Intelligent power grid operation log collection and analysis management system based on big data

Abstract

The invention discloses an intelligent power grid operation log acquisition and analysis management system based on big data, which comprises a real-time data acquisition module, a data transmission synchronization module, a log storage management module, a data cleaning processing module, an intelligent analysis mining module, an alarm notification module, a user interface visualization module and a security authority management module, abnormal conditions and problems in a power system can be found and processed in time, the real-time monitoring and response capability of the power grid state is improved, big data analysis and mining technologies are combined, intelligent decision support is provided, operation and maintenance personnel are helped to make accurate and timely decisions, and by analyzing historical data and real-time data, the operation and maintenance efficiency is improved. According to the method, the possibility of equipment failure is predicted, preventive measures are taken, and the failure and downtime of the power system are reduced, so that the method not only achieves the function of failure prediction and prevention, but also has the advantages of real-time monitoring, response and intelligent decision support.

Description

Technical Field

[0001] The present invention relates to the technical field of management systems, and in particular to a smart grid operation log collection and analysis management system based on big data. Background Art

[0002] Big data refers to a collection of data that is huge in size, diverse in type, and fast in speed, often beyond the scope of traditional data processing software. This type of data has three main characteristics: volume, variety, and velocity. Big data usually requires the use of advanced technologies and tools to collect, store, process, and analyze in order to obtain valuable information and insights from it.

[0003] Smart grid is a power grid that uses advanced communication, computing and control technologies to monitor, control and optimize all aspects of the power system, thereby improving the reliability, safety, economy and environmental protection of the power system. Smart grid integrates information and communication technologies with the power system to achieve intelligent management of power production, transmission, distribution and use to cope with the increasingly complex and dynamic power system operating environment.

[0004] Big data technology plays a key role in smart grids. Smart grids need to process a large amount of real-time and historical data from various sensors, meters, equipment and systems to achieve real-time monitoring, analysis and decision-making of power systems. Big data technology can help smart grids discover patterns, trends and anomalies from massive data and improve the efficiency and reliability of grid operations.

[0005] In smart grids, big data technology can be used for real-time data collection, storage, cleaning, analysis and mining, as well as historical data management and intelligent decision support. For example, big data analysis can be used to monitor the status of the power system in real time, predict load demand, identify abnormal conditions, optimize energy distribution, etc.

[0006] Generally speaking, traditional big data smart grid operation log collection, analysis and management solutions are unable to process large-scale real-time and historical data, which limits data analysis and mining capabilities, making it difficult to detect abnormal conditions and problems in the power system in a timely manner, resulting in weak real-time monitoring and response capabilities for the power grid status. In addition, there is a lack of intelligent decision-making support functions, making it difficult for operation and maintenance personnel to make accurate decisions when faced with complex situations.

[0007] In summary, it is necessary to propose a smart grid operation log collection and analysis management system based on big data to solve the above problems. Summary of the invention

[0008] The purpose of the present invention is to provide a smart grid operation log collection and analysis management system based on big data to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A smart grid operation log collection and analysis management system based on big data, including real-time data collection module, data transmission synchronization module, log storage management module, data cleaning processing module, intelligent analysis and mining module, alarm notification module, user interface visualization module and security authority management module:

[0011] The real-time data acquisition module is used for device data acquisition and log information acquisition;

[0012] The data transmission synchronization module is used for real-time data transmission and distributed data synchronization;

[0013] The log storage management module is used for real-time log storage and historical log management;

[0014] The data cleaning processing module is used for real-time data cleaning and log data processing;

[0015] The intelligent analysis and mining module is used for real-time data and log mining and analysis;

[0016] The alarm notification module is used for real-time alarm and log event notification;

[0017] The user interface visualization module is used to provide a real-time monitoring interface and a historical data analysis interface;

[0018] The security authority management module is used to manage data access rights and log auditing.

[0019] As a preferred technical solution for the smart grid operation log collection and analysis management system based on big data:

[0020] The real-time data acquisition module also includes a device data acquisition unit and a log information acquisition unit;

[0021] The equipment data acquisition unit is used to collect data from power grid equipment in real time, using high-frequency data sampling;

[0022] The log information collection unit is used to collect real-time log information such as device status changes and alarms.

[0023] As a preferred technical solution for the smart grid operation log collection and analysis management system based on big data:

[0024] The data transmission synchronization module also includes a real-time data transmission unit and a distributed data synchronization unit;

[0025] The real-time data transmission unit is used to transmit the real-time data to the central server using a message queue or a real-time database;

[0026] The distributed data synchronization unit is used to ensure the consistency of data among multiple data sources based on the data synchronization algorithm of the distributed system.

