CN114662925A - Data fusion and mining method for power service center - Google Patents

Data fusion and mining method for power service center Download PDF

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Publication number
CN114662925A
CN114662925A CN202210299544.7A CN202210299544A CN114662925A CN 114662925 A CN114662925 A CN 114662925A CN 202210299544 A CN202210299544 A CN 202210299544A CN 114662925 A CN114662925 A CN 114662925A
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data
power grid
application
mining
service center
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CN114662925B (en
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葛维
程超
陈博
付立衡
张亚炜
柳长发
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State Grid Hebei Electric Power Co Ltd
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State Grid Hebei Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • 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
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a data fusion and mining method of an electric power service center, which comprises the steps of constructing an m-metadata combination based on the self dimensional characteristics of covering risk events at a basic data end; determining a target data group required by current data processing at an application data end; and then, carrying out normalization model processing on the basic event data and the application data, and carrying out data presentation of the application data vector and data mining facing the risk tracing. The datamation fusion and mining method provided by the invention realizes the overall technical detail description from the basic event data group to the application data group, and has wide practicability and applicability.

Description

Data fusion and mining method for power service center
Technical Field
The invention relates to the technical field of application type database configuration and processing, in particular to an application type data configuration method and application thereof for power grid operation service.
Background
With the application and popularization of big data and cloud computing technology in modern smart grid operation and service operation, the data value gradually lies at the core position of the whole operation system. In a multi-platform and multi-dimensional data project and a gathering application network thereof, effective processing of data, including classification, conversion, fusion and compatible adaptive storage and transmission, has a very important influence on the efficiency of power grid operation service. The appeal and risk monitoring of power utilization ends by power service centers such as 95598 and the like is a centralized research direction of the current power big data application expansion, and the data processing and fusion method is a technical key point and a research and development focus which need to be solved at present in both a technical level and an application level.
Disclosure of Invention
The invention aims to provide a data fusion and mining method of an electric power service center and application thereof.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
A data fusion and mining method of a power service center comprises the following steps:
A. at a basic data end of power grid operation, acquiring data aiming at a provincial power grid marketing service center to construct an m-element data combination based on the characteristics of a power grid operation event, wherein the m groups of data have the following characteristics: the m groups of data can be acquired and stored in an acquisition mode; corresponding to the first step, the m groups of data are directly related to the characteristics of the power grid operation event and all data directions of the power grid operation event are covered; the m groups of data have independent orthogonal characteristic;
B. at an application data end of the power grid operation risk, determining a target data group of the current data processing requirement according to an actual operation risk target corresponding to a single operation group contained in single operation or combined operation, and constructing and forming an n-metadata group which is recorded as (n 1, n2, … and nn);
C. carrying out normalization model processing on the basic event data and the application data, and carrying out vectorization on the basic event data and the application data based on the orthogonal characteristic of the m-element data group to obtain a basic data vector m'; carrying out homotypic data processing on the application end data group to obtain an application data vector n';
D. data rendering of application data vectors: the following data configuration was used: n ' = n ' (m '); constructing a global mapping of each sub-data component related to m 'for any sub-data component of n', wherein the global mapping corresponds to a single-value m-element data function; thus, n single-value m-element data functions are obtained, and a single standard library processing mapping from the m-dimensional space to the n-dimensional space is integrally formed;
E. based on application data vector configuration, operation tracing oriented data mining: step D meets the data presentation requirement of the power grid operation risk, however, in practical application, the real requirement of the power grid operation risk lies in operation traceability analysis corresponding to the exceeding and floating of risk events and data out-of-range, and further data mining needs to be carried out on the application data of the power grid operation risk: for a single canonical mapping from m-dimensional space to n-dimensional space, it is first decomposed into m sets of single space mappings again, and for any set of single space mappings nk = nk (m 1, m2, …, mm), differential property mining of the data space is performed as follows: dnk/dm' = (∂ nk/∂ m1, ∂ nk/∂ m2, …, ∂ nk/∂ mm); thus obtaining n groups of data mining branches carrying data space differential characteristics; in the space vector configuration, a basic data end of an operation event adopts a unit orthogonal vector as a reference unit, a vector end curve of an operation risk application end forms a transformed curve coordinate system, the differential of the basic data end corresponds to a basic orthogonal differential manifold in a multidimensional space, the differential manifold formed by the differential of the operation risk application end is displayed by unitary vector combination of the curve coordinate system, and a similar Riemann scale tensor of the curve coordinate of the operation risk application end is completely depicted, so that the data presentation configuration of the application data vector corresponds to vector transformation from a local orthogonal coordinate to the curve coordinate, and all details from a basic data group to an application data group are completely depicted, thereby covering and displaying all microscopic sources corresponding to overproof floating and data out-of-bounds of the risk event.
