Co-resource data processing method and apparatus, device, storage medium, and product

Abstract

The application discloses a kind of co-resource data processing method, device, equipment, storage medium and product, it is related to wireless technical field, the co-resource data processing method includes: collecting and wireless network resource related initial data information;The initial data information is sequentially layered and classified processing of dimension, to obtain classified target data information;The target data information classified based on dimension is reconstructed, to obtain the co-resource body after reconstruction;The co-resource body is loaded into single table structure, to obtain co-resource pool, the application can avoid the problem that composite query condition is difficult to complete once, multiple related resource tables need to be dynamically connected when querying, reduce the query efficiency.

Description

Technical Field

[0001] This application relates to the field of wireless technology, and more particularly to a shared resource data processing method, apparatus, device, storage medium, and product. Background Technology

[0002] With the rapid development of 4G and 5G mobile communication technologies in China, as of the first half of 2024, the number of 4G and 5G mobile base stations has exceeded ten million, and wireless network resources are becoming increasingly abundant. With the continuous advancement of integration technology and the need to reduce construction costs, the sharing of wireless resources has become increasingly common.

[0003] In related technologies, queries about shared resources cannot be completed by querying a single resource. It is necessary to generate multi-level complex query scripts, and it is difficult to complete compound queries in one go. Multiple related resource tables need to be dynamically joined during the query, which reduces query efficiency. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, device, storage medium, and product for processing shared resource data, aiming to solve the technical problem of how to improve the query efficiency of shared resource data.

[0005] To achieve the above objectives, this application proposes a shared resource data processing method, which includes:

[0006] Collect initial data information related to wireless network resources;

[0007] The initial data information is sequentially classified into hierarchical and dimensional categories to obtain the classified target data information.

[0008] The classified target data information is reconstructed based on dimensions to obtain the reconstructed common resource body;

[0009] The shared resource body is loaded into a single table structure to obtain the shared resource pool.

[0010] In one embodiment, the step of sequentially performing hierarchical and dimensional classification processing on the initial data information to obtain the classified target data information includes:

[0011] The initial data information is processed in layers based on information attributes, and the relationship type of the layered initial data information is determined.

[0012] Target data information of a preset type is filtered from the initial data information;

[0013] The target data information is processed by dividing it into dimensions based on dimension labels to obtain the target data information after dimension division.

[0014] In one embodiment, the step of reconstructing the classified target data information based on dimensions to obtain the reconstructed shared resource body includes:

[0015] Determine the dimension labels corresponding to the target data information, and determine the key elements corresponding to the dimension labels;

[0016] Based on the aforementioned key elements, establish the correlation relationships between the target data information at different layers;

[0017] The target data information corresponding to the association relationship is aggregated based on the key elements to obtain the associated data corresponding to the key elements after aggregation.

[0018] The related data with the same dimensions are reconstructed through data aggregation to obtain the reconstructed shared resource body.

[0019] In one embodiment, the step of loading the shared resource body into a single table structure to obtain a shared resource pool includes:

[0020] Obtain extended information related to the key elements;

[0021] The extended information and the information in the shared resource body are processed to achieve a consistent field structure, resulting in the extended information and the shared resource body with a unified field structure.

[0022] Based on the aforementioned dimension tags, the unified shared resource body is aggregated, and the aggregated shared resource body is loaded into a single table structure to obtain a shared resource pool.

[0023] In one embodiment, the step of aggregating the unified shared resource body based on the dimension labels includes:

[0024] The information in the shared resource body from different dimensions is matched to obtain the matching results;

[0025] If the matching result is that there are different dimension tags corresponding to the shared resource body, but the information content is the same, then the shared resource bodies of different dimensions are deduplicated based on the dimension tags to obtain the deduplicated shared resource body;

[0026] Based on the aforementioned dimensional labels, the shared resource body is aggregated.

[0027] In one embodiment, the step of collecting initial data information related to wireless network resources is followed by:

[0028] The data information is structured and then temporarily stored in a resource information file.

[0029] The data information in the resource information file is loaded into a data table structure corresponding to the resource information file, and the data table structure is stored in a database;

[0030] Once the resource information file has been loaded, a message notification including the loading result is generated and sent to the local machine.

[0031] Furthermore, to achieve the above objectives, this application also proposes a shared resource data processing apparatus, which includes:

[0032] The acquisition module is used to collect initial data information related to wireless network resources;

[0033] The classification module is used to perform hierarchical and dimensional classification processing on the initial data information in sequence to obtain the classified target data information;

[0034] The reconstruction module is used to reconstruct the classified target data information based on dimensions to obtain the reconstructed common resource body;

[0035] The loading module is used to load the shared resource body into a single table structure to obtain a shared resource pool.

[0036] In addition, to achieve the above objectives, this application also proposes a shared resource data processing device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the shared resource data processing method described above.

[0037] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the shared resource data processing method described above.

[0038] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the shared resource data processing method described above.

[0039] One or more technical solutions proposed in this application have at least the following technical effects:

[0040] In contrast to related technologies, queries regarding shared resources cannot be completed through single-resource queries, requiring the generation of complex multi-level query scripts. Furthermore, complex queries are difficult to complete in one go, necessitating dynamic connections between multiple related resource tables, thus reducing query efficiency. This application addresses this by collecting initial data related to wireless network resources; performing layered processing on the data based on information attributes; determining the relationships between data belonging to different layers; reconstructing the categorized target data based on dimensions to obtain a reconstructed shared resource body; and loading the shared resource body into a single-table structure to obtain a shared resource pool. In essence, this application, after collecting initial data related to wireless network resources, layers and processes the data to obtain the relationships between data at different layers and dimensions. Based on these relationships, it reconstructs the data to obtain a shared resource body and loads it into a single-table structure to obtain a shared resource pool. This single-table structure of the shared resource pool avoids the problem of complex queries being difficult to complete in one go, requiring dynamic connections between multiple related resource tables, and thus reducing query efficiency. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating an embodiment of the shared resource data processing method of this application.

