CN108901036B - Method and device for adjusting cell network parameters, computer equipment and storage medium - Google Patents
Method and device for adjusting cell network parameters, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a method, a system, a computer device and a storage medium for adjusting cell network parameters. The method comprises the following steps: acquiring a scene type of a target cell, and determining a corresponding characteristic service set according to the scene type of the target cell; acquiring perception quality influence factors of each feature service in the feature service set, and matching target network parameters influencing the perception quality influence factors; and adjusting the target network parameters according to the preset expected performance index of the target cell. By adopting the method, the perception quality influence factor of the characteristic service under the scene category of the cell can be realized, the key network parameter can be accurately adjusted, the service performance of the cell network can be effectively improved, the network optimization effect can be improved, and the fine adjustment and optimization of the network can be realized.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for adjusting cell network parameters, a computer device, and a storage medium.
Background
In a mobile communication network, network parameters of a cell are already configured at the initial stage of cell planning construction, but the geographic environment, the network use condition and the service characteristics of one cell are continuously changed in the subsequent use process, and at this time, network parameters of wireless network equipment of the cell need to be adjusted and optimized to improve the quality of the communication network and improve the average service performance of the network.
At present, in the network optimization process, a network optimization engineer usually analyzes the performance of a communication network according to a large amount of key quality index data of cell wireless network equipment, and then adjusts cell network parameters according to personal experience. The traditional adjustment technology of cell network parameters is based on personal errors of human experience, and key parameters influencing cell network quality are difficult to realize fine adjustment, so that the network optimization effect is poor.
Disclosure of Invention
Therefore, it is necessary to provide a method, an apparatus, a computer device and a storage medium for adjusting cell network parameters, in order to solve the technical problem that the network optimization effect is poor due to the fact that it is difficult to finely adjust the key parameters affecting the cell network quality in the conventional technology.
A method for adjusting cell network parameters is characterized by comprising the following steps:
acquiring a scene type of a target cell, and determining a corresponding characteristic service set according to the scene type of the target cell;
acquiring perception quality influence factors of each feature service in the feature service set, and matching target network parameters influencing the perception quality influence factors;
and adjusting the target network parameters according to the preset expected performance index of the target cell.
An apparatus for adjusting cell network parameters, comprising:
the characteristic service set acquisition module is used for acquiring the scene type of a target cell and determining a corresponding characteristic service set according to the scene type of the target cell;
a target network parameter obtaining module, configured to obtain perceptual good/bad influence factors of each feature service in the feature service set, and match target network parameters that influence the perceptual good/bad influence factors;
and the target network parameter adjusting module is used for adjusting the target network parameters according to the preset expected performance index of the target cell.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a scene type of a target cell, and determining a corresponding characteristic service set according to the scene type of the target cell;
acquiring perception quality influence factors of each feature service in the feature service set, and matching target network parameters influencing the perception quality influence factors;
and adjusting the target network parameters according to the preset expected performance index of the target cell.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring a scene type of a target cell, and determining a corresponding characteristic service set according to the scene type of the target cell;
acquiring perception quality influence factors of each feature service in the feature service set, and matching target network parameters influencing the perception quality influence factors;
and adjusting the target network parameters according to the preset expected performance index of the target cell.
According to the cell network parameter adjusting method, the cell network parameter adjusting device, the computer equipment and the storage medium, the perception quality influence factor influencing the use of the characteristic service experience of a terminal user is determined by obtaining the characteristic service set under the scene category of the target cell, the network parameter influencing the perception quality influence factor is adjusted by combining the expected performance index of the target cell, the perception quality influence factor aiming at the characteristic service under the scene category of the cell is realized, the key network parameter is accurately adjusted, the service performance of the cell network is effectively improved, the network optimization effect is improved, and the fine adjustment and optimization of the network are realized.
Drawings
Fig. 1 is an application environment diagram of a method for adjusting cell network parameters according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a method for adjusting cell network parameters according to an embodiment of the present invention;
fig. 3 is a flowchart of determining a corresponding scene feature service set according to a scene category of the target cell in an embodiment of the present invention;
fig. 4 is a flowchart of a method for adjusting cell network parameters according to another embodiment of the present invention;
FIG. 5 is a flowchart of obtaining a scene type of a target cell in an embodiment of the present invention;
FIG. 6 is a table of the structure of a deep learning network model in an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an apparatus for adjusting cell network parameters according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an apparatus for adjusting cell network parameters according to an embodiment of the present invention;
fig. 9 is an internal structural view of a computer device in one embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The method for adjusting cell network parameters provided by the present application can be applied to the application environment shown in fig. 1. The server 110 communicates with the wireless network device 120 through a network, and the server 110 obtains the scene type of the target cell and obtains the feature service and the perceptual good and bad influence factor of the feature service according to the scene type, so as to adjust the network parameters of the wireless network device 120 in the target cell in a targeted manner. The server 110 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.
Referring to fig. 2, fig. 2 is a flowchart of a method for adjusting cell network parameters according to an embodiment of the present invention, which is described in this embodiment by taking the method applied to the server in fig. 1 as an example, and includes the following steps:
step S210: and acquiring the scene type of the target cell, and determining a corresponding characteristic service set according to the scene type of the target cell.
In this step, the scene category of the target cell may be divided based on the geographic environment, coverage factor, service characteristics, and the like of the target cell, or may be divided based on the communication behavior characteristics of the cell; one cell often carries mixed services of voice, data, multimedia and the like, different services occupy different system resources, and a characteristic service refers to a service occupying more system resources in the cell under a certain scene type; after obtaining the scene category of the cell, the server obtains the feature service features corresponding to the target cell according to the scene category, specifically, the feature service sets corresponding to the scene categories of each type may be predetermined, and after obtaining the scene category of the target cell, the server directly determines the corresponding feature service sets according to the corresponding relationship.
