CN114282598A - Multi-source heterogeneous power grid data fusion method, device, equipment and computer medium - Google Patents
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Abstract
The invention discloses a multi-source heterogeneous power grid data fusion method, a device, equipment and a computer medium; wherein the method comprises the following steps: selecting target data of current iteration from multi-source heterogeneous power grid data, and performing iteration processing on the target data; during the iterative process: calculating the similarity between the multi-source heterogeneous power grid data and the target data, and determining fusion data of current iteration based on a similarity result; clustering the fusion data to obtain a clustering result of the current iteration; and verifying the clustering result of the current iteration and the clustering result of the previous iteration, and outputting the fusion data of the current iteration if the verification result meets the preset condition. The fusion data obtained by the method is closer to the natural state of the data distribution of the multi-source heterogeneous power grid, and further power grid data analysis can be performed by using the fusion data, so that more valuable information is provided for the efficient, safe and stable operation of the power grid.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a multi-source heterogeneous power grid data fusion method, device, equipment and computer media.
Background
With the rapid development of the smart grid and the information technology, the development and the application of large electric power data are greatly promoted by massive information of multiple data types in the electric power system. The electric power system is a complex high-dimensional system, the internal data flow direction of the electric power system has different data flows such as electric power flow, business flow, fault flow, information flow and meteorological flow, and the multi-source heterogeneous problem of data information makes it difficult for people to fully mine the hidden information of big data, and the high-efficiency application of the electric power big data is greatly hindered. Therefore, the multi-source heterogeneous data in the power system are comprehensively processed to obtain high-value information meeting application requirements, and the method has important significance for efficient operation of modern power grids.
Disclosure of Invention
In order to solve the technical problems, the invention provides a multi-source heterogeneous power grid data fusion method, device, equipment and computer medium, so as to provide accurate multi-source heterogeneous data fusion information for a power grid.
In a first aspect, the invention provides a multi-source heterogeneous power grid data fusion method, which includes:
selecting target data of current iteration from multi-source heterogeneous power grid data;
performing iterative processing on the target data; during the iterative process: calculating the similarity between the multi-source heterogeneous power grid data and the target data, and determining fusion data of current iteration based on a similarity result; clustering the fusion data to obtain a clustering result of the current iteration;
and verifying the clustering result of the current iteration and the clustering result of the previous iteration, and outputting the fusion data of the current iteration if the verification result meets the preset condition.
Optionally, the method further includes: when the current round is an initial round, target data of initial round iteration are selected from the multi-source heterogeneous power grid data, iteration processing is carried out on the target data of the initial round iteration based on the iteration processing process, and a clustering result of the initial round iteration is obtained;
determining target data of next iteration of the initial round in the multi-source heterogeneous power grid data according to a clustering result of the initial round iteration, and obtaining fusion data and a clustering result of the next iteration of the initial round based on the iteration processing process;
and verifying the clustering result of the next iteration of the initial round and the clustering result of the iteration of the initial round, and outputting the fusion data of the next iteration of the initial round if the verification result meets the preset condition.
Optionally, the method further includes: if the check result does not meet the preset condition, determining target data of the next iteration in the multi-source heterogeneous power grid data according to the clustering result of the current iteration;
performing iterative processing on target data of the next iteration based on the iterative processing process to obtain fusion data and clustering results of the next iteration;
and verifying the clustering result of the next iteration and the clustering result of the current iteration, and outputting the fusion data of the next iteration if the verification result meets the preset condition.
Optionally, the method further includes: acquiring outlier data in the fusion data based on the clustering result; and determining abnormal information in the multi-source heterogeneous power grid data according to the outlier data.
Optionally, in the iterative processing process, clustering is performed on the fusion data to obtain a clustering result, which specifically includes:
clustering the fusion data by using various clustering algorithms to obtain an initial clustering result set;
performing secondary clustering on the initial clustering result set to obtain a secondary clustering result of the current iteration; and the secondary clustering result comprises a plurality of cluster centers and index information of the current iteration determined based on the cluster centers.
Optionally, the index information includes:
and in the clustering result, the distance information between each sample data and the center of the corresponding cluster.
Optionally, the multi-source heterogeneous power grid data includes: grid operating data and grid equipment status data.
