Data analysis system and method based on Hongyu-operating system

Abstract

The embodiment of the invention discloses a data analysis system and method based on an electric red operating system. The system comprises: a data processing module, which is used for collecting data of an electric red operating system; the intelligent testing and optimizing module is used for testing and optimizing the input electric red operating system data; the distributed tracking and performance monitoring module is used for tracking the data of the electric red operating system and monitoring the performance of the data processing module; the real-time data stream processing and feedback module is used for receiving the electric red operating system data from the data processing module and performing real-time analysis; and the visual interactive interface module is used for displaying the electric red-operating system data on an interactive interface in the forms of charts and graphs. According to the scheme, the data analysis system can have high expandability, and specific data processing functions can be conveniently added, modified and deleted according to different requirements of the electric red operating system.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a data analysis system and method based on a power system operation system. Background Art

[0002] As the power system continues to develop, its scale is becoming increasingly large and its structure is increasingly complex, involving many heterogeneous devices and diverse data sources. As the core support of the power system, the Dianhong operating system needs to have strong data analysis capabilities to cope with frequent version iterations, strict reliability requirements, and diverse hardware platform challenges.

[0003] In the operation of the power system, real-time performance is of vital importance. When faced with the special needs of the Dianhong operating system, traditional data analysis tools often have problems such as poor scalability, insufficient real-time data processing capabilities, and inability to deeply integrate with the operating system to achieve efficient real-time data analysis. This makes it difficult to use the Dianhong operating system and the power system to quickly respond to data changes and make corresponding adjustments. Summary of the invention

[0004] The embodiments of the present application provide a data analysis system and method based on the Dianhong operating system to improve the scalability of the Dianhong operating system and improve the real-time data processing capability.

[0005] According to one aspect of the present application, a data analysis system based on the Dianhong operating system is provided, the system comprising:

[0006] Data processing module, intelligent testing and optimization module, distributed tracking and performance monitoring module, real-time data stream processing and feedback module, and visual interactive interface module;

[0007] The data processing module is used to collect the Dianhong operating system data, clean, analyze and store the Dianhong operating system data, and transmit the Dianhong operating system data to the intelligent testing and optimization module, the distributed tracking and performance monitoring module, the real-time data stream processing and feedback module and the visual interactive interface module respectively;

[0008] The intelligent testing and optimization module is used to test and optimize the input Dianhong operating system data;

[0009] The distributed tracking and performance monitoring module is used to track the Dianhong operating system data and monitor the performance of the data processing module;

[0010] The real-time data stream processing and feedback module is used to receive the Dianhong operating system data from the data processing module, and use the distributed stream-batch integrated processing framework to perform real-time analysis on the received Dianhong operating system data. If an abnormality is found, the relevant information is fed back to the Dianhong operating system;

[0011] The visual interactive interface module is used to display the data of the electric Hong operating system on the interactive interface in the form of charts and graphs.

[0012] According to one aspect of the present application, a data analysis method based on a Dianhong operating system is provided, the method comprising:

[0013] The data processing module collects the Dianhong operating system data, cleans, analyzes and stores the Dianhong operating system data, and transmits the Dianhong operating system data to the intelligent testing and optimization module, the distributed tracking and performance monitoring module, the real-time data stream processing and feedback module and the visual interactive interface module respectively;

[0014] The intelligent testing and optimization module tests and optimizes the input data of the Dianhong operating system;

[0015] The distributed tracking and performance monitoring module tracks the Dianhong operating system data and performs performance monitoring on the data processing module;

[0016] The real-time data stream processing and feedback module receives the Dianhong operating system data from the data processing module, and uses the distributed stream-batch integrated processing framework to perform real-time analysis on the received Dianhong operating system data. If an abnormality is found, the relevant information is fed back to the Dianhong operating system;

[0017] The visual interactive interface module displays the Dianhong operating system data on the interactive interface in the form of charts and graphs.

