Data processing method based on electric power big data platform

Abstract

The invention relates to the technical field of electric power big data, and discloses an electric power big data platform-based data processing method, which comprises data acquisition, data storage, data processing, data display and data monitoring. According to the data processing method based on the electric power big data platform, the data processing method based on the electric power big data platform has remarkable beneficial effects in the aspects of data acquisition, data storage, data processing, data display, data monitoring and the like, and the operation efficiency and stability of an electric power system can be improved; according to the data processing method of the electric power big data platform, support is provided for optimization and decision-making of an electric power system, rapid acquisition, storage and processing of mass electric power data can be achieved through the data processing method of the electric power big data platform, and due to the efficient data processing capacity, the electric power system can respond to various changes more timely, and the data processing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power big data, and in particular to a data processing method based on an electric power big data platform. Background Art

[0002] With the continuous increase in application scenarios and the continuous expansion of application depth, the requirements for real-time data processing are becoming higher and higher. It is necessary to deal with different types of data, including structured, semi-structured and unstructured data, and conduct fusion correlation analysis and mining to reveal the hidden relationships, patterns and trends between data. Power big data involves a large amount of user data and power equipment data. The security and privacy of these data are very important. Measures need to be taken to ensure the security and privacy of data and prevent data leakage and abuse. The power system requires real-time monitoring and prediction, which puts higher requirements on the real-time and accuracy of the algorithm. For this reason, this application now proposes a data processing method based on a power big data platform. Summary of the invention

[0003] Technical Solution

[0004] To achieve the above purpose, the present invention provides the following technical solution: a data processing method based on a power big data platform, including data collection, data storage, data processing, data display, and data monitoring. The specific steps are as follows:

[0005] S1. Use a data acquisition device to obtain power consumption information and send real-time power consumption information to the power grid at regular intervals;

[0006] S2, receiving the electricity consumption information sent from the data acquisition device and storing it using the data storage device;

[0007] S3, the data storage device adopts different data storage methods according to the data type and data processing requirements;

[0008] S4. Use the data processor to uniformly process the stored electricity consumption information, extract abnormal data, integrate multi-source data, and generate a distributed database in batches;

[0009] S5. Further process and analyze the extracted abnormal data;

[0010] S6. Visualize the above data processing results using a data display device;

[0011] S7. Use data monitoring devices to monitor the entire data processing process to ensure the accuracy of the data and the effectiveness of the processing.

[0012] Preferably, the data collection scope covers real-time data of each link of power generation, transmission, transformation, distribution and consumption, and the data collection method is through sensors, smart meters and remote terminal units installed at each node of the power grid.

[0013] Preferably, the data storage first designs a reasonable storage architecture according to the data type, scale, and access frequency, and then selects one of a relational database, a non-relational database, or a hybrid database as the database type, removes outliers, duplicate values, and converts the format of the collected data, and finally stores the cleaned and preprocessed data in the database according to the designed storage architecture.

[0014] Preferably, the data processing includes data analysis, data conversion, and data optimization, as follows:

[0015] Data Analysis:

[0016] Use data analysis algorithms and models to mine and analyze stored data to identify patterns, trends, and anomalies in the data;

[0017] Data conversion:

[0018] Convert raw data into data with business significance according to business needs, and aggregate, group and filter the data;

[0019] Data optimization:

[0020] The data is compressed and encrypted for optimization, and the processed data is quality assessed and verified.

[0021] Preferably, the data display specific steps are as follows:

[0022] Step 1: Design an intuitive and easy-to-use data display interface based on business needs, and select a chart to display data;

[0023] Step 2: Display the processed data on the interface in the form of charts and images, provide interactive functions, and allow users to adjust the content and form of the display as needed;

[0024] Step 3: Generate regular or real-time data reports based on business needs. The reports include key indicators and analysis results.

[0025] Preferably, the data monitoring specific steps include the following:

[0026] Step 1: Set key performance indicators and thresholds based on business needs and determine the frequency and scope of monitoring;

[0027] Step 2: Use real-time monitoring technology to continuously monitor data streams and promptly detect and handle abnormal data and events;

[0028] Step 3: When the monitoring indicator exceeds the threshold, the alarm mechanism is triggered;

[0029] Step 4: Regularly analyze monitoring data to identify potential problems and areas for improvement and provide feedback for optimization.

