Method, system, equipment and medium for multidimensional detection of state of electric power communication network

Abstract

The invention discloses a method, a system, equipment and a medium for multidimensional detection of a power communication network state. The method comprises the following steps: dividing a power communication network into a plurality of areas; performing preprocessing according to the multi-dimensional monitoring data of each sub-region to obtain heterogeneous data of each sub-region; establishing a mapping relationship among different data sources, and converting data in different formats into a uniform data structure to obtain a heterogeneous data set of each sub-region; electric power communication network state indexes of all the areas are obtained through real-time calculation; according to the power communication network state index of each sub-region, analyzing the power communication network state among the regions to obtain a correlation analysis result; and according to the power communication network state index of each sub-region and the correlation analysis result, executing an aggregation operation, and performing overall state evaluation on the power communication network to obtain a power communication network state evaluation result. According to the invention, the stability and reliability of the power communication network can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power communication, and in particular to a method, system, device and medium for multi-dimensional detection of power communication network state. BACKGROUND

[0002] The rapid development of the power industry makes the power communication network, as the "nervous system" of the power system, more and more critical. This network not only has to be responsible for data transmission, remote monitoring, and automatic scheduling, but also has to undertake a series of core tasks such as relay protection. Whether its operation is stable and safe is often directly related to whether the power grid can operate safely and efficiently, and also deeply affects the power experience of millions of households. In recent years, with the continuous advancement of smart grid construction, the power communication network has more and more complex tasks to do. It not only has to ensure smooth traditional voice communication, but often also has to meet the hard requirement of millisecond-level transmission of relay protection signals, while ensuring that automatic scheduling instructions can be accurately issued and device status can be monitored in real time, and even supporting high-level functions such as fault prediction and diagnosis based on big data. These tasks have higher and higher requirements for network performance.

[0003] Due to the wide coverage, complex structure, and often changing operating environment of the power communication network, a large amount of heterogeneous data is generated in the system every day, such as device logs, traffic information, alarm records, performance indicators, and the like. These data come from various sources, have non-uniform formats, and are updated quickly. In the face of such a large amount of information flow, it is often difficult for operation and maintenance personnel to quickly sort out the clues. Sometimes, some potential fault hidden dangers or abnormal behaviors are also ignored. In particular, when a sudden network interruption or a more hidden network attack occurs, the traditional monitoring method has slow response and insufficient analysis capability, lacks the ability to process multi-source data fusion, and leads to slow fault location and low disposal efficiency. Therefore, it has become an urgent need to build a system that can monitor the state of the power communication network in real time, comprehensively, and intelligently, actively discover abnormalities, and respond to risks in a timely manner, to ensure the safe and stable operation of modern power grids. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a method, system, device and medium for multi-dimensional detection of power communication network state to solve the problem that the power communication network in the prior art involves a large amount of heterogeneous data, making it difficult to discover network faults and abnormalities in a timely and accurate manner.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a method for multi-dimensional detection of power communication network state, comprising: obtain a topology of the power communication network, and divide the power communication network into a plurality of regions; based on multi-dimensional monitoring data of each sub-region, and preprocessing, obtain heterogeneous data of each sub-region; based on the heterogeneous data of each sub-region, establish a mapping relationship between different data sources, and convert data of different formats into a unified data structure, to obtain a heterogeneous data set of each sub-region; based on the heterogeneous data set of each sub-region, obtain power communication network state indicators of each region through real-time calculation; based on the power communication network state indicators of each sub-region, analyze the power communication network state between regions to obtain correlation analysis results; based on the power communication network state indicators of each sub-region and the correlation analysis results, perform aggregation operation, and evaluate the overall state of the power communication network to obtain power communication network state evaluation results.

[0007] As a preferred scheme of the method for detecting the state of the power communication network in multiple dimensions, the power communication network is divided into a plurality of regions, including: According to the topology information of the power communication network, the key nodes, clusters and sub-networks in the power communication network are identified to obtain the network topology analysis results; According to the network topology analysis results, determine the principle of dividing the power communication network region; According to the principle of dividing the region, the power communication network is divided into a plurality of regions.