As a preferred technical scheme of the invention, in the step B, numerically, if n is greater than m, the data optimization of the n metadata groups is carried out through data elimination inspection; if n is less than or equal to m and the data processing scale is not more than 50% of the full-capacity data processing scale of the hardware system, not performing data inspection; and if n is less than or equal to m and the data processing scale is larger than 50% of the full-capacity data processing scale of the hardware system, performing data optimization on the n metadata groups through data association inspection.
As a preferred technical solution of the present invention, an array subjected to data culling inspection is directly vectorized, and for an array not subjected to data culling inspection, there are two cases: firstly, arrays are completely orthogonal, so that direct vectorization can be realized; part of data can be linearly shown, although flaws exist in theory by direct vectorization at the moment, the flaws are only represented by repeatability of output results, which can not cause bug in the operation process, and meanwhile, because data events without data removal detection are all the conditions that the data volume is less than 50% of the system data processing scale, the computing power of the system can not be redundantly consumed, and under the conditions, the application end data group is directly vectorized to obtain an application data vector n'; therefore, the application type data configuration facing the power grid operation risk and completely associated with the basic event orthogonal data set is completed.
As a preferred technical solution of the present invention, the data elimination test is: and in the data processing link and the data result output link, the repeated sets are removed in an alternative mode, and data removing remarks and reporting are carried out facing to the requirements of the power grid operation risk application end.
As a preferred technical solution of the present invention, the data association check is: and in the data processing link, the repeated groups are eliminated in an alternative mode, the functional relation between the eliminated data and alternative retained data is established, and the output of all data results is retained in the data output link based on the data function.
As a preferred technical solution of the present invention, in step a, the m-metadata group contains virtually all basic data of the grid operation risk event; if the data requirement of the application end exceeds the type of the m-metadata group in category, the first aspect is that the data requirement of the application end is fed back and returned to a provincial power grid marketing service center according to the category of the data requirement of the application end, monitoring is carried out on risk event categories in the provincial center, a collection port is additionally arranged for carrying out data expansion, and meanwhile dimension expansion is carried out on the m-metadata group according to data classification after the data expansion to keep the m-metadata group and the m-metadata group consistent; in a second aspect, corresponding to the capacity expansion requirement of the risk event category described in the first aspect, the database structure is configured to include an infinitely expandable data type storage and transmission port.
As a preferable aspect of the present invention, in the step a, the m sets of data are simultaneously given normalized characteristics, which are fourth characteristics, by linear operation: the linear operation is written as: (e 1, e2, …, em) x (m 1, m2, …, mm)TE is used as unit data in the same category data for storage and operation, and T is a transposition symbol.
As a preferred technical solution of the present invention, the provincial power grid marketing service center at least includes: the system comprises a provincial power grid marketing service center, a provincial power grid 95598 customer service center, an online national grid provincial power grid data center, a provincial power grid mobile terminal app operation center, a provincial power grid mobile terminal micro application, a provincial power grid micro information public number and a provincial power grid payment treasure service number.
As a preferred technical solution of the present invention, the adding of the monitoring and collecting port for data expansion according to the risk event category means performing at least the following expansion based on data types in the operation background of the provincial power grid marketing service center: classifying and identifying the newly added data, monitoring and collecting the newly added data, storing and transmitting the newly added data, and backing up and updating the newly added data.
As a preferred technical solution of the present invention, the n groups of data mining branches carrying data space differential characteristics have an overall data space configuration of n × m; the data rendering configuration of the application data vector corresponds to a local orthogonal coordinate to curved coordinate transformation of the n x m configuration.
