CN109379420B - Comprehensive energy service platform system based on distributed architecture - Google Patents
Comprehensive energy service platform system based on distributed architecture Download PDFInfo
- Publication number
- CN109379420B CN109379420B CN201811179574.4A CN201811179574A CN109379420B CN 109379420 B CN109379420 B CN 109379420B CN 201811179574 A CN201811179574 A CN 201811179574A CN 109379420 B CN109379420 B CN 109379420B
- Authority
- CN
- China
- Prior art keywords
- data
- energy
- distributed
- node
- library
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012545 processing Methods 0.000 claims abstract description 41
- 230000004044 response Effects 0.000 claims abstract description 27
- 238000004364 calculation method Methods 0.000 claims abstract description 20
- 230000001360 synchronised effect Effects 0.000 claims abstract description 10
- 238000000034 method Methods 0.000 claims description 32
- 238000010586 diagram Methods 0.000 claims description 26
- 230000007613 environmental effect Effects 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 16
- 238000012795 verification Methods 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 13
- 238000013500 data storage Methods 0.000 claims description 12
- 238000010248 power generation Methods 0.000 claims description 12
- 230000008859 change Effects 0.000 claims description 10
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 238000012423 maintenance Methods 0.000 claims description 7
- 238000013499 data model Methods 0.000 claims description 6
- 230000003993 interaction Effects 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000006978 adaptation Effects 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 238000012917 library technology Methods 0.000 claims description 3
- 230000002093 peripheral effect Effects 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 3
- 238000011065 in-situ storage Methods 0.000 claims description 2
- 238000003860 storage Methods 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 238000007726 management method Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 101100006584 Mus musculus Clnk gene Proteins 0.000 description 3
- 239000003595 mist Substances 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 230000006855 networking Effects 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention discloses a comprehensive energy service platform system based on a distributed architecture, which comprises: the energy collection layer is used for collecting data of each energy source in real time; the middle layer is a fog node middleware based on edge calculation, and is used for processing each energy data acquired by the energy acquisition layer to obtain local fog node cooked data and providing the cooked data to the top decentralized distributed cloud center; and the top cloud center performs convergence processing according to the clustering rule to obtain converged data, and provides support for high-speed and high-concurrency access and micro-application service of mass data. The scheme of the invention can realize that: the cloud nodes based on edge computing are self-organized and applied on site, and meanwhile, a decentralized distributed cloud center enables clustering collection and full-network N +1 synchronous backup to provide near quick response and concurrent access to mass data of a cloud and fog system.
Description
Technical Field
The invention relates to the technical field of energy, in particular to a comprehensive energy service platform system based on a distributed architecture.
Background
With the development of smart power grids, distributed energy, micro power grids and comprehensive energy technologies, more and more detailed management of sources, grids, loads, storage and utilization of energy is realized, petrochemical power generation such as coal power generation, petroleum power generation and the like which matures for many years is realized on the power generation side, clean energy such as distributed photovoltaic power generation, wind power generation, biological energy generation, geothermal energy, tidal energy and the like which rapidly rise in recent years is also realized, the load side is provided with an energy load, and cold water, hot water, electricity and gas are circularly converted and continuously consumed and utilized. Each energy source has own unique characteristic, collection mode and application mode, and is independently constructed according to respective specialties in actual monitoring, scheduling, operation and maintenance, the larger the access data is, the greater the response speed and concurrency of the system are tested, and the requirement of multi-energy collaborative complementary cross integration development of comprehensive energy sources cannot be met.
The existing energy service platform system is often single in structure, an architecture system needs to be designed in advance according to a plan, once the architecture system of the system is established, the structure of the architecture system is fixed, when a new functional module is added for the platform system, the original architecture system cannot be used, and a brand-new architecture system needs to be established for the new functional module.
In practical application, different users have different requirements, and thus, for each inherent user requirement, different architecture systems need to be constructed for the respective different requirements of the customers, so that the process of designing the comprehensive energy service platform system meeting the customer requirements for the respective different requirements of the different users is a time-consuming and labor-consuming process.
In addition, when a user has a new function demand, and a new function module is added to the existing integrated energy service platform system, the new function module is not only required to be redesigned on the basis of the original integrated energy service platform system, but also the compatibility between the technology adopted by the newly added function module and the original integrated energy service platform system is also considered, so that the process of improving the functions of the original integrated energy service platform system is extremely complicated, time-consuming and labor-consuming.
Disclosure of Invention
The embodiment of the invention aims to provide a comprehensive energy service platform system based on a distributed architecture, which is used for solving the problems of extremely complex construction, time consumption and labor consumption of the conventional comprehensive energy service platform system.
