WO2023142466A1

WO2023142466A1 - Network data storage method, related system, and storage medium

Info

Publication number: WO2023142466A1
Application number: PCT/CN2022/114359
Authority: WO
Inventors: 原朝; 王昊; 周银生; 黄骞
Original assignee: 华为技术有限公司
Priority date: 2022-01-26
Filing date: 2022-08-23
Publication date: 2023-08-03
Also published as: CN116540921A

Abstract

Embodiments of the present application provide a network data storage method, a related system, and a storage medium. The method comprises: obtaining full MR data corresponding to a plurality of grids, and dividing the plurality of grids into P sub-spaces according to the full MR data, P being an integer not less than 1; determining value levels of the P sub-spaces according to the full MR data of the P sub-spaces; performing fusion processing on the grids in the P sub-spaces according to the value levels of the P sub-spaces to obtain a fused grid of each sub-space; and storing the full MR data according to the fused grid. By using the means, in the present invention, the fused grid is used to store data, the total number of grids is reduced on the premise that information of data is not lost and the full data is stored, and a storage space is saved.

Description

Network data storage method, related system, and storage medium

This application claims the priority of a Chinese patent application with application number 202210093050.3 and titled "Network data storage method and related system, storage medium" filed with the China Patent Office on January 26, 2022, the entire contents of which are hereby incorporated by reference In this application.

technical field

The present application relates to the technical field of data processing, and in particular to a network data storage method, a related system, and a storage medium.

Background technique

In the field of wireless communication network planning and optimization, user terminal equipment reports network measurement data reports to the base station at a fixed period. In order to extract and store the data in these reports and represent the information of continuous changes in the surface space of the network, the surface space is often divided into seamless and non-overlapping equal-sized grids, and the grid is used as the basic unit to store the corresponding spatial range Network measurement data, and then through data aggregation to support network planning optimization analysis required by operator customers. According to the analysis results, Weiyou engineers optimize the network to improve the experience of network users.

With the popularity 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. Massive IoT sensors Massive amounts of data will be generated.

For the field of wireless communication network planning and optimization, the surge in data volume has brought about a series of contradictions: massive data provides rich data sources for high-level data analysis, making the analysis results more accurate, and can solve network problems well for customers. On the other hand, it also faces high overhead in data governance and data storage. In view of the important position of massive data in high-quality networks, how to reduce the cost of raster storage of massive data and extract the essence from it has become an important topic in the field of network planning and optimization in the new era. In order to make full use of the material basis provided by massive data.

When storing network data in the prior art, equal-sized grids are used for storage. In the data storage module, one grid corresponds to one data record. Considering that wireless network delivery scenarios include dense urban areas, general urban areas, suburban areas, urban and rural areas, etc., in the In areas where wireless network data is relatively sparse, such as towns, villages or suburbs, there may be no or very little wireless network data inside a single 50m grid, but the latitude and longitude of the deleted grid is still stored as a record, which causes a huge storage resource waste. With the rapid increase in the amount of data, the existing technology adopts the solution of data sampling to reduce the amount of data, by extracting a small part of data from the full amount of data instead of the full amount of data for subsequent network index aggregation processing, so as to deal with the problem of rapid increase in the amount of data , thereby reducing hardware overhead and improving query performance, but the use of data sampling will lead to a decrease in the accuracy of network planning and optimization, and a decrease in the efficiency of network planning and optimization.

Contents of the invention

The present application discloses a network data storage method, a related system, and a storage medium, which can save a full amount of data while reducing the space occupied by data storage.

In the first aspect, the embodiment of the present application provides a method for storing network data, including: acquiring the full amount of MR data corresponding to multiple grids, and dividing the multiple grids into P subspaces according to the full amount of MR data, P is an integer not less than 1; determine the value levels of the P subspaces according to the full MR data of the P subspaces; perform fusion processing on the grids in the P subspaces according to the value levels of the P subspaces , to obtain the fused grid of each subspace; store the full amount of MR data according to the fused grid.

In this embodiment of the present application, multiple grids are divided into P subspaces, and the value levels of the P subspaces are determined according to the full MR data of the P subspaces, and then the P subspaces are evaluated according to the value levels of the P subspaces. The raster in is fused, and then the full amount of MR data is stored according to the fused raster. Using this method, multiple original grids of different subspaces are merged based on different value levels, so that the number of grids is reduced, and less storage space is occupied when storing the full amount of MR data. Compared with the existing technology A very large number of equal-sized grids are used to store MR data. The present invention uses fused grids to store data. On the premise of not losing the information of the data itself and storing the full amount of data, the total number of grids is reduced, saving storage space .

As an optional implementation manner, the dividing the plurality of grids into P subspaces according to the full amount of MR data includes:

S1. Determine the data density of each grid according to the amount of MR data in each grid;

S2. Determine the dissimilarity value between any two grids in the plurality of grids according to the data density of each grid;

S3. Record the two grids with the smallest dissimilarity value between any two grids among the plurality of grids as the same group of grids;

S4. Calculating a dissimilarity value between any two grids in the plurality of grids including the same group of grids;

S5. Steps S3-S4 are repeatedly executed until each of the multiple grids is a grid in the same group of grids, and P grids of the same group are obtained, and the P subspaces are the same as the P subspaces of the same group. Corresponding to a group of grids, the number of grids contained in each of the P subspaces is not less than a first preset value.

Based on spatial proximity, the scheme divides the entire telecommunications grid area into multiple subspaces to create space carriers for subsequent value analysis and multi-scale grid storage, and provide data support and theoretical support. Take the subspace as the unit, sort its value, and provide a theoretical basis for multi-scale raster storage.

As an optional implementation, the method further includes: if there is a subspace A, wherein the number of grids contained in the subspace A is less than the first preset value, selecting from the P subspaces Obtaining subspace B, the difference of the dissimilarity value between the subspace B and the subspace A is less than a second preset value; merging the subspace A into the subspace B to update the subspace space B.

This method can make the range of the generated subspace meet the actual business distribution range, and better meet the needs of the business itself.

As an optional implementation manner, the determining the value level of the P subspaces according to the full amount of MR data of the P subspaces includes: The data density and the number of grids contained in each subspace determine the average data density of each of the P subspaces; obtain the P subspaces according to the average data density of each of the P subspaces. A value level, wherein the greater the average value of the data density, the higher the value level of the subspace.