[0044] Figure 2 This is a schematic diagram of the temporary text for the resource data processing method in this application;

[0045] Figure 3 This is an example diagram of the YAML file for the resource data processing method in this application;

[0046] Figure 4 This is a flowchart of the data acquisition process for the shared resource data processing method in this application;

[0047] Figure 5 This is a flowchart illustrating Embodiment 2 of the resource data processing method of this application;

[0048] Figure 6 This is a schematic diagram of the network resource structure representing the diverse and complex relationships of the shared resource data processing methods in this application.

[0049] Figure 7 This is a diagram illustrating the hierarchical and dimensional architecture design of the shared resource data processing method in this application.

[0050] Figure 8 This is a diagram showing the table association structure design of the shared resource body in the shared resource data processing method of this application;

[0051] Figure 9 This is a schematic diagram of the shared resource body information of the shared resource data processing method of this application;

[0052] Figure 10 A flowchart illustrating the integrity prerequisites for the shared resource body generated in the shared resource data processing method of this application;

[0053] Figure 11 This is a flowchart illustrating the data processing method for aggregating resource bodies into resource pools in the resource data processing method of this application.

[0054] Figure 12 A flowchart illustrating the integrity prerequisites for the shared resource pool in the shared resource data processing method of this application.

[0055] Figure 13 This is a schematic diagram of the module structure of the shared resource data processing device according to an embodiment of this application;

[0056] Figure 14 This is a schematic diagram of the device structure of the hardware operating environment involved in the shared resource data processing method in this application embodiment.

[0057] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0058] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0059] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0060] The main solution of this application embodiment is: to collect initial data information related to wireless network resources; to perform hierarchical processing on the data information based on information attributes, and to determine the association relationship between the data information belonging to different layers; to reconstruct the classified target data information based on dimensions to obtain the reconstructed common resource body; and to load the common resource body into a single table structure to obtain a common resource pool.

[0061] In related technologies, queries about shared resources cannot be completed by querying a single resource. It is necessary to generate multi-level complex query scripts, and it is difficult to complete compound queries in one go. Multiple related resource tables need to be dynamically joined during the query, which reduces query efficiency.

[0062] After collecting initial data information related to wireless network resources, this application processes the data information in layers and dimensions to obtain the correlation between data information in different layers and dimensions. Based on the correlation, the data information is reconstructed to obtain a common resource body, and the common resource body is loaded into a single table structure to obtain a common resource pool. Through the single table structure of the common resource pool, the problem that complex queries are difficult to complete in one go and that multiple related resource tables need to be dynamically connected during the query, which reduces the query efficiency, can be avoided.

[0063] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions. The following description uses a data processing device as an example to illustrate this embodiment and the subsequent embodiments.

[0064] Based on this, embodiments of this application provide a shared resource data processing method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the resource data processing method of this application.

[0065] In this embodiment, the shared resource data processing method includes steps S100 to S400:

[0066] Step S100: Collect initial data information related to wireless network resources;

[0067] It should be noted that the execution entity in this embodiment is a data processing device. This data processing device collects data information from the integrated resource system and the OMC system to its local storage using a big data acquisition method. Specifically, the initial data information is stored in the 4G / 5G base station northbound acquisition table: 4G / 5G base station information automatically collected through the northbound interface.

[0068] Specifically, after the data processing equipment collects and stores the data information, it will save it in the following data table:

[0069] 1. Comprehensive Resource Site Table: Records relevant information about wireless sites.

[0070] 2. Comprehensive Resource Room Table: Records resource information related to the computer room.

[0071] 3. Integrated Resource Network Resource Point Table: Records detailed information about network resource points, such as the location information of antennas and RRUs.

[0072] 4. Comprehensive Resource 4G / 5G Base Station Table: Records resource information of 4G / 5G base stations.

[0073] 5. Comprehensive Resource 4G / 5G Cell Table: Records resource information for 4G / 5G cells respectively.

[0074] 6. Integrated Resource 4G / 5G BBU Table: Records resource information of the baseband processing unit (BBU).

[0075] 7. Integrated Resource 4G / 5G RRU Table: Records resource information of remote radio unit (RRU).

[0076] 8. Comprehensive Resource Tower Table: Records tower resource information.

[0077] 9. Integrated Antenna Resource Table: Records antenna resource information.

[0078] 10. 4G / 5G Base Station Northbound Data Collection Table: 4G / 5G base station information automatically collected through the northbound interface.

[0079] 11. 4G / 5G Cell Northbound Collection Table: 4G / 5G cell information automatically collected through the northbound interface.

[0080] 12. BBU Northbound Data Collection Table: BBU information automatically collected through the northbound interface.

[0081] 13. RRU Northbound Acquisition Table: RRU information automatically acquired through the northbound interface.

[0082] 14. Chassis Data Acquisition Table: Records the resource information of the chassis.

[0083] 15. Board Data Acquisition Table: Records the resource information of the board.

[0084] Step S200: The initial data information is sequentially classified by layer and dimension to obtain the classified target data information;

[0085] Understandably, the data processing equipment first performs layered processing on the initial data information collected, and then performs dimensional processing after layering. Through layered and dimensional classification processing, the classified target data information is obtained.

