Urban power big data application system and method

Abstract

The present invention provides a city power big data application system and method, including: a platform layer for processing business requirements based on a big data platform to obtain integrated results, and mapping the integrated results on a geographic information system (GIS) platform; the platform layer includes: a data analysis module for receiving business requirements and parsing the business requirements to obtain analysis conditions; a data acquisition module for obtaining data required by the data calculation module based on the analysis conditions; a data calculation module for performing cross-calculations based on the data collected by the data acquisition module, the analysis conditions and the algorithm provided by the big data platform to obtain display data; and a data visualization module for mapping the display data on a geographic information system (GIS) platform. The present invention is based on a geographic information system (GIS) and intuitively displays data obtained through a big data platform by means of multi-dimensional visualization, providing technical support for the operator's operation management.

Description

Technical Field

[0001] The present invention relates to electric power big data, and in particular to an urban electric power big data application system and method. Background Art

[0002] As the level of informatization of the power system continues to advance, the rapid accumulation of data has caused the power system to gradually present big data characteristics. Through the cross-domain integration of data, sufficient data support can be provided for the application of big data technology. The advantages of big data technology in analyzing massive data and mining the potential value of data can bring new ideas to urban power grid planning, power grid operation management, and power supply services.

[0003] At the same time, the spatial distribution of indicators such as load / power, the location information of equipment and users, etc., play an important role in helping planning, operation, marketing and other departments understand the operation status of power grids and customer conditions in different regions, and quickly and accurately locate target objects. Therefore, combining business data with geographic information data and visually displaying various information and analysis results on the map can provide good support for the development of various businesses.

[0004] Existing information systems only provide the spatial location and topological relationship of equipment and lines, or analysis and control of a single field, lacking cross-system spatiotemporal correlation analysis and deep integration of multi-system data and cross-domain analysis. At present, the existing GIS platform has provided the spatial location and topological relationship of stations, lines, transformers, etc., but it lacks spatial information of external objects of the system such as users, electric vehicles, and new energy, and cannot associate the operation and status of equipment, lines, etc. with spatial location. It also does not have the function of analysis and mining, and cannot meet the needs of various departments to carry out advanced spatiotemporal correlation analysis and display. Summary of the invention

[0005] In order to solve the above-mentioned deficiencies in the prior art, the present invention provides an urban power big data application system and method, which comprehensively utilizes big data technology and geographic information technology, and integrates power grid topology, power grid production and operation, equipment operation and maintenance, user electricity consumption information, electric vehicle charging facilities, distributed energy information, and regional natural environment, socio-economic indicators, humanities, meteorology, traffic information and other power system external data across fields. Through data analysis and mining technology, a series of decision-making auxiliary big data application themes are formed, and these themes are comprehensively displayed on the map in a multi-level, multi-faceted and multi-angle manner, realizing the full process display of basic data, intermediate data and conclusion data, and predicting load based on power map information, assisting in charging station site selection, analyzing reliability influencing factors and user demands of various distribution lines, and providing support for power grid operation management and auxiliary decision-making.

[0006] The technical solution provided by the present invention is: an urban power big data application system, comprising:

[0007] The platform layer is used to process business requirements based on the big data platform to obtain integration results, and map the integration results on the geographic information system (GIS) platform;

[0008] The platform layer includes: a data analysis module, a data calculation module, a data acquisition module and a data visualization module;

[0009] The data analysis module is used to receive business requirements and analyze the business requirements to obtain analysis conditions;

[0010] The data acquisition module is used to obtain the data required by the data calculation module based on the analysis conditions;

[0011] The data calculation module is used to perform cross calculation based on the data collected by the data collection module, the analysis conditions and the algorithm provided by the big data platform to obtain the display data;

[0012] The data visualization module is used to map the display data on a geographic information system (GIS) platform.

[0013] Preferably, the urban power big data application system further includes: a data layer;

[0014] The data layer is used to store internal and external data of the power grid, and provide data to the data acquisition module of the platform layer in the form of a data source.

[0015] Preferably, the urban power big data application system further includes: an application layer;

[0016] The application layer is used to receive business requirements and send the business requirements to the platform layer, and provide presentation services to customers based on the presentation data of the business requirements by the platform layer.

[0017] Preferably, the data analysis module includes:

[0018] The parsing submodule is used to receive the business requirements sent by the application layer, and parse the business requirements to obtain data requirements and operation requirements;

[0019] A first sending submodule, used to send the data demand to the data acquisition module;

[0020] A second sending submodule, used to send the operation requirement to the data calculation module;

[0021] The data acquisition module comprises:

[0022] A collection and receiving submodule, used for receiving data requirements sent by the data analysis module;

[0023] The acquisition submodule is used to obtain basic data that meets the data requirements from a pre-generated basic database;

[0024] The data calculation module comprises:

[0025] A calculation receiving submodule, used for receiving the operation requirements sent by the data analysis module;

[0026] A calculation submodule, for generating intermediate data and result data by performing cross calculations using a model algorithm library, memory calculations, and batch calculations in a big data platform based on the operation requirements and the basic data collected by the collection submodule;

[0027] The data visualization module comprises:

[0028] A second sending submodule, used to send the display data consisting of the basic data, intermediate data or result data to the business layer;

[0029] The mapping submodule is used to map the display data on the Geographic Information System (GIS) platform with the help of the application layer.