Step S220: and acquiring the perception quality influence factor of each characteristic service in the characteristic service set, and matching the target network parameters influencing the perception quality influence factor.
In this step, the sensing quality factor refers to a service sensing factor that mainly affects the experience of the terminal user using the service, wherein the service sensing factor specifically includes a service success rate, a service delay time, a service rate, a service drop rate, and the like; specifically, after obtaining the feature service set of the target cell, the server may determine, according to the sensitivity of each feature service in the feature service set to different service awareness factors, an awareness benefit/disadvantage impact factor having the greatest influence on the service experience of the user from the service awareness factors, and determine, from various configuration parameters of the cell communication network, a target network parameter having the greatest influence on the awareness benefit/disadvantage impact factor.
Step S230: and adjusting the target network parameters according to the preset expected performance index of the target cell.
In this step, the expected performance index refers to an index value of a service perception factor of a feature service in the target cell; specifically, the server can design a parameter configuration method of the target network parameter according to expected performance indexes under various scene types, and the service perception factor of the feature service reaches the expected performance indexes by adjusting the target network parameter, so that the network parameter under various scene types can be adjusted in real time. The server adjusts the target network parameters according to the expected performance index value of the target cell, improves the communication network quality of the target cell, and realizes fine adjustment and optimization of the network.
In the method for adjusting the cell network parameters, the perception quality influence factor influencing the experience of terminal users using the characteristic service is determined by acquiring the characteristic service set under the scene category of the target cell, and the network parameters influencing the perception quality influence factor are adjusted by combining the expected performance index of the target cell, so that the perception quality influence factor aiming at the characteristic service under the scene category of the cell is realized, the key network parameters are accurately adjusted, the service performance of the cell network is effectively improved, the network optimization effect is improved, and the fine adjustment and optimization of the network are realized.
In one embodiment, the step of obtaining the perceptual good/bad influence factor of each feature service in the feature service set includes the following steps: acquiring quality parameters and reference values of service perception factors of all feature services in a feature service set; and determining a perception quality influence factor from the service perception factors according to the reference value corresponding to the quality parameter of the service perception factors.
In this embodiment, the Quality parameter of the Service awareness factor refers to a value obtained by real-time measurement of each characteristic Service awareness factor in the current target cell, and may be obtained from a Quality of Service (QoS) indicator of the target cell, or may be obtained from a Service Quality parameter (Key Quality Indicators, KQI) experienced by a user; the reference value of the service perception factor refers to the actual required value of each characteristic service perception factor in the current target cell, and can be obtained by initiating service dial testing by a network system or statistically analyzing user complaint information. Specifically, if the quality parameter of a certain service perception factor is smaller than the corresponding reference value, the current feature service is difficult to meet the requirements of the current network on the service perception factor, and the service perception factor is a factor mainly influencing the feeling of using the feature service by the user; similarly, if the quality parameter of a certain service perception factor is greater than the corresponding reference value, the current feature service can meet the requirements of the current network on the service perception factor.
Referring to fig. 3, fig. 3 is a flowchart illustrating a method for determining a corresponding scene feature service set according to a scene category of a target cell in an embodiment of the present invention, where the step of determining the corresponding scene feature service set according to the scene category of the target cell in this embodiment includes the following steps:
step S211: and acquiring the access volume and the flow of various services in the full service under the scene type of the target cell.
In this step, the traffic volume or traffic flow of the service has strong mobility, and under different scene types, the traffic volume and traffic flow of the service are different and have certain mobility. Specifically, the access volume and the flow of each type of service in the full service under the current scene category of the target cell are counted.
Step S212: and respectively calculating the weight values of various services in the full-volume service according to the access volume and the flow of various services.
Step S213: and determining the service with the weight value ratio exceeding a preset threshold value as the corresponding characteristic service under the scene category of the target cell, and acquiring a characteristic service set.
In this step, a service with a total weight value exceeding a preset threshold is selected as a feature service in the current scene category, so as to form a feature service set, wherein the preset threshold of the weight value can be adjusted according to the actual network condition.
In this embodiment, a service with a higher weight value in the full service is determined as a feature service according to the service volume and the service flow of the service, so that network parameters can be adjusted according to the service perception factor under the feature service, key network parameters can be accurately adjusted, the service performance of a cell network is effectively improved, the network optimization effect is improved, and a fine adjustment and optimization network is realized.
In a specific application process, the access volume and the flow of various services in the full-volume service under various types of scene categories can be acquired, the weight values of various services in the full-volume service are calculated according to the access volume and the flow of various services under various types of scene categories, so that the feature service set corresponding to each type of scene category is determined, after the server acquires the scene category of the target cell, the corresponding feature service set is directly determined according to the corresponding relation, and the network optimization efficiency is improved.
Referring to fig. 4, fig. 4 is a flowchart of a method for adjusting cell network parameters according to another embodiment of the present invention, in this embodiment, a method for adjusting cell network parameters is described in detail by taking an office building type scene as an example of a scene type of a target cell, where the method for adjusting cell network parameters includes the following steps:
step S310: and acquiring the access volume and the flow of various services in the full service under various types of scene categories.
In this step, the scene categories may derive, from the network information system, historical key performance indicator data of a plurality of cells in the whole network, which are derived year after year, as sample data, and the scene categories of the cells are determined according to the communication behavior feature data in the sample data, where the scene categories include 9 types, which are respectively a wave type, a fault type, an office building type, a discrete type, a mall type, a bar type, a residential zone type, and a village type.
Step S320: and respectively calculating the weight values of various services in the full-volume service under various types of scene categories according to the access volume and the flow of various services under various types of scene categories.
