Digital power data management method

Abstract

The invention discloses a digital electric power data management method, relates to the technical field of data management, and solves the problem that characteristic correlation degree analysis and correlation classification are not carried out on electric power data of different electric power nodes. Performing difference analysis of adjacent moment nodes on a plurality of groups of electric power data columns in the data column set, locking similar difference values based on specific change conditions among the difference values, confirming an association sequence based on related characteristics of the similar difference values, and determining the association sequence based on the confirmed sequence with the highest similarity; and the electric power data columns in the data column set are associated and exchanged, so that the data column set can reach the best performance state, and a better classification management effect on the electric power data is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a digital power data management method. Background Art

[0002] Advanced sensors are installed in various links such as power generation, transmission, transformation and distribution to collect key data such as voltage, current, power, temperature, etc. in real time. These sensors should have high accuracy, high reliability and good communication capabilities so that data can be accurately transmitted to the data management system.

[0003] The application with publication number CN117539933A discloses a digital power data management method and system. The method transmits real-time power data and non-real-time power data acquired by the source business system to the digital application platform. The real-time power data is synchronized to the digital application platform through Kafka. The non-real-time power data is extracted and calculated at regular intervals, and the calculated data is transmitted to the digital application platform; the real-time power data and non-real-time power data are stored in the database in the digital application platform; the real-time power data and non-real-time power data are displayed through the data visualization tool in the digital application platform, and abnormal power data are warned. The present invention can ensure the effectiveness of data and improve work efficiency.

[0004] In the process of managing power data, power data of the same source and the same time period are generally managed based on the corresponding data characteristics and time characteristics. However, the original data management method is relatively simple, and no feature correlation analysis is performed on the power data of different power nodes to associate and classify them, thus failing to achieve better digital power data management effects. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides a digital power data management method, which solves the problem that the power data of different power nodes are not analyzed for feature correlation so as to enable correlation classification.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A digital power data management method, comprising the following steps:

[0007] Step 1: confirm the power data columns generated by different power nodes during the collection period, and then perform correlation analysis on the power data columns of different power nodes based on the data characteristics of several groups of power data columns of different power nodes, lock the power data columns with correlation characteristics and determine the set of data columns with the same characteristics. The specific sub-steps are:

[0008] S11, based on the set collection cycle, which is a preset cycle, a plurality of groups of power data generated by the corresponding power node in the collection cycle are confirmed in sequence, the confirmed plurality of groups of power data are sorted according to the time sequence, the power data column belonging to the power node is determined, and the power data columns of other power nodes are confirmed in the same manner;

[0009] S12, based on the confirmed different power data columns of the different power nodes, based on the several groups of power data contained in the power data columns, performing mean processing on the several groups of power data to determine the associated mean values ​​J of the corresponding power data columns k , where k represents different power data columns, and the power data corresponding to different times in the power data column are calibrated as D i-k , where i represents different moments, and i = 1, 2, ..., n, where n is the total number of moments, and n is consistent with the time length of the acquisition cycle;

[0010] S13, randomly select a group of power data columns as the main column, and then determine to select a group of power data columns as the selected auxiliary columns, and associate the D i-k Marked as D i-1 , the D associated with the auxiliary column i-k Marked as D i-2 ,use Lock its evaluation coefficient P:

[0011] If P=1 or P=-1, it means that the two power data columns are positively correlated or negatively correlated, so the two sets of power data columns are marked as a set of data columns with the same characteristics;

[0012] If P≠±1, it means that there is no feature correlation between the two data columns, and no calibration is performed;

[0013] S14, based on the marked set of data columns with the same characteristics, the same method as step S13 is used to lock the data columns with the same characteristics as the set of data columns with the same characteristics from the remaining power data columns and integrate them, so that the power data columns within the set of data columns with the same characteristics are added;

[0014] For the remaining power data columns, the same determination method as the characteristic data column set is adopted to sequentially confirm the characteristic data column sets of other characteristics;

[0015] Step 2: For the determined set of data columns with the same characteristics, the main power data column is locked from the set of data columns with the same characteristics based on the power transmission direction, and then the other power data columns following the main power data column are subjected to feature association analysis, so that several groups of power data columns within the set of data columns with the same characteristics are reordered; the specific method is:

[0016] S21. Based on the power transmission directions of several power nodes, the relevant operator locks the main power node on the operation interface and marks the power data column associated with the main power node as the main power data column;

[0017] S22, determine the correlation difference between adjacent power data in the main power data column, and propose the main power data column as {D 1-k , D 2-k ,……,D n-k}, its D 1-k With D 2-k The correlation difference between them is (C 1-2 ) k =|D 1-k -D 2-k |, and then determine the associated differences of other adjacent power data in turn, and mark them as C j-q , where q = j + 1, and q ≤ n;