[0030] Preferably, the platform layer further includes:

[0031] The data storage module is used to store the basic database, basic data, intermediate data, result data and display data using a hybrid storage architecture based on data types;

[0032] Construct a basic database module, which is used to extract or import the data source provided by the data layer offline through the Extract-Interactive Transform-Load (ETL) server across platforms, and screen out and normalize the missing data, garbled data, and data that does not conform to preset rules in the data source, convert the format of the screened and normalized data source according to the preset data model, and establish the association relationship between different data tables to generate a basic database;

[0033] The partitioning storage module is used to divide the storage space in the platform layer into multiple units as needed, and store the data in the platform layer in the corresponding units through the data storage module.

[0034] Preferably, the partitioning storage module includes:

[0035] The first type unit is used to store the structured data in the basic database in a relational database, and store the unstructured data and semi-structured data in the basic database in a distributed file system, a distributed database and a distributed message queue;

[0036] A second type unit is used to store the intermediate data in a hybrid storage manner;

[0037] A third type unit is used to store the result data in a mixed storage manner;

[0038] A fourth type unit, used for storing the display data in a hybrid storage manner;

[0039] The hybrid storage method includes row-column hybrid storage, row storage and column storage.

[0040] Preferably, the application layer includes:

[0041] Urban power grid planning and operation module, equipment operation and maintenance / power supply guarantee module and user management and service module;

[0042] The urban power grid planning and operation module is used for spatial load forecasting, power supply reliability assessment and electric vehicle charging pile site selection;

[0043] The equipment operation and maintenance / power supply guarantee module is used for heavy overload warning, emergency repair resource optimization and risk assessment in the substation area;

[0044] The user management and service module is used to perform customer demand analysis, power saving potential analysis and credit analysis.

[0045] Preferably, the application layer further includes:

[0046] Spatial positioning retrieval module;

[0047] The spatial positioning retrieval module is used to combine the big data platform and the GIS platform using the ledger data identifier, and to achieve spatial positioning retrieval of the big data platform information by obtaining the coordinate position information corresponding to the ledger.

[0048] Preferably, the application layer further includes:

[0049] Interactive display module;

[0050] The interactive display module is used to display the query results from basic data, intermediate data to result data through the visualization components in the big data platform based on user needs, or drill down the coordinate location information step by step according to the zoom ratio until the customer specifies the positioning target.

[0051] Preferably, the application layer further includes:

[0052] Custom modules;

[0053] The custom module is used to draw an area of ​​any shape by monitoring the user's mouse click operations on the GIS layer, analyze the characteristic parameters of the area based on the application in the big data platform, and store and call the data generated during the analysis process to achieve comprehensive analysis, evaluation and prediction oriented to business needs.

[0054] Based on the same inventive concept, the present invention also provides a method for applying urban power big data, which is characterized by comprising:

[0055] The data analysis module of the platform layer receives the business requirements and analyzes the business requirements to obtain analysis conditions;

[0056] The data acquisition module of the platform layer obtains the data required by the data calculation module based on the analysis conditions;

[0057] The data calculation module of the platform layer performs cross calculation based on the data collected by the data collection module, the analysis conditions and the algorithm provided by the big data platform to obtain the display data;

[0058] The data visualization module of the platform layer maps the display data on the geographic information system GIS platform.

[0059] Preferably, the urban power big data application method further includes:

[0060] The data layer stores the internal and external data of the power grid and provides data to the data acquisition module of the platform layer in the form of a data source;

[0061] The application layer receives the business demand and sends the business demand to the platform layer, and provides presentation services to customers based on the presentation data of the business demand by the platform layer.

[0062] Preferably, the data analysis module of the platform layer receives the business requirements and analyzes the business requirements to obtain analysis conditions, including:

[0063] The parsing submodule in the data analysis module receives the business requirements sent by the application layer, and parses the business requirements to obtain data requirements and operation requirements;

[0064] The data requirement is sent to the data acquisition module through the first sending submodule in the data analysis module, and the operation requirement is sent to the data calculation module through the second sending submodule in the data analysis module.

[0065] Preferably, the data acquisition module of the platform layer acquires the data required by the data calculation module based on the analysis conditions, including:

[0066] The data collection and receiving submodule in the data collection module receives the data requirements sent by the data analysis module;

[0067] And the basic data that meets the data requirements is obtained from a pre-generated basic database through the acquisition submodule.

[0068] Preferably, the data calculation module of the platform layer performs cross calculation based on the data collected by the data collection module, the analysis conditions and the algorithm provided by the big data platform to obtain the display data, including:

[0069] The calculation receiving submodule in the data calculation module receives the operation requirements sent by the data analysis module;

[0070] And through the calculation submodule in the data calculation module, based on the operation requirements and the basic data collected by the collection submodule, the model algorithm library, memory calculation and batch calculation in the big data platform are used to perform cross calculations to generate intermediate data and result data.

