Intelligent analysis method for safe operation of power equipment

Abstract

The invention discloses an intelligent analysis method for safe operation of power equipment, and relates to the technical field of safety analysis of the power equipment, and a system responds to a user request, guides a configuration data interface, model parameters and a feature set, and automatically constructs a safety analysis process. A standardized weighted aggregation function is utilized, characteristic parameters and equipment monitoring indexes are dynamically associated, and the accuracy of data conversion is ensured through a weight coefficient, a historical mean value and a standard deviation; when the feature dimension exceeds the limit, automatically triggering principal component analysis to perform intelligent dimension reduction; a real-time two-way communication mechanism with an electric power monitoring system is established, reliable operation is guaranteed through protocol handshake, data verification and abnormal alarm, a dynamic security situation billboard is generated, and user interaction is creatively fused into closed-loop optimization: when a user triggers optimization, current feature weights are automatically saved, and after updated parameters are received, a model is started for retraining; and iterative evolution is realized through convergence judgment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment safety analysis, and particularly relates to an intelligent analysis method for safe operation of power equipment. BACKGROUND

[0002] In the field of safe operation management of power equipment, the traditional analysis method has many limitations. The existing technology relies on fixed data interfaces and preset models, which cannot flexibly adapt to the data collection needs of different types of power equipment, resulting in lack of pertinence in the configuration of the analysis process. At the same time, the correlation between the characteristic parameters and the monitoring indicators in the equipment operation data is a simple correspondence, without considering the dynamic weight relationship and standardization processing between them, which is easy to cause analysis deviation due to the difference or fluctuation of data dimensions.

[0003] In addition, when the input feature dimension is high, there is a lack of effective dimension reduction mechanism, which not only increases the computing load, but also may affect the analysis accuracy due to redundant information. In terms of data communication, the connection with the power monitoring system often causes poor data transmission due to protocol incompatibility, and lacks a perfect interface verification mechanism, making it difficult to ensure the real-time and reliability of the data source.

[0004] In addition, the existing scheme has a lag in model optimization, and users cannot adjust the weight of the characteristic parameters in real time according to the actual analysis results. The model retraining process is cumbersome and cannot quickly respond to the dynamic changes of the equipment operation state, resulting in insufficient timeliness and accuracy of the safety situation assessment, and difficulty in meeting the needs of real-time monitoring and intelligent analysis of the safe operation of power equipment. SUMMARY

[0005] In the exemplary embodiments of the present application, an intelligent analysis method for safe operation of power equipment is provided to improve the efficiency and accuracy of power equipment safety analysis.

[0006] The present application provides an intelligent analysis method for safe operation of power equipment, comprising the following steps:

[0007] In response to the analysis request operation triggered by the user, a safety analysis configuration interface is displayed;

[0008] The power equipment data interface, analysis model configuration information and characteristic parameters are received through the safety analysis configuration interface;

[0009] A safety analysis process is constructed according to the power equipment data interface, the analysis model configuration information and the characteristic parameters;

[0010] Real-time operation monitoring is performed on the safety analysis process.

[0011] Further, the construction of the safety analysis process specifically includes:

[0012] binding the power equipment data interface to the security analysis process;

[0013] configuring a model node of the security analysis process according to the analysis model configuration information and the feature parameters;

[0014] establishing a mapping rule of the feature parameters and a monitoring index in equipment operation data corresponding to the power equipment data interface, wherein:

[0015] the feature parameters are represented by a set P = {p1, p2,..., p n} and the monitoring index is represented by a set M = {m1, m2,..., m k};

[0016] performing a standardized weighted aggregation operation through a mapping function f: P→M:

[0017]

[0018] wherein w ij represents a weight coefficient of the i-th feature parameter to the j-th monitoring index, μ i is a historical data mean of the feature parameter p i , σ i is a historical data standard deviation of the feature parameter p i , and b j is a bias term of the j-th monitoring index;

[0019] setting an analysis logic of the model node based on the feature parameters, the analysis logic performing the following operations:

[0020] if an input feature dimension d = dim(P) exceeds a preset threshold d max , performing dimension reduction processing through principal component analysis:

[0021]

[0022] wherein, x represents an input feature vector, is a feature vector after dimension reduction;

[0023] generating a completed security analysis process.

