CN109995549B - Method and device for evaluating flow value - Google Patents
Method and device for evaluating flow value Download PDFInfo
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- CN109995549B CN109995549B CN201711481669.7A CN201711481669A CN109995549B CN 109995549 B CN109995549 B CN 109995549B CN 201711481669 A CN201711481669 A CN 201711481669A CN 109995549 B CN109995549 B CN 109995549B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/142—Network analysis or design using statistical or mathematical methods
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate
Abstract
The invention provides a method and a device for evaluating flow value, which are used for solving the problems of time and labor consumption and low accuracy in evaluating the flow value in the prior art. The method comprises the following steps: determining a prediction target; acquiring first related data according to the predicted target; performing correlation analysis on first characteristics related to the first correlation data, and determining second characteristics with high correlation with the prediction target and second correlation data corresponding to the second characteristics; preprocessing the second related data, and determining third related data after abnormal data are removed; and determining a prediction model for calculating the prediction target according to the third relevant data and a set algorithm. The invention can improve the accuracy of evaluating the flow value and reduce the consumption.
Description
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a method and an apparatus for evaluating a traffic value.
Background
With the development of the 4Generation mobile communication technology (4G), the demand of the user for the traffic is more and more outstanding in the using process, and therefore, when the service provider plans the demand value for a region, how to accurately evaluate the traffic value of the region is an urgent problem to be solved.
In the planning demand value evaluation method in the prior art, a comprehensive multidimensional evaluation system is adopted to evaluate the flow value, and specifically, the following evaluation formula is adopted: a composite score (area weight + area population weight + area economy weight + business revenue weight +4G terminal proportion weight +4G permeability weight + monthly flow weight + inter-site distance weight + planning site count weight + coverage score coverage weight + capacity score capacity weight + complaint score complaint weight + property score property weight) is calculated from the above formula, and a large amount of human resources are consumed for analysis by market scoring data in the multiple evaluation dimensions such as area economy, business revenue, etc.; coverage obtains data scores from a drive test data management system and an MR coverage data system, capacity obtains a main coverage cell through basic data and the drive test system, and telephone traffic of performance data is obtained through a network optimization platform to be used as reference scores, data needs to be manually extracted from each system platform, manual calculation is carried out, and time is consumed; target coverage area, predicted telephone traffic, special user complaints and scenes can be input by a user, certain subjective human participation factors exist, evaluation results are likely to have large differences due to input deviation, weights are manually determined through experience, and accuracy is not high.
In conclusion, the method for evaluating the flow value in the prior art has the disadvantages of large human participation factor, time and labor consumption and low accuracy.
Disclosure of Invention
The invention provides a method and a device for evaluating flow value, which are used for solving the problems of time and labor consumption and low accuracy in evaluating the flow value in the prior art.
In a first aspect, an embodiment of the present invention provides a method for evaluating a traffic value, including:
determining a prediction target; acquiring first related data according to the predicted target; performing correlation analysis on first characteristics related to the first correlation data, and determining second characteristics with high correlation with the prediction target and second correlation data corresponding to the second characteristics; preprocessing the second related data, and determining third related data after abnormal data are removed; and determining a prediction model for calculating the prediction target according to the third relevant data and a set algorithm.
Optionally, the predicted target is a flow value within a set area.
Optionally, after determining a prediction model for calculating the prediction target according to the third relevant data and a set algorithm, the method further includes: performing model evaluation on the prediction model according to a part of the third relevant data.
Optionally, the first related data is obtained by automatic data acquisition, data parsing, data normalization processing, and format output for analysis.
Optionally, the first related data is a random forest formed by automatic data acquisition, data analysis and data normalization through the set algorithm, and the random forest is composed of decision trees.
Optionally, the first related data is obtained by automatically acquiring data, analyzing data, and normalizing data, and the second characteristic includes 4G traffic, 4G client count, 4G accumulated residence time, 4GRRC user count, 4GDOU, 2G traffic, 2G backflow time, 4G average station distance, coverage area, number of stations in coverage, and the like.
