A ring network box with intelligent storage function for historical data

Abstract

The present invention relates to the field of ring network box data acquisition technology, and more specifically to a ring network box with a historical data intelligent storage function. The ring network box includes: a difference analysis module for segmenting the grid parameter data collected by the ring network box to obtain data segments; obtaining the difference distance between each two data segments; a cluster analysis module for clustering the data segments to obtain the importance coefficient of each cluster; an intra-cluster analysis module for obtaining the relative importance coefficient of each data segment within the cluster; a fusion analysis module for obtaining the final importance coefficient of the data segment based on the relative importance coefficient of the data segment and the importance coefficient of the cluster to which it belongs; a compression storage module for processing the component signals of each data segment based on the importance coefficient to obtain the updated data segment corresponding to each data segment; and compressing and storing the updated data segment. The present invention can reduce the pressure on the storage space of the ring network box and retain key data information.

Description

Technical Field

[0001] The present invention relates to the technical field of ring main box data collection, and in particular to a ring main box with a historical data intelligent storage function. Background Art

[0002] With the continuous development of distribution network automation systems and technologies, the market demand for ring main boxes (box-type switchgear) products is also increasing. The original monitoring equipment is only responsible for monitoring the operation status of the power grid, which may not meet the needs of practical on-site functions and the development of integrated monitoring and protection. Therefore, a more comprehensive ring main box (box-type switchgear) is needed. The ring main box is installed in places such as distribution rooms and switchgear. Its main function is to collect grid parameter data of the line where the monitored switch is located. It also has functions such as data collection and storage, real-time monitoring of line operation status, timely reporting when a line fault occurs, and waiting for instructions from the upper system to control the opening and closing of switches. However, as the stored data collected by the ring main box accumulates over time, it also poses certain challenges to the storage space of the device. Therefore, how to effectively reduce the storage space of this collected data without affecting the accuracy of its monitoring and analysis is of vital importance to the ring main box.

[0003] When collecting, storing, monitoring, and analyzing grid data (such as voltage and current), ring mainframe boxes can utilize traditional compression algorithms, such as ZIP and RLE, for data storage. However, due to the unique nature of grid data, these algorithms may overlook the temporal correlation of the collected data and lack a scoring system for its importance. Consequently, they often use a default, indiscriminate compression method for all grid data. If the lossy compression ratio set for the collected data is too low, it can lead to increased storage redundancy and put pressure on storage space. If the lossy compression ratio is too high, some details may be lost, affecting the accuracy of subsequent monitoring and analysis. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a ring main unit with a historical data intelligent storage function. The technical solutions adopted are as follows:

[0005] One embodiment of the present invention provides a ring network box with a historical data intelligent storage function, the ring network box comprising:

[0006] The difference analysis module is used to segment the grid parameter data collected by the ring network box to obtain data segments; and obtain the difference distance between each two data segments based on the spectrum data corresponding to each data segment;

[0007] Cluster analysis module, used to cluster all data segments using difference distance to obtain clusters; obtain the importance coefficient of a cluster based on the number of data points in a cluster and the distance between it and other clusters;

[0008] The intra-cluster analysis module is used to obtain the relative importance coefficient of a data segment within a cluster based on the time series distance of the data segment from the current moment and the difference distance between the data segment and the cluster center;

[0009] A fusion analysis module is used to obtain the final importance coefficient of a data segment based on the relative importance coefficient of the data segment and the importance coefficient of the cluster to which it belongs;

[0010] The compression storage module is used to obtain the component signal of each data segment; process the component signal of each data segment based on the importance coefficient to obtain the updated data segment corresponding to each data segment; and compress and store the updated data segment.

[0011] Preferably, obtaining the difference distance between every two data segments according to the spectrum data corresponding to each data segment includes:

[0012] The contribution of the frequency point is obtained by comparing the amplitude corresponding to a frequency point with a non-zero amplitude in the frequency data corresponding to a data segment with the sum of the amplitudes corresponding to all frequency points with non-zero amplitudes in the frequency data corresponding to the data segment; the difference distance between the two data segments is obtained based on the difference between the contributions of the frequency points with non-zero amplitudes in the spectrum data corresponding to the two data segments.

