Collection and analysis system for electric power big data

Abstract

The invention relates to the technical field of electric power data analysis, and discloses an electric power big data acquisition and analysis system which comprises data acquisition, data cleaning and conversion, data storage and management, data analysis and mining, result visual display, analysis result application and process monitoring and feedback. According to the electric power big data acquisition and analysis system, through advanced sensors and the Internet of Things technology, the electric power big data system can automatically acquire operation data of electric power equipment and a system, and through utilization of big data analysis and artificial intelligence technology, the system can process and analyze the acquired data in real time, so that the data processing speed and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power data analysis, and in particular to a system for collecting and analyzing power big data. Background Art

[0002] The application background of the power big data collection and analysis system mainly stems from the development of modern power systems and the rapid growth of data volume. With the development of the global economy and the improvement of people's living standards, the demand for electricity continues to grow. At the same time, the traditional power production and management model faces a series of problems such as resource waste and environmental pollution. In order to meet these challenges, the industry has begun to explore the application of new technologies, especially big data technology. In terms of demand, the application scenarios of power big data are very rich, covering all aspects of the power system. Through big data analysis, power dispatching can be optimized, the operating cost of the power system can be reduced, and the user's power experience can be improved. In addition, with the construction of smart grids and the access of new energy sources, the application of power big data will be more in-depth and extensive. The background technology of the power big data collection and analysis system involves data collection, data transmission, data processing and analysis, data security and privacy protection, etc. The continuous development and application of these technologies will promote the in-depth application of power big data in the power system and improve the operating efficiency and reliability of the power system. This application now proposes a power big data collection and analysis system. Summary of the invention

[0003] Technical Solution

[0004] To achieve the above purpose, the present invention provides the following technical solutions: a power big data collection and analysis system, including data collection, data cleaning and conversion, data storage and management, data analysis and mining, result visualization, analysis result application, process monitoring and feedback, the specific steps are as follows:

[0005] S1. Deploy high-precision sensors and intelligent data collection equipment at key locations in the power system. The sensors and intelligent devices transmit the collected data to the data collection center in real time via wired or wireless means;

[0006] S2. The received raw data is first preliminarily verified to remove duplicate, invalid or obviously abnormal data, and the remaining data is deeply cleaned;

[0007] S3. Select appropriate storage technology based on the characteristics and storage requirements of the data, store the cleaned and converted data into the database, and index, classify and archive the stored data;

[0008] S4. Use statistical analysis, machine learning and other algorithms to conduct in-depth analysis of stored data to extract valuable information and patterns;

[0009] S5. Design a friendly visualization interface based on analysis requirements and display the analysis results through the visualization interface;

[0010] S6. Develop a more reasonable power dispatching strategy based on the analysis results;

[0011] S7. Monitor the entire collection and analysis process in real time, regularly evaluate the performance and accuracy of the system, identify potential problems and optimize them;

[0012] S8. Collect user feedback and opinions and continuously improve system functions.

[0013] Preferably, in step S1, the data acquisition center is responsible for receiving real-time data from sensors and smart devices, and performing preliminary processing including data verification and format conversion, storing the processed data in a distributed database or data warehouse to facilitate subsequent query and analysis, implement data backup and recovery strategies, and ensure data security and reliability.

[0014] Preferably, in step S2, the remaining data is deeply cleaned, including filling missing values, correcting erroneous data, smoothing noise data, etc., to improve data quality, and the cleaned data is converted into a form suitable for analysis, such as normalization and standardization, and the data format is unified to lay the foundation for subsequent analysis.

[0015] Preferably, selecting a suitable storage technology in step S3 includes:

[0016] Analysis of data characteristics, consideration of storage requirements, and selection of storage technology. The analysis of data characteristics includes structured data and unstructured data. The consideration of storage requirements includes the following:

[0017] Data scale: For large-scale data sets, you need to choose a storage solution that is scalable and high-performance.

