Data pushing system based on electric power big data platform

Abstract

The invention discloses a data pushing system based on an electric power big data platform, and relates to the technical field of electric power big data. Comprising a control terminal, and the control terminal is connected with a data acquisition module, a data processing module, a data analysis module and a data pushing module. According to the invention, a data acquisition module is used for obtaining and pushing a power database through a power big data platform; acquiring a multi-source power database according to the pushed power database and sending the multi-source power database to a data processing module; classifying the multi-source power database into corresponding classification data pools by using the set classification data pools to generate corresponding power data packets; generating a corresponding curve model and an early warning model for each power data packet through a data analysis module, and obtaining a push log corresponding to each standard data packet according to the curve model and the early warning model; the data push module sends the push log to a corresponding push terminal; the pushing efficiency is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power big data, and specifically to a data push system based on a power big data platform. Background Art

[0002] The power big data platform collects a large amount of data such as power grid equipment, user power consumption information, and environmental parameters, providing support for power system dispatching, fault warning, and user services; among them, it includes functions such as data acquisition and integration, data storage and management, data analysis, and data visualization; and is applied to scenarios such as power system operation monitoring, fault diagnosis, dispatching optimization, power quality analysis, and power market analysis and decision-making; however, a large amount of data on the power big data platform may have diverse data, which may lead to problems such as data errors and incompleteness, and may also have data security and privacy issues as well as data processing efficiency issues; Since the power big data platform pushes different types of data to corresponding terminals for viewing, supervision, detection, warning, etc.; among them, the terminals include but are not limited to dispatching personnel terminals, operation and maintenance personnel terminals, user terminals, government terminals, etc.; however, due to data quality problems, the pushed data is inaccurate; and due to low data processing efficiency, the push is not timely and the timeliness is insufficient; and there are diverse terminals and data security and privacy issues, resulting in security problems in the push; therefore, in order to solve the above technical problems, the present invention provides a data push system based on a power big data platform. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides a data push system based on a power big data platform; The object of the present invention can be achieved by the following technical solutions: A data push system based on a power big data platform, including a control terminal, the control terminal is connected to a data acquisition module, a data processing module, a data analysis module, and a data push module; The data acquisition module is used to obtain a push power database through the power big data platform; and obtain a multi-source power database according to the push power database; The data processing module is provided with a classified data pool for classifying the multi-source power database into corresponding classified data pools to generate corresponding power data packets; The data analysis module is used to generate corresponding curve models and warning models for each power data packet, and obtain the push logs corresponding to each standard data packet according to the curve models and warning models; The data push module is used to send the push logs to the corresponding push terminals.

[0004] Further, the process of the data acquisition module obtaining the push power database through the power big data platform includes: Collect the data sources corresponding to each piece of power data and the corresponding data collection time points in the power big data platform, and obtain the data types corresponding to each piece of power data according to the data sources; the data types include operation data and electricity consumption data; and collect the transmission formats corresponding to the data types; use the collection time point as the connection data header to connect the data sources and data types corresponding to the power data to generate the storage data chain corresponding to the power data, and then connect it with the corresponding power data to generate a power data set, and integrate the power data sets corresponding to the same collection time point to generate a push power database.

[0005] Further, the process of obtaining a multi-source power database according to the push power database includes: Set the collection period, obtain the current time point, use the current time point as the end collection time point corresponding to the collection period, and then calculate backward from the current time point to obtain the start collection time point corresponding to the collection period, integrate the start collection time point and the end time point to obtain the collection time interval, mark it as the current collection time interval, and then obtain the current collection time interval according to the collection period again, and mark the previous current time interval as the historical collection time interval; Integrate the push power databases collected in the historical collection time interval to generate a historical push power database; integrate the push power databases collected in the current time interval to generate a real-time push power database; and then integrate the historical push power database and the real-time push power database to generate a multi-source power database.

[0006] Further, the process of the data processing module setting up a classified data pool includes: Set up the push terminals connected to the power big data platform, and the push terminals include operation and maintenance personnel terminals, user terminals, and management terminals; set up corresponding classified data pools according to the push terminals; Set up a push channel, and wirelessly communicate and connect the push terminals and the corresponding classified data pools through the push channel.

[0007] Further, the process of classifying the multi-source power database into the corresponding classified data pools to generate corresponding power data packets includes: Set the push data requirements corresponding to the push terminals in the classified data pool, and set an indexed data group according to the push data requirements; the indexed data group includes data types and required time periods, where the data type is a fixed indexed data, and the required time period includes a daily indexed requirement period and a self-set indexed requirement period; Collect the push power databases corresponding to the multi-source power database in real time according to the required time period; and classify the power data sets corresponding to each push power database into the corresponding classified data pools according to the data types for integration to obtain the corresponding power data packets.

