Operation risk early warning method and device of power distribution network equipment, and electronic equipment

Abstract

The invention discloses an operation risk early warning method and device for power distribution network equipment and electronic equipment, and relates to the technical field of electric power systems or other related fields, the method comprises the steps that real-time operation data of target power distribution network equipment are collected, the real-time operation data comprise operation parameters of N equipment parts in the target power distribution network equipment, and the N equipment parts in the target power distribution network equipment are connected with the real-time operation data; n is a positive integer; determining the equipment risk level of the target power distribution network equipment through a preset risk assessment model and the operation parameters of all the equipment parts; and determining a risk early warning strategy and risk early warning information based on the equipment risk level, and sending the risk early warning information to the risk prevention and control client based on the risk early warning strategy. The technical problem that safe operation of the power distribution network cannot be guaranteed due to inaccurate operation risk early warning of the power distribution network equipment in related technologies is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an operation risk early warning method and device for distribution network equipment, and electronic equipment. Background Art

[0002] With the rapid development of the power industry, the distribution network, as an important part of the power system, has a direct impact on the overall performance of the power system due to its operational safety and stability. However, distribution network equipment may fail or become abnormal due to various factors during operation, thus posing a threat to the safe operation of the distribution network.

[0003] In the related technologies, since it is impossible to accurately assess the operating risks of distribution network equipment, the safe operation of the distribution network cannot be guaranteed.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method and device for early warning the operation risks of distribution network equipment, and an electronic device, so as to at least solve the technical problem in the related art that early warning on the operation risks of distribution network equipment is inaccurate, resulting in the inability to ensure the safe operation of the distribution network.

[0006] According to one aspect of an embodiment of the present invention, there is provided an operation risk warning method for distribution network equipment, comprising: collecting real-time operation data of target distribution network equipment, wherein the real-time operation data includes operation parameters of N equipment components in the target distribution network equipment, wherein N is a positive integer; determining the equipment risk level of the target distribution network equipment by means of a preset risk assessment model and the operation parameters of all the equipment components; determining a risk warning strategy and risk warning information based on the equipment risk level, and sending the risk warning information to a risk prevention and control client based on the risk warning strategy.

[0007] Furthermore, after collecting the real-time operating data of the target distribution network equipment, the method also includes: inputting the real-time operating data into a state perception model, and outputting the equipment operating status of the target distribution network equipment, wherein the state perception model is used to calculate the health status indicator and equipment status index of the target distribution network equipment based on the operating parameters of N equipment components in the real-time operating data, and determine the equipment operating status based on the equipment status index, the health status indicator is used to quantify the health of the target distribution network equipment during operation, and the equipment status index is used to characterize the dynamic relationship between the health status indicator and the operating time; determine the maintenance strategy of the target distribution network equipment based on the equipment operating status, and perform maintenance on the target distribution network equipment according to the maintenance strategy.

[0008] Furthermore, after determining the maintenance strategy of the target distribution network equipment based on the equipment operating status, the method also includes: obtaining the equipment status index of the target distribution network equipment calculated by the status perception model; determining the equipment failure probability based on the equipment status index, and calculating the equipment failure loss based on the equipment failure probability; determining the risk warning strategy and the risk warning information based on the equipment failure loss, and sending the risk warning information to the risk prevention and control client based on the risk warning strategy.

[0009] Furthermore, after collecting the real-time operating data of the target distribution network equipment, the method also includes: performing a data cleaning operation on the real-time operating data, wherein the data cleaning operation includes: removing abnormal values ​​and removing duplicate values; identifying abnormal data in the real-time operating data, and correcting the abnormal data, wherein the abnormal data refers to operating data that is inconsistent with the normal operating mode and is used to indicate equipment failure and / or equipment operation abnormality; mapping all the operating parameters according to a preset dimensional range to obtain standardized operating data, wherein each of the operating parameters is mapped to a standardized operating parameter in the standardized operating data.

[0010] Furthermore, before determining the equipment risk level of the target distribution network equipment through a preset risk assessment model and the operating parameters of all the equipment components, the method also includes: classifying all the standardized operating parameters in the standardized operating data to obtain M membership sets, wherein each of the membership sets corresponds to an upper-level indicator type, and there is a membership relationship between the standardized operating parameters in the membership set and the upper-level indicator type, and each of the upper-level indicator type corresponds to a weight assignment standard, and M is a positive integer; for each of the membership sets, weight calculation and weight assignment are performed on all the standardized operating parameters in the membership set based on the weight assignment standard to obtain the indicator weight corresponding to each of the standardized operating parameters.

[0011] Furthermore, the step of determining the equipment risk level of the target distribution network equipment through a preset risk assessment model and the operating parameters of all the equipment components includes: inputting the standardized operating parameters corresponding to the operating parameters and the indicator weights into the preset risk assessment model, and outputting the equipment risk level of the target distribution network equipment, wherein the preset risk assessment model is used to calculate the component loss probability of each of the equipment components based on the standardized operating parameters and the indicator weights, calculate the equipment failure loss based on the component loss probabilities of N of the equipment components, and determine the equipment risk level based on the equipment failure loss.

[0012] Furthermore, after mapping all the operating parameters according to a preset dimensional range to obtain standardized operating data, the method also includes: establishing a standardized data table based on the timestamp data carried by the operating parameters and the standardized operating data corresponding to the operating parameters; during the incremental update process of the standardized data table, monitoring the real-time data volume of the standardized data table, and exporting the standardized data table when the real-time data volume meets the preset data volume threshold.

[0013] According to another aspect of an embodiment of the present invention, there is also provided an operation risk warning device for distribution network equipment, comprising: a collection unit, used to collect real-time operation data of target distribution network equipment, wherein the real-time operation data includes operation parameters of N equipment components in the target distribution network equipment, wherein N is a positive integer; a determination unit, used to determine the equipment risk level of the target distribution network equipment through a preset risk assessment model and the operation parameters of all the equipment components; a sending unit, used to determine a risk warning strategy and risk warning information based on the equipment risk level, and send the risk warning information to a risk prevention and control client based on the risk warning strategy.

