WO2022100176A1 - 调控云数据处理方法、装置及系统 - Google Patents

调控云数据处理方法、装置及系统 Download PDF

Info

Publication number
WO2022100176A1
WO2022100176A1 PCT/CN2021/112325 CN2021112325W WO2022100176A1 WO 2022100176 A1 WO2022100176 A1 WO 2022100176A1 CN 2021112325 W CN2021112325 W CN 2021112325W WO 2022100176 A1 WO2022100176 A1 WO 2022100176A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
node device
data processing
cooperating
processing
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.)
Ceased
Application number
PCT/CN2021/112325
Other languages
English (en)
French (fr)
Inventor
李大鹏
李立新
杨清波
陶蕾
黄运豪
狄方春
宋旭日
齐晓琳
杨楠
崔灿
夏文岳
叶瑞丽
武书舟
谢琳
张周杰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Electric Power Research Institute Co Ltd CEPRI
Original Assignee
China Electric Power Research Institute Co Ltd CEPRI
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by China Electric Power Research Institute Co Ltd CEPRI filed Critical China Electric Power Research Institute Co Ltd CEPRI
Priority to US17/770,332 priority Critical patent/US12547447B2/en
Publication of WO2022100176A1 publication Critical patent/WO2022100176A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5072—Grid computing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
    • 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
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • 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

