Network data storage methods and related systems and storage media

Abstract

This application provides a network data storage method, related system, and storage medium. The method includes: acquiring full MR data corresponding to multiple rasters, and dividing the multiple rasters into P subspaces based on the full MR data, where P is an integer not less than 1; determining the value level of the P subspaces based on the full MR data of the P subspaces; performing fusion processing on the rasters in the P subspaces based on the value levels of the P subspaces to obtain a fused raster for each subspace; and storing the full MR data based on the fused rasters. By employing this method, the present invention uses fused rasters to store data, reducing the total number of rasters and saving storage space without losing the data's inherent information and storing the full amount of data.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a network data storage method and related system and storage medium. Background Technology

[0002] In the field of wireless communication network planning and optimization, user terminal equipment reports network measurement data to base stations at fixed intervals. To extract and store the data in these reports and characterize the continuous changes in the network on the ground, the ground space is often divided into seamless, non-overlapping, equal-sized grids. Network measurement data for the corresponding spatial range is stored using these grids as the basic unit. This data is then aggregated to support network planning and optimization analysis required by operator customers. Based on the analysis results, engineers optimize the network to improve the user experience.

[0003] With the popularization of 5G communication technology and corresponding smart terminals, the amount of wireless network measurement data is increasing exponentially. For example, 5G communication enables the number of IoT devices per unit area to reach more than 100 times that of 4G communication, and a massive number of IoT sensors will generate massive amounts of data.

[0004] In the field of wireless communication network planning and optimization, the surge in data volume has brought about a series of contradictions: While massive amounts of data provide rich data sources for high-level data analysis, making the analysis results more accurate and better suited to solving network problems for customers, they also present significant overhead in areas such as data governance and data storage. Given the crucial role of massive data in high-quality networks, reducing the overhead of raster storage of massive amounts of data and extracting its essential components while discarding its dross has become a critical issue in the field of network planning and optimization in the new era. Only by effectively addressing the challenges in data governance and storage can we fully utilize the material foundation provided by massive amounts of data.

[0005] Existing technologies use equal-sized grid storage for network data, with one grid corresponding to one data record in the data storage module. Considering that wireless network delivery scenarios include dense urban areas, general urban areas, suburbs, and rural towns, in areas with sparse wireless network data such as rural towns or suburbs, there may be cases where a single 50m grid contains little or no wireless network data, yet the latitude and longitude of that grid are still stored as a record, resulting in significant waste of storage resources. With the rapid increase in data volume, existing technologies employ data sampling to reduce the data volume. This involves extracting a small portion of the data from the full dataset to replace the entire dataset for subsequent network metric aggregation processing, thereby addressing the problem of rapidly increasing data volume, reducing hardware overhead, and improving query performance. However, data sampling leads to a decrease in the accuracy and efficiency of network planning and optimization. Summary of the Invention

[0006] This application discloses a network data storage method and related system and storage medium, which can reduce the data storage space occupied while storing the full amount of data.

[0007] In a first aspect, embodiments of this application provide a network data storage method, comprising: acquiring full MR data corresponding to multiple rasters, and dividing the multiple rasters into P subspaces based on the full MR data, where P is an integer not less than 1; determining the value level of the P subspaces based on the full MR data of the P subspaces; performing fusion processing on the rasters in the P subspaces based on the value level of the P subspaces to obtain a fused raster for each subspace; and storing the full MR data based on the fused raster.

[0008] In this embodiment, multiple rasters are divided into P subspaces, and the value level of each P subspace is determined based on the full MR data of those subspaces. Then, the rasters in the P subspaces are merged according to their value levels, and the full MR data is stored using the merged rasters. This method merges multiple original rasters from different subspaces based on different value levels, reducing the number of rasters and thus requiring less storage space when storing the full MR data. Compared to existing technologies that use a large number of equally sized rasters to store MR data, this invention uses merged rasters to store data, reducing the total number of rasters and saving storage space without losing data information and storing the full data.

[0009] As an optional implementation, dividing the plurality of rasters into P subspaces based on the full MR data includes:

[0010] S1. Determine the data density of each grid cell based on the amount of MR data within each grid cell;

[0011] S2. Determine the dissimilarity value between any two graticles among the plurality of graticles based on the data density of each of the graticles;

[0012] S3. Record the two grids with the smallest dissimilarity values ​​among any two grids as the same group of grids;

[0013] S4. Calculate the dissimilarity value between any two graticles among the plurality of graticles containing the same group of graticles;

[0014] S5. Repeat steps S3-S4 until each of the plurality of grids is a grid in the same group, resulting in P grids in the same group. The P subspaces correspond to the P grids in the same group, and the number of grids contained in each of the P subspaces is not less than a first preset value.

[0015] This scheme, based on spatial proximity, divides the entire telecommunications grid area into multiple subspaces to create a spatial carrier for subsequent value analysis and multi-scale raster storage, and to provide data and theoretical support. The value of each subspace is ranked, providing a theoretical basis for multi-scale raster storage.

[0016] As an optional implementation, the method further includes: if there exists a subspace A, wherein the number of grids contained in the subspace A is less than the first preset value, then obtain a subspace B from the P subspaces, wherein the difference in dissimilarity value between the subspace B and the subspace A is less than the second preset value; merge the subspace A into the subspace B to update the subspace B.

[0017] This method allows the generated subspace to meet the actual business distribution range, making it more suitable for the business's own needs.

[0018] As an optional implementation, determining the value level of the P subspaces based on the full MR data of the P subspaces includes: determining the average data density of each of the P subspaces based on the data density of the raster contained in each subspace and the number of rasteres contained in each subspace; obtaining the value level of the P subspaces based on the average data density of each of the P subspaces, wherein the larger the average data density, the higher the value level of the subspace.

[0019] By determining the value level of a subspace based on the average data density, it becomes easier to determine different fusion strategies based on the value level of the subspace. Compared to existing technologies that do not differentiate between geographic spaces and apply the same data storage and processing strategies to all geographic spaces, this solution introduces the concept of "value" to differentiate geographic spaces, such as establishing "high, medium, low, and no-value areas," allowing for different processing based on the characteristics of data in various regions.

[0020] Optionally, the method further includes: obtaining the code of each grid cell based on the latitude and longitude of the grid center point of each grid cell; and performing a fusion process on the grid cells in the P subspaces based on the value levels of the P subspaces to obtain a fused grid cell for each subspace, including: determining the upper limit of the grid size of each subspace in the P subspaces based on the value levels of the P subspaces; and fusing the grid cell sets in each subspace based on the code of each grid cell to obtain a fused grid cell for each subspace, wherein the size of the fused grid cell in each subspace is not larger than that of the subspace. The upper limit of the raster size, the number of rasters in the raster set in each subspace is a preset value, and the codes of the rasters in the raster set are different only in the last bit, the rasters in the raster set are the rasters in each subspace, and / or, the rasters in the raster set are obtained by merging the rasters in each subspace; storing the full MR data according to the merged rasters includes: storing the full MR data according to the code of the merged rasters, the code of the merged rasters is obtained by deleting the last different bit in the codes of the rasters in the raster set.