By determining the value level of the subspace based on the average value of the data density, it is convenient to determine different fusion strategies based on the value level of the subspace. Compared with the existing technology that does not distinguish between geographical spaces and adopts the same data storage and processing strategy for all geographical spaces, this solution introduces the concept of "value" to distinguish geographical spaces, such as setting up "high, medium, Low and valueless areas", etc., can be processed differently according to the characteristics of various regional data.

Optionally, the method further includes: obtaining the code of each grid according to the latitude and longitude of the grid center point of each grid; The grids in the grid are fused to obtain the fused grid of each subspace, including: determining the upper limit of the grid size of each subspace in the P subspaces according to the value level of the P subspaces; Encoding of each grid, fusing the set of grids in each subspace to obtain a fused grid of each subspace, wherein the size of the fused grid in each subspace is different 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 only the last bit of the encoding of the grids in the grid set is different, The grids in the grid set are the grids in each subspace, and/or, the grids in the grid set are fused according to the grids in each subspace; Storing the full amount of MR data according to the fused grid includes: storing the full amount of MR data according to the encoding of the fused grid, where the encoding of the fused grid is the grid The encoding of the rasters in the collection is not the same as the last bit obtained by deleting.

For example, the higher the value level, the lower (smaller) the maximum grid size.

The grid set described in this application can be understood as determining a preset number of grids whose codes differ only in the last bit as a grid set. The preset number is, for example, 4 and so on. Each subspace can contain multiple collections of rasters.

Using this method, multiple original grids of different subspaces are merged based on different value levels, so that the number of grids is reduced, and less storage space is occupied when storing the full amount of MR data. Compared with the existing technology A very large number of equal-sized grids are used to store MR data. The present invention uses fused grids to store data. On the premise of not losing the information of the data itself and storing the full amount of data, the total number of grids is reduced, saving storage space .

Further, the method further includes: when the variation of MR data in any subspace C of the P subspaces exceeds a preset variation, updating the The value level of subspace C. Among them, under the same telecommunications grid, the amount of data in a single subspace will change with time, so for example, 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, it avoids repeated calculations that increase resource overhead, ensures business response speed, and at the same time ensures business accuracy.

In a second aspect, the present application provides a network data storage device, including: a processing module, configured to obtain full MR data corresponding to multiple grids, and divide the multiple grids into P subspaces, P is an integer not less than 1; the determination module is used to determine the value level of the P subspaces according to the full amount of MR data of the P subspaces; the fusion module is used to determine the value level of the P subspaces according to the value of the P subspaces The level performs fusion processing on the grids in the P subspaces to obtain a fused grid of each subspace; a storage module is configured to store the full amount of MR data according to the fused grids.

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

Wherein, the processing module is further configured to: if there is a subspace A, wherein the number of grids contained in the subspace A is less than the first preset value, then obtain the subspace B from the P subspaces , the difference of the dissimilarity value between the subspace B and the subspace A is smaller than a second preset value; the subspace A is merged into the subspace B, so as to update the subspace B.

Further, the determination module is configured to: determine the data density of each of the P subspaces according to the data density of the grids contained in each of the P subspaces and the number of grids contained in each subspace. The average value of data density; the value level of the P subspaces is obtained according to the average data density of each subspace in the P subspaces, wherein the greater the average value of the data density, the higher the value level of the subspace .

Optionally, the device further includes an encoding module, configured to: obtain the encoding of each grid according to the latitude and longitude of the grid center point of each grid; the fusion module, configured to: according to the P The value level of each subspace determines the upper limit of the grid size of each subspace in the P subspaces; according to the encoding of each grid, the grid set in each subspace is fused to obtain each The fused grids of subspaces, wherein the fused grid size in each subspace is not greater than the upper limit of the grid size of the subspace, and the number of grids in the grid set in each subspace is preset Set a value, and only the last bit of the code of the grid in the grid set is different, the grid in the grid set is the grid in each subspace, and/or, the grid The grids in the set are fused according to the grids in each subspace; the storage module is configured to: store the full amount of MR data according to the encoding of the fused grids, and the fused The code of the grid is obtained by deleting the last bit that is different from the codes of the grids in the grid set.

Further, the device further includes an update module, configured to: when the change amount of MR data in any subspace C of the P subspaces exceeds a preset change amount, according to the changed MR data in the subspace C data, update the value level of the subspace C.

In a third aspect, the present application provides a network data storage device, including a processor and a memory; wherein, the memory is used to store program codes, and the processor is used to call the program codes to execute any A method provided by an implementation.

In a fourth aspect, the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method provided in any implementation manner of the first aspect.

In a fifth aspect, the present application provides a computer program product, which, when running on a computer, causes the computer to execute the method provided in any implementation manner of the first aspect.

It can be understood that the device described in the second aspect, the device described in the third aspect, the computer storage medium described in the fourth aspect, or the computer program product described in the fifth aspect provided above are all used to execute the Either of the provided methods. Therefore, the beneficial effects that it can achieve can refer to the beneficial effects in the corresponding method, and will not be repeated here.

Description of drawings

The accompanying drawings used in the embodiments of the present application are introduced below.

Fig. 1a is a schematic framework diagram of a network data storage system provided by an embodiment of the present application;

Fig. 1b is a schematic diagram of a big data processing platform provided by an embodiment of the present application;

FIG. 2 is a schematic flow diagram of a network data storage method provided by an embodiment of the present application;

Fig. 3 is a schematic flowchart of another network data storage method provided by the embodiment of the present application;

FIG. 4 is a schematic diagram of a subspace distribution provided by an embodiment of the present application;

Fig. 5 is a schematic diagram of grid fusion provided by the embodiment of the present application;

FIG. 6 is a schematic structural diagram of a network data storage device provided by an embodiment of the present application;

FIG. 7 is a schematic structural diagram of another network data storage device provided by an embodiment of the present application.

Detailed ways

Embodiments of the present application are described below with reference to the drawings in the embodiments of the present application. The terms used in the implementation of the embodiments of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

Measurement report (Measurement Report, MR) data means that information is sent every 480ms on the traffic channel (470ms on the signaling channel), and these data can be used for network evaluation and optimization data.

Grid: Grid, by using a fixed square grid to segment the geographic space, refine the network indicators of the geographic space into the grid representation.