Step S330: and determining the service with the weight value ratio exceeding a preset threshold value under each type of scene category as the corresponding feature service under each type of scene category, and acquiring a feature service set under each type of scene category.
Specifically, the description is given to a feature service set for acquiring an office building type scene by taking the scene type as an example, and the service statistics for the office building type scene are as follows:
and taking the service with the weight value exceeding 0.1 in the table as the characteristic service under the scene category, and taking the characteristic service set under the office building type scene as { instant messaging, mailbox, browsing, financial accounting }.
Step S340: and acquiring the scene type of the target cell, and determining a corresponding characteristic service set according to the scene type of the target cell.
In this step, after the scene type of the target cell is obtained as an office building type scene, the corresponding feature service set in the office building type scene is directly determined as { instant messaging, mailbox, browsing, financial institution }.
Step S350: and acquiring the quality parameters and the reference values of the service perception factors of the feature services in the feature service set.
In this step, the quality parameter of the service perception factor may be an excellent value measured in real time within a certain time; after obtaining the feature service set in the office building type scene, measuring in real time the excellent value of the service perception factor of each feature service in the feature service set in the preset time period and obtaining the reference value of the service perception factor by service dial-up measurement and user complaint information statistics, wherein the service perception factor refers to a factor for evaluating the experience of using the service by the user, such as how well the connection power is, how fast the connection speed is, whether the service is stuck during the service using process, and the like, and is summarized as the service success rate, the service delay time and the service rate, and the reference value and the excellent value of the service perception factor of { instant messaging, mailbox, browsing, financial institution } in the office building type scene are specifically as follows:
(1) instant messaging: rate: reference value: 300 kbps; excellent values: 650kbps
Time delay: reference value: 5000 ms; excellent values: 2000ms
Success rate: the reference value is 95%
(2) Mail box: rate: reference value: 350 kbps; excellent values: 1024kbps
Time delay: reference value: 5000 ms; excellent values: 2000ms
Success rate: the reference value is 95%
(3) Browsing: rate: reference value: 350 kbps; excellent values: 1024kbps
Time delay: reference value: 5000 ms; excellent values: 2000ms
Success rate: reference value of 80%
(4) Mail box: rate: reference value: 300 kbps; excellent values: 650kbps
Time delay: reference value: 3000 ms; excellent values: 1000ms
Success rate: reference value of 98%
Step S360: and determining the perception quality influence factor from the business perception factor according to the quality parameter and the reference value of the business perception factor.
In this step, based on the reference value and the excellent value of the business perception factor of { instant messaging, mailbox, browsing, financial institution }, the perception quality influence factor in the office building type scene can be obtained as the business time delay duration and the business success rate.
Step S370: and matching target network parameters influencing the perception of the good and bad influence factors.
In this step, network parameters influencing service delay duration and service success rate are matched from network parameter configuration, and network parameters influencing perception of good and bad influence factors under an office building type scene are determined as { random access configuration parameters, admission control parameters, and control timer parameters in an RRC signaling process }.
Step S380: and adjusting the target network parameters according to the preset expected performance index of the target cell.
In the embodiment, by acquiring a feature service set { instant messaging, mailbox, browsing and financial channel } of a target cell under a scene category, determining whether a perception quality influence factor influencing the experience of a terminal user in using the feature service is service delay time and service success rate, and then adjusting a network parameter { random access configuration parameter, admission control parameter and control timer parameter in an RRC signaling process } influencing the perception quality influence factor according to an expected performance index of the target cell, the perception quality influence factor of the feature service under the scene category of the cell is realized, key network parameters are accurately adjusted, the service performance of a cell network is effectively improved, the network optimization effect is improved, and the network is finely adjusted and optimized.
Referring to fig. 5, fig. 5 is a flowchart illustrating acquiring a scene type of a target cell according to an embodiment of the present invention, where the step of acquiring the scene type of the target cell includes the following steps:
step S410: the method comprises the steps of obtaining first key performance index data of a target cell, and obtaining first communication behavior characteristic data of the target cell from the first key performance index data.
In this step, Key Performance Indicator (KPI) data refers to cell network management assessment Indicator data, which is used to describe user communication quality parameters; the communication behavior characteristic data comprises a plurality of field data representing communication behaviors in the key performance index data, the correlation among the field data is large, and the field data can comprise field data such as throughput data, communication rate and physical resource block utilization rate of a cell. Specifically, the server acquires key performance indicator data of a target cell within a certain time period, and extracts field data related to communication behaviors from the key performance indicator data as communication behavior feature data.
Step S420: and performing principal component dimensionality reduction on each first communication behavior characteristic data by using a preset principal component coefficient matrix to obtain a first communication behavior characteristic value of the target cell.
In this step, because the communication behavior characteristic data itself has stability and the change of the communication behavior characteristic data has a certain rule, the principal component analysis coefficient matrix can be determined according to the data characteristics of the communication behavior characteristic data itself; specifically, the server multiplies first communication behavior feature data in first key performance indicator data of the target cell by a preset principal component coefficient matrix, and reduces the dimension of the first communication behavior feature data to obtain a corresponding one-dimensional first communication behavior feature value.
Step S430: and generating a first communication behavior oscillogram by utilizing the first communication behavior characteristic values and combining the time granularity.
In this step, the server combines the obtained communication behavior characteristic value with the time dimension to generate a communication behavior oscillogram related to time. Specifically, the key performance indicator data may be derived by using time as a dimension, a one-dimensional communication behavior feature value representing communication characteristics of the cell at a certain time is obtained from communication behavior feature data extracted from the key performance indicator data after a principal component dimension reduction operation, and the server may generate a two-dimensional coordinate waveform diagram arranged according to time by using the communication behavior feature value in combination with the time dimension, where the two-dimensional coordinate waveform diagram represents a symbolic communication behavior distribution of the cell.