In a second aspect, an embodiment of the present invention provides an apparatus for evaluating a flow value, including: a determination module for determining a prediction target; the acquisition module is used for acquiring first related data according to the prediction target; the processing module is used for carrying out correlation analysis on the first characteristics related to the first correlation data, and determining second characteristics with high correlation with the prediction target and second correlation data corresponding to the second characteristics; the processing module is further used for preprocessing the second related data and determining third related data after abnormal data are removed; the determination module is further used for determining and calculating a prediction model of the prediction target according to the third relevant data and a set algorithm.
Optionally, the predicted target is a flow value within a set area.
Optionally, the processing module is further configured to: performing model evaluation on the prediction model according to a part of the third relevant data.
Optionally, the first related data is obtained by automatic data acquisition, data parsing, data normalization processing, and format output for analysis.
Optionally, the setting algorithm is a random forest, and the random forest is composed of decision trees.
Optionally, the second characteristic includes 4G traffic, 4G customer count, 4G accumulated residence time, 4GRRC user count, 4GDOU, 2G traffic, 2G backflow time, 4G average inter-station distance, coverage area, number of stations in the coverage, and the like.
In a third aspect, embodiments of the present invention provide a non-volatile computer storage medium, where an executable program is stored, and the executable program is executed by a processor to implement any of the above-mentioned steps of the method for evaluating a flow value.
In a fourth aspect, an embodiment of the present invention provides a computing device, including a memory, a processor, and a computer program stored on the memory, where the processor implements any of the above-mentioned steps of the method for evaluating a flow value when executing the program.
The method and the device for evaluating the flow value provided by the embodiment of the invention have the following beneficial effects: the method comprises the steps of automatically acquiring and processing first related data of a predicted target, improving the data acquisition speed, saving time consumption and labor consumption, determining and calculating a predicted model of the predicted target according to third related data obtained after data processing and a set algorithm, avoiding human factors from participating in the generation of the predicted model, and improving the accuracy of predicted flow value because weight coefficients in the predicted model are obtained through machine learning.
Drawings
The above and other objects, features and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
fig. 1 is a schematic flow chart of a method for evaluating traffic value according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of automatic data acquisition processing according to an embodiment of the present invention;
FIG. 3 is a schematic view of a processing flow of regression tree analysis according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating predicted results according to an embodiment of the present invention;
FIG. 5(a) is a schematic diagram of another predicted result according to an embodiment of the present invention;
FIG. 5(b) is a diagram illustrating a predicted result according to another embodiment of the present invention;
FIG. 5(c) is a schematic diagram of another predicted result according to the embodiment of the present invention;
FIG. 5(d) is a schematic diagram of another predicted result according to the embodiment of the present invention;
FIG. 6 is a schematic structural diagram of an apparatus for evaluating a flow value according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a hardware structure of a computing device according to an embodiment of the present invention.
Detailed Description
The method and the apparatus for evaluating traffic value provided by the embodiments of the present invention are described in detail with reference to the following embodiments.
Detailed description of the preferred embodiment
As shown in fig. 1, a method for evaluating a flow rate value according to a first embodiment of the present invention includes the following steps:
Specifically, the prediction target is a flow value in a set area.
For example, the set area includes other base stations within 0.3-5 km of the sample base station.
And 102, acquiring first related data according to the predicted target.
Specifically, first related data related to a traffic value is obtained, for example, data such as a scenic spot location, a geographic location of a base station, a type of the base station, a Location Area Code (LAC), an evolved Node B (eNodeB) ID, a traffic, a backflow time, a number of clients, a cumulative residence time, a daily average traffic, a Radio Resource Control (RRC) user number, and an average per-user traffic per month (DOU), and a related first feature of the first related data is not limited in the embodiment of the present invention.
Specifically, the second feature is not limited in the embodiment of the present invention, and the second feature includes 4G traffic, 4G customer count, 4G accumulated residence time, 4GRRC user count, 4GDOU, 2G traffic, 2G backflow time, 4G average inter-station distance, coverage area, and number of stations in a coverage area.
And 104, preprocessing the second related data, and determining third related data after abnormal data are removed.
Specifically, the abnormal data may be inconsistent and repeated values, or may be records with a vacancy value exceeding half, where records with a vacancy value less than half are assigned with a value of 0 or a default value.
And 105, determining a prediction model for calculating the prediction target according to the third relevant data and a set algorithm.
Specifically, a part of the third correlation data is used to train the prediction model of the prediction target, for example, 70% of the third correlation data is used.