[0027] As a preferred technical solution for the smart grid operation log collection and analysis management system based on big data:

[0028] The log storage management module also includes a real-time log storage unit and a historical log management unit;

[0029] The real-time log storage unit is used to store the log information collected in real time using a high-performance real-time database;

[0030] The historical log management unit is used to store historical log data using a time series database, supporting efficient time series query.

[0031] As a preferred technical solution for the smart grid operation log collection and analysis management system based on big data:

[0032] The data cleaning processing module also includes a real-time data cleaning unit and a log data processing unit;

[0033] The real-time data cleaning unit is used to filter out abnormal values ​​in real-time device data using an abnormality detection algorithm;

[0034] The log data processing unit is used to extract key information from the log text using a natural language processing (NLP) algorithm and perform structured storage.

[0035] As a preferred technical solution for the smart grid operation log collection and analysis management system based on big data:

[0036] The intelligent analysis and mining module also includes a real-time data analysis unit and a log mining and analysis unit;

[0037] The real-time data analysis unit is used to perform real-time data analysis using a machine learning algorithm to identify abnormal patterns;

[0038] The log mining and analysis unit is used to analyze historical log data using text mining and pattern recognition algorithms to discover hidden problems in power grid operation.

[0039] As a preferred technical solution for the smart grid operation log collection and analysis management system based on big data:

[0040] The alarm notification module also includes a real-time alarm unit and a log event notification unit;

[0041] The real-time alarm unit is used to generate a real-time alarm notification based on the real-time data analysis results and the anomaly detection algorithm;

[0042] The log event notification unit is used to use an event reasoning algorithm to monitor log data, generate notifications or alarms, and remind operators to pay attention to specific events.

[0043] As a preferred technical solution for the smart grid operation log collection and analysis management system based on big data:

[0044] The user interface visualization module also includes a real-time monitoring interface unit and a historical data analysis interface unit;

[0045] The real-time monitoring interface unit is used to provide an intuitive power grid operation monitoring interface using real-time data visualization technology;

[0046] The historical data analysis interface unit is used to provide a user-friendly historical data query and analysis interface using the support of a graphical database.

[0047] As a preferred technical solution for the smart grid operation log collection and analysis management system based on big data:

[0048] The security authority management module also includes a data access authority unit and a log audit unit;

[0049] The data access permission unit is used to manage user access permissions to real-time data, historical data and analysis results using a role-based access control (RBAC) algorithm;

[0050] The log audit unit is used to utilize blockchain technology to ensure the non-tamperability of data access and system operation, so as to enhance the security of the system.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention can timely discover and handle abnormal conditions and problems in the power system, improve the real-time monitoring and response capabilities of the power grid status, combine big data analysis and mining technology, provide intelligent decision support, and help operation and maintenance personnel make accurate and timely decisions. By analyzing historical data and real-time data, the possibility of equipment failure is predicted, and preventive measures are taken to reduce the failure and downtime of the power system. Therefore, the present invention not only achieves the function of fault prediction and prevention, but also has the advantages of real-time monitoring, response and intelligent decision support. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a diagram of a smart grid operation log collection and analysis management system based on big data of the present invention; DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1 , the present invention provides a technical solution:

[0055] A smart grid operation log collection and analysis management system based on big data, including real-time data collection module, data transmission synchronization module, log storage management module, data cleaning processing module, intelligent analysis and mining module, alarm notification module, user interface visualization module and security authority management module:

[0056] It should be noted that: the real-time data acquisition module is used for equipment data acquisition and log information acquisition; the data transmission and synchronization module is used for real-time data transmission and distributed data synchronization; the log storage management module is used for real-time log storage and historical log management; the data cleaning and processing module is used for real-time data cleaning and log data processing; the intelligent analysis and mining module is used for real-time data, log mining and analysis; the alarm notification module is used for real-time alarm and log event notification; the user interface visualization module is used to provide a real-time monitoring interface and a historical data analysis interface; the security permission management module is used to manage data access rights and log auditing.

[0057] Among them, the real-time data acquisition module also includes a device data acquisition unit and a log information acquisition unit; the device data acquisition unit is used to collect data from power grid equipment in real time, using high-frequency data sampling; the log information acquisition unit is used to collect real-time log information such as equipment status changes and alarms.

