CN111914037A - Power grid development-oriented multivariate information mining and analyzing method and system - Google Patents
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Abstract
The invention discloses a multivariate information mining and analyzing method and a multivariate information mining and analyzing system for power grid development, wherein the method comprises the following steps: acquiring relevant influence data in a power grid system operation scene; preprocessing data to obtain a multi-element information integrated data set of internal and external data; performing classification analysis on the spatial-temporal relevance degrees of all data sources, and determining distribution differences and characteristics of resources such as loads, power supplies and the like under different operation scenes in spatial dimension; the time-space correlation characteristics of the power grid operation information under various external environments are obtained; and determining a scheme for adjusting the power grid planning and modifying the architecture. According to internal and external multivariate information data during the operation of the power grid, data integration, data preprocessing and data analysis are carried out, an effective information set is formed according to power grid development diagnosis indexes, and the data analysis efficiency is improved by adopting parallel calculation; the whole process of power grid development is researched, and the time-space correlation characteristics and the dynamic change rule of data are obtained through data mining, so that the scheme for adjusting the power grid planning and modifying the architecture is determined.
Description
Technical Field
The invention relates to the technical field of power grid analysis, in particular to a multivariate information mining and analyzing method for power grid development.
Background
With the continuous development of the power grid, aiming at the change of the power grid time-space tide, the development of more visual angles of power grid tide time-space correlation characteristic analysis is very important. At present, the research on the trend characteristics focuses on the aspect of fault diagnosis, and the research on the characteristics such as sensitivity, fluctuation and the like in each state of steady-state operation is less. In the power grid development process, a power grid evaluation system is continuously updated, the evolution of the power grid evaluation system in a comprehensive, intelligent and actual direction is accelerated, and the development of data-driven power grid intelligent diagnosis system research is a key work for the development of the current company.
At present, the work of researching the power grid diagnosis is concentrated on one or more typical static sections, indexes capable of reflecting the process change rule of the power grid are lacked, the research is not carried out from the global perspective by combining multi-factor correlation analysis, typical parameters capable of accurately describing the state of the power grid and a comprehensive rating method are lacked, and accurate rating and development strategies cannot be carried out according to the characteristics of the power grids of various provinces. In recent years, comprehensive evaluation research in China mainly focuses on a weight processing means in an index fusion process, and comprehensive evaluation indexes are obtained by the idea of screening, rejecting and fusing basic indexes from bottom to top. The method obviously ignores the overall process characteristics of time sequence data, the differences of different power grids, the index correlation and the like, and does not meet the requirements of the current power grid development diagnosis and analysis work.
In view of the above, it is desirable to provide a multivariate information mining analysis method for power grid development, which is used for researching how to use data to drive and create a power grid development intelligent diagnosis technology and improve the power grid development quality.
Disclosure of Invention
In order to solve the technical problems, the technical scheme adopted by the invention is to provide a multivariate information mining analysis method for power grid development, which comprises the following steps:
acquiring relevant influence data in a power grid system operation scene; preprocessing data to obtain a multi-element information integrated data set of internal and external data; performing classification analysis on the spatial-temporal relevance degrees of all the data sources, and determining distribution differences and characteristics of loads and power resources under different operation scenes on spatial dimensions; the time-space correlation characteristics of the power grid operation information under various external environments are obtained; and determining a scheme for adjusting the power grid planning and modifying the architecture.
In the above method, the preprocessing the data to obtain the multiple information integrated data sets of the internal and external data specifically includes the steps of:
performing single-class data set analysis on the internal data of the power grid, and analyzing the data development state by adopting a moving average method, a regression method or a decision tree method; analyzing data change characteristics by using an edit distance algorithm or a model method, and forming an internal data characteristic set according to the data development state and the change characteristics;
according to the association rule of the power grid index mapping data table, a mapping relation is established between internal data and external data, normalization processing is carried out on the external data of the power grid, feature analysis is carried out on the normalized data set by combining a statistical algorithm and a vector machine method, and an external data feature set is constructed;
based on cluster analysis or a neural network method, the correlation characteristics among different data sets are analyzed, the data with strong correlation are collected and stored, the integrated fusion of classification, layering and priority is carried out on the whole data, and the internal and external multivariate information integrated data sets with data characteristics are extracted.