[0018] The technical solution of the embodiment of the present application provides a data analysis system for an electric-powered operating system, the system comprising: a data processing module, an intelligent testing and optimization module, a distributed tracing and performance monitoring module, a real-time data stream processing and feedback module, and a visual interactive interface module; the data processing module is used to collect electric-powered operating system data, and clean, analyze and store the electric-powered operating system data, and transmit the electric-powered operating system data to the intelligent testing and optimization module, the distributed tracing and performance monitoring module, the real-time data stream processing and feedback module, and the visual interactive interface module respectively; the intelligent testing and optimization module is used to test and optimize the input electric-powered operating system data; the distributed tracing and performance monitoring module is used to track the electric-powered operating system data and perform performance monitoring on the data processing module; the real-time data stream processing and feedback module is used to receive the electric-powered operating system data from the data processing module, and use the distributed stream-batch integrated processing framework to perform real-time analysis on the received electric-powered operating system data, and if any abnormality is found, the relevant information is fed back to the electric-powered operating system; the visual interactive interface module is used to display the electric-powered operating system data on the interactive interface in the form of charts and graphs. The above scheme can make the data analysis system highly scalable, and can easily add, modify and delete specific data processing functions according to the different needs of the Dianhong operating system.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A structural diagram of a data analysis system based on the Dianhong operating system provided in an embodiment of the present application;

[0022] Figure 2 A schematic diagram of the structure of a data processing module provided in an embodiment of the present application;

[0023] Figure 3 A schematic diagram of the structure of the intelligent testing and optimization module provided in the embodiment of the present application;

[0024] Figure 4A schematic diagram of the structure of a distributed tracking and performance monitoring module provided in an embodiment of the present application;

[0025] Figure 5 A schematic diagram of the structure of the real-time data stream processing and feedback module provided in the embodiment of the present application

[0026] Figure 6 A schematic diagram of the structure of the visual interactive interface module provided in the embodiment of the present application

[0027] Figure 7 A flowchart of a data analysis method based on the Dianhong operating system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", "third", "fourth", "actual", "preset", etc. in the specification and claims of the present application 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 interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations 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.

[0030] Figure 1 This is a structural diagram of a data analysis system based on a Dianhong operating system provided in an embodiment of the present application. The embodiment of the present application can be applied to the case of performing data analysis based on a Dianhong operating system. Figure 1 As shown, the system includes:

[0031] Data processing module 110, intelligent testing and optimization module 120, distributed tracking and performance monitoring module 130, real-time data stream processing and feedback module 140 and visual interactive interface module 150;

[0032] The data processing module 110 is used to collect the Dianhong operating system data, clean, analyze and store the Dianhong operating system data, and transmit the Dianhong operating system data to the intelligent testing and optimization module 120, the distributed tracking and performance monitoring module 130, the real-time data stream processing and feedback module 140 and the visual interactive interface module 150 respectively;

[0033] The intelligent testing and optimization module 120 is used to test and optimize the input Dianhong operating system data;

[0034] The distributed tracking and performance monitoring module 130 is used to track the Dianhong operating system data and monitor the performance of the data processing module 110;

[0035] The real-time data stream processing and feedback module 140 is used to receive the Dianhong operating system data from the data processing module 110, and use the distributed stream-batch integrated processing framework to perform real-time analysis on the received Dianhong operating system data. If an abnormality is found, the relevant information is fed back to the Dianhong operating system;

[0036] The visual interactive interface module 150 is used to display the telecom operating system data on the interactive interface in the form of charts and graphs.

[0037] The technical solution of the embodiment of the present application can make the Dianhong operating system highly scalable, and can easily add, modify and delete specific data processing functions according to the different needs of the Dianhong operating system. When a new data type and data source are added to the power system, it is only necessary to develop the corresponding microservices and connect them to the engine without affecting the stability and other functions of the entire system, facilitating the allocation and management of resources, improving the system's concurrent processing capabilities and resource utilization, and effectively responding to large-scale data processing needs.