[0024] Further, it further includes: in response to a start instruction for the security analysis process, verifying whether the power equipment data interface is associated with a real-time data source of a power monitoring system;

[0025] if the verification is passed, establishing a data communication channel with the power monitoring system;

[0026] If the verification fails, the security analysis process is rejected, and an abnormal alarm message is sent to the monitoring system.

[0027] Further, the establishing of the data communication channel comprises: sending a real-time communication protocol file to the power monitoring system, so that the power monitoring system configures a data exchange mechanism based on the protocol file;

[0028] A handshake response message returned by the power monitoring system is received to confirm that the communication channel is established.

[0029] Further, after the data communication channel is established, the method further comprises:

[0030] An analysis starting instruction is sent to the power monitoring system, and real-time feature data sets returned by the power monitoring system according to the mapping rule are received;

[0031] Based on the power equipment data interface, full equipment operation data is obtained from the power monitoring system;

[0032] According to the real-time feature data sets and the full equipment operation data, a device security situation analysis board is generated.

[0033] Further, the method further comprises: in response to a model optimization operation triggered by a user on the analysis board, a parameter update instruction is sent to the power monitoring system, so that the power monitoring system saves the feature weight of the current model node;

[0034] The optimized feature parameters fed back by the power monitoring system are received, and model retraining is performed based on the optimized feature parameters.

[0035] Further, the method further comprises: after the model retraining is completed, it is determined whether the security analysis process reaches a convergence condition;

[0036] If the convergence condition is reached, a final security evaluation report is output; if the convergence condition is not reached, the next analysis cycle is iteratively executed.

[0037] The embodiments of the application have the following beneficial effects: the technical scheme significantly improves the efficiency and accuracy of power equipment security analysis through intelligent configuration and execution process. After responding to the analysis request, the system guides the user to configure the data interface, model parameters and feature set, and automatically builds a complete security analysis process.

[0038] Heterogeneous data fusion is realized through a mathematical mapping model, and a standardized weighted aggregation function is used to dynamically associate feature parameters to equipment monitoring indicators, wherein the calculation of weight coefficients, historical mean and standard deviation ensures the accuracy of data conversion and eliminates the influence of dimension difference on analysis.

[0039] An intelligent dimension reduction mechanism is built in, which automatically triggers principal component analysis when the feature dimension exceeds the limit, and adaptively selects the optimal dimension reduction dimension, both retaining the core features and avoiding dimension disaster. Finally, a real-time two-way communication mechanism is established, which ensures the reliable execution of the analysis process through protocol handshake, data channel verification and abnormal alarm linkage with the power monitoring system.

[0040] In the running phase, the system synchronously acquires the full data and real-time feature data set, and generates dynamic visual safety situation board. The user interaction is innovatively integrated into the optimization closed loop: when the user triggers model optimization through the board, the system automatically saves the current feature weight to the monitoring system, receives the optimization parameters, starts model retraining, and realizes iterative model evolution through convergence judgment. Not only does it reduce the intensity of manual intervention, but also improves the adaptability of the model to complex working conditions through dynamic adjustment of feature weights. Under the premise of ensuring the reliability of real-time monitoring, a high-confidence safety evaluation report is output after multiple rounds of optimization, providing accurate decision support for equipment risk early warning. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0042] Figure 1 An exemplary flowchart of a power equipment safety operation intelligent analysis method provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application.

[0044] In order to further illustrate the technical solutions provided by the embodiments of the present application, the following will be described in detail with reference to the drawings and specific embodiments. Although the embodiments of the present application provide the method operation steps as shown in the following embodiments or drawings, more or less operation steps can be included in the method based on conventional or non-creative labor. The execution order of these steps is not limited to the execution order provided by the embodiments of the present application in the logical sense.

[0045] REFERENCE Figure 1 As shown in the drawings, the present application provides a power equipment safety operation intelligent analysis method, including the following steps:

[0046] S1: In response to the analysis request operation triggered by the user, display the safety analysis configuration interface.

[0047] By actively responding to user operation behavior, dynamically calling the graphical configuration interface, converting abstract power equipment safety analysis requirements into visual operation logic, establishing a technical bridge between user intent and system function, non-professionals can trigger complex analysis processes through standardized interfaces, thereby eliminating the application barriers caused by high operation threshold in traditional power system analysis.

[0048] In the overall scheme, this feature is a key component of the system interaction layer, directly determining the control logic of subsequent technical modules. By displaying the safety analysis configuration interface, the system creates the necessary conditions for receiving power equipment data interface, analysis model configuration information and feature parameters in step S2, enabling these technical elements to be standardized through structured forms. This design not only guarantees the standardization of analysis process construction, but also hides the underlying complex algorithms through graphical interaction, allowing users to focus on business logic configuration rather than technical implementation details.