[0008] As a preferred scheme of the method for detecting the state of the power communication network in multiple dimensions, the method for detecting the state of the power communication network in multiple dimensions, including: The multi-dimensional monitoring data includes device state data, network traffic data and environmental parameter data; The device state data includes the running state, fault information and performance indicators of the device; The network traffic data includes link bandwidth utilization, packet loss rate and delay; The environmental parameter data includes temperature, humidity, air quality and electromagnetic interference; The obtained device state data, network traffic data and environmental parameter data are cleaned and normalized to obtain the preprocessed data of each sub-region; The preprocessed data is subjected to isomerization treatment to obtain the heterogeneous data of each sub-region.

[0009] As a preferred scheme of the method for detecting the state of the power communication network in multiple dimensions, wherein: the mapping relationship between different data sources is established, and the data in different formats is converted into a unified data structure to obtain a heterogeneous data set of each sub-region, including: The device state data, network traffic data, and environmental parameter data are labeled to obtain labeled heterogeneous data; The key features of the labeled heterogeneous data are extracted to obtain features of different types of data; According to the features of different types of data, a mapping relationship between the features of different types of data is established, and the correlation between different types of data is calculated; According to the correlation between different types of data, a data mapping table is established; According to the data mapping table, different types of heterogeneous data are converted into a unified data structure and integrated to obtain a heterogeneous data set of each sub-region.

[0010] The beneficial effects of the preferred technical scheme are that by labeling, feature extraction, mapping relationship establishment, and correlation analysis, and based on the data mapping table, the format of multi-source heterogeneous data is unified and integrated, different types of data such as device state, network traffic, and environmental parameters can be effectively fused, the usability and consistency of the data are improved, and a high-quality data basis is provided for subsequent cross-dimensional state analysis and correlation reasoning.

[0011] As a preferred scheme of the method for detecting the state of the power communication network in multiple dimensions, wherein: the power communication network state indicators include device online rate, network delay, data packet loss rate, bandwidth utilization rate, and network stability index; The power communication network state indicators of each region are calculated in real time, including: According to the heterogeneous data set of each sub-region, the proportion of the number of online devices to the total number of devices is calculated to obtain the device online rate; According to the heterogeneous data set of each sub-region, the network delay data between each device and node are obtained, the percentile of all delay data is calculated, and the network delay is obtained; According to the heterogeneous data set of each sub-region, the number of sent and received data packets is recorded, the proportion of the number of lost data packets to the number of sent data packets is calculated, and the data packet loss rate is obtained; According to the heterogeneous data set of each sub-region, the usage amount of network bandwidth is monitored in real time, the proportion of the currently used bandwidth to the total bandwidth is calculated, and the bandwidth utilization rate is obtained; According to the network delay and the data packet loss rate, the fluctuation standard deviation of the network delay and the data packet loss rate is calculated to obtain the network stability index.

[0012] As a preferred scheme of the method for multi-dimensionally detecting the state of the power communication network, wherein: the analysis on the state of the power communication network between the regions is performed to obtain a correlation analysis result, including: comparing the index value of each region with the state index threshold to obtain a comparison result; According to the comparison result, the linear correlation coefficient of the device online rate between the regions at a plurality of time points is calculated; Based on the average value, variance and total number of devices of the device online rate of each region, statistical test is performed; According to the linear correlation coefficient and the result of the statistical test, the correlation analysis result is obtained.

[0013] The beneficial effects of the preferred technical scheme are that by comparing the region index with the preset threshold and combining the time sequence correlation analysis and statistical significance test of the device online rate, the correlation mode and difference characteristics of the state change between the regions can be accurately identified, and the positioning ability of the network abnormal propagation path and potential fault source is improved.

