Preprocessing device and method for multi-dimensional communication index data

Abstract

The invention belongs to the technical field of communication data processing, and particularly relates to a preprocessing device and method for multi-dimensional communication index data, and the device comprises a collection module which is used for collecting original communication index data from an original index library or a file server, and storing the original communication index data into a preset collection library; the configuration module is used for constructing a configuration table to form a configuration library; the initial summarizing module is used for combining all original communication index data in the acquisition library and network element information in the configuration table into a large-width table according to a preset initial dimension and storing the large-width table to form an index database; the dimension summarizing module is used for summarizing according to a preset summarizing dimension by taking the initial dimension as a base table and storing the summarizing dimension into the index database; and the index pre-summarizing module is used for performing pre-summarizing processing on complex indexes on the basis of the dimension data according to the query demand information. According to the method, the defect that multi-table query must be associated when multiple tables are stored in the prior art can be avoided, so that the application requirement of quick response of the query request is met.

Description

Technical Field

[0001] The present invention relates to the technical field of communication data processing, and in particular to a preprocessing device and method for multi-dimensional communication index data. Background Art

[0002] As current mobile communication networks become increasingly complex, operators need to monitor, analyze and mine current network performance indicator data in order to improve network quality and enhance user perception and satisfaction. They also need to compare, analyze and mine historical data with current data to discover potential network quality problems, resulting in an increasing amount of data for query and analysis.

[0003] Introducing open source databases is an effective way for enterprises to reduce costs. Open source databases such as relational databases (PostgreSQL) and MPP analytical databases (Doris) are optional components. Due to the large number of mobile communication indicators and the data stored in different tables or files, data must be stored in separate tables when loaded into the original database. Operators often need to perform multi-table association queries when analyzing data. A single open source database component can no longer effectively support the needs of data processing, writing, storage, and querying; therefore, a new solution is urgently needed to meet the application requirements of multi-table association queries, complex indicator preprocessing, low latency in data preprocessing, and fast response to query requests. Summary of the invention

[0004] In view of the technical defects mentioned in the background technology, the purpose of the embodiments of the present invention is to provide a preprocessing device and method for multi-dimensional communication indicator data to avoid the disadvantage of having to associate multiple tables for query when storing multiple tables, thereby meeting the application requirements of rapid response to query requests.

[0005] To achieve the above-mentioned purpose, in a first aspect, an embodiment of the present invention provides a preprocessing device for multi-dimensional communication indicator data, the preprocessing device comprising: The collection module is used to collect original communication indicator data from the original indicator library or file server and store it in a preset collection library; A configuration module, used to construct a configuration table to form a configuration library; wherein the configuration table includes network element information; An initial summary module is used to merge all the original communication indicator data in the collection library and the network element information in the configuration table into a large wide table according to a preset initial dimension and store them to form an indicator database; the large wide table in the indicator database includes a cell-level full indicator table, a base station-level full indicator table and a scene-level full indicator table; A dimension aggregation module, used to use the initial dimension as a base table, aggregate according to a preset aggregation dimension, and store it in an indicator database; The indicator pre-aggregation module is used to pre-aggregate complex indicators based on the query demand information and the above-mentioned dimensional data; the above-mentioned dimensional data include: a cell-level full indicator table, a base station-level full indicator table and a scene-level full indicator table.

[0006] As a specific implementation method of the present application, the configuration table includes a cell configuration table, a base station configuration table, a scene cell relationship table and an indicator aggregation formula configuration table; wherein the fields in the indicator aggregation formula configuration table include the indicator name, the indicator's time dimension aggregation method and the indicator's network element dimension aggregation method.

[0007] As a specific implementation method of the present application, the initial dimension includes a preset primary key field; wherein the primary key field includes a corresponding ID and time dimension; and the summary dimension includes a time / network element dimension summary.