[0014] Furthermore, the operation risk warning device of the distribution network equipment also includes: a first input module, used to input the real-time operation data into a state perception model, and output the equipment operation status of the target distribution network equipment, wherein the state perception model is used to calculate the health status indicator and equipment status index of the target distribution network equipment based on the operation parameters of N equipment components in the real-time operation data, and determine the equipment operation status based on the equipment status index, the health status indicator is used to quantify the health of the target distribution network equipment during operation, and the equipment status index is used to characterize the dynamic relationship between the health status indicator and the operation time; a first determination module, used to determine the maintenance strategy of the target distribution network equipment based on the equipment operation status, and repair the target distribution network equipment according to the maintenance strategy.

[0015] Furthermore, the operation risk warning device of the distribution network equipment also includes: an acquisition module, used to obtain the equipment status index of the target distribution network equipment calculated by the state perception model; a second determination module, used to determine the equipment failure probability based on the equipment status index, and calculate the equipment failure loss based on the equipment failure probability; a third determination module, used to determine the risk warning strategy and the risk warning information based on the equipment failure loss, and send the risk warning information to the risk prevention and control client based on the risk warning strategy.

[0016] Furthermore, the operation risk warning device of the distribution network equipment also includes: a data cleaning module, used to perform data cleaning operations on the real-time operation data, wherein the data cleaning operations include: removing abnormal values ​​and removing duplicate values; an identification module, used to identify abnormal data in the real-time operation data, and correct the abnormal data, wherein the abnormal data refers to operation data that is inconsistent with the normal operating mode and is used to indicate equipment failure and / or equipment operation abnormality; a mapping module, used to map all the operation parameters according to a preset dimensional range to obtain standardized operation data, wherein each of the operation parameters is mapped to a standardized operation parameter in the standardized operation data.

[0017] Furthermore, the operation risk warning device of the distribution network equipment also includes: a classification module, which is used to classify all the standardized operation parameters in the standardized operation data to obtain M membership sets, wherein each of the membership sets corresponds to an upper-level indicator type, and there is a membership relationship between the standardized operation parameters in the membership set and the upper-level indicator type, and each of the upper-level indicator type corresponds to a weight assignment standard, and M is a positive integer; a weight assignment module, which is used to perform weight calculation and weight assignment on all the standardized operation parameters in the membership set based on the weight assignment standard for each membership set to obtain the indicator weight corresponding to each standardized operation parameter.

[0018] Furthermore, the determination unit includes: a second input module, used to input the standardized operating parameters corresponding to the operating parameters and the indicator weights into the preset risk assessment model, and output the equipment risk level of the target distribution network equipment, wherein the preset risk assessment model is used to calculate the component loss probability of each of the equipment components based on the standardized operating parameters and the indicator weights, calculate the equipment failure loss based on the component loss probabilities of N of the equipment components, and determine the equipment risk level based on the equipment failure loss.

[0019] Furthermore, the operation risk warning device of the distribution network equipment also includes: an establishment module, used to establish a standardized data table based on the timestamp data carried by the operating parameters and the standardized operation data corresponding to the operating parameters; a monitoring module, used to monitor the real-time data volume of the standardized data table during the incremental update process of the data of the standardized data table, and export the standardized data table when the real-time data volume meets the preset data volume threshold.

[0020] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned operation risk warning methods for distribution network equipment.

[0021] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the above-mentioned operation risk warning methods for distribution network equipment.

[0022] In the present disclosure, a method for early warning of operation risks of distribution network equipment is proposed. First, real-time operation data of target distribution network equipment is collected, wherein the real-time operation data includes operation parameters of N equipment components in the target distribution network equipment, wherein N is a positive integer. Then, the equipment risk level of the target distribution network equipment is determined by a preset risk assessment model and the operation parameters of all equipment components. Finally, a risk early warning strategy and risk early warning information are determined based on the equipment risk level, and the risk early warning information is sent to a risk prevention and control client based on the risk early warning strategy.

[0023] In the present disclosure, by collecting real-time operating parameters of equipment components in the distribution network equipment, combining with a preset risk assessment model to evaluate the degree of impact of the operation of equipment components on the operating risk of the overall distribution network equipment, determining the equipment risk level of the target distribution network equipment, and then determining the risk warning strategy and risk warning information, executing the risk warning steps, and sending the risk warning information to the risk prevention and control client, the present invention can realize automatic and intelligent evaluation of the operating status of distribution network equipment, reduce manual intervention, improve warning accuracy and warning efficiency, estimate the equipment risk level in combination with the operating parameters of equipment components, and have a more comprehensive understanding of the operating status and hidden dangers of the distribution equipment, providing a more powerful guarantee for the safety and reliability of the power system, thereby solving the technical problem of inaccurate operating risk warning of distribution network equipment in related technologies, resulting in the inability to ensure the safe operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0025] Figure 1 is a flow chart of an optional operation risk early warning method for distribution network equipment according to an embodiment of the present invention;

[0026] Figure 2is a flow chart of an optional distribution network equipment operation risk assessment method according to an embodiment of the present invention;

[0027] Figure 3 is an architecture diagram of an optional distribution network equipment operation risk assessment system according to an embodiment of the present invention;

[0028] Figure 4 is a schematic diagram of an optional operation risk early warning device for distribution network equipment according to an embodiment of the present invention;

[0029] Figure 5 The present invention is a hardware structure block diagram of an electronic device (or mobile device) for an operation risk early warning method for distribution network equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] The following embodiments of the present invention can be applied to various systems / applications / equipment that require distribution network equipment operation risk detection and power system reliable operation guarantee, and can realize distribution network equipment operation risk warning based on risk assessment model. The present invention collects the real-time operating parameters of equipment components in the distribution network equipment, combines the preset risk assessment model to evaluate the impact of the operation of equipment components on the operation risk of the overall distribution network equipment, determines the equipment risk level of the target distribution network equipment, and then determines the risk warning strategy and risk warning information, executes the risk warning step, and sends the risk warning information to the risk prevention and control client.