The beneficial effect that adopts above-mentioned technical scheme to bring lies in:
the invention carries out data standard arrangement on the basic data end of the power grid operation risk aiming at the m-element data combination of the characteristics of the power grid operation event, forms 4 groups of standardized characteristics and lays a foundation for the construction of application end data. Further, if the data demand of the application end exceeds the type of the m-metadata group in category, the upgrading system data monitoring and collecting port of the power grid operation data center is regressed and upgraded based on the category of the data demand of the application end to perform data expansion; correspondingly, the database structure provided by the invention comprises infinitely expandable data type storage and transmission ports.
According to the method, corresponding normalization is carried out on the application data side of the power grid operation risk according to the basic data, and a data base is laid for the subsequent global vector normalization; and, according to the actual demand of the power grid operation risk processing and the computing power innovation of the system, a plurality of data inspection methods are provided: the data elimination inspection is to eliminate repeated groups in an alternative mode in a data processing link and a data result output link, and carry out data elimination remarks and delivery facing to the requirements of a power grid operation application end; the data association test is as follows: and in the data processing link, the repeated groups are eliminated in an alternative mode, the functional relation between the eliminated data and alternative retained data is established, and the output of all data results is retained in the data output link based on the data function.
The normalization model of the basic event data and the application data constructed by the invention can carry out cooperative processing on an irregular data configuration, and for the situation that the data structure is not completely orthogonal, although a flaw exists in theory in the direct vectorization, the flaw is only represented as the repeatability of an output result, which can not cause bug in the operation process, and meanwhile, the computing power of the system can not be redundantly consumed because the data events without data elimination inspection are all the situations that the data quantity is less than 50% of the processing scale of the system data.
The early-stage data construction of the invention enables the application type data facing the power grid operation risk processing to be presented simply, and the following data configuration can be directly adopted: n ' = n ' (m '); for any subdata component of n ', constructing a global mapping associated with each subdata component of m', wherein the global mapping corresponds to a single-value m-element data function; thus, n single-value m-metadata functions are obtained in total, and a single-specification vector mapping from an m-dimensional space to an n-dimensional space is formed integrally.
The application data meets the data presentation requirement of the power grid operation risk, however, in practical application, the traceability analysis of the risk event is emphasized more, and further data mining needs to be performed on the application data of the power grid operation risk. The normalized vector space model is restored and decomposed into m groups of single space mappings again, and for any group of single space mappings nk = nk (m 1, m2, … and mm), differential characteristics of a data space are mined in the following mode: dnk/dm' = (∂ nk/∂ m1, ∂ nk/∂ m2, …, ∂ nk/∂ mm); thus obtaining n groups of data mining branches carrying data space differential characteristics; in the space vector configuration, a basic data end of an operation event adopts a unit orthogonal vector as a reference unit, a vector end curve of an operation risk application end forms a transformed curve coordinate system, the differential of the basic data end corresponds to a basic orthogonal differential manifold in a multidimensional space, the differential manifold formed by the differential of the operation risk application end is displayed by unitary vector combination of the curve coordinate system, and a similar Riemann scale tensor of the curve coordinate of the operation risk application end is completely depicted, so that the data presentation configuration of the application data vector corresponds to vector transformation from a local orthogonal coordinate to the curve coordinate, and all details from a basic data group to an application data group are completely depicted, thereby covering and displaying all microscopic sources corresponding to overproof floating and data out-of-bounds of the risk event.
Detailed Description
The following examples illustrate the invention in detail. In the following description of embodiments, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather mean "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
Embodiment 1, grid operation basic event data
At a power grid operation risk basic data end, acquiring data aiming at a provincial power grid marketing service center to construct an m-element data combination based on the characteristics of a power grid operation event, wherein the m groups of data have the following characteristics: the m groups of data can be acquired and stored in an acquisition mode; corresponding to the first step, the m groups of data are directly related to the characteristics of the power grid operation event and all data directions of the power grid operation event are covered; the m groups of data have independent orthogonal characteristic; further, the m sets of data are simultaneously given normalized characteristics, which are fourth characteristics, by linear operations: the linear operation is written as: (e 1, e2, …, em) x (m 1, m2, …, mm)TE is used as unit data in the same category data for storage and operation, and T is a transposition symbol.