[0071] Preferably, the data visualization module of the platform layer maps the display data on a geographic information system (GIS) platform, including:

[0072] The second sending submodule in the data visualization module sends the display data consisting of the basic data, intermediate data or result data to the business layer;

[0073] And through the mapping submodule in the data visualization module, the display data is mapped on the geographic information system GIS platform with the help of the application layer.

[0074] Compared with the closest prior art, the technical solution provided by the present invention has the following beneficial effects:

[0075] 1. The technical solution provided by the present invention is that the platform layer is used to process business needs based on the big data platform to obtain integrated results, and map the integrated results on the geographic information system GIS platform; the platform layer includes: data analysis module, data calculation module, data acquisition module and data visualization module; the data analysis module is used to receive business needs and parse the business needs to obtain analysis conditions; the data acquisition module is used to obtain the data required by the data calculation module based on the analysis conditions; the data calculation module is used to perform cross-calculations based on the data collected by the data acquisition module, the analysis conditions and the algorithm provided by the big data platform to obtain display data; the data visualization module is used to map the display data on the geographic information system GIS platform. This architecture has strong versatility and scalability. It is based on the geographic information system GIS, and the display data obtained through the big data platform is associated with geographic information to intuitively display the overall picture of power grid production and operation, providing technical support for the operator's operation and management.

[0076] 2. The technical solution provided by the present invention comprehensively utilizes big data technology and geographic information technology to realize multi-factor spatiotemporal correlation analysis, multi-dimensional statistical analysis and multi-perspective visualization analysis of grid information, equipment information, operation information, user information, etc., and uses the location of power equipment and / or power consumption area as a carrier to convert spatial data, load data, power grid data, equipment data, and user data that seem to have no strong correlation into effective data for business applications through certain technical means, and directly combine them with geographic graphics for display.

[0077] 3. The technical solution provided by the present invention adopts the idea of ​​a unified data model to construct a logical model of system data, adopts a hybrid storage architecture, and uses different data engines for data of different data types and different application purposes, thereby improving the efficiency of data query and retrieval while meeting the storage needs of large and diversified data.

[0078] 4. The technical solution provided by the present invention involves data including various business data within the power grid, including equipment inventory, operation and maintenance data, power grid topology, marketing business data, user load data, user demand data, and external data, including meteorological data and vehicle trajectory data. The data collection frequency covers the full range from seconds, minutes, to static levels, and the spatial dimension ranges from provinces, cities, regions to single devices or single users.

[0079] 5. The technical solution provided by the present invention realizes the full-process display of basic data, intermediate data and analysis result data based on the interactive display module and the custom display module, and has strong human-computer interactivity.

[0080] 6. The technical solution provided by the present invention, with the help of analysis and mining technology, can deeply understand the characteristics of the load, the status of the equipment, customer characteristics and other information, and can intuitively display it with the help of multi-dimensional visualization means to gain insight into the spatial distribution or spatiotemporal characteristics of important parameters and indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 This is the overall architecture diagram of the urban power big data application system of the present invention;

[0082] Figure 2 A schematic diagram of the overall data and technical architecture of the urban power big data application system provided in an embodiment of the present invention;

[0083] Figure 3 A schematic diagram of hybrid storage provided in an embodiment of the present invention;

[0084] Figure 4 The hardware structure of the urban power big data application system provided in the embodiment of the present invention;

[0085] Figure 5This is a schematic diagram of the functional modules of the urban power big data application system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0086] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and examples.

[0087] The present invention proposes an urban power big data application system that provides auxiliary decision support for urban power grid planning, power grid operation management, power supply services and other fields. The present invention comprehensively utilizes big data technology and geographic information technology, and integrates power grid topology, power grid production and operation, equipment operation and maintenance, user power consumption information, electric vehicle charging facilities, distributed energy information, and regional natural environment, socio-economic indicators, humanities, meteorology, traffic information and other external data of the power system across fields. Through data analysis and mining technology, a series of auxiliary decision-making big data application themes are formed, and these themes are comprehensively displayed on the map at multiple levels, multiple sections and multiple angles, realizing the full process display of basic data, intermediate data and conclusion data, and based on the power map information, load is predicted, auxiliary charging station site selection is selected, and reliability influencing factors and user demands of various distribution lines are analyzed, providing support for power grid operation management and auxiliary decision-making.

[0088] An interactive power big data analysis application technology architecture based on GIS, which has strong versatility and scalability. Based on the geographic information system GIS, the business data such as load, equipment, and users are associated with geographic information through data aggregation and fusion technology. With the help of analysis and mining technology, the characteristics of loads, the status of equipment, customer characteristics and other information are deeply understood. With the help of multi-dimensional visualization means, the spatial distribution or spatiotemporal characteristics of important parameters and indicators are intuitively displayed, and the overall picture of power grid production and operation is intuitively displayed, providing technical support for the operator's operation and management. At the same time, by drilling down step by step, the target object can be quickly and accurately located, and the more refined characteristics and status of the target object can be understood, providing auxiliary decision-making support for the development of various businesses such as policy making, marketing, operation and inspection, and dispatching.

