CN110597792A - Multistage redundant data fusion method and device based on synchronous line loss data fusion - Google Patents
Multistage redundant data fusion method and device based on synchronous line loss data fusion Download PDFInfo
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Abstract
The invention discloses a method and a device for fusing multistage redundant data based on synchronous line loss data fusion, wherein the method comprises the following steps: cleaning the power grid data by using a button tool; carrying out abnormal data identification, system clustering analysis and positive-negative correlation analysis on the cleaned power grid data to obtain an abnormal data discrimination result; and performing multi-level redundant data check correction according to the abnormal data discrimination result to finish the correction of the power grid equipment parameters, the calculation model, the topological data and the electric quantity data. According to the fusion method provided by the embodiment of the invention, multi-source data can be treated by utilizing multi-level redundant data fusion, the data quality is improved, and the use requirement is effectively met.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to a method and a device for fusing multistage redundant data based on synchronous line loss data fusion.
Background
Compared with the traditional line loss statistics, the synchronous line loss system integrates six service systems and three platforms, and not only needs transmission, correlation and fusion of data among different systems, but also needs to ensure the accuracy of the data, so that the identification and management of abnormal data are very important.
Big data processing technology: the extraction and conversion of big data are realized by using a button tool, the button tool is an open-source ETL tool and can run on Window and Linux, and the data extraction is efficient and stable. This ETL toolset allows management of data from different databases, describing what is desired by providing a graphical user environment.
The multilevel redundant data cleaning technology comprises the following steps: the multi-stage redundant data cleaning technology comprises the following steps: a method for cleaning the poor redundant data by utilizing the fusion redundant data of the sliding window and the set theory and combining the idea of reference labels with the mild characteristics of signals is utilized. The data quality of the power system is improved in the multi-level redundant data cleaning of the power system applied by the mass data cleaning method.
Data redundancy processing techniques: the redundant data processing is mainly divided into three stages: the method comprises a data collection stage, a data identification and comparison stage and a data integration stage. The data collection stage mainly refers to that the system obtains corresponding data from the outside, and stores and classifies the collected data so as to facilitate data identification; in the data identification and comparison stage, the system judges the repeatability according to the characteristics of the stored data and adopts a corresponding algorithm to determine whether the collected data is unique or repeated; the data integration stage refers to the processing of the system on the repeated data after data identification.
Clustering analysis: the method is an exploratory analysis, people do not need to give a classification standard in the classification process, and the clustering analysis can automatically classify the samples according to the sample data. Different conclusions are often reached from the different methods used for cluster analysis. Different researchers do not necessarily obtain the same cluster number when performing cluster analysis on the same group of data.
And (3) correlation analysis: in regression and correlation analysis, the dependent variable value decreases (increases) with an increase (decrease) in the independent variable value, in which case the correlation coefficient of the dependent variable and the independent variable is negative, i.e., negative correlation. A positive correlation means that the independent variable grows, and the dependent variable also grows. The two variables have the same change direction, and when one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large.
However, the existing synchronous line loss system integrates various source end power service systems and platforms, and the accuracy of line loss calculation is seriously affected by the quality of data, so that improvement is urgently needed.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, one purpose of the invention is to provide a multistage redundant data fusion method based on synchronous line loss data fusion, and the fusion method can utilize multistage redundant data fusion to treat multi-source data, improve data quality and effectively meet use requirements.
The invention also aims to provide a multistage redundant data fusion device based on the synchronous line loss data fusion.
In order to achieve the above object, an embodiment of the present invention provides a method for fusing multilevel redundant data based on synchronous line loss data fusion, including the following steps: cleaning the power grid data by using a button tool; carrying out abnormal data identification, system clustering analysis and positive-negative correlation analysis on the cleaned power grid data to obtain an abnormal data discrimination result; and performing multi-level redundant data check correction according to the abnormal data discrimination result to finish the correction of the power grid equipment parameters, the calculation model, the topological data and the electric quantity data.