[0033] The present invention is described in detail below in conjunction with various embodiments.

[0034] Embodiment 1

[0035] According to an embodiment of the present invention, an embodiment of an operation risk warning method for distribution network equipment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] Figure 1 is a flow chart of an optional operation risk early warning method for distribution network equipment according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0037] Step S101, collecting real-time operating data of target distribution network equipment, wherein the real-time operating data includes operating parameters of N equipment components in the target distribution network equipment, wherein N is a positive integer.

[0038] Step S102: determining the equipment risk level of the target distribution network equipment through a preset risk assessment model and the operating parameters of all equipment components.

[0039] Step S103, determining a risk warning strategy and risk warning information based on the equipment risk level, and sending the risk warning information to the risk prevention and control client based on the risk warning strategy.

[0040] Through the above steps, the real-time operating data of the target distribution network equipment can be collected first, wherein the real-time operating data includes the operating parameters of N equipment components in the target distribution network equipment, wherein N is a positive integer, and then the equipment risk level of the target distribution network equipment is determined by a preset risk assessment model and the operating parameters of all equipment components, and finally the risk warning strategy and risk warning information are determined based on the equipment risk level, and the risk warning information is sent to the risk prevention and control client based on the risk warning strategy.

[0041] In an embodiment of the present invention, by collecting real-time operating parameters of equipment components in the distribution network equipment, combining with a preset risk assessment model to evaluate the degree of impact of the operation of equipment components on the operating risk of the overall distribution network equipment, determining the equipment risk level of the target distribution network equipment, and then determining the risk warning strategy and risk warning information, executing the risk warning step, and sending the risk warning information to the risk prevention and control client, the present invention can realize automatic and intelligent evaluation of the operating status of distribution network equipment, reduce manual intervention, improve warning accuracy and efficiency, estimate the equipment risk level in combination with the operating parameters of equipment components, and more comprehensively understand the operating status and hidden danger status of the distribution equipment, so as to provide a more powerful guarantee for the safety and reliability of the power system, thereby solving the technical problem of inaccurate operating risk warning of distribution network equipment in related technologies, resulting in the inability to ensure the safe operation of the distribution network.

[0042] The embodiment of the present invention is described in detail below in combination with the above steps.

[0043] The implementation subject of the embodiment of the present invention can be a power system or a power safety system. In combination with artificial intelligence technology, a neural network model is used to perform real-time monitoring and data analysis on the operating status of distribution network equipment in the power system, to achieve accurate assessment of equipment operation risks, and to provide strong protection for the safe operation of the distribution network.

[0044] Specifically, before conducting real-time monitoring and early warning, it is necessary to first build a state perception model and a preset risk assessment model for distribution network equipment, including: obtaining historical data related to distribution network equipment, and standardizing the historical data to obtain standardized data, where the relevant historical data includes equipment inventory data, operating hazard data, and external environmental impact weights; obtaining real-time operating data related to distribution network equipment and performing full-scale identification and push; building a distribution equipment state perception model based on the health status of the equipment; and establishing a distribution equipment risk assessment model based on the output results of the distribution equipment state perception and combining a multi-data weight comprehensive evaluation method.

[0045] Among them, the state perception model of distribution network equipment is realized by constructing the equation of health status indicator T. The specific equation formula of T is as follows: Where T represents the health status index of the distribution network equipment before maintenance, expressed as a percentage, p i represents the number of evaluation indicators of the equipment components of the i-th distribution network equipment, y k represents the evaluation value of the kth evaluation index, s i represents the evaluation value of the i-th evaluation index, n represents the number of components in the distribution network equipment, and the above evaluation values ​​are all the results obtained by data standardization operation.

[0046] Furthermore, the value range of the health status index of the distribution network equipment can be set to [0, 100], and the relationship between the dynamic change of the health status index of the distribution network equipment and time corresponds to the equipment status level, and the status level standard is set as follows: the health status index of the distribution equipment is greater than 80 for healthy state, the health status index of the distribution equipment is 70-80 for sub-healthy state, the health status index of the distribution equipment is 50-70 for warning state, the health status index of the distribution equipment is 35-50 for abnormal state, and the health status index of the distribution equipment is less than 35 for crisis state. The higher the value of the health status index of the distribution equipment, the better the operating state of the equipment. The specific formula for the relationship between the dynamic change of the health status index of the distribution equipment and time is: Among them, TZ represents the equipment status index during the evaluation, T represents the health status index of the power distribution equipment before maintenance, C represents the equipment aging coefficient, J represents the year when the equipment status is evaluated, and J0 represents the year when the equipment is put into operation.

[0047] Step S101, collecting real-time operating data of target distribution network equipment, wherein the real-time operating data includes operating parameters of N equipment components in the target distribution network equipment, wherein N is a positive integer.

[0048] Specifically, the real-time operation data also includes: equipment inventory data, operation hidden danger data and external environment impact weights. Among them, the equipment inventory data of the distribution network equipment refers to the recording and management of the basic information of various equipment in the distribution network, including the equipment model, capacity, manufacturer, installation location and other information. These data can help operators to effectively manage and maintain the distribution network equipment to ensure that the equipment operates normally, safely and reliably; the operation hidden danger data refers to the recording and management of possible failures, damage or other safety hazards of the distribution network equipment. By monitoring and analyzing the operation hidden danger data, potential problems can be discovered and handled in a timely manner, the equipment failure rate can be reduced, and the reliability and safety of the distribution network can be improved; the external environment impact weight refers to the evaluation and weight allocation of external environmental factors for the operation of distribution network equipment. These factors include climatic conditions, natural disasters, man-made damage, etc., which have an important impact on the operational safety and stability of distribution network equipment. By assigning different weights to external environmental factors, their impact on distribution network equipment can be better evaluated, and corresponding measures can be taken to respond and prevent them.