[0018] Access speed: For data that needs to be accessed frequently, storage media with faster access speeds, such as in-memory databases or solid-state drives, should be given priority.

[0019] Data persistence: For data that needs to be stored for a long time, a storage solution with data backup and recovery capabilities should be selected.

[0020] Before the data is stored in the database, it should be ensured that the data has been cleaned and converted to remove noise, errors and missing values, and converted into a format suitable for storage and analysis. According to the characteristics and storage requirements of the data, a reasonable database structure should be designed, including table structure, indexes, and constraints. Then, the ETL tools and custom scripts should be used to import the cleaned and converted data into the database;

[0021] Indexing, classification and archiving of said data:

[0022] Data index:

[0023] Create indexes for tables or fields in the database to improve the speed of data query and retrieval. Select the appropriate index type according to the query requirements, such as B-tree index and hash index.

[0024] Data classification:

[0025] Classify data according to its nature, purpose or source. For example, store grid operation data, equipment monitoring data, user electricity consumption data, etc. in different tables or databases. Classification helps organize and manage data and facilitates subsequent data analysis and mining.

[0026] Data Archiving:

[0027] Historical data or data that is no longer frequently accessed should be archived to save storage space and improve data access efficiency. Archived data can be stored on low-cost storage media, such as tape libraries or cloud storage. A data archiving strategy should be developed, including the archiving cycle and archiving method.

[0028] Preferably, in step S4, data mining technology is used to monitor abnormal conditions in the data in real time. Hidden patterns and trends in the data are discovered through data mining technology, such as association analysis and cluster analysis. Abnormal conditions in the data are monitored in real time, alarms are issued in time, and processing suggestions are provided.

[0029] Preferably, in step S5, the analysis results are displayed through a visual interface, providing the user with an intuitive understanding of data changes and trends and providing data screening, zooming in and out, and dynamic updating, thereby enhancing the user experience.

[0030] Preferably, in step S6, the peak and valley characteristics of the power load are identified, energy-saving measures are formulated, energy consumption and costs are reduced, the operating status of the equipment is monitored in real time, potential faults are discovered in time, and the life of the equipment is extended.

[0031] Preferably, the real-time monitoring, collection and analysis process in step S7 is as follows:

[0032] Monitoring data collection link:

[0033] Monitor the operating status of sensors and intelligent data collection equipment to ensure the accuracy and timeliness of data collection, check the stability and reliability of data transmission channels, and avoid data loss or delay;

[0034] Monitoring data preprocessing:

[0035] Track the process of data cleaning and conversion to ensure the improvement of data quality, monitor the performance of data preprocessing algorithms, and adjust algorithm parameters in a timely manner to improve processing efficiency;

[0036] Monitoring data storage link:

[0037] Monitor database storage performance and capacity, ensure data integrity and security, and check the implementation of data backup and recovery strategies to prevent data loss;

[0038] Monitoring data analysis and mining:

[0039] Track the running status of data analysis algorithms in real time to ensure the accuracy and timeliness of analysis results, monitor the quality of data mining results, and adjust mining strategies in a timely manner to improve the accuracy of discovering potential laws and patterns;

[0040] The periodic evaluation of system performance and accuracy is as follows:

[0041] Performance evaluation:

[0042] Regularly evaluate key performance indicators such as system response time and throughput to ensure efficient operation of the system, and evaluate the scalability and stability of the system to meet the needs of future business growth and increased data volume;

[0043] Accuracy Assessment:

[0044] Evaluate the accuracy of the system by comparing the analysis results with the actual power system status, and use statistical methods and machine learning algorithms to verify the analysis results to improve the accuracy of the evaluation;

[0045] The following are the potential problems to be discovered and optimized:

[0046] Through real-time monitoring and regular evaluation, potential problems in the system can be discovered, such as sensor failure, data transmission delay, and algorithm performance degradation. User feedback and opinions can be collected to understand the system's deficiencies and user needs.

