New energy data real-time storage method and device and medium

Abstract

The embodiment of the invention provides a new energy data real-time storage method and device and a medium, and the method comprises the steps: obtaining new energy heterogeneous data; analyzing the new energy heterogeneous data, and determining storage information and metadata of each data; generating a data label according to the storage information and the metadata; constructing a data directory table according to the metadata, the data tag and the storage information; associating the data directory table with the metadata, and establishing a data index; and generating a data view according to the association relationship and the data index. Therefore, cross-type data association query is realized.

Description

Technical Field

[0001] This document relates to the technical field of energy data management, and particularly to a method, device, and medium for real-time storage of new energy data. Background Art

[0002] New energy data can help predict and optimize the power generation of new energy. Through big data analysis, key factors such as weather patterns, solar radiation intensity, and wind speed can be predicted, and effective scheduling and management of new energy power generation can be carried out.

[0003] In the prior art, different data mainly relies on different storage systems. When analyzing new energy data, customized scripts are used to export data from each system, and after manual integration, it is applied to a statistical model for data analysis.

[0004] However, using different databases to store new energy data in the prior art will cause serious data fragmentation and difficulty in integration. Moreover, it is necessary to manually extract and convert data formats across systems, wasting manpower and material resources, increasing the complexity of data analysis, and making it difficult to meet the real-time new energy scheduling requirements in real time; it also cannot efficiently support time series analysis with strong real-time performance and correlation queries of cross-type data. Summary of the Invention

[0005] In view of the above solutions, the present application aims to propose a method, device, and medium for real-time storage of new energy data to solve at least one of the above technical problems.

[0006] In a first aspect, one or more embodiments of this specification provide a method for real-time storage of new energy data, including:

[0007] Obtain new energy heterogeneous data;

[0008] Analyze the new energy heterogeneous data to determine the storage information and metadata of each data;

[0009] Generate data tags according to the storage information and the metadata;

[0010] Construct a data directory table according to the metadata, the data tags, and the storage information;

[0011] Associate the data directory table with the metadata to establish a data index; and

[0012] Generate a data view according to the association relationship and the data index.

[0013] Further, analyze the new energy heterogeneous data to obtain the data type;

[0014] Select a storage engine according to the data type;

[0015] Determine the storage information of each piece of data according to the data type and the storage engine; and

[0016] Extract the metadata according to the new energy heterogeneous data.

[0017] Further, determine the storage location of each piece of data according to the storage information;

[0018] Generate data entries according to the data tags of each piece of data and the storage location; and

[0019] Obtain a data directory table according to each of the data entries.

[0020] Further, obtain the data required by the user according to the data view;

[0021] Generate a data matrix according to the data required by the user; and

[0022] Based on a correlation analysis algorithm, obtain a data analysis report according to the data matrix.

[0023] Further, obtain historical new energy heterogeneous data;

[0024] Based on a threshold analysis algorithm, determine a first detection result according to the historical new energy heterogeneous data and a preset threshold;

[0025] Based on k-means clustering analysis, cluster the historical new energy heterogeneous data to identify a second detection result of the data;

[0026] Based on an autoregressive integrated moving average model, predict a third detection result of the data according to the historical new energy heterogeneous data and the new energy heterogeneous data;

[0027] Judge the detection situations of the first detection result, the second detection result, and the third detection result,

[0028] If there are abnormal results in the first detection result, the second detection result, and the third detection result, determine that the system working condition is abnormal; and

[0029] If there are no abnormal results in the first detection result, the second detection result, and the third detection result, determine that the system working condition is normal.

[0030] Further, based on visualization technology, generate a visualization image according to the data view.

[0031] In a second aspect, an embodiment of the present application provides a new energy data real-time storage device, including:

[0032] An acquisition module, configured to acquire new energy heterogeneous data;

[0033] A determination module, configured to analyze the new energy heterogeneous data and determine the storage information and metadata of each piece of data;

[0034] A first generation module, configured to generate data tags according to the storage information and the metadata;

[0035] A construction module, configured to construct a data directory table according to the metadata, the data tags, and the storage information;

[0036] An association module, configured to associate the data directory table and the metadata to establish a data index; and

[0037] A second generation module, configured to generate a data view according to the association relationship and the data index.

[0038] Further, the determination module is configured to

[0039] Analyze the new energy heterogeneous data to obtain the data type;

[0040] Select a storage engine according to the data type;

[0041] Determine the storage information of each piece of data according to the data type and the storage engine; and

[0042] Extract the metadata according to the new energy heterogeneous data.