[0008] Further, the process by which the data analysis module generates corresponding curve models for each power data packet includes: Obtain in real time the required time period corresponding to the power data packet, generate the abscissa of the curve according to the time sequence for the required time period, and generate the ordinate of the curve for the corresponding power data; and then generate curve nodes corresponding to the power data of the power data packet according to the abscissa and ordinate of the curve. Connect the respective curve nodes corresponding to the power data of the classification data packet in the required time period in chronological order to generate a corresponding curve model.

[0009] Further, the generation process of the warning model includes: A warning threshold range is set in the classification data pool; obtain the peak value and the change value corresponding to the required time period in the curve model; the change value is used to represent the slope of two adjacent curve nodes in the curve model. Compare each curve node in the curve model with the warning threshold range. If it is within the warning threshold range, generate a warning curve node for the corresponding curve node; otherwise, do not perform any processing; and obtain and connect the respective peak values in the curve model to generate a peak curve model; and generate curve change nodes for the change values and connect them to generate a change curve model. Connect the warning curve nodes corresponding to the required time period in the classification data pool to generate a warning model.

[0010] Further, the process of obtaining the push log corresponding to each power data packet according to the curve model and the warning model includes: Extract the collection time point and power data from all the stored data chains corresponding to the required time period, and integrate them to generate a real-time power data sheet corresponding to the required time period; merge the corresponding warning model, peak curve model, and change curve model with the real-time power data sheet to generate a push log.

[0011] Further, the process by which the data push module is used to send the push log to the corresponding push terminal includes: Push the corresponding push log to the corresponding push terminal for management in real time through the push channel.

[0012] Compared with the prior art, the beneficial effects of the present invention are: 1. The present invention utilizes the data acquisition module to obtain and push the power database through the power big data platform; obtains the multi-source power database according to the pushed power database and sends it to the data processing module; collects the stored data of each power data in the power big data platform to obtain the storage data chain corresponding to the power data, and further obtains the pushed power database; obtains the historical pushed power database and the real-time pushed power database according to the acquisition period; classifies and stores a large amount of data in the power big data platform to better save storage space.

[0013] 2. Utilizes the set classification data pool to classify the multi-source power database into the corresponding classification data pool to generate the corresponding power data packet; generates the corresponding curve model and warning model for each power data packet through the data analysis module, and obtains the push log corresponding to each standard data packet according to the curve model and the warning model; classifies the corresponding power data set of each historical pushed power database into the corresponding separation data pool according to the data type for integration to obtain the corresponding power data packet; sets up an index data group, and indexes the corresponding power data set using the required time period to improve the processing efficiency of power data; 3. The data push module sends the push log to the corresponding push terminal; uses the push channel to transmit the push log to the corresponding push terminal, which can not only ensure the security of the push terminal but also improve the transmission rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0015] Figure 1 It is the schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0017] As Figure 1 shown, a data push system based on a power big data platform includes a control terminal, and the control terminal is connected to a data acquisition module, a data processing module, a data analysis module, and a data push module; The data acquisition module is used to obtain the pushed power database through the power big data platform; and obtain the multi-source power database according to the pushed power database; The data processing module is provided with a classified data pool, which is used to classify the multi-source power database into the corresponding classified data pools to generate corresponding power data packets; The data analysis module is used to generate corresponding curve models and warning models for each power data packet, and obtain the push logs corresponding to each standard data packet according to the curve models and warning models; The data push module is used to send the push logs to the corresponding push terminals.

[0018] It should be further noted that the data acquisition module obtains the pushed power database through the power big data platform, including: Collect the data sources and corresponding acquisition time points of each power data in the power big data platform, and obtain the data types corresponding to each power data according to the data sources; the data types include operation data and power consumption data; and collect the transmission formats corresponding to the data types; use the acquisition time point as the connection data header to connect the data sources and data types corresponding to the power data to generate the storage data chain corresponding to the power data, and then connect it with the corresponding power data to generate a power data set, and integrate the power data sets corresponding to the same acquisition time point to generate the pushed power database; In the above embodiment, it should be further noted that the operation data includes but is not limited to voltage, current, power, frequency, etc.; the power consumption data includes but is not limited to power consumption, electricity bills, peak and valley power consumption periods, etc.; It should be further noted that obtaining the multi-source power database according to the pushed power database includes: Set the acquisition period, obtain the current time point, use the current time point as the end acquisition time point corresponding to the acquisition period, and then calculate backward according to the current time point to obtain the start acquisition time point corresponding to the acquisition period, integrate the start acquisition time point and the end time point to obtain the acquisition time interval, mark it as the current acquisition time interval, and then obtain the current acquisition time interval again according to the acquisition period, and mark the previous current time interval as the historical acquisition time interval; Integrate the pushed power database collected in the historical acquisition time interval to generate a historical pushed power database; integrate the pushed power database collected in the current time interval to generate a real-time pushed power database; and then integrate the historical pushed power database and the real-time pushed power database to generate a multi-source power database; In the above embodiments, it should be further noted that each power data of the power big data platform is collected to obtain the storage data chain corresponding to the power data, and then the power database to be pushed is obtained; the historical pushed power database and the real-time pushed power database are obtained according to the collection period; a large amount of data on the power big data platform is classified and stored to better save storage space.