[0049] Optionally, after executing step S101, it also includes: inputting real-time operation data into a state perception model, and outputting the equipment operation status of the target distribution network equipment, wherein the state perception model is used to calculate the health status indicator and equipment status index of the target distribution network equipment based on the operation parameters of N equipment components in the real-time operation data, and determine the equipment operation status based on the equipment status index, the health status indicator is used to quantify the health of the target distribution network equipment during operation, and the equipment status index is used to characterize the dynamic relationship between the health status indicator and the operation time; determine the maintenance strategy of the target distribution network equipment based on the equipment operation status, and maintain the target distribution network equipment according to the maintenance strategy.

[0050] It should be noted that the maintenance strategy can be set as follows: for healthy distribution equipment and sub-healthy distribution equipment, maintenance is carried out through routine inspections; for alert distribution equipment, maintenance is carried out through partial maintenance; for abnormal distribution equipment, maintenance is carried out through overall maintenance; for crisis distribution equipment, maintenance is carried out by withdrawing it from operation and replacing it with new equipment.

[0051] Optionally, after determining the maintenance strategy of the target distribution network equipment based on the equipment operating status, it also includes: obtaining the equipment status index of the target distribution network equipment calculated by the state perception model; determining the equipment failure probability based on the equipment status index, and calculating the equipment failure loss based on the equipment failure probability; determining the risk warning strategy and risk warning information based on the equipment failure loss, and sending the risk warning information to the risk prevention and control client based on the risk warning strategy.

[0052] In order to facilitate the subsequent unified data processing, the embodiments of the present invention use standardized data processing means to perform standardized processing on the real-time operation data, wherein the standardized processing includes steps such as data collection, data cleaning, data completion, and data aggregation, and the original data is organized into standardized data for subsequent use and analysis. Optionally, after collecting the real-time operation data of the target distribution network equipment, it also includes: performing data cleaning operations on the real-time operation data, wherein the data cleaning operations include: removing abnormal values ​​and removing duplicate values; identifying abnormal data in the real-time operation data, and correcting the abnormal data, wherein the abnormal data refers to operation data that is inconsistent with the normal operating mode and is used to indicate equipment failure and / or abnormal equipment operation; mapping all operation parameters according to a preset dimensional range to obtain standardized operation data, wherein each operation parameter is mapped to a standardized operation parameter in the standardized operation data.

[0053] Furthermore, after standardization, multivariate data fusion technology also supports data integration and matching. By identifying the correlation and similarity between data, data from different sources can be merged and matched for subsequent analysis and mining.

[0054] Going further, feature extraction and selection can be performed on real-time operation data. Meaningful features can be extracted from multi-source data and selected and transformed to reduce the dimension and complexity of the data while retaining the most important information.

[0055] In the model building and analysis phase, the fused data is applied to prediction, classification, clustering and other tasks using machine learning, statistical modeling and other technologies to mine useful patterns and knowledge from the data. At the same time, the uncertainty and errors between different data sources need to be taken into account, so uncertainty modeling and processing is also an indispensable part. Uncertainty is modeled and managed through probability models, Bayesian inference and other methods.

[0056] In the embodiment of the present invention, it is also possible to realize the change identification, data update and full push of real-time operation data. Optionally, after all operation parameters are mapped according to the preset dimension range and standardized operation data is obtained, it also includes: establishing a standardized data table according to the timestamp data carried by the operation parameters and the standardized operation data corresponding to the operation parameters; in the process of incremental data update of the standardized data table, monitoring the real-time data volume of the standardized data table, and exporting the standardized data table when the real-time data volume meets the preset data volume threshold. The full push in the embodiment of the present invention uses multivariate data fusion software to identify changes in standardized data, and automatically extracts the entire table when the update conditions are met to realize data update.

[0057] Specifically, when there is a timestamp field on the standardized data table, which records the last update time of the record, an incremental data extraction and update process is performed based on the timestamp field. The primary key is formed based on the primary key field of the standardized data and multiple fields. The primary key is used to quickly filter the data to achieve incremental data update.

[0058] Step S102: determining the equipment risk level of the target distribution network equipment through a preset risk assessment model and the operating parameters of all equipment components.

[0059] Optionally, after executing step S102, it also includes: classifying all standardized operating parameters in the standardized operating data to obtain M membership sets, wherein each membership set corresponds to an upper-level indicator type, and there is a membership relationship between the standardized operating parameters in the membership set and the upper-level indicator type, and each upper-level indicator type corresponds to a weight assignment standard, and M is a positive integer; for each membership set, weight calculation and weight assignment are performed on all standardized operating parameters in the membership set based on the weight assignment standard to obtain the indicator weight corresponding to each standardized operating parameter.

[0060] It should be noted that the multi-data weight comprehensive evaluation method can be used to establish a preset risk assessment model for distribution network equipment; the loss of distribution equipment can also be defined as the product of the loss caused by the failure of the equipment and the probability of the equipment failing in real time. The specific calculation formula is: F j =W j *Y j Among them, F j represents the loss of device j, W j represents the loss caused by the failure of equipment j, Y j represents the probability of device j failing in real time.

[0061] It should be noted that the probability of a device failing in real time is related to its device status index. The lower the device status index, the worse the device operating status, and the higher the probability of failure. The exponential failure rate model is used to describe the relationship between the device status index and the failure rate. The specific formula of the exponential model is: β = D*e C*T ; Among them, β represents the failure rate of the equipment, D represents the fixed coefficient, C represents the circular coefficient, and T represents the state index.

