a device health monitoring process based on a self-learning feedback mechanism to obtain a device health index of each device, wherein the device health monitoring process is designed to perform dynamic weighted processing on the obtained device state parameters;

[0034] The power grid monitoring module is used for optimizing a power grid load abnormality monitoring process based on a self-learning feedback mechanism to obtain a power grid load abnormality index of the target power distribution network, wherein the power grid load abnormality monitoring process reflects the change analysis result of the power grid load rate in different time dimensions.

[0035] The operation monitoring module is used for optimizing a power grid operation monitoring process based on a self-learning feedback mechanism to obtain an operation risk abnormality index of the target power distribution network, wherein the power grid operation monitoring process reflects the abnormality analysis result of the predicted operation parameters.

[0036] The three-risk linkage evaluation module is used for inputting the time sequence correlation analysis result of the device health index, the power grid load abnormality index and the operation risk abnormality index, the device health index, the power grid load abnormality index and the operation risk abnormality index into a pre-trained deep learning model to obtain a risk monitoring result.

[0037] The risk control module is used for controlling the matched functional components of the target power distribution network to execute a risk control strategy corresponding to the risk monitoring result.

[0038] Another embodiment of the present application provides a computer readable storage medium storing a computer program, wherein when a device where the computer readable storage medium is located executes the computer program, a power distribution network operation monitoring method as described above is realized.

[0039] Compared with the prior art, the embodiment of the present application has at least one of the following advantages:

[0040] Considering that the operation state of the power distribution network is real-time changeable, static analysis is difficult to cope with the complex operation of the power grid and real-time changes, the device health monitoring process is optimized based on a self-learning feedback mechanism to obtain a device health index of each device, wherein the device health monitoring process is designed to dynamically weight the obtained state parameters of each device; the power grid load abnormality monitoring process is optimized based on the self-learning feedback mechanism to obtain a power grid load abnormality index of the target power distribution network, wherein the power grid load abnormality monitoring process reflects the change analysis result of the power grid load rate in different time dimensions; the power grid operation monitoring process is optimized based on the self-learning feedback mechanism to obtain an operation risk abnormality index of the target power distribution network, wherein the power grid operation monitoring process reflects the abnormal analysis result of the predicted operation parameters, and the monitoring of the power grid, the device and the operation is dynamically optimized through the self-learning feedback mechanism, thereby improving the accuracy of the power distribution network monitoring.

[0041] The time sequence correlation analysis result of the device health index, the power grid load abnormality index and the operation risk abnormality index, the device health index, the power grid load abnormality index and the operation risk abnormality index are input into a pre-trained deep learning model to obtain a risk monitoring result, realize dynamic fusion and comprehensive analysis of the power grid operation, the device state and the operation, control the functional components of the target power distribution network to execute the risk control strategy corresponding to the risk monitoring result, realize adaptive adjustment of the risk monitoring result through dynamic monitoring of the power grid, the device and the operation and correlation analysis among the three, enhance the response ability of the system, avoid the risk of operation failure caused by monitoring of only a single factor, ensure efficient and safe operation of the system, and further improve the accuracy of the power distribution network monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0042] Fig. 1 A flowchart of a power distribution network operation monitoring method in one of the embodiments of the present application;

[0043] Fig. 2 A structural block diagram of a power distribution network operation monitoring system in one of the embodiments of the present application;

[0044] Fig. 3 A system architecture diagram of a power distribution network operation monitoring system in one of the embodiments of the present application. DETAILED DESCRIPTION

[0045] Clearly, the described embodiments are only a part of the embodiments of the present application, and are not all the embodiments of the present application, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0046] In the description of the present application, the terms "first", "second", "third" and the like are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third" and the like can be explicitly or implicitly included one or more of the features. In the description of the present application, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0047] In the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "mounting", "connecting", "connecting" should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, it can be the communication inside two elements. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are for the purpose of description only, and are not intended to indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those skilled in the art, the specific meaning of the above terms in the present application can be understood in specific cases.

[0048] In the description of the present application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as understood by those skilled in the art. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments, and are not intended to limit the present application. For those skilled in the art, the specific meaning of the above terms in the present application can be understood in specific cases.

[0049] An embodiment of the present application provides a power distribution network operation monitoring method, specifically, please refer to Figs. 1-3 , Fig. 1 A flow chart of a power distribution network operation monitoring method in one of the embodiments of the present application is shown, Fig. 2 A structural block diagram of a power distribution network operation monitoring system in one of the embodiments of the present application is shown,Fig. 3 A system architecture diagram of a power distribution network operation monitoring system in one of the embodiments of the present application is shown.