[0008] As a specific implementation method of the present application, the aggregation according to preset aggregation dimensions specifically includes hourly / base station level full indicator table aggregation, hourly / scene level full indicator table aggregation, daily data aggregation, weekly data aggregation, monthly data aggregation and busy hour data aggregation.

[0009] As a specific implementation of the present application, the busy hour data aggregation specifically includes: First, obtain the busy hour task time, query the hourly / cell-level full indicator table for the 24 hours of the task time, group by cell ID, sort by the value of the corresponding busy hour data, obtain the first data, and insert the data into the busy hour / cell full indicator table; Then take the busy hour task time, query the hourly / base station level full indicator table for the 24 hours of the task time, group by base station ID, sort by the value of the corresponding busy hour data, take the first data, and insert the data into the busy hour / base station full indicator table.

[0010] In a second aspect, an embodiment of the present invention further provides a method for preprocessing multidimensional communication indicator data, which is applied to a device for preprocessing multidimensional communication indicator data according to the first aspect, and the method comprises the following steps: Collecting original communication indicator data from the original indicator library or file server and storing it in a preset collection library; Constructing a configuration table to form a configuration library; wherein the configuration table includes network element information; All the original communication indicator data in the collection library and the network element information in the configuration table are merged into a large wide table according to the preset initial dimension and stored to form an indicator database; the large wide table in the indicator database includes a cell-level full indicator table, a base station-level full indicator table and a scene-level full indicator table; Taking the initial dimension as the base table, summarizing according to the preset summary dimension, and storing in the indicator database; According to the query demand information, complex indicators are pre-aggregated based on the above-mentioned dimensional data; the above-mentioned dimensional data include: cell-level full indicator table, base station-level full indicator table and scene-level full indicator table.

[0011] The technical solution provided by the embodiment of the present invention combines all communication indicator data into a large wide table according to the initial dimension for storage, and summarizes other dimensions based on this table, thereby avoiding the disadvantage of having to associate multiple tables for query when storing multiple tables, reducing the IO reading amount, and thus improving the query efficiency; avoiding the repeated storage of network element attribute fields when storing multiple tables, and reducing the amount of stored data;

[0012] At the same time, the initial dimension is used as the base table to summarize the network element / time dimension, and based on this, complex indicators are preprocessed to reduce data preprocessing latency and improve query response requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the drawings required for use in the specific implementation or the description of the prior art are briefly introduced below.

[0014] Figure 1 It is a principle block diagram of a preprocessing device for multi-dimensional communication index data provided by an embodiment of the present invention;

[0015] Figure 2 is a cell configuration table provided by an embodiment of the present invention;

[0016] Figure 3 is a base station configuration table provided by an embodiment of the present invention;

[0017] Figure 4 is a scene-cell relationship table provided by an embodiment of the present invention;

[0018] Figure 5 It is an indicator aggregation formula configuration table provided by an embodiment of the present invention;

[0019] Figure 6 It is a cell full quantity indicator table provided by an embodiment of the present invention;

[0020] Figure 7 It is a base station full quantity indicator table provided by an embodiment of the present invention;

[0021] Figure 8 It is a scenario full quantity indicator table provided by an embodiment of the present invention;

[0022] Fig. 9 It is a flow chart of a method for preprocessing multi-dimensional communication indicator data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0025] It should be noted that, unless otherwise specified, the technical terms in this embodiment have the common meanings understood in the relevant technical field.

[0026] CU: Central Unit, control unit;

[0027] DU: Distributed Unit, distributed unit;

[0028] RRU: Radio Remote Unit, radio remote unit;

[0029] PRB: Physical Resource Block, physical resource block;

[0030] OMC-R: Operations and Maintenance Center-Radio wireless operations and maintenance center.