Multi-scale grids, which use grids of different sizes to represent geospatial network indicators.

The value model table in this solution can be understood as a data table that contains information such as raster coded line segment sets, corresponding densities, and value rankings contained in each subspace after the space is divided.

Referring to FIG. 1 a , it is a schematic framework diagram of a network data storage system provided by an embodiment of the present application. The system may include a base station 101 and a big data processing platform 102 . Wherein, the user terminal reports the MR data to the base station, and the base station 101 collects and transmits the MR data to the big data processing platform 102 . The big data processing platform 102 receives the full amount of MR data from the base station and marks each piece of grid data with a value label, and fuses the equal-sized grid into a multi-scale grid according to the value label and grid code to generate the full amount of MR data of the multi-scale grid. Then, based on the multi-scale grid full MR data, it is stored.

Wherein, as shown in FIG. 1 b , the big data processing platform 102 may include a value model extraction module 1021 , a business indicator aggregation module 1022 and a data storage module 1023 .

The value model extraction module 1021 is used to use the spatial clustering algorithm to aggregate the full amount of large-scale grid data, and divide multiple subspaces within the telecommunications grid area to analyze the value of the spatial dimension; the value model extraction module 1021 is also used for multiple Perform data value analysis on each subspace, such as classifying high, medium, low, and worthless subspaces according to a certain proportion, and adding value level tags to the original full amount of MR documents; the value pattern extraction module 1021 is also used to aggregate modules according to business indicators The updated key value generates line segment sets corresponding to areas of different value levels, describes the value mode, and performs time series anomaly detection on it as the original large raster data is updated, and reuses the existing value mode whether to draw clear conclusions.

The business index 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, and to update the key values of each multi-scale grid, And aggregate the corresponding business indicators.

The data storage module 1023 is used to store all MR documents and provide data sources for subsequent processing.

As an optional implementation manner, as shown in FIG. 1 a , the system further includes a network planning and network optimization platform 103 . The big data processing platform 102 transmits the full MR data of multi-scale grids to the network planning and optimization platform 103, so that the network planning and optimization platform 103 can further analyze and present the received data to support services. The network planning is the abbreviation of network planning, which refers to the planning of network construction according to the network construction goals, user needs, and local actual conditions before the construction of communication networks. The network optimization is the abbreviation of network optimization, which means to find out the reasons that affect the network quality through traffic data analysis, field test data collection, parameter analysis, hardware inspection and other means on the basis of the existing network, and to perform various optimizations on this basis .

Referring to FIG. 2 , it is a schematic flowchart of a network data storage method provided by an embodiment of the present application. As shown in Figure 2, the method includes steps 201-204, specifically as follows:

201. Acquire full MR data corresponding to multiple grids, and divide the multiple grids into P subspaces according to the full MR data, where P is an integer not less than 1;

Optionally, the execution subject of this embodiment of the present application may be a big data processing platform, such as specifically a server.

The plurality of grids mentioned above may be multiple grids of the same size, for example, may all have a size of 50*50.

Specifically, the server periodically acquires the full amount of MR data corresponding to multiple grids. The periodicity may be, for example, every day, or every few days, etc., which is not specifically limited in this solution. For example, the server obtains from the base station the full amount of MR data corresponding to all the grids in the entire telecommunications grid area of the day. The data is stored in equal-sized grids as the basic unit, and each piece of data contains one or more KPI fields to measure the quality of the grid network.

The foregoing subspace can be understood as being obtained by dividing the foregoing plurality of grids. For example, the number of grids contained in each subspace is not less than the preset value and so on.

As an implementation, the above P subspaces can be obtained by dividing according to the data volume of MR data in each grid, for example, according to the data volume of MR data in each grid, each grid can be obtained Then divide the raster with higher data density into one subspace, and divide the raster with lower data density into another subspace; or divide the raster with similar data density into a subspace, etc.

The foregoing is only an example, and this solution does not specifically limit it.

202. Determine the value levels of the P subspaces according to the full MR data of the P subspaces;

The value level of each subspace is determined based on the MR data in each of the P subspaces obtained from the above division.

For example, the value level of each subspace is determined based on the data density and the like of MR data in each subspace.

The value level may be, for example, three levels of high, medium, and low, or, in descending order, the value level may be the first level, the second level, the third level, and so on.

203. Perform fusion processing on the grids in the P subspaces according to the value levels of the P subspaces, so as to obtain the fused grids of each subspace;

By determining the value level of each subspace above, and then corresponding to different grid fusion processes based on different value levels, the fused grid of each subspace is obtained.

For example, the upper limit of the fused grid size of a subspace with a high value level is smaller, and the upper limit of the fused grid size of a subspace with a lower value level is larger.

204. Store the full amount of MR data according to the fused grid.

Among them, the fused grid can be regarded as merging multiple original grids, storing the MR data contained in the original multiple grids in a corresponding fused grid, and the fused grid is due to The number is reduced, so less storage space is occupied when storing the full amount of MR data, saving storage space.

In this embodiment of the present application, multiple grids are divided into P subspaces, and the value levels of the P subspaces are determined according to the full MR data of the P subspaces, and then the P subspaces are evaluated according to the value levels of the P subspaces. The raster in is fused, and then the full amount of MR data is stored according to the fused raster. Using this method, multiple original grids of different subspaces are merged based on different value levels, so that the number of grids is reduced, and less storage space is occupied when storing the full amount of MR data. Compared with the existing technology A very large number of equal-sized grids are used to store MR data. The present invention uses fused grids to store data. On the premise of not losing the information of the data itself and storing the full amount of data, the total number of grids is reduced, saving storage space , reducing storage overhead in the database and improving query performance.

On the other hand, this solution converts the full amount of MR data from traditional equal-sized grid storage to multi-scale grid storage, and can also solve the defect that real-time network analysis cannot be completed due to data sampling in the prior art. This solution uses the full amount of MR data for analysis. It does not need to accumulate multiple days of data to meet the analysis standards. The amount of data in a short period of time can meet the analysis requirements. By comparing with historical analysis data, it can intuitively reflect the impact of network changes. Network planning optimization and adjustment provide effect verification.