In the method, the distribution difference and the characteristics of the load and the power supply resource under different operation scenes on the spatial dimension are determined by performing classification analysis on the spatial-temporal relevance degrees of all the data sources; the method specifically comprises the following steps of:
analyzing the attributes of the multi-information integrated data set, analyzing the importance degree and distribution condition of the broken and missing data, and filling and correcting the missing data by adopting a uniform distribution method, an interpolation method and an interpolation method in sequence;
smoothing the noise data by adopting a regression method or a clustering method;
detecting the data consistency by using a function tool to finish the rapid preprocessing work of the multivariate information;
selecting a characteristic subset group from the power grid operation information of a plurality of time scales on the time dimension for analysis, and analyzing causal relationships among different characteristic subsets;
meanwhile, decomposing a power grid into different regions, different power supply access points and different voltage levels in a structural division manner from a spatial dimension, extracting different spatial sets of topographic features, performing deep analysis on the spatial sets by combining a clustering method based on an intelligent algorithm data mining analysis method, and determining distribution differences and characteristics of resources such as loads, power supplies and the like in different operation scenes in the spatial dimension;
and deeply mining the time-space correlation characteristics of the power grid operation information under various external environments.
The method also comprises the steps of acquiring the time-space correlation characteristics and the dynamic change rules of the data according to data mining, establishing the required visual scene category, realizing the low-dimensional analysis display of the large-scale data and combining the time-space characteristic display of the network geographic information; and expressing the data characteristics and the correlation between the data through related visualization graphs and tables.
The invention also provides a power grid development-oriented multivariate information mining and analyzing system, which comprises:
a data input unit: acquiring relevant influence data in a power grid system operation scene;
a data preprocessing unit: the data input unit is used for inputting data to the data input unit;
the data spatio-temporal relevance degree classification analysis unit: the system comprises a data preprocessing unit, a load analysis unit, a power supply resource analysis unit, a load analysis unit, a power supply resource analysis unit and a load analysis unit, wherein the data preprocessing unit is used for performing spatial-temporal correlation degree classification analysis on all data sources according to a multi-element information integrated data set obtained by the data preprocessing unit and determining distribution differences and characteristics of loads and power supply resources; the time-space correlation characteristics of the power grid operation information under various external environments are obtained;
a power grid planning unit: and classifying and analyzing the results by the unit according to the data space-time correlation degree.
In the above scheme, the data preprocessing unit specifically includes the following steps:
performing single-class data set analysis on the internal data of the power grid, and analyzing the data development state by adopting a moving average method, a regression method or a decision tree method; analyzing data change characteristics by using an edit distance algorithm or a model method, and forming an internal data characteristic set according to the data development state and the change characteristics;
according to the association rule of the power grid index mapping data table, a mapping relation is established between internal data and external data, normalization processing is carried out on the external data of the power grid, feature analysis is carried out on the normalized data set by combining a statistical algorithm and a vector machine method, and an external data feature set is constructed;
based on cluster analysis or a neural network method, the correlation characteristics among different data sets are analyzed, the data with strong correlation are collected and stored, the integrated fusion of classification, layering and priority is carried out on the whole data, and the internal and external multivariate information integrated data sets with data characteristics are extracted.
In the above scheme, the analysis process of the data spatiotemporal relevance classification analysis unit specifically includes:
performing single-class data set analysis on the internal data of the power grid, and analyzing the data development state by adopting a moving average method, a regression method or a decision tree method; analyzing data change characteristics by using an edit distance algorithm or a model method, and forming an internal data characteristic set according to the data development state and the change characteristics;
according to the association rule of the power grid index mapping data table, a mapping relation is established between internal data and external data, normalization processing is carried out on the external data of the power grid, feature analysis is carried out on the normalized data set by combining a statistical algorithm and a vector machine method, and an external data feature set is constructed;
based on cluster analysis or a neural network method, the correlation characteristics among different data sets are analyzed, the data with strong correlation are collected and stored, the integrated fusion of classification, layering and priority is carried out on the whole data, and the internal and external multivariate information integrated data sets with data characteristics are extracted.