[0062] Optionally, the step of determining the equipment risk level of the target distribution network equipment through a preset risk assessment model and the operating parameters of all equipment components includes: inputting standardized operating parameters and indicator weights corresponding to the operating parameters into the preset risk assessment model, and outputting the equipment risk level of the target distribution network equipment, wherein the preset risk assessment model is used to calculate the component loss probability of each equipment component based on the standardized operating parameters and indicator weights, calculate the equipment failure loss based on the component loss probability of N equipment components, and determine the equipment risk level based on the equipment failure loss.

[0063] Step S103, determining a risk warning strategy and risk warning information based on the equipment risk level, and sending the risk warning information to the risk prevention and control client based on the risk warning strategy.

[0064] In an embodiment of the present invention, historical data related to power distribution equipment is obtained and preprocessed to obtain standardized data, wherein the relevant historical data includes equipment inventory data, operation hazard data, and external environment impact weights; real-time data related to power distribution equipment is obtained, full-scale identification and push are performed, and a power distribution equipment state perception model is constructed in combination with the equipment health status; a power distribution equipment risk assessment model is established based on the output results of the power distribution equipment state perception and a multi-data weight comprehensive evaluation method; and state assessment based on the power distribution equipment is implemented based on the power distribution equipment state perception model and the power distribution equipment risk assessment model.

[0065] In the embodiment of the present invention, the cleaning and fusion of multi-source heterogeneous data can be achieved through the multivariate data fusion technology, which is conducive to data processing and uploading and downloading, greatly improving the clarity of the overall system structure and the stability of software functions, meeting the needs of real-time relational data exchange, reducing memory consumption, realizing the processing of more data and improving the CPU cache hit rate, greatly improving the integration and integration capabilities of the multivariate data fusion software, and further improving the flexibility of data processing. The idea of ​​comprehensive evaluation is used to comprehensively perceive the equipment status, provide data support for the optimal operation and maintenance decision, and combine the corresponding health level standards to determine the health of the equipment, whether it needs maintenance and estimate its remaining life, with strong logic and continuity.

[0066] The embodiments of the present invention can realize the automation and intelligence of the evaluation of the operating status of the distribution equipment, reduce manual intervention, improve work efficiency and accuracy, more comprehensively understand the operating status and hidden dangers of the distribution equipment, and provide stronger guarantee for the safe and reliable operation of the power system.

[0067] The present invention is described below in conjunction with another specific implementation manner.

[0068] The embodiment of the present invention provides a distribution network equipment operation risk assessment method, which realizes accurate assessment of equipment operation risk by real-time monitoring and data analysis of the operation status of distribution network equipment, thereby providing strong guarantee for the safe operation of the distribution network.

[0069] Figure 2 is a flow chart of an optional distribution network equipment operation risk assessment method according to an embodiment of the present invention, such as Figure 2 As shown, the method flow includes the following steps:

[0070] Step S201, obtaining historical data related to distribution network equipment, and standardizing the historical data to obtain standardized data, the historical data including equipment inventory data, operation hazard data and external environment impact weights.

[0071] Step S202, obtain real-time operation data related to the distribution network equipment, perform full-scale identification and push, and build a distribution equipment status perception model based on the health status of the equipment.

[0072] Specifically, the distribution equipment state perception model is implemented by constructing an equation for the health status indicator T. The specific equation formula of T is as follows: Where T represents the health status index of the power distribution equipment before maintenance, expressed as a percentage, p i represents the number of indicators in the i-th component, y k represents the result of the kth indicator, s iIt represents the result of the ith indicator, n represents the number of components, and the evaluation results are all obtained by data standardization operation.

[0073] Furthermore, the value range of the health status index of the distribution equipment can be set to [0,100], and the equipment status level can be corresponded by the relationship between the dynamic change of the health status index of the distribution equipment over time, and the status level standard can be set as follows: the health status index of the distribution equipment is greater than 80 for healthy state, the health status index of the distribution equipment is between 70 and 80 for sub-healthy state, the health status index of the distribution equipment is between 50 and 70 for warning state, the health status index of the distribution equipment is between 35 and 50 for abnormal state, and the health status index of the distribution equipment is less than 35 for crisis state. The higher the value of the health status index of the distribution equipment, the better the operating state of the equipment. The specific formula for the relationship between the dynamic change of the health status index of the distribution equipment over time is: Among them, TZ represents the equipment status index during the evaluation, T represents the health status index of the power distribution equipment before maintenance, C represents the equipment aging coefficient, J represents the year when the equipment status is evaluated, and J0 represents the year when the equipment is put into operation.

[0074] Furthermore, the distribution equipment in healthy state and sub-healthy state is inspected and maintained through routine inspections; the distribution equipment in alert state is inspected and maintained through partial maintenance; the distribution equipment in abnormal state is inspected and maintained through overall maintenance; the distribution equipment in crisis state is inspected and maintained by taking it out of operation and replacing it with new equipment.

[0075] Step S203: establishing a risk assessment model for power distribution equipment based on the output results of power distribution equipment status perception and in combination with a multi-data weight comprehensive evaluation method.