[0031] Please refer to Figure 1 , an embodiment of the present invention provides a preprocessing device for multi-dimensional communication indicator data, the preprocessing device comprising: The collection module is used to collect original communication indicator data from the original indicator library or file server and store it in a preset collection library; A configuration module, used to construct a configuration table to form a configuration library; wherein the configuration table includes network element information; An initial summary module, used to merge all the original communication indicator data in the collection library and the network element information in the configuration table into a large wide table according to a preset initial dimension and store them to form an indicator database; A dimension aggregation module, used to use the initial dimension as a base table, aggregate according to a preset aggregation dimension, and store it in an indicator database; The indicator pre-aggregation module is used to perform pre-aggregation processing of complex indicators based on the query demand information and the above-mentioned dimensional data.

[0032] The original communication index data includes: base station performance data, cell CU performance data, cell DU performance data, logical cell CU performance data, logical cell DU performance data, logical cell CU slice performance data, logical cell DU slice performance data, RRU performance data, optical module performance data, fronthaul link performance data, OMC-R performance data and beam performance data, etc.;

[0033] In this embodiment, the configuration table includes a cell configuration table, a base station configuration table, a scene cell relationship table, and an indicator aggregation formula configuration table; wherein the fields in the indicator aggregation formula configuration table include an indicator name, an indicator time dimension aggregation method, and an indicator network element dimension aggregation method;

[0034] The large-width table in the indicator database includes a cell-level full indicator table (hour, day, week, month, busy hour, a total of 5 tables), a base station-level full indicator table (hour, day, week, month, busy hour, a total of 5 tables) and a scene-level full indicator table (hour, day, week, month, a total of 4 tables);

[0035] It should be noted that in this embodiment, an open source database supporting column storage is introduced to realize data storage, and data models of different dimensions are designed into a column storage data structure; compared with row storage, column storage does not need to read the entire row of data when querying, but only needs to read the data of the queried column, which greatly reduces the amount of data to be read, thereby greatly improving the query efficiency;

[0036] The network element information includes the cell name, base station ID, base station engineering parameter attributes, etc., which have the same meaning as the network element attributes. Figures 2 to 5 shown.

[0037] Cell configuration table: includes primary key fields and network element attribute fields. The primary key field is the cell ID. The network element attributes include cell name, identification and other fields, base station ID, base station name, base station identification and other fields, cell engineering parameter attributes and other fields, base station engineering parameter attributes and other fields.

[0038] Base station configuration table: includes primary key fields and network element attribute fields. The primary key field is the base station ID. The network element attributes include fields such as base station name, base station identification, and base station engineering parameter attributes.

[0039] Scene-cell relationship table: includes primary key fields and network element attribute fields. The primary key fields are scene ID and cell ID. The network element attributes include scene name, scene ID and other fields.

[0040] Indicator aggregation formula configuration table: The fields include indicator name, indicator time dimension aggregation method, and indicator network element dimension aggregation method.

[0041] The above are just examples and are not intended to be limiting.

[0042] Further, the initial dimension includes a preset primary key field; wherein the primary key field includes a corresponding ID and a time dimension; the summary dimension includes a time / network element dimension summary;

[0043] The aggregation according to preset aggregation dimensions specifically includes hourly / base station level full indicator table aggregation, hourly / scenario level full indicator table aggregation, daily data aggregation, weekly data aggregation, monthly data aggregation and busy hour data aggregation.

[0044] Reference Figures 6 to 8 shown.

[0045] Cell-level full indicator table: contains 3 parts: primary key field, network element attributes, and full communication indicators. The primary key field uses the cell ID and data time, and partitions the table by time, and buckets by cell ID to disperse data storage and speed up query and data writing; network element attributes include cell name, ID, base station ID, base station name, ID and other fields, as well as cell engineering parameter attributes, base station engineering parameter attributes and other fields; the full communication indicator part includes all the original communication indicator fields at the cell level, and creates an index on the base station ID to speed up the query based on the base station ID as the filtering condition.

