Big data platform system based on power grid data and data processing method thereof

Abstract

The present application relates to a big data platform system based on power grid data and a data processing method thereof, and relates to the field of data processing technology, wherein the method comprises: first, through a data acquisition bus, the system data and dispatching data of each energy system are collected; then, through a data processing bus, object modeling based on metadata is performed according to the system data and dispatching data of each energy system to obtain a full-factor object model, and the object model is used to describe the association relationship and physical characteristic information between physical objects in each energy system in multiple dimensions; then, through a data service bus, data service requests of different applications are processed based on the object model. By applying the technical solution of the present application, a three-layer architecture of the data bus is creatively proposed, and metadata-based object modeling is performed on the equipment at the data processing layer, so that each layer is both interrelated and self-contained, and while independently completing its own functions, data interconnection and interoperability are achieved to ensure data consistency.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a big data platform system based on power grid data and a data processing method thereof. Background Art

[0002] Wind, solar and thermal power are allocated through local smart grids to coordinate electricity use in industrial areas and consume them locally, thus achieving efficiency restoration and selling surplus electricity online. In the case of insufficient local spontaneous electricity consumption, electricity can be purchased online to supplement electricity. At the same time, the coal produced by open-pit coal mines can be transported outside, in addition to being used by power plants within the local power grid, to support power generation in thermal power plants in other places.

[0003] At present, in the local power grid circular economy construction system, there are many systems such as photovoltaic, wind power, thermal power, production, and dispatching. These systems are independent of each other and have poor interoperability. Each type of energy has an element object in other systems. In this way, the object model is not unified, the data duplication is high, the correlation is not strong, and the system utilization efficiency is low. Summary of the invention

[0004] In view of this, the present application provides a big data platform system based on power grid data and its data processing method, the main purpose of which is to improve the technical problems in the current existing technology that various energy systems are isolated from each other, the interoperability between systems is poor, and then the data will have high duplication, weak correlation and low system utilization efficiency.

[0005] In a first aspect, the present application provides a big data platform system based on power grid data, including: a data acquisition module, a data processing module and a data service module;

[0006] The data acquisition module is connected to the data processing module, and the data acquisition module collects system data and scheduling data of each energy system through the data acquisition bus and sends them to the data processing bus of the data processing module;

[0007] The data processing module performs metadata-based object modeling based on the data collected by the data collection module through the data processing bus to obtain a full-factor object model, wherein the object model is used to describe the association relationship and physical property information between the physical objects in each energy system in multiple dimensions;

[0008] The data service module is connected to the data processing module, and the data service module provides data services for various applications through a data service bus based on the object model obtained by the data processing bus.

[0009] In a second aspect, the present application provides a data processing method based on a big data platform system, which is applied to the big data platform system as described in the first aspect, including:

[0010] Collect system data and dispatch data of each energy system through the data acquisition bus;

[0011] Through the data processing bus, metadata-based object modeling is performed according to the system data and scheduling data of each energy system to obtain a full-factor object model, wherein the object model is used to describe the association relationship and physical characteristic information between the physical objects in each energy system in multiple dimensions;

[0012] The data service requests of different applications are processed based on the object model through the data service bus.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data processing method based on the big data platform system described in the second aspect.

[0014] In a fourth aspect, the present application provides an electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, the data processing method based on the big data platform system described in the second aspect is implemented.

[0015] By means of the above technical scheme, the present application provides a big data platform system based on power grid data and its data processing method. Compared with the current existing technology, the present application can realize the organic integration of various energy systems, data normalization, and object panorama, forming an enterprise-level big data platform system, which can reduce data duplication, enhance the correlation between energy systems, and improve the utilization efficiency of energy systems. Specifically based on the data bus method, firstly, the system data and scheduling data of each energy system are collected through the data acquisition bus; then, through the data processing bus, metadata-based object modeling is performed according to the collected system data and scheduling data of each energy system to obtain a full-factor object model, which can be used to describe the association relationship and physical characteristic information between physical objects in each energy system in multiple dimensions; then, through the data service bus, data services are provided to each application based on the object model. By applying the technical solution of the present application, a three-layer architecture of the data bus is creatively proposed, and metadata-based object modeling is performed on the equipment in the data processing layer, so that each layer is both interrelated and self-contained, and can independently complete its own functions while achieving data interconnection and interoperability to ensure data consistency, which is conducive to building a panoramic energy object and realizing real-time online information-physical systems (Cyber-Physical Systems, CPS) and digital twins. The system architecture is innovatively used in the local circular economy system, and its results can be industrialized in the construction of distributed microgrids in new power systems.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 A schematic diagram of a local power grid circular economy workflow provided by an embodiment of the present application is shown;