[0043] Further, the construction module is configured to:

[0044] Determine the storage location of each piece of data according to the storage information;

[0045] Generate data entries according to the data tags of each piece of data and the storage location; and

[0046] Obtain a data directory table according to each of the data entries.

[0047] In a third aspect, an embodiment of the present application provides a storage medium for storing computer-executable instructions, characterized in that the computer-executable instructions, when executed, implement the steps of the new energy data real-time storage method described in any one of the first aspects.

[0048] Compared with the prior art, the present application can at least achieve the following technical effects:

[0049] By analyzing and determining the storage information of each new energy heterogeneous data, this application realizes the classification of different types of new energy data, then stores the classified data accordingly, forms data tags, and constructs a data directory based on the metadata and data tags; associates the data directory with the metadata, establishes a data index, and generates a data view, solving the problems of fragmented new energy data, difficult effective correlation analysis, and storage redundancy, making it more convenient and faster for users to search for and use new energy data in the future, and improving the data analysis efficiency of users. Description of the Drawings

[0050] In order to more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0051] Figure 1 Flowchart of a method for real-time storage of new energy data provided for one or more embodiments of this specification;

[0052] Figure 2 Schematic structural diagram of a device for real-time storage of new energy data provided for one or more embodiments of this specification. Detailed Embodiments

[0053] In order to enable those skilled in the art of this technology to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

[0054] At present, with the rapid development of the new energy industry, new energy data has grown exponentially. The new energy system covers various forms such as wind power generation, solar power generation, and hydropower generation. During the operation of various power generation facilities, a large amount of equipment operation data, environmental monitoring data, energy output data, etc. are generated. These data have a wide range of sources, huge differences in format and structure, and form a complex new energy heterogeneous data system.

[0055] Due to the complexity of new energy data, as well as the high-dimensionality, multi-source, and heterogeneity of the data, its traditional data storage, management, and analysis face many challenges.

[0056] Problem 1: Storing different types of data separately leads to data fragmentation, making it difficult to conduct effective correlation analysis. It also causes data storage redundancy and increases data storage costs.

[0057] Second, the value of new energy data lies not only in its storage, but also in its in-depth analysis to uncover potential information and provide support for energy scheduling, equipment maintenance, market decision-making, etc. Currently, data storage and data analysis are often independent processes.

[0058] Problem three: Data storage is not unified and relies on different systems (such as SCADA systems for indicator data and ERP systems for business data). Customized scripts (such as Python) are used to export data from various systems during analysis. After manual integration of the data, it is used to apply statistical models. The fragmentation of data makes integration difficult, and cross-system extraction and format conversion are time-consuming and labor-intensive, significantly increasing the complexity and economic burden of analysis.

[0059] Question 4: Heterogeneous data in the new energy industry is unique. There is a need for high-frequency collection and dynamic time window query of data such as power generation power, wind speed, and temperature. As a result, existing technologies are unable to efficiently support real-time time series analysis and cross-type data association queries when processing new energy system operation data. For example, wind farms operate 24 hours a day, and wind turbines collect operating data 50 times per second, including real-time speed (RPM), power generation power (kW), wind speed (m / s), and blade angle (degrees).

[0060] Question five: Hourly and daily power generation efficiency reports need to be generated daily for equipment performance evaluation and energy scheduling decisions. This type of data is typical indicator data in the new energy industry, with high-frequency collection and dynamic time window query requirements. The existing solution has a high-frequency data problem: 4.32 million high-frequency data per indicator per day are stored in a key-value structure. This structure lacks compression or sharding optimization for high-frequency data, resulting in high storage costs and slow retrieval speeds. For query scenarios such as "average power in the past hour", the multi-level index of the existing solution needs to scan the data one by one, which cannot be quickly aggregated, and the real-time performance is difficult to meet the needs of new energy scheduling. At the same time, the correlation analysis needs between indicator data and environmental data (such as wind speed) in wind power generation scenarios are not considered, and data management is relatively isolated.

[0061] In response to the above technical problems, this application proposes a real-time storage method for new energy data, such as Figure 1 The specific steps are as follows:

[0062] Step S1, obtaining new energy heterogeneous data.

[0063] In the embodiments of the present application, new energy heterogeneous data is obtained by deploying sensors on different devices; and then the new energy heterogeneous data is preprocessed.