Step S440: and inputting the first communication behavior oscillogram into a pre-trained deep learning network model, and acquiring the scene type of the target cell output by the pre-trained deep learning network model.
In this step, for the deep learning network model trained in advance, the communication behavior waveform map generated from the key performance indicator data has been set as an input item of the deep learning network model, and the scene category of the cell is set as an output item, so that the first communication behavior waveform map of the target cell is input to the deep learning network model trained in advance, and the scene category of the target cell is obtained. By applying the deep learning network model, the scene types of the cell can be divided under the condition of acquiring the key performance index data of the cell, so that decision assistance is provided for the optimization and construction of the cell network.
In the embodiment, by obtaining the key performance indicator data of the cell, the communication behavior characteristic value is obtained by dimensionality reduction of the field data related to the communication behavior characteristic in the key performance indicator data, and then the communication behavior oscillogram of the cell is obtained according to the communication behavior characteristic value, and by converting the key performance indicator data of the cell into a visual communication behavior oscillogram, the communication behavior oscillogram is used as a division basis of the scene category of the cell, individual errors generated by artificial division based on coarse granularities such as the geographic environment, the coverage factor and the service characteristic of the cell are effectively avoided, the accurate division of the scene category of the cell is realized, the accurate division of the scene category of the cell is used as a support basis for fine adjustment and optimization of a network, and planning construction and adjustment of the network are effectively guided.
Optionally, the communication behavior feature data includes at least one of a network throughput, an RRC connection indication value, an uplink channel physical resource block utilization rate, a downlink channel physical resource block utilization rate, an uplink service information physical resource block occupancy rate, a downlink service information physical resource block occupancy rate, and a number of downlink successful transmission initial transport blocks.
In one embodiment, the step of generating the first communication behavior oscillogram by using the first communication behavior feature values and combining the time granularity comprises the following steps: generating a two-dimensional array by utilizing the first communication behavior characteristic value and combining time granularity; generating a communication behavior original image by taking the time granularity as an abscissa and the communication behavior characteristic value as an ordinate; and removing noise in the original image of the communication behavior, and determining the original image of the communication behavior after the noise is removed as a first communication behavior oscillogram.
In this embodiment, the key performance indicator data is derived by using time as a dimension, the communication behavior feature value represents the communication behavior feature of a cell at a certain time, the server combines the communication behavior feature value with the corresponding time to generate a two-dimensional array, and then generates a two-dimensional coordinate waveform diagram by using the two-dimensional array, where the time is used as an abscissa and the communication behavior feature value is used as an ordinate, the two-dimensional coordinate waveform diagram is a communication behavior original image, and noise in the communication behavior original image often includes interference factors such as a two-dimensional coordinate axis, an image frame, and a background color, which may cause noise influence on image identification. Specifically, interference noise such as coordinate axes and frames in the original image of the communication behavior can be provided by utilizing Python programming, a waveform picture of a pure image is obtained, so that the accuracy of the waveform picture of the communication behavior for characterizing the communication behavior is improved, and the accuracy of the classification of the scene classes of the cell is improved when a subsequent classification model of the scene classes of the cell is trained by adopting the waveform picture of the communication behavior.
In one embodiment, before the step of performing principal component dimension reduction on each first communication behavior characteristic data by using a preset principal component coefficient matrix to obtain a first communication behavior characteristic value of a target cell, the method further includes the following steps: acquiring second key performance indicator data of the sample cell, and acquiring second communication behavior characteristic data of the sample cell from the second key performance indicator data; and performing principal component analysis on the second communication behavior characteristic data, and extracting a preset principal component coefficient matrix.
In the network information system, the key performance index data of a plurality of cells in the whole network can be derived by taking time as dimension to be used as sample data to a server, after obtaining the key performance index data as sample data, the server carries out bivariate correlation analysis on each field data and the communication behavior field, if the correlation between a certain field data in the key performance index data and the communication behavior field is lower than a preset threshold value, deleting the field data from the key performance indicator data, taking the residual field data in the key performance indicator data as communication behavior characteristic data, carrying out principal component analysis by the server by taking the acquired communication behavior characteristic data of a plurality of cells in the whole network as reference data to carry out dimensionality reduction, and screening the one-dimensional element according to the coefficient of the principal component analysis to obtain a preset principal component coefficient matrix for analyzing the key performance index data of the target cell.
In one embodiment, after the step of performing principal component analysis on each piece of second communication behavior feature data, the method further includes the following steps: acquiring a second communication behavior characteristic value; generating a second communication behavior oscillogram by utilizing the second communication behavior characteristic value and combining the time granularity; screening a communication behavior waveform sample graph from each second communication behavior waveform graph, and acquiring a scene type corresponding to each communication behavior waveform sample graph; and training a deep learning network model by using each communication behavior waveform sample graph and the corresponding scene category.
In the embodiment, after key performance indicator data of a plurality of cells in the whole network are derived from a network information system and are used as sample data to a server, the server extracts communication behavior characteristic data from the key performance indicator data which are used as the sample data, then performs principal component analysis on the obtained communication behavior characteristic data of the plurality of cells in the whole network as reference data to perform dimensionality reduction, screens out one-dimensional elements according to coefficients after the dimensionality reduction of the communication behavior characteristic data of each multi-dimension to obtain a preset principal component coefficient matrix and one-dimensional communication behavior characteristic values corresponding to the communication behavior characteristic data, combines the obtained communication behavior characteristic values with time dimensions to generate a communication behavior oscillogram related to time, and then screens out a plurality of communication behavior oscillograms with obvious communication behavior distribution characteristics from the communication behavior oscillograms to serve as training sample graphs, and marking corresponding scene category labels on each communication behavior waveform sample graph, inputting the scene category labels as training samples into the deep learning network model for supervised training, and improving the accuracy of the deep learning network model in the classification of the cell scene categories. The step of obtaining the second communication behavior oscillogram is the same as the step of obtaining the first communication behavior oscillogram, a two-dimensional array can be generated by using the second communication behavior characteristic value and combining time granularity, then an original communication behavior image is generated by using the time granularity as an abscissa and the communication behavior characteristic value as an ordinate, noise in the original communication behavior image is removed, and the original communication behavior image after the noise is removed is determined as the second communication behavior oscillogram.