In the scheme, the system further comprises a data correlation display unit, wherein the data correlation display unit is used for establishing a required visual scene type according to the data mining acquired data space-time correlation characteristic and dynamic change rule, and realizing low-dimensional analysis display of large-scale data and space-time characteristic display combined with network geographic information; and expressing the data characteristics and the correlation between the data through related visualization graphs and tables.
The invention also provides computer equipment comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the power grid development-oriented multivariate information mining analysis method.
The invention also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement any one of the above-mentioned methods for mining and analyzing multivariate information for power grid development.
According to internal and external multivariate information data during the operation of the power grid, data integration, data preprocessing and data analysis are carried out, an effective information set is formed according to power grid development diagnosis indexes, and the data analysis efficiency is improved by adopting parallel computation; the whole process of power grid development is researched, and the time-space correlation characteristics and the dynamic change rule of data are obtained through data mining, so that the scheme for adjusting the power grid planning and modifying the architecture is determined.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow diagram of a method provided by the present invention;
FIG. 2 is a schematic structural diagram of a process of integrating multiple information in the method provided by the present invention;
FIG. 3 is a schematic structural diagram of a temporal-spatial correlation characteristic and a dynamic change rule analysis process in the method provided by the present invention;
FIG. 4 is a schematic diagram of a system framework provided by the present invention;
fig. 5 is a schematic structural diagram of a computer device provided by the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise. Furthermore, the terms "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The invention is described in detail below with reference to specific embodiments and the accompanying drawings.
As shown in fig. 1, the invention provides a multivariate information mining analysis method for power grid development, which comprises the following steps:
and S1, acquiring relevant influence data in the operation scene of the power grid system.
In this embodiment, the inside and outside full coverage data collection is performed around the whole environment of the power grid development: the method is characterized in that investigation is respectively carried out from four aspects of benefit, safety, economy and reliability, data are collected from internal and external information sources of the power grid, and the method mainly comprises internal data such as equipment ledgers, power grid topology, operation states, load installation, power grid policies, environmental friendliness, new energy grid connection, power grid net racks, tidal current changes, various faults and critical states, and related external data such as economy, society and policies.
S2, preprocessing the data to obtain a multi-information integrated data set of internal and external data; as shown in fig. 2, the method specifically includes the following steps:
s21, performing single-class data set analysis on the internal data of the power grid, and analyzing the development state of the data (such as load power change and tidal current change) by adopting a moving average method, a regression method or a decision tree method; and analyzing data change characteristics (extreme points and step points) by using an edit distance algorithm or a model method, and forming an internal data characteristic set according to the data development state and the change characteristics.
S22, according to the association rule of the power grid index mapping data table, a mapping relation is established between internal data and external data, normalization processing is carried out on the external data of the power grid, feature analysis is carried out on the normalized data set by combining a statistical algorithm and a vector machine method, and an external data feature set is constructed.
S23, analyzing the correlation characteristics among different data sets based on cluster analysis or a neural network method, collecting and storing data with strong correlation, classifying, layering and integrating the whole data in a priority mode, and extracting internal and external multi-information integrated data sets with data characteristics (such as weather factors, historical loads and day type reflection load characteristics).