[0047] The specific optimization measures are:

[0048] Develop corresponding optimization measures for the problems found, including replacing faulty sensors, optimizing data transmission channels, adjusting algorithm parameters, and continuously improving and expanding the system's functions based on user needs and feedback to improve the system's practicality and user experience;

[0049] After implementing the optimization measures, the system is tested and verified to ensure that the optimization effect meets expectations. Based on the test results, the optimization measures are adjusted and improved to achieve the best results.

[0050] Preferably, the step S8, collecting user feedback and opinions, continuously improving system functions, and conducting testing and verification after improving system functions, the specific method is as follows:

[0051] After implementing improvement measures, conduct comprehensive testing on the system to ensure that the improvement effects meet expectations. Verify the feasibility and effectiveness of improvement measures through user testing, internal review, etc. After the improvement measures have been implemented for a period of time, collect user feedback and opinions again, evaluate the improvement effects, and further optimize and improve the system based on user feedback.

[0052] Compared with the prior art, the present invention provides a system for collecting and analyzing power big data, which has the following beneficial effects:

[0053] 1. The power big data collection and analysis system can automatically collect the operating data of power equipment and systems through advanced sensors and Internet of Things technology. By using big data analysis and artificial intelligence technology, the system can process and analyze the collected data in real time to improve the speed and accuracy of data processing.

[0054] 2. The power big data collection and analysis system can monitor the operating status and parameters of power equipment in real time and detect abnormal situations in time. By setting early warning thresholds, the system can issue early warnings when equipment fails or abnormal situations occur, reducing the failure rate and downtime. By using big data analysis and machine learning algorithms, the system can predict and diagnose failures of power equipment. By detecting potential failures in advance and taking corresponding maintenance measures, the system can reduce the equipment failure rate and improve the reliability and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The figure is a flow chart of the power big data collection and analysis system of the present invention. DETAILED DESCRIPTION

[0056] 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.

[0057] Embodiment 1:

[0058] A collection and analysis system for power big data includes data collection, data cleaning and conversion, data storage and management, data analysis and mining, result visualization, analysis result application, process monitoring and feedback. The specific steps are as follows:

[0059] S1. Deploy high-precision sensors and intelligent data collection equipment at key locations in the power system. The sensors and intelligent devices transmit the collected data to the data collection center in real time via wired or wireless means;

[0060] S2. The received raw data is first preliminarily verified to remove duplicate, invalid or obviously abnormal data, and the remaining data is deeply cleaned;

[0061] S3. Select appropriate storage technology based on the characteristics and storage requirements of the data, store the cleaned and converted data into the database, and index, classify and archive the stored data;

[0062] S4. Use statistical analysis, machine learning and other algorithms to conduct in-depth analysis of stored data to extract valuable information and patterns;

[0063] S5. Design a friendly visualization interface based on analysis requirements and display the analysis results through the visualization interface;

[0064] S6. Develop a more reasonable power dispatching strategy based on the analysis results;

[0065] S7. Monitor the entire collection and analysis process in real time, regularly evaluate the performance and accuracy of the system, identify potential problems and optimize them;

[0066] S8. Collect user feedback and opinions and continuously improve system functions.

[0067] Embodiment 2:

[0068] According to the first embodiment, a power big data collection and analysis system is proposed. The data collection center is responsible for receiving real-time data from sensors and smart devices, and performing preliminary processing including data verification and format conversion. The processed data is stored in a distributed database or data warehouse to facilitate subsequent query and analysis, implement data backup and recovery strategies to ensure data security and reliability, and perform deep cleaning on the remaining data, including filling missing values, correcting erroneous data, smoothing noise data, etc., to improve data quality. The cleaned data is converted into a form suitable for analysis, such as normalization and standardization, and the data format is unified to lay the foundation for subsequent analysis.