[0038] As a non-limiting implementation, the data processing module includes:

[0039] A data collection microservice submodule, wherein the data collection microservice submodule is connected to various data sources of the Dianhong operating system;

[0040] The data cleaning microservice submodule is used to clean the Dianhong operating system data collected by the data collection microservice submodule; remove duplicate data, correct erroneous formats, and filter out obviously abnormal data values ​​from the input Dianhong operating system data;

[0041] A data analysis microservice submodule is used to receive and analyze the cleaned data of the electric operating system by the data cleaning microservice submodule, and to process the cleaned data of the electric operating system by using a time series analysis model for power load prediction and a machine learning classification model for equipment fault diagnosis to obtain analysis results; wherein the analysis results include power load trends and potential equipment failure risk information;

[0042] The data storage microservice submodule is used to store the analysis results.

[0043] For example, Figure 2 As shown, the data processing module also includes a data acquisition microservice submodule, a data cleaning microservice submodule, a data analysis microservice submodule and a data storage microservice submodule. The data acquisition microservice submodule is connected to various data sources of the electric Hong operating system, and the data sources include power equipment sensors, databases, log file storage devices, etc. The types of collected electric Hong operating system data are diverse, covering the operating parameters of the power system, equipment status information, and user operation records, etc. The operating parameters are such as voltage, current, and power, and the equipment status information includes, for example, equipment operating status code, fault alarm information, etc. The above electric Hong operating system data collected by the data acquisition microservice submodule will be passed to the data cleaning microservice submodule for cleaning operations, and the input data will be processed to remove duplicate data, correct the wrong format (such as date format, numerical format), and filter obviously abnormal data values ​​(such as power parameter values ​​beyond a reasonable range) to improve data quality. The qualified electric Hong operating system data cleaned by the data cleaning microservice submodule will be passed to the data analysis microservice submodule for analysis, and the data will be processed using the time series analysis model for power load prediction and the machine learning classification model for equipment fault diagnosis to extract power load trends and equipment potential failure risk information. The analysis results of the data analysis microservice submodule will be passed to the data storage microservice submodule for storage. The data storage microservice submodule can be connected to external storage media, such as distributed file systems and specific power data warehouses, to ensure the secure storage and accessibility of data.

[0044] As a non-limiting implementation method, the intelligent testing and optimization module communicates data with the data cleaning microservice submodule and the data analysis microservice submodule respectively;

[0045] The intelligent testing and optimization module is also used to test the cleaned Dianhong operating system data using machine learning technology to check whether the data meets the expected business rules and data quality standards. If problems are found in the data during the test, feedback is given to the data cleaning microservice submodule for further cleaning;

[0046] During the data analysis process, the intelligent testing and optimization module is also used to test and verify the analysis results to check whether they meet the relevant analysis expectations. If the analysis results do not meet expectations, the problem is fed back to the data analysis microservice sub-module for adjustment.

[0047] For example, Figure 3 As shown, the intelligent testing and optimization module communicates data with the data cleaning microservice sub-module and the data analysis microservice sub-module respectively. The intelligent testing and optimization module uses machine learning technology (such as test cases generated by genetic algorithms) to test the cleaned Dianhong operating system data to check whether the Dianhong operating system data meets the expected business rules and data quality standards, such as whether there is duplicate Dianhong operating system data, whether the format conforms to the normal format, whether there are obviously abnormal data values, etc. If problems are found in the Dianhong operating system data during the test process, the problems will be fed back to the data cleaning microservice sub-module, and the data will be cleaned again to improve the data quality of the Dianhong operating system data.

[0048] During the data analysis process, the intelligent testing and optimization module will test and verify the analysis results to check whether they meet the relevant analysis expectations. If the analysis results do not meet expectations, the problem will be fed back to the data analysis microservice for adjustment.

[0049] Through intelligent test case generation, potential problems in data processing algorithms can be discovered in advance, improving the accuracy and reliability of data processing. During the data cleaning process, test cases for various abnormal data are automatically generated to verify the effectiveness of the cleaning algorithm and avoid system failures caused by incorrect data processing.