[0086] Step S300: Reconstruct the classified target data information based on dimensions to obtain the reconstructed common resource body;

[0087] It should be noted that the shared resource body is a comprehensive data resource that not only contains detailed data at various levels but also reflects the interrelationships between them, providing a unified data platform for subsequent queries, analysis, and decision-making. The data processing equipment reconstructs the categorized target data information based on different dimensions, resulting in the shared resource body.

[0088] Furthermore, the above-described hierarchical and dimensional approach can be applied to any complex relationship analysis.

[0089] Step S400: Load the shared resource body into a single table structure to obtain a shared resource pool.

[0090] Understandably, a single-table structure is one that can accommodate all relevant information from the spatial, entity, and logical layers. This table needs to contain enough fields to store data from each layer and their relationships. A shared resource pool is a resource management concept that integrates resource information from different levels and types into a unified table or data structure. It integrates resource data from different levels, such as the spatial, entity, and logical layers, to form a shared, comprehensive data resource set, facilitating unified querying, analysis, and management. The main purpose of a shared resource pool is to improve resource utilization efficiency, simplify resource management complexity, and provide a unified data platform for various business applications. The data processing equipment loads the reconstructed shared resource data into the designed single-table structure, forming the shared resource pool.

[0091] In one feasible implementation, step S100 may include the following steps:

[0092] The data information is structured and then temporarily stored in a resource information file.

[0093] Understandably, the data processing device is equipped with an information converter. After storing the data locally, the device transmits the data to the information converter via a file transfer protocol. The information converter extracts and converts the data into a unified format of information blocks + delimiters. The delimiters are selected based on the principle that they do not appear in the information blocks and can be one or more characters, such as |, $, / / , ※, etc. The structure of each type of data source information after conversion is shown in Table 1 below:

[0094] Table 1

[0095] Information block separator Information block separator ...... Information block separator Information block separator ...... ...... ...... ...... ...... ......

[0096] Furthermore, the data processing device is also equipped with a data loader. After the information converter performs format conversion, the data processing device temporarily stores the converted data of the same type of information in the same text file for later retrieval by the data loader. Figure 2 , Figure 2 A temporary text illustration is provided, with each piece of information in the text distinguished by a line break or carriage return.

[0097] The data information in the resource information file is loaded into a data table structure corresponding to the resource information file, and the data table structure is stored in a database;

[0098] It should be noted that the data processing device is also equipped with a data structure generator and a data loader. This data processing device loads the resource information files generated by the information converter into the database, and employs the following efficient data loading methods:

[0099] 1. Use the database's built-in efficient loading tools or commands:

[0100] The gpload command in Greenplum database loads data simultaneously with the data structure generation, making it particularly suitable for handling large amounts of data and efficiently loading text information into the Greenplum database.

[0101] The LOAD DATA INFILE command in MySQL (an open-source database) allows data from the file system to be directly loaded into a MySQL database table. It is an efficient way to load data, especially when dealing with large amounts of data.

[0102] 2. Use the SQL (Structured Query) statement INSERT INTO to insert data into the database:

[0103] The information text is converted into SQL statements, and the data is loaded into the database row by row by executing INSERT INTO statements. While this method is more flexible when processing single or small amounts of data, it may be less efficient than the first two methods when dealing with large amounts of data.

[0104] Furthermore, based on the data type of the temporary text data, the data processing device can create table structures using various tools, with each type of temporary file corresponding to a database table. For example, if the database used by the acquisition system is Greenplum, the data structure table can be created by loading the configuration file using the gpload tool. gpload is a data loading tool specific to the Greenplum database. Moreover, regardless of the database type, a new database table can be created directly using the SQL statement CREATE TABLE (create table statement) that comes with the database itself.

[0105] The following commands will load a specified YAML (not a markup language) file to create a data structure:

[0106] $GPLOAD_HOME / bin / gpload-f$GPLOAD_HOME / yaml / load_resource_tabl e_XXXX.yaml;

[0107] Furthermore, referring to Figure 3 , Figure 3 Example diagrams of YAML files are provided, and the data structure configuration file descriptions are shown in Table 2:

[0108] Table 2

[0109]

[0110]

[0111] Once the resource information file has been loaded, a message notification including the loading result is generated and sent to the local machine.

[0112] Understandably, the data processing equipment also includes a notification mechanism. The primary purpose of this mechanism is to proactively send a notification message about the loading result after the data has been loaded into the database. This mechanism ensures that subsequent steps (such as the reconstruction and generation of shared resource data) can be clearly known before execution whether the relevant information in the acquisition system has been successfully loaded into the database, thereby guaranteeing data integrity and availability. Specifically, the notification message format includes key information such as the data generation marker time, the table name of the data information, the number of records, and the actual data generation time. Furthermore, messages are separated using specific delimiters, which are selected based on the principle of not appearing in information blocks to ensure accurate message parsing. Figure 4 , Figure 4 A data acquisition flowchart is provided.

[0113] For example, the notification message format might be as follows: Data generation marker time|Table name of data information|Number of records|Actual data generation time. The separator can be characters such as |, $, / / , ※, etc. The specific choice depends on the actual data content. It's crucial to ensure that separators do not appear in the message block to facilitate subsequent parsing. In this way, the data generation notification machine can effectively and accurately transmit the data loading status to subsequent processing steps, ensuring the efficient and stable operation of the entire system. A sample message format is shown below:

[0114] 2024-09-25 00:00:00|resource_table_XXX|396659|2024-09-25 05:12:04

[0115] 2024-09-25 00:00:00|resource_table_YYY|953421|2024-09-25 05:24:11

[0116] 2024-09-25 00:00:00||resource_table_ZZZ|1322952|2024-09-25 06:35:23

[0117] In this embodiment, by layering and dividing dimensions, the multi-dimensional and multi-table data structure is transformed into a single-table structure, thereby simplifying the query of common resource information and improving the query efficiency accordingly.