[0019] It should be further noted that the data processing module sets up a classified data pool, including: A push terminal connected to the power big data platform is set up. The push terminal includes an operation and maintenance personnel terminal, a user terminal, and a management terminal; a corresponding classified data pool is set up according to the push terminal; A push channel is set up, and the push terminal and the corresponding classified data pool are wirelessly connected through the push channel; It should be further noted that classifying the multi-source power database into the corresponding classified data pool to generate the corresponding power data packet includes: The push data requirements corresponding to the push terminal are set in the classified data pool. An index data group is set according to the push data requirements. The index data group includes a data type and a required time period. Among them, the data type is a fixed index data, and the required time period includes a daily index requirement period and a self-set index requirement period; In the above embodiments, it should be further noted that if the self-set index requirement period is null, the daily index requirement period is the current collection time interval corresponding to the real-time pushed power database; The push power database corresponding to the multi-source power database is collected in real time according to the required time period; and the power data sets corresponding to each push power database are classified into the corresponding classified data pool according to the data type for integration to obtain the corresponding power data packet; In the above embodiments, it should be further noted that when the self-set index requirement period is null, the real-time pushed power database is classified into the corresponding classified data pool according to the data type to integrate and obtain the corresponding power data packet; if the self-set index period is not null, the corresponding historical pushed power database is indexed according to the self-set index period, and the power data sets corresponding to each historical pushed power database are classified into the corresponding separate data pool according to the data type for integration to obtain the corresponding power data packet; it should be further noted that an index data group is set up to index the corresponding power data set by using the required time period to improve the processing efficiency of power data.

[0020] It should be further noted that the data analysis module generates the corresponding curve model and warning model for each power data packet, including: Obtain the required time period corresponding to the power data packet in real time, generate the horizontal coordinate of the curve with the required time period in chronological order, and generate the vertical coordinate of the curve with the corresponding power data; furthermore, generate the curve nodes of the power data corresponding to the power data packet according to the horizontal and vertical coordinates of the curve. Connect the respective curve nodes corresponding to the power data of the classified data packet in chronological order according to the required time period to generate the corresponding curve model. Set a warning threshold range in the classified data pool; obtain the peak value and the change value corresponding to the required time period in the curve model; the change value is used to represent the slope of two adjacent curve nodes in the curve model. Compare each curve node in the curve model with the warning threshold range. If it is within the warning threshold range, generate the corresponding curve node as a warning curve node; otherwise, do nothing; and obtain and connect all the peak values in the curve model to generate a peak curve model; and generate curve change nodes from the change values and connect them to generate a change curve model. Connect the warning curve nodes corresponding to the required time period of the classified data pool to generate a warning model. In the above embodiment, it should be further noted that the warning threshold ranges corresponding to different classified data pools are different. It should be further noted that the push logs corresponding to each power data packet are obtained according to the curve model and the warning model; including: Extract the collection time points and power data from all the stored data chains corresponding to the required time period, and integrate them to generate a real-time power data sheet corresponding to the required time period; merge the corresponding warning model, peak curve model, and change curve model with the real-time power data sheet to generate a push log.

[0021] It should be further noted that the data push module is used to send the push log to the corresponding push terminal; including: Push the corresponding push log to the corresponding push terminal for management in real time through the push channel.

[0022] In the above embodiment, it should be further noted that using the push channel to transmit the push log to the corresponding push terminal can not only ensure the security of the push terminal but also improve the transmission rate, thereby effectively pushing the power data.

[0023] Working principle: The present invention utilizes the data acquisition module to obtain and push the power database through the power big data platform; obtains the multi-source power database according to the pushed power database and sends it to the data processing module; classifies the multi-source power database into the corresponding classification data pool by using the set classification data pool to generate the corresponding power data packet; generates the corresponding curve model and warning model for each power data packet through the data analysis module, and obtains the push log corresponding to each standard data packet according to the curve model and warning model; furthermore, the data push module sends the push log to the corresponding push terminal; the present invention effectively improves the push efficiency and transmission security.