[0021] For example, the higher the value level, the lower (smaller) the upper limit of the grid size.

[0022] The raster set described in this application can be understood as a set of raster cells whose codes differ only in the last bit. This preset number is, for example, four. Each subspace can contain multiple raster sets.

[0023] This method merges multiple original rasters in different subspaces based on different value levels, reducing the number of rasters and requiring less storage space when storing full MR data. Compared to the existing technology that uses a large number of equal-sized rasters to store MR data, this invention uses merged rasters to store data, reducing the total number of rasters and saving storage space without losing the data information itself and storing full data.

[0024] Furthermore, the method also includes: when the change in MR data in any subspace C among the P subspaces exceeds a preset change amount, updating the value level of subspace C based on the changed MR data in subspace C. Within the same telecommunications grid, the amount of data in a single subspace changes over time; therefore, time series anomaly detection can be applied to determine whether the existing value model needs to be updated. By detecting whether the current value model is applicable to new data, redundant calculations that increase resource overhead are avoided, ensuring service response speed while simultaneously guaranteeing service accuracy.

[0025] Secondly, this application provides a network data storage device, comprising: a processing module, configured to acquire full MR data corresponding to multiple rasters, and divide the multiple rasters into P subspaces based on the full MR data, where P is an integer not less than 1; a determining module, configured to determine the value level of the P subspaces based on the full MR data of the P subspaces; a fusion module, configured to perform fusion processing on the rasters in the P subspaces based on the value levels of the P subspaces to obtain a fused raster for each subspace; and a storage module, configured to store the full MR data based on the fused raster.

[0026] In this embodiment, multiple rasters are divided into P subspaces, and the value level of each P subspace is determined based on the full MR data of those subspaces. Then, the rasters in the P subspaces are merged according to their value levels, and the full MR data is stored using the merged rasters. This method merges multiple original rasters from different subspaces based on different value levels, reducing the number of rasters and thus requiring less storage space when storing the full MR data. Compared to existing technologies that use a large number of equally sized rasters to store MR data, this invention uses merged rasters to store data, reducing the total number of rasters and saving storage space without losing data information and storing the full data.

[0027] Optionally, the processing module is configured to perform the following steps:

[0028] S1. Determine the data density of each grid cell based on the amount of MR data within each grid cell;

[0029] S2. Determine the dissimilarity value between any two graticles among the plurality of graticles based on the data density of each of the graticles;

[0030] S3. Record the two grids with the smallest dissimilarity values ​​among any two grids as the same group of grids;

[0031] S4. Calculate the dissimilarity value between any two graticles among the plurality of graticles containing the same group of graticles;

[0032] S5. Repeat steps S3-S4 until each of the plurality of grids is a grid in the same group, resulting in P grids in the same group. The P subspaces correspond to the P grids in the same group, and the number of grids contained in each of the P subspaces is not less than a first preset value.

[0033] The processing module is further configured to: if there exists a subspace A, wherein the number of grids contained in the subspace A is less than the first preset value, then obtain a subspace B from the P subspaces, wherein the difference in dissimilarity value between the subspace B and the subspace A is less than the second preset value; merge the subspace A into the subspace B to update the subspace B.

[0034] Furthermore, the determining module is configured to: determine the average data density of each of the P subspaces based on the data density of the grids contained in each subspace and the number of grids contained in each subspace; and obtain the value level of the P subspaces based on the average data density of each of the P subspaces, wherein the larger the average data density, the higher the value level of the subspace.

[0035] Optionally, the device further includes an encoding module, configured to: obtain an encoding for each grid based on the latitude and longitude of the grid center point of each grid; the fusion module, configured to: determine the upper limit of the grid size for each of the P subspaces based on the value level of the P subspaces; fuse the grid sets in each subspace according to the encoding of each grid to obtain a fused grid for each subspace, wherein the size of the fused grid in each subspace is not greater than the upper limit of the grid size of that subspace, the number of grids in the grid set in each subspace is a preset value, and the encodings of the grids in the grid set are different only by the last bit, the grids in the grid set are the grids in each subspace, and / or, the grids in the grid set are obtained by fusing the grids in each subspace; the storage module, configured to: store the full MR data according to the encoding of the fused grids, wherein the encoding of the fused grids is obtained by deleting the last different bit in the encodings of the grids in the grid set.

[0036] Furthermore, the device also includes an update module, configured to: when the change in MR data in any subspace C among the P subspaces exceeds a preset change amount, update the value level of the subspace C based on the changed MR data in the subspace C.

[0037] Thirdly, this application provides a network data storage device, including a processor and a memory; wherein the memory is used to store program code, and the processor is used to call the program code to execute a method as provided in any implementation of the first aspect.

[0038] Fourthly, this application provides a computer-readable storage medium storing a computer program that is executed by a processor to implement the method provided in any implementation of the first aspect.

[0039] Fifthly, this application provides a computer program product that, when run on a computer, causes the computer to perform the method provided in any implementation of the first aspect.

[0040] It is understood that the apparatus described in the second aspect, the apparatus described in the third aspect, the computer storage medium described in the fourth aspect, or the computer program product described in the fifth aspect are all used to perform the method provided in any of the first aspects. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here. Attached Figure Description

[0041] The accompanying drawings used in the embodiments of this application are described below.

[0042] Figure 1a This is a schematic diagram of the framework of a network data storage system provided in an embodiment of this application;

[0043] Figure 1b This is a schematic diagram of a big data processing platform provided in an embodiment of this application;

[0044] Figure 2 This is a flowchart illustrating a network data storage method provided in an embodiment of this application;

[0045] Figure 3 This is a flowchart illustrating another network data storage method provided in an embodiment of this application;

[0046] Figure 4 This is a schematic diagram of a subspace distribution provided in an embodiment of this application;

[0047] Figure 5 This is a schematic diagram of grid fusion provided in an embodiment of this application;

[0048] Figure 6 This is a schematic diagram of the structure of a network data storage device provided in an embodiment of this application;

[0049] Figure 7 This is a schematic diagram of another network data storage device provided in an embodiment of this application. Detailed Implementation

[0050] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0051] Measurement Report (MR) data refers to data sent every 480ms on the traffic channel (470ms on the signaling channel). This data can be used for network evaluation and optimization.

[0052] Grid: A grid is a network of geographic indicators that are refined into a grid by dividing geographic space into fixed square grids.