In order to achieve the above object, an embodiment of the present invention provides a comprehensive energy service platform system based on a distributed architecture, where the system is a three-layer architecture system based on edge computing, and an energy collection layer based on the internet of things is arranged at a bottom layer of the three-layer architecture and is used for collecting energy data in real time; the middle layer of the three-layer framework is a fog node middleware based on edge calculation, and is used for processing each energy data acquired by the energy acquisition layer to obtain local fog node cooked data, and providing uniform data interface service for a cloud center with a top layer removed from the center while in-situ system; and the top decentralized distributed cloud center receives the fog node cooked data, and performs convergence processing on the cooked data according to a clustering rule to obtain converged data, so that distributed data processing and data service are achieved.
Optionally, the energy collection layer is further configured to obtain association information associated with collecting energy data; the association information includes at least one of: the energy source data acquisition system comprises working condition information of each acquisition device related to the acquired energy source data and environmental parameter information related to the acquired energy source data, wherein each environmental parameter information at least comprises one of the following items: the environmental current parameter information, the environmental voltage parameter information, the environmental water pressure parameter information, the environmental air pressure parameter information and the environmental temperature and humidity parameter information.
Optionally, the intermediate layer is configured to process each energy data acquired by the energy acquisition layer, and obtaining the local fog node maturity data specifically includes: the middle layer is used as a protocol adapter, and is automatically adapted to the collected energy data of different protocols provided by the collecting equipment of different manufacturers; carrying out protocol conversion on various types of energy data according to a preset protocol conversion rule to obtain each converted energy data; carrying out ETL data processing, summarizing calculation, analysis and prediction on each converted energy data to obtain corresponding calculated mature data; and storing the corresponding calculated cooked data to obtain local fog node cooked data.
Optionally, the system adopts the same preset e-CIM energy model, and the preset energy model is used for performing data processing on the data of each power generation side and/or each energy utilization side acquired by the energy acquisition layer to obtain processed data; or the preset energy model is used for carrying out topological graph processing on the data of each power generation side and/or each energy utilization side acquired by the energy acquisition layer to obtain processed topological graph data.
Optionally, the cloud centers are quick response cloud centers, the number of the cloud centers is a natural number greater than or equal to 1, each cloud center can arbitrarily expand the center nodes, and each cloud center can arbitrarily configure the center nodes with resources; performing data interaction processing on the data acquired by the energy acquisition layer according to a preset nearby rule; or, according to a preset nearby rule, quickly responding to the response request, and when the network of the nearby first response node fails, selecting a response central point with a better resource channel as a second response node according to a preset whole network load balancing rule.
Optionally, the storing of the local fog node data is based on the system having a corresponding energy source database, where the energy source database includes at least one of the following data: energy model data associated with an energy model, real-time energy data collected by the energy collection layer, historical energy data collected by the energy collection layer, topological data, spatial data and ledger data; or each cloud center can perform data synchronization on each data in the energy source database, and each cloud center can perform corresponding clustering data processing on the synchronized data, wherein the number of the cloud centers is a natural number greater than or equal to 2; and each cloud center can perform corresponding data processing on the synchronized data, specifically including: acquiring resource information of each node participating in data storage, wherein the resource information of each node at least comprises one of the following items: the method comprises the following steps of storing and occupying information, hard disk and residual space information, occupied bandwidth information, running process information and SIM card residual flow information; encrypting the data received by each node participating in data storage by adding a key corresponding to an encryption algorithm to obtain encrypted data; and when data access is carried out, carrying out key verification on the verified data by the node providing access and the data of the peripheral nodes to obtain a corresponding verification result, carrying out corresponding processing according to the verification result, covering the data of the current node by other node data if the verification result shows that the current data access is abnormal, and providing data access again until the corresponding verification result shows that the corresponding data access is normal.
Optionally, after a newly accessed energy station is acquired, the cloud center builds a model for the preset energy model to obtain a built energy model, and synchronously and remotely issues the built energy model to a corresponding fog node, wherein the preset energy model comprises a corresponding data model and a corresponding graph model; or after the energy station with the changed structure is obtained, the cloud center modifies the preset energy model to obtain a modified energy model, and synchronously and remotely issues the modified energy model to the corresponding fog node, wherein the preset energy model comprises a corresponding data model and a corresponding graph model.
Optionally, the fog node is an energy service edge end of a framework corresponding to the system, and the fog node can update an environment in which a memory energy model operates, or the fog node can process collected terminal data to obtain cooked data after operation, or the fog node can distinguish data types, or the fog node can quickly store data, or the fog node can store data regularly in a preset time, or the fog node can synchronously forward the data in a full-center network; wherein the data types distinguished by the fog nodes at least comprise one of the following items: the data type is real-time data, the data type is calculation analysis data of the early warning data, and the data type is calculation analysis data of the alarm data; the fog node can process the collected terminal data, and the obtaining of the calculated mature data specifically comprises the following steps: and (3) carrying out protocol adaptation, ETL data processing, summary calculation and analysis prediction on the acquired terminal data respectively in sequence to obtain the calculated mature data.