[0089] Example 1

[0090] This embodiment provides a city power big data application system, including:

[0091] The platform layer is used to process business requirements based on the big data platform to obtain integration results, and map the integration results on the geographic information system (GIS) platform;

[0092] The platform layer includes: a data analysis module, a data calculation module, a data acquisition module and a data visualization module;

[0093] The data analysis module is used to receive business requirements and analyze the business requirements to obtain analysis conditions;

[0094] The data acquisition module is used to obtain the data required by the data calculation module based on the analysis conditions;

[0095] The data calculation module is used to perform cross calculation based on the data collected by the data collection module, the analysis conditions and the algorithm provided by the big data platform to obtain the display data;

[0096] The data visualization module is used to map the display data on a geographic information system (GIS) platform.

[0097] In addition, the urban power big data application system also includes:

[0098] The data layer is used to store internal and external data of the power grid and provide data to the data acquisition module of the platform layer in the form of a data source.

[0099] The application layer is used to receive business requirements and send the business requirements to the platform layer, and provide display services to customers based on the display data of the business requirements by the platform layer. Specifically, it includes: receiving business requirements sent by customers and sending the business requirements to the platform layer, receiving display data sent by the platform layer, obtaining the results to be displayed based on the display data of the customers, and sending the results to be displayed to the geographic information system GIS platform of the platform layer for display.

[0100] like Figure 1 The figure shows the overall architecture of the urban power big data application system, which is divided into three parts: data layer, platform layer, and application layer. The data layer involves two types of data: internal data of the power system and external data. The platform layer includes two major blocks: big data platform and power GIS platform. The application layer can be divided into several blocks according to system functions and target users. The architecture has good configurability and scalability, provides a more intuitive, easy-to-use, and flexible way to interact with power map data, and has a strong secondary development foundation.

[0101] 1. The software architecture of the urban power big data application system is divided into three layers: original data layer, basic function layer, and application presentation layer.

[0102] (1) Original data layer: Data access objects include: distribution network operation data, electricity consumption data, electric vehicle operation data, 95598 customer service data, marketing system data, meteorological and traffic data, etc.

[0103] (2) Basic functional layer: It includes the access, storage, analysis, calculation and visualization of raw data, builds a big data center for urban power maps, and discovers the correlation and rules between cross-business / regional data through data comparison between different business fields / regions, providing support for decision makers in overall planning.

[0104] (3) Application presentation layer: Based on the different focuses and objects of each business object, a demand-oriented application scenario is established for each business object.

[0105] 2. Data and technical architecture of the urban power big data application system Figure 2 shown.

[0106] (1) The urban power big data application system adopts the idea of ​​unified data model to construct the physical architecture of system data, which is divided into four types. In this embodiment, the four types are unified layer, analysis layer, business layer, and display layer. The unified layer uses the IECSIM model and SG SIM model as the basis of the system data model. The source data extracted from internal and external systems through the interface is reorganized after preliminary cleaning and stored in the unified layer. This layer is mainly used to store all basic data such as fact data, business data, master data, reference data and dimension data with the lowest granularity. The analysis layer reorganizes the unified layer data for each analysis task. This layer no longer emphasizes the third normal form of data and reduces redundancy, with the main purpose of reducing joint queries and increasing efficiency; the business layer and the display layer are directly oriented to business needs and display, and the library table design is more customized.

[0107] (2) The data integration of the urban power big data application system adopts the extraction-interactive transformation-loading (ETL) server, and uses a variety of technical means such as the big data collector and the platform's task scheduling, data calculation, and WebService service interface to achieve access to data from different sources, formats, and time and space scales such as equipment, users, charging facilities, distributed power sources, geographic information, meteorology, and vehicle trajectories. After standardization, format conversion, and association processing of various types of data in accordance with unified data specifications, the data is stored in the data repository.

[0108] (3) The data storage of the urban power big data application system adopts a hybrid storage architecture. Different data engines are used for different data types and data for different application purposes. While meeting the storage needs of large amounts of diversified data, the efficiency of data query and retrieval is improved. For example, for the storage of source data, relational databases such as MySQL or Oracle are used to store structured data, and distributed storage architectures based on X86 cluster distributed file systems and distributed columnar databases are used to store unstructured data and semi-structured data. For the data of the analysis application layer, which involves the joint use of multi-source, multi-type, and multi-resolution data, dynamic hybrid storage is used for storage.