[0030] Compared with the prior art, the present invention provides a data processing method based on the power big data platform, which has the following beneficial effects:

[0031] 1. The data processing method based on the power big data platform shows significant beneficial effects in data acquisition, data storage, data processing, data display and data monitoring, which can improve the operating efficiency and stability of the power system and provide support for the optimization and decision-making of the power system.

[0032] 2. The data processing method based on the power big data platform can realize the rapid collection, storage and processing of massive power data. This efficient data processing capability enables the power system to respond to various changes more promptly and improve data processing efficiency.

[0033] 3. The data processing method based on the power big data platform can monitor and analyze the operating status of the power system in real time. Through data mining and analysis, problems and potential optimization space in the power system can be discovered, so as to formulate more reasonable power dispatching plans and load management strategies, which can not only improve the stability and reliability of the power system, but also reduce power loss and operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the flow chart of the power big data processing method of the present invention. DETAILED DESCRIPTION

[0035] 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.

[0036] Embodiment 1:

[0037] A data processing method based on a power big data platform includes data collection, data storage, data processing, data display, and data monitoring. The specific steps are as follows:

[0038] S1. Use a data acquisition device to obtain power consumption information and send real-time power consumption information to the power grid at regular intervals;

[0039] S2, receiving the electricity consumption information sent from the data acquisition device and storing it using the data storage device;

[0040] S3, the data storage device adopts different data storage methods according to the data type and data processing requirements;

[0041] S4. Use the data processor to uniformly process the stored electricity consumption information, extract abnormal data, integrate multi-source data, and generate a distributed database in batches;

[0042] S5. Further process and analyze the extracted abnormal data;

[0043] S6. Visualize the above data processing results using a data display device;

[0044] S7. Use data monitoring devices to monitor the entire data processing process to ensure the accuracy of the data and the effectiveness of the processing.

[0045] Embodiment 2:

[0046] According to a data processing method based on a power big data platform proposed in Example 1, the data collection scope covers real-time data of each link of power generation, transmission, transformation, distribution, and power consumption. The data collection method is through sensors, smart meters, and remote terminal units installed at various nodes of the power grid. Data collection is the basic step of power big data mining, which refers to collecting data from various power system equipment and sensors. These data include key parameters such as voltage, current, frequency, and power. When collecting data, factors such as data sampling frequency, data format, and data transmission method need to be considered. High-frequency sampling can provide more detailed power system operation status, but it will also increase the pressure of data storage and processing; standardized data format can improve data processing efficiency and reduce the complexity of data conversion; reliable data transmission method can ensure data integrity and timeliness. The beneficial effect of data collection is mainly reflected in providing original and comprehensive data resources for the power big data platform, which is the basis for subsequent data storage, processing, display, and monitoring.

[0047] Embodiment three:

[0048] According to a data processing method based on an electric power big data platform proposed in Example 1, data storage first designs a reasonable storage architecture according to data type, scale, and access frequency, and then selects one of a relational database, a non-relational database, or a hybrid database as the database type, and removes outliers, duplicate values, and converts the format of the collected data. Finally, the cleaned and pre-processed data is stored in the database according to the designed storage architecture. The data storage provides a stable and efficient data storage service for the electric power big data platform, ensures the security and reliability of the data, and provides a strong guarantee for subsequent data processing and analysis.

[0049] Embodiment 4:

[0050] According to a data processing method based on a power big data platform proposed in the first embodiment, data processing includes data analysis, data conversion, and data optimization, which are specifically as follows:

[0051] Data Analysis:

[0052] Use data analysis algorithms and models to mine and analyze stored data to identify patterns, trends, and anomalies in the data;

[0053] Data conversion:

[0054] Convert raw data into data with business significance according to business needs, and aggregate, group and filter the data;

[0055] Data optimization:

[0056] Optimize data compression and encryption, and perform quality assessment and verification on processed data;

[0057] Data processing and analysis are the core steps of power big data mining. By processing and analyzing the collected data, valuable information can be extracted to provide support for the optimization and decision-making of the power system. Data processing can extract valuable information from massive data, provide a basis for the optimization and decision-making of the power system, and improve the operating efficiency and stability of the power system.