Definitions

  • the present application belongs to the field of data processing, and relates to a method, device and system for regulating cloud data processing.
  • the regulation cloud is a cloud service platform for grid dispatching business. It is an important part of the “Three Clouds” built by the State Grid Corporation of China, and it is an innovative application of information and communication technologies such as “Cloud, Big Things, Smart Chains” in the field of regulation and control, and is an important part of supporting the power grid.
  • the important technical means of operation and regulation management implements the idea of opening up various majors horizontally and opening up multi-level business vertically, so as to realize the unified collection and management of models and data.
  • the source system re-uploads data after a data problem, which may cause the problem of repeated data uploading, which increases the bandwidth pressure on the WAN data network, resulting in
  • the centralized computing model of the cloud center is difficult to meet the development needs of regulating the cloud in the future.
  • the purpose of the present application is to overcome the above-mentioned disadvantages of cloud center computing and storage pressure and wide area data network bandwidth pressure in the prior art, and to provide a method, device and system for regulating cloud data processing.
  • a first aspect of the present application provides a control cloud data processing method, which is applied to a leading node device, and the data processing method includes:
  • the processing data sent by each cooperating node device is received, and the processing data is the collected data processed by the cooperating node device according to the data processing rule.
  • it also includes:
  • it also includes:
  • the initial collection range and initial data processing rules of each collaborative node device are preset, and sent to each collaborative node device;
  • the data collection range and data processing rules of each cooperative node device are optimized, and the optimized data collection range and data processing rules of each cooperative node device are sent to each cooperative node device.
  • it also includes:
  • the scheduling tasks sent to each cooperating node device are adjusted.
  • it also includes:
  • the global scheduling task is a power data processing task
  • the power data processing task can be decomposed into a collection task, a cleaning task, a conversion task and a storage task
  • the scheduling task includes a collection task, a cleaning task , at least one of a conversion task and a storage task
  • the data collection scope includes the equipment scope and the power data category scope, the equipment scope is all power equipment under the jurisdiction of the cooperative node equipment or the power equipment with a preset voltage level under the jurisdiction of the cooperative node equipment, and the power data category scope is all categories of power. data or several preset categories of power data, the power data categories include grid model data, grid operation data, grid management data, and external data received by the grid;
  • the data processing rules include at least one of filling missing points, smoothing mutation points, and processing illegal values.
  • a second aspect of the present application provides a control cloud data processing method, which is applied to a collaborative node device, and the data processing method includes:
  • the acquisition data within the data acquisition range is acquired, the acquired acquisition data is processed according to the data processing rules to obtain the processed data, and the processed data is sent to the leading node device.
  • it also includes:
  • a stream processing method is used for real-time data
  • a batch processing method is used for historical data.
  • the uploading of the processing data to the master node device includes:
  • a method for regulating cloud data processing includes:
  • the leading node device obtains the global scheduling task, decomposes the global scheduling task to obtain the scheduling task, and sends it to each cooperating node device;
  • the leading node device obtains the data collection range and data processing rules of each cooperating node device, and sends it to each cooperating node device;
  • Each cooperating node device receives and executes the scheduling task issued by the master node device;
  • Each cooperating node device receives the data collection range and data processing rules issued by the leading node device;
  • Each cooperating node device obtains the collected data within the data collection range based on the scheduling task, processes the obtained collected data according to the data processing rules to obtain the processed data, and sends the processed data to the leading node device;
  • the master node device receives the processing data sent by each cooperating node device.
  • it also includes:
  • the leading node device presets the initial collection range and initial data processing rules of each cooperating node device according to the control cloud data collection requirements, and sends it to each cooperating node device;
  • the leading node device evaluates the data quality of each processing data, and obtains the evaluation results of each processing data; and stores the processing data whose evaluation results are preset results;
  • the leading node device optimizes the data collection range and data processing rules of each cooperating node device according to the evaluation results of each processing data, and sends the optimized data collection range and data processing rules of each cooperating node device to each cooperating node device.
  • it also includes:
  • Each cooperating node device monitors the execution of the scheduling task and sends the execution of the scheduling task to the master node device; monitors the resource usage and sends the resource usage to the master node device; monitors the execution of the rules and reports the execution of the rules Upload to the master node device;
  • the leading node device receives the scheduling task execution status sent by each cooperating node device; obtains the resource usage of each cooperating node device; Scheduling tasks of cooperating node devices; receiving the rule execution status sent by each cooperating node device.
  • a control cloud data processing device is applied to a master node device, and the data processing device includes:
  • the task decomposition and delivery module is configured to obtain the global scheduling task, decompose the global scheduling task to obtain the scheduling task, and deliver it to each cooperating node device;
  • a data receiving module configured to receive processing data sent by each cooperating node device, the processing data being collected data processed by the cooperating node device according to the data processing rules;
  • the rule issuing module is configured to obtain the data collection range and data processing rules of each cooperating node device, and deliver it to each cooperating node device.
  • it also includes a data analysis unit
  • the data analysis unit is configured to evaluate the data quality of each processed data to obtain the evaluation result of each processed data; and store the processed data whose evaluation result is a preset result.
  • it also includes:
  • a rule preset module configured to preset the initial collection range and initial data processing rules of each cooperating node device according to the control cloud data processing requirements
  • the rule optimization module is configured to optimize the data collection range and data processing rules of each collaborative node device according to the evaluation results of each processing data;
  • the rule issuing module is further configured to deliver the optimized data collection range and data processing rules of each cooperating node device to each cooperating node device.
  • it also includes:
  • the rule monitoring module is configured to receive the rule execution status sent by each cooperating node device.
  • it also includes:
  • the task monitoring module is configured to receive the scheduling task execution status sent by each cooperating node device; obtain the resource usage status of each cooperating node device; Scheduling tasks to each cooperating node device.
  • a fifth aspect of the present application provides an apparatus for controlling cloud data processing, which is applied to collaborative node equipment, and the data processing apparatus includes:
  • the task receiving and executing module is configured to receive and execute the scheduling task issued by the master node device;
  • a rule receiving module configured to receive the data collection range and data processing rules issued by the leading node device
  • the data management module is configured to obtain the collected data within the data collection range based on the scheduling task, process the obtained collected data according to the data processing rules to obtain the processed data, and upload the processed data to the leading node device.
  • it also includes:
  • the task feedback module is configured to monitor the execution of the scheduling task and upload the execution of the scheduling task to the master node device;
  • a resource feedback module configured to monitor resource usage and upload resource usage to the master node device
  • the rule feedback module is configured to monitor the execution of the rules and upload the execution of the rules to the master node device.
  • it also includes:
  • a data format conversion module configured to convert the processed data into a preset data format
  • the data management module is further configured to upload the processing data in the preset data format to the master node device.
  • a sixth aspect of the present application provides a system for regulating cloud data processing, including: a leading node device and several cooperating node devices connected to the leading node device;
  • the lead node device includes the above-mentioned control cloud data processing device applied to the lead node device;
  • the collaborative node device includes the above-mentioned control cloud data processing apparatus applied to the collaborative node device.
  • the scheduling task is obtained by decomposing the global scheduling task, and the scheduling task is sent to each cooperating node device, so that part of the data processing work is allocated to the cooperating node device, and part of the data processing is performed by the cooperating node device,
  • the data uploaded to the leading node device is already processed data, which greatly reduces the transmission of garbage data, and effectively utilizes the computing and storage resources of the collaborative node devices on the edge side, which solves the problem of regulating cloud leading node devices.
  • Computational pressure, storage pressure, and wide-area data network bandwidth pressure enable wide-area cloud-side collaborative computing to improve the quality of data processing.
  • the data in the control field is hierarchically distributed storage, from the data and business level, there is little real-time interaction and collaborative interaction between all levels, especially after the introduction of cloud computing technology in the control field, the implementation is to horizontally open up various
  • the idea of professional and vertical opening up multi-level business so the construction of the control cloud actually considers the unified collection and management of models and data, which leads to a lot of pressure on cloud computing, storage and network, and the cloud and the edge
  • the tasks, rules and data management are independent of each other, lack of collaborative interaction, and based on the dynamic demand for data in the control field, a single edge computing capability can no longer meet the control system's dynamic processing requirements for control data.
  • this application not only performs preliminary processing of data by utilizing the processing capabilities of each collaborative node device, but also strengthens the collaborative interaction between the leading node device and each collaborative node device in combination with the actual needs in the field of regulation, and dynamically adjusts each collaborative node device by dynamically adjusting the collaborative node devices.
  • the scope of data collection and data processing rules are determined, and sent to each collaborative node device to realize the dynamic of edge data processing function, fully meet the data requirements of the control system, and fully realize the collaborative interaction between the cloud and the edge.
  • 1 is a schematic diagram of the overall architecture of the wide-area cloud-edge data collaboration oriented to the regulatory cloud architecture of the application;
  • Fig. 2 is a flow chart of a method for controlling cloud data processing in an embodiment of the application
  • FIG. 3 is a flowchart of a method for controlling cloud data processing in yet another embodiment of the application.
  • Fig. 4 is a flow chart of a method for controlling cloud data processing in yet another embodiment of the application.
  • FIG. 5 is a schematic diagram of a data flow of a data processing method in an embodiment of the present application.
  • FIG. 6 is a structural block diagram of an apparatus for controlling cloud data processing in an embodiment of the present application.
  • FIG. 7 is a structural block diagram of an apparatus for controlling cloud data processing in yet another embodiment of the application.
  • FIG. 8 is a structural block diagram of an apparatus for controlling cloud data processing in yet another embodiment of the application.
  • FIG. 9 is a schematic diagram of a regulation cloud architecture constructed by taking a power grid as an example of the present application.
  • a regulation cloud platform architecture of an implementation environment involved in various embodiments of the present application is shown, which includes a leading node device, a cooperating node device, and a source data terminal device.
  • the regulation cloud platform is based on cloud technology concepts such as virtualization, distribution and service, and is designed according to the three-layer architecture of leading nodes, collaborative nodes, and source data terminals.
  • the overall architecture of the wide-area cloud-edge data collaboration based on the regulation cloud includes three layers of regulation cloud dominant node (cloud), collaborative node (edge) and source data end (end).
  • the leading node (cloud) of the regulation cloud and the cooperating node (edge) are connected through the wide area data network, and the source data terminal (end) and the power grid acquisition equipment are connected through the local area network.
  • the leading node is responsible for wide-area cloud-side collaborative data management. Its bottom layer is based on the control of hardware, storage, network and other resources provided by the cloud infrastructure platform and the support of public components such as buses and permissions. The wide-area interaction further supports the regulation of business applications on the upper layer of the cloud.
  • the control cloud collaborative node is responsible for edge collaborative computing management. Its bottom layer is based on the hardware environment provided by the edge infrastructure. It collects the data collected by all the acquisition devices within its jurisdiction through the local area network, and conducts tasks, rules, and data through the wide area data network with the leading node. wide-area interaction.
  • the leading node device and the cooperating node device may be a server, a server cluster composed of several servers, or a cloud computing service center.
  • control cloud data processing method provided by the embodiment of this application is applied to the iterative optimization problem of wide-area data quality, and the collected data is cleaned, filtered and uploaded by coordinating the computing resources of nodes, and the control cloud center coordinates all Coordinate the computing tasks and data processing rules of the nodes, realize the distribution and feedback of tasks and rules to the coordinated nodes, and make full use of the computing resources and storage resources of the coordinated cloud coordination nodes (edges), which relieves the computing pressure and storage of the cloud center.
  • the pressure and the bandwidth pressure of the wide-area data network realize the collaborative computing of the wide-area cloud and the edge.
  • FIG. 2 a method for processing cloud data for regulation and control provided in an embodiment of the present application is shown.
  • the method is applied to a master node device.
  • the method for processing cloud data for regulation and control is applied to the implementation environment shown in FIG. 1 .
  • the control cloud data processing method includes the following steps:
  • Step 11 Acquire the global scheduling task, decompose the global scheduling task to obtain the scheduling task, and deliver it to each cooperating node device.
  • the global scheduling task is the power data processing task of the control cloud platform generated by the leading node device based on the final demand of data processing.
  • the leading node device decomposes the global scheduling task, and the power Data processing tasks can be decomposed into collection tasks, cleaning tasks, conversion tasks, and storage tasks, and the tasks that need to be specifically executed by each cooperating node are used as scheduling tasks and issued to the corresponding cooperating node devices.
  • the master node device also receives the execution status of scheduling tasks sent by each cooperating node device; acquires the resource usage status of each cooperating node device; Based on the evaluation results, adjust the scheduling tasks sent to each cooperating node device.
  • the leading node device acts as the global coordination center for controlling cloud tasks. According to the data collection scope, resource usage, and task execution of each cooperating node device, it conducts global unified coordination to give full play to the computing power of each cooperating node device while ensuring data collection. quality and efficiency.
  • the leading node device when batch processing or retransmission of existing historical data is required, once a cooperating node device has insufficient computing resources, too long data processing time, and low data upload efficiency, the leading node device will reduce the number of cooperating node devices.
  • the cooperating node device directly uploads part of the data to the leading node device without processing, and the leading node device allocates corresponding resources for processing work. After the work is completed, the leading node device re-schedules the global Adjustment of task breakdown.
  • Step 12 Acquire the data collection range and data processing rules of each cooperating node device, and send it to each cooperating node device.
  • This step is to ensure the quality of the data sent by each cooperating node device.
  • Designate each cooperating node device to collect data according to the data collection range, and perform data processing according to the data processing rules, reflecting the management function of the leading node device.
  • the system administrator manually configures the data collection scope of each cooperating node device, for example, according to the restriction requirements such as jurisdiction, voltage level, data category, etc., the data collection scope is divided into equipment scope and
  • the power data category range where the device range can be all power devices under the jurisdiction of the co-node device or the power devices with preset voltage levels under the jurisdiction of the co-node device, and the power data category range can be all categories of power data or several presets category of power data, wherein the category of power data includes grid model data, grid operation data, grid management data, and external data received by the grid.
  • the master node device also receives the rule execution status sent by each cooperating node device, wherein the rule execution status includes normal execution and abnormal execution, and the rule execution status sent by the cooperating node device is abnormal.
  • the abnormal execution prompt information of the rule is generated, and the data sent by the cooperative node device is marked as abnormal data, so that the data that has not been processed by the rule, that is, abnormal data, can be processed again through the leading node device.
  • the node equipment is normally performing data processing monitoring, and ensuring that the received data sent by each cooperating node equipment is processed by data processing rules to ensure data quality.
  • the data collection range and data processing rules of each cooperating node device are optimized according to the evaluation results of each processing data of each cooperating node device in the leading node device, for example, for data processing speed
  • the leading node device trains and upgrades data processing rules through big data analysis and mining technology, and pushes the upgraded data processing rules to the cooperating node devices to realize the self-adaptation of the data processing rules of the leading node device and each cooperating node device.
  • Step 13 Receive the processing data sent by each cooperating node device, where the processing data is the collected data processed by the cooperating node device according to the data processing rule.
  • Each cooperating node device uploads the processing data obtained from the collected data processed according to the data processing rules to the leading node device through the wide area data network. After the data is stored, the data is cached through the function of message conversion and data integration.
  • the processed data is the collected data processed by the cooperating node device according to the data processing rules, the leading node device does not need to be processed again, which greatly reduces the computational pressure of the leading node device.
  • the data in question will be found directly, avoiding the problem of repeated data uploading caused by the wrong data being uploaded, thereby reducing the bandwidth pressure of the WAN data network.
  • the leading node device evaluates the data quality of each processing data, obtains each processing data evaluation result, and stores the processing data whose evaluation result is a preset result.
  • the processing data is generally aggregated and integrated through multi-source heterogeneous data fusion technology.
  • Multi-source heterogeneous data fusion refers to the use of relevant means to integrate all the information obtained through collection and analysis, and to unify the information. Evaluation, and finally the technology of obtaining unified information, which is used to synthesize data information from various sources and different structures, absorb the characteristics of different data sources, and then extract unified, better and richer information than single data. , and then evaluate the quality of the aggregated and integrated processing data through the multi-source data quality intelligent comprehensive evaluation.
  • the multi-source data quality intelligent comprehensive evaluation combines the specific evaluation needs, from the online data parameters, model matching and data intrinsic characteristics. Data quality assessment.
  • the evaluation result is high-quality processing data, which is stored in the database that regulates the cloud leading node device. , and then provide data services for regulating cloud business applications.
  • the work of part of the data processing can be allocated to the cooperating node device, and part of the data is processed through the cooperating node device, and then sent to the cooperating node device.
  • the data of the leading node device is already processed data, which greatly reduces the transmission of garbage data, and effectively utilizes the computing and storage resources of the collaborative node devices on the edge side, which solves the computing pressure of regulating cloud leading node devices.
  • the storage pressure and the large bandwidth pressure of the wide-area data network have realized the wide-area cloud-side collaborative computing and improved the quality of data processing.
  • the data in the control field is hierarchically distributed storage, from the data and business level, there is little real-time interaction and collaborative interaction between all levels, especially after the introduction of cloud computing technology in the control field, the implementation is to horizontally open up various
  • the idea of professional and vertical opening up multi-level business so the construction of the control cloud actually considers the unified collection and management of models and data, which leads to a lot of pressure on cloud computing, storage and network, and the cloud and the edge
  • the tasks, rules and data management are independent of each other, lack of collaborative interaction, and based on the dynamic demand for data in the control field, a single edge computing capability can no longer meet the control system's dynamic processing requirements for control data.
  • this application not only performs preliminary processing of data by utilizing the processing capabilities of each collaborative node device, but also strengthens the collaborative interaction between the leading node device and each collaborative node device in combination with the actual needs in the field of regulation, and dynamically adjusts each collaborative node device by dynamically adjusting the collaborative node devices.
  • the data collection range and data processing rules are sent to each cooperating node device, changing the process or result of data processing by each cooperating node device, so that the processed data can meet the changing regulation requirements of the leading node device, and realize the edge data processing function.
  • the dynamization of the cloud can fully meet the data requirements of the control system and fully realize the collaborative interaction between the cloud and the edge.
  • control cloud data processing method includes the following steps:
  • Step 21 Receive and execute the scheduling task issued by the master node device.
  • Each cooperating node device receives the scheduling task issued by the leading node device through the wide area data network, and analyzes the task, organizes the components in the cooperating node device to work together, and starts to execute the scheduling task issued by the leading node device.
  • the cooperating node device also monitors the execution of the scheduling task, and uploads the execution of the scheduling task to the master node device; in this way, the master node device can monitor the work of each cooperating device in real time, so that Manage each cooperating device.
  • the cooperating node device also monitors resource usage, and uploads the resource usage to the dominant node device, so as to inform the dominant node device of the remaining computing power of itself, and also to provide tasks for the dominant node device Basic information for management.
  • Step 22 Receive the data collection range and data processing rules issued by the leading node device.
  • Each cooperating node device receives the data collection range and data processing rules issued by the leading node device through the wide area data network, and loads the data collection range and data processing rules into the local memory, so as to facilitate subsequent data collection through the data collection range.
  • the data processing rules process the collected data.
  • the general choice is to delete the previous data collection range and data processing rules, or directly pass New data collection scope and data processing rule coverage to realize iterative update of data collection scope and data processing rules.
  • Step 23 Based on the scheduling task, acquire the collection data within the data collection range, process the acquired collection data according to the data processing rule to obtain the processed data, and upload the processed data to the leading node device.
  • the cooperative node device calls the data collection range information, and performs data collection within the data collection range.
  • the data collection mentioned here can be obtained from the collection terminal, or directly collected.
  • data that is, the collaborative node device can have the capability of data collection.
  • call the data processing rules to process the collected data for example, smooth the mutation point data in the collected data, delete the illegal values in the collected data, and supplement the missing points in the collected data. Neighborhood value supplementation may also be other supplementary rules, which are only illustrative and not limiting.
  • the processed data is sent to the master node device.
  • the cooperative node device completes its own work and makes full use of its computing power.
  • the processing rules process the acquired data to obtain the processed data, and realize the partial processing of the data through the cooperative node device, so that the data sent to the leading node device is already processed data, which greatly reduces the transmission of garbage data, and effectively Using the computing and storage resources of the collaborative node devices on the edge side, it solves the problem of controlling the computing pressure, storage pressure and bandwidth pressure of the wide-area data network on cloud-dominant node devices. quality of data processing.
  • the process or result of the data processing is changed to meet the dynamic demand of the control system for the control data, and the leading and leading node devices can fully interact.
  • control cloud data processing method provided in another embodiment of the present application, including the operation steps of the leading node device and each cooperating node device.
  • the control cloud data processing method is applied to the method shown in FIG. 1 .
  • the control cloud data processing method includes the following steps:
  • Step 31 The leading node device acquires the global scheduling task, decomposes the global scheduling task to obtain the scheduling task, and sends it to each cooperating node device.
  • step 32 each cooperating node device receives and executes the scheduling task issued by the master node device.
  • the leading node equipment decomposes the global scheduling task into collection, cleaning, verification and evaluation, and then decomposes the global scheduling tasks into collection, cleaning, verification and evaluation.
  • the collection and cleaning tasks are sent to the cooperating node device, and the cooperating node device completes it.
  • data cleaning is the process of re-examining and verifying data, with the purpose of removing duplicate information, correcting existing errors, and providing data consistency.
  • each cooperating node device receives the collection and cleaning tasks issued by the leading node device, and performs task analysis, so as to manage the cooperating node devices to perform collection and cleaning according to the analysis content.
  • Step 33 The leading node device acquires the data collection range and data processing rules of each cooperating node device, and sends it to each cooperating node device.
  • step 34 each cooperating node device receives the data collection range and data processing rules issued by the leading node device.
  • the leading node device presets the initial collection range and cleaning rules of each cooperating node device according to the collection and cleaning requirements, and sends it to each cooperating node device, and each cooperating node device will receive the initial collection range and cleaning rules. Cleaning rules are loaded locally.
  • Step 35 each cooperating node device obtains the collection data within the data collection range based on the scheduling task, processes the obtained collection data according to the data processing rule to obtain the processing data, and uploads the processing data to the leading node device;
  • step 36 the leading node device receives the processing data sent by each cooperating node device.
  • each collaborative node device starts to acquire the grid data collected by the grid terminal acquisition devices within the data acquisition range.
  • the collaborative node device collects the data collected by all the grid acquisition devices within its jurisdiction through the local area network. It is dynamic, that is, corresponding to the data collection range, cleans the collected power grid data, deletes the duplicate information in the obtained power grid data, and supplements missing values, obtains the processed data and converts it into data packets, through the bus or
  • the protocol (according to the requirements of regulating the cloud leading node device) is sent to the leading node device through the wide area data network, and the leading node device receives the processing data sent by each cooperating node device through the wide area data network for subsequent processing.
  • the leading node device after receiving the processing data, will also evaluate the data quality of each processing data to obtain an evaluation result of each processing data; and store the processing data whose evaluation result is a preset result; then the leading node According to the evaluation results of each processing data, the equipment optimizes the data collection range and cleaning rules of each cooperative node equipment, and sends the optimized data collection range and cleaning rules of each cooperative node equipment to each cooperative node equipment. The data collection range and cleaning rules are collected and cleaned. This process is the core of iterative optimization of data quality. Through the continuous iterative process, it is ensured that the processed data sent by each cooperating node device meets the quality requirements.
  • each cooperating node device monitors the execution of the scheduling task, and sends the execution of the scheduling task to the leading node device; monitors the resource usage, and sends the resource usage to the leading node device; monitors the execution of the rules, and sends the The rule execution status is sent to the leading node device; the leading node device receives the scheduling task execution status sent by each cooperating node device; obtains the resource usage of each cooperating node device; Process the data evaluation results and adjust the scheduling tasks sent to each cooperating node device. For example, some cooperating node devices cannot complete the cleaning rule processing, then the cooperating node device is no longer required to perform the cleaning task, and the collected power grid data is directly uploaded. .
  • the master node device obtains the global scheduling task, decomposes the global scheduling task to obtain the scheduling task, and sends it to each cooperating node device, obtains the data collection range and data processing rules of each cooperating node device, and sends it to the To each cooperating node device; realize part of the data processing work is distributed to each cooperating node device.
  • Each cooperating node device receives and executes the scheduling task issued by the leading node device, receives the data collection range and data processing rules issued by the leading node device, and obtains the collected data within the data collection range based on the scheduling task, and processes it according to the data processing rules.
  • the acquired collection data is processed data, and the processed data is sent to the leading node device; part of the data processing work is completed in each cooperating node device, so that the data sent to the leading node device is already processed data, greatly It reduces the transmission of garbage data, and effectively utilizes the computing and storage resources of the collaborative node devices on the edge side.
  • Wide-area cloud-edge collaborative computing is enabled, and the quality of data processing is improved.
  • FIG. 5 a schematic diagram of the data flow of the data processing method shown in FIG. 4 of the present application is shown.
  • the data flow includes two parts: the data from the cooperating node device to the leading node device, that is, the service data flow; the leading node device and the cooperating node Data such as tasks and rules for device interaction, that is, management data flow.
  • the power grid data involved in the business data flow comes from the power grid acquisition device, and the original data is uploaded to the collaborative node device through the local area network.
  • Cooperate with node devices to deploy stream processing and batch processing functions, and perform cleaning, verification and other processing for real-time data (stream processing) and historical data (batch processing) according to the data collection range and data processing rules issued by the leading node device.
  • the high-quality data processed by the cooperating node device is converted into a data message and sent to the leading node device through the bus or protocol (according to the requirements of the control cloud leading node).
  • the leading node device After receiving the power grid data sent by each cooperating node device, the leading node device performs data caching through the function of message conversion and data integration. For the integrated data, the multi-source data quality intelligent comprehensive evaluation function is used to analyze and evaluate the data quality to determine the data quality sent by each collaborative node device. For high-quality data, it is stored in the database of the leading node device to provide data services for regulating cloud business applications.
  • Managing data flow involves the interaction of tasks and rules between master node devices and cooperating node devices.
  • the leading node device obtains data quality verification and evaluation results, and determines whether data processing rules and scheduling tasks need to be adjusted. If adjustment is required, the adjusted data processing rules and scheduling tasks are fed back to the corresponding cooperating node devices through the bus, and the cooperating node devices receive data processing rules and scheduling tasks, and perform data processing rules application and scheduling tasks in the cooperating node devices. implement.
  • the data flow also involves the rule feedback and task feedback of the coordinated node devices, and the rule monitoring and task monitoring of the control cloud center. This process is an iterative process of continuous optimization, which relieves the computing pressure of regulating the cloud-dominant node equipment and the WAN data network. Bandwidth pressure improves the quality of grid data.
  • FIG. 6 it shows a structural block diagram of a control cloud data processing apparatus provided by still another embodiment of the present application.
  • the control cloud data processing apparatus can be implemented as part or all of a master node device through software, hardware, or a combination of the two.
  • the control cloud data processing device includes: a task decomposition and issuing module, a data receiving module and a rule issuing module.
  • the task decomposition and distribution module is used to obtain the global scheduling task, decompose the global scheduling task to obtain the scheduling task, and send it to each cooperating node device;
  • the data receiving module is used to receive the processing data sent by each cooperating node device.
  • the processing data is the collected data processed by the cooperative node equipment according to the data processing rules;
  • the rule issuing module is used to obtain the data collection range and data processing rules of each cooperative node equipment, and send it to each cooperative node equipment.
  • control cloud data processing device further includes: a data analysis unit, the data analysis unit is configured to evaluate the data quality of each processed data, and obtain each processed data evaluation result; and store the evaluation result as a preset The resulting processed data.
  • the data analysis unit includes a data integration module and a data evaluation module; the data integration module is used for summarizing and integrating the processed data through multi-source heterogeneous data fusion technology; the data evaluation module is used for intelligent comprehensive evaluation of multi-source data quality. Aggregate and integrate each processing data for quality evaluation, and obtain each processing data evaluation result; and store the data whose evaluation result is a preset result.
  • control cloud data processing apparatus further includes: a rule preset module, a rule optimization module, a rule monitoring module, and a task monitoring module.
  • the rule preset module is used to preset the initial collection range and initial data processing rules of each collaborative node device according to the control cloud data processing requirements; the rule optimization module is used to optimize the data of each collaborative node device according to the evaluation results of each processed data. Collection scope and data processing rules; the rule issuing module is also used to deliver the optimized data collection scope and data processing rules of each collaborative node device to each collaborative node device; the rule monitoring module is used to receive each collaborative node device The execution of the rules sent; the task monitoring module is used to receive the execution of the scheduling tasks sent by each cooperating node device; obtain the resource usage of each cooperating node device; Based on the evaluation results, adjust the scheduling tasks sent to each cooperating node device.
  • FIG. 7 it shows a block diagram of the structure of a control cloud data processing apparatus provided by still another embodiment of the present application.
  • the control cloud data processing apparatus can be implemented as part or all of a collaborative node device through software, hardware, or a combination of the two.
  • the control cloud data processing device includes: a task receiving and executing module, a rule receiving module and a data management module.
  • the task receiving and executing module is configured to receive and execute the scheduling tasks issued by the leading node device;
  • the rule receiving module is configured to receive the data collection range and data processing rules issued by the leading node device;
  • the data management module is configured to be based on Scheduling tasks, obtain the collected data within the data collection range, process the obtained collected data according to the data processing rules to obtain the processed data, and upload the processed data to the leading node device.
  • control cloud data processing apparatus further includes: a task feedback module, a resource feedback module, a rule feedback module, and a data format conversion module.
  • the task feedback module is configured to monitor the execution of the scheduling task and send the execution of the scheduling task to the leading node device;
  • the resource feedback module is configured to monitor the resource usage and upload the resource usage to the leading node device;
  • the rule feedback module is configured to monitor the execution of the rules and upload the execution of the rules to the leading node device;
  • the data format conversion module is configured to convert the processed data into a preset data format;
  • the data management module is also configured to Send the processing data in the preset data format to the master node device.
  • FIG. 8 it shows a structural block diagram of a control cloud data processing system provided by another embodiment of the present application.
  • the control cloud data processing system includes: a leading node device and several cooperative node devices connected to the leading node device, a plurality of cooperative nodes The device and the master node device are connected through a wide area data network.
  • the leading node device includes the control cloud data processing device provided in the embodiment shown in FIG. 6 ; the cooperating node device includes the control cloud data processing device provided in the embodiment shown in FIG. 7 .
  • the control cloud data processing method of the present application can be well applied in the field of electric power automation technology, and realizes the application of computer algorithms in the field of electric automation. It is a cloud service platform for power grid dispatching business. Its architecture design reflects the characteristics of hardware resource virtualization, data standardization and application service. It is one of the "Three Clouds" constructed by State Grid Corporation of China. An important part of the power grid is the innovative application of information and communication technologies such as “cloud, big things, and intelligent chains” in the field of regulation, and it is an important technical means to support power grid operation and regulation management.
  • the regulation cloud follows a hierarchical deployment model that is compatible with the principle of "unified scheduling and hierarchical management", and builds a cross-dispatching agency "1 national (sub) leading node + N provincial (local) collaborative nodes”
  • the two-level deployment control cloud system is compatible with the principle of "unified scheduling and hierarchical management", and builds a cross-dispatching agency "1 national (sub) leading node + N provincial (local) collaborative nodes”
  • the two-level deployment control cloud system is compatible with the principle of "unified scheduling and hierarchical management”
  • Guofen Cloud as the leading node device, is composed of part or all of the control cloud data processing device shown in Figure 6, responsible for managing and controlling the operation of each collaborative node, and realizing the interconnection and information exchange with each collaborative node.
  • the full model with a network of more than 35 kV collects operating data and real-time data of more than 220 kV, and deploys relevant application functions for the main network business of the country, the province, and the province.
  • the cloud data processing device cooperates with the leading node for data collection and aggregation, integrates the provincial grid model of 10 kV and above, and collects operating data and real-time data above 10 kV. application function.
  • Guofen Cloud and Provincial Cloud can operate independently, forming an organically coordinated whole, realizing source-side maintenance, automatic integration, unified service, and global sharing of various information resources.
  • the present application further provides a storage medium, specifically a computer-readable storage medium (Memory), where the computer-readable storage medium is a memory device in a terminal device for storing programs and data.
  • the computer-readable storage medium here may include both a built-in storage medium in the terminal device, and certainly also an extended storage medium supported by the terminal device.
  • the computer-readable storage medium provides storage space in which the operating system of the terminal is stored.
  • one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions may be one or more computer programs (including program codes).
  • the computer-readable storage medium here may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory.
  • One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps in the method for regulating cloud data processing in the foregoing embodiments.
  • the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • computer-usable storage media including, but not limited to, disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions
  • the apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Strategic Management (AREA)
  • General Engineering & Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Signal Processing (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Educational Administration (AREA)
  • Marketing (AREA)
  • Development Economics (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Information Transfer Between Computers (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

本申请公开了一种调控云数据处理方法、装置及系统，所述方法包括: 主导节点设备获取全局调度任务，并将全局调度任务分解得到调度任务，并下发至各协同节点设备; 获取各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备; 各协同节点设备接收并执行主导节点设备下发的调度任务；接收主导节点设备下发的数据采集范围及数据处理规则; 基于调度任务，获取数据采集范围内的采集数据，根据数据处理规则处理获取的采集数据得到处理数据，将处理数据上送至主导节点设备; 主导节点设备接收各协同节点设备上送的处理数据。解决了云中心计算、存储压力大及数据反复上送导致的广域数据网带宽压力大的问题，提升调控云数据质量。

Description

调控云数据处理方法、装置及系统
相关申请的交叉引用
本申请基于申请号为202011243477.4、申请日为2020年11月10日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请属于数据处理领域，涉及一种调控云数据处理方法、装置及系统。
背景技术
调控云基于虚拟化、分布式及服务化等云计算技术理念，是面向电网调度业务的云服务平台，按主导节点、协同节点、源数据端三层架构设计，其架构设计体现了硬件资源虚拟化、数据标准化和应用服务化的特点，是国网公司建设的“三朵云”中的重要组成部分，是“云大物移智链”等信息通信技术在调控领域的创新应用，是支撑电网运行和调控管理的重要技术手段，贯彻的是横向打通各专业、纵向打通多级业务的思想，实现模型、数据的统一汇集和管理。随着国网调控云建设的深入推进，调控云汇集的数据呈爆发式增长，数据类型多样化加剧，数据处理时效性要求增强。
现有关于数据处理和数据质量优化的技术方案，一般都是将数据集中汇集到调控主站或者云中心，在云中心进行统一的处理和数据校验评估，当数据校验发现问题后，再由源系统对数据进行处理后重新上送。
但是，由于集中处理对云中心的计算、存储压力较大；数据出问题后由源系统重新进行数据上送，可能存在数据反复上送的问题，增加了广域数据网的带宽压力，导致基于云中心的集中式计算模式难以满足未来调控云的发展需求。
发明内容
本申请的目的在于克服上述现有技术中云中心计算、存储压力大，及广域数据网的带宽压力大的缺点，提供一种调控云数据处理方法、装置及系统。
为达到上述目的，本申请采用以下技术方案予以实现：
本申请第一方面，一种调控云数据处理方法，应用于主导节点设备， 所述数据处理方法包括：
获取全局调度任务，将全局调度任务分解得到调度任务，并下发至各协同节点设备；
获取各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备；
接收各协同节点设备上送的处理数据，所述处理数据为协同节点设备根据数据处理规则处理后的采集数据。
在一些可选实施方式中，还包括：
对各处理数据的数据质量进行评估，得到各处理数据评估结果；并存储评估结果为预设结果的处理数据。
在一些可选实施方式中，还包括：
根据调控云数据处理需求，预设各协同节点设备的初始采集范围及初始数据处理规则，并下送至各协同节点设备；
根据各处理数据评估结果，优化各协同节点设备的数据采集范围及数据处理规则，将优化后的各协同节点设备的数据采集范围及数据处理规则下送至各协同节点设备。
在一些可选实施方式中，还包括：
接收各协同节点设备上送的调度任务执行情况；
获取各协同节点设备的资源使用情况；
根据各调度任务执行情况、各资源使用情况及各处理数据评估结果，调整下发至各协同节点设备的调度任务。
在一些可选实施方式中，还包括：
接收各协同节点设备上送的规则执行情况，其中，规则执行情况包括正常执行和非正常执行，当协同节点设备上送的规则执行情况为非正常执行时，生成规则非正常执行提示信息，并将该协同节点设备上送的数据标记为异常数据。
在一些可选实施方式中，所述全局调度任务为电力数据处理任务，所述电力数据处理任务能够分解为采集任务、清洗任务、转换任务以及存储任务，所述调度任务包括采集任务、清洗任务、转换任务以及存储任务中的至少一个；
所述数据采集范围包括设备范围和电力数据类别范围，设备范围为协同节点设备管辖内的所有电力设备或协同节点设备管辖内的预设电压等级的电力设备，电力数据类别范围为所有类别的电力数据或若干预设类别的电力数据，电力数据类别包括电网模型数据、电网运行数据、电网管理数据以及电网接收的外部数据；
所述数据处理规则包括补全缺失点、平滑突变点和处理非法值中的至少一个。
本申请第二方面，一种调控云数据处理方法，应用于协同节点设备， 所述数据处理方法包括：
接收并执行主导节点设备下发的调度任务；
接收主导节点设备下发的数据采集范围及数据处理规则；
基于调度任务，获取数据采集范围内的采集数据，根据数据处理规则处理获取的采集数据得到处理数据，将处理数据上送至主导节点设备。
在一些可选实施方式中，还包括：
监控调度任务执行情况，并将调度任务执行情况上送至主导节点设备；
监控资源使用情况，并将资源使用情况上送至主导节点设备；
监控规则执行情况，并将规则执行情况上送至主导节点设备。
在一些可选实施方式中，所述根据数据处理规则处理获取的采集数据得到处理数据时，对实时数据采用流式处理方式，对历史数据采用批量处理方式。
在一些可选实施方式中，所述将处理数据上送至主导节点设备包括：
将处理数据转换为预设的数据格式；
将预设的数据格式的处理数据上送至主导节点设备。
本申请第三方面，一种调控云数据处理方法，包括：
主导节点设备获取全局调度任务，并将全局调度任务分解得到调度任务，并下发至各协同节点设备；
主导节点设备获取各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备；
各协同节点设备接收并执行主导节点设备下发的调度任务；
各协同节点设备接收主导节点设备下发的数据采集范围及数据处理规则；
各协同节点设备基于调度任务，获取数据采集范围内的采集数据，根据数据处理规则处理获取的采集数据得到处理数据，将处理数据上送至主导节点设备；
主导节点设备接收各协同节点设备上送的处理数据。
在一些可选实施方式中，还包括：
主导节点设备根据调控云数据采集需求，预设各协同节点设备的初始采集范围及初始数据处理规则，并下送至各协同节点设备；
主导节点设备对各处理数据的数据质量进行评估，得到各处理数据评估结果；并存储评估结果为预设结果的处理数据；
主导节点设备根据各处理数据评估结果，优化各协同节点设备的数据采集范围及数据处理规则，将优化后的各协同节点设备的数据采集范围及数据处理规则下送至各协同节点设备。
在一些可选实施方式中，还包括：
各协同节点设备监控调度任务执行情况，并将调度任务执行情况上送至主导节点设备；监控资源使用情况，并将资源使用情况上送至主导节点 设备；监控规则执行情况，并将规则执行情况上送至主导节点设备；
主导节点设备接收各协同节点设备上送的调度任务执行情况；获取各协同节点设备的资源使用情况；并根据各调度任务执行情况、各资源使用情况及各处理数据评估结果，调整下发至各协同节点设备的调度任务；接收各协同节点设备上送的规则执行情况。
本申请第四方面，一种调控云数据处理装置，应用于主导节点设备，所述数据处理装置包括：
任务分解及下发模块，配置为获取全局调度任务，将全局调度任务分解得到调度任务，并下发至各协同节点设备；
数据接收模块，配置为接收各协同节点设备上送的处理数据，所述处理数据为协同节点设备根据数据处理规则处理后的采集数据；以及
规则下发模块，配置为获取各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备。
在一些可选实施方式中，还包括数据分析单元；
数据分析单元，配置为对各处理数据的数据质量进行评估，得到各处理数据评估结果；并存储评估结果为预设结果的处理数据。
在一些可选实施方式中，还包括：
规则预设模块，配置为根据调控云数据处理需求，预设各协同节点设备的初始采集范围及初始数据处理规则；以及
规则优化模块，配置为根据各处理数据评估结果，优化各协同节点设备的数据采集范围及数据处理规则；
所述规则下发模块，还配置为将优化后的各协同节点设备的数据采集范围及数据处理规则下送至各协同节点设备。
在一些可选实施方式中，还包括：
规则监控模块，配置为接收各协同节点设备上送的规则执行情况。
在一些可选实施方式中，还包括：
任务监控模块，配置为接收各协同节点设备上送的调度任务执行情况；获取各协同节点设备的资源使用情况；根据各调度任务执行情况、各资源使用情况及各处理数据评估结果，调整下发至各协同节点设备的调度任务。
本申请第五方面，一种调控云数据处理装置，应用于协同节点设备，所述数据处理装置包括：
任务接收及执行模块，配置为接收并执行主导节点设备下发的调度任务；
规则接收模块，配置为接收主导节点设备下发的数据采集范围及数据处理规则；以及
数据管理模块，配置为基于调度任务，获取数据采集范围内的采集数据，根据数据处理规则处理获取的采集数据得到处理数据，将处理数据上送至主导节点设备。
在一些可选实施方式中，还包括：
任务反馈模块，配置为监控调度任务执行情况，并将调度任务执行情况上送至主导节点设备；
资源反馈模块，配置为监控资源使用情况，并将资源使用情况上送至主导节点设备；以及
规则反馈模块，配置为监控规则执行情况，并将规则执行情况上送至主导节点设备。
在一些可选实施方式中，还包括：
数据格式转换模块，配置为将处理数据转换为预设的数据格式；
所述数据管理模块还用于将预设的数据格式的处理数据上送至主导节点设备。
本申请第六方面，一种调控云数据处理系统，包括：主导节点设备以及与主导节点设备连接的若干协同节点设备；
所述主导节点设备包括上述的应用于主导节点设备的调控云数据处理装置；
所述协同节点设备包括上述的应用于协同节点设备的调控云数据处理装置。
与现有技术相比，本申请具有以下有益效果：
本申请调控云数据处理方法，通过将全局调度任务分解得到调度任务，并下发至各协同节点设备，实现将部分数据处理的工作分配至协同节点设备，通过协同节点设备进行数据的部分处理，上送至主导节点设备的数据已经是经过处理的数据，极大的减小垃圾数据传输，并且有效的利用了处于边缘侧的协同节点设备的计算和存储资源，解决了调控云主导节点设备的计算压力、存储压力和广域数据网的带宽压力大的问题，实现了广域云边协同计算，提升了数据处理的质量。由于传统情况下，调控领域的数据是分级分布式存储，从数据和业务层面，各级之间很少进行实时交互和协同互动，尤其在调控领域引入云计算技术后，贯彻的是横向打通各专业、纵向打通多级业务的思想，因此调控云的建设实际上是考虑将模型、数据进行统一汇集和管理，进而导致云端的计算、存储及网络等存在很大的压力，而且云和边之间的任务、规则和数据管理方面相互独立，缺少协同互动，并且基于调控领域对数据的动态需求，单一的边缘计算能力已不能满足调控系统对调控数据的动态处理需求。而本申请不仅通过利用各协同节点设备的处理能力对数据进行初步处理，同时，结合调控领域的实际需求，加强主导节点设备和各协同节点设备之间的协同互动，通过动态调整各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备，实现边缘数据处理功能的动态化，充分满足调控系统对数据的需求，充分实现云和边的协同互动。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。
图1为本申请的面向调控云架构的广域云边数据协同整体架构示意图；
图2为本申请的一个实施例中调控云数据处理方法流程框图；
图3为本申请的再一个实施例中调控云数据处理方法流程框图；
图4为本申请的又一个实施例中调控云数据处理方法流程框图；
图5为本申请的一个实施例中数据处理方法的数据流示意图；
图6为本申请的一个实施例中调控云数据处理装置结构框图；
图7为本申请的再一个实施例中调控云数据处理装置结构框图；
图8为本申请的又一个实施例中调控云数据处理装置结构框图；
图9为本申请的以电网为例构建的调控云架构示意图。
具体实施方式
为了使本技术领域的人员更好地理解本申请方案，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分的实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本申请保护的范围。
需要说明的是，本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
下面结合附图对本申请做进一步详细描述：
参见图1，示出了本申请各个实施例涉及的一种实施环境的调控云平台架构，其包括主导节点设备、协同节点设备以及源数据端设备。
调控云平台基于虚拟化、分布式及服务化等云技术理念，按主导节点、协同节点、源数据端三层架构设计。基于调控云的总体架构和边缘计算的理念，基于调控云的广域云边数据协同整体架构包含调控云主导节点(云)、协同节点(边)和源数据端(端)三层，其中，调控云主导节点(云)和协同节点(边)通过广域数据网相连，源数据端(端)和电网采集设备通过本地局域网相连。
主导节点负责广域云边协同数据管理，其底层基于调控云基础平台提 供的硬件、存储、网络等资源和总线、权限等公共组件支撑，通过广域数据网与协同节点进行任务、规则、数据的广域交互，进一步支撑调控云上层的业务应用。调控云协同节点负责边缘协同计算管理，其底层基于边缘基础设施提供的硬件环境，通过局域网汇集其管辖范围内的所有采集设备采集的数据，通过广域数据网与主导节点进行任务、规则、数据的广域交互。
主导节点设备及协同节点设备可以是一台服务器，也可以是若干台服务器组成的服务器集群，或者是一个云计算服务中心。
基于该架构，通过本申请实施例提供的调控云数据处理方法，应用到广域数据质量迭代优化问题中，通过协同节点的计算资源进行采集数据的清洗、筛选和上送，调控云中心协调所有协同节点的计算任务和数据处理规则，实现任务、规则向协同节点的下发和反馈，能够充分利用调控云协同节点(边)的计算资源和存储资源，缓解了调控云中心的计算压力、存储压力和广域数据网的带宽压力，实现了广域云边协同计算。
参见图2，示出了本申请一个实施例中提供的调控云数据处理方法，该方法应用于主导节点设备，本实施例以该调控云数据处理方法应用于图1所示的实施环境中来举例说明，该调控云数据处理方法包括以下步骤：
步骤11：获取全局调度任务，将全局调度任务分解得到调度任务，并下发至各协同节点设备。
全局调度任务是主导节点设备基于数据处理最终需求生成的调控云平台的电力数据处理任务，面向具体的数据采集、清洗、校验、评估等需求，主导节点设备通过将全局调度任务进行分解，电力数据处理任务能够分解为采集任务、清洗任务、转换任务以及存储任务，将其中需要各协同节点具体执行的任务作为调度任务，下发给对应的协同节点设备。
在一些可选实施方式中，主节点设备还接收各协同节点设备上送的调度任务执行情况；获取各协同节点设备的资源使用情况；根据各调度任务执行情况、各资源使用情况及各处理数据评估结果，调整下发至各协同节点设备的调度任务。主导节点设备作为调控云任务的全局协调中心，根据各协同节点设备的数据采集范围、资源使用情况及任务执行情况等，进行全局统一协调，充分发挥各协同节点设备计算能力的同时，保证数据采集质量和效率。
例如，需要进行存量历史数据批量处理或重传工作时，一旦出现某个协同节点设备计算资源不足、数据处理时间过长、数据上送效率较低等情况，主导节点设备会减小该协同节点的数据处理任务，对应的，该协同节点设备将部分数据不经过处理直接上送到主导节点设备，由主导节点设备分配相应的资源进行处理工作，该工作结束后，主导节点设备重新进行全局调度任务分解的调整。
步骤12：获取各协同节点设备的数据采集范围及数据处理规则，并下 送至各协同节点设备。
该步骤是各协同节点设备上送数据的质量保证，指定各协同节点设备按照数据采集范围进行数据采集，并按照数据处理规则进行数据处理，体现主导节点设备的管理功能。初始状态时，根据调控云数据处理需求，由系统管理员通过手工配置各协同节点设备的数据采集范围，比如按管辖范围、电压等级、数据类别等限制要求，将数据采集范围分为设备范围和电力数据类别范围，其中，设备范围可以为协同节点设备管辖内的所有电力设备或协同节点设备管辖内的预设电压等级的电力设备，电力数据类别范围可以为所有类别的电力数据或若干预设类别的电力数据，其中，电力数据类别包括电网模型数据、电网运行数据、电网管理数据以及电网接收的外部数据。以及配置各协同节点设备的初始的数据处理规则，比如补全缺失点、平滑突变点、处理非法值等中的一个或几个，并将该各协同节点设备的数据采集范围和数据处理规则下发到各协同节点设备。
在一些可选实施方式中，主节点设备还接收各协同节点设备上送的规则执行情况，其中，规则执行情况包括正常执行和非正常执行，当协同节点设备上送的规则执行情况为非正常执行时，生成规则非正常执行提示信息，并将该协同节点设备上送的数据标记为异常数据，以便后续通过主导节点设备对未进行规则处理的数据，即异常数据再次处理，实现对各协同节点设备是否正常进行数据处理的监控，以及确保接收的各协同节点设备上送的数据是经过数据处理规则处理的，保证数据质量。
在一些可选实施方式中，在系统正常运行后，根据主导节点设备内各协同节点设备的各处理数据评估结果，优化各协同节点设备的数据采集范围及数据处理规则，比如，针对数据处理速度慢的问题，适当减小该协同节点设备的数据采集范围或者降低数据处理规则的复杂性；针对处理数据的质量差、错误或无效数据较多的问题，适当完善该协同节点设备的数据处理规则。同时，主导节点设备通过大数据分析挖掘技术进行数据处理规则的训练和升级，并将升级后的数据处理规则推送到协同节点设备，实现主导节点设备和各协同节点设备的数据处理规则的自适应迭代更新，通过主导节点设备和协同节点设备之间的数据清洗协同交互技术，基于主导节点设备强大的计算能力，有效提升数据质量及清洗效率。然后将优化后的各协同节点设备的数据采集范围及数据处理规则实时下发到各协同节点设备，如此反复进行优化迭代，提升协同节点设备上送的处理数据的数据质量。
步骤13：接收各协同节点设备上送的处理数据，所述处理数据为协同节点设备根据数据处理规则处理后的采集数据。
各协同节点设备通过广域数据网，将根据数据处理规则处理后的采集数据得到的处理数据上送至主导节点设备，主导节点设备实现处理数据的接收工作，接收到各协同节点设备发送的处理数据后，通过报文转换和数 据整合功能，进行数据缓存。这里，由于处理数据是协同节点设备根据数据处理规则处理后的采集数据，就不需要主导节点设备再进行处理，极大的减轻了主导节点设备的计算压力。同时，各协同节点设备在处理过程中，有问题的数据将会直接被发现，避免将错误数据上送，导致的数据反复上送问题，进而减轻广域数据网的带宽压力。
在一些可选实施方式中，主导节点设备对各处理数据的数据质量进行评估，得到各处理数据评估结果，并存储评估结果为预设结果的处理数据。具体的，一般通过多源异构数据融合技术对各处理数据进行汇总整合，多源异构数据融合指利用相关手段将采集、分析获取到的所有信息全部综合到一起，并对信息进行统一的评价，最后得到统一的信息的技术，用于将各种不同来源、不同结构的数据信息进行综合，吸取不同数据源的特点，然后从中提取出统一的、比单一数据更好、更丰富的信息，然后通过多源数据质量智能综合评估对汇总整合的各处理数据进行质量评估，多源数据质量智能综合评估通过结合具体的评估需求，从在线数据参数、模型匹配及数据内在特征，进行多源数据质量评估。
确定每个协同节点设备上送的数据质量，进而也可依据此进行各协同节点设备的管理，同时，对于优质的数据即评估结果为优质的处理数据，存储到调控云主导节点设备的数据库中，进而为调控云业务应用提供数据服务。
综上所述，通过将全局调度任务分解得到调度任务，并下发至各协同节点设备，实现将部分数据处理的工作分配至协同节点设备，通过协同节点设备进行数据的部分处理，上送至主导节点设备的数据已经是经过处理的数据，极大的减小垃圾数据传输，并且有效的利用了处于边缘侧的协同节点设备的计算和存储资源，解决了调控云主导节点设备的计算压力、存储压力和广域数据网的带宽压力大的问题，实现了广域云边协同计算，提升了数据处理的质量。
由于传统情况下，调控领域的数据是分级分布式存储，从数据和业务层面，各级之间很少进行实时交互和协同互动，尤其在调控领域引入云计算技术后，贯彻的是横向打通各专业、纵向打通多级业务的思想，因此调控云的建设实际上是考虑将模型、数据进行统一汇集和管理，进而导致云端的计算、存储及网络等存在很大的压力，而且云和边之间的任务、规则和数据管理方面相互独立，缺少协同互动，并且基于调控领域对数据的动态需求，单一的边缘计算能力已不能满足调控系统对调控数据的动态处理需求。而本申请不仅通过利用各协同节点设备的处理能力对数据进行初步处理，同时，结合调控领域的实际需求，加强主导节点设备和各协同节点设备之间的协同互动，通过动态调整各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备，改变各协同节点设备对数据处理的过程或结果，使处理的数据满足主导节点设备不断变化的调控需求，实 现边缘数据处理功能的动态化，充分满足调控系统对数据的需求，充分实现云和边的协同互动。
参见图3，示出了本申请再一个实施例中提供的调控云数据处理方法，该方法应用于协同节点设备，本实施例以该调控云数据处理方法应用于图1所示的实施环境中来举例说明，该调控云数据处理方法包括以下步骤：
步骤21：接收并执行主导节点设备下发的调度任务。
各协同节点设备通过广域数据网接收导节点设备下发的调度任务，并进行任务解析，组织协同节点设备内各部件协同工作，开始执行主导节点设备下发的调度任务。
在一些可选实施方式中，协同节点设备还会监控调度任务执行情况，并将调度任务执行情况上送至主导节点设备；这样在主导节点设备端就能实时监控各协同设备的工作情况，以便进行各协同设备的管理。
在一些可选实施方式中，协同节点设备还会监控资源使用情况，并将资源使用情况上送至主导节点设备，以便告知主导节点设备自身的计算能力剩余情况，也是为了给主导节点设备提供任务管理的基础信息。
步骤22：接收主导节点设备下发的数据采集范围及数据处理规则。
各协同节点设备通过广域数据网接收主导节点设备下发的数据采集范围及数据处理规则，并将数据采集范围及数据处理规则加载在本地内存，以便于后续通过数据采集范围进行数据采集，根据数据处理规则对采集数据进行处理。
当本地已经加载数据采集范围及数据处理规则后，再次接收到了主导节点设备下发的数据采集范围及数据处理规则，那么一般的选择是将之前的数据采集范围及数据处理规则删除，或者直接通过新的数据采集范围及数据处理规则覆盖，以实现数据采集范围及数据处理规则的迭代更新。
步骤23：基于调度任务，获取数据采集范围内的采集数据，根据数据处理规则处理获取的采集数据得到处理数据，将处理数据上送至主导节点设备。
基于调度任务，也就是响应于调度任务，协同节点设备调用数据采集范围信息，并在数据采集范围内进行数据采集，这里所说的数据采集，可以是从采集终端获取数据，也可以是直接采集的数据，即该协同节点设备可以具有数据采集的能力。同时，调用数据处理规则对采集的数据进行处理，比如，将采集数据中的突变点数据进行平滑处理，将采集数据中出现的非法值删除，将采集数据中出现的缺失点进行补充，可以通过邻域值补充，也可以是其他的补充规则，此处仅为举例说明，不做限制。处理完成后，将处理数据上送至主导节点设备，至此，协同节点设备完成自身的工作，充分利用了其计算能力。
综上所述，通过接收并执行主导节点设备下发的调度任务，接收主导节点设备下发的数据采集范围及数据处理规则，并且，基于调度任务，获 取数据采集范围内的采集数据，根据数据处理规则处理获取的采集数据得到处理数据，实现通过协同节点设备进行数据的部分处理，使得上送至主导节点设备的数据已经是经过处理的数据，极大的减小垃圾数据传输，并且有效的利用了处于边缘侧的协同节点设备的计算和存储资源，解决了调控云主导节点设备的计算压力、存储压力和广域数据网的带宽压力大的问题，实现了广域云边协同计算，提升了数据处理的质量。同时，基于接收主导节点设备下发的数据采集范围及数据处理规则，改变对数据处理的过程或结果来满足调控系统对调控数据的动态需求，实现主和导节点设备充分互动。
参见图4，示出了本申请再一个实施例中提供的调控云数据处理方法，包括主导节点设备及各协同节点设备的操作步骤，本实施例以该调控云数据处理方法应用于图1所示的实施环境中，以应用于电网调控系统来举例说明，该调控云数据处理方法包括以下步骤：
步骤31：主导节点设备获取全局调度任务，并将全局调度任务分解得到调度任务，并下发至各协同节点设备。
对应的，步骤32：各协同节点设备接收并执行主导节点设备下发的调度任务。
具体的，基于电网调控系统对模型数据、运行数据、管理数据及外部数据的采集、清洗、转换及存储的需求，主导节点设备将全局调度任务分解为采集、清洗、校验以及评估，并将采集及清洗任务下发至协同节点设备，由协同节点设备来完成。其中，数据清洗(Data cleaning)是对数据进行重新审查和校验的过程，目的在于删除重复信息、纠正存在的错误，并提供数据一致性。
对应的，各协同节点设备接收主导节点设备下发的采集及清洗任务，并进行任务解析，以根据解析内容管理协同节点设备执行采集及清洗。
步骤33：主导节点设备获取各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备。
对应的，步骤34：各协同节点设备接收主导节点设备下发的数据采集范围及数据处理规则。
具体的，初始状态下，主导节点设备根据采集及清洗需求，预设各协同节点设备的初始采集范围及清洗规则，并下发至各协同节点设备，各协同节点设备将接收的初始采集范围及清洗规则加载在本地。
步骤35：各协同节点设备基于调度任务，获取数据采集范围内的采集数据，根据数据处理规则处理获取的采集数据得到处理数据，将处理数据上送至主导节点设备；
对应的，步骤36：主导节点设备接收各协同节点设备上送的处理数据。
具体的，各协同节点设备基于采集任务，开始获取数据采集范围内各电网终端采集设备采集的电网数据，这里协同节点设备通过局域网汇集其 管辖范围内的所有电网采集设备采集的数据，其管辖范围是动态变化的，即对应数据采集范围，并将采集到的电网数据进行清洗，将获取的电网数据内的重复信息删除，并补充缺失值，得到处理数据并转换为数据报文，通过总线或者协议(根据调控云主导节点设备的要求)的方式，通过广域数据网上送至主导节点设备，主导节点设备通过广域数据网接收各协同节点设备上送的处理数据，进行后续处理。
在一些可选实施方式中，主导节点设备接收处理数据后，还会对各处理数据的数据质量进行评估，得到各处理数据评估结果；并存储评估结果为预设结果的处理数据；然后主导节点设备根据各处理数据评估结果，优化各协同节点设备的数据采集范围及清洗规则，将优化后的各协同节点设备的数据采集范围及清洗规则下送至各协同节点设备，各协同节点设备按照新的数据采集范围及清洗规则进行采集和清洗。这个过程是数据质量迭代优化的核心，通过不断的迭代过程，保证各协同节点设备上送的处理数据满足质量需求。
在一些可选实施方式中，为了有效管理各协同节点设备，需要获取各协同节点设备的反馈信息，以此为基础进行反馈调节。具体的，各协同节点设备监控调度任务执行情况，并将调度任务执行情况上送至主导节点设备；监控资源使用情况，并将资源使用情况上送至主导节点设备；监控规则执行情况，并将规则执行情况上送至主导节点设备；主导节点设备接收各协同节点设备上送的调度任务执行情况；获取各协同节点设备的资源使用情况；并根据各调度任务执行情况、各资源使用情况及各处理数据评估结果，调整下发至各协同节点设备的调度任务，比如有的协同节点设备无法完成清洗规则处理，那么就不再需要该协同节点设备进行清洗任务，直接将采集的电网数据上送。
综上所述，主节点设备通过获取全局调度任务，并将全局调度任务分解得到调度任务，并下发至各协同节点设备，获取各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备；实现将数据的部分处理工作分配至各协同节点设备。各协同节点设备接收并执行主导节点设备下发的调度任务，接收主导节点设备下发的数据采集范围及数据处理规则，并基于调度任务，获取数据采集范围内的采集数据，根据数据处理规则处理获取的采集数据得到处理数据，将处理数据上送至主导节点设备；实现在各协同节点设备完成数据的部分处理工作，使得上送至主导节点设备的数据已经是经过处理的数据，极大的减小垃圾数据传输，并且有效的利用了处于边缘侧的协同节点设备的计算和存储资源，解决了调控云主导节点设备的计算压力、存储压力和广域数据网的带宽压力大的问题，实现了广域云边协同计算，提升了数据处理的质量。
参见图5，示出了本申请图4所示的数据处理方法的数据流示意图，数据流包含两部分：从协同节点设备到主导节点设备的数据，即业务数据流； 主导节点设备和协同节点设备交互的任务、规则等数据，即管理数据流。
以电网数据为例，(1)业务数据流。业务数据流涉及的电网数据来源于电网采集设备，通过本地局域网将原始数据上送到协同节点设备。协同节点设备部署流式处理和批量处理功能，根据主导节点设备下发的数据采集范围和数据处理规则，分别针对实时数据(流式处理)和历史数据(批量处理)进行清洗、校验等处理。协同节点设备处理后的优质数据，转换为数据报文，通过总线或者协议(根据调控云主导节点的要求)的方式发送到主导节点设备。
主导节点设备接收到各协同节点设备发送的电网数据后，通过报文转换和数据整合功能，进行数据缓存。对整合后的数据，通过多源数据质量智能综合评估功能，进行数据质量分析评估，确定每个协同节点设备上送的数据质量。对于优质的数据，存储到主导节点设备的数据库中，为调控云业务应用提供数据服务。
(2)管理数据流。管理数据流涉及主导节点设备和协同节点设备之间的任务和规则交互。在上述业务数据流中，主导节点设备获取数据质量校验、评估结果，确定数据处理规则和调度任务是否需要调整。如果需要调整的话，将调整后的数据处理规则和调度任务通过总线反馈给相应的协同节点设备，协同节点设备进行数据处理规则和调度任务接收，并在协同节点设备进行数据处理规则应用和调度任务执行。该数据流同时涉及协同节点设备的规则反馈、任务反馈和调控云中心的规则监控、任务监控，该过程是不断优化迭代的过程，缓解了调控云主导节点设备的计算压力和广域数据网的带宽压力，提升了电网数据的质量。
下述为本申请的装置实施例，可以用于执行本申请方法实施例。对于装置实施例中未纰漏的细节，请参照本申请方法实施例。
参见图6，示出了本申请再一个实施例提供的调控云数据处理装置的结构方框图，该调控云数据处理装置可以通过软件、硬件或者两者结合实现为主导节点设备的一部分或全部。该调控云数据处理装置包括：任务分解及下发模块、数据接收模块以及规则下发模块。
其中，任务分解及下发模块用于获取全局调度任务，将全局调度任务分解得到调度任务，并下发至各协同节点设备；数据接收模块用于接收各协同节点设备上送的处理数据，所述处理数据为协同节点设备根据数据处理规则处理后的采集数据；规则下发模块用于获取各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备。
在一些可选实施方式中，该调控云数据处理装置还包括：数据分析单元，数据分析单元用于对各处理数据的数据质量进行评估，得到各处理数据评估结果；并存储评估结果为预设结果的处理数据。具体的，数据分析单元包括数据整合模块和数据评估模块；数据整合模块用于通过多源异构数据融合技术对各处理数据进行汇总整合；数据评估模块用于通过多源数 据质量智能综合评估对汇总整合的各处理数据进行质量评估，得到各处理数据评估结果；并存储评估结果为预设结果的数据。
在一些可选实施方式中，该调控云数据处理装置还包括：规则预设模块、规则优化模块、规则监控模块以及任务监控模块。
其中，规则预设模块用于根据调控云数据处理需求，预设各协同节点设备的初始采集范围及初始数据处理规则；规则优化模块用于根据各处理数据评估结果，优化各协同节点设备的数据采集范围及数据处理规则；所述规则下发模块还用于将优化后的各协同节点设备的数据采集范围及数据处理规则下送至各协同节点设备；规则监控模块用于接收各协同节点设备上送的规则执行情况；任务监控模块用于接收各协同节点设备上送的调度任务执行情况；获取各协同节点设备的资源使用情况；根据各调度任务执行情况、各资源使用情况及各处理数据评估结果，调整下发至各协同节点设备的调度任务。
参见图7，示出了本申请再一个实施例提供的调控云数据处理装置的结构方框图，该调控云数据处理装置可以通过软件、硬件或者两者结合实现为协同节点设备的一部分或全部。该调控云数据处理装置包括：任务接收及执行模块、规则接收模块以及数据管理模块。
其中，任务接收及执行模块，配置为接收并执行主导节点设备下发的调度任务；规则接收模块，配置为接收主导节点设备下发的数据采集范围及数据处理规则；数据管理模块，配置为基于调度任务，获取数据采集范围内的采集数据，根据数据处理规则处理获取的采集数据得到处理数据，将处理数据上送至主导节点设备。
在一些可选实施方式中，该调控云数据处理装置还包括：任务反馈模块、资源反馈模块、规则反馈模块以及数据格式转换模块。
其中，任务反馈模块，配置为监控调度任务执行情况，并将调度任务执行情况上送至主导节点设备；资源反馈模块，配置为监控资源使用情况，并将资源使用情况上送至主导节点设备；规则反馈模块，配置为监控规则执行情况，并将规则执行情况上送至主导节点设备；数据格式转换模块，配置为将处理数据转换为预设的数据格式；所述数据管理模块，还配置为将预设的数据格式的处理数据上送至主导节点设备。
参见图8，示出了本申请再一个实施例提供的调控云数据处理系统的结构方框图，该调控云数据处理系统包括：主导节点设备以及与主导节点设备连接的若干协同节点设备，若干协同节点设备与主导节点设备之间通过广域数据网连接。
其中，主导节点设备包括图6所示实施例中提供的调控云数据处理装置；协同节点设备包括图7所示实施例中提供的调控云数据处理装置。
本申请调控云数据处理方法，能够较好的应用于电力自动化技术领域，实现计算机算法在电气自动化领域应用，具体的，参见图9，以电网为例构 建的调控云，基于虚拟化、分布式及服务化等云计算技术理念，是面向电网调度业务的云服务平台，其架构设计体现了硬件资源虚拟化、数据标准化和应用服务化的特点，是国网公司建设的“三朵云”中的重要组成部分，是“云大物移智链”等信息通信技术在调控领域的创新应用，是支撑电网运行和调控管理的重要技术手段。结合调控业务生产组织模式，调控云遵循与“统一调度、分级管理”原则相适应的分级部署模式，构建跨调度机构的“1个国(分)主导节点+N个省(地)协同节点”的两级部署调控云体系。
其中，国分云作为主导节点设备，由图6所示的调控云数据处理装置组成部分或全部，负责管理、控制各协同节点的运行，并实现与各协同节点的互联和信息交换，集成了全网35千伏以上的全模型，汇集了220千伏以上运行数据和实时数据，面向国分省调主网业务，部署了相关应用功能；省地云作为协同节点设备，包括图7所示的调控云数据处理装置，配合主导节点进行数据归集和汇聚，集成了10千伏及以上省网模型，汇集了10千伏以上运行数据和实时数据，面向省地县调区域电网业务，部署了相关应用功能。国分云和省地云在统一架构的基础上，可各自独立运行，构成有机协同的整体，实现各类信息资源的源端维护、自动集成、统一服务、全局共享。
再一个实施例中，本申请还提供了一种存储介质，具体为计算机可读存储介质(Memory)，所述计算机可读存储介质是终端设备中的记忆设备，用于存放程序和数据。可以理解的是，此处的计算机可读存储介质既可以包括终端设备中的内置存储介质，当然也可以包括终端设备所支持的扩展存储介质。计算机可读存储介质提供存储空间，该存储空间存储了终端的操作系统。并且，在该存储空间中还存放了适于被处理器加载并执行的一条或一条以上的指令，这些指令可以是一个或一个以上的计算机程序(包括程序代码)。需要说明的是，此处的计算机可读存储介质可以是高速RAM存储器，也可以是非不稳定的存储器(non-volatile memory)，例如至少一个磁盘存储器。可由处理器加载并执行计算机可读存储介质中存放的一条或一条以上指令，以实现上述实施例中有关调控云数据处理方法的相应步骤。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中 的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
最后应当说明的是：以上实施例仅用以说明本申请的技术方案而非对其限制，尽管参照上述实施例对本申请进行了详细的说明，所属领域的普通技术人员应当理解：依然可以对本申请的具体实施方式进行修改或者等同替换，而未脱离本申请精神和范围的任何修改或者等同替换，其均应涵盖在本申请的权利要求保护范围之内。

Claims (22)

  1. 一种调控云数据处理方法，包括：
    获取全局调度任务，将全局调度任务分解得到调度任务，并下发至各协同节点设备；
    获取各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备；
    接收各协同节点设备上送的处理数据，所述处理数据为协同节点设备根据数据处理规则处理后的采集数据。
  2. 根据权利要求1所述的调控云数据处理方法，其中，还包括：
    对各处理数据的数据质量进行评估，得到各处理数据评估结果；并存储评估结果为预设结果的处理数据。
  3. 根据权利要求2所述的调控云数据处理方法，其中，还包括：
    根据调控云数据处理需求，预设各协同节点设备的初始采集范围及初始数据处理规则，并下送至各协同节点设备；
    根据各处理数据评估结果，优化各协同节点设备的数据采集范围及数据处理规则，将优化后的各协同节点设备的数据采集范围及数据处理规则下送至各协同节点设备。
  4. 根据权利要求2所述的调控云数据处理方法，其中，还包括：
    接收各协同节点设备上送的调度任务执行情况；
    获取各协同节点设备的资源使用情况；
    根据各调度任务执行情况、各资源使用情况及各处理数据评估结果，调整下发至各协同节点设备的调度任务。
  5. 根据权利要求1所述的调控云数据处理方法，其中，还包括：
    接收各协同节点设备上送的规则执行情况，其中，规则执行情况包括正常执行和非正常执行，当协同节点设备上送的规则执行情况为非正常执行时，生成规则非正常执行提示信息，并将该协同节点设备上送的数据标记为异常数据。
  6. 根据权利要求1所述的调控云数据处理方法，其中，所述全局调度任务为电力数据处理任务，所述电力数据处理任务能够分解为采集任务、清洗任务、转换任务以及存储任务，所述调度任务包括采集任务、清洗任务、转换任务以及存储任务中的至少一个；
    所述数据采集范围包括设备范围和电力数据类别范围，设备范围为协同节点设备管辖内的所有电力设备或协同节点设备管辖内的预设电压等级的电力设备，电力数据类别范围为所有类别的电力数据或若干预设类别的电力数据，电力数据类别包括电网模型数据、电网运行数据、电网管理数据以及电网接收的外部数据；
    所述数据处理规则包括补全缺失点、平滑突变点和处理非法值中的至少一个。
  7. 一种调控云数据处理方法，包括：
    接收并执行主导节点设备下发的调度任务；
    接收主导节点设备下发的数据采集范围及数据处理规则；
    基于调度任务，获取数据采集范围内的采集数据，根据数据处理规则处理获取的采集数据得到处理数据，将处理数据上送至主导节点设备。
  8. 根据权利要求7所述的调控云数据处理方法，其中，还包括：
    监控调度任务执行情况，并将调度任务执行情况上送至主导节点设备；
    监控资源使用情况，并将资源使用情况上送至主导节点设备；
    监控规则执行情况，并将规则执行情况上送至主导节点设备。
  9. 根据权利要求7所述的调控云数据处理方法，其中，所述根据数据处理规则处理获取的采集数据得到处理数据时，对实时数据采用流式处理方式，对历史数据采用批量处理方式。
  10. 根据权利要求7所述的调控云数据处理方法，其中，所述将处理数据上送至主导节点设备包括：
    将处理数据转换为预设的数据格式；
    将预设的数据格式的处理数据上送至主导节点设备。
  11. 一种调控云数据处理方法，包括：
    主导节点设备获取全局调度任务，并将全局调度任务分解得到调度任务，并下发至各协同节点设备；
    主导节点设备获取各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备；
    各协同节点设备接收并执行主导节点设备下发的调度任务；
    各协同节点设备接收主导节点设备下发的数据采集范围及数据处理规则；
    各协同节点设备基于调度任务，获取数据采集范围内的采集数据，根据数据处理规则处理获取的采集数据得到处理数据，将处理数据上送至主导节点设备；
    主导节点设备接收各协同节点设备上送的处理数据。
  12. 根据权利要求11所述的调控云数据处理方法，其中，还包括：
    主导节点设备根据调控云数据采集需求，预设各协同节点设备的初始采集范围及初始数据处理规则，并下送至各协同节点设备；
    主导节点设备对各处理数据的数据质量进行评估，得到各处理数据评估结果；并存储评估结果为预设结果的处理数据；
    主导节点设备根据各处理数据评估结果，优化各协同节点设备的数据采集范围及数据处理规则，将优化后的各协同节点设备的数据采集范围及数据处理规则下送至各协同节点设备。
  13. 根据权利要求11所述的调控云数据处理方法，其中，还包括：
    各协同节点设备监控调度任务执行情况，并将调度任务执行情况上送至主导节点设备；监控资源使用情况，并将资源使用情况上送至主导节点设备；监控规则执行情况，并将规则执行情况上送至主导节点设备；
    主导节点设备接收各协同节点设备上送的调度任务执行情况；获取各协同节点设备的资源使用情况；并根据各调度任务执行情况、各资源使用情况及各处理数据评估结果，调整下发至各协同节点设备的调度任务；接收各协同节点设备上送的规则执行情况。
  14. 一种调控云数据处理装置，包括：
    任务分解及下发模块，配置为获取全局调度任务，将全局调度任务分解得到调度任务，并下发至各协同节点设备；
    数据接收模块，配置为接收各协同节点设备上送的处理数据，所述处理数据为协同节点设备根据数据处理规则处理后的采集数据；以及
    规则下发模块，配置为获取各协同节点设备的数据采集范围及数据处理规则，并下送至各协同节点设备。
  15. 根据权利要求14所述的调控云数据处理装置，其中，还包括数据分析单元；
    数据分析单元，配置为对各处理数据的数据质量进行评估，得到各处理数据评估结果；并存储评估结果为预设结果的处理数据。
  16. 根据权利要求14所述的调控云数据处理装置，其中，还包括：
    规则预设模块，配置为根据调控云数据处理需求，预设各协同节点设备的初始采集范围及初始数据处理规则；以及
    规则优化模块，配置为根据各处理数据评估结果，优化各协同节点设备的数据采集范围及数据处理规则；
    所述规则下发模块，还配置为将优化后的各协同节点设备的数据采集范围及数据处理规则下送至各协同节点设备。
  17. 根据权利要求14所述的调控云数据处理装置，其中，还包括：
    规则监控模块，配置为接收各协同节点设备上送的规则执行情况。
  18. 根据权利要求14所述的调控云数据处理装置，其中，还包括：
    任务监控模块，配置为接收各协同节点设备上送的调度任务执行情况；获取各协同节点设备的资源使用情况；根据各调度任务执行情况、各资源使用情况及各处理数据评估结果，调整下发至各协同节点设备的调度任务。
  19. 一种调控云数据处理装置，包括：
    任务接收及执行模块，配置为接收并执行主导节点设备下发的调度任务；
    规则接收模块，配置为接收主导节点设备下发的数据采集范围及数据处理规则；以及
    数据管理模块，配置为基于调度任务，获取数据采集范围内的采集数 据，根据数据处理规则处理获取的采集数据得到处理数据，将处理数据上送至主导节点设备。
  20. 根据权利要求19所述的调控云数据处理装置，其中，还包括：
    任务反馈模块，配置为监控调度任务执行情况，并将调度任务执行情况上送至主导节点设备；
    资源反馈模块，配置为监控资源使用情况，并将资源使用情况上送至主导节点设备；以及
    规则反馈模块，配置为监控规则执行情况，并将规则执行情况上送至主导节点设备。
  21. 根据权利要求19所述的调控云数据处理装置，其中，还包括：
    数据格式转换模块，配置为将处理数据转换为预设的数据格式；
    所述数据管理模块还用于将预设的数据格式的处理数据上送至主导节点设备。
  22. 一种调控云数据处理系统，包括：主导节点设备以及与主导节点设备连接的若干协同节点设备；
    所述主导节点设备包括权利要求14至18任一项所述的调控云数据处理装置；
    所述协同节点设备包括权利要求19至21任一项所述的调控云数据处理装置。
PCT/CN2021/112325 2020-11-10 2021-08-12 调控云数据处理方法、装置及系统 Ceased WO2022100176A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US17/770,332 US12547447B2 (en) 2020-11-10 2021-08-12 Dispatching and control cloud data processing method, apparatus and system

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202011243477.4 2020-11-10
CN202011243477.4A CN112104751B (zh) 2020-11-10 2020-11-10 调控云数据处理方法、装置及系统

Publications (1)

Publication Number Publication Date
WO2022100176A1 true WO2022100176A1 (zh) 2022-05-19

Family

ID=73785848

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/112325 Ceased WO2022100176A1 (zh) 2020-11-10 2021-08-12 调控云数据处理方法、装置及系统

Country Status (3)

Country Link
US (1) US12547447B2 (zh)
CN (1) CN112104751B (zh)
WO (1) WO2022100176A1 (zh)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114978216A (zh) * 2022-05-19 2022-08-30 泉州市拓科信息技术有限公司 一种基于物联网的多源数据处理系统
CN115002217A (zh) * 2022-05-23 2022-09-02 中国电信股份有限公司 调度方法、装置、设备及介质
CN115484265A (zh) * 2022-09-19 2022-12-16 合肥合锻智能制造股份有限公司 基于云边协同的多源异构数据的管理系统
CN116014718A (zh) * 2022-12-26 2023-04-25 国网河北省电力有限公司电力科学研究院 基于云边协同计算模型的电力调度方法、装置以及设备
CN116156191A (zh) * 2022-11-25 2023-05-23 天翼数字生活科技有限公司 一种基于国标的分省调度系统、方法、设备和存储介质
CN116566793A (zh) * 2023-06-21 2023-08-08 中国工商银行股份有限公司 设备的分配方法、装置、电子设备及存储介质
CN118643276A (zh) * 2024-08-14 2024-09-13 山东京博控股集团有限公司 一种覆土储罐的多源监测数据处理方法及设备
CN119766809A (zh) * 2024-12-16 2025-04-04 中国电子科技集团公司第十五研究所 一种基于软总线的多设备协同管理与优化系统

Families Citing this family (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112104751B (zh) 2020-11-10 2021-02-12 中国电力科学研究院有限公司 调控云数据处理方法、装置及系统
CN112650585A (zh) * 2020-12-24 2021-04-13 山东大学 一种新型边缘-云协同边缘计算平台、方法及存储介质
CN113155197A (zh) * 2021-05-07 2021-07-23 南京邮电大学 一种智能水物联网系统
CN113157795A (zh) * 2021-05-18 2021-07-23 国网宁夏电力有限公司 适用于移动应用的电网调控运行多源数据建模与管理系统
CN114064714B (zh) * 2021-10-20 2025-03-11 广州番禺电缆集团有限公司 一种智能电缆的海量业务数据处理方法及装置
CN114580482A (zh) * 2021-12-08 2022-06-03 成都中星世通电子科技有限公司 基于边缘计算节点与数据中心的无线电信号特征采集方法
CN116471627A (zh) * 2022-01-11 2023-07-21 中兴通讯股份有限公司 流数据的处理方法、系统、节点、电子设备及存储介质
CN114492194A (zh) * 2022-02-11 2022-05-13 国网冀北电力有限公司电力科学研究院 新能源数据处理系统、方法及装置
CN117097722A (zh) * 2022-05-13 2023-11-21 中国电信股份有限公司 一种调度方法及系统
CN115185663B (zh) * 2022-07-26 2023-04-07 贵州开放大学(贵州职业技术学院) 一种基于大数据的智慧化数据处理系统
CN116090614A (zh) * 2022-12-26 2023-05-09 国网河北省电力有限公司电力科学研究院 基于数字孪生的电网数据采集方法、装置、设备及介质
CN116629351B (zh) * 2023-07-19 2025-03-25 支付宝(杭州)信息技术有限公司 数据处理方法及装置
CN118413867B (zh) * 2024-07-02 2024-09-20 西安羚控电子科技有限公司 一种基于业务数据降级的集群数据同步方法及装置
CN119066718B (zh) * 2024-11-06 2025-01-24 贵州电网有限责任公司 一种电网接入数据校验方法及系统
CN119629200B (zh) * 2024-12-05 2025-10-24 广东电网有限责任公司 一种分布式光伏数据采集方法及装置
CN119891535B (zh) * 2024-12-26 2026-01-23 深圳供电局有限公司 一种保供电关键设备监控方法及其系统、电子设备、介质
CN119886954B (zh) * 2024-12-30 2025-11-25 中国电力科学研究院有限公司 调控云平台数据质量协同优化系统、方法、设备及介质
CN119813537B (zh) * 2025-01-03 2025-10-28 国网湖北省电力有限公司信息通信公司 一种电力系统运行信息智能采集系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105894174A (zh) * 2016-01-29 2016-08-24 国家电网公司 电力系统移动互联网云计算平台及调度、数据分析方法
CN109379420A (zh) * 2018-10-10 2019-02-22 上海方融科技有限责任公司 一种基于分布式架构的综合能源服务平台系统
CN110609512A (zh) * 2019-09-25 2019-12-24 新奥（中国）燃气投资有限公司 一种物联网平台和物联网设备监控方法
US20200314204A1 (en) * 2019-03-27 2020-10-01 Siemens Aktiengesellschaft Method for operating a network-aware container orchestration system
CN112104751A (zh) * 2020-11-10 2020-12-18 中国电力科学研究院有限公司 调控云数据处理方法、装置及系统

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007199811A (ja) * 2006-01-24 2007-08-09 Hitachi Ltd プログラム制御方法、計算機およびプログラム制御プログラム
CN102170365B (zh) * 2010-02-26 2013-12-25 阿里巴巴集团控股有限公司 实现软件系统热部署的方法及系统
JP2012094030A (ja) * 2010-10-28 2012-05-17 Hitachi Ltd 計算機システム及び処理制御方法
US9128763B2 (en) * 2011-08-23 2015-09-08 Infosys Limited System and method for job scheduling optimization
US9292373B2 (en) * 2013-03-15 2016-03-22 International Business Machines Corporation Query rewrites for data-intensive applications in presence of run-time errors
US10127264B1 (en) * 2015-09-17 2018-11-13 Ab Initio Technology Llc Techniques for automated data analysis
US10235076B2 (en) * 2015-12-16 2019-03-19 Accenture Global Solutions Limited Data pipeline architecture for cloud processing of structured and unstructured data
CN107103009B (zh) * 2016-02-23 2020-04-10 杭州海康威视数字技术股份有限公司 一种数据处理方法及装置
US10496605B2 (en) * 2016-04-29 2019-12-03 Splunk Inc. Application deployment for data intake and query system
US10791155B2 (en) * 2017-01-18 2020-09-29 Electronics And Telecommunications Research Institute Infrastructure apparatus and method of providing collaboration between thing devices
CN107733986B (zh) * 2017-09-15 2021-01-26 中国南方电网有限责任公司 支持一体化部署及监控的保护运行大数据支撑平台

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105894174A (zh) * 2016-01-29 2016-08-24 国家电网公司 电力系统移动互联网云计算平台及调度、数据分析方法
CN109379420A (zh) * 2018-10-10 2019-02-22 上海方融科技有限责任公司 一种基于分布式架构的综合能源服务平台系统
US20200314204A1 (en) * 2019-03-27 2020-10-01 Siemens Aktiengesellschaft Method for operating a network-aware container orchestration system
CN110609512A (zh) * 2019-09-25 2019-12-24 新奥（中国）燃气投资有限公司 一种物联网平台和物联网设备监控方法
CN112104751A (zh) * 2020-11-10 2020-12-18 中国电力科学研究院有限公司 调控云数据处理方法、装置及系统

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114978216A (zh) * 2022-05-19 2022-08-30 泉州市拓科信息技术有限公司 一种基于物联网的多源数据处理系统
CN114978216B (zh) * 2022-05-19 2023-11-10 泉州市拓科信息技术有限公司 一种基于物联网的多源数据处理系统
CN115002217A (zh) * 2022-05-23 2022-09-02 中国电信股份有限公司 调度方法、装置、设备及介质
CN115002217B (zh) * 2022-05-23 2024-02-06 中国电信股份有限公司 调度方法、装置、设备及介质
CN115484265A (zh) * 2022-09-19 2022-12-16 合肥合锻智能制造股份有限公司 基于云边协同的多源异构数据的管理系统
CN116156191A (zh) * 2022-11-25 2023-05-23 天翼数字生活科技有限公司 一种基于国标的分省调度系统、方法、设备和存储介质
CN116014718A (zh) * 2022-12-26 2023-04-25 国网河北省电力有限公司电力科学研究院 基于云边协同计算模型的电力调度方法、装置以及设备
CN116566793A (zh) * 2023-06-21 2023-08-08 中国工商银行股份有限公司 设备的分配方法、装置、电子设备及存储介质
CN118643276A (zh) * 2024-08-14 2024-09-13 山东京博控股集团有限公司 一种覆土储罐的多源监测数据处理方法及设备
CN119766809A (zh) * 2024-12-16 2025-04-04 中国电子科技集团公司第十五研究所 一种基于软总线的多设备协同管理与优化系统

Also Published As

Publication number Publication date
US12547447B2 (en) 2026-02-10
CN112104751B (zh) 2021-02-12
US20240143391A1 (en) 2024-05-02
CN112104751A (zh) 2020-12-18

Similar Documents

Publication Publication Date Title
WO2022100176A1 (zh) 调控云数据处理方法、装置及系统
CN116739236B (zh) 一种基于云边融合架构的智能电网系统及调度方法
CN112698953A (zh) 一种基于微服务的电网智能运检平台
CN104348254B (zh) 面向服务的变电站监控系统架构
CN114363345B (zh) 一种面向工业异构网络管理的边云服务协同方法
CN111385127B (zh) 一种智能化处理系统及方法
WO2023098374A1 (zh) 网络资源部署方法、装置、电子设备及存储介质
CN108052634A (zh) 一种电网生产控制大区与资产管理大区多信息系统的集成方法
CN109379217B (zh) 一种城域网异厂家业务编排器
CN112365366A (zh) 一种基于智能化5g切片的微电网管理方法及系统
CN108924007B (zh) 通信运营信息的大数据采集及存储系统和方法
CN102026228B (zh) 通信网络性能数据的统计方法和设备
CN114968981A (zh) 基于电网数据的大数据平台系统及其数据处理方法
CN111242492B (zh) 一种基于WebService的资源聚合商调控信息交互系统及方法
CN112486666A (zh) 一种模型驱动的参考架构方法及平台
CN106130065B (zh) 一种分布式光伏集群系统
CN115733756A (zh) 一种基于人工智能的配电网全数据监测系统及方法
CN114492194A (zh) 新能源数据处理系统、方法及装置
CN106022648A (zh) 一种电力调度报表协调与生成方法
CN111783053A (zh) 一种交互式统一大数据编程计算平台
CN114500530B (zh) 一种民用边缘信息系统自动调整方法
CN114862112A (zh) 一种用于工业制造单元的智能群控方法、系统及电子设备
CN106027420A (zh) 一种资源模型创建方法和装置
CN115459315B (zh) 基于云边协同的充电站聚合管理方法
CN112636975A (zh) 一种边缘计算网关网络管理系统

Legal Events

Date Code Title Description
WWE Wipo information: entry into national phase

Ref document number: 17770332

Country of ref document: US

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 08/09/2023)

122 Ep: pct application non-entry in european phase

Ref document number: 21890705

Country of ref document: EP

Kind code of ref document: A1

WWG Wipo information: grant in national office

Ref document number: 17770332

Country of ref document: US