In one embodiment, the step of screening the communication behavior waveform sample map from each of the second communication behavior waveform maps and obtaining a scene type corresponding to each of the communication behavior waveform sample maps includes the following steps: acquiring the number of target scene categories; according to the number of the target scene categories, dividing each second communication behavior oscillogram into different scene clusters with the number of the target scene categories by using a clustering algorithm, and acquiring each scene cluster center and a scene category corresponding to each scene cluster center; taking a second communication behavior waveform chart with the distance value between the second communication behavior waveform chart and each scene clustering center smaller than a preset distance value as a communication behavior waveform sample chart; and determining the scene type corresponding to the communication behavior waveform sample graph according to the scene type corresponding to each scene clustering center.
In this embodiment, the number of target scene categories refers to the number of scene categories; after the server determines the number of the target scene categories, the communication behavior oscillograms with similar characteristics can be divided into one cluster by using a clustering algorithm, so that each communication behavior oscillogram is divided into different scene clusters with the number of the target scene categories respectively, a corresponding scene cluster center is obtained, the scene category of each scene cluster is determined according to the communication behavior characteristics, then a circle region is drawn by taking each scene cluster center as the center and taking a preset distance value as a radius, the communication behavior oscillograms in each circle region are taken as communication behavior waveform sample maps, a plurality of communication behavior oscillograms with obvious communication behavior distribution characteristics are rapidly screened as training samples, the cost of manually screening the training samples is saved, the screening of the training samples is more objective and accurate, and the accuracy of the division of the small-area scene categories is improved.
In one embodiment, the step of obtaining each scene clustering center and a scene category corresponding to each scene clustering center by dividing each second communication behavior oscillogram into different scene clusters with the number of target scene categories by using a clustering algorithm includes the following steps: randomly selecting second communication behavior oscillograms with the number being the number of the target scene categories as a first clustering center of each scene cluster; calculating the distance value from the remaining second communication behavior oscillograms to each first clustering center, dividing each remaining second communication behavior oscillogram into a scene clustering with the smallest distance value from the first clustering center, and obtaining the scene clustering results of the division of each second communication behavior oscillogram into different scene clustering; calculating a second clustering center of each scene cluster according to the scene clustering result, judging whether the first clustering center is equal to the second clustering center, if so, taking each second clustering center as each scene clustering center, and determining the scene type of each scene clustering center; and if the first clustering center is not equal to the second clustering center, taking the second clustering center as the first clustering center, and skipping to execute calculation of the distance value from the remaining second communication behavior oscillogram to each first clustering center.
In this embodiment, K second communication behavior oscillograms are randomly selected from the plurality of communication behavior oscillograms as a first clustering center, where K is the number of categories of the target scene, and then the distance between each second communication behavior oscillogram and the first clustering center is calculated, so that the second communication behavior oscillograms are classified into the cluster where the first clustering center closest to the second communication behavior oscillogram is located. And calculating the average value of the newly formed second communication behavior oscillograms of each cluster to obtain a second cluster center, finishing clustering if the cluster centers of two adjacent clusters are not changed, regarding the second cluster center as the first cluster center if the cluster centers of two adjacent clusters are changed, dividing each communication behavior oscillogram into different cluster center categories again, and iteratively calculating the cluster centers until the cluster centers of two adjacent clusters are not changed.
The following describes the details of the scene type of the target cell with reference to a specific embodiment:
and acquiring second key performance index data of the sample cell, and acquiring second communication behavior characteristic data of the sample cell from the second key performance index data. The method comprises the steps of leading out historical key performance index data of a plurality of cells in the whole network in a year and a whole year in a network information system by taking time as a dimension to serve as sample data to a server, carrying out basic cleaning on the sample key performance index data by the server after a large amount of sample key performance index data are collected, carrying out zero filling operation on zero values in the sample key performance index data, deleting repeated items in the sample key performance index data, reducing the influence of data noise to the maximum extent, clustering field data in the field data by taking network throughput, call-on rate and other fields related to communication characteristic behaviors of users in the cells as cores, and removing noise fields.
Specifically, each key performance indicator data includes fields including: time, Cell name CGI (Cell Global Identifier), antenna angle, Cell bandwidth, operating frequency band, network throughput, average throughput of uplink physical resource blocks, average throughput of downlink physical resource blocks, maximum number of RRC connections, number of RRC connection establishment requests, number of RRC connection establishment successes, average utilization of uplink physical resource blocks, average utilization of downlink physical resource blocks, occupancy of uplink service information physical resource blocks, occupancy of downlink service information physical resource blocks, average uplink interference level of physical resource blocks, number of QPSK mode downlink successful initial transmission blocks, number of 16QAM mode downlink successful initial transmission blocks, number of 64QAM mode downlink successful initial transmission blocks, E-RAB drop rate, and wireless drop rate.
