CN118071009A - Data prediction method and system - Google Patents
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- CN118071009A CN118071009A CN202410110050.9A CN202410110050A CN118071009A CN 118071009 A CN118071009 A CN 118071009A CN 202410110050 A CN202410110050 A CN 202410110050A CN 118071009 A CN118071009 A CN 118071009A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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Abstract
The invention provides a data prediction method and a data prediction system, and belongs to the technical field of data prediction. The method comprises the following steps: determining the association relation between the power plant historical operation data according to the preprocessed power plant historical operation data; configuring each data node of the knowledge graph model based on the data characteristics of each power plant historical operation data and the association relation between each power plant historical operation data; and acquiring power plant operation data in real time, and inputting the power plant operation data into corresponding data nodes in the knowledge graph model based on corresponding data features so as to perform data prediction and obtain a data prediction result. The method and the system realize data prediction based on the knowledge graph, thereby realizing the purpose of real-time and accurate prediction of the operation data of the power plant, helping users to rapidly predict target data and providing decision support for the users.
Description
Technical Field
The present invention relates to the field of data prediction technology, and in particular, to a data prediction method, a data prediction system, a machine-readable storage medium, and an electronic device.
Background
A power plant (also called a power plant) is a plant that converts various primary energy sources stored in nature into electric energy (secondary energy sources). With the development of power plants, real-time prediction of operation data of the power plants is required, and decision support is provided for users. At present, the operation data prediction of the power plant only stays in the artificial arrangement prediction stage, and the objectivity and the accuracy are lacking.
Therefore, how to predict the operation data of the power plant accurately in real time, so as to help the user predict the target data quickly, and providing decision support is a problem to be solved urgently.
Disclosure of Invention
The embodiment of the invention aims to provide a data prediction method and a system, which at least solve the problem that the prediction of the operation data of a power plant lacks objectivity and accuracy.
To achieve the above object, a first aspect of the present invention provides a data prediction method, including:
Determining the association relation between the power plant historical operation data according to the preprocessed power plant historical operation data;
configuring each data node of the knowledge graph model based on the data characteristics of each power plant historical operation data and the association relation between each power plant historical operation data;
and acquiring power plant operation data in real time, and inputting the power plant operation data into corresponding data nodes in the knowledge graph model based on corresponding data features so as to perform data prediction and obtain a data prediction result.
Optionally, the data node includes a plurality of child nodes and a plurality of parent nodes;
The configuring each data node of the knowledge graph model based on the association relationship between the data characteristics of each power plant historical operation data and each power plant historical operation data includes:
Determining data characteristics corresponding to each sub-node based on the data characteristics of the historical operation data of each power plant and the association relation between the historical operation data of each power plant; wherein, the data characteristics of the historical operation data of each power plant are in one-to-one correspondence with the data characteristics corresponding to each sub-node;
Based on the data to be predicted and the historical operation data of the power plant, performing prediction rule configuration, and embedding the prediction rule into a corresponding father node in the knowledge graph model;
based on the prediction rules, the connection between each child node and the corresponding parent node is established.
Optionally, the prediction rules include a machine learning algorithm and a mathematical model;
the configuration of the prediction rule based on the data to be predicted and the historical operation data of the power plant comprises the following steps:
Determining a logical relationship between the data to be predicted and historical operating data of the power plant;
Based on the logical relationship between the data to be predicted and the power plant historical operating data, a machine learning algorithm and/or mathematical model of the corresponding parent node is matched.
Optionally, the data prediction method further includes:
historical operating data of the power plant is obtained based on a power plant DCS database, an API interface and/or a crawler technology.
Optionally, the preprocessing process of the power plant historical operation data includes:
Deduplication processing, missing value processing, and/or outlier processing.
Optionally, the data node includes a central node, configured to display a data prediction result;
the display mode of the data prediction result at least comprises a chart, a table and/or characters.