[0118] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 5 In step S200, the shared resource data processing method further includes steps S01 to S04:

[0119] Step S01: Perform layered processing on the data information based on information attributes, and determine the relationship type of the layered data information;

[0120] It should be noted that information attributes include physical location, logical function, and network element to which they belong. Relationship types include one-to-one, one-to-many, many-to-one, and many-to-many relationships. Based on the attributes of the data information, such as physical location, logical function, and network element to which they belong, the data processing equipment divides the data into different layers, such as spatial, entity, and logical layers, and determines the relationship types of the data information after layering.

[0121] Specifically, spatial layer resources include near-endpoints and remote points, sites, data centers and network remote points, etc. The spatial layer is the top layer of the resource layering structure, mainly focusing on the geographical location, regional scope and spatial attributes of resources. Resources in this layer provide spatial positioning and deployment information for other layers and are the geographical foundation of the entire resource management system.

[0122] Physical layer resources include baseband processing units (BBU), remote radio frequency units (RRU), towers and antennas, etc. The physical layer is located in the middle layer of the resource layering structure. It mainly contains specific material resources. These resources have physical forms and can be directly observed, used and maintained. They are the material basis for realizing various functions.

[0123] Logical layer resources, such as base stations and cells, are at the bottom of the resource layering structure. They mainly contain abstract and non-physical resources. These resources are associated with the spatial and physical layers through virtual information and are more focused on business applications and function implementation.

[0124] Furthermore, the layered structure of resources applies not only to wireless network resources but also to all resources. Just as computer networks construct a layered model, all computer network structures fall within the seven layers of the physical layer, data link layer, network layer, transport layer, session layer, presentation layer, and application layer. The resource layering structure of data processing equipment, from top to bottom, is the spatial layer, the entity layer, and the logical layer. The spatial layer refers to resources related to geographical location, area, and spatial attributes. The entity layer refers to concrete material resources such as equipment, materials, machinery, tools, and hardware. These resources have a physical form and can be directly observed and used; these resources belong to the entity layer. The logical layer refers to resources without concrete material resources. The relationship between resources in this layer and those in the spatial and entity layers is achieved by establishing virtual information associations (for example, the virtual information in base station resources is the station number, and the virtual information in cell resources is the cell number (CGI). If corresponding virtual information is also added to the resources in the entity and spatial layers, the logical layer can establish relationships with other layers). Logical layer resources are biased towards business applications.

[0125] Specifically, there are diverse relationships among wireless network resources. For example, at the physical layer, the BBU device typically corresponds to a one-to-one near-endpoint at the spatial layer. This configuration means that each BBU device is directly connected to a specific near-endpoint. This setup helps simplify connections, clarify responsibility division, and facilitate maintenance and management. At the logical layer, one base station can correspond to multiple cells. This one-to-many relationship reflects the flexibility of base station logical partitioning and resource allocation. One base station can cover multiple cells, each corresponding to a different geographical area or user group. Multiple cells may correspond to one RRU. This many-to-one relationship reflects the role of the RRU in resource aggregation. As a remote radio unit, the RRU can receive and transmit signals from multiple cells. Multiple RRUs correspond to multiple antennas. This many-to-many relationship is the most complex part of the communication network, involving signal transmission and coordination between multiple RRUs and multiple antennas. This configuration can achieve more flexible network coverage and capacity expansion. Figure 6 , Figure 6 A schematic diagram of the network resource structure with diverse and complex relationships is provided.

[0126] Specifically, the relation types are described below:

[0127] One-to-many: One upper-level resource can be associated with multiple lower-level resources.

[0128] Many-to-1: Multiple lower-level resources can be associated with one upper-level resource.

[0129] Many-to-many: There are complex relationships between resources. One resource can be associated with multiple other resources, and vice versa.

[0130] Step S02: Filter out target data information of a preset type from the data information;

[0131] Understandably, the preset types are one-to-many, many-to-one, and many-to-many relationship types. The data processing device filters out the one-to-many, many-to-one, and many-to-many relationship types from the data information to obtain the target data information. This data information can more clearly describe the relationships between data, facilitating data querying, analysis, and management. As shown in Table 3, Table 3 provides a hierarchical dimension relationship table:

[0132] Table 3

[0133]

[0134]

[0135] Step S03: The target data information is processed by dimension division based on dimension labels to obtain the target data information after dimension division;

[0136] It should be noted that dimension labels are used to identify the characteristics and attributes of data. The data processing equipment determines the dimension labels corresponding to the hierarchically divided data information, and based on different dimensions, performs dimensionality-based processing on the layered target data information to obtain the dimensionalized target data information. The resource layering and dimensionality-based method based on spatial, entity, and logical layers can effectively clarify the complex relationships between different types of resources, providing a theoretical basis and technical support for the efficient utilization of resources. Figure 7 , Figure 7 It provides a shared resource layered and dimensioned architecture design diagram.