Optionally, the cloud center divides different user stations into different energy station spaces, merges each energy station into a corresponding energy station space, and manages a corresponding cloud node cluster in one energy station space, where the energy station space is used to provide a corresponding network channel address for the user station, or the energy station space is used to build a data communication network of a virtual private network, or the energy station space is used to implement load balancing in the virtual private network, or the energy station space is used to implement data security cloud and mist interaction in the virtual private network.
Optionally, the energy station corresponding to the system is a distributed five-dimensional library, and the five-dimensional library at least includes one of the following items: a distributed real-time library, a distributed historical library, a distributed graphic library, a distributed topology library and a distributed file library; the distributed real-time library is a distributed real-time library based on the whole-network memory library technology, and is used for carrying out real-time data acquisition on at least one data item of operation condition data of each terminal and operation parameter data of each terminal, or is used for carrying out data storage on at least one data item of operation condition data of each terminal and operation parameter data of each terminal, or is used for carrying out data forwarding on at least one data item of operation condition data of each terminal and operation parameter data of each terminal; the distributed historical library is used for carrying out multi-dimensional and multi-granularity data storage processing on each energy data according to preset data dimensions, or the distributed historical library is used for carrying out quick retrieval on each energy data according to preset quick retrieval rules to obtain each energy data meeting preset requirements; the distributed graphic library is used for providing visual and graphical data for the cloud center and each fog node, and the graphics included in the distributed graphic library at least comprise one of the following items: the system comprises a wiring diagram stored in a cloud center, a network deployment diagram stored in the cloud center, a network node working condition diagram stored in the cloud center, a measuring point working condition diagram stored in the cloud center, each wiring diagram stored in each fog node, each network deployment diagram stored in each fog node, each node working condition diagram stored in each fog node, and each measuring point working condition diagram stored in each fog node; the distributed topology library is used for describing an association relationship among the devices, or the distributed topology library is used for visually displaying first change information of the corresponding associated device when the current device is switched between a normal operation state and a stop operation state, or the distributed topology library is used for visually displaying second change information of the corresponding associated device when the current device is switched between the normal operation state and an alarm operation state, or the distributed topology library is used for visually displaying third change information of the corresponding associated device when the current device is switched between a maintenance state and a monitoring state; the distributed file library is used for displaying images of all devices and panoramic images of the energy station in an auxiliary mode, wherein data corresponding to the distributed file library at least comprises one of the following items: the video data of each recognition, the standing book data of each equipment, the operation parameter data of each equipment, and the operation and maintenance inspection field photo data of each equipment.
The embodiment of the invention has the following advantages: the embodiment of the invention provides a comprehensive energy service platform system based on a distributed architecture, which can realize the following steps: the cloud nodes based on edge computing are self-organized and applied on site, and meanwhile, a decentralized distributed cloud center enables clustering collection and full-network N +1 synchronous backup to provide near quick response and concurrent access to mass data of a cloud and fog system.
Drawings
Fig. 1 is a schematic structural diagram of an integrated energy service platform system based on a distributed architecture in a specific application provided in embodiment 2 of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions under which the present invention can be implemented, so that the present invention has no technical significance, and any structural modification, ratio relationship change, or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention. In addition, the terms such as "upper", "lower", "left", "right" and "middle" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and changes or modifications of the relative relationship may be made without substantial technical changes.
Example 1
According to an embodiment of the present invention, there is provided a distributed architecture-based integrated energy service platform system, including: the system is a three-layer architecture system based on edge calculation, and the bottom layer of the three-layer architecture is an energy collection layer based on the Internet of things and used for collecting energy data in real time; the middle layer of the three-layer framework is a fog node middleware based on edge calculation, and is used for processing each energy data acquired by the energy acquisition layer to obtain local fog node cooked data, and providing uniform data interface service for a top-layer decentralized cloud center while the local system is in place; the top-layer decentralized distributed cloud center receives the fog node cooked data, and performs convergence processing on the cooked data according to a clustering rule to obtain converged data so as to achieve distributed data processing and data service; thus, the system provided in embodiment 1 of the present invention can: the cloud nodes based on edge computing are self-organized and applied on site, and meanwhile, a decentralized distributed cloud center enables clustering collection and full-network N +1 synchronous backup to provide near quick response and concurrent access to mass data of a cloud and fog system.
It should be noted that, the fog node based on the middleware technology: the method has the advantages that data acquisition, protocol conversion, ETL data processing and analysis of different devices, different protocols (modbus, 101, 103, 104, DLT645, factory-defined rules and the like), different interfaces (232, 485, TCP, zigbee, NB-IoT and the like) and different applications (device working condition, environment monitoring, energy real-time monitoring and the like) of different manufacturers at the bottom layer are realized, cooked data required by the cloud center is formed, unified acquisition standards and unified application interface standards are realized, and unified data standards and interactive interfaces are provided for the cloud center while on-site attendance is realized; the clustering rule is a rule set by a conventional clustering method, and is not described herein again.