After a large amount of sample key performance indicator data are collected, a server respectively carries out bivariate correlation analysis on field data and communication behavior fields, such as network throughput, in the key performance indicator data, field data with correlation lower than a threshold value and without generating large change on cell communication behavior characteristics, such as time, cell name CGI, antenna angle, cell bandwidth and working frequency field, in the key performance indicator data are removed, influence of data noise is reduced, the field data with low correlation with the communication behavior fields are finally screened out to serve as communication behavior characteristic data for research, and eleven field data comprise network throughput, maximum RRC connection number, RRC connection establishment request number, RRC connection establishment success number, data with correlation higher than 0.5 threshold value, The method comprises the following steps of uplink channel physical resource block utilization rate, downlink channel physical resource block utilization rate, uplink service information physical resource block occupancy rate, downlink service information physical resource block occupancy rate, QPSK mode downlink successful transmission initial transmission block quantity, 16QAM mode downlink successful transmission initial transmission block quantity and 64QAM mode downlink successful transmission initial transmission block quantity.
And performing principal component analysis on each second communication behavior characteristic data to obtain a second communication behavior characteristic value and extract a preset principal component coefficient matrix. The communication behavior characteristic data comprises a plurality of field data related to communication behaviors; the server performs principal component analysis to reduce dimensions by taking the acquired communication behavior characteristic data of a plurality of cells in the whole network as reference data, obtains a one-dimensional communication behavior characteristic value after reducing the dimensions of each multi-dimensional communication behavior characteristic data, and screens out one-dimensional elements according to coefficients of the principal component analysis to obtain a preset principal component coefficient matrix. The preset principal component coefficient matrix is specifically as follows: network throughput 0.943+ maximum number of RRC connections 0.893+ number of RRC connection establishment requests 0.866+ number of RRC connection establishment successes 0.868+ average utilization of upstream PRBs 0.881+ average utilization of downstream PRBs 0.948+ occupancy of upstream traffic information PRBs 0.864+ occupancy of downstream traffic information PRBs 0.955+ 0.947+ initial number of successful transmissions in QPSK mode downlink 0.884+ initial number of successful transmissions in QAM mode downlink TB 0.766.
And generating a second communication behavior oscillogram by utilizing the second communication behavior characteristic value and combining the time granularity. The key performance index data is derived by taking hours as dimensions, after bivariate correlation analysis and principal component analysis operations, communication characteristics of a cell at a certain time can be represented by one-dimensional communication behavior characteristic values according to the key performance index data, a server combines the communication behavior characteristic values at different times in the same day belonging to the same cell with the corresponding time to generate two-dimensional arrays which are sequentially arranged according to 24 hours, a 24 x 24 two-dimensional coordinate waveform diagram taking time as a horizontal coordinate and the communication behavior characteristic values as a vertical coordinate is formed according to the two dimensions, interference noises such as two-dimensional coordinate axes, image frames, background colors and the like in the two-dimensional coordinate waveform diagram are removed, and a pure image waveform diagram is obtained, so that the communication behavior waveform diagram representing the communication behavior characteristics of all cells in the whole year in the whole network is obtained.
And acquiring the number of target scene categories. The traditional scene category number is usually from 6 to 13, on the basis, the clustering algorithm can be utilized to divide each communication behavior oscillogram into a plurality of categories of scene categories according to the scene category number from 6 to 13, so as to obtain clustering results from 6 to 13, compare indexes such as the intensity of the communication behavior oscillograms in each scene category in each clustering result, the difference among the various scene categories and the like, and finally, the scene category number category with the best overall quality of the clustering result is 9, so that the target scene category number is determined to be 9. After dividing the communication behavior oscillograms into 9 classes, determining scene categories of each class of communication behavior oscillograms according to the characteristics of each class of communication behavior oscillograms, and finally defining nine classes of scene categories as follows: wave type, fault type, shift area office building type, discrete type, business district type, bar type, accommodation district type, and village type.
And according to the number of the target scene categories, dividing each second communication behavior oscillogram into different scene clusters with the number of the target scene categories by utilizing a clustering algorithm, and acquiring each scene cluster center and the scene category corresponding to each scene cluster center.
And taking the second communication behavior waveform diagram with the distance value from each scene clustering center smaller than the preset distance value as a communication behavior waveform sample diagram.
And determining the scene type corresponding to the communication behavior waveform sample graph according to the scene type corresponding to each scene clustering center.
And training the deep learning network model by utilizing each communication behavior waveform sample diagram and the corresponding scene category. The server acquires the cell scene categories corresponding to the communication behavior oscillograms according to the graphic features of the communication behavior oscillograms, and then inputs the communication behavior oscillograms and the scene categories corresponding to the communication behavior oscillograms as training samples into the deep learning network model for supervised training. Specifically, the server may divide the communication behavior oscillogram into a plurality of categories according to the cell symbolic communication behavior distribution characteristics in the communication behavior oscillogram, each category corresponds to a cell scene category, so as to obtain the scene categories corresponding to the communication behavior oscillograms, then, each communication behavior oscillogram is used as an input item of a deep learning network model, the deep learning network model carries out analysis training according to the input communication behavior oscillogram, an output recognition result is compared with a scene type corresponding to the communication behavior oscillogram, parameters of each layer of network in the deep learning network model are reversely corrected according to a comparison result, the structural table of the deep learning network model is shown in fig. 6, the pooling layer in the deep learning network model uses two types, namely a maximum pooling layer and a random pooling layer, and the step length of the random pooling layer is set.
The iteration number of the training of the deep learning network model is set to be 100000 times, an Adadelta optimizer is used by the optimizer, the learning rate is set to be 0.001, and the regular term coefficient is set to be 0.0005. By using the random pooling layer, the fuzzy recognition effect of the deep learning network model is improved, and the robustness of the deep learning network model is improved.