[0064] Specifically, targeted data acquisition devices are deployed at each key link of the new energy system to obtain new energy heterogeneous data in real time. Among them, the new energy heterogeneous data includes various new energy data such as equipment operation data, monitoring indicators, and inspection reports. For example, high-precision sensors are installed at key parts of new energy power generation equipment to collect power generation and equipment operation data; light intensity sensors, current and voltage sensors are set on solar panels to obtain information such as light intensity, output current, and voltage. For monitoring indicator data, an energy management system and a monitoring platform are constructed to obtain power generation statistics data, power generation efficiency evaluation indicators, power factor monitoring values, etc. of the new energy power station. For inspection report data, the inspection personnel rely on regular on-site inspections of new energy facilities, record information such as the appearance of the equipment, the aging of components, and potential on-site environmental hazards in a written report, and at the same time collect on-site pictures and video materials with the help of portable shooting equipment. The equipment operation data, monitoring indicator data, and inspection report data obtained from different channels above together constitute the new energy heterogeneous data. These data have wide sources, diverse formats, and different structures, providing a rich data basis for the comprehensive management and in-depth analysis of the new energy system.

[0065] The new energy heterogeneous data is preprocessed from three aspects: data cleaning, standardization, and verification. Among them, data cleaning mainly includes removing outliers and handling missing values. Removing outliers means identifying and removing obviously unreasonable data points. When handling missing values, determine the reason for the missing values, and according to the characteristics and missing situation of the data, use methods such as mean filling and interpolation method for filling. Mean filling is for numerical data, and the mean of the variable can be used for filling. Interpolation methods include linear interpolation, polynomial interpolation, etc. For time series data, time-weighted interpolation can be performed according to the data of adjacent time points before and after.

[0066] Data standardization is to unify data from different sources, different formats, and different dimensions into a data format that conforms to specific specifications and standards for data integration and analysis. In new energy data management, the units and precisions of the collected data are converted into pre-set standard units and precisions. In addition, the format of the data is standardized, and the data is converted into a unified format to ensure that the data can be smoothly exchanged and processed between different modules.

[0067] Data verification is for data consistency. For data after different preprocessing steps, the processed data is compared and analyzed with the original data to check whether the data has changed.

[0068] Step S2: Analyze the new energy heterogeneous data to determine the storage information and metadata of each data item.

[0069] In the embodiment of the present application, the new energy heterogeneous data is analyzed to obtain the data type; according to the data type, a storage engine is selected; according to the data type and the storage engine, the storage information of each data item is determined; and the metadata is extracted according to the new energy heterogeneous data.

[0070] Specifically, first, the new energy heterogeneous data is classified into business data, indicator data, and file data according to the data type. Each data type has significant differences in structure, generation frequency, and usage. Among them, business data is static structured data used to describe the basic attributes of equipment or systems, such as equipment numbers, locations, capacities, etc. Indicator data is dynamic time-series data used to reflect the real-time performance of equipment or systems, such as power generation, real-time power, wind speed, etc. File data is unstructured file-type data used to describe documents, images, or videos, such as inspection reports, drone images, etc. After classification, the corresponding storage engine is selected according to different data types. The business data is stored in a relational database to support associative queries. The indicator data is stored in a time-series data storage engine to support fast writing of high-frequency data and data analysis functions based on time windows. The file data is stored in an object storage engine to support the management requirements of a large number of files.

[0071] Secondly, record the storage information of each piece of data in the new energy heterogeneous data. Here, the storage information refers to the relevant ancillary information generated after classifying and storing the new energy heterogeneous data. For example, data type, storage engine, storage path, etc. For different storage engines, the storage information required is different. For example, library and table information in a relational storage engine; library, table, policy, and indicator coding in a time-series storage engine; file storage path, file format, data size, etc. information in an object storage engine.

[0072] Step S3: Generate data tags according to the storage information and the metadata.

[0073] In the implementation of this application, data tags are obtained from stored information and metadata. The data tags summarize the key attributes of the data (such as type, source, location, identifier, etc.) to support the construction of a data catalog and the association of cross-type data. For example, the input data is the operating data of a wind turbine (equipment identifier: CN000001AAB01RC5000FA-122-XXX, equipment location: Rongcheng Wind Farm, fan capacity: 4.2MW, rated wind speed: 11.2m / s, etc.). After data classification and metadata extraction, data tags are formed. For example: (equipment identifier: CN000001AAB01RC5000FA-122-XXX, equipment location: Rongcheng Wind Farm, fan capacity: 4.2MW, rated wind speed: 11.2m / s, data type: business data, storage engine: structured data storage).