[0046] The cell-level full-volume indicator table is divided into hourly table, daily table, weekly table, and monthly table according to the time dimension, which store hourly data, daily data, weekly data, and monthly data respectively. The partition granularity is hourly, daily, weekly, and monthly respectively. It is used to speed up the query of daily, weekly, and monthly dimension data.

[0047] Base station level full indicator table: contains 3 parts: primary key field, network element attributes, and full communication indicators. The primary key field uses the base station ID and data time, and the table is partitioned by time and bucketed by base station ID to disperse data storage and speed up query and data writing; network element attributes include fields such as base station name and identification, as well as fields such as base station engineering parameter attributes; the full communication indicator part includes all (cell and base station level) original communication indicator fields.

[0048] The base station-level full-volume indicator table is divided into hourly table, daily table, weekly table, and monthly table according to the time dimension, which store hourly data, daily data, weekly data, and monthly data respectively, and the partition granularity is hourly, daily, weekly, and monthly respectively. It is used to speed up the query of daily, weekly, and monthly dimension data.

[0049] Scenario-level full indicator table: contains 3 parts: primary key field, network element attributes, and full communication indicators. The primary key field uses the scenario ID and data time, and partitions the table by time and buckets by scenario ID to disperse data storage and speed up query and data writing; network element attributes include fields such as scenario name, ID, and scenario engineering parameter attributes; the full communication indicator part includes all the original communication indicator fields at the cell level.

[0050] The scenario-level full-volume indicator table is divided into hourly table, daily table, weekly table, and monthly table according to the time dimension, which store hourly data, daily data, weekly data, and monthly data respectively, and the partition granularity is hourly, daily, weekly, and monthly respectively. It is used to speed up the query of daily, weekly, and monthly dimension data.

[0051] In this embodiment, the above-mentioned dimensional data include: cell-level full indicator table (hour, day, week, month, busy hour, a total of 5 tables), base station-level full indicator table (hour, day, week, month, busy hour, a total of 5 tables) and scene-level full indicator table (hour, day, week, month, a total of 4 tables)

[0052] Initial Dimension Summary

[0053] Summarize the hourly / cell-level full indicator table, take the hourly task time as the data time, query the cell configuration table, obtain the cell ID and network element attribute fields, use the cell ID and data time as the primary key, insert the data into the cell-level full indicator table, and complete the storage of the primary key field and network element attribute field.

[0054] Take the hourly task time as the data time, and query the cell-level original communication indicator table C1 from the original indicator library; Table C1, namely, the cell CU, DU performance data, logical cell CU, DU, CU slice and DU slice performance data in the original indicator library, obtain the cell ID by associating the cell configuration table with the network element ID, and obtain the time dimension aggregation method of all communication indicators in Table C1 through the indicator aggregation formula configuration table, aggregate the data to the cell hour dimension according to the cell ID and data time as the primary key, and import the data into the hourly / cell-level full indicator table.

[0055] Repeat the previous step to import the data of all cell-level original indicator tables in the original indicator library into the hourly / cell-level full indicator table in sequence.

[0056] Time / Network Element Dimension Summary

[0057] Summary of hourly / base station level full-scale indicator table

[0058] Take the hourly task time as the data time, query the hourly / cell-level full indicator table, obtain the network element dimension aggregation method of all communication indicators in the cell full indicator table through the standard aggregation formula configuration table, aggregate the data into the base station hour dimension based on the base station ID and data time as the primary key, and insert the data into the hourly / base station-level full indicator table.

[0059] Take the hourly task time as the data time, and query the base station-level original communication indicator table B1 from the original indicator library; Table B1 refers to the base station performance data, RRU performance data, optical module performance data, fronthaul link performance data, OMC-R performance data and beam performance data in the original indicator library. The base station ID is obtained by associating the base station configuration table with the network element ID. The time dimension aggregation method of all communication indicators in Table B1 is obtained through the indicator aggregation formula configuration table. The data is aggregated to the base station hour dimension according to the base station ID and the data time as the primary key, and the data is imported into the hourly / base station-level full indicator table.