In a second aspect, the present invention provides a multi-source heterogeneous power grid data fusion apparatus, including:
the data acquisition module is used for selecting target data of current iteration from multi-source heterogeneous power grid data;
the iteration processing module is used for carrying out iteration processing on the target data; during the iterative process: calculating the similarity between the multi-source heterogeneous power grid data and the target data, and determining fusion data of current iteration based on a similarity result; clustering the fusion data to obtain a clustering result of the current iteration;
and the result checking module is used for checking the clustering result of the current iteration and the clustering result of the previous iteration, and outputting the fusion data of the current iteration if the checking result meets the preset condition.
In a third aspect, the present invention provides a data processing apparatus, including a processor, where the processor is coupled with a memory, where the memory stores a program, and the program is executed by the processor, so that the data processing apparatus executes the multi-source heterogeneous power grid data fusion method according to the first aspect.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the multi-source heterogeneous power grid data fusion method according to the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
according to the multi-source heterogeneous power grid data fusion method, the similarity between multi-source heterogeneous power grid data and the selected target power grid data is calculated, heterogeneous power grid data are fused into low-dimensional isomorphic power grid fusion data, the distribution state of the fusion data is obtained in a clustering integration mode, new target power grid data are selected according to the distribution state, the fusion process of the multi-source heterogeneous power grid data is iterated, and finally output fusion data are close to the natural state of the distribution of the multi-source heterogeneous power grid data; and further power grid data analysis is carried out on the output fusion data, so that more valuable information can be provided for the efficient, safe and stable operation of the power grid.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a multi-source heterogeneous power grid data fusion method provided by an embodiment of the invention;
fig. 2 is a schematic structural diagram of a multi-source heterogeneous power grid data fusion device provided in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
As shown in fig. 1, in a first aspect, an embodiment of the present invention provides a multi-source heterogeneous power grid data fusion method, which specifically includes the following steps.
S1: and selecting target data of the current iteration from the multi-source heterogeneous power grid data.
It can be understood that the multi-source heterogeneous power grid data come from different service systems in the power grid, and the data volume is often numerous and varied; in this embodiment, the multi-source heterogeneous power grid data specifically includes power grid operation data and power grid equipment state data.
When the multi-source heterogeneous power grid data are subjected to data fusion, a plurality of representative data are selected from the multi-source heterogeneous power grid data and used as target data of current iteration.
It should be noted that the target data may be randomly selected from the multi-source heterogeneous power grid data or selected correspondingly based on a limiting condition.
S2: performing iterative processing on the target data, wherein in the iterative processing process: calculating the similarity between the multi-source heterogeneous power grid data and the target data, and determining fusion data of current iteration based on a similarity result; and clustering the fusion data to obtain a clustering result of the current iteration.
In this embodiment, the multi-source heterogeneous power grid data is fused into low-dimensional homogeneous power grid fusion data by calculating the similarity between the multi-source heterogeneous power grid data and the target data.
Specifically, in the iterative processing process, the specific process of clustering the fusion data to obtain a clustering result is as follows: clustering the fusion data by using various clustering algorithms to obtain an initial clustering result set; and performing secondary clustering on the initial clustering result set to obtain a secondary clustering result of the current iteration.
And the secondary clustering result comprises a plurality of cluster centers and index information of the current iteration determined based on the cluster centers.
In this embodiment, the index information refers to information of a distance between each sample data and a center of a corresponding cluster in the clustering result.
It should be noted that the present invention does not limit the category of the clustering algorithm used in the above iteration process.
In the data fusion process, the iteration processing is carried out on the target data selected in each round to obtain fusion data and a clustering result corresponding to each round; and the clustering result is used for verifying the accuracy of the fused data and is also used as a limiting condition for selecting target data of the next iteration.
S3: and verifying the clustering result of the current iteration and the clustering result of the previous iteration, and outputting the fusion data of the current iteration if the verification result meets the preset condition.
The clustering result is verified based on the preset condition, so that the distribution state of the finally output power grid isomorphic fusion data is closer to the distribution state of the original multi-source heterogeneous power grid data, and the accuracy of the obtained fusion data is higher.
In this embodiment, if the check result does not satisfy the preset condition, determining target data of a next iteration in the multi-source heterogeneous power grid data according to a clustering result of a current iteration, and performing iteration processing on the target data of the next iteration based on the iteration processing process, so as to obtain fusion data and a clustering result of the next iteration.