[0109] Dynamic hybrid storage is modeled using hierarchical key-value pairs, that is, a (Key, Value) pair is used to represent a basic attribute value. An entity contains several attribute values, and its corresponding data object will be mapped to a set of (Key, Value) pairs. For the (Key, Value) pairs in the same shard, row storage or column storage can be used. In the same data table, row-column hybrid storage can be implemented at the same time according to the data access specification. If all columns in the table are placed in a column group for row storage, traditional row storage can be implemented; and if all columns in the table are placed in a column group for column storage, or each column is placed in a column group, complete column storage can be implemented. The row-based method is suitable for efficient access to data items, and the column-based method is suitable for the storage of sparse data, which reduces storage space.

[0110] like Figure 3 As shown, for the data in the analysis application layer, the row, column, or mixed storage method is set according to the characteristics of the data and the needs of the analysis application.

[0111] (4) The urban power big data application system meets computing needs of different timeliness through the memory computing, batch computing and other distributed computing technologies provided by the big data platform. Memory computing supports interactive analysis, such as online statistics on the number of electric vehicle charging piles, and batch computing supports offline analysis of large batches of data, such as load characteristic indicator analysis of custom areas.

[0112] (5) The data analysis of the urban power big data application system uses a separate OLAP and data mining server to build a distributed data mining algorithm library to form a unified analysis and decision-making component, providing basic technical support for the construction of analysis and decision-making applications.

[0113] 3. The urban power big data application system uses the ArcGIS ledger data interface, coordinate data storage, etc. to obtain the corresponding coordinate location information (MapPoint) of the ledger through the ledger data identification of power grid equipment, users, charging facilities, distributed power sources, etc. The system uses the obtained coordinate location information to perform custom graphic visualization rendering on the ArcGIS layer, thereby realizing the spatial positioning retrieval of information such as power grid equipment, users, charging facilities, distributed power sources, etc., and supports flexible configuration of the display content and display method of related spatial objects and attribute information.

[0114] 4. The urban power big data application system is based on ArcGIS and adopts multiple visualization display methods such as ARCGIS, ECHARTS, FLEX, HTML5, CSS3, etc. to realize the monitoring and graphic display of collected data. It supports the whole process display from basic data, process data to result data, and supports drilling down step by step through coordinate location information and Map scale to accurately locate the target; it has interactive display function and supports automatic selection of display objects, display range and resolution, etc.

[0115] 5. The urban power big data application system monitors the user's mouse click operations on the ArcGIS layer, draws points, constructs surfaces from points, customizes areas of arbitrary shapes through selective drawing, realizes the characteristic parameter calculation function of customized areas for different themes, and stores and calls data.

[0116] 6. Urban power big data application hardware architecture Figure 4 As shown in the figure, the whole system consists of 6 servers, including 1 database server, which is used as the basic library of the big data platform for data storage and backup; 3 computing servers form a big data computer group to realize data preprocessing, statistical analysis, prediction and evaluation and other analysis and mining functions; 1 data acquisition server completes data collection and extraction functions; 1 application server completes the storage and display of analysis results. These hardware can be either physical machines or virtual machines on the cloud platform.

[0117] Example 2

[0118] The urban power big data application system provided in this embodiment has the following functions:

[0119] like Figure 5 The functional module diagram of the system shown in the figure shows that the system functions mainly include three modules: basic functional module, status characteristic analysis and display module and advanced application module. Each module has multiple secondary functional modules and can be adjusted and expanded according to needs.

[0120] (1) Basic functional modules. Relying on the functions of the big data platform, combined with the GIS system and auxiliary tools, it realizes the functions of data access, cleaning, storage, retrieval, calculation, and visualization, providing support for subsequent analysis and processing and advanced applications. In addition to the basic functions of the big data platform, the basic module of the urban power big data application system also has four major extended functions: multi-source data aggregation and fusion, spatial positioning retrieval, interactive multi-dimensional visual analysis and display, and custom area.

[0121] 1) Multi-source data convergence and fusion: Realize the access, cleaning and storage of data from different sources, types and time and space scales inside and outside the power system, including power grid topology, power grid operation, equipment operation and maintenance, marketing business, user information, electric vehicle charging facilities, distributed energy information, as well as geography, meteorology, economy, humanities and society, transportation, etc.

[0122] 2) Spatial positioning retrieval: Match the object information of lines, substations, distribution transformers, power users, charging facilities, distributed power sources, metering and collection devices involved in the urban power grid with their spatial location information, and mark them on the map, supporting query positioning and two-way retrieval of relevant information; supporting flexible configuration of the display content and display method of related spatial objects and attribute information.

[0123] 3) Interactive multi-dimensional visualization analysis and display: Based on GIS, it adopts various visualization methods to realize multi-level, multi-faceted and multi-angle comprehensive display of collected data and analysis topics, supports the whole process display from basic data, intermediate data to result data, and drills down step by step to locate the target; it can display alarms for equipment abnormalities or failures. It has interactive display function and supports automatic selection of display objects, display range and resolution, etc.

[0124] 4) Customized area: Customize areas of any shape through a variety of selective drawing methods, calculate the characteristic parameters of customized areas for different themes, and store and call the data.