[0060] Repeat the previous step to import the data of all base station-level original indicator tables in the original indicator library into the hourly / base station-level full indicator table in sequence.

[0061] Summary of hourly / scenario-level full-scale indicator tables

[0062] Take the hourly task time as the data time, query the hourly / cell-level full indicator table, associate the scene-cell relationship table with the cell ID, obtain the scene ID, obtain the network element dimension aggregation method of all cell indicators through the indicator aggregation formula configuration table, aggregate the data into the scene hour dimension based on the scene ID and data time as the primary key, and insert the data into the hourly / scenario-level full indicator table.

[0063] Daily data summary

[0064] Take the daily task time as the data time, query the hourly / cell-level full indicator table, and obtain the time dimension aggregation method of all communication indicators in the cell full indicator table through the standard aggregation formula configuration table. Aggregate the data to the cell day dimension by cell ID and data time, and insert the data into the daily / cell-level full indicator table.

[0065] Take the daily task time as the data time, query the hourly / base station level full indicator table, obtain the time dimension aggregation method of all communication indicators in the base station full indicator table through the standard aggregation formula configuration table, aggregate the data to the base station day dimension by base station ID and data time, and insert the data into the daily / base station level full indicator table.

[0066] Take the daily task time as the data time, query the hourly / scenario-level full indicator table, and obtain the time dimension aggregation method of all communication indicators in the scenario-level full indicator table through the standard aggregation formula configuration table. Aggregate the data into the scenario-day dimension by scene ID and data time, and insert the data into the daily / scenario-level full indicator table.

[0067] Weekly data summary

[0068] Take the weekly task time as the data time, query the daily / cell-level full indicator table, and obtain the time dimension aggregation method of all communication indicators in the cell full indicator table through the standard aggregation formula configuration table. Aggregate the data to the cell weekly dimension by cell ID and data time, and insert the data into the weekly / cell-level full indicator table.

[0069] Take the weekly task time as the data time, query the daily / base station level full indicator table, obtain the time dimension aggregation method of all communication indicators in the base station full indicator table through the standard aggregation formula configuration table, aggregate the data to the base station weekly dimension according to the base station ID and data time, and insert the data into the weekly / base station level full indicator table.

[0070] Take the weekly task time as the data time, query the daily / scenario-level full indicator table, obtain the time dimension aggregation method of all communication indicators in the scenario-level full indicator table through the standard aggregation formula configuration table, aggregate the data into the scenario-week dimension by scenario ID and data time, and insert the data into the weekly / scenario-level full indicator table.

[0071] Monthly data summary

[0072] Take the monthly task time as the data time, query the daily / cell-level full indicator table, and obtain the time dimension aggregation method of all communication indicators in the cell full indicator table through the standard aggregation formula configuration table. Aggregate the data to the cell monthly dimension by cell ID and data time, and insert the data into the monthly / cell-level full indicator table.

[0073] Take the monthly task time as the data time, query the daily / base station level full indicator table, obtain the time dimension aggregation method of all communication indicators in the base station full indicator table through the standard aggregation formula configuration table, aggregate the data to the base station monthly dimension according to the base station ID and data time, and insert the data into the monthly / base station level full indicator table.

[0074] Take the monthly task time as the data time, query the daily / scenario-level full indicator table, obtain the time dimension aggregation method of all communication indicators in the scenario-level full indicator table through the standard aggregation formula configuration table, aggregate the data into the scenario-month dimension by scenario ID and data time, and insert the data into the monthly / scenario-level full indicator table.

[0075] In this embodiment, the busy hour data aggregation specifically includes: First, obtain the busy hour task time, query the hourly / cell-level full indicator table for the 24 hours of the task time, group by cell ID, sort by the value of the corresponding busy hour data, obtain the first data, and insert the data into the busy hour / cell full indicator table; Then take the busy hour task time, query the hourly / base station level full indicator table for the 24 hours of the task time, group by base station ID, sort by the value of the corresponding busy hour data, take the first data, and insert the data into the busy hour / base station full indicator table.