[0020] Figure 2 A schematic diagram of the structure of a big data platform system based on power grid data provided by an embodiment of the present application is shown;

[0021] Figure 3 A schematic diagram of a data processing method based on a big data platform system provided in an embodiment of the present application is shown;

[0022] Figure 4 A schematic diagram of the architecture of a big data platform system provided in an embodiment of the present application is shown;

[0023] Figure 5 A schematic diagram of the construction framework of a big data platform system based on power grid data provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0025] like Figure 1 As shown in the figure, this is the circular economy workflow of the local power grid. The wind and solar energy plant system will allocate power to the local smart grid, the local smart grid will allocate electricity to industrial enterprises and consume it on site. The local smart grid will allocate power to the open-pit coal mine system and consume it on site. The open-pit coal mine system can also provide coal and drainage water to the thermal power plant. The thermal power plant system provides recycled water and heating for municipal electricity consumption, and reproduces the efficiency to the large power grid, etc.

[0026] In this local power grid circular economy construction system, there are many systems such as photovoltaic, wind power, thermal power, production, and dispatching. Various systems are independent of each other and have poor interoperability. Each energy source has an element object in other systems. In this way, the object model is not unified, the data duplication is high, the correlation is not strong, the system utilization efficiency is low, and the local comprehensive energy mutual assistance, mutual protection and interconnection cannot be formed, which is not conducive to the safe and low-carbon utilization of the local power grid and cannot truly achieve the circular economy operation effect of the local power grid.

[0027] In order to improve the above-mentioned local power grid circular economy workflow, each energy system is isolated from each other, the interoperability between systems is poor, which will lead to high data duplication, weak correlation, and low system utilization efficiency. This embodiment provides a big data platform system based on power grid data, such as Figure 2 As shown, the big data platform system includes: a data acquisition module 11, a data processing module 12, and a data service module 13.

[0028] Among them, the data acquisition module 11 is connected to the data processing module 12. The data acquisition module 11 collects system data and scheduling data (such as point table timing data, relationship data, ledger data, video data and other structured and unstructured data) of various energy systems (such as photovoltaic systems, wind power systems, thermal power systems, etc., which can specifically correspond to various energy systems in the local power grid circular economy construction system) through the data acquisition bus, and sends it to the data processing bus of the data processing module 12.

[0029] Optionally, the data acquisition bus may specifically include: protocol adaptation middleware of different protocol standard interfaces; the data acquisition module 11 collects system data and scheduling data of each energy system through the protocol adaptation middleware for multi-source aggregation, and performs distributed sinking denoising. The data obtained after denoising is then sent to the data processing bus of the data processing module 12. This optional method can effectively remove noise data, thereby ensuring the accuracy of the subsequent metadata-based object modeling by the data processing module 12.

[0030] Optionally, the data acquisition module 11 sends the system data and scheduling data of each energy system to the data acquisition bus, and forwards it to the data processing bus through a wired or wireless network to provide it to the data processing module 12 for processing.

[0031] The data processing module 12 performs metadata-based object modeling based on the data collected by the data acquisition module 11 through the data processing bus to obtain a full-factor object model, wherein the full-factor object model can be used to describe the association relationship and physical property information between physical objects in various energy systems in multiple dimensions, which is equivalent to an object-based full-factor description.

[0032] Optionally, the multiple dimensions may specifically include: spatial dimension (such as the spatial location of entity objects such as equipment and facilities), topological dimension, real-time dimension (such as the current status of entity objects such as equipment and facilities), historical dimension (such as the historical status of entity objects such as equipment and facilities), relationship dimension (such as the relationship between entity objects such as equipment and facilities), etc.