Optionally, after configuring each data node of the knowledge-graph model based on the data characteristic of each power plant historical operation data and the association relationship between each power plant historical operation data, the method further includes:
Taking the preprocessed power plant historical operation data as input, and testing the configured knowledge graph model;
And (3) based on the test result, optimizing and debugging the knowledge graph model.
A second aspect of the present invention provides a data prediction system comprising:
The association relation determining module is used for determining association relation among the power plant historical operation data according to the preprocessed power plant historical operation data;
The data node configuration module is used for configuring each data node of the knowledge graph model based on the data characteristics of each power plant historical operation data and the association relation between each power plant historical operation data;
The data prediction module is used for acquiring the power plant operation data in real time, inputting the power plant operation data into corresponding data nodes in the knowledge graph model based on corresponding data features so as to perform data prediction and obtain a data prediction result.
In a third aspect the invention provides a machine-readable storage medium having stored thereon instructions which, when executed by a processor, cause the processor to be configured to perform the data prediction method described above.
In a fourth aspect of the present invention, an electronic device is provided, the electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the data prediction method described above when executing the computer program.
Through the technical scheme, the data prediction method and the system are provided, and the preprocessed power plant historical operation data are analyzed to determine the association relation among the power plant historical operation data. And configuring each data node of the knowledge graph model according to the data characteristics of each power plant historical operation data and the association relation between each power plant historical operation data. And carrying out data prediction on the power plant operation data acquired in real time by using the configured knowledge graph model so as to obtain a data prediction result of the target prediction data. The method and the system realize data prediction based on the knowledge graph, construct data nodes of the knowledge graph model through historical operation data of the power plant, and utilize the configured knowledge graph model to perform data prediction based on the power plant operation data acquired in real time. Therefore, the purpose of real-time and accurate prediction of the operation data of the power plant is achieved, a user is helped to rapidly predict target data, and decision support is provided for the user.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a data prediction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of node connection of a knowledge-graph model according to an embodiment of the present invention;
FIG. 3 is a block diagram of a data prediction system provided by one embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a preferred embodiment of the present invention.
Description of the reference numerals
10-Electronic device, 100-processor, 101-memory, 102-computer program.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
Fig. 1 is a flowchart of a data prediction method according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides a data prediction method, including:
S110: determining the association relation between the power plant historical operation data according to the preprocessed power plant historical operation data;
Specifically, the historical operation data of each power plant includes entities (such as people, places, events, etc.), and the power system of the power plant may involve a plurality of different devices and links (such as power transmission lines, substations, etc.), so that the correlation relationship between the historical operation data of each power plant can be determined by carrying out statistics and analysis on the event and operation data (i.e. the historical operation data of the power plant) which have occurred in the power plant.
In some implementations of this embodiment, the data prediction method further includes: historical operating data of the power plant is obtained based on a power plant DCS database, an API interface and/or a crawler technology.
In particular, the power plant historical operating data may be sourced from a number of sources, such as a power plant DCS database, API interface acquisition, crawler technology (where the crawler technology may be extracted from a number of media data such as pages, text, images, and audio). The key to data collection is to determine the data source, obtain the rights and compliance of the data, and ensure the integrity and accuracy of the data. Thus integrating information (including structured data, semi-structured data, and unstructured data) from different data sources to construct a unified knowledge-graph system.
In the knowledge graph model of the method, the power plant historical operation data belongs to knowledge graph data (including information such as entities, relations and attributes). Knowledge-graph data can be obtained by a knowledge-graph construction tool, a data extraction tool or a manual annotation and the like.
In some implementations of this embodiment, the preprocessing of the power plant historical operating data includes: deduplication processing, missing value processing, and/or outlier processing.
Specifically, preprocessing of data cleaning is required in view of the possible noise, redundancy and inconsistencies in the collected historical operating data of the power plant. The data cleaning process comprises operations such as deduplication, missing value processing, outlier processing, standardization, normalization and the like so as to ensure the quality and accuracy of historical operation data of the power plant.