The multistage redundant data fusion method based on the synchronous line loss data fusion disclosed by the embodiment of the invention is based on the multistage redundant data fusion, and is used for cleaning, analyzing and applying the multistage fused redundant data, so that the abnormal data of the power grid can be quickly and automatically identified and corrected, the data quality is improved, the calculation accuracy is improved, the offline verification workload of business personnel is reduced, and the use requirement is effectively met.
In addition, the multistage redundant data fusion method based on the synchronous line loss data fusion according to the above embodiment of the present invention may further have the following additional technical features:
further, in an embodiment of the present invention, the performing abnormal data identification, system clustering analysis and positive-negative correlation analysis on the washed multilevel fusion redundancy number includes: analyzing the power grid data by using a big data processing method to screen out first abnormal data; analyzing the power grid data by using a system clustering analysis method to screen out second abnormal data; and analyzing the power grid data by using a positive and negative correlation analysis method to screen out third abnormal data.
Further, in an embodiment of the present invention, the method further includes: and carrying out transmission check on the data by using a cyclic redundancy check method.
Further, in an embodiment of the present invention, the cleaning the multi-level fused redundant data by using a button tool further includes: performing fusion redundant data cleaning by using a sliding window and a set theory; and/or redundant data washing is performed according to the reference label idea and the signal light characteristic.
Further, in an embodiment of the present invention, the performing multi-stage redundant data check and correction according to the abnormal data screening result includes: and (4) verifying and correcting parameters of equipment at all levels of a main network, a distribution network and a distribution area, topological data of the power grid, a calculation model and collected data by using the multi-level redundant data.
In order to achieve the above object, an embodiment of another aspect of the present invention provides a multi-level redundant data fusion apparatus based on synchronous line loss data fusion, including: the cleaning module is used for cleaning the power grid data by using a button tool; the screening module is used for carrying out abnormal data identification, system clustering analysis and positive and negative correlation analysis on the cleaned power grid data to obtain an abnormal data screening result; and the correction module is used for carrying out multi-level redundancy data check correction according to the abnormal data discrimination result so as to finish the correction of the power grid equipment parameters, the calculation model, the topological data and the electric quantity data.
The multistage redundant data fusion device based on the synchronous line loss data fusion disclosed by the embodiment of the invention is based on the multistage redundant data fusion, and is used for cleaning, analyzing and applying the multistage fused redundant data, so that the abnormal data of the power grid can be quickly and automatically identified and corrected, the data quality is improved, the calculation accuracy is improved, the offline verification workload of business personnel is reduced, and the use requirement is effectively met.
In addition, the multistage redundant data fusion device based on the synchronous line loss data fusion according to the above embodiment of the present invention may further have the following additional technical features:
further, in an embodiment of the present invention, the selection module includes: the first screening unit is used for analyzing the power grid data by using a big data processing method so as to screen out first abnormal data; the second screening unit is used for analyzing the power grid data by using a system clustering analysis method so as to screen out second abnormal data; and the third screening unit is used for analyzing the power grid data by using a positive and negative correlation analysis method so as to screen out third abnormal data.
Further, in an embodiment of the present invention, the method further includes: and the checking module is used for carrying out transmission checking on the data by utilizing a cyclic redundancy checking method.
Further, in an embodiment of the present invention, the washing module is further configured to perform a fused redundant data washing using a sliding window and a set theory, and/or perform a redundant data washing according to a reference tag concept in combination with a signal light feature.
Further, in an embodiment of the present invention, the correction module is further configured to perform verification correction on parameters of devices at each level of a main network, a distribution network, and a distribution area, topology data of a power grid, a calculation model, and collected data by using multi-level redundant data.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a multi-stage redundant data fusion method based on contemporaneous line loss data fusion according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for multi-level redundant data fusion based on contemporaneous line loss data fusion according to an embodiment of the present invention;
fig. 3 is a block diagram of a multi-stage redundant data fusion apparatus based on synchronous line loss data fusion according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes a method and an apparatus for fusing multilevel redundant data based on synchronous line loss data fusion according to an embodiment of the present invention with reference to the accompanying drawings, and first, a method for fusing multilevel redundant data based on synchronous line loss data fusion according to an embodiment of the present invention will be described with reference to the accompanying drawings.