[0049] When users trigger requests according to different power equipment analysis scenarios, the system can dynamically generate customized configuration interfaces based on a unified interaction protocol, providing front-end support for the flexible construction of safety analysis processes in step S3.

[0050] This interaction mechanism ensures that subsequent model node configuration, feature parameter mapping and other key technical operations are derived from user-expressed business requirements, thereby ensuring the relevance and effectiveness of the analysis process at the source,

[0051] S2: Receive power equipment data interface, analysis model configuration information and feature parameters through the safety analysis configuration interface.

[0052] This step S2 receives power equipment data interface, analysis model configuration information and feature parameters through the safety analysis configuration interface. The purpose of this setting is to provide basic input information for subsequent construction of safety analysis processes, ensuring that the analysis process can accurately interface with actual operation data of power equipment, and configure appropriate analysis models and key feature parameters based on user needs and equipment characteristics.

[0053] In the overall scheme, this step plays a role in connecting data and models. The reception of power equipment data interface provides interface basis for subsequent data communication with the power monitoring system and acquisition of equipment operation data, which is a prerequisite for data interaction; the reception of analysis model configuration information determines the model framework and related parameter settings used in safety analysis, laying the foundation for model node configuration.

[0054] The receiving of the characteristic parameters as the core input of the analysis is directly related to the monitoring indicators in the equipment operation data, and through subsequent mapping rules and analysis logic, the effective evaluation of the safety state of the equipment is realized, which is the key link connecting the actual operation data of the equipment and the safety analysis model, and guarantees the pertinence and accuracy of the safety analysis process

[0055] S3: Constructing a safety analysis process according to the power equipment data interface, the analysis model configuration information and the characteristic parameters.

[0056] By synergistically integrating the three elements of data input source, model algorithm logic and feature processing rule, the discrete technical components are converted into executable analysis links, a standardized process construction mechanism is established, the business parameters configured by the user can be converted into a calculation topology with clear data flow and logical relationship, thereby solving the problem of insufficient adaptability caused by process solidification in traditional power safety analysis.

[0057] Further, the construction of the safety analysis process specifically includes:

[0058] Binding the power equipment data interface to the safety analysis process.

[0059] According to the analysis model configuration information and the characteristic parameters, configuring the model nodes of the safety analysis process.

[0060] Establishing a mapping rule of the monitoring indicators in the equipment operation data corresponding to the characteristic parameters and the power equipment data interface, wherein:

[0061] The characteristic parameters are represented by a set P = {p1, p2,..., p n}, and the monitoring indicators are represented by a set M = {m1, m2,..., m k}.

[0062] Performing a standardized weighted aggregation operation through a mapping function f: P→M:

[0063]

[0064] Where w ij represents the weight coefficient of the i-th characteristic parameter to the j-th monitoring indicator, μ i is the historical data mean of the characteristic parameter p i , σ i is the historical data standard deviation of the characteristic parameter p i , and b j is the bias term of the j-th monitoring indicator.

[0065] The operation realizes the quantitative conversion of the characteristic parameter set P to the monitoring indicator set M through the mapping function f: P→M, and its core function is to eliminate the differences between different characteristic parameters p idimensional differences and data distribution deviations. The specific implementation process In particular, The historical data mean μ i and standard deviation σ i are standardized to convert physical parameters such as temperature and voltage with different dimensions into comparable dimensionless values, providing a data foundation for subsequent model node analysis.

[0066] In the overall scheme, this operation constitutes the technical core of monitoring index generation. The introduction of weight coefficient w ij establishes a quantitative relationship between feature parameter p i and monitoring index m j , for example, in the transformer oil temperature analysis scene, the winding temperature feature can be given a higher weight to improve the accuracy of the key index.

[0067] The bias term b j provides a compensation mechanism for individual differences in equipment, making the same monitoring index comparable between different specifications of equipment. This design significantly enhances the adaptability of the safety analysis process, enabling the equipment safety situation analysis dashboard generated in step S4 to accurately reflect the nature of the equipment operating state.

[0068] Further, the operation supports the closed-loop optimization capability of the scheme through a dynamic weight mechanism. When the user triggers the model optimization operation, the updated feature parameter P' will re-execute the aggregation operation, and by adjusting w ij , the accuracy of the monitoring index m j is improved.