[0076] Specifically, taking the busy hour of "RRC connection establishment request times" as an example, that is, the time point with the largest value of "RRC connection establishment request times" in 24 hours a day is used as the busy hour point of "RRC connection establishment request times" of the network element.

[0077] 1. Get the busy hour task time, query the hourly / cell-level full indicator table for the 24 hours of the task time, group by cell ID, sort by the "RRC connection establishment request number" value (in descending order), get the first data, and insert the data into the busy hour / cell full indicator table.

[0078] 2. Get the busy hour task time, query the hourly / base station level full indicator table for the 24 hours of the task time, group by base station ID, sort by the "RRC connection establishment request number" value (in descending order), get the first data, and insert the data into the busy hour / base station full indicator table.

[0079] 3. For other types of busy hour summary replacement indicators, repeat the above two steps.

[0080] It should be noted that the complex index pre-aggregation, taking the cell value scoring aggregation as an example, scores the network element based on two indicators: the total number of users and the total traffic volume of the network element on that day.

[0081] Query the daily / cell-level full indicator table, exclude NB network elements, group by city, sort by the total number of users, obtain the total number of network elements in the city where the network element is located, and the ranking of the network element in the city based on the number of users, divide the ranking by the total network element data, and get the user number score of the cell.

[0082] Query the daily / cell-level full indicator table, exclude NB network elements, group by city, sort by total traffic volume, obtain the total number of network elements in the city where the network element is located, and the ranking of the network element in the city based on traffic volume, divide the ranking by the total network element data to get the traffic volume score of the cell.

[0083] Value score = (user score*0.7 + traffic score*0.3)*100

[0084] Query application examples

[0085] High-load cell query: According to the different cell bandwidths, different thresholds are set for the total number of occupied downlink PRBs in the cell, the total number of available downlink PRBs in the cell, and the maximum number of RRC connected users, and qualified cells are queried.

[0086] Cell bandwidth = m1, m1a1 <= total number of occupied downlink PRBs in the cell < m1a2, m1b1 <= total number of available downlink PRBs in the cell < m1b2, m1c1 <= maximum number of RRC connected users < m1c2;

[0087] Cell bandwidth = m2, m2a1 <= total number of occupied downlink PRBs in the cell < m2a2, m2b1 <= total number of available downlink PRBs in the cell < m2b2, m2c1 <= maximum number of RRC connected users < m2c2;

[0088] The description is as follows: m1, m2: cell bandwidth parameter values, such as: 2.1G, 3.5G; m1a1,m1a2,m2a1,m2a2: indicator value, total number of downlink PRBs occupied in the cell, such as 80; m1b1,m1b2,m2b1,m2b2: indicator value, total number of available downlink PRBs in the cell, such as 100; m1c1,m1c2,m2c1,m2c2: indicator value, maximum number of RRC connected users, such as: 40; Set the query conditions accordingly. Among them: the cell bandwidth comes from the cell parameter attributes, the total number of cell downlink PRB occupancy and the total number of cell downlink PRB available come from the cell DU performance data, and the maximum number of RRC connected users comes from the logical cell CU performance data. This avoids the disadvantage of having to associate multiple tables for query when storing multiple tables, reduces the IO reading amount, and thus improves the efficiency of the query.

[0089] The above solution combines all communication indicator data into a large wide table according to the initial dimension for storage, and then summarizes other dimensions based on this table, avoiding the disadvantage of having to associate multiple tables for query when storing multiple tables, reducing the IO reading amount, and thus improving the query efficiency; avoiding the repeated storage of network element attribute fields when storing multiple tables, and reducing the amount of stored data;

[0090] At the same time, the initial dimension is used as the base table to summarize the network element / time dimension, and based on this, complex indicators are preprocessed to reduce data preprocessing latency and improve query response requirements;

[0091] The data is loaded and overwritten according to the preset primary key. Data re-collection and re-exchange do not require deletion of the original data. Data entry is simpler and more efficient than before.