In an optional example, the energy collection layer is further configured to obtain association information associated with collecting the energy data; the association information includes at least one of: the energy source data acquisition system comprises working condition information of each acquisition device related to the acquired energy source data and environmental parameter information related to the acquired energy source data, wherein each environmental parameter information at least comprises one of the following items: each environment current parameter information, each environment voltage parameter information, each environment water pressure parameter information, each environment air pressure parameter information, each environment temperature and humidity parameter information; in this way, the energy collection layer of the system provided in embodiment 1 of the present invention can not only collect energy data, but also acquire related information associated with the energy data.
In an optional example, the intermediate layer is configured to process each energy data collected by the energy collection layer, and the obtaining of the local cloud node maturity data specifically includes: the middle layer is used as a protocol adapter, and is automatically adapted to the collected energy data of different protocols provided by the collecting equipment of different manufacturers; carrying out protocol conversion on various types of energy data according to a preset protocol conversion rule to obtain each converted energy data; carrying out ETL data processing, summarizing calculation, analysis and prediction on each converted energy data to obtain corresponding calculated mature data; and storing the corresponding calculated cooked data to obtain local fog node cooked data.
In an optional example, the system adopts the same preset e-cim (energy Common Information model) energy model, and the preset energy model is used for performing data processing on data of each power generation side and/or each energy utilization side collected by the energy collection layer to obtain processed data; or the preset energy model is used for carrying out topological graph processing on the data of each power generation side and/or each energy utilization side acquired by the energy acquisition layer to obtain processed topological graph data.
It should be noted that the same preset e-CIM energy model is a preset energy model established by a conventional energy model modeling method, and the specific modeling method is not described herein again.
In an optional example, the cloud centers are fast response cloud centers, the number of the cloud centers is a natural number greater than or equal to 1, each cloud center can arbitrarily extend the center node, and each cloud center can arbitrarily configure the center node for resources; performing data interaction processing on data acquired by the energy acquisition layer according to a preset nearby rule; or, according to a preset nearby rule, quickly responding to the response request, and when the network of the nearby first response node fails, selecting a response central point with a better resource channel as a second response node according to a preset whole network load balancing rule; therefore, through a preset proximity rule, when the network of the first response node fails, a second response node corresponding to the first response node can be selected nearby, and the second response node has a better resource channel as a substitute response node.
It should be noted that the preset proximity rule is a preset proximity rule established by a conventional proximity method, and the rule is convenient for determining a second response node that is in proximity to the first response node, and details thereof are not repeated herein.
In an optional example, the storing of the local nodes data is based on the system having a corresponding energy source database, wherein the energy source database comprises at least one of the following data: energy model data associated with the energy model, real-time energy data collected by the energy collection layer, historical energy data collected by the energy collection layer, topological data, spatial data and standing book data; or each cloud center can perform data synchronization on each data in the energy database, and each cloud center can perform corresponding clustering data processing on the synchronized data, wherein the number of the cloud centers is a natural number greater than or equal to 2; each cloud center can perform corresponding data processing on the synchronized data, and the data processing specifically includes: acquiring resource information of each node participating in data storage, wherein the resource information of each node at least comprises one of the following items: the method comprises the following steps of storing and occupying information, hard disk and residual space information, occupied bandwidth information, running process information and SIM card residual flow information; encrypting the data received by each node participating in data storage by adding a key corresponding to an encryption algorithm to obtain encrypted data; and when data access is carried out, carrying out key verification on the verified data by the access-providing node and the data of the peripheral nodes to obtain a corresponding verification result, carrying out corresponding processing according to the verification result, covering the data of the current node by the data of other nodes if the verification result shows that the current data access is abnormal, and providing data access again until the corresponding verification result shows that the corresponding data access is normal.
In an optional example, after a newly accessed energy station is obtained, the cloud center builds a model for a preset energy model to obtain a built energy model, and synchronously and remotely issues the built energy model to a corresponding fog node, wherein the preset energy model comprises a corresponding data model and a corresponding graph model; or after the energy station with the changed structure is obtained, the cloud center modifies the preset energy model to obtain a modified energy model, and synchronously and remotely issues the modified energy model to the corresponding fog node, wherein the preset energy model comprises a corresponding data model and a corresponding graph model; therefore, the cloud center can synchronously and remotely issue the constructed energy model to the corresponding fog node, or the cloud center can synchronously and remotely issue the modified energy model to the corresponding fog node.