[0024] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solution and advantages of the present application clearer, the above combines the accompanying drawings and specific embodiments to further describe the present application in detail; it should be understood that the specific embodiments described herein are only intended to explain the present application, rather than limiting the present application; for those skilled in the art, the present application can be implemented without some of these specific details; the above description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0025] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A data push system based on a power big data platform, comprising a control terminal, characterized in that: The control terminal is connected to the data acquisition module, the data processing module, the data analysis module and the data push module; The data acquisition module is used to obtain a push power database through the power big data platform; and obtain a multi-source power database based on the push power database; The data processing module is provided with a classification data pool, which is used to classify the multi-source power database into the corresponding classification data pool to generate the corresponding power data packet; The data analysis module is used to generate a corresponding curve model and early warning model for each power data packet, and obtain a push log corresponding to each standard data packet according to the curve model and the early warning model; The data push module is used to send the push log to the corresponding push terminal.

2. According to claim 1, a data push system based on a power big data platform is characterized in that: The process of the data acquisition module acquiring and pushing the power database through the power big data platform includes: Collect the data source and the corresponding collection time point corresponding to each power data on the power big data platform, and obtain the data type corresponding to each power data according to the data source; the data type includes operation data and power consumption data; and collect the transmission format corresponding to the data type; use the collection time point as the connection data header to connect the data source and data type corresponding to the power data to generate a storage data chain corresponding to the power data, and then connect with the corresponding power data to generate a power data set, and integrate the power data sets corresponding to the same collection time point to generate a push power database.

3. According to claim 2, a data push system based on a power big data platform is characterized in that: The process of obtaining a multi-source power database based on a push power database includes: Set the collection cycle, obtain the current time point, take the current time point as the end collection time point corresponding to the collection cycle, and then calculate backwards from the current time point to obtain the start collection time point corresponding to the collection cycle, integrate the start collection time point and the end time point to obtain the collection time interval, mark it as the current collection time interval, and then obtain the current collection time interval again according to the collection cycle, and mark the previous time interval as the historical collection time interval; The push power database collected in the historical collection time interval is integrated to generate a historical push power database; the push power database collected in the current time interval is integrated to generate a real-time push power database; and then the historical push power database and the real-time push power database are integrated to generate a multi-source power database.

4. According to claim 3, a data push system based on a power big data platform is characterized in that: The process of the data processing module setting the classification data pool includes: Setting a push terminal connected to the power big data platform, wherein the push terminal includes an operation and maintenance personnel terminal, a user terminal, and a management terminal; setting a corresponding classification data pool according to the push terminal; A push channel is set up, and the push terminal and the corresponding classification data pool are connected through wireless communication through the push channel.

5. According to claim 4, a data push system based on a power big data platform is characterized in that: The process of classifying the multi-source power database into the corresponding classification data pool to generate the corresponding power data packet includes: The push data demand corresponding to the push terminal is set in the classified data pool, and an index data group is set according to the push data demand; the index data group includes a data type and a demand time period, wherein the data type is fixed index data, and the demand time period includes a daily index demand period and an autonomously set index demand period; The push power database corresponding to the multi-source power database is collected in real time according to the demand time period; and the power data sets corresponding to each push power database are classified into the corresponding classification data pool according to the data type for integration to obtain the corresponding power data packet.

6. A data push system based on a power big data platform according to claim 5, characterized in that: The process of the data analysis module generating a corresponding curve model from each power data packet includes: Obtain the demand time period corresponding to the power data packet in real time, generate a curve abscissa from the demand time period in chronological order, and generate a curve ordinate from the corresponding power data; and then generate a curve node corresponding to the power data packet according to the curve abscissa and the curve ordinate; The power data of the classified data packet corresponding to the demand time period is connected to the corresponding curve nodes in chronological order to generate the corresponding curve model.

7. A data push system based on a power big data platform according to claim 6, characterized in that: The generation process of the early warning model includes: A warning threshold range is set in the classification data pool; a peak value and a change value corresponding to a demand time period in the curve model are obtained; the change value is used to represent the slope of two adjacent curve nodes in the curve model; Compare each curve node in the curve model with the warning threshold range. If the node is within the warning threshold range, generate a warning curve node for the corresponding curve node; otherwise, do nothing; obtain each peak value in the curve model and connect them to generate a peak curve model; and generate a curve change node for the change value and connect them to generate a change curve model; The early warning curve nodes corresponding to the demand time period of the classification data pool are connected to generate an early warning model.

8. The data push system based on the electric power big data platform according to claim 7 is characterized in that: The process of obtaining the push logs corresponding to each power data packet according to the curve model and the early warning model includes: Extract the collection time points and power data from all storage data chains corresponding to the demand time period, and integrate them to generate a real-time power data sheet corresponding to the demand time period; merge the corresponding early warning model, peak curve model, change curve model and real-time power data sheet to generate a push log.

9. The data push system based on the electric power big data platform according to claim 8 is characterized in that: The process of the data push module for sending the push log to the corresponding push terminal includes: The corresponding push log is pushed to the corresponding push terminal in real time through the push channel for management.

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