Further, verifying the clustering result of the next iteration and the clustering result of the current iteration, and outputting the fusion data of the next iteration if the verification result meets a preset condition; if not, the process is repeated.
It should be noted that, in this embodiment, when the current round is an initial round, target data of initial round iteration is selected from the multi-source heterogeneous power grid data; performing iterative processing on the target data of the initial round of iteration based on the iterative processing process to obtain a clustering result of the initial round of iteration; and determining target data of next iteration of the initial round in the multi-source heterogeneous power grid data according to the clustering result of the initial round of iteration, and obtaining fusion data and clustering result of next iteration of the initial round based on the iteration processing process.
And at the moment, verifying the clustering result of the next iteration of the initial round and the clustering result of the initial round, and outputting the fusion data of the next iteration of the initial round if the verification result meets the preset condition.
After the fused data corresponding to the multi-source heterogeneous power grid data are obtained through the method, the outlier data in the fused data can be obtained based on the clustering result of the fused data, the abnormal information in the multi-source heterogeneous power grid data is further obtained according to the outlier data, and the equipment or the component with the potential fault in the power grid can be determined through the abnormal information.
The fusion process of the multi-source heterogeneous power grid data will be specifically described below by an embodiment.
In this embodiment, the smart grid is constructed by combining the information system and the grid physical system, and therefore, the multi-source heterogeneous data set in the smart grid includes grid system data and information system data.
1) Selecting K from multi-source heterogeneous data set0Calculating each multi-source heterogeneous data and K according to the target data0Similarity between target data, at which time K is calculated0And the similarity results form new isomorphic power grid fusion data.
In this embodiment, K0Is 10.
In this embodiment, a multi-source heterogeneous dataset may be represented as
Wherein,
represents the data of the grid system and the grid system,
representing information system data; specifically, xi＝{xij|j＝1,2,...,N}TAnd yi＝{yij|j＝1,2,...,N}TDenotes xiAnd yiCorresponding data columnVector, n1+n2＝n。
In this embodiment, the number N of the multi-source heterogeneous data is 300, and the dimension N of the data is 30.
Selected K0The target data is expressed as
Correspondingly, a multi-source heterogeneous data set can be represented as
For the distance between the data of each power grid system in the multi-source heterogeneous data set, the Euclidean distance can be used for calculation:
the distance between the various information system data is then calculated using cosine similarity:
wherein:
therefore, in the multi-source heterogeneous data set X, the distance between each heterogeneous data and the target data can be calculated by the following formula:
for any heterogeneous data d in the multi-source heterogeneous data set XiThe distance between the heterogeneous data and the target data can be calculated based on the distance calculation formula, and then the heterogeneous data d is calculatediAnd target data rkSimilarity between them:
sim(di,rk)＝exp(-qd(di,rk)),q>0
in the present embodiment, q is 2.
Further, the obtained similarity result is expressed as zikI.e. zik＝exp(-qd(di,rk))。
Further, there are:
zi＝{zik|k＝1,2,...,K0}；Z＝{zi|i＝1,2,...,N}
at this time, Z is isomorphic fusion data after multi-source heterogeneous data conversion, wherein Z isiAnd diAnd correspond to each other.
2) And (3) selecting various clustering algorithms to cluster the fused data Z obtained in the step (1), calculating a relation matrix between corresponding fused data Z based on each clustering algorithm, and classifying and summarizing the calculated relation matrixes to construct a plurality of co-joined matrixes.
Specifically, in this embodiment, typical 8 types of clustering algorithms are selected, and each type of algorithm includes 2 algorithms, so that there are 16 types of clustering algorithms, which are represented as
Further, 16 clustering algorithms are classified and numbered as shown in table 1.
TABLE 1 clustering algorithm and Categories
The fused data Z were clustered using the 16 clustering algorithms described above, and the obtained clustering result is expressed as Θ ═ P1,P2,...,P16In which P iskAnd representing the clustering result of the kth clustering algorithm.
Further, a relation matrix Q corresponding to each clustering algorithm is constructed according to the obtained clustering resultskSpecifically, the following is shown:
wherein,
k＝1,2,...16。
further, the mean value of the relationship matrixes corresponding to the clustering algorithms belonging to the same type is calculated through the following formula:
i＝1,2,...,8。
a co-joined matrix can be obtained based on the mean result of the relationship matrix
Is particularly shown as
3) For the co-connection matrix obtained in the step 2
And integrating to obtain a final co-joined matrix among the fused data Z, and performing secondary clustering on the fused data based on the final co-joined matrix.