S120: configuring each data node of the knowledge graph model based on the data characteristics of each power plant historical operation data and the association relation between each power plant historical operation data;
Referring to fig. 2, fig. 2 is a schematic diagram of node connection of a knowledge graph model according to an embodiment of the invention. In the knowledge graph model, the data characteristics of the entity corresponding to the historical operation data of each power plant and the association relation between the historical operation data of each power plant can be represented in a graph form, the entity is a node in the graph, and the association relation is a connecting line between the nodes. By representing entities, associations, and attributes between historical operating data of each power plant through a graph structure, rich semantic information, such as associations between entities, characteristics of attributes, and the like, can be described. The data prediction of the knowledge graph is to predict new entity attributes, relationships or other related information according to the existing knowledge graph data.
In some implementations of this embodiment, the data nodes include a plurality of child nodes and a plurality of parent nodes; the configuring each data node of the knowledge graph model based on the association relationship between the data characteristics of each power plant historical operation data and each power plant historical operation data includes:
Determining data characteristics corresponding to each sub-node based on the data characteristics of the historical operation data of each power plant and the association relation between the historical operation data of each power plant; wherein, the data characteristics of the historical operation data of each power plant are in one-to-one correspondence with the data characteristics corresponding to each sub-node;
Specifically, in the process of constructing the knowledge graph model, the arrangement positions of the sub-nodes corresponding to the data features of the entity corresponding to the historical operation data of each power plant are required to be determined according to the association relation between the historical operation data of each power plant, so as to determine the data structure of each sub-node of the knowledge graph model. And connecting the historical operation data of each power plant to the corresponding sub-node according to the data characteristics of the historical operation data of each power plant and the association relation between the historical operation data of each power plant to form a complete knowledge graph.
Based on the data to be predicted and the historical operation data of the power plant, performing prediction rule configuration, and embedding the prediction rule into a corresponding father node in the knowledge graph model;
Wherein the prediction rules comprise a machine learning algorithm and a mathematical model; the configuration of the prediction rule based on the data to be predicted and the historical operation data of the power plant comprises the following steps: determining a logical relationship between the data to be predicted and historical operating data of the power plant; based on the logical relationship between the data to be predicted and the power plant historical operating data, a machine learning algorithm and/or mathematical model of the corresponding parent node is matched.
Specifically, according to the predicted demand (i.e., data to be predicted) and the power plant historical operation data, calculating logic between the power plant historical operation data and the predicted demand is analyzed, a corresponding mathematical model, a machine learning algorithm or other calculating methods are selected, and the selected prediction rules such as the machine learning algorithm, the mathematical model and the like are configured in the father node.
Based on the prediction rules, the connection between each child node and the corresponding parent node is established.
Specifically, according to the calculation logic relation reflected by the prediction rule, the child nodes to be connected with each father node are determined, so that each child node is connected with the corresponding father node, and the configuration of each data node of the knowledge graph model is completed.
S130: and acquiring power plant operation data in real time, and inputting the power plant operation data into corresponding data nodes in the knowledge graph model based on corresponding data features so as to perform data prediction and obtain a data prediction result.
Specifically, the operation data of each power plant is input to the corresponding data node in the knowledge graph model based on the corresponding data characteristics, the knowledge graph model takes the historical operation data of the power plant as the standard, and the operation data of each power plant is comprehensively analyzed according to the association relation between the configured data nodes and the prediction rules such as a machine learning algorithm, a mathematical model and the like configured in the father node, so that the data prediction result of the target prediction data (namely the data to be predicted) is obtained, and the purpose of accurate real-time prediction is realized.