Embodiment 2, grid operation risk application data
At the application data end of the grid operation risk, determining a target data group of the current data processing requirement according to an actual operation risk target corresponding to a single operation group contained in the single operation or the combined operation, and recording the target data group as (n 1, n2, … and nn); numerically, if n is greater than m, performing data optimization of the n metadata groups through data elimination inspection; if n is less than or equal to m and the data processing scale is not more than 50% of the full-capacity data processing scale of the hardware system, not performing data inspection; and if n is less than or equal to m and the data processing scale is larger than 50% of the full-capacity data processing scale of the hardware system, performing data optimization on the n metadata groups through data association inspection.
Example 3 vector normalization data model
Performing normalization model processing on the basic event data in the embodiment 1 and the application data in the embodiment 2, and performing vectorization on the basic event data based on the orthogonality of the m-element data group to obtain a basic data vector m'; for an application-side data group, vectorization is directly performed on an array subjected to data culling inspection, while for an array not subjected to data culling inspection, there are two cases: firstly, arrays are completely orthogonal, so that direct vectorization can be realized; part of data can be linearly expressed, although the direct vectorization has flaws theoretically at the moment, the flaws are only represented by the repeatability of output results, which can not cause bug in the operation process, and meanwhile, because data events without data elimination inspection are all the conditions that the data volume is less than 50% of the system data processing scale, the computing power of the system can not be redundantly consumed, and in such a condition, the application end data group is directly vectorized to obtain an application data vector n'; therefore, the application type data configuration facing the power grid operation risk and completely associated with the basic event orthogonal data set is completed.
Example 4 expansibility of software and hardware System
The m-metadata group contains practically all basic data of the power grid operation risk event; if the data requirement of the application end exceeds the type of the m-metadata group in category, the first aspect is that the data requirement of the application end is fed back and returned to a provincial power grid marketing service center according to the category of the data requirement of the application end, monitoring is carried out on risk event categories in the provincial center, a collection port is additionally arranged for carrying out data expansion, and meanwhile dimension expansion is carried out on the m-metadata group according to data classification after the data expansion to keep the m-metadata group and the m-metadata group consistent; in a second aspect, corresponding to the capacity expansion requirement of the risk event category described in the first aspect, the database structure is configured to include an infinitely expandable data type storage and transmission port.
Example 5 data center
The provincial power grid marketing service center at least comprises: the system comprises a provincial power grid marketing service center, a provincial power grid 95598 customer service center, an online national grid provincial power grid data center, a provincial power grid mobile terminal app operation center, a provincial power grid mobile terminal micro application, a provincial power grid micro information public number and a provincial power grid payment treasure service number.
In embodiment 4, the data expansion performed by adding the monitoring and collecting port for the risk event category means that the data type-based expansion is performed in the operation background of the provincial power grid marketing service center, where the data type-based expansion is at least as follows: the method comprises the steps of new data classification and identification, new data monitoring and collection, new data storage and transmission, and new data backup and update.
Example 6 data culling inspection method
The data elimination inspection method comprises the following steps: and in the data processing link and the data result output link, the repeated sets are removed in an alternative mode, and data removing remarks and reporting are carried out facing to the requirements of the power grid operation risk application end.
Example 7 data correlation test method
The data correlation inspection method comprises the following steps: and in the data processing link, the repeated groups are eliminated in an alternative mode, the functional relation between the eliminated data and alternative retained data is established, and the output of all data results is retained in the data output link based on the data function.
Example 8 application type data presentation method
Data rendering of application data vectors: the following data configuration was used: n ' = n ' (m '); constructing a global mapping of each sub-data component related to m 'for any sub-data component of n', wherein the global mapping corresponds to a single-value m-element data function; thus, n single-value m-metadata functions are obtained in total, and a single-specification library processing mapping from the m-dimensional space to the n-dimensional space is formed integrally.