[0076] Specifically, the risk assessment model of power distribution equipment is established in combination with the multi-data weight comprehensive evaluation method, including: the multi-data weight comprehensive evaluation method includes that the evaluation object has i sub-indicators, and there are j upper-level indicators on the sub-indicators, and the sub-indicators are divided into multiple subsets according to the affiliation relationship between the i sub-indicators and the j upper-level indicators; the evaluation standard level is established, and a certain indicator of the object to be evaluated is set for an evaluation standard, and the indicator weight is calculated respectively, and the membership function is selected to calculate the membership value of each indicator corresponding to the evaluation set, and the membership set is determined. The specific calculation formula is: Z = Hx + v;

[0077] i=1,2,…,m;J(x)=(z-Hx) T (z-Hx); J w (x) = (z - Hx) T W(z-Hx),

[0078] Among them, Hx is the measurement function vector, v is the error, z is the measurement equation, Hi (x) is the i-th measurement function vector, i is the truncated series, j is the number of iterations, H ij x j is to iterate the i-th measurement function vector j times, J(x) is the objective function established according to the least squares criterion, J w (x) is the weighted objective function; W is a weighted positive definite matrix, let W = R -1 , R is the measurement error variance matrix, then

[0079] Furthermore, the risk assessment model for power distribution equipment established in combination with the multi-data weight comprehensive evaluation method also includes: defining the loss of power distribution equipment as the product of the loss caused by the failure of the equipment and the probability of the equipment failing in real time. The specific calculation formula is: F j =W j *Y j , where F j represents the loss of device j, W j represents the loss caused by the failure of equipment j, Y j represents the probability of device j failing in real time;

[0080] In addition, the probability of a device failing in real time is related to its device status index. The lower the device status index, the worse the device operating status, and the higher the probability of failure. The exponential failure rate model is used to describe the relationship between the device status index and the failure rate. The specific formula of the exponential model is: β = D*e C*T , where β represents the failure rate of the equipment, D represents the fixed coefficient, C represents the circular coefficient, and T represents the state index.

[0081] Furthermore, the steps of full identification and push include: identifying changes in standardized data through multivariate data fusion software, and automatically extracting the entire table when the update conditions are met to achieve data update; when there is a timestamp field on the standardized data table, which records the last update time of the record, based on the timestamp field, an incremental data extraction and update process is performed, based on the primary key field of the standardized data and multiple fields jointly forming a primary key, the primary key is used to quickly filter the data to achieve incremental data update.

[0082] Step S204: Perform distribution network equipment operation risk assessment based on the distribution equipment state perception model and the distribution equipment risk assessment model.

[0083] Figure 3 is an architecture diagram of an optional distribution network equipment operation risk assessment system according to an embodiment of the present invention, such as Figure 3 As shown, the system includes the following modules: data acquisition module, data preprocessing module, risk assessment module and early warning module.

[0084] The data acquisition module is used to collect the operating data of the distribution network equipment in real time through the sensor network. These sensors can include voltage sensors, current sensors, temperature sensors, humidity sensors, etc., which are used to monitor various operating parameters of the equipment. The collected data is sent to the data processing center through the data transmission network to provide raw data for subsequent data preprocessing and risk assessment.

[0085] The data preprocessing module is used to receive the operating data from the data acquisition module and perform a series of data processing operations. First, through data cleaning operations, outliers, duplicate values ​​and other data that do not meet the requirements are removed. Then, outlier processing is performed, and statistical methods or machine learning algorithms are used to identify and correct abnormal data. Finally, through data standardization operations, the data is converted into a unified dimensional range so that the subsequent risk assessment model can better process and analyze the data.

[0086] The risk assessment module uses machine learning algorithms to build an equipment operation risk assessment model. First, historical operation data is collected as a training data set, including equipment data in normal operation and fault state. Then, a suitable machine learning algorithm (such as support vector machine, random forest, etc.) is selected for model training to establish a mapping relationship between equipment operation status and risk level. The trained model can be saved for subsequent real-time risk assessment.

[0087] The early warning module is used to generate corresponding risk early warning information based on the risk level output by the risk assessment module. When the equipment operation risk level exceeds the preset threshold, the early warning module will trigger the early warning mechanism and send early warning information to the operation and maintenance personnel through SMS, email or system pop-up windows. The early warning information includes the risk level, equipment location, possible causes and recommended countermeasures, etc., to help the operation and maintenance personnel quickly locate and deal with potential operation risks.

[0088] Compared with the prior art, the advantages of the embodiments of the present invention are that historical data related to distribution equipment is obtained and pre-processed to obtain standardized data, wherein the relevant historical data includes equipment inventory data, operation hazard data and external environment impact weights; real-time data related to distribution equipment is obtained, full-scale identification and push are performed, and a distribution equipment state perception model is constructed in combination with the equipment health status; a distribution equipment risk assessment model is established based on the output results of distribution equipment state perception and a multi-data weight comprehensive evaluation method; and state assessment based on distribution equipment is realized based on the distribution equipment state perception model and the distribution equipment risk assessment model.

[0089] The embodiment of the present invention can realize the cleaning and fusion of multi-source heterogeneous data through multi-data fusion technology, which is conducive to data processing and uploading and downloading, greatly improving the clarity of the overall system structure and the stability of software functions, meeting the needs of real-time relational data exchange, reducing memory consumption, realizing the processing of more data and improving the CPU cache hit rate, greatly improving the integration and integration capabilities of multi-data fusion software, and further improving the flexibility of data processing. It adopts a comprehensive evaluation approach to comprehensively perceive the equipment status, provide data support for the optimal operation and maintenance decision, and combine the corresponding health level standards to determine the health of the equipment, whether it needs maintenance and estimate its remaining life, with strong logic and continuity.

[0090] The embodiments of the present invention can realize the automation and intelligence of the evaluation of the operating status of the distribution equipment, reduce manual intervention, improve work efficiency and accuracy, more comprehensively understand the operating status and hidden dangers of the distribution equipment, and provide stronger guarantee for the safe and reliable operation of the power system.

[0091] The present invention is described below in conjunction with another optional embodiment.

[0092] Embodiment 2

[0093] An operation risk warning device for distribution network equipment provided in this embodiment includes multiple implementation units, each of which corresponds to each implementation step in the above-mentioned embodiment 1.