[0050] As a non-limiting implementation method, the distributed tracing and performance monitoring module includes:

[0051] A distributed tracing submodule, wherein the distributed tracing submodule communicates data with the data collection microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule respectively, and is used to record the data flow process between each data collection microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule;

[0052] A performance monitoring submodule, wherein the performance monitoring submodule communicates data with the data acquisition microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule respectively, and is used to continuously monitor the performance indicators of the data acquisition microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule; wherein the performance indicators include data processing throughput, CPU and memory utilization, and network bandwidth occupancy.

[0053] like Figure 4 As shown, the distributed tracing submodule transmits data bidirectionally with the data collection microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule. The distributed tracing submodule records the flow of data between various microservices, including which data source is collected, which microservices are processed, the processing time, and detailed information on the status of the processing results. The performance monitoring submodule transmits data bidirectionally with the data collection microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule. The performance monitoring submodule continuously monitors the performance indicators of each microservice, including data processing throughput, CPU and memory utilization, and network bandwidth occupancy.

[0054] In a distributed system environment, traditional data analysis engines find it difficult to quickly locate performance bottlenecks and failure points in the data processing process. The distributed tracking and performance monitoring module of the present invention can accurately locate the specific microservices and links where data processing delays or errors occur through detailed call chain tracking, greatly shortening the troubleshooting time.

[0055] As a non-limiting implementation method, the real-time data stream processing and feedback module is also used to receive the analysis result data from the data analysis microservice submodule and the electronic operating system data from the data acquisition microservice submodule;

[0056] The real-time data stream processing and feedback module is also used to transmit the analysis results and the Dianhong operating system data to the visual interactive interface module so that the operation and maintenance personnel can understand the real-time operating status of the system.

[0057] For example, Figure 5 As shown, the real-time data stream processing and feedback module receives the analysis result data from the data analysis microservice submodule and the original data stream of the data acquisition microservice submodule; the real-time data stream processing and feedback module transmits the real-time data processing results and event information to the visual interactive interface module for operation and maintenance personnel to understand the real-time operation status of the system.

[0058] As a non-limiting implementation method, the visual interaction interface module is used to obtain historical analysis results through the data storage microservice submodule;

[0059] The analysis results and the Dianhong operating system data are obtained through the real-time data stream processing and feedback module, the performance data and call information are obtained through the distributed tracking and performance monitoring module, and the external operation and maintenance personnel and system administrators are interacted with, operation instructions are received, and the operation instructions are passed to the corresponding modules.

[0060] For example, Figure 6As shown, the visual interactive interface module obtains historical analysis results through the data storage microservice submodule, obtains real-time analysis results and the Dianhong operating system data through the real-time data stream processing and feedback module; and obtains performance data and call information through the distributed tracking and performance monitoring module. Through the visual interactive interface module, it is possible to interact with external operation and maintenance personnel and system administrators, receive operation instructions from external operation and maintenance personnel and system administrators, such as data screening, query, and parameter setting instructions for the data analysis engine, and pass these instructions to the corresponding modules (such as data acquisition microservice adjustment of acquisition parameters, intelligent testing and optimization module).

[0061] As a non-limiting implementation method, the real-time data stream processing and feedback module is specifically used to use the distributed stream-batch integrated processing framework to perform statistical analysis and abnormal threshold detection on the received telecom operating system data based on a time window.

[0062] As a non-limiting implementation method, the real-time data stream processing and feedback module is specifically used to calculate the statistical indicators of average load, maximum load and minimum load within a preset time window for power load data;

[0063] In each preset time window, the power load data is accumulated using a preset function, and the average value is calculated to determine the change trend of the power load.

[0064] As a non-limiting implementation method, the real-time data stream processing and feedback module is specifically used to set an abnormal threshold value according to the operating characteristics and historical data of the power system;

[0065] The power load data is compared with the abnormal threshold, and if the power load data exceeds the abnormal threshold, it is determined that an abnormal situation exists.