[0013] Preferably, obtaining the difference distance between the two data segments according to the difference between the contributions of the frequency points with non-zero amplitudes in the spectrum data corresponding to the two data segments includes:

[0014] Obtain the union of the frequency points with non-zero amplitudes in the spectrum data corresponding to the two data segments; use the Euclidean distance calculation formula to calculate the contribution of each frequency point in the union to the spectrum data of the two data segments to obtain the difference distance between the two data segments.

[0015] Preferably, obtaining the importance coefficient of a cluster based on the number of data points in the cluster and the distance between the cluster and other clusters includes:

[0016] Calculate the average value of the difference distances between the cluster center of a cluster and the cluster centers of other clusters, and record it as the average distance of the cluster; compare the number of data segments in the cluster with the number of data segments in all clusters to obtain the proportion of the number of data segments of the cluster; compare the average distance of the cluster with the average distance of all clusters to obtain the distance proportion of the cluster; use a first preset value to perform negative correlation mapping on the proportion of the number of data segments of the cluster and multiply it with the distance proportion to obtain the importance coefficient of the cluster.

[0017] Preferably, within a cluster, the relative importance coefficient of a data segment is obtained according to the temporal distance of the data segment from the current moment and the difference distance between the data segment and the cluster center, including:

[0018] Take the time length from the middle moment of a data segment in a cluster to the current moment, and use an exponential function with a natural constant as the base to negatively map the time length to obtain the time series related eigenvalue of the data segment; use an exponential function with a natural constant as the base to map the difference distance between the data segment and the cluster center of the cluster where the data segment is located to obtain the distance distribution eigenvalue; normalize the time series related eigenvalue and the distance distribution eigenvalue and perform weighted summation to obtain the relative importance coefficient of the data segment.

[0019] Preferably, obtaining the final importance coefficient of a data segment according to the relative importance coefficient of the data segment and the importance coefficient of the cluster to which the data segment belongs includes:

[0020] Compare the relative importance coefficient of a data segment with the relative importance coefficient of the data segment closest to the cluster center within the cluster where the data segment is located to obtain the importance ratio corresponding to the data segment; multiply the configuration constant term, the importance coefficient of the cluster where the data segment is located, and the importance ratio corresponding to the data segment and normalize them to obtain the final importance coefficient of the data segment.

[0021] Preferably, obtaining the component signal of each data segment includes:

[0022] Each data segment is decomposed using EMD to obtain the component signals corresponding to each data segment.

[0023] Preferably, processing the component signals of each data segment based on the importance coefficient to obtain an updated data segment corresponding to each data segment includes:

[0024] Arrange the component signals of a data segment in the order in which they are obtained during decomposition to obtain a component signal sequence; extract component signals from the last component signal in the component signal sequence forward until the ratio of the number of extracted component signals to the number of component signals in the component signal sequence is equal to the final importance coefficient of the data segment, stop extracting the component signals, and the extracted component signals are the retained component signals of the data segment. Perform an inverse transformation on the retained component signals to obtain an updated data segment of the data segment.

[0025] Embodiments of the present invention have at least the following beneficial effects: the present application segments the grid parameter data collected by the ring network box to obtain data segments, then performs Fourier transform on different data segments to obtain spectrum data, then obtains the difference distance between any two data segments based on the spectrum data, then performs cluster analysis on the analyzed data segments based on the difference distance between the data segments to obtain the importance coefficient of each cluster cluster. However, since the density of different cluster clusters may vary, the relative importance of specific data within the cluster cluster is further analyzed to obtain the relative importance coefficient of the data segment, and then combines the importance coefficient of the cluster cluster to obtain the final importance coefficient of each data segment. Then, each data segment is subjected to EMD decomposition to obtain different component signals, and then the component signals of the data segment are retained based on the importance coefficient to obtain an updated data segment corresponding to each data segment. The data segments processed by the compression algorithm are then compressed to achieve adaptive lossy compression of different data segments, thereby reducing the pressure on the storage space of the ring network box and reducing the impact of accuracy deviation caused by data compression loss during subsequent system monitoring and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 This is a structural block diagram of a ring network box with historical data intelligent storage function provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a ring main unit (RME) with intelligent historical data storage capabilities. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0029] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0030] The following describes in detail a specific solution of a ring network box with a historical data intelligent storage function provided by the present invention with reference to the accompanying drawings.

[0031] Example:

[0032] The main application scenario of the present invention is: in the process of collecting, storing and monitoring data in the power grid (such as voltage, current and other data) by the ring network box, the existing compression algorithm ignores the analysis of the time correlation and importance of these data when compressing and storing these data, and compresses and stores all data, resulting in a waste of storage resources. Therefore, it is necessary to analyze the collected data and determine the compressed data.