[0058] Embodiment five:

[0059] According to a data processing method based on a power big data platform proposed in Example 1, the specific steps of data display are as follows:

[0060] Step 1: Design an intuitive and easy-to-use data display interface based on business needs, and select a chart to display data;

[0061] Step 2: Display the processed data on the interface in the form of charts and images, provide interactive functions, and allow users to adjust the content and form of the display as needed;

[0062] Step 3: Generate regular or real-time data reports based on business needs, including key indicators and analysis results;

[0063] Data visualization is the display of data analysis results in the form of charts, dashboards, etc., so that decision makers can understand and use this information more intuitively. Line charts can show the changing trend of data, bar charts can compare different categories of data, pie charts can show the proportion of data, and heat maps can show the density and distribution of data. Data display is to simplify complex data and analysis results into easy-to-understand graphics and charts, improve the efficiency of information transmission, and help decision makers understand data and information more intuitively, so as to make more informed decisions.

[0064] Embodiment six:

[0065] According to a data processing method based on a power big data platform proposed in the first embodiment, the specific steps of data monitoring include the following:

[0066] Step 1: Set key performance indicators and thresholds based on business needs and determine the frequency and scope of monitoring;

[0067] Step 2: Use real-time monitoring technology to continuously monitor data streams and promptly detect and handle abnormal data and events;

[0068] Step 3: When the monitoring indicator exceeds the threshold, the alarm mechanism is triggered;

[0069] Step 4: Analyze monitoring data regularly to identify potential problems and improvement points and provide feedback for optimization;

[0070] Data monitoring refers to the real-time monitoring and management of the operating status of the power big data platform to ensure the stability and security of the platform. Data monitoring can monitor abnormal changes in data, equipment failure status, etc., issue alarms in time and take corresponding measures to deal with them. At the same time, data monitoring can also monitor and manage data access rights and data security to ensure data security and privacy. Data monitoring can promptly detect and handle abnormal situations and failure status of the power big data platform to ensure the stability and security of the platform; at the same time, it can also monitor and manage data access rights and data security to protect user privacy and data security.

[0071] 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 data processing method based on a power big data platform, characterized in that: Including data collection, data storage, data processing, data display, and data monitoring. The specific steps are as follows: S1. Use a data acquisition device to obtain power consumption information and send real-time power consumption information to the power grid at regular intervals; S2, receiving the electricity consumption information sent from the data acquisition device and storing it using the data storage device; S3, the data storage device adopts different data storage methods according to the data type and data processing requirements; S4. Use the data processor to uniformly process the stored electricity consumption information, extract abnormal data, integrate multi-source data, and generate a distributed database in batches; S5. Further process and analyze the extracted abnormal data; S6. Visualize the above data processing results using a data display device; S7. Use a data monitoring device to monitor the entire data processing process.

2. According to claim 1, a data processing method based on a power big data platform is characterized in that: The data collection scope covers real-time data of each link of power generation, transmission, transformation, distribution and consumption, and the data collection method is through sensors, smart meters and remote terminal units installed at each node of the power grid.

3. The data processing method based on the power big data platform according to claim 1 is characterized in that: The data storage first designs a reasonable storage architecture according to the data type, scale, and access frequency, and then selects one of a relational database, a non-relational database, or a hybrid database as the database type, removes outliers, duplicate values, and converts the format of the collected data, and finally stores the cleaned and pre-processed data in the database according to the designed storage architecture.

4. The data processing method based on the power big data platform according to claim 1 is characterized in that: The data processing includes data analysis, data conversion, and data optimization, as follows: Data Analysis: Use data analysis algorithms and models to mine and analyze stored data to identify patterns, trends, and anomalies in the data; Data conversion: Convert raw data into data with business significance according to business needs, and aggregate, group and filter the data; Data optimization: The data is compressed and encrypted for optimization, and the processed data is quality assessed and verified.

5. The data processing method based on the power big data platform according to claim 1 is characterized in that: The specific steps of data display are as follows: Step 1: Design an intuitive and easy-to-use data display interface based on business needs, and select a chart to display data; Step 2: Display the processed data on the interface in the form of charts and images, provide interactive functions, and allow users to adjust the content and form of the display as needed; Step 3: Generate regular or real-time data reports based on business needs. The reports include key indicators and analysis results.

6. The data processing method based on the power big data platform according to claim 1 is characterized in that: The specific steps of data monitoring include the following: Step 1: Set key performance indicators and thresholds based on business needs and determine the frequency and scope of monitoring; Step 2: Use real-time monitoring technology to continuously monitor data streams and promptly detect and handle abnormal data and events; Step 3: When the monitoring indicator exceeds the threshold, the alarm mechanism is triggered; Step 4: Regularly analyze monitoring data to identify potential problems and areas for improvement and provide feedback for optimization.

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