[0033] Correspondingly, the data processing module 12 can instantiate the data collected by the data acquisition module 11 according to the data processing standards of these dimensions, construct a panoramic object instance based on the information-physical system (Common Information Model, CIM), and obtain a full-factor object model. The object model can be a universal information model based on different international power standards, and an extended model of various energy sources and applications can be extended. For example, the obtained full-factor object model based on metadata can be described in multiple dimensions such as spatial dimension, topological dimension, real-time dimension, historical dimension, and relational dimension, from the spatial position of the object, the relationship between the objects, the current state of the object, the previous state of the object, and the object's own description (such as shape, color, behavior, etc.). Figure 1 The relationship between the physical objects in each energy system shown and the physical property information, etc.

[0034] The data processing module 12 can also predict the behavior information of the physical object in the object model based on the object analysis result of the full-factor object model. For example, the behavior information of the physical object in the object model can be predicted based on the object analysis result of the full-factor object model through an artificial intelligence (AI) object analysis algorithm.

[0035] The data service module 13 is connected to the data processing module 12. The data service module 13 provides data services to various applications through a data service bus based on a full-factor object model obtained from the data processing bus to meet different application service requirements.

[0036] The construction of this big data platform system is based on the data bus method. The data of various energy systems are sent to the data acquisition bus, and then forwarded to the data processing bus through wired / wireless networks and the software and hardware environment infrastructure layer based on Infrastructure as a Service (IAAS). Metadata-based object modeling is performed to form a CPS physical object mapping of digital twin-based equipment and facilities and topological associations. Through the data service bus, unified data services are provided for various applications to meet the real-time object display of system platform applications.

[0037] The construction of this big data platform system realizes the complete online physical and information of equipment and equipment CPS through object-oriented modeling of equipment and equipment, and fully describes the object integrity, data normalization and consistency, and spatial topological relationships, solves the information deviation of objects and unifies the upstream and downstream data service interfaces of the system, effectively improving system efficiency.

[0038] Compared with the current existing technology, the big data platform system based on power grid data provided by this embodiment can realize the organic integration of various energy systems, data normalization, and object panorama, forming an enterprise-level big data platform system, which can reduce data duplication, enhance the correlation between energy systems, and improve the utilization efficiency of energy systems. It can form a local comprehensive energy mutual assistance and mutual protection interconnection, which is conducive to the safe and low-carbon utilization of the local power grid, and can truly realize the circular economy operation effect of the local power grid. By applying the technical solution of this embodiment, a three-layer architecture of the data bus is creatively proposed, and the equipment is modeled based on metadata objects at the data processing layer, so that each layer is both interrelated and self-contained. While independently completing its own functions, it also realizes data interconnection and ensures data consistency, which is conducive to the construction of panoramic energy objects and the realization of real-time online CPS and digital twins. The system architecture is innovatively used in the local circular economy system, and its achievements can be industrialized in the construction of distributed microgrids in new power systems.

[0039] Furthermore, in order to illustrate the data processing process of the above-mentioned big data platform system, a data processing method based on the big data platform system is provided, which can be applied to Figure 2 The big data platform system based on power grid data shown in Figure 3 As shown, the method includes:

[0040] Step 201: Collect system data and dispatch data of each energy system through the data acquisition bus.

[0041] For example, the data acquisition bus provides multiple access standards such as standard interfaces based on Modbus communication protocol, TCP / UDP protocol (Transmission Control Protocol / User Datagram Protocol) 101-104, application programming interface (API), object linking and embedding process control (OLE for Process Control, OPC), standard acquisition methods based on serial ports, wired and wireless networks, standard communication interfaces between systems based on Web Services, message mechanisms, etc., which aggregate structured and unstructured data such as point table time series data, relational data, ledger data, video data, etc. of existing systems and intelligent transformation acquisition terminals, and then send them to the Platform as a Service (PAAS) layer through network resources such as 4G / 5G, network cables, and optical fibers.

[0042] Step 202: Through the data processing bus, metadata-based object modeling is performed according to the system data and scheduling data of each energy system to obtain an object model of all elements.

[0043] Among them, the object model is used to describe the association relationship and physical property information between physical objects in various energy systems in multiple dimensions.