Fig. 1 is a flowchart of a multi-stage redundant data fusion method based on contemporaneous line loss data fusion according to an embodiment of the present invention.
As shown in fig. 1, the method for fusing multilevel redundant data based on synchronous line loss data fusion includes the following steps:
in step S101, the grid data is cleaned by using a button tool.
It can be understood that, as shown in fig. 2, for redundant data cleaning, a big data tool is used to perform multi-stage redundant data cleaning of device parameters, topological relations, operation data and the like on multi-source fusion data of a contemporaneous line loss system, that is, a big data tool key is used to collect power grid data including transformer substations, transformers, switches, compensation devices, power transmission lines, power distribution lines, distribution transformers, transformer bays, users, operation data, graphic data and the like.
Further, in an embodiment of the present invention, the cleaning the multi-level fused redundant data by using a button tool further includes: performing fusion redundant data cleaning by using a sliding window and a set theory; and/or redundant data washing is performed according to the reference label idea and the signal light characteristic.
It can be understood that, as shown in fig. 2, for multi-level redundant data cleaning, multi-source fusion redundant data cleaning method and multi-source cross redundant data cleaning method are used to implement multi-level redundant data cleaning of the power grid.
Specifically, the multilevel redundant data cleansing technique: the method is a method for cleaning fused redundant data by utilizing a sliding window and a set theory. Firstly, initializing a fusion tag list by taking an original data unique identifier as a key field, wherein nodes in the fusion tag list are tuple cache queues formed by original data tuples; secondly, trial receiving data streams from a plurality of readers, analyzing the unique identification attribute of the tuple, determining the storage position of the reader tuple cache queue in the fusion tag list according to the unique identification of the tuple, and inserting the new tuple original data set into the tuple cache queue to ensure time ascending; then, according to the size of a time sliding window and a noise threshold value, noise filtering and single-point redundancy elimination are carried out on each tuple cache queue; and a last random algorithm selects the unique identifier of the tuple of any tuple in the group to clean the unique identifier of the tuple of the data at last, and then the data is cleaned. And secondly, a poor redundant data cleaning method combining the idea of reference labels with the light characteristics of signals is used for reference. Firstly, reading an affiliation relationship between a configuration file binding parameter label and a reader-writer, creating an initial cross label list, and trying to update the cross label list from an original data stream which is not short-circuited and pushed by the reader-writer; secondly, detecting whether a reference label is a label to be arbitrated in the cross label list, if not, filtering out overdue cross information tuples in the cross label list to keep reasonable memory occupation amount, and if so, detecting tuple cache queues in the label groups according to a set sliding window; then, calculating the reader-writer signal intensity of the tag which is monitored correspondingly to the tag to be arbitrated in the time sliding window; and finally, calculating the Euclidean distance of the reader-writer reference label signal intensity vector corresponding to the label to be arbitrated. The trusting reader-writer with poor redundant data is arbitrated by utilizing a method of minimizing relative position similarity, and cross redundant data is eliminated through mutual exclusion processing after arbitration.
In step S102, abnormal data identification, system clustering analysis, and positive-negative correlation analysis are performed on the cleaned power grid data, so as to obtain an abnormal data discrimination result.
In an embodiment of the present invention, the performing abnormal data identification, system clustering analysis and positive-negative correlation analysis on the cleaned multilevel fusion redundancy number includes: analyzing the power grid data by using a big data processing method to screen out first abnormal data; analyzing the power grid data by using a system clustering analysis method to screen out second abnormal data; and analyzing the power grid data by using a positive and negative correlation analysis method to screen out third abnormal data.