[0125] (2) Status feature analysis and display module. This module conducts statistical, correlation, classification, clustering and other analysis and mining of various data from different angles and depths according to application requirements to reflect the status or characteristics of the power grid, equipment or users, and stores and displays the analysis results. This module mainly realizes the analysis and result display of a certain type of characteristics of the power grid, equipment or users, and provides auxiliary decision-making reference information for power grid operation and management. Taking into account the multiple analysis results of this module, the evaluation or prediction function is realized, and it is expanded in depth and breadth to form an advanced application module. At present, the functions realized by this module include multi-dimensional display of city and power grid data, load characteristic analysis, analysis of factors affecting distribution network reliability, and analysis of user demands.

[0126] 1) Multi-dimensional display of city and power grid data: Using geographic information technology, the historical and current data of different topics such as the power grid in the selected area, user static data, power grid operation information, power user electricity consumption information, electric vehicle charging pile information, new energy and distributed energy information are comprehensively displayed on the map in multiple aspects and angles, including panoramic data display, query positioning, dynamic display of historical data, indicator statistics, etc.

[0127] 2) Load characteristics analysis: Comprehensively consider multiple characteristics such as regional load density, user density, load growth rate, and regional construction time to classify load areas, and display the distribution of typical areas on the map. At the same time, analyze and display the load characteristics of a single area and its type change trend, providing support for discovering the load characteristics and development laws of typical areas. The area can be a power supply area or a power supply community, or you can use the custom area function of this system to set the analysis area independently.

[0128] 3) Power supply reliability analysis: Analyze the reliability changes of various types of lines in various types of areas, explore the relationship between various influencing factors such as topology, user density, user type and equipment level and power supply reliability from multiple angles, and provide support for power supply reliability assessment and finding ways to improve power supply reliability.

[0129] 4) User demand analysis: Starting from the work order information of the customer service 95598 system, according to the different types of demands, user demands are classified into four aspects: power application, power outage, power supply quality, and others (service quality, etc.). The distribution of user demands in each power supply area and distribution transformer is analyzed and displayed based on the power supply area and distribution transformer, providing support for the power management department to understand the operation status of the power grid and customer satisfaction.

[0130] (3) Advanced applications. This module contains several big data applications to realize comprehensive analysis, evaluation or prediction functions for business needs. Each application can be used as a relatively complete whole to provide support in a certain field for power grid planning, power grid operation, equipment operation and maintenance or user services. At present, the advanced applications that have been realized by the system include site selection and planning of electric vehicle charging stations and prediction, warning and risk assessment of heavy overload in substations.

[0131] 1) Site selection and planning of electric vehicle charging stations: This application includes three functions: charging area demand analysis, electric vehicle charging station operation analysis, and line load analysis. With the help of the GIS system, the spatial distribution of the main vehicle operation routes, high-density gathering points, and long-term stay areas, as well as the load size of surrounding lines and the utilization of electric vehicle charging stations are intuitively displayed on the map, assisting planning and design personnel to determine the location and capacity of electric vehicle charging piles.

[0132] 2) Substation heavy overload prediction, warning and risk assessment: This application includes four functions: multi-dimensional analysis of historical heavy overloads, analysis of heavy overload-related factors, substation heavy overload warning and risk assessment. It can accurately locate the heavy overloaded substations, and go deeper step by step. By displaying the results of multi-dimensional analysis, it can explore the causes, laws and affected users of heavy overloads, and provide support for daily operation and maintenance and power supply during important periods.

[0133] In addition to the above-mentioned business functions, the urban power big data application system also has system management and maintenance functions, mainly including user role management, system authority management, configuration management, log maintenance management, security audit management and other functions.

[0134] Example 3

[0135] This embodiment takes the site selection of electric vehicle charging piles as an example to illustrate the following aspects:

[0136] When the application layer receives the business demand for selecting a site for an electric vehicle charging pile, it sends the business demand to the platform layer;

[0137] The data analysis module in the platform layer receives the business demand and parses it to obtain the analysis conditions, including: mining the running trajectory of electric vehicles, calculating the capacity and load of the power grid in the specified area, as well as the number of electric vehicles and the number of distributed power sources;

[0138] Send the data that needs to be collected in the analysis conditions to the data acquisition module, such as the number of electric vehicles and the number of distributed power sources. The data acquisition module obtains basic data from the basic database and waits for the data calculation module or the data analysis module to call. When receiving the requirements of the data calculation module or the data analysis module, the data acquisition module sends data according to the corresponding requirements;

[0139] When the operation requirements include algorithms, the data analysis module will perform the operation, for example: mining the running trajectory of electric vehicles and sending the operation requirements to the data calculation module; for example: calculating the capacity and load of the power grid in a specified area.

[0140] The data calculation module includes: a calculation receiving submodule and a calculation submodule.