[0044] Optionally, step 201 may specifically include: collecting system data and scheduling data in different formats of each energy system through different protocols; correspondingly, step 202 may specifically include: first, according to the preset conversion rules of each energy system in different scenarios and businesses (each energy system may have its own corresponding different preset conversion rules in different scenarios and businesses), format-converting the system data and scheduling data in different formats of each energy system to obtain data that conforms to the general information model standard; then object-modeling the obtained data that conforms to the general information model standard based on metadata to obtain a full-factor object model.

[0045] Exemplarily, the multiple dimensions include: spatial dimension, topological dimension, real-time dimension, historical dimension, and relational dimension; the data that conforms to the general information model standard is object-modeled based on metadata to obtain a full-factor object model, which may specifically include: according to the data processing standards of the multiple dimensions (spatial dimension, topological dimension, real-time dimension, historical dimension, and relational dimension), the data that conforms to the general information model standard is instantiated into objects, a panoramic object instance based on the information-physical system is constructed, and a full-factor object model is obtained. The object model may be a general information model based on different international power standards, and an extended model of various energy sources and applications can be extended.

[0046] After obtaining the full-factor object model, the method of this embodiment may also include: obtaining database data of the multidimensional spatiotemporal distributed database of the object model, wherein the database data may specifically include: spatial data, business data, operation data, topological data, equipment data, etc.; then performing object analysis based on these database data and the full-factor object model; finally, according to the object analysis results, predicting the behavior information of the physical objects in the object model.

[0047] In this embodiment, the standardized design of the data processing bus can provide a CIM model based on the international power standard IEC61968 / IEC61970, expand the e-CIM extended model of various energy sources and applications, and model and objectify the "source, grid, load, storage and use" of energy such as "wind, light, electricity, and storage" from the dimensions of "space, topology, real-time, history, and relationship" to form a standardized five-dimensional data processing standard for energy data; then instantiate the data of the data acquisition bus according to the standardized design specifications to construct a panoramic object instance based on CPS. Combined with the AI ​​analysis, prediction and evaluation algorithm of the energy system, the energy instantiated object is given an observable, predictable, and operable digital twin behavior of thought and behavior, and finally a complete description of the energy system is formed.

[0048] In order to remove noise data, optionally, before the above-mentioned data that conforms to the general information model standard is subjected to metadata-based object modeling to obtain the object model of all elements, the method of this embodiment may also include: performing distributed sinking denoising processing on the data that conforms to the general information model standard; in this embodiment, filtering rules can be established based on the object model of all elements, and the filtering rules can be used to denoise the data.

[0049] Accordingly, the above-mentioned object modeling based on metadata of the data that meets the general information model standard is performed to obtain the object model of all elements, which may specifically include: performing object modeling based on metadata on the data obtained after denoising to obtain the object model of all elements. This optional method can effectively remove noise data, thereby ensuring the accuracy of subsequent object modeling based on metadata.

[0050] Step 203: Process data service requests of different applications based on the full-factor object model through the data service bus.

[0051] The data service bus can provide open RESTful, JS-API, and microservice component interface services based on international standards such as HSDA, TSDA, GDA, and GES. This embodiment can convert the system data and scheduling data of the safety instrumented system (Safety Instrumented System, SIS) of thermal power plants, wind farms, and photovoltaic power stations into data object models through the bus. The same type of models are grouped together with the same attributes, and code-free programming and dragging are used to establish automatic associated objects. The data of each object has physical characteristics and associations. Various model algorithms are implemented through physical connections, which is conducive to monitoring, analyzing, evaluating, and utilizing the data of each object to form comprehensive value data. Through registration, subscription, etc., it provides raw data, behavioral data, and comprehensive data services for various applications of energy system software as a service (Software as a Service, SaaS), and provides big data applications that display and comprehensively analyze and reflect transaction value for local circular economy.

[0052] For example, Figure 4 The figure shows the architecture diagram of the entire big data platform system. In the energy system, there may be various energy systems such as "thermal power", "nuclear power", "wind power", "photovoltaic", "energy storage", "natural gas", "raw coal", "steam", and "carbon emission factor". This big data platform system collects the data of these energy systems in a distributed manner through the data collection bus to the IAAS layer, and performs virtual processing of computing resources, storage resources, network resources, virtual machines (VPC), load balancing processing of server load balancer (SLB), etc.