Specifically, as shown in fig. 2, for abnormal data identification, a synchronous line loss system integrates six service systems and three platforms, and a big data processing technology is used for identifying abnormal data such as missing, jumping and the like; for cluster analysis, analyzing the power grid data by using a system cluster analysis method, and analyzing and discriminating abnormal data; and (4) positive and negative correlation analysis, namely analyzing the power grid data by using a positive and negative correlation analysis method, and analyzing and discriminating abnormal data.
For example, a big data processing technology is utilized to perform data identification and comparison on the collected data, repeated judgment is performed according to the characteristics of the data, a corresponding algorithm is adopted to determine the uniqueness of the collected data, and redundant data is processed.
Further, abnormal data such as data loss, operation data acquisition failure, operation data jumping and the like of equipment such as a transformer substation, a transformer, a switch, compensation equipment, a power transmission line, a power distribution line, a distribution transformer, a transformer area and a user are screened by utilizing a big data processing technology aiming at the data of a synchronous line loss system integrating six service systems and three platforms.
Further, the system clustering analysis is a method for determining that the clusters mainly comprise the parameters of the same type of equipment and the operating data of the same equipment by adopting a traditional statistical clustering analysis method. Firstly, the parameters of the same type of equipment, such as an S9-250/10 type transformer, are influenced by manufacturers, and parameters such as no-load loss, short-circuit voltage percentage, no-load current percentage, resistance, reactance and the like are distributed in a certain range, and if abnormal jumping occurs, the parameters are abnormal data; secondly, the operation data of the same equipment has certain continuity of operation data fluctuation, and the sudden change of the operation data can be accurately positioned through clustering.
And further, correlation analysis is carried out, wherein positive and negative correlation analysis power grid data are used for analysis, and abnormal data are analyzed and discriminated. The correlation is the uncertain dependence between the variables, and the correlation analysis is a common statistical method for researching the uncertain dependence between the variables and the degree of closeness thereof, and is usually measured by using a correlation coefficient which is used for describing the variablesStatistics of degree and direction of correlation, usually denoted r, and satisfying-1 ≦ r ≦ 1, given the variable's data (x)i，yi) I is 1, 2, … …, n, the correlation coefficient of the sample data is calculated as follows:
the correlation analysis is the analysis of the closeness degree of the variables, and the task of the correlation analysis is to make a practical interpretation on whether necessary connection exists between the variables, the closeness degree of the connection and the direction of change, determine the closeness degree of the connection and check the effectiveness of the connection. In the process of multi-source data fusion, a certain amount of abnormal data exists, the data are visually far away from other data, the closeness degree among variables is reduced due to the existence of the abnormal data, and the abnormal data in the multi-source data are accurately positioned and corrected by utilizing correlation analysis to guide data management work to be developed.
In step S103, multi-level redundant data check and correction are performed according to the abnormal data discrimination result to complete correction of the power grid device parameters, the calculation model, the topology data, and the electric quantity data.
Further, in an embodiment of the present invention, the performing multi-stage redundant data check and correction according to the abnormal data screening result includes: and (4) verifying and correcting parameters of equipment at all levels of a main network, a distribution network and a distribution area, topological data of the power grid, a calculation model and collected data by using the multi-level redundant data.
It can be understood that, as shown in fig. 2, for multi-level redundant data check and correction, on the basis of redundant data cleaning and abnormal data analysis and discrimination, the multi-level redundant data is used to check and correct abnormal data, including device parameters, topological data, calculation models, electric quantity data, and the like, so as to improve data quality. In other words, the multi-level redundant data is used for verifying and correcting the parameters of equipment at each level of a main network, a distribution network and a platform area, topological data of a power grid, a calculation model and collected data, so that the abnormal data of the power grid can be quickly and automatically identified and corrected, the data quality is improved, and the calculation accuracy is improved.
Further, in an embodiment of the present invention, the method of an embodiment of the present invention further includes: and carrying out transmission check on the data by using a cyclic redundancy check method.