[0092] Reference Fig. 9Based on the same inventive concept, an embodiment of the present invention further provides a method for preprocessing multidimensional communication indicator data, which is applied to a device for preprocessing multidimensional communication indicator data according to the first aspect, and the method comprises the following steps:

[0093] S101, collecting original communication indicator data from an original indicator library or a file server, and storing it in a preset collection library;

[0094] S102, constructing a configuration table to form a configuration library; wherein the configuration table includes network element information;

[0095] S103, merging all the original communication indicator data in the collection library and the network element information in the configuration table into a large wide table according to a preset initial dimension and storing them to form an indicator database;

[0096] S104, taking the initial dimension as a base table, summarizing according to a preset summary dimension, and storing in an indicator database;

[0097] S105, according to the query demand information, based on the above-mentioned dimensional data, pre-aggregation processing of complex indicators is performed.

[0098] Further, in this embodiment, the configuration table includes a cell configuration table, a base station configuration table, a scene cell relationship table, and an indicator aggregation formula configuration table; wherein the fields in the indicator aggregation formula configuration table include an indicator name, an indicator time dimension aggregation method, and an indicator network element dimension aggregation method;

[0099] The large wide table in the indicator database includes a cell-level full indicator table, a base station-level full indicator table and a scene-level full indicator table.

[0100] The initial dimension includes a preset primary key field; wherein the primary key field includes a corresponding ID and a time dimension; the summary dimension includes a time / network element dimension summary;

[0101] The aggregation according to the preset aggregation dimensions specifically includes hourly / base station-level full-amount indicator table aggregation, hourly / scenario-level full-amount indicator table aggregation, daily data aggregation, weekly data aggregation, monthly data aggregation, and busy-hour data aggregation;

[0102] The busy hour data summary specifically includes: First, obtain the busy hour task time, query the hourly / cell-level full indicator table for the 24 hours of the task time, group by cell ID, sort by the value of the corresponding busy hour data, obtain the first data, and insert the data into the busy hour / cell full indicator table; Then take the busy hour task time, query the hourly / base station level full indicator table for the 24 hours of the task time, group by base station ID, sort by the value of the corresponding busy hour data, take the first data, and insert the data into the busy hour / base station full indicator table.

[0103] It should be noted that for a more specific description of the workflow of the method embodiment, please refer to the aforementioned device embodiment part, which will not be repeated here.

[0104] The entire solution combines all communication indicator data into a large wide table according to the initial dimension for storage, avoiding the disadvantage of having to associate multiple tables for query when storing multiple tables, thereby meeting the application requirements of fast response to query requests;

[0105] Taking the initial dimension as the base table, the network element / time dimension is summarized, and complex indicators are preprocessed based on this, thereby reducing data preprocessing latency and improving query response requirements.

[0106] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A preprocessing device for multidimensional communication index data, characterized in that: The pre-processing device comprises: The collection module is used to collect original communication indicator data from the original indicator library or file server and store it in a preset collection library; A configuration module, used to construct a configuration table to form a configuration library; wherein the configuration table includes network element information; An initial summary module is used to merge all the original communication indicator data in the collection library and the network element information in the configuration table into a large wide table according to a preset initial dimension and store them to form an indicator database; the large wide table in the indicator database includes a cell-level full indicator table, a base station-level full indicator table and a scene-level full indicator table; A dimension aggregation module, used to use the initial dimension as a base table, aggregate according to a preset aggregation dimension, and store it in an indicator database; The indicator pre-aggregation module is used to pre-aggregate complex indicators based on the query demand information and the above-mentioned dimensional data; the above-mentioned dimensional data include: a cell-level full indicator table, a base station-level full indicator table and a scene-level full indicator table.