[0069] Embodiment three:

[0070] According to the first embodiment, a power big data collection and analysis system is proposed, and the selection of a suitable storage technology includes:

[0071] Analysis of data characteristics, consideration of storage requirements, and selection of storage technology. The analysis of data characteristics includes structured data and unstructured data. The consideration of storage requirements includes the following:

[0072] Data scale: For large-scale data sets, you need to choose a storage solution that is scalable and high-performance.

[0073] Access speed: For data that needs to be accessed frequently, storage media with faster access speeds, such as in-memory databases or solid-state drives, should be given priority.

[0074] Data persistence: For data that needs to be stored for a long time, a storage solution with data backup and recovery capabilities should be selected.

[0075] Before the data is stored in the database, it should be ensured that the data has been cleaned and converted to remove noise, errors and missing values, and converted into a format suitable for storage and analysis. According to the characteristics and storage requirements of the data, a reasonable database structure should be designed, including table structure, indexes, and constraints. Then, the ETL tools and custom scripts should be used to import the cleaned and converted data into the database;

[0076] Indexing, classification and archiving of said data:

[0077] Data index:

[0078] Create indexes for tables or fields in the database to improve the speed of data query and retrieval. Select the appropriate index type according to the query requirements, such as B-tree index and hash index.

[0079] Data classification:

[0080] Classify data according to its nature, purpose or source. For example, store grid operation data, equipment monitoring data, user electricity consumption data, etc. in different tables or databases. Classification helps organize and manage data and facilitates subsequent data analysis and mining.

[0081] Data Archiving:

[0082] Historical data or data that is no longer frequently accessed should be archived to save storage space and improve data access efficiency. Archived data can be stored on low-cost storage media, such as tape libraries or cloud storage. A data archiving strategy should be developed, including the archiving cycle and archiving method.

[0083] Embodiment 4:

[0084] According to the first embodiment, a system for collecting and analyzing electric power big data is proposed. In step S4, data mining technology is used to monitor abnormal conditions in the data in real time. Through data mining technologies such as association analysis and cluster analysis, hidden rules and trends in the data are discovered, and abnormal conditions in the data are monitored in real time, alarms are issued in time, and processing suggestions are provided; in step S5, the analysis results are displayed through a visual interface to provide users with an intuitive understanding of data changes and trends, and provide data screening, zooming in and out, and dynamic updates to enhance user experience; in step S6, the peak and valley characteristics of the power load are identified, energy-saving measures are formulated, energy consumption and costs are reduced, the operating status of the equipment is monitored in real time, potential faults are discovered in time, and the life of the equipment is extended.

[0085] Embodiment five:

[0086] According to the first embodiment, a system for collecting and analyzing power big data is proposed. The real-time monitoring, collecting and analyzing process in step S7 is as follows:

[0087] Monitoring data collection link:

[0088] Monitor the operating status of sensors and intelligent data collection equipment to ensure the accuracy and timeliness of data collection, check the stability and reliability of data transmission channels, and avoid data loss or delay;

[0089] Monitoring data preprocessing:

[0090] Track the process of data cleaning and conversion to ensure the improvement of data quality, monitor the performance of data preprocessing algorithms, and adjust algorithm parameters in a timely manner to improve processing efficiency;

[0091] Monitoring data storage link:

[0092] Monitor database storage performance and capacity, ensure data integrity and security, and check the implementation of data backup and recovery strategies to prevent data loss;

[0093] Monitoring data analysis and mining:

[0094] Track the running status of data analysis algorithms in real time to ensure the accuracy and timeliness of analysis results, monitor the quality of data mining results, and adjust mining strategies in a timely manner to improve the accuracy of discovering potential laws and patterns;

[0095] The periodic evaluation of system performance and accuracy is as follows:

[0096] Performance evaluation:

[0097] Regularly evaluate key performance indicators such as system response time and throughput to ensure efficient operation of the system, and evaluate the scalability and stability of the system to meet the needs of future business growth and increased data volume;

[0098] Accuracy Assessment:

[0099] Evaluate the accuracy of the system by comparing the analysis results with the actual power system status, and use statistical methods and machine learning algorithms to verify the analysis results to improve the accuracy of the evaluation;

[0100] The following are the potential problems to be discovered and optimized:

[0101] Through real-time monitoring and regular evaluation, potential problems in the system can be discovered, such as sensor failure, data transmission delay, and algorithm performance degradation. User feedback and opinions can be collected to understand the system's deficiencies and user needs.