Embodiment 9 application-based data mining for grid operation risk
Based on application data vector configuration and operation source-oriented data mining: the application end data meets the data presentation requirement of the power grid operation risk, however, in practical application, the real requirement of the power grid operation risk lies in operation traceability analysis corresponding to the exceeding and floating risk event and the data out-of-range, and the application data of the power grid operation risk needs to be further subjected to data mining: for a single canonical mapping from m-dimensional space to n-dimensional space, it is first decomposed into m sets of single space mappings again, and for any set of single space mappings nk = nk (m 1, m2, …, mm), differential property mining of the data space is performed as follows: dnk/dm' = (∂ nk/∂ m1, ∂ nk/∂ m2, …, ∂ nk/∂ mm); thus obtaining n groups of data mining branches carrying data space differential characteristics; in the space vector configuration, a basic data end of an operation event adopts a unit orthogonal vector as a reference unit, a vector end curve of an operation risk application end forms a transformed curve coordinate system, the differential of the basic data end corresponds to a basic orthogonal differential manifold in a multidimensional space, the differential manifold formed by the differential of the operation risk application end is displayed by unitary vector combination of the curve coordinate system, and a similar Riemann scale tensor of the curve coordinate of the operation risk application end is completely depicted, so that the data presentation configuration of the application data vector corresponds to vector transformation from a local orthogonal coordinate to the curve coordinate, and all details from a basic data group to an application data group are completely depicted, thereby covering and displaying all microscopic sources corresponding to overproof floating and data out-of-bounds of the risk event.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
In various embodiments, the hardware implementation of the technology may directly employ existing intelligent devices, including but not limited to industrial personal computers, PCs, smart phones, handheld stand-alone machines, floor stand-alone machines, and the like. The input device preferably adopts a screen keyboard, the data storage and calculation module adopts the existing memory, calculator and controller, the internal communication module adopts the existing communication port and protocol, and the remote communication adopts the existing gprs network, the web and the like.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Each functional unit in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the embodiments of the method. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, and software distribution media, etc. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.

Claims (10)

1. A data fusion and mining method of an electric power service center is characterized in that: the method comprises the following steps:
A. at a basic data end of power grid operation, acquiring data aiming at a provincial power grid marketing service center to construct an m-element data combination based on the characteristics of a power grid operation event, wherein the m groups of data have the following characteristics: the m groups of data can be acquired and stored in an acquisition mode; corresponding to the first step, the m groups of data are directly related to the characteristics of the power grid operation event and all data directions of the power grid operation event are covered; the m groups of data have independent orthogonal characteristic;
B. at an application data end of the power grid operation risk, determining a target data group of the current data processing requirement according to an actual operation risk target corresponding to a single operation group contained in single operation or combined operation, and constructing and forming an n-metadata group which is recorded as (n 1, n2, … and nn);
C. carrying out normalization model processing on the basic event data and the application data, and carrying out vectorization on the basic event data and the application data based on the orthogonal characteristic of the m-element data group to obtain a basic data vector m'; carrying out data processing of the same type on the application terminal data group to obtain an application data vector n';
D. data rendering of application data vectors: the following data configuration was used: n ' = n ' (m '); constructing a global mapping of each sub-data component related to m 'for any sub-data component of n', wherein the global mapping corresponds to a single-value m-element data function; thus, n single-value m-element data functions are obtained, and a single standard library processing mapping from the m-dimensional space to the n-dimensional space is integrally formed;
E. based on application data vector configuration, operation tracing oriented data mining: step D, meeting the data presentation requirement of the power grid operation risk, wherein the real requirement of the power grid operation risk in practical application lies in operation traceability analysis corresponding to the exceeding and floating of risk events and data out-of-range, and further data mining needs to be carried out on the application data of the power grid operation risk: for a single canonical mapping from m-dimensional space to n-dimensional space, it is first decomposed into m sets of single space mappings again, and for any set of single space mappings nk = nk (m 1, m2, …, mm), differential property mining of the data space is performed as follows: dnk/dm' = (∂ nk/∂ m1, ∂ nk/∂ m2, …, ∂ nk/∂ mm); thus obtaining n groups of data mining branches carrying data space differential characteristics; in the space vector configuration, a basic data end of an operation event adopts a unit orthogonal vector as a reference unit, a vector end curve of an operation risk application end forms a transformed curve coordinate system, the differential of the basic data end corresponds to a basic orthogonal differential manifold in a multidimensional space, the differential manifold formed by the differential of the operation risk application end is displayed by unitary vector combination of the curve coordinate system, and a similar Riemann scale tensor of the curve coordinate of the operation risk application end is completely depicted, so that the data presentation configuration of the application data vector corresponds to vector transformation from a local orthogonal coordinate to the curve coordinate, and all details from a basic data group to an application data group are completely depicted, thereby covering and displaying all microscopic sources corresponding to overproof floating and data out-of-bounds of the risk event.