In an optional example, the fog node is an energy service edge end of a framework corresponding to the system, and the fog node can update an environment in which the memory energy model operates, or the fog node can process collected terminal data to obtain cooked data after operation, or the fog node can distinguish data types, or the fog node can quickly store the data, or the fog node can store the data regularly in a preset time, or the fog node can synchronously forward the data in a full-center network; the data types distinguished by the fog nodes at least comprise one of the following items: the data type is real-time data, the data type is calculation analysis data of the early warning data, and the data type is calculation analysis data of the alarm data; the fog node can process the collected terminal data, and the acquired mature data after operation specifically comprises the following steps: and (3) carrying out protocol adaptation, ETL data processing, summary calculation and analysis prediction on the acquired terminal data respectively in sequence to obtain the calculated mature data.
It should be noted that ETL is an abbreviation of Extract-Transform-Load in english, and is used to describe a process of extracting (Extract), transforming (Transform), and loading (Load) data from a source to a destination. The term ETL is more commonly used in data warehouses, but its objects are not limited to data warehouses. The ETL is an important ring for constructing a data warehouse, and a user extracts required data from a data source, and finally loads the data into the data warehouse according to a predefined data warehouse model after data cleaning.
In an alternative example, the cloud center divides different user stations into different energy station spaces, merges the energy stations into corresponding energy station spaces, manages a corresponding mist node cluster in one energy station space, and the energy station spaces are used for providing corresponding network channel addresses for the user stations, or the energy station spaces are used for building a data communication network of a virtual private network, or the energy station spaces are used for realizing load balancing in the virtual private network, or the energy station spaces are used for realizing data security cloud and mist interaction in the virtual private network.
In an alternative example, the energy station corresponding to the system is a distributed five-dimensional library, and the five-dimensional library at least comprises one of the following items: a distributed real-time library, a distributed historical library, a distributed graphic library, a distributed topology library and a distributed file library;
the distributed real-time library is a distributed real-time library based on the whole-network memory library technology, and is used for carrying out real-time data acquisition on one data at least comprising operation condition data of each terminal and operation parameter data of each terminal, or the distributed real-time library is used for carrying out data storage on one data at least comprising the operation condition data of each terminal and the operation parameter data of each terminal, or the distributed real-time library is used for carrying out data forwarding on one data at least comprising the operation condition data of each terminal and the operation parameter data of each terminal;
the distributed historical library is used for carrying out multi-dimensional and multi-granularity data storage processing on each energy data according to preset data dimensions, or the distributed historical library is used for carrying out quick retrieval on each energy data according to preset quick retrieval rules to obtain each energy data meeting preset requirements;
the distributed graphic library is used for providing visual and graphical data for the cloud center and each fog node, and the graphs included in the distributed graphic library at least comprise one of the following items: the system comprises a wiring diagram stored in a cloud center, a network deployment diagram stored in the cloud center, a network node working condition diagram stored in the cloud center, a measuring point working condition diagram stored in the cloud center, each wiring diagram stored in each fog node, each network deployment diagram stored in each fog node, each node working condition diagram stored in each fog node and each measuring point working condition diagram stored in each fog node;
the distributed topology library is used for describing the association relationship among the devices, or the distributed topology library is used for visually displaying first change information of the corresponding associated device when the current device is switched between a normal operation state and a stop operation state, or the distributed topology library is used for visually displaying second change information of the corresponding associated device when the current device is switched between the normal operation state and an alarm operation state, or the distributed topology library is used for visually displaying third change information of the corresponding associated device when the current device is switched between a maintenance state and a monitoring state;
the distributed file library is used for assisting in showing images of all the devices and panoramic images of the energy stations, wherein data corresponding to the distributed file library at least comprises one of the following items: the video data of each recognition, the standing book data of each equipment, the operation parameter data of each equipment, and the operation and maintenance inspection field photo data of each equipment.
In summary, the integrated energy service platform system based on the distributed architecture provided in embodiment 1 of the present invention can: the cloud nodes based on edge computing are self-organized and applied on site, and meanwhile, a decentralized distributed cloud center enables clustering collection and full-network N +1 synchronous backup to provide near quick response and concurrent access to mass data of a cloud and fog system.
In addition, the energy service platform system provided in embodiment 1 of the present invention further has the following advantages, which are specifically described as follows:
the method has the advantages that: the cloud platform system has elasticity;
the method has the advantages that: the cloud platform system is distributed, can be seamlessly expanded, and supports mass data access;
the method has the advantages that: the cloud platform system has fusion;
the advantages are that: the cloud platform system is convenient for unified management of equipment, connection, calculation and application;
the advantages are that: the cloud platform system is an edge intelligent cloud platform system;
the method has the advantages that: the cloud platform system is a full-layer domain intelligent system with information decision, behavior feedback, automatic networking and load balancing.
Example 2
According to an embodiment of the present invention, a distributed architecture-based integrated energy service platform system in specific applications is provided, and as shown in fig. 1, a schematic structural diagram of a distributed architecture-based integrated energy service platform system in specific applications provided in embodiment 2 of the present invention is provided.
For detailed description of the system shown in fig. 1, please refer to the same parts as those in embodiment 1, and further description is omitted here.