[0033] See also Figure 1 , which shows a structural block diagram of a ring network box with a historical data intelligent storage function provided by an embodiment of the present invention. The ring network box includes the following modules:

[0034] The difference analysis module is used to segment the grid parameter data collected by the ring network box to obtain data segments; and obtain the difference distance between every two data segments based on the spectrum data corresponding to each data segment.

[0035] The main function of the ring main box is to collect grid parameter data such as voltage and current in the power grid, and then use a large amount of historical data as training sample data for abnormal monitoring models, power control and other decision-making models for training and analysis. However, since traditional algorithms store electrical parameter data collected by the ring main box, such as voltage and current, due to the increased time, it also brings certain challenges to the storage space pressure of the ring main box.

[0036] Therefore, it is necessary to analyze the grid parameter data collected by the ring network box, so as to minimize the impact on the accuracy of the monitoring function of the ring network box, effectively improve the compression ratio of these data, thereby reducing the pressure on the storage space of the ring network box and extending the service life of the equipment.

[0037] The grid parameter data collected by the ring main unit (RMB) consists of time-series data such as voltage and current. This data is then analyzed and processed to reduce storage space requirements in the RMU. Therefore, a voltage and current data acquisition module is required to collect real-time grid voltage and current data. This data is then processed by the analysis and processing module, and then compressed and stored using the compression algorithm of the compression module. The grid parameter data consists of current and voltage data. The grid parameter data is collected at the same frequency as the conventional RMU.

[0038] Since the current and voltage in the power grid undergo certain dynamic and random changes during use, their changing characteristics are different in different time periods, and their importance may also be different. Therefore, it is necessary to reduce the space occupied by data storage as much as possible while minimizing the impact on the accuracy of data monitoring and analysis after processing.

[0039] Furthermore, a kind of grid parameter data is analyzed, and the current data is used as an example for analysis in the embodiment of the present application. Taking the historical current data two hours from the current moment as an example for analysis, the current data collected is first divided into a small segment according to a certain time period. Here, 50 Hz AC is taken as an example, and its one cycle is 20 milliseconds. Therefore, when dividing it into small segments, at least one cycle of the AC is used as the lower limit, and a suitable length is selected. The length selected in this case is 60 milliseconds as a preset segment length (specifically selected based on the space and computing power of the device. The smaller the segment length, the more detailed the analysis and the better the compression effect. If you want fast calculation speed and low complexity, longer segments are required. Therefore, the selection of segments is more based on the storage space size and computing performance of the device for self-configuration and decision).

[0040] The grid parameter data (current data) collected by the ring network box is segmented using a preset segment length to obtain data segments. For each data segment, a fast Fourier transform is performed to obtain the corresponding spectrum data for each data segment.

[0041] For any segment of current data, the amplitude corresponding to any frequency represents its contribution to the current signal. By comparing the segmented data after introducing frequency contributions, we can more accurately reflect the actual impact of different frequency components in the signal, avoiding the problem of simple amplitude differences masking changes in key features. In particular, for power grid signals characterized by primary frequency and low frequency characteristics, the contribution-based difference calculation can more sensitively capture changes in the signal's energy distribution at the main frequencies, thereby improving the rationality and accuracy of segment classification and subsequent compression decisions.

[0042] The difference distance between every two data segments is obtained based on the spectrum data corresponding to each data segment. Specifically, the amplitude corresponding to a frequency point with a non-zero amplitude in the frequency data corresponding to a data segment is compared with the sum of the amplitudes corresponding to all frequency points with non-zero amplitudes in the frequency data corresponding to the data segment to obtain the contribution of the frequency point.

[0043] The specific calculation model of contribution is: ,

[0044] in, Indicates the contribution of the i-th frequency point whose amplitude is not zero in the frequency data corresponding to the a-th data segment. Indicates the amplitude corresponding to the i-th frequency point whose amplitude is not zero in the frequency data corresponding to the a-th data segment (if the amplitude is 0, its contribution is 0, and the calculation is skipped directly). The number of frequency points with non-zero amplitude in the frequency data corresponding to the a-th data segment; It represents the ratio of the amplitude of the i-th frequency point with non-zero amplitude in the frequency data corresponding to the a-th data segment to the sum of the amplitudes of all frequency points with non-zero amplitude in the frequency data corresponding to the a-th data segment, that is, the contribution of this frequency point to the current signal segment.