2. A preprocessing device for multidimensional communication index data according to claim 1, characterized in that: The configuration table includes a cell configuration table, a base station configuration table, a scene-cell relationship table and an indicator aggregation formula configuration table; wherein the fields in the indicator aggregation formula configuration table include the indicator name, the indicator's time dimension aggregation method and the indicator's network element dimension aggregation method.

3. A preprocessing device for multidimensional communication index data as claimed in claim 2, characterized in that: The initial dimension includes a preset primary key field; wherein the primary key field includes a corresponding ID and a time dimension; and the summary dimension includes a time / network element dimension summary.

4. A preprocessing device for multidimensional communication index data as claimed in claim 3, characterized in that: The aggregation according to preset aggregation dimensions specifically includes hourly / base station level full indicator table aggregation, hourly / scenario level full indicator table aggregation, daily data aggregation, weekly data aggregation, monthly data aggregation and busy hour data aggregation.

5. A preprocessing device for multi-dimensional communication index data as claimed in claim 4, characterized in that: The busy hour data summary specifically includes: First, obtain the busy hour task time, query the hourly / cell-level full indicator table for the 24 hours of the task time, group by cell ID, sort by the value of the corresponding busy hour data, obtain the first data, and insert the data into the busy hour / cell full indicator table; Then take the busy hour task time, query the hourly / base station level full indicator table for the 24 hours of the task time, group by base station ID, sort by the value of the corresponding busy hour data, take the first data, and insert the data into the busy hour / base station full indicator table.

6. A method for preprocessing multidimensional communication index data, characterized in that: The method for preprocessing multidimensional communication indicator data applied to claim 1 comprises the following steps: Collecting original communication indicator data from the original indicator library or file server and storing it in a preset collection library; Constructing a configuration table to form a configuration library; wherein the configuration table includes network element information; All the original communication indicator data in the collection library and the network element information in the configuration table are merged into a large wide table according to the preset initial dimension and stored to form an indicator database; the large wide table in the indicator database includes a cell-level full indicator table, a base station-level full indicator table and a scene-level full indicator table; Taking the initial dimension as the base table, aggregating according to the preset aggregation dimension and storing it in the indicator database; According to the query demand information, complex indicators are pre-aggregated based on the above-mentioned dimensional data; the above-mentioned dimensional data include: cell-level full indicator table, base station-level full indicator table and scene-level full indicator table.

7. A method for preprocessing multidimensional communication index data as claimed in claim 6, characterized in that: The configuration table includes a cell configuration table, a base station configuration table, a scene-cell relationship table and an indicator aggregation formula configuration table; wherein the fields in the indicator aggregation formula configuration table include the indicator name, the indicator's time dimension aggregation method and the indicator's network element dimension aggregation method.

8. A method for preprocessing multidimensional communication index data as claimed in claim 7, characterized in that: The initial dimension includes a preset primary key field; wherein the primary key field includes a corresponding ID and a time dimension; the summary dimension includes a time / network element dimension summary; The aggregation according to preset aggregation dimensions specifically includes hourly / base station level full indicator table aggregation, hourly / scenario level full indicator table aggregation, daily data aggregation, weekly data aggregation, monthly data aggregation and busy hour data aggregation.

9. A method for preprocessing multidimensional communication index data as claimed in claim 8, characterized in that: The busy hour data summary specifically includes: First, obtain the busy hour task time, query the hourly / cell-level full indicator table for the 24 hours of the task time, group by cell ID, sort by the value of the corresponding busy hour data, obtain the first data, and insert the data into the busy hour / cell full indicator table; Then take the busy hour task time, query the hourly / base station level full indicator table for the 24 hours of the task time, group by base station ID, sort by the value of the corresponding busy hour data, take the first data, and insert the data into the busy hour / base station full indicator table.

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