[0137] In one feasible implementation, step S300 further includes the following steps:

[0138] Determine the dimension labels corresponding to the target data information, and determine the key elements corresponding to the dimension labels;

[0139] Understandably, a key element is the basic unit of a data record. A key element refers to a field of information within a resource that shares information with other resources and is typically the focus of business applications. The data processing device determines the dimension labels corresponding to the target data information and identifies the key elements corresponding to those dimension labels. Specifically, the various dimensions of wireless network resources and their corresponding dimension labels and key elements are shown in Table 4 below:

[0140] Table 4

[0141]

[0142]

[0143] Based on the aforementioned key elements, establish the correlation relationships between the target data information at different layers;

[0144] It should be noted that a relationship refers to a logical connection established between different data tables or datasets based on certain common attributes. This relationship allows for data to be queried and manipulated across different tables, thereby achieving data integration and analysis. Data processing equipment provides key elements and establishes relationships between them and target data information in each data table. By determining the relationships between data information at different levels, and by processing data information through hierarchical and dimensional methods, the relationship between resources at different dimensions and levels can be clearly defined, flattening the complex multi-table relationship structure and reducing the complexity of single-table transformations.

[0145] Furthermore, the handling of relationships can be divided into the following three methods:

[0146] 1. Direct association

[0147] Definition: A field value in one resource table is directly equal to a field value in another resource table.

[0148] Processing method: Match fields from two tables using equality conditions (=).

[0149] Example: Table A has a field id, and Table B also has a field id. The two tables can be directly linked using A.id = B.id.

[0150] 2. Includes association

[0151] Definition: A field value in one resource table is a part of a field value in another resource table.

[0152] Solution: First, add delimiters before and after the data in the relevant data tables, and then use SQL query functions such as LIKE, INSTR, SUBSTRING, and regular expressions to achieve matching.

[0153] Example: Table A has a field `value` with records containing "X", and Table B has a field `values` with records containing "X,Y". Convert the data in the `value` field of table A to ",X," and the data in the `values` field of table B to ",X,Y,". Use an SQL query to find if a record in table A is contained as a substring in a record in table B.

[0154] 3. Relationship after segmentation

[0155] Definition: This involves splitting a single record in one table into multiple records based on multiple values ​​in a certain field, and then associating these records with the corresponding records in another table.

[0156] Example: Table A has two records with values ​​"X" and "Z" for a certain field, and Table B has two records with values ​​"X,Y" and "Y,Z" for a certain field. By performing a split operation on the field in Table B, "X,Y" will be split into two records with values ​​"X" and "Y" respectively. Similarly, "Y,Z" will be split into two records with values ​​"Y" and "Z" respectively. After the splitting, Table B can be directly joined with the corresponding fields in Table A.

[0157] Application scenario examples

[0158] Taking antennas and cells in wireless network resources as an example:

[0159] Integrated Resource Antenna Table: The smallest unit of record is an antenna hardware device, one of the fields of which is the cell name, which may contain multiple cell names.

[0160] Comprehensive Resource 4G / 5G Cell Table: The smallest unit of record is a cell.

[0161] Handling method:

[0162] Segmentation and Association: The cell name field in the integrated resource antenna table is segmented, and the field value containing multiple cell names is split into multiple records, with each record corresponding to one cell name.

[0163] Direct association: The segmented antenna table and cell table are directly associated through the cell name field, thereby obtaining the antenna hardware device relationship for each cell.

[0164] By using the above relationship processing methods, data from different resource information tables can be effectively integrated to generate a shared resource body across various dimensions, facilitating subsequent analysis and queries.

[0165] Specifically, the following examples illustrate the key elements and relationships of each resource data table:

[0166] 1. OMC (Operation and Maintenance Center) Northbound Chassis Table:

[0167] Key element: related_managedelement (network element).

[0168] Relationships: It is directly associated with the OMC Northbound 4G Base Station Table, OMC Northbound 4G Network Element Table, OMC Northbound 5G Base Station Table, and OMC Northbound 5G Network Element Table through related_managedelement(network element), indicating the base station to which the chassis belongs.

[0169] 2. OMC Northbound Board Table:

[0170] Key element: related_managedelement (network element).

[0171] Association Relationships: It is directly associated with the OMC Northbound 4G Cell Table and the OMC Northbound 5G Cell Table through related_managedelement(network element), indicating the cell to which the board belongs; it is also associated with the OMC Northbound RRU Table through related_managedelement(network element) (the OMC Northbound RRU Table uses RRU hardware devices as the basic unit, and one RRU may belong to multiple network elements), indicating the RRU to which the board belongs.

[0172] 3. OMC Northbound 4G Base Station Table, OMC Northbound 4G Network Element Table, OMC Northbound 5G Base Station Table, OMC Northbound 5G Network Element Table:

[0173] Key element: enbid (enodebid).

[0174] Relationship: It is directly associated with the Comprehensive Assets 4G Base Station Table and the Comprehensive Assets 5G Base Station Table through enbid (enodebid), indicating the connection and integration of OMC northbound base station and Comprehensive Assets base station information.

[0175] 4. Comprehensive 4G base station list, comprehensive 5G base station list:

[0176] Key element: Resource ID (base station ID).

[0177] Association: It is associated with the Comprehensive Resource RRU table through the resource ID (base station ID) (the base station table uses base stations as the basic unit, and a base station may belong to multiple RRUs), indicating the RRU to which the base station belongs.

[0178] 5. OMC Northbound 4G Cell Table, OMC Northbound 5G Cell Table:

[0179] Key element: Cellular CGI.

[0180] Relationship: It is directly associated with the Comprehensive Assets 4G Cell Table and Comprehensive Assets 5G Cell Table through the cell CGI, indicating the connection and integration of OMC northbound cell information and Comprehensive Assets cell information.

[0181] 6. Comprehensive Antenna Table:

[0182] Key elements: Resource ID (tower ID), zh_name (comprehensive resource community name).