[0045] Furthermore, the difference distance between the two data segments is obtained according to the difference between the contribution degrees of the frequency points with non-zero amplitudes in the spectrum data corresponding to the two data segments.

[0046] Specifically, the union of frequency points with non-zero amplitudes in the spectrum data corresponding to the two data segments is obtained; the Euclidean distance calculation formula is used to calculate the contribution of each frequency point in the union in the spectrum data of the two data segments to obtain the difference distance between the two data segments.

[0047] The calculation model of the difference distance is: ,

[0048] in, Indicates the difference between the currently collected a-th and b-th data segments. 、 Respectively represent the number of frequency points with non-zero amplitude in the spectrum data corresponding to the ath and bth data segments, Indicates the number of frequency points in the union of frequency points with non-zero amplitudes in the spectrum data corresponding to the a-th and b-th data segments, and Respectively represent the contribution of the ith frequency point in the spectrum data of the ath and bth data segments respectively; It represents the contribution difference of the i-th frequency point with non-zero amplitude in the spectrum data of the a-th and b-th data segments. The larger the value, the greater the difference between the two data segments. This calculation method is equivalent to using the Euclidean distance calculation formula to calculate the Euclidean distance between two points in multidimensional space. and It represents two values ​​under one dimension. From this, we can get the difference distance between each two data segments.

[0049] The cluster analysis module is used to cluster all data segments using difference distance to obtain clusters; based on the number of data points in a cluster and the distance between it and other clusters, the importance coefficient of the cluster is obtained.

[0050] Through the above, the difference distance between any two data segments in the currently collected power grid parameter data can be obtained, and then the data segments of the currently collected power grid parameter data are clustered using density peak clustering (DPC). This algorithm is a well-known technology and will not be described in detail here. That is, clustering is performed with one data segment as one element.

[0051] Compared to the data in the power grid, normal parameter data accounts for a relatively large proportion of the overall data size, and usually has general similarities. After clustering, if the distance between a cluster and other clusters is farther, then the data of the cluster is more different from the data of other clusters, that is, the greater the difference between the data of the cluster and the data of other clusters, the more important the cluster may be. Similarly, if the size of a cluster is relatively small, then it means that compared to other data, these data features are relatively rare in the entire time period, and they may play an important role in the monitoring and other analysis processes, and their importance may also be higher.

[0052] The importance coefficient of a cluster is obtained based on the number of data points in the cluster and the distance between the cluster and other clusters. Specifically, the average difference distance between the cluster center of a cluster and the cluster centers of other clusters is calculated and recorded as the average distance of the cluster; the number of data segments in the cluster is compared with the number of data segments in all clusters to obtain the proportion of the number of data segments in the cluster; the average distance of the cluster is compared with the average distance of all clusters to obtain the distance proportion of the cluster; the first preset value is used to perform a negative correlation mapping on the proportion of the number of data segments in the cluster and multiply it by the distance proportion to obtain the importance coefficient of the cluster.

[0053] The calculation model of the average distance is: ,

[0054] in, Represents the average value of the difference distance between the sth cluster and other clusters, that is, the average distance of the sth cluster; represents the difference distance between the cluster center of the sth cluster and the cluster center of the rth cluster, It represents the mean of the sum of the difference distances between the cluster center of the sth cluster and the cluster centers of each cluster in the remaining clusters. The larger the value, the greater the distance between the cluster and other clusters.

[0055] After obtaining the distance between any cluster and other clusters, the importance coefficient of the cluster can be calculated in combination with the number of data segments within the cluster. The specific calculation model of the importance coefficient is: ,

[0056] In the formula Indicates the importance coefficient of the sth cluster in the current collected data, Represents the number of elements in the sth cluster (that is, the number of data segments within the cluster); Indicates the number of all elements in all current clusters (that is, the number of all divided data segments); represents the average distance of the sth cluster, Z represents the number of clusters, is the ratio of the number of data segments of the sth cluster, which means the ratio of the number of elements in the sth cluster to the number of all elements (i.e., segmented data) of all current clusters. The smaller the value, the higher the The larger the value, the smaller the size of the cluster is compared with other clusters, and the more important the cluster may be. It is the distance ratio of the sth cluster, which means the ratio of the distance of the sth cluster compared to other clusters to the sum of the distances of all clusters compared to other clusters. The larger the value, the farther the cluster is from other clusters, and the more important it may be.