[0069] In the model retraining process, the comparison and analysis of the operation results with the full data of the equipment operation form the basis for loss function calculation, driving the weight coefficient to evolve towards the convergence condition. This technical design makes the output of the safety assessment report not only based on real-time data, but also integrates historical operation rules and human experience,

[0070] By binding the power equipment data interface to the analysis process, the data supply integrity in the subsequent real-time monitoring stage is ensured; and based on the analysis model configuration information, the model node provides pluggable analysis capabilities for different power equipment scenarios.

[0071] Specifically, by defining the mapping function between the feature parameter set and the monitoring index set, and performing the standardization and weighted aggregation operation, this feature realizes the normalization of multi-source heterogeneous data, effectively eliminating the dimensional differences and data distribution deviations of the equipment monitoring index, and providing high-quality feature input for subsequent model nodes.

[0072] Based on the feature parameters, the analysis logic of the model node is set, which performs the following operations:

[0073] If the input feature dimension d = dim(P) exceeds the preset threshold d max Then, dimensionality reduction is performed using principal component analysis:

[0074]

[0075] in, x represents the input feature vector. These are the eigenvectors after dimensionality reduction.

[0076] Generate a configured security analysis workflow.

[0077] In its implementation, the system first detects the input feature dimension d = dim(P) and the preset threshold d. max Numerical relationship: when d > d max Automatically trigger principal component analysis for dimensionality reduction. Among them, the dimensionality reduction target dimension The setting strategy significantly reduces computational complexity while preserving the main information of features, effectively avoiding the curse of dimensionality caused by high-dimensional data.

[0078] Within the overall technical architecture, this feature constitutes the intelligent decision-making center of the security analysis process. This is achieved through the feature parameter p. i With monitoring indicator m j The mapping rule f: P→M establishes a bridge between the feature space and the model space, enabling the standardized weighted aggregation operation results to be transformed. It can directly drive the judgment logic of model nodes.

[0079] When the feature dimension d = dim(P) exceeds the preset threshold d max At that time, the system automatically triggers principal component analysis dimensionality reduction. The strategy for determining the value of the target dimension k in dimensionality reduction. While ensuring information retention, computational complexity was effectively controlled.

[0080] Not only does it avoid the curse of dimensionality caused by high-dimensional features, but it also... The dimensional constraint mechanism achieves a balance between computational resources and model accuracy, providing underlying support for the stable operation of the security analysis process in power systems of different scales.

[0081] When the input feature dimension d = dim(P) exceeds the preset threshold d max Automatically triggered, it projects the original feature space x to a low-dimensional orthogonal space through a linear transformation. Its core function is to eliminate multicollinearity among features while preserving the main variation information of the data. In specific implementation, the dimensionality reduction target dimension... The setting strategy has dual technical advantages: dmax The constraint guarantees the controllability of the computing resources for real-time analysis, while The information retention rate is self-adaptively adjusted according to the feature size, so that the computing complexity and model accuracy achieve the optimal balance in engineering practice.

[0082] In the overall technical architecture, this operation constitutes a key link of feature preprocessing.

[0083] By performing The generated reduced-dimension feature vector is input to the analysis logic of the model node instead of the original high-dimensional data x, which significantly reduces the computing load of the subsequent standardized weighted aggregation operation .

[0084] In particular, this operation converts the highly correlated high-dimensional feature parameters p i into linearly independent principal component vectors through eigenvalue decomposition of the covariance matrix, essentially eliminating the information redundancy interference in the calculation of the monitoring index m j in step S3, thereby improving the data reliability of the equipment safety situation analysis board.

[0085] Furthermore, this dimension reduction mechanism enhances the adaptability of the system through dynamic dimension control. When the power equipment monitoring points are expanded, resulting in an increase in the feature dimension d , the calculation rule causes the system to automatically expand the principal component retention dimension, avoiding the loss of important feature information; in a resource-limited scenario, the d max threshold value forces the compression of the feature space, ensuring the real-time performance of the analysis process.

[0086] S4: performing real-time operation monitoring on the safety analysis process.

[0087] Further, it also includes: in response to a start instruction for the safety analysis process, verifying whether the power equipment data interface is associated with a real-time data source of the power monitoring system.

[0088] If the verification is passed, a data communication channel is established with the power monitoring system.