[0058] Among them, the data transmission synchronization module also includes a real-time data transmission unit and a distributed data synchronization unit; the real-time data transmission unit is used to transmit real-time data to a central server using a message queue or a real-time database; the distributed data synchronization unit is used to ensure the consistency of data between multiple data sources based on the data synchronization algorithm of the distributed system.

[0059] Among them, the log storage management module also includes a real-time log storage unit and a historical log management unit; the real-time log storage unit is used to use a high-performance real-time database to store real-time collected log information; the historical log management unit is used to use a time series database to store historical log data and support efficient time series queries.

[0060] Among them, the data cleaning processing module also includes a real-time data cleaning unit and a log data processing unit; the real-time data cleaning unit is used to filter outliers in real-time device data using an anomaly detection algorithm; the log data processing unit is used to extract key information from log text using a natural language processing (NLP) algorithm for structured storage.

[0061] Among them, the intelligent analysis and mining module also includes a real-time data analysis unit and a log mining and analysis unit; the real-time data analysis unit is used to use machine learning algorithms to perform real-time data analysis and identify abnormal patterns; the log mining and analysis unit is used to use text mining and pattern recognition algorithms to analyze historical log data and discover hidden problems in power grid operation.

[0062] Among them, the alarm notification module also includes a real-time alarm unit and a log event notification unit; the real-time alarm unit is used to generate real-time alarm notifications based on real-time data analysis results and anomaly detection algorithms; the log event notification unit is used to use event reasoning algorithms to monitor log data, generate notifications or alarms, and remind operators to pay attention to specific events.

[0063] Among them, the user interface visualization module also includes a real-time monitoring interface unit and a historical data analysis interface unit; the real-time monitoring interface unit is used to use real-time data visualization technology to provide an intuitive power grid operation monitoring interface; the historical data analysis interface unit is used to use graphic database support to provide a user-friendly historical data query and analysis interface.

[0064] Among them, the security permission management module also includes a data access permission unit and a log audit unit; the data access permission unit is used to use a role-based access control (RBAC) algorithm to manage user access permissions to real-time data, historical data, and analysis results; the log audit unit is used to use blockchain technology to ensure the non-tamperability of data access and system operations to enhance the security of the system.

[0065] Example

[0066] In practical applications, a smart grid operation log collection and analysis management system based on big data is established, which includes a real-time data collection module, a data transmission synchronization module, a log storage management module, a data cleaning and processing module, an intelligent analysis and mining module, an alarm notification module, a user interface visualization module and a security authority management module. The real-time data collection module is used for equipment data collection and log information collection; the data transmission synchronization module is used for real-time data transmission and distributed data synchronization; the log storage management module is used for real-time log storage and historical log management; the data cleaning processing module is used for real-time data cleaning and log data processing; the intelligent analysis and mining module is used for real-time data, log mining and analysis; the alarm notification module is used for real-time alarm and log event notification; the user interface visualization module is used to provide a real-time monitoring interface and a historical data analysis interface; the security authority management module is used to manage data access rights and log auditing.

[0067] Specifically, the real-time data acquisition module also includes a device data acquisition unit and a log information acquisition unit; the device data acquisition unit is used to collect data from power grid equipment in real time, using high-frequency data sampling; the log information acquisition unit is used to collect real-time log information such as device status changes and alarms.

[0068] The data transmission synchronization module also includes a real-time data transmission unit and a distributed data synchronization unit; the real-time data transmission unit is used to transmit real-time data to a central server using a message queue or a real-time database; the distributed data synchronization unit is used to ensure the consistency of data between multiple data sources based on the data synchronization algorithm of the distributed system.

[0069] The log storage management module also includes a real-time log storage unit and a historical log management unit; the real-time log storage unit is used to use a high-performance real-time database to store real-time collected log information; the historical log management unit is used to use a time series database to store historical log data and support efficient time series queries.

[0070] The data cleaning processing module also includes a real-time data cleaning unit and a log data processing unit; the real-time data cleaning unit is used to filter outliers in real-time device data using an anomaly detection algorithm; the log data processing unit is used to extract key information from log text using a natural language processing (NLP) algorithm for structured storage.

[0071] The intelligent analysis and mining module also includes a real-time data analysis unit and a log mining and analysis unit; the real-time data analysis unit is used to use machine learning algorithms to perform real-time data analysis and identify abnormal patterns; the log mining and analysis unit is used to use text mining and pattern recognition algorithms to analyze historical log data and discover hidden problems in power grid operation.