[0014] As a preferred scheme of the method for multi-dimensionally detecting the state of the power communication network, wherein: the analysis on the state of the power communication network between the regions is performed to obtain a correlation analysis result, including: According to the state index of each region and the correlation analysis result, the state index of each region is weighted and aggregated to obtain a whole state index, wherein the weight corresponding to each region is determined according to the importance of the region in the network and the number of devices; The whole state index is compared with the evaluation result threshold; According to the comparison result, the power communication network state evaluation result is output; The evaluation result includes normal state, warning state and abnormal state.

[0015] In a second aspect, the present application provides a system for multi-dimensionally detecting the state of a power communication network, comprising: A region division module is configured to obtain the topology structure of the power communication network and divide the power communication network into a plurality of regions; A data preprocessing module is configured to preprocess the multi-dimensionally monitored data of each sub-region based on the multi-dimensionally monitored data of each sub-region to obtain heterogeneous data of each sub-region; A heterogeneous data fusion module is configured to establish a mapping relationship between different data sources based on the heterogeneous data of each sub-region, convert data in different formats into a unified data structure, and obtain a heterogeneous data set of each sub-region; A state index calculation module is configured to obtain the state index of each region by real-time calculation based on the heterogeneous data set of each sub-region; A regional correlation analysis module is configured to analyze the power communication network state between regions according to the power communication network state indicators of each sub-region, and obtain a correlation analysis result. A whole state evaluation module is configured to perform an aggregation operation according to the power communication network state indicators of each sub-region and the correlation analysis result, and evaluate the whole state of the power communication network, and obtain a power communication network state evaluation result.

[0016] In a third aspect, the present application provides an electronic device, comprising: A memory is configured to store a program. A processor is configured to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, realize the steps of the method for detecting the power communication network state in multiple dimensions.

[0017] In a fourth aspect, the present application provides a computer readable storage medium, comprising: the program, when executed by the processor, realizes the steps of the method for detecting the power communication network state in multiple dimensions.

[0018] The present application has the following beneficial effects: the present application realizes reasonable partitioning of the network, improves the refinement and structure of monitoring and management, through the technical means of identifying key nodes, clusters and sub-networks based on the power communication network topology structure, and determining the division principle according to the analysis result to perform regional division; the present application realizes quality improvement and preliminary integration of multi-source heterogeneous data, and provides a reliable data basis for subsequent fusion analysis, through the technical means of collecting device state data, network flow data and environmental parameter data of each sub-region, and performing preprocessing such as cleaning and normalization; the present application realizes deep fusion of device, network and environmental multi-dimensional data, and enhances the comparability and correlation of data, through the technical means of marking heterogeneous data, extracting key features, establishing mapping relationship between features and calculating correlation, and then constructing a data mapping table to realize conversion and integration of different format data into a unified structure; the present application realizes comprehensive quantitative evaluation of the network operation state, and improves the completeness of the monitoring dimension, through the technical means of real-time calculation of the device online rate, network delay, data packet loss rate, bandwidth utilization rate and network stability index of each region as state indicators; the present application realizes accurate identification of the state correlation mode and significant difference between regions, and enhances the ability to understand abnormal propagation paths and potential fault sources, through the technical means of comparing the regional indicators with the threshold value, and combining the time series linear correlation coefficient calculation and statistical test of the device online rate for inter-regional state analysis; the present application realizes scientific and dynamic evaluation of the whole operation state of the power communication network, and improves the accuracy and timeliness of operation and maintenance decisions, through the technical means of weighting aggregation of the state indicators based on the regional importance and the number of devices, and combining threshold value comparison to output the whole evaluation result. BRIEF DESCRIPTION OF DRAWINGS

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a basic flowchart illustrating a method for multidimensional detection of the status of a power communication network, provided as an embodiment of the present invention. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0021] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for multidimensional detection of the status of a power communication network is provided, comprising: S100: Obtain the topology of the power communication network and divide the power communication network into several regions; S200: Based on the multidimensional monitoring data of each sub-region, and after preprocessing, heterogeneous data for each sub-region is obtained; S200: Based on the heterogeneous data of each sub-region, establish the mapping relationship between different data sources, and convert data of different formats into a unified data structure to obtain the heterogeneous dataset of each sub-region; S400: Based on the heterogeneous dataset of each sub-region, the power communication network status indicators of each region are obtained through real-time calculation. S500: Based on the power communication network status indicators of each sub-region, analyze the power communication network status between regions to obtain correlation analysis results; S600: Based on the power communication network status indicators and correlation analysis results for each sub-region, perform aggregation operations and evaluate the overall status of the power communication network to obtain the power communication network status evaluation results.