[0018] In the same way, the correlation differences of adjacent power data in other power data columns are determined one by one and calibrated as (D j-q ) o , where o represents the remaining different power data columns in the same characteristic data column set;

[0019] S23, a plurality of groups of associated difference values ​​C determined based on the main power data column j-q , identify the correlation values ​​XG between other power data columns and the main power data column belonging to the same period o , its XG o =|C j-q -(D j-q ) o |, based on the label o, there will be several groups of related values ​​XG for the same label o o Perform summation and lock the combined value HB o , based on different combined values ​​HB generated by different power data columns o , from several groups and values ​​HB o Select HB omin The corresponding power data column is used as the associated data column of the main power data column, and the associated data column is sorted after the main power data column;

[0020] S24, taking the determined associated data column as the main power data column, and confirming and sorting the associated data columns of the main power data column, and similarly re-sorting the different power data columns in the same characteristic data column set;

[0021] Step 3: Based on the re-sorted data column set with the same characteristics and the remaining power data columns, the read and write rates of the data column set with the same characteristics and the power data columns are initially determined, and based on the determined specific rate parameters, the data column set with the same characteristics or the power data columns with similar rate parameter characteristics are associated and stored, and the specific method is as follows:

[0022] S31, pre-read and write the data column set with the same characteristics and the remaining power data column determined in this acquisition cycle, and average the read and write rates of several groups generated in the pre-read and write period, and mark them as Vz, where z represents different read and write objects, and the read and write objects are the data column set with the same characteristics or the remaining power data column, and the pre-read and write period is the preset period;

[0023] S32. Based on the several groups of Vz determined in this acquisition cycle, sort the several groups of Vz in ascending order to determine the rate sequence, lock the same fluctuation parameter set from the rate sequence, mark the first group of read and write rates of the rate sequence as V1, divide the related rate parameters belonging to [V1, V1+Y1] into the same fluctuation parameter set, and then perform subsequent division. The subsequent value ranges are: (V1+Y1, V1+2Y1], (V1+2Y1, V1+3Y1], ..., (V1+hY1, V1+mY1], where h=m-1, and confirm the subsequent same fluctuation parameter sets one by one;

[0024] S33. Different read-write objects belonging to the same fluctuation parameter set are stored in the same storage partition, wherein the read-write objects are the same characteristic data column set or the remaining power data columns.

[0025] Preferably, the (C 1-2 ) k The subscript 1-2 in represents that the correlation difference is the data difference between time 1 and time 2.

[0026] The present invention provides a digital power data management method. Compared with the prior art, it has the following advantages:

[0027] Beneficial effects:

[0028] The present invention confirms the power data columns associated with different power nodes, and then confirms the correlation change characteristics of the confirmed different power data columns. Based on the confirmed correlation change characteristics, the corresponding evaluation coefficient is determined. Based on the specific performance state of the evaluation coefficient, the mutually related data columns are combined, and the corresponding sets of data columns with the same characteristics are locked, so that the data columns with the same characteristics are combined, and the power data are initially managed;

[0029] For the determined data column set, several groups of power data columns within the data column set are subjected to difference analysis of adjacent time nodes, similar differences are locked based on the specific changes between the differences, and the associated sequences are confirmed based on the relevant features of the similar differences. Based on the confirmed sequence with the highest similarity, the power data columns within the data column set are associated and exchanged, so that the data column set can achieve the best performance state and achieve better classification management effect on power data;

[0030] When storing collections and data columns, based on the corresponding read and write rates, read and write objects with similar read and write rates are classified for effective storage, and read and write objects with the same characteristics are divided into the same related storage partitions to achieve better storage processing effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the process of the present invention;

[0032] Figure 2 A schematic diagram of the storage process of the read-write object with the same characteristics of the present invention;

[0033] Figure 3 It is a schematic diagram for determining a set of data columns with the same characteristics according to the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] First embodiment

[0036] See also Figure 1 , the present application provides a digital power data management method, comprising the following steps:

[0037] Step 1: confirm the power data columns generated by different power nodes within the collection period, and then perform correlation analysis on the power data columns of different power nodes based on the data features of several groups of power data columns of different power nodes, lock the power data columns with correlation features and determine the set of data columns with the same features. Specifically, for different power data associated with different power nodes, if the power nodes are related or there are power nodes with data circulation, the change features of the corresponding data are relatively consistent, so the power data with high similar features can be determined as a set, and the corresponding data column set can be locked;