The network data storage method provided by the embodiment of the present application will be described in detail below. Referring to FIG. 3 , it is a schematic diagram of a network data storage method provided by an embodiment of the present application. As shown in Figure 3, the method includes steps 301-307. In this embodiment, based on the space-time clustering algorithm Zorder-Hclustering, subspace division is performed, and then grid fusion is performed, as follows:

301. Obtain full MR data corresponding to multiple grids;

For example, the server acquires the full amount of MR data from the base station.

302. Divide the plurality of grids into P subspaces according to the full amount of MR data, where P is an integer not less than 1;

As a specific implementation manner, based on the space-time clustering algorithm Zorder-Hclustering, the subspace is divided. Correspondingly, step 302 may include the following steps S1-S5, specifically as follows:

Given that the area of a single grid in the telecommunications grid is the same, the data density Density of each grid can be determined based on the corresponding size of each grid and the data volume Q of MR data corresponding to each grid.

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

The dissimilarity value D between any two grids Grid_1 and Grid_2 can be expressed as:

D＝|Density(Grid_1)-Density(Grid_2)|;

For example, if the dissimilarity value between Grid_1 and Grid_2 is the smallest, then Grid_1 and Grid_2 are the same group of grids.

That is to say, the same group of grids is regarded as a grid, and then the dissimilarity value between the same group of grids and any other grid is calculated, and then based on the previously calculated The dissimilarity value between any other two rasters and the dissimilarity value between the same group of rasters and other arbitrary rasters, and determine the minimum dissimilarity value.

Wherein, the data density of the grids in the same group is obtained by dividing the data volume of all the grids in the same group by the total area of the grids in the same group.

That is to say, when each grid has the same group of grids, and P subspaces are obtained, and the number of grids contained in each subspace is not less than the first preset value, the iteration is stopped. The first preset value can be understood as the preset minimum number of grids contained in each subspace.

Wherein, if there is a subspace A, wherein the number of grids contained in the subspace A is less than the first preset value, the subspace B is obtained from the P subspaces, and the subspace B and the If the dissimilarity value difference of the subspace A is less than a second preset value, the subspace A is merged into the subspace B, so as to update the subspace B.

Specifically, the subspace B whose dissimilarity value difference with the subspace A is smaller than a second preset value may be determined from the subspaces spatially adjacent to the subspace A.

For example, by merging the subspaces containing the number of grids less than the first preset value and the subspace with the closest dissimilarity value, so that the number of grids contained in each of the finally obtained P subspaces is equal to not less than the first preset value. This method can make the range of the generated subspace meet the actual business distribution range, and better meet the needs of the business itself.

As an implementation manner, for the above steps S1-S5, an example may be as follows:

Let each grid be a separate group, construct a matrix M according to the dissimilarity value between any two grids and the grid number, and the value in the matrix M is the dissimilarity value between all groups. It is assumed that there are i grids (groups) in total, where D _{i-1, i} represent the dissimilarity value between grids Grid_i-1 and Grid_i. Matrix M can be shown in Table 1.

Table I

DD.	Grid_1Grid_1	Grid_2Grid_2	Grid_3Grid_3	Grid_4Grid_4	Grid_5Grid_5	……...	Grid_i-1Grid_i-1	Grid_iGrid_i
Grid_1Grid_1	00	D _1,2 D _1,2	D _1,3 D _1,3	D _1,4 D _1,4	D _1,5 D _1,5	……...	D _1,i-1 D _1,i-1	D _1,i D _1,i
Grid_2Grid_2	D _2,1 D _2,1	00	D _2,3 D _2,3	D _2,4 D _2,4	D _2,5 D _2,5	……...	D _2,i-1 D _2,i-1	D _2,i D _2,i
Grid_3Grid_3	D _3,1 D _3,1	D _3,2 D _3,2	00	D _3,4 D _3,4	D _3,5 D _3,5	the	D _3,i-1 D _3,i-1	D _3,i D _3,i
Grid_4Grid_4	D _4,1 D _4,1	D _4,2 D _4,2	D _4,3 D _4,3	00	D _4,5 D _4,5	the	D _4,i-1 D _4,i-1	D _4,i D _4,i
Grid_5Grid_5	D _5,1 D _5,1	D _5,2 D _5,2	D _5,3 D _5,3	D _5,4 D _5,4	00	the	D _5,i-1 D _5,i-1	D _5,i D _5,i
……...	……...	……...	the	the	the	00	……...	……...
Grid_i-1Grid_i-1	D _i-1,1 D _i-1,1	D _i-1,2 D _i-1,2	D _i-1,3 D _i-1,3	D _i-1,4 D _i-1,4	D _i-1,5 D _i-1,5	……...	00	D _i-1,i D _i-1,i
Grid_iGrid_i	D _i,1 D _i,1	D _i,2 D _i,2	D _i,3 D _i,3	D _i,4 D _i,4	D _i,5 D _i,5	……...	D _i,i-1 D _i,i-1	00

By traversing the matrix M, the two grid groups with the minimum dissimilarity value are regarded as the same group of grids, and the matrix M is updated.

For example: D _1,2 is the minimum value in the matrix M, then merge the group Grid_1 and the group Grid_2 into the group Grid_1_2, and update its Density and matrix M, as shown in Table 2 for the updated matrix M:

Table II

DD.	Grid_1_2Grid_1_2	Grid_3Grid_3	Grid_4Grid_4	Grid_5Grid_5	……...	Grid_i-1Grid_i-1	Grid_iGrid_i
Grid_1_2Grid_1_2	00	D _{1_2,3} D _{1_2,3}	D _{1_2,4} D _{1_2,4}	D _{1_2,5} D _{1_2,5}	……...	D _{1_2,i-1} D _{1_2,i-1}	D _{1_2,i} D _{1_2,i}
Grid_3Grid_3	D _{3,1_2} D _{3,1_2}	00	D _3,4 D _3,4	D _3,5 D _3,5	the	D _3,i-1 D _3,i-1	D _3,i D _3,i
Grid_4Grid_4	D _{4,1_2} D _{4,1_2}	D _4,3 D _4,3	00	D _4,5 D _4,5	the	D _4,i-1 D _4,i-1	D _4,i D _4,i
Grid_5Grid_5	D _{5,1_2} D _{5,1_2}	D _5,3 D _5,3	D _5,4 D _5,4	00	the	D _5,i-1 D _5,i-1	D _5,i D _5,i
……...	……...	the	the	the	00	……...	……...
Grid_i-1Grid_i-1	D _{i-1,1_2} D _{i-1,1_2}	D _i-1,3 D _i-1,3	D _i-1,4 D _i-1,4	D _i-1,5 D _i-1,5	……...	00	D _i-1,i D _i-1,i
Grid_iGrid_i	D _{i,1_2} D _{i,1_2}	D _i,3 D _i,3	D _i,4 D _i,4	D _i,5 D _i,5	……...	D _i,i-1 D _i,i-1	00

By repeatedly merging the two groups with the minimum dissimilarity value, until the number of grids in P groups is not less than the first preset value.