In the implementation process, the method analyzes the preprocessed power plant historical operation data to determine the association relation among the power plant historical operation data. And configuring each data node of the knowledge graph model according to the data characteristics of each power plant historical operation data and the association relation between each power plant historical operation data. And carrying out data prediction on the power plant operation data acquired in real time by using the configured knowledge graph model so as to obtain a data prediction result of the target prediction data. The method realizes the data prediction based on the knowledge graph, constructs the data node of the knowledge graph model through the historical operation data of the power plant, and performs the data prediction based on the power plant operation data acquired in real time by utilizing the configured knowledge graph model. Therefore, the purpose of real-time and accurate prediction of the operation data of the power plant is achieved, a user is helped to rapidly predict target data, and decision support is provided for the user.
In some implementations of this embodiment, the data node includes a central node configured to display a data prediction result; the display mode of the data prediction result at least comprises a chart, a table and/or characters.
Specifically, the data structure of the central node is designed, and the data prediction result is displayed to the user in a chart, a table or other visual form through the central node, so that the user interaction and the query are convenient.
In some implementations of the present embodiment, after configuring each data node of the knowledge-graph model based on the association between the data characteristic of each power plant historical operational data and each power plant historical operational data, the method further includes: taking the preprocessed power plant historical operation data as input, and testing the configured knowledge graph model; and (3) based on the test result, optimizing and debugging the knowledge graph model.
Specifically, the knowledge-graph model after configuration is tested and optimized by utilizing the preprocessed power plant historical operation data so as to ensure the accuracy, stability and performance of the knowledge-graph model after configuration.
FIG. 3 is a block diagram of a data prediction system provided in one embodiment of the present invention. As shown in fig. 3, an embodiment of the present invention provides a data prediction system, including:
The association relation determining module is used for determining association relation among the power plant historical operation data according to the preprocessed power plant historical operation data;
The data node configuration module is used for configuring each data node of the knowledge graph model based on the data characteristics of each power plant historical operation data and the association relation between each power plant historical operation data;
The data prediction module is used for acquiring the power plant operation data in real time, inputting the power plant operation data into corresponding data nodes in the knowledge graph model based on corresponding data features so as to perform data prediction and obtain a data prediction result.
Specifically, the system analyzes the preprocessed historical operating data of the power plant to determine the association relationship between the historical operating data of each power plant. And configuring each data node of the knowledge graph model according to the data characteristics of each power plant historical operation data and the association relation between each power plant historical operation data. And carrying out data prediction on the power plant operation data acquired in real time by using the configured knowledge graph model so as to obtain a data prediction result of the target prediction data. The system realizes data prediction based on the knowledge graph, constructs data nodes of the knowledge graph model through historical operation data of the power plant, and performs data prediction based on the power plant operation data acquired in real time by using the configured knowledge graph model. Therefore, the purpose of real-time and accurate prediction of the operation data of the power plant is achieved, a user is helped to rapidly predict target data, and decision support is provided for the user.
Embodiments of the present invention also provide a machine-readable storage medium having stored thereon instructions that, when executed by the processor 100, cause the processor 100 to be configured to perform the data prediction method described above.
Machine-readable storage media include both permanent and non-permanent, removable and non-removable media, and information storage may be implemented by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer 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 Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
The embodiment of the present invention further provides an electronic device 10, where the electronic device 10 includes a memory 101, a processor 100, and a computer program 102 stored in the memory 101 and executable on the processor 100, and the processor 100 implements the data prediction method described above when executing the computer program 102.
Fig. 4 is a schematic diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 4, the electronic device 10 of this embodiment includes: a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. The steps of the method embodiments described above are implemented by the processor 100 when executing the computer program 102. Or the processor 100, when executing the computer program 102, performs the functions of the modules/units of the apparatus embodiments described above.
By way of example, computer program 102 may be partitioned into one or more modules/units that are stored in memory 101 and executed by processor 100 to accomplish the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing particular functions to describe the execution of the computer program 102 in the electronic device 10. For example, the computer program 102 may be partitioned into an association determination module, a data node configuration module, and a data prediction module.