[0053] The collected data is sent to the data processing bus, and then enters the PAAS layer from the IAAS layer. Using a unified access standard, the access data is converted, integrated, and verified. Specifically, it is based on object-oriented modeling of metadata, and the e-CIM extension model of various energy sources and applications is extended based on the CIM model of the international power standard IEC61968 / IEC61970. Specifically, it may include power models, new energy models, thermal power models, load models, etc. The e-CIM extension model may be based on data from a five-dimensional spatiotemporal distributed database, including: spatial data, business data, operation data, topology data, and equipment data. Taking the power model as an example, it involves information content in data types such as current, voltage, and power, as well as information content in equipment such as distribution rooms, switches, and protection devices.

[0054] In data processing, there are distributed supports such as distributed relations, distributed graphics, distributed real-time, distributed historical libraries, distributed files, distributed processing, distributed messages, distributed transactions, and real-time monitoring, which improve the efficiency of data processing. In addition, the AI ​​dual-carbon brain technology engine is also provided in the data processing bus, which can provide intelligent perception, intelligent duty, expert system, intelligent prediction, self-learning and other services based on the analysis results of AI objects.

[0055] For example, risk prediction can be performed for the operating conditions of equipment objects. The object model can be used to describe the threshold when the equipment is at risk (through object analysis and measurement). Then, based on the description of the risk threshold, risk prediction can be accurately performed. For another example, the dispatch strategy description of the energy system can be used to provide an economically optimal, safe and stable dispatch strategy solution, and future dispatch strategies can also be provided through the analysis results of AI objects. For another example, the cause of the problem can also be analyzed through the analysis results of AI objects, and corresponding solutions can be provided.

[0056] The data service bus can be based on the e-CIM extension model to provide basic application services including: control strategy engine (control rule editing, control strategy issuance, control strategy optimization), data processing services (energy carbon data calculation, data statistics alarm, report data preprocessing), spatial topology analysis (energy supply range analysis, energy power analysis, carbon path analysis), comprehensive data visualization (energy carbon map, operation and maintenance map, weather cloud map). It also provides external interface standards, such as RESTful API, JS API, reusable components, open plug-ins, etc.

[0057] The SAAS layer can provide energy monitoring (image recognition, voice recognition, image and model integration), energy analysis (load forecasting, multi-energy coupling, alarm filtering), carbon emission applications (knowledge graph, intelligent optimization, trading strategies), intelligent reports (energy consumption reports, operation reports, dual-carbon reports), energy economics (electricity price model, green carbon trading, blockchain trading), smart terminals (business flow data processing, mobile data management, message push), etc.

[0058] This big data platform system can ensure data security, application security, system security, cloud platform security, network security, etc. in terms of security assurance.

[0059] This big data platform system uses a three-layer data bus method of data acquisition bus, data processing bus, and data service bus to convert various types of data in different formats collected by different protocols such as IEC870-5-104, Modbus TCP, OPC, API, etc. through the acquisition bus and data processing bus, and becomes CIM standard data. It has various standard attributes of object-oriented (loops, lines, buses, etc.), and objects have multi-dimensional characteristics such as time, space, topology, and relationships. It provides data services for various applications on the data service bus layer, realizes object-oriented and model-based standard analysis and processing, and provides data support services for energy monitoring, energy analysis, energy reporting, energy economy, smart terminals, etc. at the SaaS layer.

[0060] For example, loop data is stored according to the address of the Device Information File (INF) after being parsed according to the IEC870-5-104 protocol. This includes different types of collected circuit breaker positions, disconnector positions, protection alarm signals, voltage, current, power, power factor and other data. These data are isolated and scattered. After passing through the data processing bus, they are collected into the same loop. In this way, when the circuit breaker is opened, the current must be 0 and the power must also be 0. They correspond to each other at the physical level. The superior and subordinate relationships of the loops are one-to-one in physical management. When performing superior and subordinate topological analysis of the loops, the data are equal and related, which is conducive to achieving more, deeper and more accurate analysis.

[0061] The technical solution provided in this embodiment can realize the organic integration of multiple systems, data normalization, and object panorama, forming an enterprise-level big data platform, which not only retains the normal operation of the original system, but also meets the various new business needs of Internet+, and solves the problems of repeated investment and inefficient application of multiple systems of enterprises; makes data measurable and evaluable, solves the blind spots and pain points of enterprise energy utilization, and is conducive to enterprises guiding the standardization of business buses through data buses, guiding the optimization of energy structure and business processes; constructs a full range of energy objects based on digital models and behaviors of five-dimensional data, which is conducive to the comprehensive intelligent scheduling of local microgrids, and provides a basic support environment for safe, economical and low-carbon operation of local circular economy; and provides an industry reference for the construction of microgrid energy systems under new power systems.