[0038] Combination Figure 3,The specific sub-steps of performing correlation analysis on the power data columns of different power nodes are:

[0039] S11. Based on the set collection cycle, which is a preset cycle and is prepared in advance by relevant operators based on experience, several groups of power data generated by the corresponding power nodes within the collection cycle are confirmed in sequence, and the confirmed groups of power data are sorted according to the time sequence, and the power data column belonging to the power node is determined, and then the power data columns of other power nodes are confirmed in the same way;

[0040] S12, based on the confirmed different power data columns of the different power nodes, based on the several groups of power data contained in the power data columns, performing mean processing on the several groups of power data to determine the associated mean values ​​J of the corresponding power data columns k , where k represents different power data columns, and the power data corresponding to different times in the power data column are calibrated as D i-k , where i represents different moments, and i = 1, 2, ..., n, where n is the total number of moments, and n is consistent with the time length of the acquisition cycle;

[0041] S13, randomly select a group of power data columns as the main column, and then determine to select a group of power data columns as the selected auxiliary columns, and associate the D i-k Marked as D i-1 , the D associated with the auxiliary column i-k Marked as D i-2 ,use Lock its evaluation coefficient P:

[0042] If P=1 or P=-1, it means that the two power data columns are positively correlated or negatively correlated, so the two sets of power data columns are marked as a set of data columns with the same characteristics;

[0043] If P≠±1, it means that there is no feature correlation between the two data columns, and no calibration is performed;

[0044] S14, based on the identified data column set with the same characteristics, the same method as step S13 is used to lock the data columns with the same characteristics as the data column set with the same characteristics from the remaining power data columns and integrate them, so that the power data columns within the data column set with the same characteristics are added. For example, the power data columns associated with the data column set with the same characteristics are A and B, and there are still other data columns C, D and E. C has the same characteristics, and A or B can be selected as the relevant main column to identify whether the subsequent C, D and E have the same characteristics, and the data column C with the same characteristics is planned to be included in the data column set with the same characteristics between A and B;

[0045] For the remaining power data columns, the same determination method as the characteristic data column set is adopted to sequentially confirm the characteristic data column sets of other characteristics;

[0046] Step 2: for the determined set of data columns with the same characteristics, based on the power transmission direction, the main power data column is locked from the set of data columns with the same characteristics, and then the characteristic association analysis is performed on other power data columns following the main power data column, so that several groups of power data columns within the set of data columns with the same characteristics are reordered. Specifically, there are multiple groups of different power data columns in the set of data columns with the same characteristics. In order to make the association characteristics between different power data columns closer, a better correlation management effect of the set of data columns with the same characteristics is achieved, which is convenient for the subsequent correlation management of the set of data columns with the same characteristics, and a better data column management effect is achieved;

[0047] The specific method of reordering is as follows:

[0048] S21. Based on the power transmission direction of several power nodes, the relevant operator locks the main power node on the operation interface, and marks the power data column associated with the main power node as the main power data column (that is, power is transmitted from this power node to other power nodes, and the proposed power transmission direction is ABC, then A belongs to the main power node, and the relevant power data columns belonging to the same characteristic data column set generally belong to power nodes in the same transmission direction, and the power transmission parameters of the power nodes are all related, so they will be determined to be the same characteristic data column set);

[0049] S22, determine the correlation difference between adjacent power data in the main power data column, and propose the main power data column as {D 1-k , D 2-k ,……,D n-k}, its D 1-k With D 2-k The correlation difference between them is (C 1-2 ) k =|D 1-k -D 2-k |, where C 1-2 The subscript 1-2 in the above represents that the correlation difference is the data difference between time 1 and time 2. Then, the correlation differences of other adjacent power data are determined in turn and marked as C. j-q , where q = j + 1, and q ≤ n;

[0050] In the same way, the correlation differences of adjacent power data in other power data columns are determined one by one and calibrated as (D j-q ) o , where o represents the remaining different power data columns in the same characteristic data column set;