[0066] The statistical analysis based on the time window is specifically as follows:

[0067] Sliding window setting: define the sliding window. For example, if the window size is set to 5 minutes and the sliding step is set to 1 minute, statistical analysis will be performed on the data in the past 5 minutes every 1 minute. For power load data, the statistical indicators of average load, maximum load, and minimum load in the window can be calculated;

[0068] Data processing within the window: In each window, the reduce function is used to accumulate the load values ​​and calculate the average value, which can observe the changing trend of the power load in a short period of time in real time.

[0069] The abnormal threshold detection is specifically:

[0070] Setting thresholds: According to the operating characteristics and historical data of the power system, set abnormal thresholds. For example, for the power load of a region, set the maximum load threshold for normal operation to 800MW.

[0071] Real-time comparison: During data stream processing, each data point is compared with the set threshold. When the load value exceeds the threshold, it is determined that an abnormal situation may have occurred.

[0072] Figure 7 This is a flowchart of a data analysis method based on the Dianhong operating system provided in the embodiment of the present application. For details not described in detail in the embodiment of the present application, please refer to the above embodiment. Figure 7 As shown, a method for monitoring power transmission equipment provided by an embodiment of the present application includes:

[0073] S110, the data processing module collects the Dianhong operating system data, cleans, analyzes and stores the Dianhong operating system data, and transmits the Dianhong operating system data to the intelligent testing and optimization module, the distributed tracking and performance monitoring module, the real-time data stream processing and feedback module and the visual interactive interface module respectively.

[0074] S120, the intelligent testing and optimization module tests and optimizes the input Dianhong operating system data.

[0075] S130, the distributed tracking and performance monitoring module tracks the Dianhong operating system data and monitors the performance of the data processing module.

[0076] S140. The real-time data stream processing and feedback module receives the Dianhong operating system data from the data processing module, and uses the distributed stream-batch integrated processing framework to perform real-time analysis on the received Dianhong operating system data. If any abnormality is found, the relevant information is fed back to the Dianhong operating system.

[0077] S150. The visual interactive interface module displays the Dianhong operating system data on the interactive interface in the form of charts and graphs.

[0078] The technical solution of the embodiment of the present application splits the different functions of the data processing module of the data analysis engine into multiple independent microservices, including a data acquisition microservice submodule, a data cleaning microservice submodule, a data analysis microservice submodule, and a data storage microservice submodule. This microservice architecture makes the data analysis engine highly scalable and can easily add, modify, and delete specific data processing functions according to the different needs of the electric power operating system. When a new data type and data source are added to the power system, it is only necessary to develop the corresponding microservice and connect it to the engine without affecting the stability and other functions of the entire system, facilitating the allocation and management of resources, improving the system's concurrent processing capabilities and resource utilization, and effectively responding to large-scale data processing needs.

[0079] The data processing module collects the data of the Dianhong operating system, cleans, analyzes and stores the data of the Dianhong operating system, and transmits the data of the Dianhong operating system to the intelligent testing and optimization module, the distributed tracking and performance monitoring module, the real-time data stream processing and feedback module and the visual interactive interface module respectively, including: the data processing module includes a data collection microservice submodule, a data cleaning microservice submodule, a data analysis microservice submodule, and a data storage microservice submodule. The data collection microservice submodule is connected to various data sources of the Dianhong operating system.

[0080] The data cleaning microservice submodule performs cleaning operations on the Dianhong operating system data collected by the data collection microservice submodule; removes duplicate data, corrects incorrect formats, and filters obviously abnormal data values ​​from the input Dianhong operating system data;

[0081] The data analysis microservice submodule receives the cleaned electric operating system data by the data cleaning microservice submodule for analysis, and processes the cleaned electric operating system data using a time series analysis model for power load prediction and a machine learning classification model for equipment fault diagnosis to obtain analysis results; wherein the analysis results include power load trends and equipment potential failure risk information;

[0082] The data storage microservice submodule stores the analysis results.