[0050] The flow chart of the power distribution network operation monitoring method in one of the embodiments of the present application comprises the following steps S1-S5, which are as follows:

[0051] Step S1: Based on the self-learning feedback mechanism, the device health monitoring process is optimized to obtain the device health index of each device, wherein the device health monitoring process is designed to dynamically weight the obtained device state parameters.

[0052] It should be noted that the present application continuously collects power grid operation parameters, device state parameters and operation parameters through a widely deployed sensor network. After processing, these data are transmitted to the central data processing unit through wireless communication or optical fiber and enter the next analysis link.

[0053] It should be further noted that, considering that the operation condition of the power distribution network is real-time changing, static analysis is difficult to cope with the complex operation condition and real-time change of the power grid. In order to improve the real-time performance of the power distribution network monitoring, dynamic monitoring and analysis of the power grid, devices and operations are required.

[0054] Specifically, the device health index is calculated by dynamically weighting the collected device state parameters; the dynamic weighting is designed to adjust the weight of each device state parameter in real time based on the deviation between the first real-time data and the corresponding first historical data of each device state parameter, and the weighted sum of the first real-time data obtained by real-time adjustment is taken as the device health index.

[0055] In this step, the weight of each device state parameter is adjusted in real time based on the deviation between the first real-time data and the corresponding first historical data of each device state parameter, including: taking the average value of all first historical data of each device state parameter as the state reference value of each device state parameter; taking the absolute value of the difference between the first real-time data and the corresponding state reference value of each device state parameter as the state deviation degree of each device state parameter; and taking the result of normalizing all state deviation degrees as the update weight of each device state parameter.

[0056] It should be noted that the evaluation of the device health index is based on key parameters such as device temperature, vibration amplitude and load rate, and is dynamically adjusted through real-time data and historical operation mode correlation analysis. According to the deviation amplitude of the real-time data and the historical mode, when the real-time vibration or temperature deviates from the normal range, the weight of the corresponding parameter is automatically increased, so that the evaluation result can better reflect the current health condition of the device.

[0057] Step S2: optimizing the power grid load abnormality monitoring process based on the self-learning feedback mechanism to obtain a power grid load abnormality index of the target power distribution network, wherein the power grid load abnormality monitoring process reflects the change analysis result of the power grid load rate in different time dimensions.

[0058] It should be noted that the power grid operation parameters at least include the operation power of the target power distribution network.

[0059] Specifically, the power grid load rate is calculated according to the linear relationship between the rated power and the collected operation power of the target power distribution network; the second real-time data of the power grid load rate is dynamically threshold-comparison analyzed based on the distribution characteristics of the second historical data of the power grid load rate, and the power grid load abnormality index is calculated.

[0060] As an embodiment of the present application, the power grid load rate is the ratio of the operation power of the target power distribution network to the rated power.

[0061] In this step, the power grid load abnormality index is calculated based on the dynamic threshold-comparison analysis of the second real-time data of the power grid load rate according to the distribution characteristics of the second historical data of the power grid load rate, including: taking the sum of the average value and the corresponding standard deviation of all second historical data as the upper limit of the early warning, and taking the absolute value of the difference between the second real-time data and the upper limit of the early warning as the upper limit deviation; taking the difference between the average value and the corresponding standard deviation of all second historical data as the lower limit of the early warning, and taking the absolute value of the difference between the second real-time data and the lower limit of the early warning as the lower limit deviation; taking the minimum value of the upper limit deviation and the lower limit deviation as the power grid load abnormality index.

[0062] It should be noted that in order to improve the adaptability of the power grid load abnormality index calculation, the range of the power grid load rate is dynamically adjusted in combination with the fluctuation range of the historical data of the power grid load rate to avoid false positives or false negatives. When the historical data fluctuates less, the range is narrowed to improve the sensitivity; when the fluctuation is larger, the range is appropriately relaxed to reduce false positives.

[0063] Step S3: optimizing the power grid operation monitoring process based on the self-learning feedback mechanism to obtain an operation risk abnormality index of the target power distribution network, wherein the power grid operation monitoring process reflects the abnormal analysis result of the predicted operation parameter.

[0064] Specifically, the third historical data of each operation parameter collected is predicted and analyzed by using a long short-term memory network model to obtain predicted data of each operation parameter, and the operation risk abnormality index is obtained by performing abnormality detection on all predicted data.

[0065] In the process of prediction analysis, the weight parameters of the long short-term memory network model are adjusted based on the deviation between the prediction data of each operation parameter and the third real-time data at the corresponding moment.