[0022] It should be noted that existing methods for detecting the status of power communication networks face a series of challenges during operation. These include: relying solely on single-dimensional data (such as equipment alarms or link performance) for independent analysis, making it difficult to integrate heterogeneous data from multiple sources such as equipment status, network traffic, and environmental parameters, resulting in prominent information silos; lacking in-depth utilization of network topology, with arbitrary regional divisions that fail to reflect the hierarchical relationship between key nodes and subnets; status assessments often remain at the level of threshold exceeding alarms, lacking dynamic analysis of the correlation and differences in status changes between regions, making it difficult to discover hidden faults or abnormal propagation paths across regions; and the assessment of the overall network status often adopts simple averaging and aggregation methods, failing to consider the differences in importance between different regions, leading to one-sided assessment results that cannot accurately reflect the true operating status of the network, thus affecting the timeliness and accuracy of fault tracing and maintenance decisions.

[0023] Therefore, in response to the problem that existing technologies involve a large amount of heterogeneous data in power communication networks, making it difficult to detect network faults and anomalies in a timely and accurate manner, the steps S100-S600 are used to achieve comprehensive perception, accurate assessment, and anomaly tracing of network operation status. This is achieved by integrating multi-source heterogeneous data from the power communication network, dividing regions based on topology, constructing a unified data model, and combining real-time status indicator calculation with inter-regional correlation difference analysis. This improves the intelligence level of status monitoring and the accuracy of operation and maintenance decisions.

[0024] Example 2, this is an embodiment of the present invention, which provides a method for multi-dimensional detection of the status of a power communication network based on the previous embodiment, including: In this embodiment of the application, step S100 divides the power communication network into several regions, including: Based on the topology information of the power communication network, key nodes, clusters and subnetworks in the power communication network are identified, and the results of network topology analysis are obtained. Based on the results of network topology analysis, the principles for dividing power communication network areas are determined; Based on the principle of dividing areas, the power communication network is divided into several areas.

[0025] In this embodiment of the application, the determination of the region division principle in step S100 is made by analyzing the topology information of the power communication network, identifying key nodes, clusters and sub-networks, and determining the principle based on the network hierarchy and connection relationship.

[0026] In an optional implementation, the determination of the regional division principle in step S100 can also be achieved by obtaining the geographical location information of each device or node in the power communication network, and clustering them according to their physical proximity or administrative division, thereby dividing the network into several geographical regions.

[0027] In an optional implementation, the determination of the area division principle in step S100 can also be achieved by identifying the service types carried by each device or link in the power communication network and classifying them according to different functional categories such as control, monitoring, and management, thereby dividing the network into several functional areas.

[0028] In this embodiment of the application, step S200 involves preprocessing the multidimensional monitoring data of each sub-region to obtain heterogeneous data for each sub-region, including: Multidimensional monitoring data includes equipment status data, network traffic data, and environmental parameter data; Equipment status data includes the equipment's operating status, fault information, and performance indicators; Network traffic data includes link bandwidth utilization, packet loss rate, and latency; Environmental parameters include temperature, humidity, air quality, and electromagnetic interference; The acquired device status data, network traffic data, and environmental parameter data are cleaned and normalized to obtain preprocessed data for each sub-region. The preprocessed data is heterogeneously processed to obtain heterogeneous data for each sub-region.

[0029] In this embodiment of the application, the feature extraction of multi-source heterogeneous data in step S200 includes extracting key features, such as fault codes, delay percentiles, temperature and humidity change rates, based on preset rules or domain knowledge after labeling device status data, network traffic data and environmental parameter data, so as to obtain representative features of different types of data.