[0051] S23, a plurality of groups of associated difference values ​​C determined based on the main power data column j-q , identify the correlation values ​​XG between other power data columns and the main power data column belonging to the same period o , its XG o =|C j-q -(D j-q ) o |, based on the label o, there will be several groups of related values ​​XG for the same label o o Perform summation and lock the combined value HB o , based on different combined values ​​HB generated by different power data columns o , from several groups and values ​​HB o Select HB omin The corresponding power data column is used as the associated data column of the main power data column, and this associated data column is sorted after the main power data column. For example, the power data columns contained in the proposed data column set with the same characteristics are ABCDE, and the corresponding power data column B is marked as the main power data column. The main power column {D 1-B , D 2-B ,……,D n-B}, then the main power data column generates several sets of related differences: C 1-2 =|D 1-B -D 2-B |、C 2-3 =|D 2-B -D 3-B |、……、C (n-1)-n =|D (n-1)-B -D n-B |, then other power data columns can be determined in the same way as the corresponding correlation difference. Based on the same subscripts (1-2), (2-3), ..., etc., the corresponding correlation difference can be processed to determine the corresponding correlation value, and then the specific combined value can be locked for the determined groups of correlation values. The minimum value can be locked from the determined combined values ​​to determine the most correlated data column. When the feature is processed, the correlation value of the correlation difference in the same period is XG o The smaller it is, the closer the data trends of different data columns in the corresponding period are, and their amplitude trends are relatively close, which can ensure the characteristic sorting effect of its power data column;

[0052] S24. The determined associated data column is used as the main power data column, and the associated data column of the main power data column is confirmed and sorted. Similarly, different power data columns in the same characteristic data column set are reordered. Specifically, by reordering the same characteristic data column set, a better characteristic sorting effect can be obtained, so that the same characteristic data column set can better display characteristics.

[0053] Second embodiment

[0054] The following steps are also included:

[0055] Step 3: Combination Figure 2 , based on the reordered data column set with the same characteristics and the remaining power data columns (this power data column is the related data column that is not divided into the data column set with the same characteristics, and this type of data column is the remaining power data column), the read and write rates of the data column set with the same characteristics and the power data column are initially determined, and based on the determined specific rate parameters, the data column set with the same characteristics or the power data column with similar rate parameter characteristics are associated and stored. Specifically, the sets and data columns with relatively overlapping corresponding characteristics are stored in the same storage partition, which can ensure the corresponding storage effect. When such data is subsequently read and written, the read and write loads of the corresponding storage partition can be effectively balanced, and the query and read and write rates are faster, which can achieve better read and write effects;

[0056] The specific sub-steps for associative storage are as follows:

[0057] S31. Pre-read and write the data column set with the same characteristics and the remaining power data column determined in this acquisition cycle, and average the read and write rates of several groups generated in the pre-read and write period, and mark them as Vz, where z represents different read and write objects, and the read and write objects are the data column set with the same characteristics or the remaining power data column, and the pre-read and write period is a preset period, which is generally 2 minutes or even shorter;

[0058] S32. Based on the several groups of Vz determined in this acquisition cycle, sort the several groups of Vz in ascending order to determine the rate sequence, lock the same fluctuation parameter set from the rate sequence, mark the first group of read and write rates in the rate sequence as V1, divide the related rate parameters belonging to [V1, V1+Y1] into the same fluctuation parameter set, and then perform subsequent division. The subsequent value ranges are: (V1+Y1, V1+2Y1], (V1+2Y1, V1+3Y1], ..., (V1+hY1, V1+mY1], where h=m-1, m represents the last group of value ranges, and the subsequent same fluctuation parameter sets are confirmed one by one;

[0059] S33. Different read-write objects belonging to the same fluctuation parameter set are stored in the same storage partition, wherein the read-write objects are the same characteristic data column set or the remaining power data columns.

[0060] Specifically, in order to enable the corresponding storage partition to achieve better storage-related effects, based on the read and write rates determined in the pre-read and write process, the relevant characteristic parameters of the corresponding read and write objects are confirmed, and the same characteristic read and write objects are divided into the same related storage partition by using the numerical range confirmation method, so as to achieve better storage processing effects.

[0061] Third embodiment

[0062] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0063] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0064] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A digital power data management method, characterized in that: The following steps are involved: Step 1: confirm the power data columns generated by different power nodes during the collection period, and then perform correlation analysis on the power data columns of different power nodes based on the data features of several groups of power data columns of different power nodes, lock the power data columns with correlation features and determine the set of data columns with the same features; Step 2: For the determined set of data columns with the same characteristics, the main power data column is locked from the set of data columns with the same characteristics based on the power transmission direction, and then the other power data columns following the main power data column are subjected to feature association analysis, so that several groups of power data columns within the set of data columns with the same characteristics are reordered.