As shown in fig. 1, the cloud platform system provided in embodiment 2 of the present invention is a distributed architecture system based on cloud technology,
the architecture system is a cloud technology distributed architecture which adopts edge computing, a fog node management middleware and cloud centralized control center services.
In addition, the cloud platform system comprises a distributed real-time library, a distributed historical library, a distributed relational database, a distributed network library and a distributed graphic topology library.
Compared with the existing system, the cloud platform system provided by the embodiment 2 of the invention has the following advantages:
the method has the advantages that: the cloud platform system has elasticity;
the method has the advantages that: the cloud platform system is distributed, can be seamlessly expanded, and supports mass data access;
the method has the advantages that: the cloud platform system has fusion;
the advantages are that: the cloud platform system is convenient for unified management of equipment, connection, calculation and application;
the advantages are that: the cloud platform system is an edge intelligent cloud platform system;
the method has the advantages that: the cloud platform system is a full-layer domain intelligent system with information decision, behavior feedback, automatic networking and load balancing.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. An integrated energy service platform system based on a distributed architecture, comprising:
the system is a three-layer architecture system based on edge calculation, and the bottom layer of the three-layer architecture is an energy collection layer based on the Internet of things and used for collecting energy data in real time;
the middle layer of the three-layer framework is a fog node middleware based on edge calculation, and is used for processing each energy data acquired by the energy acquisition layer to obtain local fog node cooked data, and providing uniform data interface service for a cloud center with a top layer removed from the center while in-situ system;
and the top decentralized distributed cloud center receives the fog node cooked data, and performs convergence processing on the cooked data according to a clustering rule to obtain converged data, so that distributed data processing and data service are achieved.
2. The system of claim 1, wherein the energy collection layer is further configured to obtain association information associated with collecting energy data;
the association information includes at least one of:
the energy source data acquisition system comprises working condition information of each acquisition device related to the acquired energy source data and environmental parameter information related to the acquired energy source data, wherein each environmental parameter information at least comprises one of the following items: the environmental current parameter information, the environmental voltage parameter information, the environmental water pressure parameter information, the environmental air pressure parameter information and the environmental temperature and humidity parameter information.
3. The system according to claim 1, wherein the intermediate layer is configured to process each energy data collected by the energy collection layer to obtain local fog node maturity data, and specifically includes:
the middle layer is used as a protocol adapter, and is automatically adapted to the collected energy data of different protocols provided by the collecting equipment of different manufacturers;
carrying out protocol conversion on various types of energy data according to a preset protocol conversion rule to obtain each converted energy data;
carrying out ETL data processing, summarizing calculation, analysis and prediction on each converted energy data to obtain corresponding calculated mature data;
and storing the corresponding calculated cooked data to obtain local fog node cooked data.
4. The system according to any one of claims 1 to 3,
the system adopts the same preset e-CIM energy model,
the preset energy model is used for carrying out data processing on the data of each power generation side and/or each energy utilization side collected by the energy collection layer to obtain processed data; or,
the preset energy model is used for carrying out topological graph processing on the data of each power generation side and/or each energy utilization side collected by the energy collection layer to obtain processed topological graph data.
5. The system of claim 4,
the cloud centers are quick response cloud centers, the number of the cloud centers is a natural number which is more than or equal to 1, each cloud center can freely expand the center nodes, and each cloud center can freely allocate the center nodes for resources; performing data interaction processing on the data acquired by the energy acquisition layer according to a preset nearby rule;
or, according to a preset nearby rule, quickly responding to the response request, and when the network of the nearby first response node fails, selecting a response central point with a better resource channel as a second response node according to a preset whole network load balancing rule.
6. The system of claim 5,
the storage of the local fog node data is based on that the system has a corresponding energy source database, wherein the energy source database at least comprises one of the following data: energy model data associated with an energy model, real-time energy data collected by the energy collection layer, historical energy data collected by the energy collection layer, topological data, spatial data and ledger data; or,
each cloud center can perform data synchronization on each data in the energy source database, and each cloud center can perform corresponding clustering data processing on the synchronized data, wherein the number of the cloud centers is a natural number which is more than or equal to 2; and
each cloud center can perform corresponding data processing on the synchronized data, and the corresponding data processing specifically includes:
acquiring resource information of each node participating in data storage, wherein the resource information of each node at least comprises one of the following items: the method comprises the following steps of storing and occupying information, hard disk and residual space information, occupied bandwidth information, running process information and SIM card residual flow information;
encrypting the data received by each node participating in data storage by adding a key corresponding to an encryption algorithm to obtain encrypted data;
and when data access is carried out, carrying out key verification on the verified data by the node providing access and the data of the peripheral nodes to obtain a corresponding verification result, carrying out corresponding processing according to the verification result, covering the data of the current node by other node data if the verification result shows that the current data access is abnormal, and providing data access again until the corresponding verification result shows that the corresponding data access is normal.