[0053] Multi-scale raster refers to the use of raster of different sizes to represent geospatial network indicators.

[0054] The value pattern table in this scheme can be understood as a data table containing information such as the set of raster-coded line segments, corresponding density, and value ranking contained in each subspace after the space is partitioned.

[0055] Reference Figure 1a The diagram shown illustrates the framework of a network data storage system according to an embodiment of this application. The system may include a base station 101 and a big data processing platform 102. User terminals report MR data to the base station, which collects and transmits the data to the big data processing platform 102. The big data processing platform 102 receives the full MR data from the base station and labels each raster data with a value tag. Based on the value tags and raster codes, it merges equal-sized rasters into multi-scale rasters, generating multi-scale raster full MR data, which is then stored.

[0056] Among them, such as Figure 1b As shown, the big data processing platform 102 may include a value pattern extraction module 1021, a business indicator aggregation module 1022, and a data storage module 1023.

[0057] The value pattern extraction module 1021 is used to aggregate the full set of equal-sized grid data using a spatial clustering algorithm, and to divide the telecommunications grid area into multiple subspaces for spatial dimension value analysis. The value pattern extraction module 1021 is also used to perform data value analysis on multiple subspaces, such as classifying high, medium, low, and no-value subspaces according to a certain proportion, and adding value level labels to the original full set of MR documents. The value pattern extraction module 1021 is also used to generate line segment sets for corresponding regions based on the key values ​​updated by the business indicator aggregation module, to characterize value patterns, and to perform time series anomaly detection on the original equal-sized grid data as it is updated, and to make a clear conclusion on whether the existing value patterns can be reused.

[0058] The business indicator aggregation module 1022 is used to generate multi-scale grids in different value subspaces according to the value tags generated by the value model extraction module 1021, refer to the predefined multi-scale grid usage strategy, update the key values ​​of each multi-scale grid, and aggregate the corresponding business indicators.

[0059] The data storage module 1023 is used to store all MR documents, providing a data source for subsequent processing.

[0060] As an optional implementation method, such as Figure 1a As shown, the system also includes a network planning and optimization platform 103. The big data processing platform 102 transmits multi-scale raster full-volume MR data to the network planning and optimization platform 103, enabling the platform to perform further data analysis and presentation to support business operations. "Network planning" refers to the planning of network construction based on network goals, user needs, and local conditions before building a communication network. "Network optimization" refers to identifying factors affecting network quality through traffic data analysis, on-site test data collection, parameter analysis, and hardware checks, and then performing various optimizations based on this identification.

[0061] Reference Figure 2 The diagram shown is a flowchart illustrating a network data storage method provided in an embodiment of this application. Figure 2 As shown, the method includes steps 201-204, as detailed below:

[0062] 201. Obtain the full MR data corresponding to multiple rasters, and divide the multiple rasters into P subspaces based on the full MR data, where P is an integer not less than 1;

[0063] Optionally, the execution entity in this application embodiment may be a big data processing platform, such as a server.

[0064] The aforementioned grids can be multiple grids of the same size, for example, all of them can be 50*50 pixels.

[0065] Specifically, the server periodically retrieves full MR data corresponding to multiple rasters. This periodicity can be daily, or every few days, etc., and this solution does not impose specific limitations on it. For example, the server retrieves full MR data for the current day for all rasters within the entire telecom grid area from the base station. This data is stored in equal-sized rasters as the basic unit, and each data entry contains one or more KPI fields that measure the quality of the network in that raster.

[0066] The aforementioned subspace can be understood as being obtained by dividing the aforementioned multiple grids. For example, each subspace contains no less than a preset number of grids.

[0067] As one implementation, the aforementioned P subspaces can be obtained by dividing the raster based on the amount of MR data in each raster. For example, the data density of each raster can be obtained based on the amount of MR data in each raster, and then the raster with higher data density can be divided into one subspace, and the raster with lower data density can be divided into another subspace; or, the raster with similar data density can be divided into one subspace, etc.

[0068] The above is merely an example, and this solution does not impose any specific limitations on it.

[0069] 202. Determine the value level of the P subspaces based on the full MR data of the P subspaces;

[0070] Based on the MR data in each of the P subspaces obtained from the above division, the value level of each subspace is determined.

[0071] For example, the value level of each subspace can be determined based on factors such as the data density of the MR data in each subspace.

[0072] This value level can be, for example, three levels: high, medium, and low, or, in descending order, first level, second level, third level, etc.

[0073] 203. Based on the value level of the P subspaces, perform a fusion process on the grids in the P subspaces to obtain the fused grid for each subspace;

[0074] The above method determines the value level of each subspace and then performs different raster fusion processes based on different value levels to obtain the fused raster for each subspace.

[0075] For example, the upper limit of the grid size after merging subspaces with high value levels is smaller, while the upper limit of the grid size after merging subspaces with low value levels is larger.

[0076] 204. Store the full MR data according to the fused raster.

[0077] The merged raster can be seen as a combination of multiple original rasters, where the MR data contained in the original multiple rasters is stored in a corresponding merged raster. Because the number of merged rasters is reduced, less storage space is occupied when storing the full MR data, thus saving storage space.

[0078] In this embodiment, multiple rasters are divided into P subspaces, and the value level of each of the P subspaces is determined based on the full MR data. Then, the rasters in the P subspaces are merged according to their value levels, and the full MR data is stored using the merged rasters. This method merges multiple original rasters from different subspaces based on different value levels, reducing the number of rasters and the storage space required for storing the full MR data. Compared to existing technologies that use a large number of equally sized rasters to store MR data, this invention uses merged rasters to store data, reducing the total number of rasters without losing data information and storing the full data, thus saving storage space, reducing database storage overhead, and improving query performance.

[0079] On the other hand, this solution transforms the traditional one-size raster storage of full MR data into multi-scale raster storage, which also solves the deficiency in existing technologies that cannot complete real-time network analysis due to data sampling. This solution uses full MR data for analysis, eliminating the need to accumulate data over multiple days to meet analysis standards. The amount of data within a shorter time frame is sufficient to meet the analysis requirements. By comparing with historical analysis data, the impact of network changes is intuitively demonstrated, providing effectiveness verification for network planning and optimization adjustments.

[0080] The network data storage method provided in the embodiments of this application will be described in detail below. (Refer to...) Figure 3 The diagram shown is a schematic representation of a network data storage method provided in an embodiment of this application. Figure 3 As shown, the method includes steps 301-307. This embodiment uses the spatiotemporal clustering algorithm Zorder-Hclustering to perform subspace partitioning and then raster fusion, as detailed below:

[0081] 301. Obtain the full MR data corresponding to multiple rasters;

[0082] For example, the server obtains the full MR data from the base station.