The method comprises the steps of obtaining first key performance index data of a target cell, and obtaining first communication behavior characteristic data of the target cell from the first key performance index data. The server carries out bivariate correlation analysis on the sample data, determines field data serving as communication behavior characteristic data in the key performance index data, and directly acquires corresponding field data serving as first communication behavior characteristic data after acquiring first key performance index data of a target cell.
And performing principal component dimensionality reduction on each first communication behavior characteristic data by using a preset principal component coefficient matrix to obtain a first communication behavior characteristic value of the target cell. And the server multiplies the first communication behavior characteristic data of the target cell by a preset principal component coefficient matrix, and reduces the dimension of the first communication behavior characteristic data to obtain a corresponding one-dimensional first communication behavior characteristic value.
And generating a first communication behavior oscillogram by utilizing the first communication behavior characteristic values and combining the time granularity.
And inputting the first communication behavior oscillogram into a pre-trained deep learning network model, and acquiring the scene type of the target cell output by the pre-trained deep learning network model. The communication behavior characteristic data is obtained from the key performance index data of the target cell, and the communication behavior oscillogram is generated from the communication behavior characteristic data, so that the cells are quickly divided into different scene categories according to the communication behavior oscillogram, the synchronous updating efficiency of the scene categories of the cells is improved, and an important basis is provided for customizing a planning construction scheme and determining a network optimization strategy.
It should be understood that, although the steps in the flowcharts of fig. 2 to 4 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-4 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
The present invention also provides a device for adjusting cell network parameters according to the method for adjusting cell network parameters, and an embodiment of the device for adjusting cell network parameters of the present invention is described in detail below.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an apparatus for adjusting cell network parameters according to an embodiment of the present invention, in this embodiment, the apparatus for adjusting cell network parameters includes:
a feature service set obtaining module 510, configured to obtain a scene type of a target cell, and determine a corresponding feature service set according to the scene type of the target cell;
a target network parameter obtaining module 520, configured to obtain perceptual quality impact factors of each feature service in the feature service set, and match target network parameters that affect the perceptual quality impact factors;
the target network parameter adjusting module 530 is configured to adjust the target network parameter according to a preset expected performance index of the target cell.
In one embodiment, the target network parameter obtaining module 520 is configured to obtain a quality parameter and a reference value of a service awareness factor of each feature service in the feature service set; and determining the perception quality influence factor from the business perception factor according to the quality parameter and the reference value of the business perception factor.
In one embodiment, the feature service set obtaining module 510 is configured to obtain the access volume and the traffic volume of each type of service in the full service under the scene type of the target cell; respectively calculating the weight values of various services in the full-volume service according to the access volume and the flow of various services; and determining the service with the weight value ratio exceeding a preset threshold value as the corresponding characteristic service under the scene category of the target cell, and acquiring a characteristic service set.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an apparatus for adjusting cell network parameters in an embodiment of the present invention, in this embodiment, the apparatus for adjusting cell network parameters further includes a scene type obtaining module 540;
the scene category obtaining module 540 is configured to obtain first key performance indicator data of the target cell, and obtain first communication behavior feature data of the target cell from the first key performance indicator data; performing principal component dimensionality reduction on each first communication behavior characteristic data by using a preset principal component coefficient matrix to obtain a first communication behavior characteristic value of a target cell; generating a first communication behavior oscillogram by utilizing the first communication behavior characteristic values and combining time granularity; and inputting the first communication behavior oscillogram into a pre-trained deep learning network model, and acquiring the scene type of the target cell output by the pre-trained deep learning network model.
In one embodiment, the scene category obtaining module 540 is configured to generate a two-dimensional array by using the first communication behavior feature value in combination with the time granularity; generating a communication behavior original image by taking the time granularity as an abscissa and the communication behavior characteristic value as an ordinate; and removing noise in the original image of the communication behavior, and determining the original image of the communication behavior after the noise is removed as a first communication behavior oscillogram.
In one embodiment, the scene category obtaining module 540 is configured to obtain second key performance indicator data of the sample cell, and obtain second communication behavior feature data of the sample cell from the second key performance indicator data; and performing principal component analysis on the second communication behavior characteristic data, and extracting a preset principal component coefficient matrix.
In one embodiment, the scene category obtaining module 540 is further configured to obtain a second communication behavior feature value; generating a second communication behavior oscillogram by utilizing the second communication behavior characteristic value and combining the time granularity; screening a communication behavior waveform sample graph from each second communication behavior waveform graph, and acquiring a scene type corresponding to each communication behavior waveform sample graph; and training a deep learning network model by using each communication behavior waveform sample graph and the corresponding scene category.
For specific limitations of the adjusting apparatus for cell network parameters, reference may be made to the above limitations of the adjusting method for cell network parameters, which are not described herein again. The modules in the device for adjusting cell network parameters may be implemented wholly or partially by software, hardware and their combination. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing key quality index data, expected index performance and other parameters. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of adjusting a cell network parameter.
Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the invention provides a computer device comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring the scene type of a target cell, and determining a corresponding characteristic service set according to the scene type of the target cell;
acquiring perception quality influence factors of each characteristic service in the characteristic service set, and matching target network parameters influencing the perception quality influence factors;
and adjusting the target network parameters according to the preset expected performance index of the target cell.