[0074] Step S4, construct a data catalog table according to the metadata, the data tags, and the stored information.

[0075] In the embodiment of this application, according to the stored information, determine the storage location of each data; generate data entries according to the data tags of each data and the storage location; and obtain a data catalog table according to each data entry.

[0076] Specifically, generate data entries based on the data tags and storage locations of each piece of data, and summarize the data entries into a unified data catalog table. Among them, the data catalog is a structured index table that summarizes the key information (data type, identifier, storage location, etc.) of new energy heterogeneous data. Organize the data catalog through data tags to provide a data map for the system.

[0077] Step S5, associate the data catalog table with the metadata to establish a data index.

[0078] Specifically, perform data association between the data catalog and relevant metadata information, analyze the metadata to identify the association relationships between business data, metric data, and file data, and establish a data index.

[0079] For example: taking a wind farm as an example, the system processes the data of wind turbine A. The data catalog includes business data (equipment data), metric data (power output), and file data (inspection records). First, by analyzing the equipment identifier A in the metadata tags, identify the association relationships between the three types of data. For example, the equipment information is associated with the equipment identifier A, the power metric label is associated with the equipment identifier A, and the inspection record is associated with the equipment identifier A. At the same time, the data catalog is associated with the equipment identifier A and an index table is established to record the storage locations of each data.

[0080] Step S6, generate a data view according to the association relationship and the data index.

[0081] Specifically, by means of these association relationships and data indexes, a unified data view is generated to achieve the integration of cross-type data.

[0082] For example: By means of the above association relationships and indexes, a unified data view is formed to integrate device information, power data, and inspection image files, achieving cross-type data integration. When a user queries the operating status and inspection records of device A through the view, the storage type and storage location of the device information are found through the data directory index, the description information of device A is queried and displayed through the business data query engine. At the same time, the power index data entry of device A is queried through the data directory index, the index data of device A in the power index data is queried and the power curve is displayed through the index data query engine. The storage path of the inspection image file is obtained by querying the file data entry of device A through the data directory index, and the inspection file link is obtained through the file storage engine.

[0083] In this application, storing different types of data via the unified data view can provide a more convenient and efficient query service for subsequent analysis of new energy heterogeneous data.

[0084] Furthermore, according to the data view, the data required by the user is obtained; according to the data required by the user, a data matrix is generated; and based on the correlation analysis algorithm, according to the data matrix, a data analysis report is obtained.

[0085] Specifically, the data required by the user is queried and analyzed according to the data view to form a data matrix; then the data matrix is analyzed through the correlation analysis algorithm, and a data analysis report is formed based on the data analysis results. For example: Analyze the correlation between solar power generation efficiency and solar radiation intensity, ambient temperature, and ambient humidity. First, query the operation index data of the solar panel power equipment at the station and the ambient variable index data (temperature, humidity, radiation intensity, etc.) at the same time period at the station through the unified data view, and use the correlation analysis algorithm to calculate the correlation coefficients between the variables. Three situations can be obtained. Situation 1, the correlation coefficient between radiation intensity and power generation efficiency is 0.87, indicating a strong positive linear correlation between the two. Situation 2, the correlation coefficient between temperature and power generation efficiency is -0.68, indicating a negative correlation between the increase in temperature and the decrease in efficiency. Situation 3, the correlation coefficient between humidity and solar power generation efficiency is -0.55, revealing that an increase in humidity may non-linearly reduce the efficiency. Finally, through the correlation coefficients, the dependence relationship between the metric variables and the efficiency is quantified to provide data support for the optimal operation of the new energy system.

[0086] Further, obtain historical new energy heterogeneous data; based on a threshold analysis algorithm, determine a first detection result according to the historical new energy heterogeneous data and a preset threshold; based on k-means clustering analysis, cluster the historical new energy heterogeneous data to identify a second detection result of the data; based on an autoregressive integrated moving average model, predict a third detection result of the data according to the historical new energy heterogeneous data and the new energy heterogeneous data; judge the detection situations of the first detection result, the second detection result, and the third detection result. If there are abnormal results in the first detection result, the second detection result, and the third detection result, determine that the system working condition is abnormal; and if there are no abnormal results in the first detection result, the second detection result, and the third detection result, determine that the system working condition is normal.