[0030] In an optional implementation, the feature extraction of multi-source heterogeneous data in step S200 can also be achieved by inputting the preprocessed device status, network traffic and environmental parameter data into a trained unsupervised learning model, and using the model to automatically learn and output low-dimensional feature vectors that can characterize the distribution and pattern of the original data, so as to realize the automatic extraction of features.

[0031] In an optional implementation, the feature extraction of multi-source heterogeneous data in step S200 can also be achieved by calculating statistics (such as mean, variance, trend slope) or frequency domain features within a sliding window based on historical data of device status, network traffic, and environmental parameters, in order to capture the dynamic patterns of various types of data changing over time and extract the corresponding time-series features.

[0032] In this embodiment of the application, step S300 establishes a mapping relationship between different data sources and converts data of different formats into a unified data structure to obtain a heterogeneous dataset for each sub-region, including: Label device status data, network traffic data, and environmental parameter data to obtain labeled heterogeneous data; Key features are extracted from labeled heterogeneous data to obtain features of different types of data; Based on the characteristics of different types of data, establish the mapping relationship between the characteristics of different types of data, and calculate the correlation between different types of data; Establish a data mapping table based on the correlation between different types of data; Based on the data mapping table, different types of heterogeneous data are transformed into a unified data structure and integrated to obtain heterogeneous datasets for each sub-region.

[0033] In this embodiment of the application, the power communication network status indicators in step S400 include equipment online rate, network latency, packet loss rate, bandwidth utilization, and network stability index; Real-time calculations yield the status indicators of the power communication network in each region, including: Based on the heterogeneous datasets of each sub-region, the ratio of the number of online devices to the total number of devices is calculated to obtain the device online rate. Based on the heterogeneous dataset of each sub-region, obtain the network latency data between each device and node, calculate the percentile of all latency data, and obtain the network latency. Based on the heterogeneous dataset of each sub-region, record the number of data packets sent and received, calculate the ratio of the number of lost data packets to the number of sent data packets, and obtain the data packet loss rate; Based on the heterogeneous datasets of each sub-region, the network bandwidth usage is monitored in real time, and the ratio of the currently used bandwidth to the total bandwidth is calculated to obtain the bandwidth utilization rate. Based on network latency and packet loss rate, the standard deviation of the fluctuation of network latency and packet loss rate is calculated to obtain the network stability index.

[0034] In this embodiment of the application, step S500 analyzes the status of the power communication network between different regions to obtain correlation analysis results, including: The indicator values ​​of each region are compared with the status indicator thresholds to obtain the comparison results; Based on the comparison results, calculate the linear correlation coefficient of equipment online rate between different regions at multiple time points; Statistical tests were performed based on the average and variance of the equipment online rate in each region and the total number of equipment. The correlation analysis results are obtained based on the linear correlation coefficient and the results of the statistical test.

[0035] In this embodiment of the application, the statistical analysis of inter-regional correlation in step S500 includes quantifying the synchronicity of changes by calculating the linear correlation coefficient of the online rate of each region's equipment at multiple time points, and combining it with a t-test based on the mean, variance, and total number of equipment to determine the significance of the differences in status between regions, thereby comprehensively obtaining the results of the inter-regional correlation analysis.

[0036] In an optional implementation, the statistics of the inter-regional correlation analysis in step S500 can also be evaluated by calculating the mutual information value between the time series of online rates of equipment in each region, so as to quantify the degree of information sharing between the two under nonlinear relationship, thereby assessing the correlation strength of inter-regional state changes.

[0037] In an optional implementation, the statistics of the inter-regional correlation analysis in step S500 can also be used to determine whether there is a causal relationship between regions and its direction of influence by examining whether the historical changes in the online rate of equipment in one region have statistical predictive power for the current state of another region.