[0083] The intelligent testing and optimization module communicates data with the data cleaning microservice submodule and the data analysis microservice submodule respectively;

[0084] The intelligent testing and optimization module uses machine learning technology to test the cleaned Dianhong operating system data to check whether the data meets the expected business rules and data quality standards. If problems are found in the data during the test, feedback is given to the data cleaning microservice submodule for further cleaning;

[0085] During the data analysis process, the intelligent testing and optimization module tests and verifies the analysis results to check whether they meet the relevant analysis expectations. If the analysis results do not meet expectations, the problem is fed back to the data analysis microservice sub-module for adjustment.

[0086] The distributed tracing and performance monitoring module includes a distributed tracing submodule and a performance monitoring submodule. The distributed tracing submodule communicates data with the data collection microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule respectively. The performance monitoring submodule communicates data with the data collection microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule respectively.

[0087] The distributed tracking submodule records the data flow process between each data collection microservice submodule, data cleaning microservice submodule, and data analysis microservice submodule. The performance monitoring submodule is used to continuously monitor the performance indicators of the data collection microservice submodule, data cleaning microservice submodule, and data analysis microservice submodule; wherein the performance indicators include data processing throughput, CPU and memory utilization, and network bandwidth occupancy.

[0088] The real-time data stream processing and feedback module receives the analysis result data from the data analysis microservice submodule and the Dianhong operating system data from the data acquisition microservice submodule;

[0089] The real-time data stream processing and feedback module transmits the analysis results and the Dianhong operating system data to the visual interactive interface module, so that the operation and maintenance personnel can understand the real-time operating status of the system.

[0090] The visual interactive interface module obtains historical analysis results through the data storage microservice sub-module; obtains the analysis results and the Dianhong operating system data through the real-time data stream processing and feedback module, obtains performance data and call information through the distributed tracking and performance monitoring module, interacts with external operation and maintenance personnel and system administrators, receives operation instructions, and passes the operation instructions to the corresponding modules.

[0091] The real-time data stream processing and feedback module utilizes the distributed stream-batch integrated processing framework to perform statistical analysis and abnormal threshold detection on the received telecom operating system data based on a time window.

[0092] The real-time data stream processing and feedback module calculates statistical indicators of average load, maximum load and minimum load within a preset time window for the power load data;

[0093] In each preset time window, the power load data is accumulated using a preset function, and the average value is calculated to determine the change trend of the power load.

[0094] The real-time data stream processing and feedback module sets an abnormal threshold value according to the operating characteristics and historical data of the power system;

[0095] The power load data is compared with the abnormal threshold, and if the power load data exceeds the abnormal threshold, it is determined that an abnormal situation exists.

[0096] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the information expected by the technical solution of this application can be achieved, and this document is not limited here.

[0097] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A data analysis system based on the Dianhong operating system, characterized in that: The system comprises: It includes data processing module, intelligent testing and optimization module, distributed tracking and performance monitoring module, real-time data stream processing and feedback module and visual interactive interface module; The data processing module is used to collect the Dianhong operating system data, clean, analyze and store the Dianhong operating system data, and transmit the Dianhong operating system data to the intelligent testing and optimization module, the distributed tracking and performance monitoring module, the real-time data stream processing and feedback module and the visual interactive interface module respectively; The intelligent testing and optimization module is used to test and optimize the input Dianhong operating system data; The distributed tracking and performance monitoring module is used to track the Dianhong operating system data and monitor the performance of the data processing module; The real-time data stream processing and feedback module is used to receive the Dianhong operating system data from the data processing module, and use the distributed stream-batch integrated processing framework to perform real-time analysis on the received Dianhong operating system data. If an abnormality is found, the relevant information is fed back to the Dianhong operating system; The visual interactive interface module is used to display the data of the electric Hong operating system on the interactive interface in the form of charts and graphs.

2. The method according to claim 1, characterized in that: The data processing module comprises: A data collection microservice submodule, wherein the data collection microservice submodule is connected to various data sources of the Dianhong operating system; The data cleaning microservice submodule is used to clean the Dianhong operating system data collected by the data collection microservice submodule; remove duplicate data, correct erroneous formats, and filter out obviously abnormal data values ​​from the input Dianhong operating system data; A data analysis microservice submodule is used to receive and analyze the cleaned data of the electric operating system by the data cleaning microservice submodule, and to process the cleaned data of the electric operating system by using a time series analysis model for power load prediction and a machine learning classification model for equipment fault diagnosis to obtain analysis results; wherein the analysis results include power load trends and potential equipment failure risk information; The data storage microservice submodule is used to store the analysis results.