[0087] Specifically, the system working condition is judged in three ways. Way 1: Set an upper threshold and a lower threshold by using historical new energy heterogeneous data, and use threshold monitoring to detect abnormal points beyond the range. Way 2: Use k-means clustering analysis to cluster historical new energy heterogeneous data to identify whether the working condition is in a normal operation mode or an abnormal operation mode. Way 3: Use an autoregressive integrated moving average model to model the power index data, predict future index values to provide a comparison benchmark, and perform difference analysis with real-time values to achieve dynamic monitoring and determine the current working condition. If any of the monitoring results of the three ways judges an abnormality, manual maintenance is required; otherwise, the system is normal.

[0088] This application combines three methods of threshold monitoring, clustering analysis, and autoregressive integrated moving average model to perform closed-loop management on the system working condition from historical modeling to real-time analysis, timely discover and respond to abnormal working conditions, and improve energy production efficiency and equipment life.

[0089] Further, based on visualization technology, generate a visualization image according to the data view.

[0090] Specifically, in terms of energy production monitoring, use a dynamic line chart to display the change curves of the power generation amount and power generation power index data of a solar power station, which can clearly present the fluctuations of power generation efficiency at different times, facilitate users to grasp the operating status of power generation equipment, quickly detect the time nodes of power generation abnormalities, and timely troubleshoot faults. For a wind farm, the wind speed rose chart and the power generation power scatter chart can be combined to display the fan power index data, and environmental index data such as wind direction and wind speed can intuitively reflect the power generation power distribution under different wind direction and wind speed conditions, and assist in determining the best installation orientation and operation parameter adjustment strategy of the fan.

[0091] In the presentation of the operating status of the equipment, key parameters of new energy equipment, such as temperature, pressure, vibration amplitude, etc., are visualized in the form of a dashboard or progress bar, and the health status of the equipment is alerted to the operation and maintenance personnel with intuitive color changes (e.g., green for the normal range, yellow for the critical value, and red for the dangerous area), facilitating timely maintenance work to prevent downtime losses caused by equipment failures. At the same time, by drawing a pie chart of the equipment failure types, the proportion of each type of failure is clearly shown, helping technicians focus on the high-occurrence failure types, deeply analyze the root causes of the failures, and formulate targeted improvement measures.

[0092] In the display of new energy resource distribution, with the help of a Geographic Information System (GIS) platform, data such as solar irradiance and wind energy resource density are superimposed on the map in the form of a heat map, vividly presenting the richness and distribution differences of new energy resources in different regions, providing a scientific and intuitive basis for the site selection and planning of new energy projects, assisting enterprises in accurately laying out new energy facilities, and achieving the maximization of resource utilization.

[0093] This application converts complex and abstract new energy heterogeneous data into intuitive, easy-to-understand, and highly insightful graphical images through data visualization, enabling users to quickly extract valuable insights from massive data and thus make more informed choices.

[0094] An embodiment of this application provides a new energy data real-time storage device, as Figure 2 shown, including:

[0095] An acquisition module 101, configured to acquire new energy heterogeneous data;

[0096] A determination module 102, configured to analyze the new energy heterogeneous data and determine the storage information and metadata of each data;

[0097] A first generation module 103, configured to generate data tags according to the storage information and the metadata;

[0098] A construction module 104, configured to construct a data directory table according to the metadata, the data tags, and the storage information;

[0099] An association module 105, configured to associate the data directory table and the metadata to establish a data index; and

[0100] A second generation module 106, configured to generate a data view according to the association relationship and the data index.

[0101] Further, the determination module is configured to,

[0102] Analyze the new energy heterogeneous data to obtain the data type;

[0103] Select a storage engine according to the data type;

[0104] Determine the storage information of each piece of data according to the data type and the storage engine; and

[0105] Extract the metadata according to the new energy heterogeneous data.

[0106] Furthermore, the building module is configured to:

[0107] Determine the storage location of each piece of data according to the storage information;

[0108] Generate data entries according to the data tags of each piece of data and the storage location; and

[0109] Obtain a data directory table according to each of the data entries.

[0110] An embodiment of the present application provides a storage medium for storing computer-executable instructions, characterized in that the computer-executable instructions, when executed, implement the steps of the new energy data real-time storage method described in any one of the above embodiments.

[0111] It should be noted that the embodiment of the storage medium in this specification and the embodiment of the new energy data real-time storage method in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the corresponding implementation of the new energy data real-time storage method described above, and the repeated parts will not be elaborated.