[0072] The alarm notification module also includes a real-time alarm unit and a log event notification unit; the real-time alarm unit is used to generate real-time alarm notifications based on real-time data analysis results and anomaly detection algorithms; the log event notification unit is used to use event reasoning algorithms to monitor log data, generate notifications or alarms, and remind operators to pay attention to specific events.

[0073] The user interface visualization module also includes a real-time monitoring interface unit and a historical data analysis interface unit; the real-time monitoring interface unit is used to use real-time data visualization technology to provide an intuitive power grid operation monitoring interface; the historical data analysis interface unit is used to use graphic database support to provide a user-friendly historical data query and analysis interface.

[0074] The security permission management module also includes a data access permission unit and a log audit unit; the data access permission unit is used to manage user access permissions to real-time data, historical data and analysis results using a role-based access control (RBAC) algorithm; the log audit unit is used to use blockchain technology to ensure the immutability of data access and system operations to enhance the security of the system.

[0075] Based on the above system, the present invention is applied in maintaining efficient and stable operation of the power grid to ensure that the daily life and industrial activities of urban residents are not affected and monitoring and managing the power supply of a city, including the following steps:

[0076] (1) Real-time data collection:

[0077] Install sensors and monitoring equipment on various key equipment, including generators and transformers;

[0078] The real-time data acquisition unit regularly collects the operating data of these devices, including current, voltage, frequency, and temperature;

[0079] At the same time, the log information collection unit records the device's status changes and alarm information log data;

[0080] It should be noted that: in the real-time data collection stage, sensors and monitoring equipment are first installed on various key equipment, such as generators and transformers. These equipment monitor key parameters such as current, voltage, frequency, and temperature in milliseconds or seconds through sensors in real time. The real-time data collection unit collects operating data from these equipment. These data are transmitted in digital or analog form and transmitted to the central server after sampling and conversion. At the same time, the log information collection unit is responsible for recording the status changes and alarm information log data of the equipment. These log data record the operating status, abnormal conditions and other important events of the equipment for subsequent analysis and troubleshooting. The whole process is continuous and automatic to ensure real-time monitoring and recording of the power grid operation status.

[0081] (2) Data transmission and synchronization:

[0082] The real-time data transmission unit transmits the collected data to the central server through the network;

[0083] The distributed data synchronization unit ensures the synchronization and consistency of data between multiple data sources;

[0084] (2.1) Data transmission: The collected real-time data and log information are sent to a central server, usually through the network, using Ethernet, Wi-Fi or other communication protocols to transmit data;

[0085] (2.2) Data synchronization, once the data arrives at the central server, the data synchronization process begins;

[0086] Ensure that data collected from different devices is properly associated and synchronized;

[0087] For example, if data generated by a transformer and a generator need to be considered together in subsequent analysis, the data from these two data sources need to be synchronized to the same time point and data format;

[0088] (2.3) Distributed system support. If the system adopts a distributed architecture, data synchronization involves coordination and communication between multiple nodes.

[0089] Ensure that data collected even on different servers can be properly synchronized and integrated;

[0090] (2.4) Consistency assurance: The data synchronization process needs to ensure data consistency, that is, the data between different data sources should be accurate, complete, and consistent;

[0091] This is achieved by adopting transaction processing and data version control methods;

[0092] (2.5) Real-time requirements: data transmission and synchronization processes need to maintain real-time performance as much as possible to ensure that the collected data can be analyzed and processed in a timely manner, thereby achieving timely monitoring and response to the power grid operation status;

[0093] (3) Log storage management:

[0094] The real-time log storage unit uses a high-performance database to store device status changes and alarm information log data in real time;

[0095] The historical log management unit stores these log data in time series and provides efficient query and retrieval functions;

[0096] (3.1) Real-time log storage, real-time log data such as equipment status changes and alarm information are stored in high-performance databases that have fast writing and retrieval capabilities to ensure that real-time data can be recorded and accessed in a timely manner;

[0097] (3.2) Data archiving: Periodically or according to set conditions, real-time log data will be archived to historical log storage, ensuring the long-term preservation of real-time data and freeing up real-time storage resources so that they can be used to receive new real-time data;

[0098] (3.3) Storage in time series: Archived log data is stored in time series, using timestamps or dates to organize data for subsequent retrieval and analysis;