S3, performing classification analysis on the spatial-temporal relevance degrees of all data sources, and determining distribution differences and characteristics of resources such as loads and power supplies under different operation scenes in spatial dimension; and the time-space correlation characteristics of the power grid operation information under various external environments. As shown in fig. 3, the method specifically includes the following steps:
s31, analyzing the attributes of the integrated data set, analyzing the importance degree and distribution condition of the data break-missing part, and filling and correcting the missing data by adopting a uniform distribution method, an interpolation method and an interpolation method in sequence;
s32, smoothing the noise data by adopting a regression method or a clustering method;
s33, detecting data consistency by using a function tool to finish the rapid preprocessing work of the multivariate information;
s34, selecting a characteristic subset group in a time dimension, analyzing the characteristic subset group from the power grid operation information of a plurality of time scales (short-term, medium-term and long-term), and analyzing causal relationships among different characteristic subset groups (such as research on the energy transmission characteristics of the intra-provincial power grid and the flow propagation rule in the network);
s35, meanwhile, decomposing a power grid into different regions, different power supply access points and different voltage levels in a structure division mode from the spatial dimension, extracting different spatial sets such as terrain features (height, gradient, slope direction and the like), performing depth analysis on the spatial sets by combining a clustering method based on an intelligent algorithm data mining analysis method, and determining distribution differences and features of resources such as loads, power supplies and the like in different operation scenes in the spatial dimension;
and S36, deeply mining the time-space correlation characteristics of the power grid operation information under various external environments (such as weather, day type, user, load and power grid efficiency under the constraint of a grid structure).
And S4, determining and adjusting a power grid planning and transformation framework scheme according to the step S3, and providing more instructive opinions for power grid transformation and construction.
Preferably, the required visual scene category is established according to the data mining acquired data space-time correlation characteristics and dynamic change rules, so that low-dimensional analysis display of large-scale data, space-time characteristic display combined with network geographic information and the like are realized; and expressing the data characteristics and the correlation between the data through related visualization graphs and tables. Therefore, a user can more intuitively know the data characteristics, the influence degree among the data and the main area of power grid transformation and construction.
According to the embodiment, data integration, data preprocessing and data analysis are carried out according to internal and external multivariate information data during the operation of a power grid, an effective information set is formed according to power grid development diagnosis indexes, and the data analysis efficiency is improved by adopting parallel computation; researching the whole power grid development process, and acquiring the time-space correlation characteristics and the dynamic change rule of data through data mining so as to determine a scheme for adjusting power grid planning and reconstructing a framework; in addition, the required visual scene category can be established according to the requirement; based on visual software application, a multi-dimensional analysis display scheme of large-scale data is provided, a data sharing and dynamic interaction interface is constructed, and a visual chart making method is provided.
As shown in fig. 4, the present invention further provides a multivariate information mining analysis system for power grid development, which includes a data input unit: acquiring relevant influence data in a power grid system operation scene;
in this embodiment, the inside and outside full coverage data collection is performed around the whole environment of the power grid development: the method is characterized in that investigation is respectively carried out from four aspects of benefit, safety, economy and reliability, data are collected from internal and external information sources of the power grid, and the method mainly comprises internal data such as equipment ledgers, power grid topology, operation states, load installation, power grid policies, environmental friendliness, new energy grid connection, power grid net racks, tidal current changes, various faults and critical states, and related external data such as economy, society and policies.
A data preprocessing unit: the data input unit is used for inputting data to the data input unit; the data preprocessing specifically comprises the following steps:
a21, performing single-class data set analysis on the internal data of the power grid, and analyzing the data development state by adopting a moving average method, a regression method or a decision tree method; and analyzing the data change characteristics by using an edit distance algorithm or a model method, and forming an internal data characteristic set according to the data development state and the change characteristics.
A22, according to the association rule of the power grid index mapping data table, establishing a mapping relation between internal data and external data, carrying out normalization processing on the external data of the power grid, and carrying out feature analysis on the normalized data set by combining a statistical algorithm and a vector machine method to construct an external data feature set.
A23, analyzing the correlation characteristics among different data sets based on cluster analysis or a neural network method, collecting and storing data with strong correlation, performing integrated fusion of classification, layering and priority on the whole data, and extracting an integrated data set with internal and external multivariate information integrated data characteristics.
The data spatio-temporal relevance degree classification analysis unit: the system comprises a data preprocessing unit, a data analysis unit and a data analysis unit, wherein the data preprocessing unit is used for integrating a data set according to multi-information obtained by the data preprocessing unit, carrying out classification analysis on the spatial and temporal relevance of all data sources and determining the distribution difference and characteristics of resources such as loads, power supplies and the like under different operation scenes on a spatial dimension; and the time-space correlation characteristics of the power grid operation information under various external environments.