[0083] 302. Divide the plurality of grids into P subspaces based on the full MR data, where P is an integer not less than 1;

[0084] As a specific implementation method, the subspace is partitioned based on the spatiotemporal clustering algorithm Zorder-Hclustering. Accordingly, step 302 may include the following steps S1-S5, as follows:

[0085] S1. Determine the data density of each grid cell based on the amount of MR data within each grid cell;

[0086] Since the area of ​​each individual grid cell within the telecommunications grid is the same, the data density Density of each grid cell can be determined based on the size of each grid cell and the amount of MR data Q corresponding to each grid cell.

[0087] For example, when the grid size is 50m, the data density can be expressed as Density = Q / 50*50.

[0088] S2. Determine the dissimilarity value between any two grids among the plurality of grids based on the data density of each grid; the dissimilarity value D between any two grids Grid_1 and Grid_2 can be expressed as:

[0089] D=|Density(Grid_1)-Density(Grid_2)|;

[0090] S3. Record the two grids with the smallest dissimilarity values ​​among any two grids as the same group of grids;

[0091] For example, if the dissimilarity value between grids Grid_1 and Grid_2 is the smallest, then grids Grid_1 and Grid_2 are in the same group.

[0092] S4. Calculate the dissimilarity value between any two graticles among the plurality of graticles containing the same group of graticles;

[0093] In other words, the same group of rasters is treated as a single raster, and then the dissimilarity value between the same group of rasters and any other raster is calculated. Then, based on the dissimilarity values ​​between any two other rasters besides the same group of rasters that have been calculated, as well as the dissimilarity values ​​between the same group of rasters and any other raster, the minimum dissimilarity value is determined.

[0094] The data density of a group of rasters is obtained by dividing the data volume of all rasters in the group by the total area of ​​the group of rasters.

[0095] S5. Repeat steps S3-S4 until each of the plurality of grids is a grid in the same group, resulting in P grids in the same group. The P subspaces correspond to the P grids in the same group, and the number of grids contained in each of the P subspaces is not less than a first preset value.

[0096] In other words, the iteration stops when each grid cell has a group of grid cells and P subspaces are obtained, and the number of grid cells in each subspace is not less than a first preset value. This first preset value can be understood as the minimum number of grid cells contained in each subspace.

[0097] If there exists a subspace A, and the number of grids contained in the subspace A is less than the first preset value, then a subspace B is obtained from the P subspaces. If the difference in dissimilarity value between the subspace B and the subspace A is less than the second preset value, then the subspace A is merged into the subspace B to update the subspace B.

[0098] Specifically, a subspace B can be determined from the subspaces adjacent to subspace A, where the difference in dissimilarity value between subspace A and subspace B is less than a second preset value.

[0099] For example, by merging subspaces containing fewer than a first preset value with the subspace whose dissimilarity value is closest to the first preset value, the number of grids in each of the resulting P subspaces is not less than the first preset value. This method allows the range of the generated subspaces to meet the actual business distribution range and better suit the needs of the business itself.

[0100] This scheme, based on spatial proximity, divides the entire telecommunications grid area into multiple subspaces to create a spatial carrier for subsequent value analysis and multi-scale raster storage, and to provide data and theoretical support. The value of each subspace is ranked, providing a theoretical basis for multi-scale raster storage.

[0101] As one implementation method, the following example can be used for steps S1-S5 above:

[0102] Let each grid cell be a separate group. Construct a matrix M based on the dissimilarity value between any two grid cells and the grid number. The values ​​in matrix M are the dissimilarity values ​​between all groups. Let there be i grid cells (groups) in total, where D i-1,i This represents the dissimilarity value between grids Grid_i-1 and Grid_i. Matrix M can be seen in Table 1.

[0103] Table 1

[0104] D Grid_1 Grid_2 Grid_3 Grid_4 Grid_5 …… Grid_i-1 Grid_i Grid_1 0 <![CDATA[D 1,2 ]]> <![CDATA[D 1,3 ]]> <![CDATA[D 1,4 ]]> <![CDATA[D 1,5 ]]> …… <![CDATA[D 1,i-1 ]]> <![CDATA[D 1,i ]]> Grid_2 <![CDATA[D 2,1 ]]> 0 <![CDATA[D 2,3 ]]> <![CDATA[D 2,4 ]]> <![CDATA[D 2,5 ]]> …… <![CDATA[D 2,i-1 ]]> <![CDATA[D 2,i ]]> Grid_3 <![CDATA[D 3,1 ]]> <![CDATA[D 3,2 ]]> 0 <![CDATA[D 3,4 ]]> <![CDATA[D 3,5 ]]> <![CDATA[D 3,i-1 ]]> <![CDATA[D 3,i ]]> Grid_4 <![CDATA[D 4,1 ]]> <![CDATA[D 4,2 ]]> <![CDATA[D 4,3 ]]> 0 <![CDATA[D 4,5 ]]> <![CDATA[D 4,i-1 ]]> <![CDATA[D 4,i ]]> Grid_5 <![CDATA[D 5,1 ]]> <![CDATA[D 5,2 ]]> <![CDATA[D 5,3 ]]> <![CDATA[D 5,4 ]]> 0 <![CDATA[D 5,i-1 ]]> <![CDATA[D 5,i ]]> …… …… …… 0 …… …… Grid_i-1 <![CDATA[D i-1,1 ]]> <![CDATA[D i-1,2 ]]> <![CDATA[D i-1,3 ]]> <![CDATA[D i-1,4 ]]> <![CDATA[D i-1,5 ]]> …… 0 <![CDATA[D i-1,i ]]> Grid_i <![CDATA[D i,1 ]]> <![CDATA[D i,2 ]]> <![CDATA[D i,3 ]]> <![CDATA[D i,4 ]]> <![CDATA[D i,5 ]]> …… <![CDATA[D i,i-1 ]]> 0

[0105] By traversing matrix M, two grid groups with the minimum dissimilarity value are considered as the same grid group, and matrix M is updated.