2. A digital power data management method according to claim 1, characterized in that: In step 1, the specific sub-steps of performing correlation analysis on the power data columns of different power nodes are: S11, based on the set collection cycle, which is a preset cycle, a plurality of groups of power data generated by the corresponding power node in the collection cycle are confirmed in sequence, the confirmed plurality of groups of power data are sorted according to the time sequence, the power data column belonging to the power node is determined, and the power data columns of other power nodes are confirmed in the same manner; S12, based on the confirmed different power data columns of the different power nodes, based on the several groups of power data contained in the power data columns, performing mean processing on the several groups of power data to determine the associated mean values ​​J of the corresponding power data columns k , where k represents different power data columns, and the power data corresponding to different times in the power data column are calibrated as D i-k , where i represents different moments, and i = 1, 2, ..., n, where n is the total number of moments, and n is consistent with the time length of the acquisition cycle; S13, randomly select a group of power data columns as the main column, and then determine to select a group of power data columns as the selected auxiliary column, and associate the D i-k Marked as D i-1 , the D associated with the auxiliary column i-k Marked as D i-2 ,use Lock its evaluation coefficient P: If P=1 or P=-1, it means that the two power data columns are positively correlated or negatively correlated, so the two sets of power data columns are marked as a set of data columns with the same characteristics; S14, based on the marked set of data columns with the same characteristics, the same method as step S13 is used to lock and integrate the data columns with the same characteristics as the set of data columns with the same characteristics from the remaining power data columns, so that the power data columns within the set of data columns with the same characteristics are added.

3. A digital power data management method according to claim 2, characterized in that: The step S14 further includes: For the remaining power data columns, the same determination method as the characteristic data column set is adopted to sequentially confirm the characteristic data column sets of other characteristics.

4. A digital power data management method according to claim 2, characterized in that: In step S13, if P≠±1, it means that there is no feature association between the two data columns, and no calibration is performed.

5. A digital power data management method according to claim 1, characterized in that: In the step 2, the specific method of reordering the set of data columns with the same characteristics is: S21. Based on the power transmission directions of several power nodes, the relevant operator locks the main power node on the operation interface and marks the power data column associated with the main power node as the main power data column; S22, determine the correlation difference between adjacent power data in the main power data column, and propose the main power data column as {D 1-k , D 2-k ,……,D n-k }, its D 1-k With D 2-k The correlation difference between them is (C 1-2 ) k =|D 1-k -D 2-k |, and then determine the associated differences of other adjacent power data in turn, and mark them as C j-q , where q = j + 1, and q ≤ n; In the same way, the correlation differences of adjacent power data in other power data columns are determined one by one and calibrated as (D j-q ) o , where o represents the remaining different power data columns in the same characteristic data column set; S23, a plurality of groups of associated difference values ​​C determined based on the main power data column j-q , identify the correlation values ​​XG between other power data columns and the main power data column belonging to the same period o , its XG o =|C j-q -(D j-q ) o |, based on the label o, there will be several groups of related values ​​XG for the same label o o Perform summation and lock the combined value HB o , based on different combined values ​​HB generated by different power data columns o , from several groups and values ​​HB o Select HB omin The corresponding power data column is used as the associated data column of the main power data column, and the associated data column is sorted after the main power data column; S24, taking the determined associated data column as the main power data column, and confirming and sorting the associated data columns of the main power data column, and similarly re-sorting the different power data columns in the same characteristic data column set.

6. A digital power data management method according to claim 5, characterized in that: The 1-2 ) k The subscript 1-2 in represents that the correlation difference is the data difference between time 1 and time 2.

7. A digital power data management method according to claim 1, characterized in that: Also includes: Step 3: Based on the reordered data column set with the same characteristics and the remaining power data columns, the read and write rates of the data column set with the same characteristics and the power data columns are initially determined, and based on the determined specific rate parameters, the data column set with the same characteristics or the power data columns with similar rate parameter characteristics are associated and stored.

8. A digital power data management method according to claim 7, characterized in that: In step 3, the specific method of associating and storing the same characteristic data column set or power data column is as follows: S31, pre-read and write the data column set with the same characteristics and the remaining power data column determined in this acquisition cycle, and average the read and write rates of several groups generated in the pre-read and write period, and mark them as Vz, where z represents different read and write objects, and the read and write objects are the data column set with the same characteristics or the remaining power data column, and the pre-read and write period is the preset period; S32. Based on the several groups of Vz determined in this acquisition cycle, sort the several groups of Vz in ascending order to determine the rate sequence, lock the same fluctuation parameter set from the rate sequence, mark the first group of read and write rates in the rate sequence as V1, divide the related rate parameters belonging to [V1, V1+Y1] into the same fluctuation parameter set, and then perform subsequent division. The subsequent value ranges are: (V1+Y1, V1+2Y1], (V1+2Y1, V1+3Y1], ..., (V1+hY1, V1+mY1], where h=m-1, and confirm the subsequent same fluctuation parameter sets one by one; S33. Different read-write objects belonging to the same fluctuation parameter set are stored in the same storage partition, wherein the read-write objects are the same characteristic data column set or the remaining power data columns.

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