In particular, the co-joined matrix
The integration process of (a) is as follows:
first, a co-connection matrix is calculated
Two matrices arbitrarily different in
And
similarity between them:
further, let
And calculates each co-connection matrix U according to the calculatedkWeight ω of (d)kThe method specifically comprises the following steps:
using the resulting weight ωkFinal co-connection matrix capable of calculating fusion data Z
Which is represented as
Then according to the final co-connection matrix
Selecting the algorithm M of the 16 clustering algorithms in the table 111(Spectral using a sparse similarity matrix) carries out secondary clustering on the fusion data Z to obtain a clustering result.
4) Selecting K based on the clustering result obtained in the step 31Taking the fusion data as a cluster center, and taking K corresponding to the cluster center1Number of multiple source isomerismAccording to a new target data and based on K1And (4) repeating the steps 1 to 3 for each new target data until the clustering result meets the preset condition.
The specific steps of step 4 are as follows:
first, K in the clustering result of the fusion data Z is calculated1A cluster of classes is represented as
And calculates each class cluster CiThe mean value of (a); in cluster CiSelecting the fusion data with the minimum Euclidean distance from the mean value as a cluster center of the cluster, and recording the cluster center as the cluster center
And calculating index information WSS:
further, the cluster center c is divided into a plurality of clustersiCorresponding multi-source heterogeneous data is represented as
By using
As new target data to replace the target data R of the previous round, and based on the target data
Repeating the steps 1 to 3 to obtain new fusion data
And novel K2Individual cluster
Selecting the cluster
Central point of (2)
And calculating index information of the round
For the index information WSS and the index information
Checking, if the preset condition is met:
stopping the iteration and outputting the fused data
If not, repeating the steps 1 to 4 until the final fusion data is output.
In another embodiment, the preset condition may be further set to K1＝K2Namely: the clustering result of the current iteration is the same as that of the previous iteration.
It should be noted that, in the step 4, after the secondary clustering of the fused data Z is completed, if there is fused data, the fused data Z is determined to be not fused data
If the data does not belong to any cluster, the fused data
Is discrete data.
It will be appreciated that discrete data
Corresponding multi-source heterogeneous data
The contained corresponding information is greatly different from the information contained in other heterogeneous data, so the multi-source heterogeneous data
The corresponding grid device or component is a potentially faulty device or component.
According to the multi-source heterogeneous power grid data fusion method provided by the embodiment of the invention, heterogeneous power grid data are fused into low-dimensional isomorphic fusion data by calculating the similarity between the multi-source heterogeneous power grid data and the selected target data; and then obtaining the distribution state of the fused data in a clustering integration mode, selecting new target data according to the distribution state, and further iterating the data fusion process. The fusion data result obtained finally after iteration is more accurate and is closer to the natural state of power grid data distribution; and meanwhile, the data distribution state of the original multi-source heterogeneous power grid can be obtained.
The obtained fusion data is used for further power grid data analysis, and more valuable information can be provided for efficient, safe and stable operation of a power grid.
Referring to fig. 2, in a second aspect, an embodiment of the present invention further provides a multi-source heterogeneous power grid data fusion apparatus, including a data obtaining module 101, an iteration processing module 102, and a result checking module 103.
The data obtaining module 101 is configured to select target data of a current iteration from multi-source heterogeneous power grid data.
The iterative processing module 102 is configured to perform iterative processing on the target data; during the iterative process: calculating the similarity between the multi-source heterogeneous power grid data and the target data, and determining fusion data of current iteration based on a similarity result; and clustering the fusion data to obtain a clustering result of the current iteration.
The result checking module 103 is configured to check the clustering result of the current iteration with the clustering result of the previous iteration, and output fusion data of the current iteration if the checking result meets a preset condition.
Since the content of information interaction, execution process and the like among the modules in the device is based on the same concept as the embodiment of the multi-source heterogeneous power grid data fusion method provided by the first aspect of the present invention, specific content can be referred to the description in the embodiment of the method of the present invention, and details are not repeated here.