[0038] In this embodiment of the application, based on the comparison results, the linear correlation coefficient of the device online rate between each region at multiple time points is calculated. Specifically, the following formula is used to calculate the region. and Correlation coefficient of equipment online rate between : in, For the region and The correlation coefficient of equipment online rate , They are respectively regions and In the Device online rate at a given time point , They are respectively regions and The average online rate of the equipment. The total number of points in time; In this embodiment, a statistical test is performed based on the average and variance of the online rate of devices in each region and the total number of devices. Specifically, the following formula is used to test the region. and Differences in state indicators between them: in, To account for the differences in status indicators between different regions, , They are respectively for the region and The average online rate of the equipment. , They are respectively regions and Equipment online rate variance They are respectively regions and The total number of devices.

[0039] In this embodiment of the application, step S600 involves performing an aggregation operation and assessing the overall state of the power communication network to obtain a power communication network state assessment result, including: Based on the status indicators and correlation analysis results of each region, the status indicators of each region are weighted and aggregated to obtain the overall status indicators. The weight of each region is determined according to the importance of the region in the network and the number of devices. Compare the overall status indicators with the evaluation result thresholds; Based on the comparison results, output the power communication network status assessment results; The assessment results include normal status, warning status, and abnormal status.

[0040] In this embodiment of the application, the aggregation strategy for overall status assessment in step S600 includes determining the weights of each region based on its importance in the network and the number of devices, weighting and aggregating the status indicators of each region to obtain an overall status indicator, and comparing it with the assessment result threshold to output an overall status assessment result that is normal, warning, or abnormal.

[0041] In an optional implementation, the aggregation strategy for overall state assessment in step S600 can also use the state indicators and correlation analysis results of each region as input variables, perform inference operations using a preset fuzzy rule base and membership function, and finally output the fuzzy judgment result of the overall state and defuzzify it into a specific assessment level.

[0042] In an optional implementation, the aggregation strategy for overall state assessment in step S600 can also use the state indicators and correlation analysis results of each region as feature inputs, and use a pre-trained machine learning classification model to perform reasoning to directly output the category judgment result of the overall state of the power communication network.

[0043] In this embodiment, the threshold values ​​for status indicators are set separately for different types of indicators. For example, the threshold for device online rate can be set to 90%, the threshold for network latency can be set to 100ms, the threshold for packet loss rate can be set to 1%, and the threshold for bandwidth utilization can be set to 85%. The values ​​of the thresholds are based on the statistical results of historical operating data of the power communication network, industry technical standards, equipment manufacturer recommendations, or on-site operation and maintenance experience, and can be adjusted periodically or dynamically according to network expansion, equipment upgrades, or seasonal load changes.

[0044] In this embodiment, the evaluation result threshold defines the boundaries between three status levels: "normal," "warning," and "abnormal." For example, when the overall status indicator (such as the weighted online rate) is greater than or equal to 90%, it is determined to be in a "normal state"; between 70% and 89%, it is determined to be in a "warning state"; and below 70%, it is determined to be in an "abnormal state." This threshold range is set based on the overall reliability target of the power communication network, the critical service assurance requirements, and historical fault statistics, and can be manually adjusted through configuration files or management interfaces to adapt to the evaluation needs of different operational stages.

[0045] In this embodiment of the application, during the weighted aggregation process, the weight corresponding to each region not only considers the region's hierarchical position in the network topology (such as the core layer, aggregation layer, and access layer), but also combines the total number of devices, service carrying capacity, and fault impact range within the region for comprehensive evaluation. The weight values ​​are normalized to ensure that the sum is 1, so as to ensure the rationality of the overall status indicators.

[0046] In this embodiment of the application, the data mapping table is established based on offline training or expert knowledge base. By analyzing the historical correlation patterns between device status, network traffic and environmental parameters, key features are extracted and their mapping relationships are determined. The mapping table can be updated periodically to adapt to changes in network structure or operating environment.

[0047] In this embodiment of the application, the system continuously collects multi-dimensional monitoring data of each area at preset time intervals (such as every minute), executes the processing flow from S200 to S600, realizes real-time or near-real-time status assessment, and ensures that the assessment results can reflect the latest operating status of the network.