[0099] (3.4) Efficient query and retrieval functions. The historical log management system provides efficient query and retrieval functions, allowing users to easily access archived log data. This includes data retrieval based on time range, keyword search, etc., and provides a variety of data display and export options to meet the needs of different users;

[0100] (3.5) Data compression and optimization: In order to save storage space and improve query efficiency, historical log data will be compressed and optimized, which includes data compression, index optimization, data cleaning and other operations to ensure that data is stored in the best state;

[0101] (3.6) Backup and recovery: For data security and reliability, historical log data will be backed up regularly and a corresponding recovery mechanism will be established to prevent data loss or damage;

[0102] (4) Data cleaning processing:

[0103] The real-time data cleaning unit uses anomaly detection algorithms to filter out abnormal values ​​in the collected data to ensure the accuracy and reliability of the data;

[0104] The log data processing unit uses natural language processing algorithms to parse and structure log text for subsequent analysis and mining;

[0105] (4.1) The real-time data cleaning unit first receives the collected data and uses an anomaly detection algorithm to analyze and process the data. This process aims to identify and filter out abnormal values ​​in the data, such as unreasonable extreme values ​​or sudden abnormal fluctuations. The anomaly detection algorithm is based on statistical methods, machine learning models, or rule engines. It performs multi-dimensional analysis on the data, identifies data points that are inconsistent with the normal operating mode, and marks them as outliers;

[0106] (4.2) The log data processing unit receives the collected log text and uses the natural language processing (NLP) algorithm to parse and structure it. This process aims to convert the natural language text into structured data for subsequent analysis and mining. The NLP algorithm includes lexical analysis, syntactic analysis, named entity recognition and other technologies, which are used to extract key information from the log text and convert it into a format that can be understood and processed by computer programs. The structured log data can include timestamps, event types, keywords, exception codes and other information, which can be effectively used by subsequent analysis algorithms;

[0107] (4.3) In the process of implementing anomaly detection and log text parsing, data cleaning and conversion are required to deal with noise and inconsistency in the data, which includes data normalization, missing value processing, data format conversion and other operations to ensure data consistency and accuracy;

[0108] (4.4) In the process of data cleaning and processing, new errors or deviations are introduced. Therefore, the system needs to implement quality control measures to check and verify the processed data to ensure the quality and reliability of the data;

[0109] (5) Intelligent analysis and mining:

[0110] The real-time data analysis unit uses machine learning algorithms to analyze real-time data and identify potential abnormal patterns or trends, including predicting the potential for equipment failure.

[0111] The log mining and analysis unit uses text mining and pattern recognition algorithms to analyze historical log data and discover hidden problems in power grid operation, including typical equipment failure modes;

[0112] (5.1) The real-time data analysis unit uses machine learning algorithms to analyze real-time data. The algorithms may include supervised learning, unsupervised learning, or semi-supervised learning methods. Through supervised learning, the system learns the characteristics of the normal operation mode of the equipment and detects abnormal patterns or trends in the real-time data. For example, the system trains models based on historical data to predict the possibility of equipment failure and promptly issues alarms or takes preventive measures. Unsupervised learning algorithms discover hidden patterns or clusters in the data and identify potential abnormalities. The algorithms are able to discover new patterns in the absence of labeled data and provide the system with more comprehensive analysis results;

[0113] (5.2) The log mining and analysis unit uses text mining and pattern recognition algorithms to analyze historical log data. The algorithms can automatically identify key information and patterns in the text. Text mining techniques include word frequency statistics, topic modeling, and sentiment analysis methods, which are used to extract useful information from a large amount of log text. Pattern recognition algorithms identify typical equipment failure modes or potential problems in power grid operation. By analyzing historical log data, the system discovers potential problems in power grid operation and provides corresponding suggestions or solutions.

[0114] For example, typical equipment failure modes can be identified and preventive maintenance measures can be proposed to reduce future failures;

[0115] (5.3) Based on real-time data analysis and historical log mining results, the system provides intelligent decision support, including automated alarm generation, fault diagnosis, and optimization suggestion functions to help operation and maintenance personnel make correct decisions quickly. The intelligent decision support system provides personalized suggestions and solutions based on the current grid status and historical data analysis results to maximize the reliability and efficiency of the grid;

[0116] (6) Alarm notification:

[0117] The real-time alarm unit generates real-time alarm notifications based on real-time data analysis results and anomaly detection algorithms, and promptly notifies operation and maintenance personnel of problems;