[0106] For example: D 1,2 If the minimum value is found in matrix M, then merge group Grid_1 and group Grid_2 into group Grid_1_2, and update its Density and matrix M. Table 2 shows the updated matrix M:

[0107] Table 2

[0108] D Grid_1_2 Grid_3 Grid_4 Grid_5 …… Grid_i-1 Grid_i Grid_1_2 0 <![CDATA[D 1_2,3 ]]> <![CDATA[D 1_2,4 ]]> <![CDATA[D 1_2,5 ]]> …… <![CDATA[D 1_2,i-1 ]]> <![CDATA[D 1_2,i ]]> Grid_3 <![CDATA[D 3,1_2 ]]> 0 <![CDATA[D 3,4 ]]> <![CDATA[D 3,5 ]]> <![CDATA[D 3,i-1 ]]> <![CDATA[D 3,i ]]> Grid_4 <![CDATA[D 4,1_2 ]]> <![CDATA[D 4,3 ]]> 0 <![CDATA[D 4,5 ]]> <![CDATA[D 4,i-1 ]]> <![CDATA[D 4,i <!-- 8 -->]]> Grid_5 <![CDATA[D 5,1_2 ]]> <![CDATA[D 5,3 ]]> <![CDATA[D 5,4 ]]> 0 <![CDATA[D 5,i-1 ]]> <![CDATA[D 5,i ]]> …… …… 0 …… …… Grid_i-1 <![CDATA[D i-1,1_2 ]]> <![CDATA[D i-1,3 ]]> <![CDATA[D i-1,4 ]]> <![CDATA[D i-1,5 ]]> …… 0 <![CDATA[D i-1,i ]]> Grid_i <![CDATA[D i,1_2 ]]> <![CDATA[D i,3 ]]> <![CDATA[D i,4 ]]> <![CDATA[D i,5 ]]> …… <![CDATA[D i,i-1 ]]> 0

[0109] By repeatedly merging the two groups with the minimum dissimilarity value, until there are P groups in which the number of grid cells is not less than the first preset value.

[0110] If there is a group A with fewer than the first preset value in a single group, group A is merged into group B by determining the group B that is adjacent to group A and has the closest dissimilarity value.

[0111] For example, if the number of grid cells in Grid_4 is less than the first preset value, then compare D. 3,4 With D 4,5 Size.

[0112] If D 3,4 <D 4,5 Then merge Grid_3 and Grid_4 into Grid_3_4, and update its Density and matrix M, as shown in Table 3.

[0113] If D 3,4 >D 4,5 Then merge Grid_4 and Grid_5 into Grid_4_5, and update its Density and matrix M, as shown in Table 4.

[0114] Table 3

[0115] D Grid_1_2 Grid_3_4 Grid_5 …… Grid_i-1 Grid_i Grid_1_2 0 <![CDATA[D 1_2,3_4 ]]> <![CDATA[D 1_2,5 ]]> …… <![CDATA[D 1_2,i-1 ]]> <![CDATA[D 1_2,i ]]> Grid_3_4 <![CDATA[D 3_4,1_2 ]]> 0 <![CDATA[D 3_4,5 ]]> <![CDATA[D 3_4,i-1 ]]> <![CDATA[D 3_4,i ]]> Grid_5 <![CDATA[D 5,1_2 ]]> <![CDATA[D 5,3_4 ]]> 0 <![CDATA[D 5,i-1 ]]> <![CDATA[D 5,i ]]> …… …… 0 …… …… Grid_i-1 <![CDATA[D i-1,1_2 ]]> <![CDATA[D i-1,3_4 ]]> <![CDATA[D i-1,5 ]]> …… 0 <![CDATA[D i-1,i ]]> Grid_i <![CDATA[D i,1_2 ]]> <![CDATA[D i,3_4 ]]> <![CDATA[D i,5 ]]> …… <![CDATA[D i,i-1 ]]> 0

[0116] Table 4

[0117] D Grid_1_2 Grid_3 Grid_4_5 …… Grid_i-1 Grid_i Grid_1_2 0 <![CDATA[D 1_2,3 ]]> <![CDATA[D 1_2,4_5 ]]> …… <![CDATA[D 1_2,i-1 ]]> <![CDATA[D 1_2,i ]]> Grid_3 <![CDATA[D 3,1_2 ]]> 0 <![CDATA[D 3,4_5 ]]> <![CDATA[D 3,i-1 ]]> <![CDATA[D 3,i ]]> Grid_4_5 <![CDATA[D 4_5,1_2 ]]> <![CDATA[D 4_5,3 ]]> 0 <![CDATA[D 4_5,i-1 ]]> <![CDATA[D 4_5,i ]]> …… …… 0 …… …… Grid_i-1 <![CDATA[D i-1,1_2 ]]> <![CDATA[D i-1,3 ]]> <![CDATA[D i-1,4_5 ]]> …… 0 <![CDATA[D i-1,i ]]> Grid_i <![CDATA[D i,1_2 ]]> <![CDATA[D i,3 ]]> <![CDATA[D i,4_5 ]]> …… <![CDATA[D i,i-1 ]]> 0

[0118] Repeat the above merging process until the number of individual grouped grid cells is less than the first preset value, at which point the algorithm ends.

[0119] 303. Determine the average data density of each of the P subspaces based on the data density of the raster contained in each subspace and the number of raster contained in each subspace;

[0120] Specifically, the data density of each raster is determined based on the amount of MR data within each raster, and then the data densities of all rasteres contained in each subspace are summed to obtain the data density of the raster contained in each subspace.

[0121] The number of rasters contained in each subspace can be determined based on the number of rasters encoded within that subspace. The raster encoding is derived from the latitude and longitude of the raster center point.

[0122] For example, based on the quaternary Z-order curve algorithm, the raster code can be obtained by inputting the latitude and longitude of a 50-meter raster.

[0123] This method is only one example; other methods may also be used, and this solution does not specifically limit them.

[0124] The average data density of each subspace is the ratio of the sum of the data densities of the rasters in that subspace to the number of rasters.

[0125] For example, key-value pairs can be constructed using the Z-order quaternary raster encoding inherent in MR data. The basic format is: Key: Value => Z-order quaternary raster encoding: corresponding raster data density. Correspondingly, the aforementioned P subspaces can be understood as P raster groups containing a series of key-value pairs.

[0126] Based on the number of keys (i.e., raster codes) in each subspace containing key-value pairs, which is the number of rasters contained in each subspace, the average data density of each of the P subspaces can be determined.

[0127] 304. The value level of the P subspaces is obtained based on the average data density of each subspace, wherein the higher the average data density, the higher the value level of the subspace.

[0128] By sorting the average data density above, we can obtain the value levels of P subspaces.

[0129] Preferably, the two-dimensional raster space can be converted into a one-dimensional set of points and line segments. After the conversion, when labeling MR data, the code of a data point is regarded as a point, and a series of consecutive codes are equivalent to a line. Using one-dimensional points and lines instead of two-dimensional rasters to determine the affiliation of raster codes is faster.

[0130] like Figure 4 As shown, when P is 4, that is, there exist subspaces Group1, Group2, Group3, and Group4, if:

[0131] Density Group1 Density Group2 Density Group3 Density Group4 Then we have:

[0132] Group 1 includes: 013, 021, 023, 030-033, 102-123…;

[0133] Group 2 includes: 133, 303, 310-313, 321-323…;

[0134] Group 3 includes: 002-012, 020-030, 032, 100, 101, 110, 111…;

[0135] Group 4 includes: 000, 001;

[0136] Among them, Group1, Group2, Group3 and Group4 correspond to high, medium, low and no-value areas, respectively.