At this time, if there is a group A whose number of grids in a single group is less than the first preset value, group A is merged into group B by determining group B which is adjacent to group A and has the closest dissimilarity value.

For example, if the number of grids in Grid_4 is less than the first preset value, compare the sizes of D _3,4 and D _4,5 .

If D _3,4 < D _4,5 , merge Grid_3 and Grid_4 into Grid_3_4, and update its Density and matrix M, as shown in Table 3.

If D _3,4 >D _4,5 , merge Grid_4 and Grid_5 into Grid_4_5, and update its Density and matrix M, as shown in Table 4.

Table three

DD.	Grid_1_2Grid_1_2	Grid_3_4Grid_3_4	Grid_5Grid_5	……...	Grid_i-1Grid_i-1	Grid_iGrid_i
Grid_1_2Grid_1_2	00	D _{1_2,3_4} D _{1_2,3_4}	D _{1_2,5} D _{1_2,5}	……...	D _{1_2,i-1} D _{1_2,i-1}	D _{1_2,i} D _{1_2,i}
Grid_3_4Grid_3_4	D _{3_4,1_2} D _{3_4,1_2}	00	D _{3_4,5} D _{3_4,5}	the	D _{3_4,1-1} D _{3_4,1-1}	D _{3_4,i} D _{3_4, i}
Grid_5Grid_5	D _{5,1_2} D _{5,1_2}	D _{5,3_4} D _{5,3_4}	00	the	D _5,i-1 D _5,i-1	D _5,i D _5,i
……...	……...	the	the	00	……...	……...
Grid_i-1Grid_i-1	D _{i-1,1_2} D _{i-1,1_2}	D _{i-1,3_4} D _{i-1,3_4}	D _i-1,5 D _i-1,5	……...	00	D _i-1,i D _i-1,i
Grid_iGrid_i	D _{i,1_2} D _{i,1_2}	D _{i,3_4} D _{i,3_4}	D _i,5 D _i,5	……...	D _i,i-1 D _i,i-1	00

Table four

DD.	Grid_1_2Grid_1_2	Grid_3Grid_3	Grid_4_5Grid_4_5	……...	Grid_i-1Grid_i-1	Grid_iGrid_i
Grid_1_2Grid_1_2	00	D _{1_2,3} D _{1_2,3}	D _{1_2,4_5} D _{1_2,4_5}	……...	D _{1_2,i-1} D _{1_2,i-1}	D _{1_2,i} D _{1_2,i}
Grid_3Grid_3	D _{3,1_2} D _{3,1_2}	00	D _{3,4_5} D _{3,4_5}	the	D _3,i-1 D _3,i-1	D _3,i D _3,i
Grid_4_5Grid_4_5	D _{4_5,1_2} D _{4_5,1_2}	D _{4_5,3} D _{4_5,3}	00	the	D _{4_5,i-1} D _{4_5,i-1}	D _{4_5,i} D _{4_5,i}
……...	……...	the	the	00	……...	……...
Grid_i-1Grid_i-1	D _{i-1,1_2} D _{i-1,1_2}	D _i-1,3 D _i-1,3	D _{i-1,i_5} D _{i-1,i_5}	……...	00	D _i-1,i D _i-1,i
Grid_iGrid_i	D _{i,1_2} D _{i,1_2}	D _i,3 D _i,3	D _{i,4_5} D _{i,4_5}	……...	D _i,i-1 D _i,i-1	00

The above merging process is repeated until the number of individual grouped grids is less than the first preset value, and the algorithm ends.

303. Determine the average data density of each of the P subspaces according to the data density of the grids contained in each of the P subspaces and the number of grids contained in each subspace;

Specifically, according to the amount of MR data in each grid, the data density of each grid is determined, and then the data densities of all grids contained in each subspace are summed, that is, the data density contained in each subspace is obtained The data density of the raster.

The number of grids contained in each subspace can be determined according to the number of codes of the grids in the subspace. Wherein, the coding of the grid is obtained according to the latitude and longitude of the grid center point of each grid.

For example, according to the quaternary Z-order curve algorithm, the grid code can be obtained by inputting the latitude and longitude of a 50-meter grid.

This means is only an example, and it may also be in other manners, which are not specifically limited in this solution.

The average data density of each subspace is the ratio of the sum of the data densities of the grids in the subspace to the number of grids.

For example, the key-value pair can be constructed by using the built-in Z-order quaternary raster code in MR data. The basic format is: key Key: value Value => Z-order quaternary raster code: corresponding to the data density of the raster. Correspondingly, the above P subspaces can be understood as P grid groups containing a series of key-value pairs.

According to the number of keys (that is, raster codes) containing key-value pairs in each subspace, that is, the number of rasters contained in each subspace, the average data density of each subspace in the P subspaces can be determined.

304. Obtain the value level of the P subspaces according to the average data density of each of the P subspaces, wherein the greater the average value of the data density, the higher the value level of the subspace;

Sorting the average values of the above data densities can then obtain the value levels of the P subspaces.

Preferably, the two-dimensional grid space can be converted into a one-dimensional point set and line segment set, so that when labeling MR data after conversion, the code of a piece of data is regarded as a point, and a series of continuous codes is equivalent to It is faster to use one-dimensional dotted lines instead of two-dimensional grids to determine the attribution of grid codes.

As shown in Figure 4, when P is 4, there are subspaces Group1, Group2, Group3 and Group4, if:

Density _Group1 > Density _Group2 > Density _Group3 > Density _Group4 , then:

Group1 includes: 013, 021, 023, 030-033, 102-123…;

Group2 includes: 133, 303, 310-313, 321-323…;

Group3 includes: 002-012, 020-030, 032, 100, 101, 110, 111…;

Group4 includes: 000,001;

Among them, Group1, Group2, Group3 and Group4 correspond to high, medium, low and valueless areas respectively.