[0102] The specific optimization measures are:

[0103] Develop corresponding optimization measures for the problems found, including replacing faulty sensors, optimizing data transmission channels, adjusting algorithm parameters, and continuously improving and expanding the system's functions based on user needs and feedback to improve the system's practicality and user experience;

[0104] After implementing the optimization measures, the system is tested and verified to ensure that the optimization effect meets expectations. Based on the test results, the optimization measures are adjusted and improved to achieve the best results.

[0105] Embodiment six:

[0106] According to the first embodiment, a power big data collection and analysis system is proposed. In step S8, user feedback and opinions are collected to continuously improve the system function. After the system function is improved, testing and verification are performed. The specific method is as follows:

[0107] After implementing improvement measures, conduct comprehensive testing on the system to ensure that the improvement effects meet expectations. Verify the feasibility and effectiveness of improvement measures through user testing, internal review, etc. After the improvement measures have been implemented for a period of time, collect user feedback and opinions again, evaluate the improvement effects, and further optimize and improve the system based on user feedback.

[0108] 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 system for collecting and analyzing power big data, characterized by: It includes data collection, data cleaning and conversion, data storage and management, data analysis and mining, result visualization, analysis result application, process monitoring and feedback. The specific steps are as follows: S1. Deploy high-precision sensors and intelligent data collection equipment at key locations in the power system. The sensors and intelligent devices transmit the collected data to the data collection center in real time via wired or wireless means; S2. The received raw data is first preliminarily verified to remove duplicate, invalid or obviously abnormal data, and the remaining data is deeply cleaned; S3. Select appropriate storage technology based on the characteristics and storage requirements of the data, store the cleaned and converted data into the database, and index, classify and archive the stored data; S4. Use statistical analysis, machine learning and other algorithms to conduct in-depth analysis of stored data to extract valuable information and patterns; S5. Design a friendly visualization interface based on analysis requirements and display the analysis results through the visualization interface; S6. Develop a more reasonable power dispatching strategy based on the analysis results; S7. Monitor the entire collection and analysis process in real time, regularly evaluate the performance and accuracy of the system, identify potential problems and optimize them; S8. Collect user feedback and opinions and continuously improve system functions.

2. The power big data collection and analysis system according to claim 1 is characterized by: In step S1, the data acquisition center is responsible for receiving real-time data from sensors and smart devices, and performing preliminary processing including data verification and format conversion, storing the processed data in a distributed database or data warehouse, and implementing data backup and recovery strategies to ensure data security and reliability.

3. The power big data collection and analysis system according to claim 1 is characterized by: In step S2, the remaining data is deeply cleaned, including filling missing values, correcting erroneous data, smoothing noise data, etc., to improve data quality, convert the cleaned data into a form suitable for analysis, perform normalization and standardization, and unify the data format.

4. The power big data collection and analysis system according to claim 1 is characterized by: The step S3 of selecting a suitable storage technology comprises: Analysis of data characteristics, consideration of storage requirements, and selection of storage technology. The analysis of data characteristics includes structured data and unstructured data. The consideration of storage requirements includes the following: Data scale: For large-scale data sets, you need to choose a storage solution that is scalable and high-performance; Access speed: For data that needs to be accessed frequently, storage media with faster access speeds are given priority, including in-memory databases or solid-state drives; Data persistence: For data that needs to be stored for a long time, choose a storage solution with data backup and recovery capabilities; Before the data is stored in the database, ensure that the data has been cleaned and converted to remove noise, errors and missing values, and converted into a format suitable for storage and analysis. According to the characteristics and storage requirements of the data, design a reasonable database structure, including table structure, index, and constraints, and then use ETL tools and custom scripts to import the cleaned and converted data into the database; Indexing, classification and archiving of said data: Data index: Create indexes for tables or fields in the database, and select appropriate index types based on query requirements, such as B-tree indexes and hash indexes; Data classification: Classify the data according to its nature, purpose or source, and store the grid operation data, equipment monitoring data, user power consumption data, etc. in different tables and databases; Data Archiving: Archive historical data or data that is no longer frequently accessed.