In one embodiment, the present invention further provides a computer readable storage medium having a computer program stored thereon, the computer program when executed by a processor implementing the steps of:
acquiring the scene type of a target cell, and determining a corresponding characteristic service set according to the scene type of the target cell;
acquiring perception quality influence factors of each characteristic service in the characteristic service set, and matching target network parameters influencing the perception quality influence factors;
and adjusting the target network parameters according to the preset expected performance index of the target cell.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (12)
1. A method for adjusting cell network parameters is characterized by comprising the following steps:
acquiring first key performance indicator data of a target cell, and acquiring first communication behavior characteristic data of the target cell from the first key performance indicator data;
performing principal component dimensionality reduction on each first communication behavior characteristic data by using a preset principal component coefficient matrix to obtain a first communication behavior characteristic value of the target cell;
generating a two-dimensional array by utilizing the first communication behavior characteristic value and combining time granularity;
generating a first communication behavior oscillogram by taking the time granularity as an abscissa and the communication behavior characteristic value as an ordinate;
inputting the first communication behavior oscillogram into a pre-trained deep learning network model, and acquiring the scene type of the target cell output by the pre-trained deep learning network model;
acquiring the access volume and the flow of various services in the full service under the scene type of the target cell;
respectively calculating the weight values of various services in the full-volume service according to the access volume and the flow of various services;
determining the service with the weight value ratio exceeding a preset threshold value as a corresponding characteristic service under the scene category of the target cell, and determining a corresponding characteristic service set according to a plurality of obtained characteristic services;
acquiring perception quality influence factors of each feature service in the feature service set, and matching target network parameters influencing the perception quality influence factors;
and adjusting the target network parameters according to the preset expected performance index of the target cell.
2. The method of claim 1, wherein the step of obtaining the perceptual goodness impact factor of each feature service in the feature service set comprises the steps of:
acquiring quality parameters and reference values of service perception factors of each feature service in the feature service set;
and determining a perception quality influence factor from the service perception factors according to the quality parameters and the reference values of the service perception factors.
3. The method of claim 1, wherein the step of generating the first communication behavior waveform map with the time granularity as abscissa and the communication behavior feature value as ordinate comprises the steps of:
generating a communication behavior original image by taking the time granularity as an abscissa and the communication behavior characteristic value as an ordinate;
and removing noise in the original image of the communication behavior, and determining the original image of the communication behavior after the noise is removed as a first communication behavior oscillogram.
4. The method for adjusting cell network parameters according to claim 1, wherein before the step of performing principal component dimension reduction on each of the first communication behavior feature data by using a preset principal component coefficient matrix to obtain the first communication behavior feature value of the target cell, the method further comprises the following steps:
acquiring second key performance indicator data of a sample cell, and acquiring second communication behavior characteristic data of the sample cell from the second key performance indicator data;
and performing principal component analysis on each second communication behavior characteristic data, and extracting a preset principal component coefficient matrix.
5. The method of claim 4, wherein after the step of performing the principal component analysis on each of the second communication behavior feature data, the method further comprises the following steps:
acquiring a second communication behavior characteristic value;
generating a second communication behavior oscillogram by utilizing the second communication behavior characteristic value and combining time granularity;
screening a communication behavior waveform sample graph from each second communication behavior waveform graph, and acquiring a scene type corresponding to each communication behavior waveform sample graph;
and training a deep learning network model by using each communication behavior waveform sample graph and the corresponding scene category.
6. An apparatus for adjusting cell network parameters, comprising:
the characteristic service set acquisition module is used for acquiring first key performance index data of the target cell and acquiring first communication behavior characteristic data of the target cell from the first key performance index data; performing principal component dimensionality reduction on each first communication behavior characteristic data by using a preset principal component coefficient matrix to obtain a first communication behavior characteristic value of a target cell; generating a two-dimensional array by utilizing the first communication behavior characteristic value and combining time granularity; generating a first communication behavior oscillogram by taking the time granularity as an abscissa and the communication behavior characteristic value as an ordinate; inputting the first communication behavior oscillogram into a pre-trained deep learning network model, and acquiring the scene type of a target cell output by the pre-trained deep learning network model; acquiring the access volume and the flow of various services in the full service under the scene type of the target cell; respectively calculating the weight values of various services in the full-volume service according to the access volume and the flow of various services; determining the service with the weight value ratio exceeding a preset threshold value as a corresponding characteristic service under the scene category of the target cell, and determining a corresponding characteristic service set according to a plurality of obtained characteristic services;
a target network parameter obtaining module, configured to obtain perceptual good/bad influence factors of each feature service in the feature service set, and match target network parameters that influence the perceptual good/bad influence factors;
and the target network parameter adjusting module is used for adjusting the target network parameters according to the preset expected performance index of the target cell.
7. The adjusting apparatus according to claim 6, wherein the target network parameter obtaining module is configured to obtain a quality parameter and a reference value of a service awareness factor of each feature service in the feature service set; and determining the perception quality influence factor from the business perception factor according to the quality parameter and the reference value of the business perception factor.
8. The adjusting apparatus according to claim 6, wherein the feature service set obtaining module is further configured to generate an original image of the communication behavior by using the time granularity as an abscissa and the communication behavior feature value as an ordinate; and removing noise in the original image of the communication behavior, and determining the original image of the communication behavior after the noise is removed as a first communication behavior oscillogram.
9. The adjusting apparatus according to claim 6, wherein the scene category obtaining module is further configured to obtain second key performance indicator data of the sample cell, and obtain second communication behavior characteristic data of the sample cell from the second key performance indicator data; and performing principal component analysis on the second communication behavior characteristic data, and extracting a preset principal component coefficient matrix.
10. The adjusting apparatus according to claim 9, wherein the scene category obtaining module is further configured to obtain a second communication behavior feature value; generating a second communication behavior oscillogram by utilizing the second communication behavior characteristic value and combining the time granularity; screening a communication behavior waveform sample graph from each second communication behavior waveform graph, and acquiring a scene type corresponding to each communication behavior waveform sample graph; and training a deep learning network model by using each communication behavior waveform sample graph and the corresponding scene category.
11. A computer arrangement comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, carries out the steps of the method of adjusting cell network parameters according to any of claims 1 to 5.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for adjusting cell network parameters of any one of claims 1 to 5.
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