In the existing technology, no distinction is made between geographical spaces, and the same data storage and processing strategies are adopted for all geographical spaces; however, this solution introduces the concept of "value" to distinguish geographical spaces, such as setting up "high, medium, low, Valueless areas", etc., can be processed differently according to the characteristics of various regional data.

Further, after obtaining the above value levels, a value pattern table can be constructed. The line segment set of the internal subspace of a single telecom grid is stored by constructing a table, as shown in Table 5:

Table five

The form contains the following fields:

"GroupID" is the value area number of the further subdivided subspace within a single telecom grid;

"Contains" is a collection of coded points/line segments contained in a single subspace Group;

"Data" and "Area" are the total data volume and total area of the grid corresponding to the code point/segment contained in a single Group;

"Density" is the data density of a single Group;

"ValueOrder" is the value order among the groups.

Then, associate the MR document (that is, the structured MR data obtained after storage), and fill in the corresponding value order. After generating the corresponding value mode table, according to the Z-order quaternary raster code ("CODE") in the full raster MR data, compare the "Contains" field in the value mode table, and append the raster in the value mode table to each record. The value level "ValueOrder" of the corresponding value area in the table provides a partition basis for subsequent generation of multi-scale grids. An example is the full MR document structure after updating "ValueOrder" as shown in Table 6:

Table six

Through this table, you can clearly and intuitively understand the value level corresponding to each raster code.

305. Determine the upper limit of the grid size of each of the P subspaces according to the value levels of the P subspaces;

Among them, grid storage strategies of different sizes are formulated according to different value levels. The grid size trend can become larger as the value level of "ValueOrder" decreases, and becomes smaller as it increases.

Multi-scale grid usage strategy: a grid usage plan based on using grids of different sizes to store MR data, where the multi-scale grid size is 2n times of 50 meters, such as 50*50, 100*100, 200 *200…

Examples of specific strategies are as follows:

Strategy 1: Combining with the value level analysis results, use different size grids for different levels of value areas, for example, use 50*50 grids for high-value areas, use grids with an upper limit of 100*100 for medium-value areas, and use grids for low-value areas. The upper limit of grid size is 200*200, the upper limit of grid size for useless areas is 400*400, etc.

Strategy 2: Combined with the value level analysis results, use 50-meter and 100-meter-level grids for high-value and medium-value areas, and do not perform aggregation calculations for low-value and non-value areas.

Strategy 3: By default, only some features with many applications, such as coverage, are calculated, but an on-demand compensation mechanism for other non-default calculation features is provided, such as single-user, traffic, and network performance.

306. According to the encoding of each grid, fuse the set of grids in each subspace to obtain a fused grid of each subspace, where the fused grid in each subspace The grid size 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 coding of the grids in the grid set is only the last bit Not the same, the grids in the grid set are the grids in each subspace, and/or, the grids in the grid set are obtained by fusion according to the grids in each subspace of;

As shown in Figure 5, based on the quaternary Z-sequence coding, fusion is performed on the basis of the existing 50-meter-level grid, and each time the grid level is doubled, the last grid is deleted based on the existing grid coding. One bit, and fuse 4 rasters with the same number. This process is repeated until the fused rasters reach the maximum size specified by the policy and there are no more rasters to continue merging. The fusion process is shown in Table 7. The upper limit of the fusion scale is 200*200:

Table seven

307. Store the full amount of MR data according to the encoding of the fused grid, the encoding of the fused grid is obtained by deleting the last bit that is different from the encoding of the grids in the grid set of.

After the multi-scale grid is generated, the final value model is updated. For example, after the original 50-meter-level grid value model in Figure 5 is converted to the maximum 200-meter-level grid model, the number of grids can be reduced to about 40%. The value model "Contains" in the table is updated to 02,030,032, 2,300,302,320. Based on this update the "Contains" field of the value model table and integrate the MR documents according to the "CODE" mapping relationship. Based on this integration, storage space can be effectively saved.

On the basis of this embodiment, where under the same telecommunications grid, the amount of data in a single subspace, namely "Data", will change with time, so 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, it avoids repeated calculations that increase resource overhead, ensures business response speed, and at the same time ensures business accuracy.

Specifically, combined with the 3-sigma principle and the sliding window method, the trend of historical data is comprehensively analyzed, and the reusability of the value model is detected in the time domain space. If no abnormal value is found in each subspace, the historical value level is directly reused. . For example, a value is considered an outlier if it is n times the standard deviation away from the mean.

If an abnormal situation is found, for new unprocessed data, keep "GroupID", "Contains", and "Area" in the value mode unchanged, and update "Density" and "ValueOrder" with "Data" to determine the new value order , the purpose of this update is to be compatible with different value levels on the time dimension of the same region.

The above abnormality may be that the change amount of MR data in any subspace exceeds the preset change amount, and the value level of the subspace is updated according to the changed MR data in the subspace.

It should be noted that, the embodiment of the present application takes network data as MR data as an example for illustration, other wireless network data such as call history report (call history report, CHR) data, etc. may also be applicable to this solution. This plan does not specifically limit this.

Referring to Figure 6, based on the description of the above-mentioned embodiment of the network data storage method, the embodiment of the present invention also discloses a network data storage device, referring to Figure 6, Figure 6 is a network data storage device provided by the embodiment of the present invention A schematic structural diagram of the network data storage device including a processing module 601, a determination module 602, a fusion module 603 and a storage module 604; wherein:

A processing module 601, configured to acquire full MR data corresponding to multiple grids, and divide the multiple grids into P subspaces according to the full MR data, where P is an integer not less than 1;

Determining module 602, for determining the value level of the P subspaces according to the full amount of MR data of the P subspaces;

A fusion module 603, configured to perform fusion processing on the grids in the P subspaces according to the value levels of the P subspaces, so as to obtain the fused grids of each subspace;

A storage module 604, configured to store the full amount of MR data according to the fused grid.

Wherein, the processing module 601 is configured to perform the following steps:

Further, the processing module 601 is also used for:

If there is a subspace A, wherein the number of grids contained in the subspace A is less than the first preset value, obtain a subspace B from the P subspaces, and the subspace B and the subspace The difference of the dissimilarity value of A is smaller than the second preset value;

The subspace A is merged into the subspace B to update the subspace B.