[0057] In this way, the importance coefficient of each cluster can be obtained.

[0058] The intra-cluster analysis module is used to obtain the relative importance coefficient of a data segment within a cluster based on the time series distance of the data segment from the current moment and the difference distance between the data segment and the cluster center.

[0059] The importance coefficient of any cluster is obtained through the above. Although the cluster center of the same cluster can reflect the basic characteristics of the cluster data as a whole, the distance between each element (i.e., segmented data) within each cluster and the cluster center and the time period of actual collection are different. For example, when monitoring, the closer the distance of certain data segments is to the monitoring moment (current moment), the greater the reference significance when monitoring or analyzing. Then the degree of retention of the information collected during this time period may need to be higher.

[0060] Therefore, it is necessary to further analyze each data segment within the cluster so that its importance can be evaluated in a more fine-grained manner, achieving more accurate and flexible lossy compression, and effectively balancing data redundancy reduction and key information retention.

[0061] The relative importance coefficient of a data segment is obtained within a cluster according to the time series distance of the data segment from the current moment and the difference distance between the data segment and the cluster center.

[0062] Specifically, the time length from the middle moment of a data segment in a cluster to the current moment is obtained, and the time length is negatively correlated with the exponential function with the natural constant as the base to obtain the time series related characteristic value of the data segment; the difference distance between the data segment and the cluster center of the cluster where the data segment is located is mapped using the exponential function with the natural constant as the base to obtain the distance distribution characteristic value; the time series related characteristic value and the distance distribution characteristic value are normalized and weighted summed to obtain the relative importance coefficient of the data segment.

[0063] The specific calculation model of the relative importance coefficient is:

[0064]

[0065] In the formula Represents the relative importance coefficient of the jth element (data segment) in the sth cluster; 、 Indicates the weight coefficient, here the value is 0.4 and 0.6, the empirical value (specifically set according to the actual demand for historical data. If the analysis has a smaller dependence on the earlier historical data, then you can set A larger weight is set, and a smaller weight is set). represents a linear normalization function, and e represents a natural constant; It represents the time distance between the middle moment of the jth data segment in the s clusters and the latest sampling moment (i.e. the current moment). The smaller the value, the closer the time series related feature value is. The larger the value is, the closer the data segment is to the current time, and the higher its importance may be. Indicates the distance between the jth data segment in s clusters and the center of the cluster. The larger the value, the closer the distance distribution characteristic value The larger it is, the farther the data segment is from the center of the cluster. In this cluster, the difference between the data segment and the representative data segment closest to the center of the cluster may be greater, and its relative importance in the current cluster may be higher.

[0066] The fusion analysis module is used to obtain the final importance coefficient of a data segment based on the relative importance coefficient of the data segment and the importance coefficient of the cluster to which it belongs.

[0067] Through the above, we can get the importance coefficient of any cluster and the relative importance coefficient of each cluster, and then combine the two to obtain the final importance coefficient of each data segment.

[0068] The final importance coefficient of a data segment is obtained based on the relative importance coefficient of the data segment and the importance coefficient of the cluster to which it belongs. Specifically, the relative importance coefficient of a data segment is compared with the relative importance coefficient of the data segment closest to the cluster center within the cluster to obtain the corresponding importance ratio of the data segment. The final importance coefficient of the data segment is obtained by multiplying the configuration constant term, the importance coefficient of the cluster to which the data segment belongs, and the corresponding importance ratio of the data segment and normalizing them.

[0069] The calculation model of the final importance coefficient is: ,

[0070] In the formula Represents the final importance coefficient of the j-th data segment in the s-th cluster. and They represent the importance coefficient of the sth cluster and the relative importance coefficient of the jth data segment in the sth cluster respectively; G represents the configuration constant term, and its value range here is By configuring this parameter, you can manually control the importance of each segment signal, thereby indirectly controlling the degree of information loss of each segment. The default value here is 1 (select according to the actual environment). The smaller the value, the higher the information loss of the segment during subsequent compression. Indicates the relative importance coefficient of the data segment closest to the cluster center in the sth cluster. It represents the ratio of the relative importance coefficient of the jth data segment in the sth cluster to the relative importance coefficient of the data segment closest to the cluster center in the sth cluster, that is, the importance ratio corresponding to the data segment. If the value is larger, it means that the importance coefficient of the data segment may be relatively high relative to the cluster, then the importance of the data segment may be higher than the initial estimate of the cluster, otherwise it is lower, thereby making a more accurate assessment of the importance of the data segment.