3. The method according to claim 2, characterized in that The intelligent testing and optimization module communicates data with the data cleaning microservice submodule and the data analysis microservice submodule respectively; The intelligent testing and optimization module is also used to test the cleaned Dianhong operating system data using machine learning technology to check whether the data meets the expected business rules and data quality standards. If problems are found in the data during the test, feedback is given to the data cleaning microservice submodule for further cleaning; During the data analysis process, the intelligent testing and optimization module is also used to test and verify the analysis results to check whether they meet the relevant analysis expectations. If the analysis results do not meet expectations, the problem is fed back to the data analysis microservice sub-module for adjustment.

4. The method according to claim 2, characterized in that: The distributed tracing and performance monitoring module includes: A distributed tracing submodule, wherein the distributed tracing submodule communicates data with the data collection microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule respectively, and is used to record the data flow process between each data collection microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule; A performance monitoring submodule, wherein the performance monitoring submodule communicates data with the data acquisition microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule respectively, and is used to continuously monitor the performance indicators of the data acquisition microservice submodule, the data cleaning microservice submodule, and the data analysis microservice submodule; wherein the performance indicators include data processing throughput, CPU and memory utilization, and network bandwidth occupancy.

5. The method according to claim 2, characterized in that: The real-time data stream processing and feedback module is also used to receive the analysis result data from the data analysis microservice submodule and the Dianhong operating system data from the data acquisition microservice submodule; The real-time data stream processing and feedback module is also used to transmit the analysis results and the Dianhong operating system data to the visual interactive interface module so that the operation and maintenance personnel can understand the real-time operating status of the system.

6. The method according to claim 5, characterized in that The visual interaction interface module is used to obtain historical analysis results through the data storage microservice submodule; The analysis results and the Dianhong operating system data are obtained through the real-time data stream processing and feedback module, the performance data and call information are obtained through the distributed tracking and performance monitoring module, and the external operation and maintenance personnel and system administrators are interacted with, operation instructions are received, and the operation instructions are passed to the corresponding modules.

7. The method according to claim 1, characterized in that The real-time data stream processing and feedback module is specifically used to utilize the distributed stream-batch integrated processing framework to perform statistical analysis and abnormal threshold detection on the received telecom operating system data based on a time window.

8. The method according to claim 7, characterized in that The real-time data stream processing and feedback module is specifically used to calculate the statistical indicators of average load, maximum load and minimum load within a preset time window for the power load data; In each preset time window, the power load data is accumulated using a preset function, and the average value is calculated to determine the change trend of the power load.

9. The method according to claim 7, characterized in that: The real-time data stream processing and feedback module is specifically used to set an abnormal threshold value according to the operating characteristics and historical data of the power system; The power load data is compared with the abnormal threshold, and if the power load data exceeds the abnormal threshold, it is determined that an abnormal situation exists.

10. A data analysis method based on the Dianhong operating system, characterized in that: The method comprises: The data processing module collects the Dianhong operating system data, cleans, analyzes and stores the Dianhong operating system data, and transmits the Dianhong operating system data to the intelligent testing and optimization module, the distributed tracking and performance monitoring module, the real-time data stream processing and feedback module and the visual interactive interface module respectively; The intelligent testing and optimization module tests and optimizes the input data of the Dianhong operating system; The distributed tracking and performance monitoring module tracks the Dianhong operating system data and performs performance monitoring on the data processing module; The real-time data stream processing and feedback module receives the Dianhong operating system data from the data processing module, and uses the distributed stream-batch integrated processing framework to perform real-time analysis on the received Dianhong operating system data. If an abnormality is found, the relevant information is fed back to the Dianhong operating system; The visual interactive interface module displays the Dianhong operating system data on the interactive interface in the form of charts and graphs.

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