2. The method for the data fusion and mining of the power service center according to claim 1, wherein the method comprises the following steps: in the step B, numerically, if n is larger than m, optimizing data of the n metadata groups through data elimination inspection; if n is less than or equal to m and the data processing scale is not more than 50% of the full-capacity data processing scale of the hardware system, not performing data inspection; and if n is less than or equal to m and the data processing scale is larger than 50% of the full-capacity data processing scale of the hardware system, performing data optimization on the n metadata groups through data association inspection.
3. The method for the data fusion and mining of the power service center according to claim 2, wherein the method comprises the following steps: the vectorization is directly performed on the array subjected to the data culling check, while for the array not subjected to the data culling check, there are two cases: firstly, arrays are completely orthogonal, so that direct vectorization can be realized; part of data can be linearly shown, although flaws exist in theory by direct vectorization at the moment, the flaws are only represented by repeatability of output results, which can not cause bug in the operation process, and meanwhile, because data events without data removal detection are all the conditions that the data volume is less than 50% of the system data processing scale, the computing power of the system can not be redundantly consumed, and under the conditions, the application end data group is directly vectorized to obtain an application data vector n'; therefore, the application type data configuration facing to the power grid operation risk and completely associated with the basic event orthogonal data set is completed.
4. The method for the data fusion and mining of the power service center according to claim 2, wherein the method comprises the following steps: the data elimination test is as follows: and in the data processing link and the data result output link, the repeated sets are removed in an alternative mode, and data removing remarks and reporting are carried out facing to the requirements of the power grid operation risk application end.
5. The method for the datamation fusion and mining of the power service center according to claim 2, wherein the method comprises the following steps: the data association check is as follows: and in the data processing link, the repeated groups are eliminated in an alternative mode, the functional relation between the eliminated data and alternative retained data is established, and the output of all data results is retained in the data output link based on the data function.
6. The method for the datamation fusion and mining of the power service center according to claim 1, wherein the method comprises the following steps: in the step A, the m metadata group actually contains all basic data of the power grid operation risk event; if the data requirement of the application end exceeds the type of the m-metadata group in category, the first aspect is that the data requirement of the application end is fed back and returned to a provincial power grid marketing service center according to the category of the data requirement of the application end, monitoring is carried out on risk event categories in the provincial center, a collection port is additionally arranged for carrying out data expansion, and meanwhile dimension expansion is carried out on the m-metadata group according to data classification after the data expansion to keep the m-metadata group and the m-metadata group consistent; in a second aspect, corresponding to the risk event category capacity expansion requirement described in the first aspect, the database structure is constructed and arranged to include an infinitely expandable data type storage and transmission port.
7. The method for the data fusion and mining of the power service center according to claim 1, wherein the method comprises the following steps: in step a, normalization characteristics, which are fourth characteristics, are further given to the m sets of data simultaneously by linear operation: the linear operation is written as: (e 1, e2, …, em) x (m 1, m2, …, mm)TE, storing and operating as unit data in the same category data,t is the transposed symbol.
8. The method for the data fusion and mining of the power service center according to claim 1, wherein the method comprises the following steps: the provincial power grid marketing service center at least comprises: the system comprises a provincial power grid marketing service center, a provincial power grid 95598 customer service center, an online national grid provincial power grid data center, a provincial power grid mobile terminal app operation center, a provincial power grid mobile terminal micro application, a provincial power grid micro information public number and a provincial power grid payment treasure service number.
9. The method for the data fusion and mining of the power service center according to claim 6, wherein the method comprises the following steps: the data expansion by adding the monitoring and collecting ports aiming at the risk event category is that at least the following expansion based on data types is performed on the operation background of the provincial power grid marketing service center: the method comprises the steps of new data classification and identification, new data monitoring and collection, new data storage and transmission, and new data backup and update.
10. The method for the data fusion and mining of the power service center according to claim 1, wherein the method comprises the following steps: the n groups of data mining branches carrying data space differential characteristics have an overall data space configuration of n multiplied by m; the data representation configuration of the application data vector corresponds to a local orthogonal coordinate to curved coordinate transformation of the n x m configuration.
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