[0137] Existing technologies do not differentiate between geographic spaces and apply the same data storage and processing strategies to all geographic spaces. However, this solution introduces the concept of "value" to differentiate geographic spaces, such as establishing "high, medium, low, and no-value areas," which allows for different processing based on the characteristics of data in various regions.

[0138] Furthermore, after obtaining the above value levels, a value pattern table can be constructed. This table stores the set of line segments within a single telecommunications grid's internal subspace, as shown in Table 5:

[0139] Table 5

[0140]

[0141] This table contains the following fields:

[0142] "GroupID" is the value area number of a subspace within a single telecommunications grid that is further subdivided.

[0143] "Contains" refers to the set of coded points / segments contained within a single subspace Group;

[0144] “Data” and “Area” represent the total amount of data and the total area of ​​the raster corresponding to the coded points / segments contained in a single Group;

[0145] "Density" refers to the data density of a single group;

[0146] "ValueOrder" is the value ranking among the groups.

[0147] Then, associate the MR documents (i.e., the structured MR data obtained after storage) and populate the corresponding value order. After generating the corresponding value pattern table, based on the Z-order quaternary raster code ("CODE") in the full raster MR data, and referring to the "Contains" field in the value pattern table, append the value level "ValueOrder" of the corresponding value area in the table for each record. This provides a partitioning basis for the subsequent generation of multi-scale rasters. The full MR document structure after updating "ValueOrder" is shown in Table 6 as an example:

[0148] Table 6

[0149]

[0150] This table provides a clear and intuitive understanding of the value level corresponding to each raster code.

[0151] 305. Determine the upper limit of the grid size for each of the P subspaces based on the value level of the P subspaces;

[0152] Specifically, different grid storage strategies are implemented based on different value levels. The grid size trend can increase as the "ValueOrder" value level decreases and decrease as it increases.

[0153] Multi-scale raster usage strategy: A raster usage scheme based on storing MR data using raster cells of varying sizes, where the multi-scale raster size is 50 meters. n Multiples, such as 50*50, 100*100, 200*200...

[0154] Specific strategy examples are as follows:

[0155] Strategy 1: Based on the value level analysis results, use different grid sizes for different value areas. For example, use a 50*50 grid for high-value areas, a grid size limit of 100*100 for medium-value areas, a grid size limit of 200*200 for low-value areas, and a grid size limit of 400*400 for no-value areas.

[0156] Strategy 2: Based on the value level analysis results, use 50-meter and 100-meter grids for high-value and medium-value areas, while temporarily omitting aggregation calculations for low-value and no-value areas.

[0157] Strategy 3: By default, only some features that are frequently used, such as coverage, are calculated. However, an on-demand supplementary calculation mechanism is provided for other non-default features, such as single-user features, call center features, and network performance features.

[0158] 306. According to the code of each grid, the grid sets in each subspace are merged to obtain the merged grid of each subspace, wherein the size of the merged grid in each subspace is not greater than the upper limit of the grid size of the subspace, the number of grids in the grid set in each subspace is a preset value, and the codes of the grids in the grid set are different only in the last bit, the grids in the grid set are the grids in each subspace, and / or, the grids in the grid set are obtained by merging the grids in each subspace;

[0159] like Figure 5As shown, based on quaternary Z-sequence encoding, fusion is performed on existing 50-meter level grids. Each time the grid level doubles, the last digit of the existing grid code is removed, and four grids with the same number are merged. This process is repeated until the merged grid reaches the maximum scale specified by the strategy, and no more grids can be merged. The fusion process is shown in Table 7, with an upper limit of 200*200.

[0160] Table 7

[0161]

[0162] 307. Store the full MR data according to the encoding of the fused raster, wherein the encoding of the fused raster is obtained by deleting the last different bit in the encoding of the rasters in the raster set.

[0163] After the multi-scale raster is generated, the final value pattern is updated, for example: Figure 5 After transforming the original 50-meter raster value model to a maximum 200-meter raster model, the number of raster cells can be reduced to approximately 40%. The "Contains" field in the value model table is updated to 02,030,032, 2,300,302,320. Based on this, the "Contains" field in the value model table is updated, and MR documents are integrated according to the "CODE" mapping relationship. This integration effectively saves storage space.

[0164] Based on this embodiment, where the amount of data in a single subspace, i.e. "Data", changes over time within the same telecommunications grid, time series anomaly detection can be applied to determine whether the existing value model needs to be updated.

[0165] By detecting whether the current value model is applicable to new data, we can avoid redundant calculations that increase resource consumption, ensure business response speed, and at the same time guarantee business accuracy.

[0166] Specifically, by combining the 3-sigma principle and the sliding window method, a comprehensive analysis of historical data trends is conducted to test the reusability of value patterns in the time domain. If no outliers are found in any subspace, the historical value level is directly reused. For example, if a value is n times the standard deviation from the mean, it is determined to be an outlier.

[0167] If an anomaly is detected, for new data that has not been processed, keep "GroupID", "Contains", and "Area" unchanged in the value model, and update "Density" and "ValueOrder" along with "Data" to determine the new value order. The purpose of this update is to be compatible with different value levels in the same region over time.

[0168] The above-mentioned anomaly could be caused by the change in MR data in any subspace exceeding a preset change amount. In such cases, the value level of the subspace is updated based on the changed MR data in that subspace.

[0169] This method merges multiple original rasters in different subspaces based on different value levels, reducing the number of rasters and requiring less storage space when storing full MR data. Compared to the existing technology that uses a large number of equal-sized rasters to store MR data, this invention uses merged rasters to store data, reducing the total number of rasters and saving storage space without losing the data information itself and storing full data.

[0170] It should be noted that this application uses MR data as an example for illustration, but other wireless network data such as call history report (CHR) data can also be applied to this solution. This solution does not impose specific limitations on this.

[0171] Reference Figure 6 As shown, based on the description of the above network data storage method embodiments, this embodiment of the invention also discloses a network data storage device, with reference to... Figure 6 , Figure 6 This is a schematic diagram of a network data storage device provided in an embodiment of the present invention. The network data storage device includes a processing module 601, a determining module 602, a fusion module 603, and a storage module 604; wherein:

[0172] The processing module 601 is used to acquire the full MR data corresponding to multiple rasters, and divide the multiple rasters into P subspaces based on the full MR data, where P is an integer not less than 1;

[0173] The determination module 602 is used to determine the value level of the P subspaces based on the full MR data of the P subspaces;

[0174] The fusion module 603 is used to perform fusion processing on the grids in the P subspaces according to the value level of the P subspaces, so as to obtain the fused grid of each subspace;

[0175] Storage module 604 is used to store the full MR data according to the fused raster.