[0112] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0113] In the 1930s, it was obvious to distinguish whether an improvement in a technology was a hardware improvement (e.g., improvement in circuit structures such as diodes, transistors, switches, etc.) or a software improvement (improvement in method processes). However, with the development of technology, many improvements in method processes today can be regarded as direct improvements in hardware circuit structures. Almost all designers obtain the corresponding hardware circuit structures by programming the improved method processes into the hardware circuits. Therefore, it cannot be said that an improvement in a method process cannot be implemented with hardware entity modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user's programming of the device. Designers can program by themselves to "integrate" a digital system on a piece of PLD without having to ask the chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a Hardware Description Language (HDL). And there is not only one type of HDL, but many types, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. Currently, the most commonly used ones are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply performing a little logical programming on the method process with the above-mentioned several hardware description languages and programming it into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method process.

[0114] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that, in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to logically program the method steps to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, and embedded microcontrollers to achieve the same function. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or the structures within the hardware component.

[0115] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0116] For the convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0117] Those skilled in the art should understand that one or more embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, one or more embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0118] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the specification. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one or more of the flows Figure 1 or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0119] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0121] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0122] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0123] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0124] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0125] One or more embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. One or more embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0126] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0127] The above are only examples of this document and are not intended to limit this document. For those skilled in the art, various changes and modifications can be made to this document. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of this document shall be included within the scope of the claims of this document.

Claims

1. A real-time storage method for new energy data, characterized in that Including: Obtain new energy heterogeneous data; Analyze the new energy heterogeneous data to determine the storage information and metadata of each data; Generate data tags according to the storage information and the metadata; Construct a data directory table according to the metadata, the data tags and the storage information; Associate the data directory table with the metadata to establish a data index; And Generate a data view according to the association relationship and the data index.

2. The method according to claim 1, wherein Analyzing the new energy heterogeneous data to determine the storage information and metadata of each data includes: Analyze the new energy heterogeneous data to obtain the data type; Select a storage engine according to the data type; Determine the storage information of each data according to the data type and the storage engine; and Extract the metadata according to the new energy heterogeneous data.

3. The method according to claim 1, wherein Constructing a data directory table according to the metadata, the data tags and the storage information includes: Determine the storage location of each data according to the storage information; Generate data entries according to the data tags and the storage locations of each data; and Obtain a data directory table according to each of the data entries.

4. The method according to claim 1, wherein The method further includes: Obtain the data required by the user according to the data view; Generate a data matrix according to the data required by the user; and Based on a correlation analysis algorithm, obtain a data analysis report according to the data matrix.

5. The method according to claim 1, wherein The method further includes: Obtain historical new energy heterogeneous data; Based on a threshold analysis algorithm, determine a first detection result according to the historical new energy heterogeneous data and a preset threshold; Based on k-means clustering analysis, cluster the historical new energy heterogeneous data to identify a second detection result of the data; Based on an autoregressive integrated moving average model, predict a third detection result of the data according to the historical new energy heterogeneous data and the new energy heterogeneous data; Judge the detection situations of the first detection result, the second detection result and the third detection result, If there are abnormal results in the first detection result, the second detection result and the third detection result, determine that the system working condition is abnormal; and If there are no abnormal results in the first detection result, the second detection result and the third detection result, determine that the system working condition is normal.

6. The method according to claim 1, wherein The method further includes: Generate a visualization image according to the data view based on visualization technology.

7. A real-time storage device for new energy data, characterized in that Including: An acquisition module for acquiring new energy heterogeneous data; A determination module for analyzing the new energy heterogeneous data to determine the storage information and metadata of each data; A first generation module for generating data tags according to the storage information and the metadata; A construction module for constructing a data directory table according to the metadata, the data tags and the storage information; An association module for associating the data directory table with the metadata to establish a data index; And A second generation module for generating a data view according to the association relationship and the data index.

8. The device according to claim 7, characterized in that The determination module is configured to Analyze the new energy heterogeneous data to obtain the data type; Select a storage engine according to the data type; Determine the storage information of each piece of data according to the data type and the storage engine; and Extract the metadata according to the new energy heterogeneous data.

9. The device according to claim 7, wherein The construction module is configured to: Determine the storage location of each piece of data according to the storage information; Generate data entries according to the data tags of each piece of data and the storage location; and Obtain a data directory table according to each of the data entries.

10. A storage medium for storing computer-executable instructions, characterized in that, The computer-executable instructions, when executed, implement the steps of the new energy data real-time storage method according to any one of claims 1-6.

Images