[0118] The log event notification unit monitors log data, identifies potential problems through event reasoning algorithms, and generates notifications or alarms so that operation and maintenance personnel can handle them in a timely manner;

[0119] (7) User interface visualization:

[0120] The real-time monitoring interface unit provides an intuitive grid operation monitoring interface, displays real-time data and alarm information, and supports user-customized display methods;

[0121] The historical data analysis interface unit provides historical data query and analysis functions, including data visualization and trend analysis, to help managers understand the historical status and trends of power grid operation;

[0122] (8) Security authority management:

[0123] The data access permission unit manages access permissions to real-time data, historical data, and analysis results based on user roles to ensure data security and privacy;

[0124] The log audit unit uses blockchain technology to ensure the immutability of data access and system operations, improving the security and credibility of the system.

[0125] In summary, the present invention has the following advantages:

[0126] Real-time monitoring and response: abnormal conditions and problems in the power system can be discovered and handled in a timely manner, improving the real-time monitoring and response capabilities of the power grid status;

[0127] Intelligent decision support: Combining big data analysis and mining technology, it can provide intelligent decision support to help operation and maintenance personnel make accurate and timely decisions;

[0128] Fault prediction and prevention: By analyzing historical and real-time data, the possibility of equipment failure is predicted and preventive measures are taken to reduce failures and downtime of the power system.

[0129] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart grid operation log collection and analysis management system based on big data, including a real-time data collection module, a data transmission synchronization module, a log storage management module, a data cleaning processing module, an intelligent analysis and mining module, an alarm notification module, a user interface visualization module and a security authority management module, characterized in that: The real-time data acquisition module is used for device data acquisition and log information acquisition; The data transmission synchronization module is used for real-time data transmission and distributed data synchronization; The log storage management module is used for real-time log storage and historical log management; The data cleaning processing module is used for real-time data cleaning and log data processing; The intelligent analysis and mining module is used for real-time data and log mining and analysis; The alarm notification module is used for real-time alarm and log event notification; The user interface visualization module is used to provide a real-time monitoring interface and a historical data analysis interface; The security authority management module is used to manage data access rights and log auditing.

2. According to the big data-based smart grid operation log collection and analysis management system of claim 1, it is characterized by: The real-time data acquisition module also includes a device data acquisition unit and a log information acquisition unit; The equipment data acquisition unit is used to collect data from power grid equipment in real time; The log information collection unit is used to collect real-time log information of device status changes and alarms.

3. According to the big data-based smart grid operation log collection and analysis management system of claim 2, it is characterized by: The data transmission synchronization module also includes a real-time data transmission unit and a distributed data synchronization unit; The real-time data transmission unit is used to transmit real-time data to a central server; The distributed data synchronization unit is used for data synchronization.

4. According to the big data-based smart grid operation log collection and analysis management system of claim 3, it is characterized by: The log storage management module also includes a real-time log storage unit and a historical log management unit; The real-time log storage unit is used for collecting log information in real time; The historical log management unit is used to support efficient time series query.

5. According to the big data-based smart grid operation log collection and analysis management system of claim 4, it is characterized by: The data cleaning processing module also includes a real-time data cleaning unit and a log data processing unit; The real-time data cleaning unit is used to filter outliers in real-time device data; The log data processing unit is used to extract key information from the log text.

6. The big data-based smart grid operation log collection and analysis management system according to claim 5, characterized in that: The intelligent analysis and mining module also includes a real-time data analysis unit and a log mining and analysis unit; The real-time data analysis unit is used to perform real-time data analysis using a machine learning algorithm; The log mining and analysis unit is used to analyze historical log data.

7. The big data-based smart grid operation log collection and analysis management system according to claim 6, characterized in that: The alarm notification module also includes a real-time alarm unit and a log event notification unit; The real-time alarm unit is used to generate a real-time alarm notification; The log event notification unit is used to generate a notification or an alarm.

8. The big data-based smart grid operation log collection and analysis management system according to claim 7, characterized in that: The user interface visualization module also includes a real-time monitoring interface unit and a historical data analysis interface unit; The real-time monitoring interface unit is used to provide an intuitive power grid operation monitoring interface; The historical data analysis interface unit is used to provide a user-friendly historical data query and analysis interface.

9. The big data-based smart grid operation log collection and analysis management system according to claim 8, characterized in that: The security authority management module also includes a data access authority unit and a log audit unit; The data access permission unit is used to manage the user's access permissions to real-time data, historical data and analysis results; The log audit unit is used to enhance the security of the system.

Images