[0048] In this embodiment, the network stability index is used as a comprehensive indicator. Its standard deviation is calculated based on the network latency and packet loss rate sequence at multiple consecutive time points. It can effectively capture short-term oscillations and long-term trend changes in network performance and improve the sensitivity to potential unstable states.

[0049] Example 3 is an embodiment of the present invention. This embodiment differs from the first embodiment in that it provides a system for multidimensional detection of the status of a power communication network.

[0050] It should be noted that the technical solution of the system for multidimensional detection of the state of a power communication network is based on the same concept as the technical solution of the method for multidimensional detection of the state of a power communication network described above. For details not described in detail in the technical solution of the system for multidimensional detection of the state of a power communication network in this embodiment, please refer to the description of the technical solution of the method for multidimensional detection of the state of a power communication network described above.

[0051] This embodiment of a system for multidimensional detection of the status of a power communication network includes: The region division module is used to obtain the topology of the power communication network and divide the power communication network into several regions. The data preprocessing module is used to preprocess the multidimensional monitoring data of each sub-region to obtain heterogeneous data for each sub-region. The heterogeneous data fusion module is used to establish mapping relationships between different data sources based on the heterogeneous data of each sub-region, and to convert data of different formats into a unified data structure to obtain heterogeneous datasets for each sub-region; The status index calculation module is used to calculate the status index of the power communication network in each region in real time based on the heterogeneous dataset of each sub-region. The regional correlation analysis module is used to analyze the status of the power communication network between different regions based on the power communication network status indicators of each sub-region, and obtain the correlation analysis results. The overall status assessment module is used to perform aggregation operations based on the status indicators and correlation analysis results of the power communication network in each sub-region, and to assess the overall status of the power communication network to obtain the power communication network status assessment results.

[0052] This embodiment also provides an electronic device applicable to a method for multidimensional detection of the status of a power communication network, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a method for multidimensional detection of the status of a power communication network, as proposed in the above embodiments.

[0053] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for multidimensional detection of the status of a power communication network as proposed in the above embodiments.

[0054] The storage medium proposed in this embodiment and the method for implementing a multi-dimensional detection of the status of a power communication network proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0055] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0056] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for multidimensional detection of the status of a power communication network, characterized in that, include: Obtain the topology of the power communication network and divide the power communication network into several regions; Based on the multidimensional monitoring data of each sub-region, and after preprocessing, heterogeneous data for each sub-region is obtained; Based on the heterogeneous data of each sub-region, a mapping relationship between different data sources is established, and data of different formats are converted into a unified data structure to obtain the heterogeneous dataset of each sub-region. Based on the heterogeneous datasets of each sub-region, the status indicators of the power communication network in each region are obtained through real-time calculation. Based on the power communication network status indicators of each sub-region, the power communication network status between each region is analyzed to obtain the correlation analysis results; Based on the power communication network status indicators and correlation analysis results for each sub-region, an aggregation operation is performed to assess the overall status of the power communication network and obtain the power communication network status assessment results.

2. The method for multidimensional detection of power communication network status as described in claim 1, characterized in that: The division of the power communication network into several regions includes: Based on the topology information of the power communication network, key nodes, clusters and subnetworks in the power communication network are identified, and the results of network topology analysis are obtained. Based on the results of the network topology analysis, the principles for dividing the power communication network areas are determined; Based on the aforementioned principle of zoning, the power communication network is divided into several regions.

3. The method for multidimensional detection of power communication network status as described in claim 1 or 2, characterized in that: The process involves preprocessing the multidimensional monitoring data from each sub-region to obtain heterogeneous data for each sub-region, including: The multidimensional monitoring data includes equipment status data, network traffic data, and environmental parameter data; The equipment status data includes the equipment's operating status, fault information, and performance indicators; The network traffic data includes link bandwidth utilization, packet loss rate, and latency; The environmental parameter data includes temperature, humidity, air quality, and electromagnetic interference; The acquired device status data, network traffic data, and environmental parameter data are cleaned and normalized to obtain preprocessed data for each sub-region. The preprocessed data is heterogeneously processed to obtain heterogeneous data for each sub-region.