[0141] The calculation receiving submodule is used to receive the operation requirements sent by the data analysis module;

[0142] A calculation submodule is used to generate intermediate data and result data by cross-calculating using the model algorithm library, memory calculation and batch calculation in the big data platform based on the operation requirements and the basic data collected by the collection submodule;

[0143] The result data includes: the basic data and the intermediate data generate the first result data by using the mining algorithm in the model algorithm library of the big data platform, and the basic data generate the intermediate data and the second result data by using the memory calculation and batch calculation in the big data platform;

[0144] Send the display data consisting of basic data, intermediate data or result data in the platform layer to the business layer;

[0145] The data storage module adopts a hybrid storage architecture to store the basic database, basic data, intermediate data, result data and display data.

[0146] Send the display data to the business layer for display.

[0147] Example 4

[0148] Based on the same inventive concept, this embodiment also provides a method for applying urban power big data, including:

[0149] The data analysis module of the platform layer receives the business requirements and analyzes the business requirements to obtain analysis conditions;

[0150] The data acquisition module of the platform layer obtains the data required by the data calculation module based on the analysis conditions;

[0151] The data calculation module of the platform layer performs cross calculation based on the data collected by the data collection module, the analysis conditions and the algorithm provided by the big data platform to obtain the display data;

[0152] The data visualization module of the platform layer maps the display data on the geographic information system GIS platform.

[0153] In the embodiment, the urban power big data application method further includes:

[0154] The data layer stores the internal and external data of the power grid and provides data to the data acquisition module of the platform layer in the form of a data source;

[0155] The application layer receives the business demand and sends the business demand to the platform layer, and provides presentation services to customers based on the presentation data of the business demand by the platform layer.

[0156] In the embodiment, the data analysis module of the platform layer receives the business requirements and analyzes the business requirements to obtain analysis conditions, including:

[0157] The parsing submodule in the data analysis module receives the business requirements sent by the application layer, and parses the business requirements to obtain data requirements and operation requirements;

[0158] The data requirement is sent to the data acquisition module through the first sending submodule in the data analysis module, and the operation requirement is sent to the data calculation module through the second sending submodule in the data analysis module.

[0159] In an embodiment, the data acquisition module of the platform layer acquires the data required by the data calculation module based on the analysis conditions, including:

[0160] The data collection and receiving submodule in the data collection module receives the data requirements sent by the data analysis module;

[0161] And the basic data that meets the data requirements is obtained from a pre-generated basic database through the acquisition submodule.

[0162] In an embodiment, the data calculation module of the platform layer performs cross calculation based on the data collected by the data collection module, the analysis conditions and the algorithm provided by the big data platform to obtain the display data, including:

[0163] The calculation receiving submodule in the data calculation module receives the operation requirements sent by the data analysis module;

[0164] And through the calculation submodule in the data calculation module, based on the operation requirements and the basic data collected by the collection submodule, the model algorithm library, memory calculation and batch calculation in the big data platform are used to perform cross calculations to generate intermediate data and result data.

[0165] In the embodiment, the data visualization module of the platform layer maps the display data on the geographic information system GIS platform, including:

[0166] The second sending submodule in the data visualization module sends the display data consisting of the basic data, intermediate data or result data to the business layer;

[0167] And through the mapping submodule in the data visualization module, the display data is mapped on the geographic information system GIS platform with the help of the application layer.

[0168] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0169] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0170] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0171] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0172] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A city power big data application system, characterized in that: include: The platform layer is used to process business requirements based on the big data platform to obtain integration results, and map the integration results on the geographic information system (GIS) platform; The platform layer includes: a data analysis module, a data calculation module, a data acquisition module and a data visualization module; The data analysis module is used to receive business requirements and analyze the business requirements to obtain analysis conditions; The data acquisition module is used to obtain the data required by the data calculation module based on the analysis conditions; The data calculation module is used to perform cross calculation based on the data collected by the data collection module, the analysis conditions and the algorithm provided by the big data platform to obtain the display data; The data visualization module is used to map the display data on a geographic information system (GIS) platform; The data analysis module comprises: The parsing submodule is used to receive the business requirements sent by the application layer, and parse the business requirements to obtain data requirements and operation requirements; A first sending submodule, used to send the data demand to the data acquisition module; A second sending submodule, used to send the operation requirement to the data calculation module; The data acquisition module comprises: A collection and receiving submodule, used for receiving data requirements sent by the data analysis module; The acquisition submodule is used to obtain basic data that meets the data requirements from a pre-generated basic database; The data calculation module comprises: A calculation receiving submodule, used for receiving the operation requirements sent by the data analysis module; A calculation submodule, for generating intermediate data and result data by performing cross calculations using a model algorithm library, memory calculations, and batch calculations in a big data platform based on the operation requirements and the basic data collected by the collection submodule; The data visualization module comprises: A second sending submodule, used to send the display data consisting of the basic data, intermediate data or result data to the business layer; The mapping submodule is used to map the display data on the Geographic Information System (GIS) platform with the help of the application layer.

2. The urban power big data application system according to claim 1, characterized in that: Also includes: Data layer; The data layer is used to store internal and external data of the power grid, and provide data to the data acquisition module of the platform layer in the form of a data source.

3. The urban power big data application system according to claim 2, characterized in that: Also includes: Application layer; The application layer is used to receive business requirements and send the business requirements to the platform layer, and provide presentation services to customers based on the presentation data of the business requirements by the platform layer.