[0094] Figure 4 is a schematic diagram of an optional operation risk warning device for distribution network equipment according to an embodiment of the present invention, such as Figure 4 As shown, the device may include: a collecting unit 41, a determining unit 42, and a sending unit 43.

[0095] The acquisition unit 41 is used to acquire real-time operation data of the target distribution network equipment, wherein the real-time operation data includes operation parameters of N equipment components in the target distribution network equipment, wherein N is a positive integer.

[0096] The determination unit 42 is used to determine the equipment risk level of the target distribution network equipment through a preset risk assessment model and the operating parameters of all equipment components.

[0097] The sending unit 43 is used to determine the risk warning strategy and risk warning information based on the equipment risk level, and send the risk warning information to the risk prevention and control client based on the risk warning strategy.

[0098] The above-mentioned operation risk warning device for distribution network equipment can first collect real-time operation data of the target distribution network equipment through the collection unit 41, wherein the real-time operation data includes the operation parameters of N equipment components in the target distribution network equipment, wherein N is a positive integer, and then the determination unit 42 determines the equipment risk level of the target distribution network equipment through a preset risk assessment model and the operation parameters of all equipment components, and finally the sending unit 43 determines the risk warning strategy and risk warning information based on the equipment risk level, and sends the risk warning information to the risk prevention and control client based on the risk warning strategy.

[0099] In an embodiment of the present invention, by collecting real-time operating parameters of equipment components in the distribution network equipment, combining with a preset risk assessment model to evaluate the degree of impact of the operation of equipment components on the operating risk of the overall distribution network equipment, determining the equipment risk level of the target distribution network equipment, and then determining the risk warning strategy and risk warning information, executing the risk warning step, and sending the risk warning information to the risk prevention and control client, the present invention can realize automatic and intelligent evaluation of the operating status of distribution network equipment, reduce manual intervention, improve warning accuracy and efficiency, estimate the equipment risk level in combination with the operating parameters of equipment components, and more comprehensively understand the operating status and hidden danger status of the distribution equipment, so as to provide a more powerful guarantee for the safety and reliability of the power system, thereby solving the technical problem of inaccurate operating risk warning of distribution network equipment in related technologies, resulting in the inability to ensure the safe operation of the distribution network.

[0100] Optionally, the operation risk warning device of the distribution network equipment also includes: a first input module, used to input real-time operation data into a state perception model, and output the equipment operation status of the target distribution network equipment, wherein the state perception model is used to calculate the health status indicator and equipment status index of the target distribution network equipment based on the operation parameters of N equipment components in the real-time operation data, and determine the equipment operation status based on the equipment status index, the health status indicator is used to quantify the health of the target distribution network equipment during operation, and the equipment status index is used to characterize the dynamic relationship between the health status indicator and the operation time; a first determination module, used to determine the maintenance strategy of the target distribution network equipment based on the equipment operation status, and repair the target distribution network equipment according to the maintenance strategy.

[0101] Optionally, the operation risk warning device of the distribution network equipment also includes: an acquisition module, used to obtain the equipment status index of the target distribution network equipment calculated by the state perception model; a second determination module, used to determine the equipment failure probability based on the equipment status index, and calculate the equipment failure loss based on the equipment failure probability; a third determination module, used to determine the risk warning strategy and risk warning information based on the equipment failure loss, and send the risk warning information to the risk prevention and control client based on the risk warning strategy.

[0102] Optionally, the operation risk warning device of the distribution network equipment also includes: a data cleaning module, which is used to perform data cleaning operations on the real-time operation data, wherein the data cleaning operations include: removing abnormal values ​​and removing duplicate values; an identification module, which is used to identify abnormal data in the real-time operation data and correct the abnormal data, wherein the abnormal data refers to operation data that is inconsistent with the normal operating mode and is used to indicate equipment failure and / or equipment operation abnormality; a mapping module, which is used to map all operation parameters according to a preset dimensional range to obtain standardized operation data, wherein each operation parameter is mapped to a standardized operation parameter in the standardized operation data.

[0103] Optionally, the operation risk warning device of the distribution network equipment also includes: a classification module, which is used to classify all standardized operation parameters in the standardized operation data to obtain M membership sets, wherein each membership set corresponds to an upper-level indicator type, and there is a membership relationship between the standardized operation parameters in the membership set and the upper-level indicator type, and each upper-level indicator type corresponds to a weight assignment standard, and M is a positive integer; a weight assignment module, which is used to calculate and assign weights to all standardized operation parameters in each membership set based on the weight assignment standard to obtain the indicator weight corresponding to each standardized operation parameter.

[0104] Optionally, the determination unit includes: a second input module, used to input standardized operating parameters and indicator weights corresponding to the operating parameters into a preset risk assessment model, and output the equipment risk level of the target distribution network equipment, wherein the preset risk assessment model is used to calculate the component loss probability of each equipment component based on the standardized operating parameters and indicator weights, calculate the equipment failure loss based on the component loss probability of N equipment components, and determine the equipment risk level based on the equipment failure loss.

[0105] Optionally, the operation risk warning device of the distribution network equipment also includes: an establishment module, which is used to establish a standardized data table based on the timestamp data carried by the operating parameters and the standardized operation data corresponding to the operating parameters; a monitoring module, which is used to monitor the real-time data volume of the standardized data table during the incremental update process of the standardized data table, and export the standardized data table when the real-time data volume meets the preset data volume threshold.

[0106] The above-mentioned operation risk warning device of the distribution network equipment may also include a processor and a memory. The above-mentioned acquisition unit 41, determination unit 42, sending unit 43, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions.

[0107] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the risk warning strategy and risk warning information are determined based on the risk level of the device by adjusting the kernel parameters, and the risk warning information is sent to the risk prevention and control client based on the risk warning strategy.