7. The system of claim 4,
after a newly accessed energy station is obtained, the cloud center builds a model for the preset energy model to obtain a built energy model, and synchronously and remotely issues the built energy model to a corresponding fog node, wherein the preset energy model comprises a corresponding data model and a corresponding graph model; or,
after the energy station with the changed structure is obtained, the cloud center modifies the preset energy model to obtain a modified energy model, and synchronously and remotely issues the modified energy model to the corresponding fog node, wherein the preset energy model comprises a corresponding data model and a corresponding graph model.
8. The system of claim 7,
the fog nodes are energy service edge terminals of a framework corresponding to the system, and can update the running environment of a memory energy model, or can process collected terminal data to obtain calculated mature data, or can distinguish data types, or can quickly store the data, or can store the data regularly in a preset time, or can synchronously forward the data in a full-center network;
wherein the data types distinguished by the fog nodes at least comprise one of the following items: the data type is real-time data, the data type is calculation analysis data of the early warning data, and the data type is calculation analysis data of the alarm data;
the fog node can process the collected terminal data, and the obtaining of the calculated mature data specifically comprises the following steps:
and (3) carrying out protocol adaptation, ETL data processing, summary calculation and analysis prediction on the acquired terminal data respectively in sequence to obtain the calculated mature data.
9. The system of claim 1,
the cloud center divides different user stations into different energy station spaces, merges all energy stations into corresponding energy station spaces, and manages a corresponding fog node cluster in one energy station space, wherein the energy station space is used for providing corresponding network channel addresses for the user stations, or the energy station space is used for building a data communication network of a virtual private network, or the energy station space is used for realizing load balancing in the virtual private network, or the energy station space is used for realizing data safety cloud and fog interaction in the virtual private network.
10. The system of claim 1,
the energy station corresponding to the system is a distributed five-dimensional library, and the five-dimensional library at least comprises one of the following items: a distributed real-time library, a distributed historical library, a distributed graphic library, a distributed topology library and a distributed file library;
the distributed real-time library is a distributed real-time library based on the whole-network memory library technology, and is used for carrying out real-time data acquisition on at least one data item of operation condition data of each terminal and operation parameter data of each terminal, or is used for carrying out data storage on at least one data item of operation condition data of each terminal and operation parameter data of each terminal, or is used for carrying out data forwarding on at least one data item of operation condition data of each terminal and operation parameter data of each terminal;
the distributed historical library is used for carrying out multi-dimensional and multi-granularity data storage processing on each energy data according to preset data dimensions, or the distributed historical library is used for carrying out quick retrieval on each energy data according to preset quick retrieval rules to obtain each energy data meeting preset requirements;
the distributed graphic library is used for providing visual and graphical data for the cloud center and each fog node, and the graphics included in the distributed graphic library at least comprise one of the following items: the system comprises a wiring diagram stored in a cloud center, a network deployment diagram stored in the cloud center, a network node working condition diagram stored in the cloud center, a measuring point working condition diagram stored in the cloud center, each wiring diagram stored in each fog node, each network deployment diagram stored in each fog node, each node working condition diagram stored in each fog node, and each measuring point working condition diagram stored in each fog node;
the distributed topology library is used for describing an association relationship among the devices, or the distributed topology library is used for visually displaying first change information of the corresponding associated device when the current device is switched between a normal operation state and a stop operation state, or the distributed topology library is used for visually displaying second change information of the corresponding associated device when the current device is switched between the normal operation state and an alarm operation state, or the distributed topology library is used for visually displaying third change information of the corresponding associated device when the current device is switched between a maintenance state and a monitoring state;
the distributed file library is used for displaying images of all devices and panoramic images of the energy station in an auxiliary mode, wherein data corresponding to the distributed file library at least comprises one of the following items: the video data of each recognition, the standing book data of each equipment, the operation parameter data of each equipment, and the operation and maintenance inspection field photo data of each equipment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811179574.4A CN109379420B (en) | 2018-10-10 | 2018-10-10 | Comprehensive energy service platform system based on distributed architecture |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811179574.4A CN109379420B (en) | 2018-10-10 | 2018-10-10 | Comprehensive energy service platform system based on distributed architecture |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109379420A CN109379420A (en) | 2019-02-22 |