Specifically, the set algorithm is a random forest which is composed of decision trees and is scientifically modeled and automatically corrected through machine learning.
The method for evaluating the flow value provided by the embodiment of the invention has the following beneficial effects: the method comprises the steps of automatically acquiring and processing first related data of a predicted target, improving the data acquisition speed, saving time consumption and labor consumption, determining and calculating a predicted model of the predicted target according to third related data obtained after data processing and a set algorithm, avoiding human factors from participating in the generation of the predicted model, and improving the accuracy of predicted flow value because weight coefficients in the predicted model are obtained through machine learning.
As a possible implementation manner, the first relevant data is obtained by automatic data acquisition, data parsing, data normalization processing, and format output for analysis, and a specific flow is shown in fig. 2.
Such as auto-acquisition performance, Memory Read (MR), test, basic data, credit, economic, etc. data.
And step 203, carrying out data normalization processing.
In a possible implementation manner, after determining a prediction model for calculating the prediction target according to the third relevant data and a setting algorithm, the method further includes:
performing model evaluation on the prediction model according to a part of the third relevant data.
For example, 30% of the third data is used for model evaluation of the prediction model.
The above process is illustrated by a specific example.
Firstly sampling the flow values of 78623 4G and 2G base stations in the whole province, then carrying out data processing, classifying according to the longitude and latitude and the region type of the 4G base station, respectively carrying out statistics on the 4G and 2G base stations, removing abnormal data such as base stations with normal indexes and 0 flow in a coverage range, and the like to obtain processed data, determining characteristic variables of a prediction model, such as 4G flow, 4G customer number, 4G accumulated residence time, 4GRRC user number, 4GDOU, 2G flow, 2G backflow time, 4G average station spacing, coverage area and station number in the coverage range, setting an algorithm in the prediction model as a random forest, setting forest trees as 500, selecting 70% of processed data as a training set to train the prediction model, determining the prediction model, and testing the produced prediction model by using 30% of processed data as a testing set, the random forest in the prediction model is composed of a regression tree, wherein the regression tree is one of decision trees, and the processing procedure of regression tree analysis is shown in fig. 3, taking the analysis of four characteristics such as 2G flow, 4GDOU, 4G accumulated residence time, 4G flow and the like as an example. When the test is performed according to the data after 30% of processing, the result diagram shown in fig. 4 is obtained, and it can be seen that the point reaching 87% (i.e. the predicted flow value) is distributed between the line 2 times of the actual value and the line 1/2, i.e. the accuracy reaches 87%.
According to the determined prediction model, the flow values of 78623 4G and 2G base stations in the original data province are randomly sampled for 4 times, test results such as those in FIG. 5(a), FIG. 5(b), FIG. 5(c) and FIG. 5(d) are obtained, and most points can be determined to be still between the two lines of 2 times and 1/2 of the actual values. Therefore, the prediction model has better prediction capability on unprocessed original data.
Detailed description of the invention
A second embodiment of the present invention provides a device for evaluating a flow value, as shown in fig. 6, including:
a determining module 601, configured to determine a prediction target.
An obtaining module 602, configured to obtain first relevant data according to the predicted target.
The processing module 603 is configured to perform correlation analysis on the first feature related to the first correlation data, and determine a second feature having a large correlation with the prediction target and second correlation data corresponding to the second feature.
The processing module 603 is further configured to preprocess the second related data, and determine third related data from which the abnormal data is removed.
The determining module 601 is further configured to determine a prediction model for calculating the prediction target according to the third relevant data and a set algorithm.
The device for evaluating the flow value provided by the embodiment of the invention has the following beneficial effects: the method comprises the steps of automatically acquiring and processing first related data of a predicted target, improving the data acquisition speed, saving time consumption and labor consumption, determining and calculating a predicted model of the predicted target according to third related data obtained after data processing and a set algorithm, avoiding human factors from participating in the generation of the predicted model, and improving the accuracy of predicted flow value because weight coefficients in the predicted model are obtained through machine learning.
The embodiment of the invention also provides a nonvolatile computer storage medium, wherein the computer storage medium stores an executable program, and the executable program is executed by a processor to realize any step of the method for evaluating the flow value provided by the first embodiment.