In the embodiment of the invention, a Cyclic Redundancy Check (CRC) technology is utilized to realize the data transmission check function and improve the data transmission quality. Specifically, a Cyclic Redundancy Check (CRC) technique is utilized to implement the data transmission check function. The data transmitting side and the data receiving side have a production polynomial G (X) as a divisor polynomial in advance. The data bit sequence to be transmitted is taken as the coefficient of a polynomial F (X), which is a dividend polynomial, and the coefficient of a remainder polynomial R (X) obtained by dividing the dividend polynomial F (X) by a divisor polynomial G (X) is the CRC code. The CRC code is added to the sequence of binary data bits to be transmitted, forming a transmission code. After receiving the sending code, the receiving party also considers the sending code as a coefficient sequence of a polynomial, divides the polynomial by the same generator polynomial, and if the remainder is zero, the transmission has no errors; otherwise, the transmission is in error.
In summary, as shown in fig. 2, on the basis of the contemporaneous line loss multi-source data fusion, the multi-level fusion redundant data is cleaned, analyzed and corrected by using the big data button tool, so as to implement a method for governing the multi-source data by using the multi-level redundant data and improving the data quality, wherein the data cleaning includes: performing data collection, data identification and comparison and data integration by using a button to realize the cleaning work of multi-source data redundant data; after redundant data is cleaned, abnormal data identification, system clustering analysis and positive and negative correlation analysis are carried out, and mass data abnormity is discriminated by utilizing a big data processing technology. According to the abnormal data discrimination result, the correction of the power grid equipment parameters, the calculation model, the topological data and the electric quantity data is realized by utilizing the multi-level redundant data checking and correcting function, the multi-level redundant data is fully utilized to provide a method for treating the multi-source fusion data, the functions of quickly and automatically identifying and correcting the abnormal data of the power grid are realized, the data quality is improved, and the calculation accuracy is improved.
According to the multistage redundant data fusion method based on the synchronous line loss data fusion, the multistage redundant data fusion is carried out, cleaning, analysis and application are carried out on the multistage redundant data fusion, the abnormal data of the power grid are rapidly and automatically identified and corrected, the data quality is improved, the calculation accuracy is improved, the workload of offline verification of business personnel is reduced, and the use requirement is effectively met.
Next, a multi-stage redundant data fusion apparatus based on synchronous line loss data fusion according to an embodiment of the present invention will be described with reference to the drawings.
Fig. 3 is a block diagram of a multi-stage redundant data fusion apparatus based on synchronous line loss data fusion according to an embodiment of the present invention.
As shown in fig. 3, the multistage redundant data fusion apparatus 10 based on the contemporaneous line loss data fusion includes: a cleaning module 100, a selection module 200, and a modification module 300.
The cleaning module 100 is used for cleaning the power grid data by using a button tool. The screening module 200 is used for performing abnormal data identification, system clustering analysis and positive-negative correlation analysis on the cleaned power grid data to obtain an abnormal data screening result. The correction module 300 is configured to perform multi-level redundancy data check correction according to the abnormal data discrimination result to complete correction of the power grid device parameters, the calculation model, the topology data, and the electric quantity data. The fusion device 10 provided by the embodiment of the invention can utilize multi-level redundant data fusion to manage multi-source data, improve the data quality and effectively meet the use requirement.
Further, in one embodiment of the present invention, the selection module 200 includes: the device comprises a first screening unit, a second screening unit and a third screening unit. The first screening unit is used for analyzing the power grid data by using a big data processing method so as to screen out first abnormal data. The second screening unit is used for analyzing the power grid data by using a system clustering analysis method so as to screen out second abnormal data. The third screening unit is used for analyzing the power grid data by using a positive and negative correlation analysis method so as to screen out third abnormal data.
Further, in an embodiment of the present invention, the fusion device 10 of the embodiment of the present invention further includes: and (5) a checking module. The check module is used for carrying out transmission check on the data by using a cyclic redundancy check method.
Further, in an embodiment of the present invention, the washing module 100 is further configured to perform a fused redundant data washing using a sliding window and a set theory, and/or perform a redundant data washing according to a reference tag concept in combination with a signal light feature.