[0062] In order to further illustrate the specific implementation process of the above embodiment, the following application scenarios are given, which are not limited thereto:

[0063] This big data platform system can be deployed and applied in local circular economy demonstration sites, which may involve energy generation systems such as wind, solar, thermal power, coal mines, and energy storage. At the load end, there are scenarios such as electrolytic aluminum, electric hydrogen production, and heavy truck battery replacement. Each piece is a self-contained system with isolated data. It is necessary to integrate the resources of various systems and unify data standardization according to the requirements of local circular economy construction to form a five-dimensional spatiotemporal object model of data, build a big data platform, provide unified interface data services and application support for various business systems, and provide advanced, open, and scalable solutions for subsequent business extension and new energy access.

[0064] For example, Figure 5 As shown in the figure, it is a schematic diagram of the construction framework of this big data platform system. It includes: data acquisition bus, data processing bus and data service bus. The data of various systems (such as the dispatching OPEN5000 system, A / B / C / D power plant SIS system, equipment management system (PMS) / energy management system (EMS), photovoltaic system, wind power system, energy storage system, aluminum price and other external systems) are collected through the data acquisition bus, which involves protocol adaptation middleware, multi-source convergence, distributed sinking denoising processing, etc. Then it is uploaded to various servers, firewalls, isolation devices and other infrastructure, operating systems, and time series databases, and then transmitted to the data processing bus for metadata-based object modeling to obtain the e-CIM object model, which involves object five-dimensional databases and AI object analysis algorithms. The data service bus can provide open microservices, code-free programming, report services, hidden danger identification services, visualization services, online electricity shopping services, etc., and can then provide big data application displays (such as large screens, PCs, WeChat applets, APPs, etc.).

[0065] The construction of this big data platform system covers the energy supply of the circular economy source end of "wind power, solar power and energy storage", the safe dispatching of the network side of the smart grid dispatching center, and the energy system of low-carbon economic production and operation on the load side of the aluminum side (electrolytic aluminum, electric hydrogen production, and heavy truck battery replacement). It uses the data bus architecture and object-oriented modeling ideas to effectively integrate the resources of various energy flows in the local power grid, form a local circular economy that is data-based, information-based, and digital, reduce repeated investment in information technology, and provide unified big data resources and efficient operation strategies for the safe, low-carbon, and economic coordinated dispatching of the local power grid, and provide a demonstration effect for the construction of local microgrids in new power systems.

[0066] Based on the above Figure 3 The method shown in the embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Figure 3 The data processing method based on the big data platform system is shown.

[0067] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0068] Based on the above Figure 3 In order to achieve the above-mentioned purpose, the embodiment of the present application also provides an electronic device, which can be a server, a computer, etc., and the device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 3 The data processing method based on the big data platform system is shown.

[0069] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0070] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0071] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0072] Through the description of the above implementation methods, the technicians in this field can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the scheme of this embodiment, the organic integration and data normalization of various energy systems and the panoramic view of objects can be realized, forming an enterprise-level big data platform system, which can reduce data duplication, enhance the correlation between energy systems, and improve the utilization efficiency of energy systems. It can form a local comprehensive energy mutual assistance and mutual protection interconnection, which is conducive to the safety and low-carbon utilization of the local power grid, and can truly realize the circular economy operation effect of the local power grid. By applying the technical scheme of this embodiment, a three-layer architecture of the data bus is creatively proposed, and the equipment is modeled based on metadata objects at the data processing layer, so that each layer is both interrelated and self-contained. While independently completing its own functions, it also realizes data interconnection and ensures data consistency, which is conducive to the construction of panoramic energy objects, and realizes real-time online CPS and digital twins. The system architecture is innovatively used in the local circular economy system, and its achievements can be industrialized in the construction of distributed microgrids in new power systems.