[0183] Association relationships: It is directly associated with the Comprehensive Asset Management Tower Table through the resource ID (tower ID), indicating the tower to which the antenna belongs; it is associated with the Comprehensive Asset Management 4G Cell Table and Comprehensive Asset Management 5G Cell Table through zh_name (comprehensive asset management cell name) after splitting, indicating the cell to which the antenna belongs.

[0184] 7. Comprehensive data on tower structures:

[0185] Key elements: resourcedot_name (remote dot name), room_name (data center name).

[0186] Relationships: It is directly associated with the remote point table of the comprehensive resource network through resourcedot_name (remote point name), indicating the remote point to which the tower belongs; it is directly associated with the data center table of the comprehensive resource network through room_name (data center name), indicating the near point to which the tower belongs.

[0187] 8. Comprehensive Asset RRU Table:

[0188] Key elements: Resource ID (remote endpoint ID), Resource ID (near endpoint ID).

[0189] Association Relationship: It is directly associated with the remote point of the integrated resource network through the resource ID (remote point ID), indicating the remote point to which the RRU belongs; it is directly associated with the data center table of the integrated resource network through the resource ID (near endpoint ID), indicating the near endpoint to which the RRU belongs.

[0190] 9. Comprehensive Resource Site List:

[0191] Key element: Resource ID (site ID).

[0192] Association: It is directly associated with the Comprehensive Data Center table through the resource ID (site ID), indicating the near endpoint to which the site belongs.

[0193] The target data information corresponding to the association relationship is aggregated based on the key elements to obtain the associated data corresponding to the key elements after aggregation.

[0194] It should be noted that the data processing equipment aggregates target data information that belongs to the same key elements to obtain aggregated related data.

[0195] The related data with the same dimensions are reconstructed through data aggregation to obtain the reconstructed shared resource body.

[0196] Understandably, the data processing device adds corresponding dimension labels to each aggregated data record to identify the dimension to which the data belongs. For example, the dimension label for the spatial layer could be SPOSITION, the dimension label for the entity layer could be BBU, RRU, etc., and the dimension label for the logical layer could be BS, CELL, etc. Based on these different dimension labels, the data is integrated into the corresponding data tables to form a shared resource entity. Figure 8 , Figure 8 A diagram of the table association structure for shared resource entities is provided.

[0197] Specifically, taking the generation of shared resource information as a BBU dimension as an example, as shown in Table 4, the BBU serial number is the core key element of the dimension. When aggregating or merging other data based on the BBU serial number, a delimiter is used for separation. The delimiter is a half-width comma, and the dimension label flag is "BBU". In this way, all resource information related to BBU is integrated into the BBU dimension. For details, refer to... Figure 9 , Figure 9 A schematic diagram of some information about the shared resources entity is provided.

[0198] Furthermore, splitting, merging, or aggregating data is a common task in database operations. Built-in database functions can simplify this process and improve efficiency. For Greenplum, the `array_agg` function aggregates multiple rows of data values ​​into an array, which can then be converted into a string using the `array_to_string` function, allowing the specification of delimiters for output formatting. When data splitting is required, the `regexp_split_to_table` function provides a method that splits a string into multiple parts based on regular expression patterns and returns each part as a separate row in tabular form.

[0199] In MySQL, a similar operation can be accomplished using the GROUP_CONCAT function, which merges a group of rows into a single string and supports custom delimiters. SQL Server provides the STRING_AGG function to achieve the same purpose, namely, aggregating strings by group, and also allows setting delimiters and controlling the output order.

[0200] In summary, by leveraging these built-in database functions, users can efficiently handle data aggregation and splitting needs without relying on backend programs, achieving the desired results. This not only simplifies the development process but also improves query performance and flexibility.

[0201] Furthermore, the generation of the shared resource body requires the collection of data from all relevant resource tables to proceed. The message receiving system receives messages from the notification machine and determines the validity of resources based on the time, number of records, and resource name in the message, marking them as valid. A set containing the names of required valid resource tables is defined. Each time a valid resource table message is received, the table name is added to the set variable. If the custom set is included in the set variable, the resource table is complete, and shared resource body generation can begin; otherwise, the resource table is incomplete, and data collection must continue. Figure 10 , Figure 10 A flowchart of the integrity prerequisites for generating shared resource entities is provided.

[0202] In one feasible implementation, step S400 further includes the following steps:

[0203] Obtain extended information related to the key elements;

[0204] It should be noted that extended information refers to other information related to the key elements. Due to the limitation of the dimensional range, extended information will not increase the data record volume of the shared resource pool. Data information that can be associated with dimensions can be added to the extended information. The data processing device acquires the extended information related to the key elements.

[0205] The extended information and the information in the shared resource body are processed to achieve a consistent field structure, resulting in the extended information and the shared resource body with a unified field structure.

[0206] Understandably, the data processing device will include the key elements of all resource bodies to form a unified field structure for the common resource body, and other information surrounding the key elements to form a unified field structure for extended information.

[0207] Based on the aforementioned dimension tags, the unified shared resource body is aggregated, and the aggregated shared resource body is loaded into a single table structure to obtain a shared resource pool.

[0208] Understandably, the data processing device groups data records based on the dimension labels in the shared resource data, with each dimension label corresponding to a group of data records. These records share the same dimensional features, and the grouped shared resource data is loaded into a single table structure to obtain the shared resource pool. Figure 11 , Figure 11 A flowchart is provided to illustrate the data processing flow of aggregating resources into resource pools.

[0209] In one feasible implementation, the step of aggregating the unified shared resource body based on the dimension labels further includes the following steps:

[0210] The information in the shared resource body from different dimensions is matched to obtain the matching results;

[0211] It should be noted that the data processing device matches information in shared resource bodies of different dimensions. Based on the information content in the shared resource bodies, it compares whether the data content between different shared resource bodies is the same, and then obtains the matching result.