[0066] It should be noted that after each operation is completed, the real-time data and the prediction results are compared, and the deviation between the actual risk and the predicted risk is analyzed. When a high prediction error is found, the system automatically adjusts the input weight and training parameters of the long short-term memory network model.

[0067] Step S4: input the time sequence correlation analysis results of the device health index, the power grid load abnormality index and the operation risk abnormality index, the device health index, the power grid load abnormality index and the operation risk abnormality index into the pre-trained deep learning model to obtain the risk monitoring result.

[0068] It should be noted that only considering one factor in the power grid, the device or the operation is easy to ignore the influence of other factors on the system, resulting in incomplete risk assessment. Through the linkage evaluation, the interaction between the power grid operation state, the device health condition and the operation process can be considered comprehensively, and the potential systemic risk can be identified.

[0069] Specifically, the time sequence correlation analysis results of the device health index, the power grid load abnormality index and the operation risk abnormality index, the device health index, the power grid load abnormality index and the operation risk abnormality index are input into the pre-trained deep learning model to obtain the risk monitoring result.

[0070] It should be noted that the risk monitoring result includes mild risk, moderate risk and severe risk.

[0071] Step S5: control the functional components matched with the target power distribution network to execute the risk control strategy corresponding to the risk monitoring result.

[0072] Specifically, the risk control strategy includes a preventive reminder strategy corresponding to the mild risk, a forced reminder strategy corresponding to the moderate risk, and a forced intervention strategy corresponding to the severe risk.

[0073] It should be noted that the target power distribution network is divided into multiple levels, i.e., mild, moderate and severe risks, through the risk monitoring result, and different response measures are taken by the system for different risk levels. When the risk is mild, the system only issues a warning to remind the operator; when the risk is moderate, the system will issue a mandatory prompt, suggesting that the operator take preventive measures; and when the risk is severe, the system will automatically execute a forced intervention operation, such as cutting off the faulty line or locking the equipment. When the system identifies a high-level risk, it will select an intervention means according to the preset strategy, including power grid load adjustment, equipment shutdown, personnel evacuation, etc. All intervention operations are executed through an automatic control system to ensure that risks can be quickly and effectively eliminated in extreme cases to ensure the safety of the power grid.

[0074] The power distribution network operation monitoring method provided by the embodiment of the application has the beneficial effects that, compared with the prior art,

[0075] Considering that the operation condition of the power distribution network is real-time changing, static analysis is difficult to cope with the complex operation condition and real-time change of the power grid, the device health monitoring process is optimized based on a self-learning feedback mechanism to obtain a device health index of each device, wherein the device health monitoring process is designed to perform dynamic weighted processing on the obtained device state parameters; the power grid load abnormality monitoring process is optimized based on the self-learning feedback mechanism to obtain a power grid load abnormality index of the target power distribution network, wherein the power grid load abnormality monitoring process reflects the change analysis result of the power grid load rate in different time dimensions; the power grid operation monitoring process is optimized based on the self-learning feedback mechanism to obtain an operation risk abnormality index of the target power distribution network, wherein the power grid operation monitoring process reflects the abnormal analysis result of the predicted operation parameter, and the monitoring of the power grid, the device and the operation is dynamically optimized through the self-learning feedback mechanism, thereby improving the accuracy of the power distribution network monitoring.

[0076] The time sequence correlation analysis result of the device health index, the power grid load abnormality index and the operation risk abnormality index, the device health index, the power grid load abnormality index and the operation risk abnormality index are input into a pre-trained deep learning model to obtain a risk monitoring result, realize dynamic fusion and comprehensive analysis of the power grid operation, the device state and the operation, control the functional components of the target power distribution network to execute the risk control strategy corresponding to the risk monitoring result, realize adaptive adjustment of the risk level through dynamic monitoring of the power grid, the device and the operation and correlation analysis among the three, enhance the response capability of the system, avoid the risk of operation failure caused by monitoring of only a single factor, and further improve the accuracy of the power distribution network monitoring.

[0077] The embodiment of the application further provides a power distribution network operation monitoring system, which comprises:

[0078] The device monitoring module 11 is configured to optimize the device health monitoring process based on the self-learning feedback mechanism, and obtain a device health index of each device, wherein the device health monitoring process is designed to perform dynamic weighting processing on the obtained device state parameters.

[0079] The power grid monitoring module 12 is configured to optimize the power grid load abnormality monitoring process based on the self-learning feedback mechanism, and obtain a power grid load abnormality index of the target distribution network, wherein the power grid load abnormality monitoring process reflects the change analysis result of the power grid load rate in different time dimensions.