Optionally, the determining module 602 is configured to:

Determine the average data density of each of the P subspaces according to the data density of the grids contained in each of the P subspaces and the number of grids contained in each subspace;

The value level of the P subspaces is obtained according to the average data density of each of the P subspaces, wherein the greater the average data density, the higher the value level of the subspace.

Wherein, the device also includes an encoding module for:

Obtain the code of each grid according to the latitude and longitude of the grid center point of each grid;

The fusion module 603 is configured to:

determining the upper limit of the grid size of each subspace in the P subspaces according to the value level of the P subspaces;

According to the encoding of each grid, the grid set in each subspace is fused to obtain the fused grid of each subspace, wherein the size of the fused grid in each subspace 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 only different 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 fused according to the grids in each subspace;

The storage module 604 is configured to:

The full amount of MR data is stored according to the coding of the fused grid, which is obtained by deleting the last bit that is different from the coding of the grids in the grid set.

Further, the device also includes an update module, configured to:

When the variation of MR data in any subspace C of the P subspaces exceeds a preset variation, the value level of the subspace C is updated according to the changed MR data in the subspace C.

It is worth pointing out that, for the specific function implementation manner of the network data storage device, reference may be made to the description of the above network data storage method, which will not be repeated here. Each unit or module in the network data storage device can be separately or all combined into one or several other units or modules to form, or one (some) units or modules can be split into functionally smaller ones. Multiple units or modules can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units or modules are divided based on logical functions. In practical applications, the functions of one unit (or module) can also be realized by multiple units (or modules), or the functions of multiple units (or modules) can be realized by one unit (or module) implementation.

Based on the descriptions of the foregoing method embodiments and device embodiments, embodiments of the present invention further provide a network data storage device.

Please refer to FIG. 7 , which is a schematic structural diagram of a network data storage device provided by an embodiment of the present invention. The network data storage device 700 shown in FIG. 7 (the device 700 may specifically be a computer device) includes a memory 701 , a processor 702 , a communication interface 703 and a bus 704 . Wherein, the memory 701 , the processor 702 , and the communication interface 703 are connected to each other through a bus 704 .

The memory 701 may be a read-only memory (Read Only Memory, ROM), a static storage device, a dynamic storage device or a random access memory (Random Access Memory, RAM).

The memory 701 may store programs, and when the programs stored in the memory 701 are executed by the processor 702, the processor 702 and the communication interface 703 are used to execute various steps of the network data storage method of the embodiment of the present application.

The processor 702 may be a general-purpose central processing unit (Central Processing Unit, CPU), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a graphics processing unit (graphics processing unit, GPU) or one or more The integrated circuit is used to execute related programs to realize the functions required by the units in the network data storage device of the embodiment of the present application, or to execute the network data storage method of the method embodiment of the present application.

The processor 702 may also be an integrated circuit chip, which has a signal processing capability. During implementation, each step of the network data storage method of the present application may be completed by an integrated logic circuit of hardware in the processor 702 or instructions in the form of software. The above-mentioned processor 702 can also be a general-purpose processor, a digital signal processor (Digital Signal Processing, DSP), an application-specific integrated circuit (ASIC), a ready-made programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components. Various methods, steps, and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register. The storage medium is located in the memory 701, and the processor 702 reads the information in the memory 701, and combines its hardware to complete the functions required by the units included in the network data storage device of the embodiment of the application, or execute the network data storage device of the method embodiment of the application. data storage method.

The communication interface 703 implements communication between the apparatus 700 and other devices or communication networks by using a transceiver device such as but not limited to a transceiver. For example, data can be acquired through the communication interface 703 .

The bus 704 may include pathways for transferring information between various components of the device 700 (eg, memory 701 , processor 702 , communication interface 703 ).

It should be noted that although the device 700 shown in FIG. 7 only shows a memory, a processor, and a communication interface, in the specific implementation process, those skilled in the art should understand that the device 700 also includes other devices necessary for normal operation . Meanwhile, according to specific needs, those skilled in the art should understand that the apparatus 700 may also include hardware devices for implementing other additional functions. In addition, those skilled in the art should understand that the device 700 may only include components necessary to realize the embodiment of the present application, and does not necessarily include all the components shown in FIG. 7 .

The embodiment of the present application also provides a chip system, the chip system is applied to electronic equipment; the chip system includes one or more interface circuits, and one or more processors; the interface circuit and the processor Interconnected by wires; the interface circuit is used to receive signals from the memory of the electronic device and send the signals to the processor, the signals include computer instructions stored in the memory; when the processor executes When the computer instructs, the electronic device executes the network data storage method.

The embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium stores instructions, and when it is run on a computer or a processor, the computer or the processor executes one of the above-mentioned methods or multiple steps.

The embodiment of the present application also provides a computer program product including instructions. When the computer program product is run on the computer or the processor, the computer or the processor is made to perform one or more steps in any one of the above methods.

Those skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the above-described system, device and unit can refer to the specific description of the corresponding steps in the foregoing method embodiments, which will not be repeated here. repeat.

It should be understood that in the description of this application, unless otherwise specified, "/" means that the objects associated with each other are an "or" relationship, for example, A/B can mean A or B; where A and B can be singular or plural. And, in the description of the present application, unless otherwise specified, "plurality" means two or more than two. "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 item (piece) of a, b, or c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, c can be single or multiple . In addition, in order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with basically the same function and effect. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. Meanwhile, in the embodiments of the present application, words such as "exemplary" or "for example" are used as examples, illustrations or illustrations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application shall not be interpreted as being more preferred or more advantageous than other embodiments or design schemes. To be precise, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete manner for easy understanding.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods may be implemented in other ways. For example, the division of this unit is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored, or not implement. The mutual coupling, or direct coupling, or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

A unit described as a separate component may or may not be physically separated, and a component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In the above embodiments, all or part of them may be implemented by software, hardware, firmware or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present application will be generated in whole or in part. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable device. The computer instructions may be stored in or transmitted over a computer-readable storage medium. The computer instructions can be sent from one website site, computer, server, or data center to another by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) A website site, computer, server or data center for transmission. The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center integrated with one or more available media. The usable medium can be read-only memory (read-only memory, ROM), or random access memory (random access memory, RAM), or magnetic medium, for example, floppy disk, hard disk, magnetic tape, magnetic disk, or optical medium, such as , a digital versatile disc (digital versatile disc, DVD), or a semiconductor medium, for example, a solid state disk (solid state disk, SSD) and the like.