[0071] The compression storage module is used to obtain the component signal of each data segment; process the component signal of each data segment based on the importance coefficient to obtain the updated data segment corresponding to each data segment; and compress and store the updated data segment.

[0072] Through the above, we can get the final importance coefficient of different data segments in the ring network box current data Each data segment is then decomposed using EMD (Empirical Mode Decomposition) to obtain different component signals. This means that each data segment has its own corresponding component signal. Following the order in which EMD decomposes the signal, the frequencies of the resulting component signals decrease from high to low. High-frequency signals often contain greater fluctuations and noise, while low-frequency signals represent the main frequency and overall variation characteristics of the entire signal.

[0073] Therefore, after retaining The component signal (low frequency part) with a high ratio is more conducive to signal reconstruction and compression efficiency improvement, because the low frequency component contains the core characteristics of the signal, which can effectively reduce redundant data and maintain the main information of the signal. The proportional component (high-frequency component) usually contains more noise and small fluctuations, and contributes less to the main characteristics of the signal, so it can be ignored.

[0074] The component signals of each data segment are processed based on the importance coefficient to obtain the updated data segment corresponding to each data segment. Specifically, the component signals of a data segment are arranged in the order of the component signals obtained during decomposition to obtain a component signal sequence; component signals are taken from the last component signal in the component signal sequence forward until the ratio of the number of component signals taken out to the number of component signals in the component signal sequence is equal to the final importance coefficient of the data segment, and the component signal extraction is stopped. The extracted component signals are the retained component signals of the data segment, and the retained component signals are inversely transformed to obtain the updated data segment of the data segment.

[0075] In addition, it should be noted that the empirical mode decomposition and the inverse transformation of the component signals are prior arts and will not be described in detail here. In actual application, the ratio of the number of component signals extracted to the number of component signals in the component signal sequence may not be equal to the final importance coefficient of the data segment. In this case, it is necessary to make the ratio of the number of component signals extracted to the number of component signals in the component signal sequence slightly larger than the final importance coefficient. That is, when the ratio is slightly smaller than the final importance coefficient, continue to extract component signals until the ratio is larger than the final importance coefficient, at which time stop extracting component signals.

[0076] The updated data segment obtained by inverse transforming the retained component signal can replace the original data of that data segment. The same applies to any other data segment. The processed data is then compressed using a compression algorithm such as ZIP before storage. This allows adaptive lossy compression of the acquired data to reduce data storage space pressure. Similarly, the voltage data is processed and compressed for storage.

[0077] In summary, the present application segments the grid parameter data collected by the ring network box, and then obtains the differences between different data segments, and then clusters the data segments based on the differences between the different data segments, and then performs analysis outside and inside the cluster cluster, respectively obtaining the importance coefficient of the cluster cluster and the relative importance coefficient of the data segment within the cluster cluster, and then obtaining the final importance coefficient of each data segment, and then using EMD to decompose the data segment to obtain the component signal, and use the final importance coefficient of the data segment to determine the component signal that needs to be retained, and then perform an inverse transformation to obtain the updated data segment corresponding to the data segment, and then compress and store it to reduce its storage space and reduce the pressure on the ring network box storage.

[0078] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A ring main unit with intelligent storage function for historical data, characterized in that: The ring main box includes: The difference analysis module is used to segment the grid parameter data collected by the ring network box to obtain data segments; the difference distance between each two data segments is obtained based on the difference in the proportion of the amplitude of the frequency points with non-zero amplitude in the spectrum data corresponding to each two data segments; The cluster analysis module is used to cluster all data segments using difference distance to obtain clusters; the importance coefficient of a cluster is obtained based on the proportion of the number of data segments in a cluster and the proportion of the average distance between the cluster and other clusters; The intra-cluster analysis module is used to obtain the relative importance coefficient of a data segment within a cluster based on the time series distance of the data segment from the current moment and the difference distance between the data segment and the cluster center; A fusion analysis module is used to obtain a final importance coefficient of a data segment based on the ratio of the relative importance coefficient of a data segment to the relative importance coefficient of the data segment closest to the cluster center in the cluster where the data segment is located, and the importance coefficient of the cluster where the data segment is located; The compression storage module is used to obtain the component signal of each data segment; screen the component signal of each data segment based on the final importance coefficient of each data segment, and obtain the updated data segment corresponding to each data segment; and compress and store the updated data segment.