5. The power big data collection and analysis system according to claim 1 is characterized by: In step S4, data mining technology is used to monitor abnormal situations in the data in real time. Through data mining technology, including association analysis and cluster analysis, hidden patterns and trends in the data are discovered, and abnormal situations in the data are monitored in real time, and alarms are issued in time and processing suggestions are provided.

6. The power big data collection and analysis system according to claim 1 is characterized by: In step S5, the analysis results are displayed through a visual interface, providing users with an intuitive understanding of data changes and trends, and providing data screening, zooming in and out, and dynamic updating, thereby enhancing user experience.

7. The power big data collection and analysis system according to claim 1 is characterized by: In step S6, the peak and valley characteristics of the power load are identified, energy-saving measures are formulated, energy consumption and costs are reduced, the operating status of the equipment is monitored in real time, potential faults are discovered in time, and the life of the equipment is extended.

8. The power big data collection and analysis system according to claim 1 is characterized by: The real-time monitoring, collection and analysis process in step S7 is as follows: Monitoring data collection link: Monitor the operating status of sensors and intelligent data collection equipment to ensure the accuracy and timeliness of data collection, check the stability and reliability of data transmission channels, and avoid data loss or delay; Monitoring data preprocessing: Track the process of data cleaning and conversion to ensure the improvement of data quality, monitor the performance of data preprocessing algorithms, and adjust algorithm parameters in a timely manner to improve processing efficiency; Monitoring data storage link: Monitor database storage performance and capacity, ensure data integrity and security, and check the implementation of data backup and recovery strategies to prevent data loss; Monitoring data analysis and mining: Track the running status of data analysis algorithms in real time to ensure the accuracy and timeliness of analysis results, monitor the quality of data mining results, and adjust mining strategies in a timely manner to improve the accuracy of discovering potential laws and patterns; The periodic evaluation of system performance and accuracy is as follows: Performance evaluation: Regularly evaluate key performance indicators such as system response time and throughput to ensure efficient operation of the system, and evaluate the scalability and stability of the system to meet the needs of future business growth and increased data volume; Accuracy Assessment: Evaluate the accuracy of the system by comparing the analysis results with the actual power system status, and use statistical methods and machine learning algorithms to verify the analysis results to improve the accuracy of the evaluation; The following describes how to discover potential problems and optimize them: Through real-time monitoring and regular evaluation, potential problems in the system can be discovered, such as sensor failure, data transmission delay, and algorithm performance degradation. User feedback and opinions can be collected to understand the shortcomings of the system and user needs. The specific optimization measures are: Develop corresponding optimization measures for the problems found, including replacing faulty sensors, optimizing data transmission channels, adjusting algorithm parameters, and continuously improving and expanding the system's functions based on user needs and feedback to improve the system's practicality and user experience; After implementing the optimization measures, the system is tested and verified to ensure that the optimization effect meets expectations. Based on the test results, the optimization measures are adjusted and improved to achieve the best results.

9. The power big data collection and analysis system according to claim 1, characterized in that: The step S8, collecting user feedback and opinions, continuously improving system functions, and conducting testing and verification after improving system functions, the specific method is as follows: After implementing improvement measures, conduct comprehensive testing on the system to ensure that the improvement effects meet expectations. Verify the feasibility and effectiveness of improvement measures through user testing, internal review, etc. After the improvement measures have been implemented for a period of time, collect user feedback and opinions again, evaluate the improvement effects, and further optimize and improve the system based on user feedback.

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