[0073] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0074] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A big data platform system based on power grid data, characterized in that: include: Data acquisition module, data processing module and data service module; The data acquisition module is connected to the data processing module, and the data acquisition module collects system data and scheduling data of each energy system through the data acquisition bus and sends them to the data processing bus of the data processing module; The data processing module performs metadata-based object modeling based on the data collected by the data collection module through the data processing bus to obtain a full-factor object model, wherein the object model is used to describe the association relationship and physical property information between the physical objects in each energy system in multiple dimensions; the multiple dimensions include: spatial dimension, topological dimension, real-time dimension, historical dimension and relationship dimension; The data service module is connected to the data processing module, and the data service module provides data services for each application through a data service bus based on the object model obtained by the data processing bus; The data acquisition module collects system data and dispatch data of each energy system through the data acquisition bus, including: Collect system data and dispatch data of different formats of each energy system through different protocols; The data processing module performs metadata-based object modeling according to the system data and scheduling data of each energy system through the data processing bus to obtain a full-factor object model, including: According to the preset conversion rules of each energy system in different scenarios and businesses, the system data and scheduling data of each energy system in different formats are converted into formats to obtain data that conforms to the general information model standard; The data that conforms to the general information model standard is object-modeled based on metadata to obtain a full-factor object model, specifically including: according to the multi-dimensional data processing standard, the data that conforms to the general information model standard is instantiated into objects, and a panoramic object instance based on the information-physical system is constructed to obtain the object model. The object model is a general information model based on different international power standards, and an extended model of various energy sources and applications is extended.

2. The big data platform system according to claim 1, characterized in that: The data processing module also predicts behavior information of the physical object in the object model based on the object analysis result of the object model.

3. The big data platform system according to claim 1, characterized in that: The data acquisition bus includes: protocol adaptation middleware of different protocol standard interfaces; The data acquisition module collects the system data and scheduling data of each energy system through the protocol adaptation middleware for multi-source aggregation, and performs distributed sinking denoising processing.

4. The big data platform system according to claim 1, characterized in that: The data acquisition module sends the system data and scheduling data of each energy system to the data acquisition bus, and forwards them to the data processing bus through a wired or wireless network to provide them to the data processing module for processing.

5. A data processing method based on a big data platform system, applied to the big data platform system as claimed in any one of claims 1 to 4, characterized in that: include: Collect system data and dispatch data of each energy system through the data acquisition bus; Through the data processing bus, metadata-based object modeling is performed according to the system data and scheduling data of each energy system to obtain a full-factor object model, wherein the object model is used to describe the association relationship and physical property information between the physical objects in each energy system in multiple dimensions; the multiple dimensions include: spatial dimension, topological dimension, real-time dimension, historical dimension and relationship dimension; Processing data service requests of different applications based on the object model through a data service bus; Wherein, the system data and dispatching data of each energy system are collected through the data collection bus, including: Collect system data and dispatch data of different formats of each energy system through different protocols; The data processing bus is used to perform metadata-based object modeling according to the system data and scheduling data of each energy system to obtain a full-factor object model, including: According to the preset conversion rules of each energy system in different scenarios and businesses, the system data and scheduling data of each energy system in different formats are converted into formats to obtain data that conforms to the general information model standard; The data that conforms to the general information model standard is object-modeled based on metadata to obtain a full-factor object model, specifically including: according to the multi-dimensional data processing standard, the data that conforms to the general information model standard is instantiated into objects, and a panoramic object instance based on the information-physical system is constructed to obtain the object model. The object model is a general information model based on different international power standards, and an extended model of various energy sources and applications is extended.

6. The method according to claim 5, characterized in that After obtaining the object model, the method further includes: Acquire database data of the multidimensional spatiotemporal distributed database of the object model, the database data including: spatial data, business data, operation data, topology data and equipment data; Performing object analysis based on the database data and the object model; According to the object analysis result, the behavior information of the physical object in the object model is predicted.

7. The method according to claim 5, characterized in that Before performing object-oriented modeling based on metadata on the data conforming to the general information model standard to obtain an object model of all elements, the method further includes: Performing distributed sinking denoising processing on the data that complies with the general information model standard; The data that complies with the general information model standard is subjected to object-oriented modeling based on metadata to obtain an object model of all elements, specifically including: The data obtained after denoising is subjected to object modeling based on metadata to obtain an object model of all elements.

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