[0108] The above-mentioned memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0109] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the following method steps: collecting real-time operating data of the target distribution network equipment, wherein the real-time operating data includes operating parameters of N equipment components in the target distribution network equipment, wherein N is a positive integer; determining the equipment risk level of the target distribution network equipment through a preset risk assessment model and the operating parameters of all equipment components; determining a risk warning strategy and risk warning information based on the equipment risk level, and sending the risk warning information to a risk prevention and control client based on the risk warning strategy.

[0110] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the operation risk warning method for distribution network equipment of any one of the above-mentioned embodiments one.

[0111] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement the operation risk warning method for distribution network equipment of any one of the above-mentioned embodiments one.

[0112] Figure 5 1 is a hardware structure block diagram of an electronic device (or mobile device) for an operation risk warning method for distribution network equipment according to an embodiment of the present invention. Figure 5 As shown, the electronic device may include one or more ( Figure 5502a, 502b, ..., 502n are used to illustrate) processor 502 (processor 502 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), memory 504 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 5 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 5 More or fewer components as shown, or with Figure 5 Different configurations are shown.

[0113] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0114] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0116] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0117] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0118] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0119] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for early warning of operation risks of distribution network equipment, characterized in that: include: Collecting real-time operating data of the target distribution network equipment, wherein the real-time operating data includes operating parameters of N equipment components in the target distribution network equipment, where N is a positive integer; Determine the equipment risk level of the target distribution network equipment by using a preset risk assessment model and the operating parameters of all the equipment components; A risk warning strategy and risk warning information are determined based on the equipment risk level, and the risk warning information is sent to a risk prevention and control client based on the risk warning strategy.

2. The operation risk early warning method according to claim 1 is characterized in that: After collecting the real-time operation data of the target distribution network equipment, the method further includes: Input the real-time operation data into the state perception model, and output the equipment operation status of the target distribution network equipment, wherein the state perception model is used to calculate the health status indicator and equipment status index of the target distribution network equipment based on the operation parameters of the N equipment components in the real-time operation data, and determine the equipment operation status based on the equipment status index, the health status indicator is used to quantify the health of the target distribution network equipment during operation, and the equipment status index is used to characterize the dynamic relationship between the health status indicator and the operation time; A maintenance strategy for the target distribution network equipment is determined based on the equipment operating status, and the target distribution network equipment is maintained according to the maintenance strategy.

3. The operation risk early warning method according to claim 2 is characterized in that: After determining the maintenance strategy of the target distribution network equipment based on the equipment operation status, the method further includes: Acquire the device state index of the target distribution network device calculated by the state perception model; Determining a probability of equipment failure based on the equipment status index, and calculating equipment failure losses based on the equipment failure probability; The risk warning strategy and the risk warning information are determined based on the equipment failure loss, and the risk warning information is sent to the risk prevention and control client based on the risk warning strategy.

4. The operation risk early warning method according to claim 1 is characterized in that: After collecting the real-time operation data of the target distribution network equipment, the method further includes: Performing a data cleaning operation on the real-time operation data, wherein the data cleaning operation includes: removing abnormal values ​​and removing duplicate values; Identifying abnormal data in the real-time operation data and correcting the abnormal data, wherein the abnormal data refers to operation data that is inconsistent with a normal operation mode and is used to indicate a device failure and / or abnormal operation of the device; All the operating parameters are mapped according to a preset dimensional range to obtain standardized operating data, wherein each of the operating parameters is mapped to a standardized operating parameter in the standardized operating data.

5. The operation risk early warning method according to claim 4 is characterized in that: Before determining the equipment risk level of the target distribution network equipment by using a preset risk assessment model and the operating parameters of all the equipment components, the method further includes: Classifying all the standardized operating parameters in the standardized operating data to obtain M membership sets, wherein each membership set corresponds to an upper-layer indicator type, there is a membership relationship between the standardized operating parameters in the membership set and the upper-layer indicator type, each upper-layer indicator type corresponds to a weight assignment standard, and M is a positive integer; For each of the membership sets, weight calculation and weight assignment are performed on all the standardized operating parameters in the membership set based on the weight assignment standard to obtain the indicator weight corresponding to each of the standardized operating parameters.

6. The operation risk early warning method according to claim 5 is characterized in that: The step of determining the equipment risk level of the target distribution network equipment by using a preset risk assessment model and the operating parameters of all the equipment components comprises: The standardized operating parameters corresponding to the operating parameters and the indicator weights are input into the preset risk assessment model, and the equipment risk level of the target distribution network equipment is output, wherein the preset risk assessment model is used to calculate the component loss probability of each of the equipment components based on the standardized operating parameters and the indicator weights, calculate the equipment failure loss based on the component loss probabilities of N of the equipment components, and determine the equipment risk level based on the equipment failure loss.

7. The operation risk early warning method according to claim 4 is characterized in that: After all the operating parameters are mapped according to the preset dimension range to obtain standardized operating data, the method further includes: Establishing a standardized data table according to the timestamp data carried by the operating parameters and the standardized operating data corresponding to the operating parameters; During the data incremental update process of the standardized data table, the real-time data volume of the standardized data table is monitored, and when the real-time data volume meets a preset data volume threshold, the standardized data table is exported.

8. An operation risk early warning device for distribution network equipment, characterized in that: include: A collection unit, used to collect real-time operation data of the target distribution network equipment, wherein the real-time operation data includes operation parameters of N equipment components in the target distribution network equipment, wherein N is a positive integer; A determination unit, configured to determine the equipment risk level of the target distribution network equipment by using a preset risk assessment model and the operating parameters of all the equipment components; The sending unit is used to determine the risk warning strategy and risk warning information based on the risk level of the equipment, and send the risk warning information to the risk prevention and control client based on the risk warning strategy.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the operation risk early warning method for distribution network equipment according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the operation risk warning method of distribution network equipment as described in any one of claims 1 to 7.

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