[0080] The operation monitoring module 13 is configured to optimize the power grid operation monitoring process based on the self-learning feedback mechanism, and obtain an operation risk abnormality index of the target distribution network, wherein the power grid operation monitoring process reflects the abnormal analysis result of the predicted operation parameter.

[0081] The three-risk linkage evaluation module 14 is configured to input the time sequence correlation analysis result of the device health index, the power grid load abnormality index and the operation risk abnormality index, the device health index, the power grid load abnormality index and the operation risk abnormality index into a pre-trained deep learning model, and obtain a risk monitoring result.

[0082] The risk control module 15 is configured to control the function components matched with the target distribution network to execute a risk control strategy corresponding to the risk monitoring result.

[0083] It should be noted that the power distribution network operation monitoring system provided in the embodiment further comprises a central control unit.

[0084] The device monitoring module 11 is mainly responsible for monitoring the operating conditions of various power devices in the power distribution network, including the real-time states of transformers, switches, circuit breakers and other devices. Sensors will monitor the temperature, vibration, leakage current, insulation resistance and other parameters of the devices to ensure that the devices operate within a safe range. Once abnormal parameters such as high temperature, excessive vibration or reduced insulation resistance are detected, the system will immediately trigger an alarm and record the abnormal data to help subsequent analysis and decision-making.

[0085] The power grid monitoring module 12 collects real-time core data of the power grid operation through a distributed sensor network, such as voltage, current, power, frequency and other parameters. These data can reflect the current operating state of the power grid, especially in the case of load fluctuation, line abnormality, voltage fluctuation and other situations, and can timely feedback the operation risk of the power grid. The data is transmitted to the central control unit through wireless communication or optical fiber communication for further risk analysis.

[0086] The operation monitoring module 13 mainly monitors the operation process of the operator, especially the high-risk operation steps. In the maintenance and repair of the power distribution network, the operation behavior of personnel is crucial. This module monitors the operation actions, environmental conditions, etc. of the operator in real time by combining video monitoring, sensors and operation records. The system can identify non-standard operation behavior or high-risk operation state and give warnings or automatic intervention when necessary to ensure operation safety.

[0087] The three-risk linkage evaluation module 14 is the core module, responsible for comprehensive analysis and linkage evaluation of data in three aspects of power grid, equipment and operation. Through big data analysis and artificial intelligence technology, the system can realize fusion analysis of multi-source data and identify potential comprehensive risks. Linkage analysis not only considers the risk of a single factor such as power grid overload or equipment failure, but also combines the behavior patterns of the operator and the operation environment to form a dynamic and real-time comprehensive risk evaluation.

[0088] The risk control module 15 can judge the risk level of the system in real time based on the risk monitoring results of the three-risk linkage evaluation module and take corresponding warning and intervention measures. When the system detects a high comprehensive risk, it will remind the operator through sound and light alarm, SMS notification and other ways and automatically intervene according to the preset strategy. For example, when the equipment has a serious fault or the operator operates improperly, the system can automatically disconnect the faulty equipment or stop the operation process to prevent the accident from further expanding.

[0089] Another embodiment of the present application provides a computer readable storage medium storing a computer program, wherein when the computer readable storage medium is executed by a device, the computer program realizes a power distribution network operation monitoring method as described above.

[0090] The above-described embodiments only express several embodiments of the present application, which are described in detail and specifically, but should not be understood as limiting the scope of the present patent. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present patent should be subject to the appended claims.

Claims

1. A method for monitoring the operation of a power distribution network, characterized in that, The method includes: The device health monitoring process is optimized based on a self-learning feedback mechanism to obtain the device health index of each device. The device health monitoring process is designed to dynamically weight the acquired device status parameters. The power grid load anomaly monitoring process is optimized based on a self-learning feedback mechanism to obtain the power grid load anomaly index of the target distribution network. The power grid load anomaly monitoring process reflects the analysis results of the power grid load rate change under different time dimensions. The power grid operation monitoring process is optimized based on a self-learning feedback mechanism to obtain the operation risk anomaly index of the target distribution network. The power grid operation monitoring process reflects the anomaly analysis results of the predicted operation parameters. The time-series correlation analysis results of the equipment health index, the power grid load anomaly index, and the operation risk anomaly index, along with the equipment health index, the power grid load anomaly index, and the operation risk anomaly index, are input into a pre-trained deep learning model to obtain risk monitoring results. Control the functional components that match the target distribution network to execute risk control strategies corresponding to the risk monitoring results.