[0089] The establishment of the data communication channel includes: sending a real-time communication protocol file to the power monitoring system, so that the power monitoring system configures a data exchange mechanism based on the protocol file.

[0090] A handshake response message returned by the power monitoring system is received to confirm that the communication channel is established.

[0091] By establishing strict interface verification logic, it is ensured that the safety analysis process can accurately identify valid data sources when it is started, solving the problems of legality and integrity of real-time data acquisition in power equipment analysis.

[0092] When the system responds to the start instruction, first perform data interface relevance verification, the verification mechanism by checking the interface configuration metadata and power monitoring system registration information matching degree, from the source to eliminate the risk of analysis distortion caused by the wrong data source access, for subsequent real-time analysis to lay a data credible foundation.

[0093] After verification, trigger the communication channel establishment process, by sending real-time communication protocol file to the power monitoring system to realize system protocol alignment, the protocol file contains data format specification, transmission frequency parameters and encryption verification rules and other key technical elements, so that the originally heterogeneous system can configure data exchange mechanism based on unified standards.

[0094] In particular, through the confirmation mechanism of handshake response message, a closed-loop control structure of bidirectional authentication is formed, which not only guarantees the reliability of the communication link, but also provides channel protection for the stable transmission of subsequent real-time feature data set.

[0095] When the verification fails, the abnormal alarm information sending operation triggered not only prevents the execution of invalid analysis process, but also sends diagnostic information to the monitoring system, so that the operation and maintenance personnel can locate the interface configuration fault point in time.

[0096] Significantly improve the self-diagnosis ability of power equipment safety analysis system, avoid the whole process execution error caused by communication exception. At the same time, the dynamic delivery mechanism of protocol file in the communication channel establishment process supports the system's compatible adaptation ability to multiple power monitoring protocols, so that the safety analysis process can be flexibly deployed in different specifications of power system environment, and provides core support for the real-time data supply of equipment safety situation analysis board.

[0097] Further, after establishing the data communication channel, further comprising:

[0098] Send analysis start instruction to the power monitoring system, and receive real-time feature data set returned by the power monitoring system according to the mapping rule.

[0099] If the verification fails, refuse to execute the safety analysis process, and send abnormal alarm information to the monitoring system.

[0100] Based on the power equipment data interface, obtain the equipment operation full data from the power monitoring system.

[0101] According to the real-time feature data set and the equipment operation full data, generate the equipment safety situation analysis board.

[0102] By bidirectional instruction interaction and data fusion processing, the technical elements configured in the early stage are converted into visual analysis results, a dynamic analysis closed loop is established, and the problems of real-time deficiency and information fragmentation in power equipment safety monitoring are solved. After the communication channel is established, the analysis start instruction is sent to the power monitoring system to trigger the data supply process, so that the system can actively return the standardized feature data set according to the mapping rule f: P→M defined in step S3. This mechanism effectively guarantees the logical consistency between feature extraction and model analysis.

[0103] In the overall scheme, this feature builds a multi-dimensional analysis system of equipment safety situation. Based on the equipment operation full data obtained through the power equipment data interface, the equipment operation full data and the real-time feature data set form a complementary relationship: the former provides a complete mirror of the original operation state of the equipment, and the latter carries the standardized feature information after weighted aggregation processing.

[0104] By fusing the safety situation analysis dashboard generated by the two types of heterogeneous data, the association mapping of the key monitoring indicators mj and the equipment operation underlying data is realized, so that the operation and maintenance personnel can not only macroscopically grasp the safety score trend, but also trace and locate specific abnormal data points.

[0105] In particular, the active push mechanism of abnormal alarm information is bound with the verification failure scenario to form a fault rapid response channel. When the data interface association failure is detected, the analysis process is immediately blocked and a diagnostic code is sent to the monitoring system, which significantly improves the system defense capability.

[0106] Further, the feature supports the decision optimization value of the technical scheme through the data visualization engine. The generation process of the analysis dashboard is embedded with a dynamic rendering algorithm, which associates and displays the standardized weighted aggregation calculation results in the feature data set with the time and space dimensions of the full data, forming a visualization matrix with weight heat distribution.

[0107] The quantitative evaluation results of the equipment safety situation are intuitively presented, which provides an interactive data verification interface for subsequent user-triggered model optimization operations, so that the adjustment of key parameters such as weight coefficients wij has visual feedback basis.

[0108] Specifically, it also includes: in response to the model optimization operation triggered by the user on the analysis dashboard, sending a parameter update instruction to the power monitoring system to save the feature weight of the current model node.