The electronic device 10 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The electronic device 10 may include, but is not limited to, a processor 100, a memory 101. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the electronic device 10 and is not intended to limit the electronic device 10, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may further include an input-output device, a network access device, a bus, etc.
The Processor 100 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 101 may be an internal storage unit of the electronic device 10, such as a hard disk or a memory of the electronic device 10. The memory 101 may also be an external storage device of the electronic device 10, such as a plug-in hard disk provided on the electronic device 10, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like. Further, the memory 101 may also include both internal storage units and external storage devices of the electronic device 10. The memory 101 is used to store computer programs and other programs and data required by the electronic device 10. The memory 101 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program 102 product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program 102 product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program 102 products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program 102 instructions. These computer program 102 instructions may be provided to a processor 100 of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor 100 of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program 102 instructions may also be stored in a computer-readable memory 101 that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory 101 produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program 102 instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. A method of data prediction, comprising:
Determining the association relation between the power plant historical operation data according to the preprocessed power plant historical operation data;
configuring each data node of the knowledge graph model based on the data characteristics of each power plant historical operation data and the association relation between each power plant historical operation data;
and acquiring power plant operation data in real time, and inputting the power plant operation data into corresponding data nodes in the knowledge graph model based on corresponding data features so as to perform data prediction and obtain a data prediction result.
2. The method of claim 1, wherein the data nodes comprise a plurality of child nodes and a plurality of parent nodes;
The configuring each data node of the knowledge graph model based on the data characteristics of each power plant historical operation data and the association relation between each power plant historical operation data comprises the following steps:
Determining data characteristics corresponding to each sub-node based on the data characteristics of the historical operation data of each power plant and the association relation between the historical operation data of each power plant; wherein, the data characteristics of the historical operation data of each power plant are in one-to-one correspondence with the data characteristics corresponding to each sub-node;
Based on the data to be predicted and the historical operation data of the power plant, performing prediction rule configuration, and embedding the prediction rule into a corresponding father node in a knowledge graph model;
and establishing connection between each child node and a corresponding parent node based on the prediction rule.
3. The method of claim 2, wherein the prediction rules include a machine learning algorithm and a mathematical model;
The predicting rule configuration based on the data to be predicted and the power plant historical operation data comprises the following steps:
Determining a logical relationship between the data to be predicted and historical operating data of the power plant;
Based on the logical relationship between the data to be predicted and the power plant historical operating data, a machine learning algorithm and/or mathematical model of the corresponding parent node is matched.
4. The method of claim 1, further comprising:
historical operating data of the power plant is obtained based on a power plant DCS database, an API interface and/or a crawler technology.
5. The method of claim 1, wherein the preprocessing of the power plant historical operating data comprises:
Deduplication processing, missing value processing, and/or outlier processing.
6. The data prediction method according to claim 1, wherein the data node comprises a central node for presenting the data prediction result;
the display mode of the data prediction result at least comprises a chart, a table and/or characters.
7. The data prediction method according to claim 1, further comprising, after configuring each data node of the knowledge-graph model based on a correlation between data features of each power plant historical operation data and each power plant historical operation data:
Taking the preprocessed power plant historical operation data as input, and testing the configured knowledge graph model;
And (3) based on the test result, optimizing and debugging the knowledge graph model.
8. A data prediction system, comprising:
The association relation determining module is used for determining association relation among the power plant historical operation data according to the preprocessed power plant historical operation data;
The data node configuration module is used for configuring each data node of the knowledge graph model based on the data characteristics of each power plant historical operation data and the association relation between each power plant historical operation data;
The data prediction module is used for acquiring the power plant operation data in real time, inputting the power plant operation data into corresponding data nodes in the knowledge graph model based on corresponding data features so as to perform data prediction and obtain a data prediction result.
9. A machine-readable storage medium having instructions stored thereon, which when executed by a processor cause the processor to be configured to perform the data prediction method of any of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the data prediction method of any one of claims 1 to 7 when executing the computer program.
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