4. The method for multidimensional detection of power communication network status as described in claim 3, characterized in that: The process of establishing mapping relationships between different data sources and converting data of different formats into a unified data structure to obtain heterogeneous datasets for each sub-region includes: Label device status data, network traffic data, and environmental parameter data to obtain labeled heterogeneous data; Key features are extracted from the labeled heterogeneous data to obtain features of different types of data; Based on the characteristics of the different types of data, establish the mapping relationship between the characteristics of the different types of data, and calculate the correlation between the different types of data; Based on the correlation between the different types of data, a data mapping table is established; Based on the data mapping table, different types of heterogeneous data are converted into a unified data structure and integrated to obtain heterogeneous datasets for each sub-region.

5. The method for multidimensional detection of power communication network status as described in claim 4, characterized in that: The power communication network status indicators include equipment online rate, network latency, data packet loss rate, bandwidth utilization, and network stability index. The real-time calculation yields the power communication network status indicators for each region, including: Based on the heterogeneous datasets of each sub-region, the ratio of the number of online devices to the total number of devices is calculated to obtain the device online rate. Based on the heterogeneous dataset of each sub-region, obtain the network latency data between each device and node, calculate the percentile of all latency data, and obtain the network latency. Based on the heterogeneous dataset of each sub-region, record the number of data packets sent and received, calculate the ratio of the number of lost data packets to the number of sent data packets, and obtain the data packet loss rate; Based on the heterogeneous datasets of each sub-region, the network bandwidth usage is monitored in real time, and the ratio of the currently used bandwidth to the total bandwidth is calculated to obtain the bandwidth utilization rate. Based on network latency and packet loss rate, the standard deviation of the fluctuation of network latency and packet loss rate is calculated to obtain the network stability index.

6. The method for multidimensional detection of power communication network status as described in claim 5, characterized in that: The analysis of the power communication network status between different regions yields correlation analysis results, including: The indicator values ​​of each region are compared with the status indicator thresholds to obtain the comparison results; Based on the comparison results, calculate the linear correlation coefficient of the equipment online rate between different regions at multiple time points; Statistical tests were performed based on the average and variance of the equipment online rate in each region and the total number of equipment. Based on the linear correlation coefficient and the results of the statistical test, the correlation analysis results are obtained.

7. The method for multidimensional detection of power communication network status as described in claim 6, characterized in that: The process of performing aggregation operations and assessing the overall state of the power communication network to obtain a power communication network state assessment result includes: Based on the status indicators and correlation analysis results of each region, the status indicators of each region are weighted and aggregated to obtain the overall status indicators. The weight of each region is determined according to the importance of the region in the network and the number of devices. The overall status index is compared with the evaluation result threshold; Based on the comparison results, output the power communication network status assessment results; The assessment results include normal status, warning status, and abnormal status.

8. A system for multidimensional detection of the status of a power communication network, using the method described in any one of claims 1-7, characterized in that, include: The region division module is used to obtain the topology of the power communication network and divide the power communication network into several regions. The data preprocessing module is used to preprocess the multidimensional monitoring data of each sub-region to obtain heterogeneous data for each sub-region. The heterogeneous data fusion module is used to establish mapping relationships between different data sources based on the heterogeneous data of each sub-region, and to convert data of different formats into a unified data structure to obtain heterogeneous datasets for each sub-region; The status index calculation module is used to calculate the status index of the power communication network in each region in real time based on the heterogeneous dataset of each sub-region. The regional correlation analysis module is used to analyze the status of the power communication network between different regions based on the power communication network status indicators of each sub-region, and obtain the correlation analysis results. The overall status assessment module is used to perform aggregation operations based on the status indicators and correlation analysis results of the power communication network in each sub-region, and to assess the overall status of the power communication network to obtain the power communication network status assessment results.

9. An electronic device, characterized in that, include: Memory, used to store programs; A processor for loading the program to perform the steps of the method as claimed in any one of claims 1-7.

10. A computer-readable storage medium storing a program, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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