4. The urban power big data application system according to claim 1, characterized in that: The platform layer also includes: The data storage module is used to store the basic database, basic data, intermediate data, result data and display data using a hybrid storage architecture based on data types; Construct a basic database module, which is used to extract or import the data source provided by the data layer offline through the Extract-Interactive Transform-Load (ETL) server across platforms, and screen out and normalize the missing data, garbled data, and data that does not conform to preset rules in the data source, convert the format of the screened and normalized data source according to the preset data model, and establish the association relationship between different data tables to generate a basic database; The partitioning storage module is used to divide the storage space in the platform layer into multiple units as needed, and store the data in the platform layer in the corresponding units through the data storage module.

5. The urban power big data application system according to claim 4, characterized in that: The partitioning storage module comprises: The first type unit is used to store the structured data in the basic database in a relational database, and store the unstructured data and semi-structured data in the basic database in a distributed file system, a distributed database and a distributed message queue; A second type unit is used to store the intermediate data in a hybrid storage manner; A third type unit is used to store the result data in a mixed storage manner; A fourth type unit, used for storing the display data in a hybrid storage manner; The hybrid storage method includes row-column hybrid storage, row storage and column storage.

6. The urban power big data application system according to claim 3, characterized in that: The application layer includes: Urban power grid planning and operation module, equipment operation and maintenance / power supply guarantee module and user management and service module; The urban power grid planning and operation module is used for spatial load forecasting, power supply reliability assessment and electric vehicle charging pile site selection; The equipment operation and maintenance / power supply guarantee module is used for heavy overload warning, emergency repair resource optimization and risk assessment in the substation area; The user management and service module is used to perform customer demand analysis, power saving potential analysis and credit analysis.

7. The urban power big data application system according to claim 3, characterized in that: The application layer also includes: Spatial positioning retrieval module; The spatial positioning retrieval module is used to combine the big data platform and the GIS platform using the ledger data identifier, and to achieve spatial positioning retrieval of the big data platform information by obtaining the coordinate position information corresponding to the ledger.

8. The urban power big data application system according to claim 7, characterized in that: The application layer also includes: Interactive display module; The interactive display module is used to display the query results from basic data, intermediate data to result data through the visualization components in the big data platform based on user needs, or drill down the coordinate location information step by step according to the zoom ratio until the customer specifies the positioning target.

9. The urban power big data application system according to claim 8, characterized in that: The application layer also includes: Custom modules; The custom module is used to draw an area of ​​any shape by monitoring the user's mouse click operations on the GIS layer, analyze the characteristic parameters of the area based on the application in the big data platform, and store and call the data generated during the analysis process to achieve comprehensive analysis, evaluation and prediction oriented to business needs.

10. A method for applying urban power big data, characterized in that: include: The data analysis module of the platform layer receives the business requirements and analyzes the business requirements to obtain analysis conditions; The data acquisition module of the platform layer obtains the data required by the data calculation module based on the analysis conditions; The data calculation module of the platform layer performs cross calculation based on the data collected by the data collection module, the analysis conditions and the algorithm provided by the big data platform to obtain the display data; The data visualization module of the platform layer maps the display data on the Geographic Information System (GIS) platform; The data analysis module of the platform layer receives the business requirements and analyzes the business requirements to obtain analysis conditions, including: The parsing submodule in the data analysis module receives the business requirements sent by the application layer, and parses the business requirements to obtain data requirements and operation requirements; The data requirement is sent to the data acquisition module through the first sending submodule in the data analysis module, and the operation requirement is sent to the data calculation module through the second sending submodule in the data analysis module; The data acquisition module of the platform layer obtains the data required by the data calculation module based on the analysis conditions, including: The data collection and receiving submodule in the data collection module receives the data requirements sent by the data analysis module; And obtaining basic data that meets the data requirements from a pre-generated basic database through a collection submodule; The data calculation module of the platform layer performs cross calculation based on the data collected by the data collection module, the analysis conditions and the algorithm provided by the big data platform to obtain the display data, including: The calculation receiving submodule in the data calculation module receives the operation requirements sent by the data analysis module; And through the calculation submodule in the data calculation module, based on the operation requirements and the basic data collected by the collection submodule, the model algorithm library, memory calculation and batch calculation in the big data platform are used to perform cross calculations to generate intermediate data and result data; The data visualization module of the platform layer maps the display data on the geographic information system GIS platform, including: The second sending submodule in the data visualization module sends the display data consisting of the basic data, intermediate data or result data to the business layer; And through the mapping submodule in the data visualization module, the display data is mapped on the geographic information system GIS platform with the help of the application layer.

11. The urban power big data application method according to claim 10, characterized in that: Also includes: The data layer stores the internal and external data of the power grid and provides data to the data acquisition module of the platform layer in the form of a data source; The application layer receives the business demand and sends the business demand to the platform layer, and provides presentation services to customers based on the presentation data of the business demand by the platform layer.

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