| CN109379420B true CN109379420B (en) | 2021-03-26 |
Family
ID=65404027
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811179574.4A Active CN109379420B (en) | 2018-10-10 | 2018-10-10 | Comprehensive energy service platform system based on distributed architecture |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109379420B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110225075A (en) * | 2019-03-25 | 2019-09-10 | 北京快电科技有限公司 | A kind of building energy internet wisdom operation cloud operating system |
| CN109946967A (en) * | 2019-03-25 | 2019-06-28 | 北京快电科技有限公司 | A kind of building energy internet information interchanger of combination of edge intelligence |
| CN109905489B (en) * | 2019-04-01 | 2020-10-30 | 重庆大学 | Multi-sensor data relevance processing method and system based on data fusion algorithm |
| CN111800443B (en) * | 2019-04-08 | 2022-04-29 | 阿里巴巴集团控股有限公司 | Data processing system and method, device and electronic equipment |
| CN110084415A (en) * | 2019-04-19 | 2019-08-02 | 苏州尚能物联网科技有限公司 | A kind of building energy consumption forecasting system and method based on side cloud collaboration hybrid modeling strategy |
| CN111586091B (en) * | 2020-03-25 | 2021-03-19 | 光控特斯联(重庆)信息技术有限公司 | Edge computing gateway system for realizing computing power assembly |
| CN111726403B (en) * | 2020-06-11 | 2021-01-29 | 和宇健康科技股份有限公司 | Cross-cloud-platform big data management method and system |
| CN112104751B (en) * | 2020-11-10 | 2021-02-12 | 中国电力科学研究院有限公司 | Control cloud data processing method, device and system |
| CN112468569A (en) * | 2020-11-23 | 2021-03-09 | 华能国际电力股份有限公司 | Production real-time supervision framework based on 'cloud computing' industrial video cascade |
| CN113422392A (en) * | 2021-06-18 | 2021-09-21 | 南方电网数字电网研究院有限公司 | Intelligent power station equipment control system based on edge calculation |
| CN113934795A (en) * | 2021-10-19 | 2022-01-14 | 国网山东省电力公司平邑县供电公司 | Power grid early warning system based on distributed management |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104184209A (en) * | 2014-08-15 | 2014-12-03 | 国家电网公司 | User-side distributed energy power generation management system |
| CN106372069A (en) * | 2015-07-20 | 2017-02-01 | 深圳市奇辉电气有限公司 | Method and system for monitoring electric energy quality |
| CN107688882A (en) * | 2016-08-05 | 2018-02-13 | 上海天齐建设股份有限公司 | Distributed energy dispatching management information system |
-
2018
- 2018-10-10 CN CN201811179574.4A patent/CN109379420B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104184209A (en) * | 2014-08-15 | 2014-12-03 | 国家电网公司 | User-side distributed energy power generation management system |
| CN106372069A (en) * | 2015-07-20 | 2017-02-01 | 深圳市奇辉电气有限公司 | Method and system for monitoring electric energy quality |
| CN107688882A (en) * | 2016-08-05 | 2018-02-13 | 上海天齐建设股份有限公司 | Distributed energy dispatching management information system |
Non-Patent Citations (1)
| Title |
|---|
| 基于云平台的分布式新能源监控数据分析方法研究;王尔玺;《能源与环保》;20180430;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109379420A (en) | 2019-02-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109379420B (en) | Comprehensive energy service platform system based on distributed architecture | |
| CN112698953A (en) | Power grid intelligent operation and detection platform based on micro-service | |
| CN103597707B (en) | There is the distributed intelligence framework of the most reversely/forward cloud | |
| CN104102790B (en) | Power supply figure automatic mapping system and method based on GIS | |
| CN108694244A (en) | A kind of electric network synthetic application platform based on three-dimensional data | |
| CN106203890A (en) | Battalion based on CIM auxiliary tone integration data modeling method | |
| Akrivopoulos et al. | A fog computing-oriented, highly scalable iot framework for monitoring public educational buildings | |
| CN108848132B (en) | Power distribution scheduling main station system based on cloud | |
| KR102508817B1 (en) | High availability distribution intelligence system using message transmission bus | |
| CN110019106A (en) | A kind of power marketing method and system for processing mass data of smart grid | |
| CN104966330A (en) | Remote machine room intelligent inspection system based on cloud computer | |
| CN102426525A (en) | Panoramic modeling method for multi-application system | |
| CN106599293B (en) | A CIM XML Power Grid Model for Allocating Integrated System | |
| CN108446396A (en) | A kind of electric power data processing method based on modified CIM model | |
| CN104320495A (en) | Massive data storage method and system of power utilization information collecting system | |
| CN104091034A (en) | Automatic circuit contact graph forming system and method based on graph matching | |
| CN113094174A (en) | Method for coordination control of power distribution network terminal autonomous networking based on edge computing technology | |
| CN119419954A (en) | A distribution network symbiosis simulation system and method based on digital twin | |
| CN105303292A (en) | Distribution data storage method and device | |
| CN112286884B (en) | Method, system and medium for automatically generating topology of ring main unit of power distribution network | |
| CN102868746B (en) | Method for realizing mobile informationization | |
| CN111597166B (en) | Power database model construction method, device, computer equipment and storage medium | |
| CN109687464B (en) | Analysis method and device, storage medium and processor for power flow of power grid | |
| CN105471926A (en) | Diagnostic data cloud model constructing system based on autonomous decentralized system | |
| Song et al. | Research on multi-parameter data monitoring system of distribution station based on edge computing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |