CN112163047A - Data center and computing equipment - Google Patents
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Abstract
The embodiment of the invention discloses a data center station, which comprises: the data acquisition module is suitable for acquiring relevant data of the power system; the data processing module is suitable for processing the collected related data; the data analysis module is suitable for analyzing the processed related data to obtain the relationship between the related data; and the data display module is suitable for displaying the processed related data and the relationship between the related data. The embodiment of the invention also discloses corresponding computing equipment.
Description
Technical Field
The invention relates to the field of data processing of power systems, in particular to a data center and computing equipment.
Background
The new strategy of national energy security requires the construction of a multi-supply system to further promote the development of new energy, and meanwhile, the coming of the digital economic era makes the deep integration of the digital revolution and the energy revolution become an important development trend of the energy and power industry. Therefore, how to promote the efficient consumption of new energy becomes one of the problems to be solved urgently in the operation of the power grid company. The appearance of the data center technology provides a global, intelligent and agile multifunctional platform for power grid enterprises, a digital solution is provided for the new energy consumption problem by constructing the data center oriented to new energy consumption, the new energy consumption efficiency is improved by relying on big data mining, and the method is an effective way for relieving the new energy consumption problem.
But existing data center stations have very limited processing in terms of new energy consumption. Therefore, a more advanced data center oriented to new energy consumption is needed.
Disclosure of Invention
To this end, embodiments of the present invention provide a data center station and computing device in an effort to solve, or at least alleviate, the above-identified problems.
According to an aspect of an embodiment of the present invention, there is provided a data center, including: the data acquisition module is suitable for acquiring relevant data of the power system; the data processing module is suitable for processing the collected related data; the data analysis module is suitable for analyzing the processed related data to obtain the relationship between the related data; and the data display module is suitable for displaying the processed related data and the relationship between the related data.
Optionally, in a method according to an embodiment of the present invention, the data collection module is adapted to collect the relevant data of the power system by using at least one of the following data collection engines: kafka, Flume, Scribe and Chukwa.
Optionally, in the method according to the embodiment of the present invention, the relevant data of the power system includes internal data of the power system including new energy installed capacity, utilization hours, electricity sales data and user electricity consumption data, and external data including weather data, geographic data and socioeconomic data.
Optionally, in the method according to the embodiment of the present invention, the data processing module is adapted to perform the following processing on the collected related data: data cleaning, data conversion, data classification, data quick recovery, data correctness checking and data backup.
Optionally, in the method according to the embodiment of the present invention, the data processing module is adapted to convert the relevant data into a predetermined format according to different application scenarios, perform data fusion and merging processing according to data content by using an information measure technology, a rough set theory technology and a D-S evidence theory technology, classify the data by using an intelligent tag technology, and establish a catalog and an index for the processed standardized data by using an incremental real-time indexing technology.
Optionally, in the method according to the embodiment of the present invention, the application scenario includes at least one of: the method comprises a new energy source installation planning scene, a new energy source unit state monitoring and early warning scene, a power grid planning and designing scene, a power grid scheduling scene, a load forecasting scene and a policy suggestion scene.
Optionally, in the method according to the embodiment of the present invention, the data analysis module is adapted to analyze the processed related data by using at least one of the following data development tools: strom, Spark, and Map-reduce.
Optionally, in a method according to an embodiment of the invention, the data display module is adapted to display the processed related data and the relationship between the related data using the following data visualization tools: highcharts, Yixin BI, and Echarts.
According to another aspect of an embodiment of the present invention, there is provided a computing device including: one or more processors; and a memory; one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications comprising a data center according to an embodiment of the present invention.
According to another aspect of an embodiment of the present invention, there is provided a computing device including: one or more processors; a memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications comprising the above apparatus according to an embodiment of the present invention.
According to the data center platform scheme provided by the embodiment of the invention, the adaptability of the data center platform and a new energy consumption scene is researched by analyzing the connotation and technical characteristics of the data center platform and combining the key problems of new energy consumption, the data center platform facing the new energy consumption is constructed on the basis, and a data acquisition module, a data processing module, a data analysis module and a data display module are utilized to capture and collect data, improve the data value density, externalize the data value and visualize the data. The data center platform for new energy consumption can assist the power grid to improve the data management level, improve the efficiency and the capacity of new energy consumption, and can be used for the sustainable development of the service energy field.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the embodiments of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.
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To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which are indicative of various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings. Throughout this disclosure, like reference numerals generally refer to like parts or elements.
FIG. 1 shows a schematic diagram of a computing device 100, according to one embodiment of the invention; and
fig. 2 shows a schematic diagram of a data center station 200 according to one embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
FIG. 1 shows a schematic diagram of a computing device 100, according to one embodiment of the invention. As shown in FIG. 1, in a basic configuration 107, computing device 100 typically includes system memory 106 and one or more processors 104. A memory bus 108 may be used for communication between the processor 104 and the system memory 106.
Depending on the desired configuration, the processor 104 may be any type of processor, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 104 may include one or more levels of cache, such as a level one cache 110 and a level two cache 112, a processor core 114, and registers 116. The example processor core 114 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 118 may be used with the processor 104, or in some implementations the memory controller 118 may be an internal part of the processor 104.
Depending on the desired configuration, system memory 106 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 106 may include an operating system 120, one or more applications 122, and program data 124. In some implementations, the application 122 can be arranged to execute instructions on an operating system with program data 124 by one or more processors 104.
Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 102 via the bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 152 or HDMI interfaces. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, remote input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 158. An example communication device 146 may include a network controller 160, which may be arranged to facilitate communications with one or more other computing devices 162 over a network communication link via one or more communication ports 164.
A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
Computing device 100 may be implemented as a server, such as a database server, an application server, a WEB server, and the like, or as a personal computer including both desktop and notebook computer configurations. Of course, computing device 100 may also be implemented as a small-sized portable (or mobile) electronic device.
In an embodiment in accordance with the invention, the application 122 of the computing device 100 includes executing a technical desk 200 in accordance with an embodiment of the invention.
How to manage and utilize huge data resources becomes a hot problem under the background of big data era. The data center is generally defined as an intelligent service platform between a business foreground and a system background, and realizes business datamation by acquiring, storing and processing mass data, unifying standards, analyzing and mining, sharing and exchanging, displaying applications and the like, so that the data center can be enabled to be applied to the foreground, and efficient service is provided for clients. Although the frame structure of the data center station is not uniformly defined at present, the data center station generally comprises modules of data acquisition, data storage, data management, data analysis, data application and the like, and is divided into a four-layer architecture of data acquisition, data storage and management, data analysis and data application.
The data center station can design different data application scenes according to business requirements, and meets the requirements of data sharing, value mining and application analysis between horizontal cross-professional rooms and vertical cross-layer levels by promoting data real-time linkage, global optimization and service continuous upgrading innovation by means of construction promotion and construction assistance. The current data center is mainly applied to the fields of internet, business service, new media and the like. The data center station has the following significant advantages:
(1) and the data development efficiency is improved. When the traditional data platform is used for application development, data acquisition and data analysis mining are often required to be carried out again, so that the efficiency of data processing and analysis is poor, the period is long, and the data development speed cannot be matched with the application development speed. The construction data middle platform forms standard uniform and clear-function data assets through fusion processing and classification labeling processing of multi-source heterogeneous data, provides a foundation for data multiplexing, improves the data development speed, and quickly responds to application development and business innovation.
(2) Compression platform construction costs. On one hand, the application value is used as the guide when the data middlewares are built, the data assets of the data middlewares form various data sets facing to the service scenes, and the corresponding data sets are reused by matching the scenes when data development is performed next time, so that a special independent service system data platform does not need to be built again, and the cost of repeated construction is reduced. On the other hand, the data center station reasonably stores the data in a classified manner, so that data redundancy is reduced, storage space is saved, and storage cost is reduced.
(3) Effectively breaking the data barrier. When a traditional data platform is built, all systems are built to form single application taking a main body of each system as a center, and an information isolated island is easily formed due to professional, business and other barriers. The data center station extracts and integrates common data, has flexible and strong shared service capability, is used for front-end service application construction or data analysis direct calling, realizes data interconnection and intercommunication, and practically improves the cross-border capability in data application.
With the continuous progress of new energy power generation technology and the rise of energy revolution strategy, the new energy industry is continuously and rapidly developed, and the current new energy consumption problem mainly shows the following aspects:
firstly, the installation is excessive, because advantages such as clean environmental protection of new forms of energy electricity generation and the relevant policy of new forms of energy promote vigorously, new forms of energy installation proportion, installed capacity are constantly rising, but the total power consumption increase rate of our country's society is far less than new forms of energy installation increase rate, consequently shows the whole surplus of new forms of energy installation, has further aggravated and has abandoned wind and abandoned light scheduling problem.
And secondly, the power grid transmission capacity is insufficient. The large-scale transmission across provinces and regions is one of the important ways for new energy consumption. In the remote new energy transmission process, on one hand, network loss can be generated during actual transmission of a power grid, voltage drop is formed on a transformer and a distribution line, and the transmission capacity of the power grid is reduced. On the other hand, new weak links are brought to the actual transmission of the power grid by new energy grid connection, and the weak links become new bottlenecks which restrict the transmission capacity of the power grid, such as insufficient reactive voltage supporting capacity, frequent voltage fluctuation of the alternating-current side of a direct-current system and the like.
And thirdly, the balance adjustment problem of the time-varying power system. Due to the particularity of power resources, power generation, power transmission, power supply and power utilization in a power system need to be guaranteed to be completed simultaneously, and power loads have obvious time-varying characteristics. The principle of system balance is to adjust the power output to track the load change and keep dynamic balance. However, the output of the new energy power generation is limited by variable weather conditions, has randomness, intermittence and fluctuation, is difficult to predict and has low accuracy, and the peak regulation capability of the power system is insufficient, so that the output of the power supply is difficult to keep up with the change of the load, and the dynamic balance of the time-varying power system cannot be ensured.
Fourth, the matching mechanism is not complete, and from the power supply side, an electricity price mechanism capable of effectively reflecting the power supply and demand relationship is not formed at the power generation side, so that the low marginal cost advantage of new energy is not brought into play; from the side of a power grid, a perfect transaction mechanism for new energy consumption across province and district is lacked when the new energy consumption is used for power transmission across province and district; from the load side, the willingness of the user to participate in peak shaving is low, and the activity of the user to participate in peak shaving is not motivated by a more reasonable and perfect incentive policy.
The data center station provides a data base platform for new energy consumption, and provides the following technical effects for relieving the problem of new energy consumption from the direction of data intelligence:
(1) the data center station improves the data development speed through data multiplexing, improves the speed of realizing the service value of new energy consumption related data, realizes the quick reappearance of data assets, quickly responds to changes of environment, running state and the like based on the efficient development of data, adapts to the high variability brought by the new energy access, and realizes flexible management and control.
(2) The data center station can effectively break through data barriers, potential association between social macroscopic data and energy and power industries and new energy consumption is mined, the internal value of more cross-industry and cross-field data is found, for example, association between the social macroscopic data and load requirements is mined by analyzing mobile communication data, so that a predicted value of a power grid peak is obtained, and data support is provided for peak shaving.
(3) The data center platform is combined with the difficult problems faced by new energy consumption and corresponding requirements to develop application scenes, data acquisition, storage, ordered management and comprehensive analysis are carried out according to scene requirements, the problem of new energy consumption is further relieved, data fusion and sharing are promoted internally, related enterprises are promoted to cooperate externally, the informatization degree of each link is improved comprehensively, and therefore the capacity and the level of new energy consumption are improved better.
FIG. 2 shows a schematic diagram of a station 200 in the art, according to one embodiment of the invention. As shown in fig. 2, the technical desk 200 includes a data acquisition module 210, a data processing module 220, a data analysis module 230, and a data display module 240.
The data acquisition module 210 (also referred to as a data source layer) is adapted to acquire relevant data of the power system, and implement multi-source multi-dimensional data stereo integration. The relevant data of the power system comprises internal data (namely relevant data of each link of the power) of the power system and external data, wherein the internal data comprises new energy installed capacity, utilization hours, electricity selling data and user electricity consumption data, and the external data comprises meteorological data, geographic data and socioeconomic data. The data collection module 210 performs data fusion on the large and unordered high-dimensional source heterogeneous data, so as to realize cross-domain data collection.
The data center platform for new energy consumption needs massive scattered data from different systems and different fields as data support and foundation, so that the capture and collection of the data play a vital role in the data center platform construction. When the data center station captures data, not only integrity and comprehensiveness of the data are ensured, but also the efficiency and stability of data capture are guaranteed according to uncertainty and burstiness of data such as new energy power generation equipment operation data and fault information. Therefore, the data acquisition module 210 captures log data through data acquisition engines such as Kafka, Flume, Scribe, Chukwa and the like, captures network data in a targeted manner through distributed crawlers, intelligent acquisition scheduling, data acquisition agents and the like, and captures and collects data of each channel with high reliability and high fault tolerance by coupling a flexible data capture strategy with various acquisition technologies, so that new energy consumption is realized.
The data processing module 220 (also referred to as a data asset layer) is adapted to process the collected relevant data. The data processing module 220 can implement differentiation and lean management of data by constructing a common data module or an extraction data module. The public data module mainly comprises data information such as equipment information, power grid operation information, user information, financial information, market information and the like, and the data of all levels of departments can be used as required. The extraction data module is mainly used for establishing a data system according to scene requirements, such as a power generation prediction data system, an operation data system, a maintenance data system and the like, and meeting the requirement of extracting complex data sequences of related scenes in real time. Meanwhile, in order to quickly and timely search useful data from a huge data system, a data resource layer carries out catalogue compilation, and efficient retrieval is realized. The data asset layer catalog needs to be maintained and updated regularly to ensure timeliness. The data processing module 220 realizes the co-construction and sharing of enterprise-level data and improves the lean management level of the enterprise data.
The data types stored in the data acquisition module 210 include characters, charts and videos, and the data has wide sources, various forms and various varieties, and is subject to risks of partial data loss, data errors and data redundancy, and the data value density is low. Therefore, the data processing module 220 needs to effectively improve the data value density and lay the foundation of data value externalization through data cleaning, data conversion, data classification, data rapid recovery, data correctness verification and data backup. The disordered and disordered data of the data source layer are identified and filtered through a data cleaning technology, useless, repeated or wrong data are cleaned, data with large deviation are detected and removed through outliers, and effective information elements are automatically extracted through intelligent analysis. The data processing module 220 may convert the data into a suitable format according to different application scenarios. And then data fusion and merging are carried out through an information measure, a rough set theory technology and a D-S evidence theory technology according to data content, and the data availability is improved. And finally, classifying the data by using an intelligent tag technology, and establishing a catalogue and an index for the processed standardized data by using an incremental real-time index technology to realize the rapid retrieval of the data.
It should be understood that the construction of the station in the data can effectively alleviate the main problems faced by new energy consumption, and a new idea for solving the new energy consumption problem can be provided in different application scenarios from the aspects of data utilization, resource integration, information sharing and the like. The data center platform collects, sorts, shares and multiplexes data in all links of 'source network load storage' of the power system, realizes interconnection and intercommunication of all links, further improves the fast and efficient coordination and interaction capacity of the power system source network, and optimizes dispatching to improve the new energy consumption level.
The application scenario at least comprises one of the following: the method comprises a new energy source installation planning scene, a new energy source unit state monitoring and early warning scene, a power grid planning and designing scene, a power grid scheduling scene, a load forecasting scene and a policy suggestion scene.
In a new energy installation planning scene, a data center station carries out catalog retrieval on a data resource layer, obtains data information of resources such as wind energy, solar energy, fossil energy and the like through index positioning, utilizes an analysis layer to carry out correlation analysis, space and time sequence analysis, researches energy space distribution conditions and time sequence change conditions reflected by the data, carries out regional resource development potential analysis and new energy installation feasibility analysis, then utilizes big data analysis and data mining to calculate different regional absorption capacities through data such as equipment operation data, regional load data, meteorological data and the like, and combines the existing power grid planning information to provide data support and suggestions for new energy power station planning, site selection and installation scale.
In the condition monitoring and early warning scene of the new energy source unit, the data center station acquires information such as protection signal data, trip data, overload data, bad working condition data and the like generated when a system is abnormal or fails by using a data source layer, and integrates the information in the data source layer to form an early warning data system. And then, the device state evolution process analysis and trend analysis are carried out on an analysis layer by combining with the real-time state change data, so that the real-time running state of the new energy power generation device is fed back in time, the timeliness and the accuracy of the device state evaluation are improved, the new energy power generation device is in an optimal state in running through early sensing and early warning, the stability and the controllability of new energy power generation are guaranteed, and the stable grid-connected consumption of the new energy power generation is realized at the maximum efficiency.
In a power grid planning and designing scene, a data center platform utilizes a data source layer to collect data information such as power grid equipment data, transformer substation data, regional grid information data, weather and meteorological data, geographic and geomorphic data, national economy data, population and social data, and forms a load demand data system, a meteorological data system and the like in a data resource layer after data classification and arrangement. And then, the new energy power generation is accurately predicted by using flow calculation, multiple regression analysis, machine learning and the like through related data information such as new energy installation, weather and the like in an analysis layer, new energy grid-connected capacity prediction and inter-provincial delivery electric quantity prediction are performed by combining geographic environment, equipment operation and load demand data of various regions, a basis is provided for novel power grid structure design, power distribution network frame planning and the like in a transmission and distribution link, and the power grid has enough acceptance capacity to new energy.
In a power grid dispatching scene, a data center analyzes the power forecasting deviation of new energy according to historical new energy power generation actual and forecasting power data, comprehensively considers load information, transmission electric quantity information and controllable resource information of a demand side of the whole power grid, calculates new energy consumption space, power grid channel transmission capacity, thermal power unit peak regulation capacity and the like of each region by combining with relevant data of a power grid structure, realizes global situation perception of the power grid through rapid and accurate analysis and information sharing, guides a dispatching center to carry out optimized dispatching decisions of different time scales, forms a dynamic optimal dispatching scheme, realizes large-range resource optimized configuration, guarantees safe, economic and environment-friendly operation of the power grid, and maximally accepts renewable energy such as wind power and the like.
In a load forecasting scene, the more obvious influence factors are the air temperature and the region type, the heating and cooling loads can be put into operation on a large scale due to the severe change of the weather, and in addition, data such as population, national economy, load type and the like also have great influence on load forecasting. Therefore, the data center station acquires relevant data information by using the data source layer, combines steady-state data and dynamic data of power grid operation, performs data multidimensional joint analysis such as coupled cluster analysis, regression analysis and time sequence analysis on the analysis layer, accurately identifies key influence factors, constructs a load characteristic model, a load dynamic model and an incremental load prediction model based on data driving, accurately predicts the load condition for a long time, and provides a basis for new energy installation planning, power grid planning, new energy policy suggestion and the like.
In the policy suggestion scene, the implementation effect of the policy related to new energy is influenced by the social psychology of various resources endowment, energy production and application participants. And the data center station searches the internal association relation of each influence factor based on historical and current data, and provides a suggestion for the optimization of a new energy policy mechanism of the government. For example, a new energy flexible electricity price mechanism and a new energy trans-provincial and trans-regional trading mechanism are popularized through new energy power generation prediction, regional load requirements and complete margin of consumed space of each region; according to the load condition and the power grid peak regulation capacity, the large users are encouraged to voluntarily shift peak power utilization through a large user direct purchase mechanism and power generation right replacement transaction, a peak regulation auxiliary service market rule is formulated, a subsidy mechanism is perfected, the enthusiasm of power generation enterprises for participating in peak regulation is improved, peak regulation pressure is relieved, and therefore new energy consumption is promoted.
The data analysis module 230 (also referred to as an analysis layer) is adapted to analyze the processed related data to obtain a relationship between the related data. The data analysis module 230 is intended to extract deep knowledge from data rapidly and put it into engineering application, and mainly includes key technologies such as big data retrieval, data mining, machine learning, etc., and related algorithm models such as recurrent neural network method, genetic algorithm, decision tree method, mixed model clustering algorithm, fuzzy set, etc. The data analysis module 230 finds out potential rules from the data based on application requirements, and realizes effective and reasonable utilization of data resources to support multidimensional lean application. When the user energy consumption behavior portrayal is carried out, user side data characteristics are extracted from a user data system through a distributed data mining algorithm and the like, and potential user energy consumption behavior rules are revealed, so that optimization scheduling is assisted, and new energy consumption related decisions are served.
The data analysis module 230 is adapted to analyze the processed relevant data using at least one of the following data development tools: the method comprises the steps of Strom, Spark and Map-reduce, so that distributed machine learning, retrieval and parallel operation of large-scale data sets are realized, and the efficiency and quality of data value externalization of the data center station are improved practically. The Strom has efficient and reliable stream data processing capacity, and meets the application scene requirements with high real-time requirements, such as state monitoring and early warning of a new energy unit, real-time analysis of the running state of a power grid and the like; spark completes large-scale data processing through distributed memory calculation, provides high-performance mass data analysis, and is suitable for power grid planning, load prediction and other scenes; the Map-reduce realizes large-scale off-line data batch processing with high fault tolerance and reliability, and can be used for new energy installation planning and other scenes.
The data display module 240 (also referred to as an application layer) is adapted to display the processed related data and the relationship between the related data. The data display module 240 internally supports the relevant management and operation applications of enterprises such as power grid planning, power grid operation and customer service, provides the relevant new energy consumption services such as equipment state early warning, new energy power generation prediction and new energy installation planning for the interior of the enterprises, improves the operation efficiency of the power system, improves the safety of the power grid and further improves the new energy consumption capacity. The method provides basis for the government industry to make a new energy consumption related policy, supports high-efficiency and accurate government decision making, provides related services such as trend prejudgment, accurate marketing, business site selection planning and the like for the industries related to other external enterprises, guides enterprise planning development, improves enterprise management level, improves enterprise benefits and accordingly improves the activity of the enterprises participating in new energy consumption. Meanwhile, a suggestion of optimizing energy utilization is provided for electricity utilization customers, and the problem of new energy consumption is relieved from the user side.
The data display module 240 needs to be able to visually and dynamically display the relationship between related data, such as a new energy power generation prediction curve, a load distribution condition, a real-time power grid operation state, and the like, and therefore data visualization needs to be implemented through an interactive and visual tool. The data visualization tool is commonly used by Highcharts, Yixin BI or Echarts, wherein the Highcharts are convenient to customize and have extremely high degree of freedom, and when the data volume of a new energy consumption related scene reaches ten thousand levels, the Highcharts can realize flexible multi-dimensional display of data resources through multi-table linkage and automatic zooming. The billion information BI is internally provided with dozens of visual elements and graphs, rich visual effects can be realized through simple data relation definition, secondary development is not needed, the operation is simple, and the user service friendliness of the data center platform is realized. Echarts contains rich chart types, has obvious advantages in the aspect of 3D drawing, and can realize dynamic visualization of scenes such as power grid running state and the like.
In conclusion, the building data center is an important direction for digital transformation of the power grid, and is an effective way for solving the problem of new energy consumption. The adaptability of the data center platform and a new energy consumption scene is researched by analyzing the connotation and technical characteristics of the data center platform and combining the key problems of new energy consumption, the data center platform facing the new energy consumption is constructed on the basis, the structure of the data center platform is explained by the connotation and characteristics of a data source layer, a data resource layer, an analysis layer and an application layer, the possible application scene of the data center platform in the new energy consumption is finally analyzed, and the technical support is provided for realizing the corresponding functions of each layer of the data center platform by key technologies of data capturing and gathering, data value density improvement, data value externalization, data visualization and the like. The data center platform for new energy consumption can assist the power grid to improve the data management level, improve the efficiency and the capacity of new energy consumption, and can be used for the sustainable development of the service energy field.
The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of embodiments of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as removable hard drives, U.S. disks, floppy disks, CD-ROMs, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing embodiments of the invention.
In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to perform the methods of embodiments of the present invention according to instructions in the program code stored in the memory.
By way of example, and not limitation, readable media may comprise readable storage media and communication media. Readable storage media store information such as computer readable instructions, data structures, program modules or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of readable media.
In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with examples of embodiments of the invention. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best modes of embodiments of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of an embodiment of this invention.
Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of and form different embodiments of the invention. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Furthermore, some of the above embodiments are described herein as a method or combination of elements of a method that can be performed by a processor of a computer system or by other means for performing the functions described above. A processor having the necessary instructions for carrying out the method or method elements described above thus forms a means for carrying out the method or method elements. Furthermore, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.
As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.
While embodiments of the invention have been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the embodiments of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive embodiments. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present embodiments are disclosed by way of illustration and not limitation, the scope of embodiments of the invention being defined by the appended claims.
Claims (9)
1. A station for data comprising:
the data acquisition module is suitable for acquiring relevant data of the power system;
the data processing module is suitable for processing the collected related data;
the data analysis module is suitable for analyzing the processed related data to obtain the relationship between the related data; and
and the data display module is suitable for displaying the processed related data and the relationship between the related data.
2. The method of claim 1, wherein the data collection module is adapted to collect data related to the power system using at least one of the following data collection engines: kafka, Flume, Scribe and Chukwa.
3. The method of claim 1, wherein the data related to the power system comprises internal data of the power system including new energy installed capacity, hours of utilization, electricity sold data, and user electricity usage data, and external data including weather data, geographic data, and socio-economic data.
4. The method of claim 1, wherein the data processing module is adapted to perform the following on the collected relevant data: data cleaning, data conversion, data classification, data quick recovery, data correctness checking and data backup.
5. The method as claimed in claim 4, wherein the data processing module is adapted to convert the related data into a predetermined format according to different application scenarios, perform data fusion and merging processing according to data content through information measure technology, rough set theory technology and D-S evidence theory technology, classify data using intelligent label technology, and create catalogues and indexes for the processed standardized data through incremental real-time indexing technology.
6. The method of claim 5, wherein the application scenario includes at least one of: the method comprises a new energy source installation planning scene, a new energy source unit state monitoring and early warning scene, a power grid planning and designing scene, a power grid scheduling scene, a load forecasting scene and a policy suggestion scene.
7. The method of claim 1, wherein the data analysis module is adapted to analyze the processed relevant data using at least one of the following data development tools: strom, Spark, and Map-reduce.
8. The method of claim 1, wherein the data display module is adapted to display the processed related data and the relationship between the related data using the following data visualization tool: highcharts, Yixin BI, and Echarts.
9. A computing device, comprising:
one or more processors; and
a memory;
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications comprising the data center of claims 1-8.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112817958A (en) * | 2021-02-25 | 2021-05-18 | 广东电网有限责任公司 | Electric power planning data acquisition method and device and intelligent terminal |
| CN113052393A (en) * | 2021-04-09 | 2021-06-29 | 新奥数能科技有限公司 | Enterprise site selection method and device |
| CN113361832A (en) * | 2021-08-10 | 2021-09-07 | 睿至科技集团有限公司 | Electric power data center station and working method |
| CN115439015A (en) * | 2022-10-20 | 2022-12-06 | 国家电投集团科学技术研究院有限公司 | Local area power grid data management method, device and equipment based on data middleboxes |
| CN117312281A (en) * | 2023-06-30 | 2023-12-29 | 江苏中科西北星信息科技有限公司 | Automatic fusion method, system, equipment and storage medium for multi-source heterogeneous data |
| CN117312281B (en) * | 2023-06-30 | 2024-05-24 | 江苏中科西北星信息科技有限公司 | Automatic fusion method, system, equipment and storage medium for multi-source heterogeneous data |
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2020
- 2020-09-21 CN CN202010997076.1A patent/CN112163047A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112817958A (en) * | 2021-02-25 | 2021-05-18 | 广东电网有限责任公司 | Electric power planning data acquisition method and device and intelligent terminal |
| CN113052393A (en) * | 2021-04-09 | 2021-06-29 | 新奥数能科技有限公司 | Enterprise site selection method and device |
| CN113052393B (en) * | 2021-04-09 | 2024-03-08 | 新奥数能科技有限公司 | Enterprise site selection method and device |
| CN113361832A (en) * | 2021-08-10 | 2021-09-07 | 睿至科技集团有限公司 | Electric power data center station and working method |
| CN115439015A (en) * | 2022-10-20 | 2022-12-06 | 国家电投集团科学技术研究院有限公司 | Local area power grid data management method, device and equipment based on data middleboxes |
| CN117312281A (en) * | 2023-06-30 | 2023-12-29 | 江苏中科西北星信息科技有限公司 | Automatic fusion method, system, equipment and storage medium for multi-source heterogeneous data |
| CN117312281B (en) * | 2023-06-30 | 2024-05-24 | 江苏中科西北星信息科技有限公司 | Automatic fusion method, system, equipment and storage medium for multi-source heterogeneous data |
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