The above is only the specific implementation of the embodiment of the application, but the protection scope of the embodiment of the application is not limited thereto, and any changes or replacements within the technical scope disclosed in the embodiment of the application shall be covered by this application. Within the scope of protection of the application examples. Therefore, the protection scope of the embodiments of the present application should be based on the protection scope of the claims.

Claims

A network data storage method, characterized in that, comprising:

Acquiring full MR data corresponding to multiple grids, and dividing the multiple grids into P subspaces according to the full MR data, where P is an integer not less than 1;

Determine the value level of the P subspaces according to the full amount of MR data in the P subspaces;

performing fusion processing on the grids in the P subspaces according to the value levels of the P subspaces, so as to obtain the fused grids of each subspace;

The full amount of MR data is stored according to the fused grid.
The method according to claim 1, wherein said dividing said plurality of grids into P subspaces according to said full amount of MR data comprises:

S1. Determine the data density of each grid according to the amount of MR data in each grid;

S2. Determine the dissimilarity value between any two grids in the plurality of grids according to the data density of each grid;

S3. Record the two grids with the smallest dissimilarity value between any two grids among the plurality of grids as the same group of grids;

S4. Calculating a dissimilarity value between any two grids in the plurality of grids including the same group of grids;

S5. Steps S3-S4 are repeatedly executed until each of the multiple grids is a grid in the same group of grids, and P grids of the same group are obtained, and the P subspaces are the same as the P subspaces of the same group. Corresponding to a group of grids, the number of grids contained in each of the P subspaces is not less than a first preset value.
The method according to claim 2, further comprising:

If there is a subspace A, wherein the number of grids contained in the subspace A is less than the first preset value, obtain a subspace B from the P subspaces, and the subspace B and the subspace The difference of the dissimilarity value of A is smaller than the second preset value;

The subspace A is merged into the subspace B to update the subspace B.
The method according to claim 2 or 3, wherein the determination of the value levels of the P subspaces according to the full amount of MR data of the P subspaces includes:

Determine the average data density of each of the P subspaces according to the data density of the grids contained in each of the P subspaces and the number of grids contained in each subspace;

The value level of the P subspaces is obtained according to the average data density of each of the P subspaces, wherein the greater the average data density, the higher the value level of the subspace.
The method according to claim 4, characterized in that the method further comprises:

Obtain the code of each grid according to the latitude and longitude of the grid center point of each grid;

The step of merging the grids in the P subspaces according to the value levels of the P subspaces to obtain the fused grids of each subspace includes:

determining the upper limit of the grid size of each subspace in the P subspaces according to the value level of the P subspaces;

According to the encoding of each grid, the grid set in each subspace is fused to obtain the fused grid of each subspace, wherein the size of the fused grid in each subspace 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 only different 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 fused according to the grids in each subspace;

The storing the full amount of MR data according to the fused grid includes:

The full amount of MR data is stored according to the coding of the fused grid, which is obtained by deleting the last bit that is different from the coding of the grids in the grid set.
The method according to any one of claims 1 to 5, characterized in that the method further comprises:

When the variation of MR data in any subspace C of the P subspaces exceeds a preset variation, the value level of the subspace C is updated according to the changed MR data in the subspace C.
A network data storage device, characterized in that it comprises:

A processing module, configured to obtain full MR data corresponding to multiple grids, and divide the multiple grids into P subspaces according to 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 according to the full amount of MR data of the P subspaces;

a fusion module, configured to perform fusion processing on the grids in the P subspaces according to the value levels of the P subspaces, so as to obtain the fused grids of each of the subspaces;

A storage module, configured to store the full amount of MR data according to the fused grid.
The device according to claim 7, wherein the processing module is configured to perform the following steps:

S1. Determine the data density of each grid according to the amount of MR data in each grid;

S2. Determine the dissimilarity value between any two grids in the plurality of grids according to the data density of each grid;

S3. Record the two grids with the smallest dissimilarity value between any two grids among the plurality of grids as the same group of grids;

S4. Calculating a dissimilarity value between any two grids in the plurality of grids including the same group of grids;

S5. Steps S3-S4 are repeatedly executed until each of the multiple grids is a grid in the same group of grids, and P grids of the same group are obtained, and the P subspaces are the same as the P subspaces of the same group. Corresponding to a group of grids, the number of grids contained in each of the P subspaces is not less than a first preset value.
The device according to claim 8, wherein the processing module is also used for:

If there is a subspace A, wherein the number of grids contained in the subspace A is less than the first preset value, obtain a subspace B from the P subspaces, and the subspace B and the subspace The difference of the dissimilarity value of A is smaller than the second preset value;

The subspace A is merged into the subspace B to update the subspace B.
The device according to claim 8 or 9, wherein the determination module is configured to:

Determine the average data density of each of the P subspaces according to the data density of the grids contained in each of the P subspaces and the number of grids contained in each subspace;

The value level of the P subspaces is obtained according to the average data density of each of the P subspaces, wherein the greater the average data density, the higher the value level of the subspace.
The device according to claim 10, wherein the device further comprises an encoding module, configured to:

Obtain the code of each grid according to the latitude and longitude of the grid center point of each grid;

The fusion module is used for:

determining the upper limit of the grid size of each subspace in the P subspaces according to the value level of the P subspaces;

According to the encoding of each grid, the grid set in each subspace is fused to obtain the fused grid of each subspace, wherein the size of the fused grid in each subspace 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 only different 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 fused according to the grids in each subspace;

The storage module is used for:

The full amount of MR data is stored according to the coding of the fused grid, which is obtained by deleting the last bit that is different from the coding of the grids in the grid set.
The device according to any one of claims 7 to 11, wherein the device further includes an update module, configured to:

When the variation of MR data in any subspace C of the P subspaces exceeds a preset variation, the value level of the subspace C is updated according to the changed MR data in the subspace C.
A network data storage device, characterized by comprising a processor and a memory; wherein the memory is used to store program codes, and the processor is used to call the program codes to execute any one of claims 1 to 6 the method described.
A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 6.
A computer program product, characterized in that, when the computer program product is run on a computer, the computer is made to execute the method according to any one of claims 1 to 6.