2. The ring main unit with intelligent storage function of historical data according to claim 1, characterized in that: The obtaining the difference distance between each two data segments according to the difference in the proportion of the amplitudes of the frequency points with non-zero amplitudes in the spectrum data corresponding to each two data segments includes: The contribution of the frequency point is obtained by comparing the amplitude corresponding to a frequency point with a non-zero amplitude in the frequency data corresponding to a data segment with the sum of the amplitudes corresponding to all frequency points with non-zero amplitudes in the frequency data corresponding to the data segment; the difference distance between the two data segments is obtained based on the difference between the contributions of the frequency points with non-zero amplitudes in the spectrum data corresponding to the two data segments.

3. The ring main unit with intelligent storage function for historical data according to claim 2, characterized in that: The obtaining the difference distance between the two data segments according to the difference between the contribution degrees of the frequency points having non-zero amplitudes in the spectrum data corresponding to the two data segments includes: Obtain the union of the frequency points with non-zero amplitudes in the spectrum data corresponding to the two data segments; use the Euclidean distance calculation formula to calculate the contribution of each frequency point in the union to the spectrum data of the two data segments to obtain the difference distance between the two data segments.

4. The ring main unit with intelligent storage function for historical data according to claim 1, characterized in that: The obtaining of the importance coefficient of a cluster based on the proportion of the number of data segments in a cluster and the proportion of the average distance between the cluster and other clusters includes: Calculate the average value of the difference distances between the cluster center of a cluster and the cluster centers of other clusters, and record it as the average distance of the cluster; compare the number of data segments in the cluster with the number of data segments in all clusters to obtain the proportion of the number of data segments of the cluster; compare the average distance of the cluster with the average distance of all clusters to obtain the distance proportion of the cluster; use a first preset value to perform negative correlation mapping on the proportion of the number of data segments of the cluster and multiply it with the distance proportion to obtain the importance coefficient of the cluster.

5. The ring main unit with intelligent storage function for historical data according to claim 1, characterized in that: The step of obtaining a relative importance coefficient of a data segment within a cluster according to a temporal distance between a data segment and a current moment and a difference distance between the data segment and a cluster center includes: Take the time length from the middle moment of a data segment in a cluster to the current moment, and use an exponential function with a natural constant as the base to negatively map the time length to obtain the time series related eigenvalue of the data segment; use an exponential function with a natural constant as the base to map the difference distance between the data segment and the cluster center of the cluster where the data segment is located to obtain the distance distribution eigenvalue; normalize the time series related eigenvalue and the distance distribution eigenvalue and perform weighted summation to obtain the relative importance coefficient of the data segment.

6. The ring main unit with intelligent storage function for historical data according to claim 1, characterized in that: The method of obtaining a final importance coefficient of a data segment based on a ratio of a relative importance coefficient of a data segment to a relative importance coefficient of a data segment closest to the cluster center in the cluster where the data segment is located, and an importance coefficient of the cluster where the data segment is located, includes: Compare the relative importance coefficient of a data segment with the relative importance coefficient of the data segment closest to the cluster center within the cluster where the data segment is located to obtain the importance ratio corresponding to the data segment; multiply the configuration constant term, the importance coefficient of the cluster where the data segment is located, and the importance ratio corresponding to the data segment and normalize them to obtain the final importance coefficient of the data segment.

7. The ring main unit with historical data intelligent storage function according to claim 1, characterized in that: The obtaining of the component signal of each data segment includes: Each data segment is decomposed using EMD to obtain the component signals corresponding to each data segment.

8. The ring main unit with historical data intelligent storage function according to claim 1, characterized in that: The filtering of the component signals of each data segment based on the final importance coefficient of each data segment and obtaining the updated data segment corresponding to each data segment includes: Arrange the component signals of a data segment in the order in which they are obtained during decomposition to obtain a component signal sequence; extract component signals from the last component signal in the component signal sequence forward until the ratio of the number of extracted component signals to the number of component signals in the component signal sequence is equal to the final importance coefficient of the data segment, stop extracting the component signals, and the extracted component signals are the retained component signals of the data segment. Perform an inverse transformation on the retained component signals to obtain an updated data segment of the data segment.

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