The analysis process of the data space-time relevance classification analysis unit specifically comprises the following steps:
s31, analyzing the attributes of the integrated data set, analyzing the importance degree and distribution condition of the data break-missing part, and filling and correcting the missing data by adopting a uniform distribution method, an interpolation method and an interpolation method in sequence;
s32, smoothing the noise data by adopting a regression method or a clustering method;
s33, detecting data consistency by using a function tool to finish the rapid preprocessing work of the multivariate information;
s34, selecting a characteristic subset group to analyze the power grid operation information of multiple time scales in a time dimension, and analyzing causal relationships among different characteristic subsets;
s35, meanwhile, decomposing a power grid into different regions, different power supply access points and different voltage levels in a structure division mode from the spatial dimension, extracting different spatial sets such as terrain features, performing depth analysis on the spatial sets by combining a clustering method based on an intelligent algorithm data mining analysis method, and determining distribution differences and features of resources such as loads and power supplies under different operation scenes in the spatial dimension;
and S36, deeply mining the time-space correlation characteristics of the power grid operation information under various external environments.
A power grid planning unit: and determining a scheme for adjusting the power grid planning and reconstructing the architecture according to the analysis result of the data space-time correlation classification analysis unit.
Preferably, the system further comprises a data correlation display unit, which is used for establishing a required visual scene type according to the data mining acquired data space-time correlation characteristics and dynamic change rules, and realizing low-dimensional analysis display of large-scale data, space-time characteristic display combined with network geographic information and the like; and expressing the data characteristics and the correlation between the data through related visualization graphs and tables.
As shown in fig. 5, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the multivariate information mining analysis method for power grid development in the above embodiment, or the computer program, when executed by the processor, implements the multivariate information mining analysis method for power grid development in the above embodiment.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A multivariate information mining analysis method for power grid development is characterized by comprising the following steps:
acquiring relevant influence data in a power grid system operation scene; preprocessing data to obtain a multi-element information integrated data set of internal and external data; performing classification analysis on the spatial-temporal relevance degrees of all the data sources, and determining distribution differences and characteristics of loads and power resources under different operation scenes on spatial dimensions; the time-space correlation characteristics of the power grid operation information under various external environments are obtained; and determining a scheme for adjusting the power grid planning and modifying the architecture.
2. The multivariate information mining analysis method for power grid development as defined in claim 1, wherein the preprocessing of the data to obtain multivariate information integration data sets of internal and external data specifically comprises the steps of:
performing single-class data set analysis on the internal data of the power grid, and analyzing the data development state by adopting a moving average method, a regression method or a decision tree method; analyzing data change characteristics by using an edit distance algorithm or a model method, and forming an internal data characteristic set according to the data development state and the change characteristics;
according to the association rule of the power grid index mapping data table, a mapping relation is established between internal data and external data, normalization processing is carried out on the external data of the power grid, feature analysis is carried out on the normalized data set by combining a statistical algorithm and a vector machine method, and an external data feature set is constructed;
based on cluster analysis or a neural network method, the correlation characteristics among different data sets are analyzed, the data with strong correlation are collected and stored, the integrated fusion of classification, layering and priority is carried out on the whole data, and the internal and external multivariate information integrated data sets with data characteristics are extracted.
3. The multivariate information mining analysis method for power grid development as claimed in claim 2, wherein the classification analysis of the spatial-temporal relevance degrees is performed on all data sources to determine the distribution differences and characteristics of loads and power resources under different operation scenes in a spatial dimension; the method specifically comprises the following steps of:
analyzing the attributes of the multi-information integrated data set, analyzing the importance degree and distribution condition of the broken and missing data, and filling and correcting the missing data by adopting a uniform distribution method, an interpolation method and an interpolation method in sequence;
smoothing the noise data by adopting a regression method or a clustering method;
detecting the data consistency by using a function tool to finish the rapid preprocessing work of the multivariate information;
selecting a characteristic subset group from the power grid operation information of a plurality of time scales on the time dimension for analysis, and analyzing causal relationships among different characteristic subsets;
meanwhile, decomposing a power grid into different regions, different power supply access points and different voltage levels in a structural division manner from a spatial dimension, extracting different spatial sets of topographic features, performing deep analysis on the spatial sets by combining a clustering method based on an intelligent algorithm data mining analysis method, and determining distribution differences and characteristics of loads and power supply resources in different operating scenes in the spatial dimension;
and deeply mining the time-space correlation characteristics of the power grid operation information under various external environments.