[0212] If the matching result is that there are different dimension labels corresponding to the shared resource body, but the information content is the same, then the shared resource bodies of different dimensions are deduplicated based on the dimension labels to obtain the deduplicated shared resource body;

[0213] Understandably, if the data processing device determines that the matching results show that there are shared resource bodies with the same information content but different dimension labels, it will perform deduplication processing, deduplicating these shared resource bodies based on dimension labels, retaining the unique information content, and merging the different dimension labels to obtain the deduplicated shared resource bodies, with the different dimension labels separated by delimiters.

[0214] Based on the aforementioned dimensional labels, the shared resource body is aggregated.

[0215] It should be noted that the data processing device aggregates shared resource entities based on dimension labels, integrating shared resource entities from different dimensions into a more comprehensive dataset for subsequent querying and analysis. Through the above steps, shared resource entity data from different dimensions can be effectively integrated, ensuring data consistency and integrity, while improving data query efficiency and management convenience. After the shared resource data is generated, the data processing device deletes the source data and the resource table information collected and stored. The generated shared resource single table structure merges duplicate fields, reduces related fields, aggregates shared resource information, and reduces the amount of index data in each resource table. Therefore, it occupies less storage space, and data maintenance work only needs to focus on the shared resource single table, reducing the maintenance work of a large amount of table information.

[0216] Furthermore, data expansion can be performed during or after the shared resource pool is generated. Expanded data only needs to match any one dimension from the spatial, entity, or logical layers. Business application-related data can be directly integrated as dimensions at the business layer and overlaid onto the resource body, then aggregated into the resource pool along with the resource body; alternatively, data connections can be achieved by associating with existing dimension information in the resource pool. The data expansion process is simple and straightforward.

[0217] Furthermore, the generation of the shared resource pool can only be carried out after the data acquisition of all relevant shared resource entities is completed. The message receiving system in the device will receive messages from the shared resource entity data generation notification machine, and use these messages to mark the resources corresponding to the required resource entity set as valid. The validity of the resources is determined by information such as time, number of records, and resource name in the received resource entity data generation message, and a validity mark is added after confirmation. Figure 12 , Figure 12 A flowchart of the integrity prerequisites for generating the shared resource pool is provided.

[0218] Furthermore, each record in the shared resource pool contains data information from all dimensions. By defining query conditions based on one or more dimensions, the required resource data information can be drilled down. Since the shared resource pool adopts a single-table structure, generating SQL query statements is relatively simple.

[0219] In this implementation, by integrating the complex multi-dimensional table data of wireless resources into a single table, centralized management and maintenance of resource data are achieved, avoiding data redundancy and inconsistency issues, reducing database storage requirements, and thus lowering costs. Complex one-to-one, one-to-many, and many-to-many relationships between resources are connected through direct association, inclusive association, and partitioned association methods to form resource entities. These entities are then integrated using dimension labels, merging the data to form a resource pool. Queries in the single resource pool table can directly query shared resource information across multiple dimensions, significantly improving query efficiency. Users can obtain the information they need faster without needing to perform data association and queries across multiple tables. Furthermore, this single-table approach simplifies data maintenance, reducing potential errors and additional workload when updating and synchronizing multiple tables, and facilitates data expansion. New data types or fields can be easily added to the resource pool's extended information area without redesigning or adjusting multiple tables.

[0220] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the resource data processing method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0221] This application also provides a shared resource data processing device, please refer to... Figure 13 The shared resource data processing device includes:

[0222] The acquisition module 10 is used to acquire initial data information related to wireless network resources;

[0223] The classification module 20 is used to perform hierarchical and dimensional classification processing on the initial data information in sequence to obtain the classified target data information;

[0224] Reconstruction module 30 is used to reconstruct the classified target data information based on dimensions to obtain the reconstructed common resource body;

[0225] The loading module 40 is used to load the shared resource body into a single table structure to obtain a shared resource pool.

[0226] Optionally, the classification module includes:

[0227] The filtering submodule is used to perform hierarchical processing on the initial data information based on information attributes, and determine the relationship type of the hierarchical initial data information; filter out target data information of a preset type from the initial data information; and perform dimensional processing on the target data information based on dimension labels to obtain the dimensionalized target data information.

[0228] Optionally, the reconstruction module includes:

[0229] A submodule is established to determine the dimension labels corresponding to the target data information and the key elements corresponding to the dimension labels; based on the key elements, the association relationship between the target data information at different layers is established; the target data information corresponding to the association relationship is aggregated based on the key elements to obtain the associated data corresponding to the aggregated key elements; the associated data with the same dimension is reconstructed through data aggregation to obtain the reconstructed shared resource body.

[0230] Optionally, the loading module includes:

[0231] The aggregation submodule is used to obtain extended information related to the key elements; to perform consistent field processing on the extended information and the information in the shared resource body to obtain the extended information and the shared resource body with unified field structure; to aggregate the unified shared resource body based on the dimension tags, and to load the aggregated shared resource body into a single table structure to obtain the shared resource pool.

[0232] Optionally, the aggregation submodule includes:

[0233] The deduplication unit is used to match information in the shared resource body of different dimensions to obtain a matching result; if the matching result is that the shared resource body has different dimension labels but the information content is the same, then the shared resource bodies of different dimensions are deduplicated based on the dimension labels to obtain the deduplicated shared resource body; and the shared resource body is aggregated based on the dimension labels.