The above-described embodiments of the apparatus are merely illustrative, and the modules described as separate components may or may not be physically separate, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the units can be selected according to actual needs to achieve the purpose of the method of the embodiment.
In a third aspect, the present invention provides a data processing apparatus, including a processor, where the processor is coupled with a memory, where the memory stores a program, and the program is executed by the processor, so that the data processing apparatus executes the multi-source heterogeneous power grid data fusion method according to the first aspect.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the multi-source heterogeneous power grid data fusion method according to the first aspect.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium, and may include the processes of the embodiments of the methods when executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A multi-source heterogeneous power grid data fusion method is characterized by comprising the following steps:
selecting target data of current iteration from multi-source heterogeneous power grid data;
performing iterative processing on the target data; during the iterative process: calculating the similarity between the multi-source heterogeneous power grid data and the target data, and determining fusion data of current iteration based on a similarity result; clustering the fusion data to obtain a clustering result of the current iteration;
and verifying the clustering result of the current iteration and the clustering result of the previous iteration, and outputting the fusion data of the current iteration if the verification result meets the preset condition.
2. The multi-source heterogeneous power grid data fusion method according to claim 1, further comprising:
when the current round is an initial round, target data of initial round iteration are selected from the multi-source heterogeneous power grid data, iteration processing is carried out on the target data of the initial round iteration based on the iteration processing process, and a clustering result of the initial round iteration is obtained;
determining target data of next iteration of the initial round in the multi-source heterogeneous power grid data according to a clustering result of the initial round iteration, and obtaining fusion data and a clustering result of the next iteration of the initial round based on the iteration processing process;
and verifying the clustering result of the next iteration of the initial round and the clustering result of the iteration of the initial round, and outputting the fusion data of the next iteration of the initial round if the verification result meets the preset condition.
3. The multi-source heterogeneous power grid data fusion method according to claim 1, further comprising:
if the check result does not meet the preset condition, determining target data of the next iteration in the multi-source heterogeneous power grid data according to the clustering result of the current iteration;
performing iterative processing on target data of the next iteration based on the iterative processing process to obtain fusion data and clustering results of the next iteration;
and verifying the clustering result of the next iteration and the clustering result of the current iteration, and outputting the fusion data of the next iteration if the verification result meets the preset condition.
4. The multi-source heterogeneous power grid data fusion method according to claim 1, further comprising:
acquiring outlier data in the fusion data based on the clustering result;
and determining abnormal information in the multi-source heterogeneous power grid data according to the outlier data.
5. The multi-source heterogeneous power grid data fusion method according to claim 1, wherein in the iterative processing process, the fusion data is clustered to obtain a clustering result, and specifically, the clustering result is:
clustering the fusion data by using various clustering algorithms to obtain an initial clustering result set;
performing secondary clustering on the initial clustering result set to obtain a secondary clustering result of the current iteration; and the secondary clustering result comprises a plurality of cluster centers and index information of the current iteration determined based on the cluster centers.
6. The multi-source heterogeneous power grid data fusion method according to claim 5, wherein the index information comprises:
and in the clustering result, the distance information between each sample data and the center of the corresponding cluster.
7. The multi-source heterogeneous power grid data fusion method according to claim 1, wherein the multi-source heterogeneous power grid data comprises: grid operating data and grid equipment status data.
8. A multi-source heterogeneous power grid data fusion device is characterized by comprising:
the data acquisition module is used for selecting target data of current iteration from multi-source heterogeneous power grid data;
the iteration processing module is used for carrying out iteration processing on the target data; during the iterative process: calculating the similarity between the multi-source heterogeneous power grid data and the target data, and determining fusion data of current iteration based on a similarity result; clustering the fusion data to obtain a clustering result of the current iteration;
and the result checking module is used for checking the clustering result of the current iteration and the clustering result of the previous iteration, and outputting the fusion data of the current iteration if the checking result meets the preset condition.
9. A data processing apparatus, characterized by comprising:
a processor coupled to a memory, the memory storing a program for execution by the processor to cause the data processing apparatus to perform the multi-source heterogeneous power grid data fusion method of any of claims 1-7.
10. A computer storage medium, wherein the computer storage medium stores computer instructions for executing the multi-source heterogeneous power grid data fusion method according to any one of claims 1 to 7.
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