2. The method for monitoring the operation of a power distribution network according to claim 1, characterized in that, The optimization of the equipment health monitoring process based on the self-learning feedback mechanism to obtain the equipment health index of each device includes: The collected status parameters of each device are dynamically weighted to calculate the device health index; The dynamic weighting process is designed to adjust the weight of each device status parameter in real time based on the deviation between the first real-time data and the corresponding first historical data of each device status parameter, and then use the updated weight obtained in real time to perform a weighted summation on the first real-time data as the device health index.

3. The method for monitoring the operation of a power distribution network according to claim 1, characterized in that, The optimization of the power grid load anomaly monitoring process based on the self-learning feedback mechanism to obtain the power grid load anomaly index of the target distribution network includes: The grid load factor is calculated based on the linear relationship between the rated power and the operating power of the target distribution network. Based on the distribution characteristics of the second historical data of the power grid load rate, a dynamic threshold comparison analysis is performed on the second real-time data of the power grid load rate to calculate the power grid load anomaly index.

4. The method for monitoring the operation of a power distribution network according to claim 1, characterized in that, The optimization of the power grid operation monitoring process based on the self-learning feedback mechanism to obtain the operation risk anomaly index of the target distribution network includes: A long short-term memory network model is used to predict and analyze the third historical data of each collected operation parameter to obtain the predicted data of each operation parameter. Anomaly detection is performed on all the predicted data to obtain the operation risk anomaly index.

5. A method for monitoring the operation of a power distribution network according to claim 4, characterized in that, The optimization of the power grid operation monitoring process based on the self-learning feedback mechanism to obtain the operation risk anomaly index of the target distribution network also includes: The weight parameters of the Long Short-Term Memory Network model are adjusted based on the deviation between the predicted data for each of the operation parameters and the third real-time data at the corresponding time.

6. The method for monitoring the operation of a power distribution network according to claim 2, characterized in that, The real-time adjustment of the weight of each device status parameter based on the deviation between the first real-time data and the corresponding first historical data of each device status parameter includes: The average value of all the first historical data for each device status parameter is used as the status reference value for each device status parameter. The absolute value of the difference between the first real-time data of each device status parameter and the corresponding status reference value is taken as the degree of status deviation of each device status parameter; The result of normalizing all the deviations from the stated state is used as the update weight for each of the stated device state parameters.

7. The method for monitoring the operation of a power distribution network according to claim 3, characterized in that, The distribution characteristics of the second historical data of the power grid load rate are used to perform dynamic threshold comparison analysis on the second real-time data of the power grid load rate, and the power grid load anomaly index is calculated, including: The sum of the average value and the corresponding standard deviation of all the second historical data is used as the warning upper limit, and the absolute value of the difference between the second real-time data and the warning upper limit is used as the upper limit deviation degree. The difference between the average value and the corresponding standard deviation of all the second historical data is used as the lower limit of the warning, and the absolute value of the difference between the second real-time data and the lower limit of the warning is used as the degree of deviation of the lower limit. The minimum value of the upper limit deviation and the lower limit deviation is used as the power grid load anomaly index.

8. The method for monitoring the operation of a power distribution network according to claim 1, characterized in that, The risk monitoring results include mild risk, moderate risk, and severe risk; The risk control strategy includes a prevention and reminder strategy corresponding to the mild risk, a mandatory reminder strategy corresponding to the moderate risk, and a mandatory intervention strategy corresponding to the severe risk.

9. A power distribution network operation monitoring system, characterized in that, The system includes: The equipment monitoring module is used to optimize the equipment health monitoring process based on a self-learning feedback mechanism to obtain the equipment health index of each of the devices. The equipment health monitoring process is designed to dynamically weight the acquired status parameters of each device. The power grid monitoring module is used to optimize the power grid load anomaly monitoring process based on a self-learning feedback mechanism to obtain the power grid load anomaly index of the target distribution network. The power grid load anomaly monitoring process reflects the analysis results of the power grid load rate change under different time dimensions. The operation monitoring module is used to optimize the power grid operation monitoring process based on a self-learning feedback mechanism to obtain the operation risk anomaly index of the target distribution network. The power grid operation monitoring process reflects the anomaly analysis results of the predicted operation parameters. The three-risk linkage assessment module is used to input the time-series correlation analysis results of the equipment health index, the power grid load anomaly index, and the operation risk anomaly index, as well as the equipment health index, the power grid load anomaly index, and the operation risk anomaly index, into a pre-trained deep learning model to obtain risk monitoring results; The risk control module is used to control the functional components that match the target power distribution network to execute risk control strategies corresponding to the risk monitoring results.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements a power distribution network operation monitoring method as described in any one of claims 1 to 8.

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