[0176] The processing module 601 is used to perform the following steps:

[0177] S1. Determine the data density of each grid cell based on the amount of MR data within each grid cell;

[0178] S2. Determine the dissimilarity value between any two graticles among the plurality of graticles based on the data density of each of the graticles;

[0179] S3. Record the two grids with the smallest dissimilarity values ​​among any two grids as the same group of grids;

[0180] S4. Calculate the dissimilarity value between any two graticles among the plurality of graticles containing the same group of graticles;

[0181] S5. Repeat steps S3-S4 until each of the plurality of grids is a grid in the same group, resulting in P grids in the same group. The P subspaces correspond to the P grids in the same group, and the number of grids contained in each of the P subspaces is not less than a first preset value.

[0182] Furthermore, the processing module 601 is also used for:

[0183] If there exists a subspace A, and the number of grids contained in the subspace A is less than the first preset value, then subspace B is obtained from the P subspaces, and the difference in dissimilarity value between the subspace B and the subspace A is less than the second preset value.

[0184] Subspace A is merged into subspace B to update subspace B.

[0185] Optionally, the determining module 602 is used for:

[0186] The average data density of each of the P subspaces is determined based on the data density of the raster contained in each subspace and the number of raster contained in each subspace;

[0187] The value level of the P subspaces is obtained based on the average data density of each subspace, wherein the higher the average data density, the higher the value level of the subspace.

[0188] The device further includes an encoding module, used for:

[0189] The code for each grid is obtained based on the latitude and longitude of the grid center point;

[0190] The fusion module 603 is used for:

[0191] The upper limit of the grid size of each of the P subspaces is determined based on the value level of the P subspaces;

[0192] Based on the encoding of each grid, the grid sets in each subspace are merged to obtain a merged grid for each subspace. The size of the merged grid in each subspace is not greater than the upper limit of the grid size of that subspace. The number of grids in the grid set in each subspace is a preset value, and the encoding of the grids in the grid set is different only by the last bit. The grids in the grid set are the grids in each subspace, and / or the grids in the grid set are obtained by merging the grids in each subspace.

[0193] The storage module 604 is used for:

[0194] The full MR data is stored according to the encoding of the fused raster, wherein the encoding of the fused raster is obtained by deleting the last different bit in the encoding of the raster in the raster set.

[0195] Furthermore, the device also includes an update module for:

[0196] When the change in MR data in any subspace C among the P subspaces exceeds a preset change amount, the value level of subspace C is updated based on the changed MR data in subspace C.

[0197] In this embodiment, multiple rasters are divided into P subspaces, and the value level of each of the P subspaces is determined based on the full MR data. Then, the rasters in the P subspaces are merged according to their value levels, and the full MR data is stored using the merged rasters. This method merges multiple original rasters from different subspaces based on different value levels, reducing the number of rasters and the storage space required for storing the full MR data. Compared to existing technologies that use a large number of equally sized rasters to store MR data, this invention uses merged rasters to store data, reducing the total number of rasters without losing data information and storing the full data, thus saving storage space, reducing database storage overhead, and improving query performance.

[0198] On the other hand, this solution transforms the traditional one-size raster storage of full MR data into multi-scale raster storage, which also solves the deficiency in existing technologies that cannot complete real-time network analysis due to data sampling. This solution uses full MR data for analysis, eliminating the need to accumulate data over multiple days to meet analysis standards. The amount of data within a shorter time frame is sufficient to meet the analysis requirements. By comparing with historical analysis data, the impact of network changes is intuitively demonstrated, providing effectiveness verification for network planning and optimization adjustments.

[0199] It is worth noting that the specific functional implementation of the network data storage device can be found in the description of the network data storage method above, and will not be repeated here. The various units or modules in the network data storage device can be individually or entirely merged into one or more other units or modules, or some of the units or modules can be further divided into multiple functionally smaller units or modules. This achieves the same operation without affecting the technical effects of the embodiments of the present invention. The aforementioned units or modules are based on logical functional division. In practical applications, the function of one unit (or module) can also be implemented by multiple units (or modules), or the function of multiple units (or modules) can be implemented by one unit (or module).

[0200] Based on the description of the above method and device embodiments, this invention also provides a network data storage device.

[0201] Please see Figure 7 This is a schematic diagram of the structure of a network data storage device provided in an embodiment of the present invention. Figure 7 The network data storage device 700 shown (specifically, this device 700 can be a computer device) includes a memory 701, a processor 702, a communication interface 703, and a bus 704. The memory 701, processor 702, and communication interface 703 are interconnected via the bus 704.

[0202] The memory 701 may be a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM).

[0203] The memory 701 can store programs. When the program stored in the memory 701 is executed by the processor 702, the processor 702 and the communication interface 703 are used to execute the various steps of the network data storage method of the present application embodiment.

[0204] The processor 702 may be a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), graphics processing unit (GPU), or one or more integrated circuits, used to execute relevant programs to achieve the functions required by the units in the network data storage device of this application embodiment, or to execute the network data storage method of this application method embodiment.

[0205] The processor 702 can also be an integrated circuit chip with signal processing capabilities. In implementation, each step of the network data storage method of this application can be completed by the integrated logic circuits in the hardware of the processor 702 or by instructions in software form. The processor 702 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory 701. The processor 702 reads the information in the memory 701 and, in conjunction with its hardware, performs the functions required by the units included in the network data storage device of this application embodiment, or executes the network data storage method of this application method embodiment.

[0206] The communication interface 703 uses transceiver devices, such as, but not limited to, transceivers, to enable communication between the device 700 and other devices or communication networks. For example, data can be acquired through the communication interface 703.

[0207] Bus 704 may include a pathway for transmitting information between various components of device 700 (e.g., memory 701, processor 702, communication interface 703).

[0208] It should be noted that, although Figure 7 The illustrated device 700 only shows the memory, processor, and communication interface. However, those skilled in the art should understand that in specific implementations, device 700 may also include other devices necessary for normal operation. Furthermore, depending on specific needs, those skilled in the art should understand that device 700 may also include hardware devices for implementing other additional functions. Moreover, those skilled in the art should understand that device 700 may only include the devices necessary for implementing the embodiments of this application, and may not necessarily include... Figure 7 All the devices shown.

[0209] This application also provides a chip system applied to an electronic device; the chip system includes one or more interface circuits and one or more processors; the interface circuits and the processors are interconnected via lines; the interface circuits are used to receive signals from the memory of the electronic device and send the signals to the processors, the signals including computer instructions stored in the memory; when the processor executes the computer instructions, the electronic device executes the network data storage method.

[0210] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps of any of the above methods.

[0211] This application also provides a computer program product containing instructions. When the computer program product is run on a computer or processor, it causes the computer or processor to perform one or more steps of any of the methods described above.