[0109] Receive the optimized feature parameters fed back by the power monitoring system, and perform model retraining based on the optimized feature parameters.

[0110] After the model retraining is completed, it is determined whether the safety analysis process meets the convergence condition.

[0111] If the convergence condition is reached, a final safety assessment report is output. If the convergence condition is not reached, the next analysis cycle is iteratively performed.

[0112] The present application is described in reference to the flow diagrams and / or block diagrams of the method, apparatus (system) and computer program product according to this application. It will be understood that each block of the flow diagrams and / or block diagrams, and combinations of blocks in the flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more flow or flow diagrams and / or block diagrams Figure 1 means for carrying out the function specified by the flow or flow diagrams and / or block or blocks.

[0113] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flow diagrams and / or block diagrams flow or flows and / or block or blocks. Figure 1 one or more flow or flow diagrams and / or block diagrams Figure 1 means for carrying out the function specified by the flow or flow diagrams and / or block or blocks.

[0114] The computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow diagrams and / or block diagrams flow or flows and / or block or blocks. Figure 1 one or more flow or flow diagrams and / or block diagrams Figure 1 means for carrying out the function specified by the flow or flow diagrams and / or block or blocks.

[0115] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for intelligent analysis of the safe operation of power equipment, characterized in that, Includes the following steps: In response to a user-triggered analysis request, the security analysis configuration interface is displayed; The security analysis configuration interface receives power equipment data interfaces, analysis model configuration information, and feature parameters. A security analysis process is constructed based on the power equipment data interface, the analysis model configuration information, and the feature parameters. Real-time operational monitoring is performed on the security analysis process.

2. The method according to claim 1, characterized in that, The security analysis process specifically includes: Bind the power equipment data interface to the security analysis process; Configure the model nodes of the security analysis process based on the analysis model configuration information and the feature parameters; Establish a mapping rule between the feature parameters and the monitoring indicators in the equipment operation data corresponding to the power equipment data interface, wherein: The characteristic parameters are defined by the set P = {p1, p2, ..., p...} n The monitoring indicators are represented by the set M = {m1, m2, ..., m}. k }express; Perform standardized weighted aggregation operation using the mapping function f: P→M: Among them, w ij μ represents the weighting coefficient of the i-th feature parameter with respect to the j-th monitoring indicator. i For the characteristic parameter p i Historical data mean, σ i For the characteristic parameter p i Historical data standard deviation, b j This is the bias term for the j-th monitoring indicator; Based on the aforementioned feature parameters, the analysis logic for the model nodes is defined, and the analysis logic performs the following operations: If the input feature dimension d = dim(P) exceeds the preset threshold d max Then, dimensionality reduction is performed using principal component analysis: in, x represents the input feature vector. These are the feature vectors after dimensionality reduction; Generate a configured security analysis workflow.

3. The method according to claim 2, characterized in that, Also includes: In response to the start command for the security analysis process, verify whether the power equipment data interface is associated with the real-time data source of the power monitoring system; If the verification is successful, a data communication channel is established with the power monitoring system; If the verification fails, the security analysis process will be refused and an abnormal alarm message will be sent to the monitoring system.

4. The method according to claim 3, characterized in that, The establishment of the data communication channel includes: Send a real-time communication protocol file to the power monitoring system so that the power monitoring system can configure a data exchange mechanism based on the protocol file; The system receives a handshake response message from the power monitoring system to confirm that the communication channel has been established.

5. The method according to claim 4, characterized in that, After establishing the data communication channel, the following is also included: Send an analysis start command to the power monitoring system and receive the real-time feature dataset returned by the power monitoring system according to the mapping rules; Based on the power equipment data interface, obtain full data on equipment operation from the power monitoring system; Based on the real-time feature dataset and the full data of device operation, a device security status analysis dashboard is generated.

6. The method according to claim 5, characterized in that, Also includes: In response to the model optimization operation triggered by the user on the analysis dashboard, a parameter update instruction is sent to the power monitoring system so that the power monitoring system saves the feature weights of the current model node; The system receives optimized feature parameters from the power monitoring system and performs model retraining based on the optimized feature parameters.

7. The method according to claim 6, characterized in that, Also includes: After the model is retrained, it is determined whether the security analysis process has reached the convergence condition. If the convergence condition is met, the final security assessment report will be output. If the convergence condition is not met, the next analysis cycle will be executed iteratively.

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