An embodiment of the present invention further provides a computing device, configured to execute any method for evaluating a traffic value in the first embodiment, as shown in fig. 7, which is a schematic diagram of a hardware structure of the computing device in the fourth embodiment of the present invention, where the computing device may specifically be a desktop computer, a portable computer, a smart phone, a tablet computer, and the like. The computing device may include a memory 701, a processor 702 and a computer program stored on the memory, the processor implementing the steps of the method of assessing flow value in one embodiment when executing the program. Memory 701 may include Read Only Memory (ROM) and Random Access Memory (RAM), among other things, and provides processor 702 with program instructions and data stored in memory 701.
Further, the computing device may further include an input device 703, an output device 707, and the like. The input device 703 may include a keyboard, mouse, touch screen, or the like; the output device 707 may include a Display device such as a Liquid Crystal Display (LCD), a Cathode Ray Tube (CRT), or the like. The memory 701, the processor 702, the input device 703 and the output device 707 may be connected by a bus or other means, such as the bus connection shown in fig. 7.
The processor 702 calls the program instructions stored in the memory 701 and executes the method of evaluating traffic value provided by the first embodiment according to the obtained program instructions.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (12)
1. A method of assessing flow value, comprising:
determining a prediction target;
acquiring first related data according to the predicted target;
performing correlation analysis on first characteristics related to the first correlation data, and determining second characteristics with high correlation with the prediction target and second correlation data corresponding to the second characteristics;
preprocessing the second related data, and determining third related data after abnormal data are removed;
and determining a prediction model for calculating the prediction target according to the third relevant data and a set algorithm, wherein the set algorithm is a random forest which consists of decision trees.
2. The method of claim 1, wherein the predicted target is a flow value within a set area.
3. The method of claim 1, wherein after determining a predictive model to calculate the predicted target based on the third correlation data and a set algorithm, the method further comprises:
performing model evaluation on the prediction model according to a part of the third relevant data.
4. The method of claim 1, wherein the first correlation data is obtained by automatic data collection, data parsing, data normalization processing, and format output for analysis.
5. The method of claim 1, wherein the second characteristics comprise 4G traffic, 4G customer count, 4G cumulative residence time, 4GRRC user count, 4GDOU, 2G traffic, 2G flow back time, 4G average inter-site distance, coverage area, number of in-coverage sites, and the like.
6. An apparatus for assessing flow value, comprising:
a determination module for determining a prediction target;
the acquisition module is used for acquiring first related data according to the prediction target;
the processing module is used for carrying out correlation analysis on the first characteristics related to the first correlation data, and determining second characteristics with high correlation with the prediction target and second correlation data corresponding to the second characteristics;
the processing module is further used for preprocessing the second related data and determining third related data after abnormal data are removed;
the determining module is further used for determining a prediction model for calculating the prediction target according to the third relevant data and a set algorithm, wherein the set algorithm is a random forest, and the random forest is composed of a decision tree.
7. The apparatus of claim 6, wherein the predicted target is a flow value within a set area.
8. The apparatus of claim 6, wherein the processing module is further to:
performing model evaluation on the prediction model according to a part of the third relevant data.
9. The apparatus of claim 6, wherein the first related data is obtained by automatic data collection, data parsing, data normalization processing, and format output for analysis.
10. The apparatus of claim 6, wherein the second characteristics comprise 4G traffic, 4G customer count, 4G cumulative residence time, 4GRRC user count, 4GDOU, 2G traffic, 2G flow back time, 4G average inter-site distance, coverage area, number of in-coverage sites, and the like.
11. A non-transitory computer storage medium storing an executable program for execution by a processor to perform the steps of the method of any one of claims 1 to 5.
12. A computing device comprising a memory, a processor and a computer program stored on the memory, the processor implementing the steps of the method of any one of claims 1 to 5 when executing the program.
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| CN113347659B (en) * | 2021-06-01 | 2022-12-23 | 深圳市大数据研究院 | Flow prediction method and device |
| CN113453096B (en) * | 2021-06-04 | 2022-12-13 | 中国联合网络通信集团有限公司 | Method and device for predicting PON port flow of passive optical network |
| CN114390563A (en) * | 2021-12-30 | 2022-04-22 | 中国电信股份有限公司 | 5G cell residence capacity evaluation method and device and electronic equipment |
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