[0212] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the specific descriptions of the corresponding steps in the foregoing method embodiments, and will not be repeated here.

[0213] It should be understood that in the description of this application, unless otherwise stated, " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B can represent A or B; where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" do not necessarily imply difference. In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0214] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling, direct coupling, or communication connection shown or discussed between each other may be indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0215] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0216] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be read-only memory (ROM), random access memory (RAM), or magnetic media, such as floppy disks, hard disks, magnetic tapes, magnetic disks, or optical media, such as digital versatile discs (DVDs), or semiconductor media, such as solid-state disks (SSDs).

[0217] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the claims.

Claims

1. A network data storage method, characterized in that, include: Obtain the full MR data corresponding to multiple rasters, and divide the multiple rasters into P subspaces based on the full MR data, where P is an integer not less than 1; Based on the full MR data of the P subspaces, determine the value level of the P subspaces; The grids in the P subspaces are merged according to their value levels to obtain the merged grid for each subspace. The full MR data is stored according to the fused raster.

2. The method according to claim 1, characterized in that, The step of dividing the plurality of rasters into P subspaces based on the full MR data includes: S1. Determine the data density of each grid cell based on the amount of MR data within each grid cell; S2. Determine the dissimilarity value between any two graticles among the plurality of graticles based on the data density of each of the graticles; S3. Record the two grids with the smallest dissimilarity values ​​among any two grids as the same group of grids; S4. Calculate the dissimilarity value between any two graticles among the plurality of graticles containing the same group of graticles; S5. Repeat steps S3-S4 until each of the plurality of grids is a grid in the same group, resulting in P grids in the same group. The P subspaces correspond to the P grids in the same group, and the number of grids contained in each of the P subspaces is not less than a first preset value.

3. The method according to claim 2, characterized in that, The method further includes: If there exists a subspace A, and the number of grids contained in the subspace A is less than the first preset value, then subspace B is obtained from the P subspaces, and the difference in dissimilarity value between the subspace B and the subspace A is less than the second preset value. Subspace A is merged into subspace B to update subspace B.

4. The method according to claim 2 or 3, characterized in that, The step of determining the value level of the P subspaces based on the full MR data of the P subspaces includes: The average data density of each of the P subspaces is determined based on the data density of the raster contained in each subspace and the number of raster contained in each subspace; The value level of the P subspaces is obtained based on the average data density of each subspace, wherein the higher the average data density, the higher the value level of the subspace.

5. The method according to claim 4, characterized in that, The method further includes: The code for each grid is obtained based on the latitude and longitude of the grid center point; The step of fusing the grids in the P subspaces according to their value levels to obtain the fused grid for each subspace includes: The upper limit of the grid size of each of the P subspaces is determined based on the value level of the P subspaces; Based on the encoding of each grid, the grid sets in each subspace are merged to obtain a merged grid for each subspace. The size of the merged grid in each subspace is not greater than the upper limit of the grid size of that subspace. The number of grids in the grid set in each subspace is a preset value, and the encoding of the grids in the grid set is different only by the last bit. The grids in the grid set are the grids in each subspace, and / or the grids in the grid set are obtained by merging the grids in each subspace. The step of storing the full MR data according to the fused raster includes: The full MR data is stored according to the encoding of the fused raster, wherein the encoding of the fused raster is obtained by deleting the last different bit in the encoding of the raster in the raster set.

6. The method according to claim 1, 2, 3 or 5, characterized in that, The method further includes: When the change in MR data in any subspace C among the P subspaces exceeds a preset change amount, the value level of subspace C is updated based on the changed MR data in subspace C.

7. A network data storage device, characterized in that, include: The processing module is used to acquire the full MR data corresponding to multiple rasters, and divide the multiple rasters into P subspaces based on the full MR data, where P is an integer not less than 1; The determination module is used to determine the value level of the P subspaces based on the full MR data of the P subspaces; The fusion module is used to fuse the grids in the P subspaces according to their value levels to obtain the fused grids for each subspace. A storage module is used to store the full MR data according to the fused raster.

8. The apparatus according to claim 7, characterized in that, The processing module is used to perform the following steps: S1. Determine the data density of each grid cell based on the amount of MR data within each grid cell; S2. Determine the dissimilarity value between any two graticles among the plurality of graticles based on the data density of each of the graticles; S3. Record the two grids with the smallest dissimilarity values ​​among any two grids as the same group of grids; S4. Calculate the dissimilarity value between any two graticles among the plurality of graticles containing the same group of graticles; S5. Repeat steps S3-S4 until each of the plurality of grids is a grid in the same group, resulting in P grids in the same group. The P subspaces correspond to the P grids in the same group, and the number of grids contained in each of the P subspaces is not less than a first preset value.

9. The apparatus according to claim 8, characterized in that, The processing module is further configured to: If there exists a subspace A, and the number of grids contained in the subspace A is less than the first preset value, then subspace B is obtained from the P subspaces, and the difference in dissimilarity value between the subspace B and the subspace A is less than the second preset value. Subspace A is merged into subspace B to update subspace B.

10. The apparatus according to claim 8 or 9, characterized in that, The determining module is used for: The average data density of each of the P subspaces is determined based on the data density of the raster contained in each subspace and the number of raster contained in each subspace; The value level of the P subspaces is obtained based on the average data density of each subspace, wherein the higher the average data density, the higher the value level of the subspace.

11. The apparatus according to claim 10, characterized in that, The device further includes an encoding module for: The code for each grid is obtained based on the latitude and longitude of the grid center point; The fusion module is used for: The upper limit of the grid size of each of the P subspaces is determined based on the value level of the P subspaces; Based on the encoding of each grid, the grid sets in each subspace are merged to obtain a merged grid for each subspace. The size of the merged grid in each subspace is not greater than the upper limit of the grid size of that subspace. The number of grids in the grid set in each subspace is a preset value, and the encoding of the grids in the grid set is different only by the last bit. The grids in the grid set are the grids in each subspace, and / or the grids in the grid set are obtained by merging the grids in each subspace. The storage module is used for: The full MR data is stored according to the encoding of the fused raster, wherein the encoding of the fused raster is obtained by deleting the last different bit in the encoding of the raster in the raster set.

12. The apparatus according to claim 7, 8, 9 or 11, characterized in that, The device further includes an update module for: When the change in MR data in any subspace C among the P subspaces exceeds a preset change amount, the value level of subspace C is updated based on the changed MR data in subspace C.

13. A network data storage device, characterized in that, It includes a processor and a memory; wherein the memory is used to store program code, and the processor is used to call the program code to perform the method as described in any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the method of any one of claims 1 to 6.

15. A computer program product, characterized in that, When the computer program product is run on a computer, it causes the computer to perform the method as described in any one of claims 1 to 6.

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