4. The grid-development-oriented multivariate information mining analysis method as recited in claim 3, further comprising:
acquiring data space-time correlation characteristics and dynamic change rules according to data mining, establishing a required visual scene category, and realizing low-dimensional analysis display of large-scale data and space-time characteristic display combined with network geographic information; and expressing the data characteristics and the correlation between the data through related visualization graphs and tables.
5. A multivariate information mining analysis system for power grid development is characterized by comprising:
a data input unit: acquiring relevant influence data in a power grid system operation scene;
a data preprocessing unit: the data input unit is used for inputting data to the data input unit;
the data spatio-temporal relevance degree classification analysis unit: the system comprises a data preprocessing unit, a load analysis unit, a power supply resource analysis unit, a load analysis unit, a power supply resource analysis unit and a load analysis unit, wherein the data preprocessing unit is used for performing spatial-temporal correlation degree classification analysis on all data sources according to a multi-element information integrated data set obtained by the data preprocessing unit and determining distribution differences and characteristics of loads and power supply resources; the time-space correlation characteristics of the power grid operation information under various external environments are obtained;
a power grid planning unit: and classifying and analyzing the results by the unit according to the data space-time correlation degree.
6. The grid-development-oriented multivariate information mining analysis system as claimed in claim 5, wherein the data preprocessing unit is used for preprocessing data and specifically comprises the following steps:
performing single-class data set analysis on the internal data of the power grid, and analyzing the data development state by adopting a moving average method, a regression method or a decision tree method; analyzing data change characteristics by using an edit distance algorithm or a model method, and forming an internal data characteristic set according to the data development state and the change characteristics;
according to the association rule of the power grid index mapping data table, a mapping relation is established between internal data and external data, normalization processing is carried out on the external data of the power grid, feature analysis is carried out on the normalized data set by combining a statistical algorithm and a vector machine method, and an external data feature set is constructed;
based on cluster analysis or a neural network method, the correlation characteristics among different data sets are analyzed, the data with strong correlation are collected and stored, the integrated fusion of classification, layering and priority is carried out on the whole data, and the internal and external multivariate information integrated data sets with data characteristics are extracted.
7. The power grid development-oriented multivariate information mining analysis system as claimed in claim 5, wherein the analysis process of the data space-time correlation degree classification analysis unit specifically comprises:
performing single-class data set analysis on the internal data of the power grid, and analyzing the data development state by adopting a moving average method, a regression method or a decision tree method; analyzing data change characteristics by using an edit distance algorithm or a model method, and forming an internal data characteristic set according to the data development state and the change characteristics;
according to the association rule of the power grid index mapping data table, a mapping relation is established between internal data and external data, normalization processing is carried out on the external data of the power grid, feature analysis is carried out on the normalized data set by combining a statistical algorithm and a vector machine method, and an external data feature set is constructed;
based on cluster analysis or a neural network method, the correlation characteristics among different data sets are analyzed, the data with strong correlation are collected and stored, the integrated fusion of classification, layering and priority is carried out on the whole data, and the internal and external multivariate information integrated data sets with data characteristics are extracted.
8. The grid-evolving multivariate information mining analysis system of claim 5, further comprising
The data correlation display unit is used for acquiring data space-time correlation characteristics and dynamic change rules according to data mining, establishing required visual scene categories, and realizing low-dimensional analysis display of large-scale data and space-time characteristic display combined with network geographic information; and expressing the data characteristics and the correlation between the data through related visualization graphs and tables.
9. Computer arrangement comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor, when executing said computer program, implements a grid-oriented multivariate information mining analysis method according to any of claims 1 to 4.
10. Computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements a grid-oriented multivariate information mining analysis method according to any one of claims 1 to 4.
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