[0234] Optionally, the acquisition module includes:

[0235] The structuring submodule is used to perform structuring processing on the data information, temporarily storing the structuring data information in a resource information file; loading the data information in the resource information file into a data table structure corresponding to the resource information file, the data table structure being stored in a database; and generating a message notification including the loading result and sending it locally until the resource information file is fully loaded.

[0236] The shared resource data processing apparatus provided in this application, employing the shared resource data processing method in the above embodiments, can solve the technical problems of shared resource data processing. Compared with the prior art, the beneficial effects of the shared resource data processing apparatus provided in this application are the same as the beneficial effects of the shared resource data processing method provided in the above embodiments, and other technical features in the shared resource data processing apparatus are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0237] This application provides a shared resource data processing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the shared resource data processing method in the first embodiment described above.

[0238] The following is for reference. Figure 14 The diagram illustrates a structural schematic of a shared resource data processing device suitable for implementing embodiments of this application. The shared resource data processing device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, tablets, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 14 The shared resource data processing device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0239] like Figure 14 As shown, the shared resource data processing device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the shared resource data processing device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the shared resource data processing device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a shared resource data processing device with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0240] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0241] The shared resource data processing device provided in this application, employing the shared resource data processing method in the above embodiments, can solve the technical problem of shared resource data processing. Compared with the prior art, the beneficial effects of the shared resource data processing device provided in this application are the same as the beneficial effects of the shared resource data processing method provided in the above embodiments, and other technical features in the shared resource data processing device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0242] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0243] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0244] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the shared resource data processing method in the above embodiments.

[0245] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0246] The aforementioned computer-readable storage medium may be included in the shared resource data processing device; or it may exist independently and not assembled into the shared resource data processing device.

[0247] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the shared resource data processing device, the shared resource data processing device causes the following actions: to collect initial data information related to wireless network resources; to perform hierarchical and dimensional classification processing on the initial data information in sequence to obtain classified target data information; to reconstruct the classified target data information based on dimensions to obtain a reconstructed shared resource body; and to load the shared resource body into a single table structure to obtain a shared resource pool.

[0248] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0249] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0250] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0251] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described shared resource data processing method, thereby solving the technical problem of shared resource data processing. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the shared resource data processing method provided in the above embodiments, and will not be repeated here.

[0252] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the shared resource data processing method described above.

[0253] The computer program product provided in this application can solve the technical problem of shared resource data processing. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the shared resource data processing method provided in the above embodiments, and will not be repeated here.

[0254] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for processing shared resource data, characterized in that, The shared resource data processing method includes: Collect initial data information related to wireless network resources; The initial data information is sequentially classified into hierarchical and dimensional categories to obtain the classified target data information. The classified target data information is reconstructed based on dimensions to obtain the reconstructed common resource body; The shared resource body is loaded into a single table structure to obtain the shared resource pool.

2. The shared resource data processing method as described in claim 1, characterized in that, The step of performing hierarchical and dimensional classification processing on the initial data information to obtain the classified target data information includes: The initial data information is processed in layers based on information attributes, and the relationship type of the layered initial data information is determined. Target data information of a preset type is filtered from the initial data information; The target data information is processed by dividing it into dimensions based on dimension labels to obtain the target data information after dimension division.

3. The shared resource data processing method as described in claim 1, characterized in that, The step of reconstructing the classified target data information based on dimensions to obtain the reconstructed shared resource body includes: Determine the dimension labels corresponding to the target data information, and determine the key elements corresponding to the dimension labels; Based on the aforementioned key elements, establish the correlation relationships between the target data information at different layers; The target data information corresponding to the association relationship is aggregated based on the key elements to obtain the associated data corresponding to the key elements after aggregation. The related data with the same dimensions are reconstructed through data aggregation to obtain the reconstructed shared resource body.

4. The shared resource data processing method as described in claim 3, characterized in that, The step of loading the shared resource body into a single table structure to obtain the shared resource pool includes: Obtain extended information related to the key elements; The extended information and the information in the shared resource body are processed to achieve a consistent field structure, resulting in the extended information and the shared resource body with a unified field structure. Based on the aforementioned dimension tags, the unified shared resource body is aggregated, and the aggregated shared resource body is loaded into a single table structure to obtain a shared resource pool.

5. The shared resource data processing method as described in claim 4, characterized in that, The step of aggregating the unified shared resource body based on the dimensional labels includes: The information in the shared resource body from different dimensions is matched to obtain the matching results; If the matching result is that there are different dimension tags corresponding to the shared resource body, but the information content is the same, then the shared resource bodies of different dimensions are deduplicated based on the dimension tags to obtain the deduplicated shared resource body; Based on the aforementioned dimensional labels, the shared resource body is aggregated.

6. The shared resource data processing method as described in claim 1, characterized in that, The step of collecting initial data information related to wireless network resources is followed by: The data information is structured and then temporarily stored in a resource information file. The data information in the resource information file is loaded into a data table structure corresponding to the resource information file, and the data table structure is stored in a database; Once the resource information file has been loaded, a message notification including the loading result is generated and sent to the local machine.

7. A data processing apparatus, characterized in that, The device includes: The acquisition module is used to collect initial data information related to wireless network resources; The classification module is used to perform hierarchical and dimensional classification processing on the initial data information in sequence to obtain the classified target data information; The reconstruction module is used to reconstruct the classified target data information based on dimensions to obtain the reconstructed common resource body; The loading module is used to load the shared resource body into a single table structure to obtain a shared resource pool.

8. A data processing device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the shared resource data processing method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the shared resource data processing method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the shared resource data processing method as described in any one of claims 1 to 6.

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