Further, in an embodiment of the present invention, the modification module 300 is further configured to perform verification and modification on device parameters, power grid topology data, calculation models, and collected data of each level of the main network, the distribution network, and the distribution area by using multi-level redundant data.
It should be noted that the foregoing explanation of the embodiment of the method for fusing multilevel redundant data based on synchronous line loss data fusion is also applicable to the apparatus for fusing multilevel redundant data based on synchronous line loss data fusion of this embodiment, and is not repeated here.
According to the multistage redundant data fusion device based on the synchronous line loss data fusion, the multistage redundant data fusion is carried out, the multistage fused redundant data is cleaned, analyzed and applied, the abnormal data of the power grid is rapidly and automatically identified and corrected, the data quality is improved, the calculation accuracy is improved, the workload of offline verification of business personnel is reduced, and the use requirement is effectively met.
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, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A multi-level redundant data fusion method based on synchronous line loss data fusion is characterized by comprising the following steps:
cleaning the power grid data by using a button tool;
carrying out abnormal data identification, system clustering analysis and positive-negative correlation analysis on the cleaned power grid data to obtain an abnormal data discrimination result; and
and performing multi-level redundant data check correction according to the abnormal data discrimination result to finish the correction of the power grid equipment parameters, the calculation model, the topological data and the electric quantity data.
2. The method according to claim 1, wherein the performing of abnormal data identification, system clustering analysis and positive-negative correlation analysis on the washed multi-stage fusion redundancy number comprises:
analyzing the power grid data by using a big data processing method to screen out first abnormal data;
analyzing the power grid data by using a system clustering analysis method to screen out second abnormal data;
and analyzing the power grid data by using a positive and negative correlation analysis method to screen out third abnormal data.
3. The method of claim 1, further comprising:
and carrying out transmission check on the data by using a cyclic redundancy check method.
4. The method of claim 1, wherein the washing the multi-level fused redundant data using a button tool further comprises:
performing fusion redundant data cleaning by using a sliding window and a set theory; and/or
And carrying out redundant data washing according to the idea of the reference label and the combination of signal light characteristics.
5. The method according to any one of claims 1 to 4, wherein the performing multi-stage redundancy data check correction according to the abnormal data discrimination result comprises:
and (4) verifying and correcting parameters of equipment at all levels of a main network, a distribution network and a distribution area, topological data of the power grid, a calculation model and collected data by using the multi-level redundant data.
6. The utility model provides a multistage redundant data fusion device based on contemporary line loss data fusion which characterized in that includes:
the cleaning module is used for cleaning the power grid data by using a button tool;
the screening module is used for carrying out abnormal data identification, system clustering analysis and positive and negative correlation analysis on the cleaned power grid data to obtain an abnormal data screening result; and
and the correction module is used for carrying out multi-level redundancy data check correction according to the abnormal data discrimination result so as to finish the correction of the power grid equipment parameters, the calculation model, the topological data and the electric quantity data.
7. The apparatus of claim 6, wherein the selection module comprises:
the first screening unit is used for analyzing the power grid data by using a big data processing method so as to screen out first abnormal data;
the second screening unit is used for analyzing the power grid data by using a system clustering analysis method so as to screen out second abnormal data;
and the third screening unit is used for analyzing the power grid data by using a positive and negative correlation analysis method so as to screen out third abnormal data.
8. The apparatus of claim 6, further comprising:
and the checking module is used for carrying out transmission checking on the data by utilizing a cyclic redundancy checking method.
9. The apparatus of claim 6, wherein the washing module is further configured to perform fused redundant data washing using sliding window and set theory, and/or perform redundant data washing according to reference tag concept in combination with signal light feature.
10. The device according to any one of claims 6 to 9, wherein the correction module is further configured to perform verification correction on device parameters, grid topology data, calculation models and collected data at each level of a main grid, a distribution network and a platform area by using multi-level redundant data.
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