CN108093418B - A method for call prediction and dynamic base station access based on bill information mining based on K-nearest neighbor algorithm - Google Patents

A method for call prediction and dynamic base station access based on bill information mining based on K-nearest neighbor algorithm Download PDF

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CN108093418B
CN108093418B CN201711366689.XA CN201711366689A CN108093418B CN 108093418 B CN108093418 B CN 108093418B CN 201711366689 A CN201711366689 A CN 201711366689A CN 108093418 B CN108093418 B CN 108093418B
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曹万鹏
罗云彬
李鹏
李�浩
徐青
史辉
林绍福
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Beijing University of Technology
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Abstract

本发明公开一种基于K最近邻算法的话单信息挖掘通话预测、动态基站接入方法，包括：步骤1、在话单大数据中寻找出基站一段时间内，时间点相对规律性的、固定的通话信息，以及该基站所能承担的最大移动终端接入量；2、对上述具有规律性电话号码，以其通话时间点，接入连续性，不同通话时间方差等参数作为统计信息为特征，基于K最近邻算法对每一号码进行精确分类；3、当基站预测将达到饱和程度时，提前规划通知附近基站，将其他的临时接入移动终端连接申请主动导入其他附近基站；4、判断该基站每天的繁忙、空闲程度，决定是否为该基站扩容以及在附近部署新的固定或临时性基站，或将周围基站暂时关闭、拆撤。

Figure 201711366689

The invention discloses a method for mining call prediction and dynamic base station access based on bill information based on K-nearest neighbor algorithm. Call information, and the maximum number of mobile terminal access that the base station can undertake; 2. For the above regular phone numbers, the parameters such as call time point, access continuity, and variance of different call times are used as statistical information. Accurately classify each number based on the K-nearest neighbor algorithm; 3. When the base station predicts that it will reach the saturation level, plan and notify nearby base stations in advance, and actively import other temporary access mobile terminal connection applications to other nearby base stations; 4. Determine the The daily busyness and idleness of the base station determines whether to expand the capacity of the base station and deploy new fixed or temporary base stations nearby, or temporarily shut down or dismantle the surrounding base stations.

Figure 201711366689

Description

Call bill information mining call prediction and dynamic base station access method based on K nearest neighbor algorithm
Technical Field
The invention relates to a data statistical method, in particular to a call bill information mining call prediction and dynamic base station access method based on a K nearest neighbor algorithm (KNN).
Background
The number and frequency of base stations accessing the telephone on a daily basis is variable and non-uniform. The access information of different base stations and different time period telephones is mastered, so that the capacity design of the base stations and the construction layout of the base stations are guided, under the condition of minimum investment, each base station device is fully utilized, the waste of design layout is avoided, the base station connection and telephone communication experience of users is ensured, and the intelligent channel service is realized.
Obviously, if accurate prediction of the access telephone quantity and the access time of each base station can be realized, the dynamic adjustment and the overall planning of the telephone access base station are undoubtedly facilitated, and meanwhile, the planning of the construction and the layout of the base stations is facilitated. In addition, the preparation and prediction of the connection of the telephone in advance are beneficial to realizing the access of the mobile phone more quickly and rapidly, quickening the connection speed of the user and improving the connection experience of the user base station.
Disclosure of Invention
Aiming at the requirements and the defects of the traditional base station capacity design, the base station planning layout, the telephone base station connection and the like, a call bill information mining call prediction and dynamic base station access method based on the K nearest neighbor algorithm is provided, and regular characteristic quantities are mined through statistics of big data of call bills connected with the base station, so that guidance for base station layout planning and telephone access preparation is formed, and an intelligent telecommunication service mode is realized.
A call bill information mining conversation prediction and dynamic base station access method based on a K nearest neighbor algorithm comprises the following steps:
1. starting from the communication habits of people and the working rules of base stations in different areas and at different time points, searching fixed call information (or telephone access volume) of the base stations at certain time points in a period of time and the maximum mobile terminal access volume which can be borne by the base stations in the call bill big data;
2. the regular telephone numbers are characterized in that parameters such as call time points, access continuity, variance of different call time and the like are taken as statistical information, and each number is accurately classified based on a K nearest neighbor algorithm. In the subsequent operation, different scheduling means and flow control rules are adopted for different classified numbers, the conversation and the like are accurately predicted, when the time point or the time period is about to be reached, the saturation degree of the base station is checked, and according to the situation, a space is reserved for the class numbers of the mobile terminals which are relatively fixedly connected with the base station;
3. when the base station predicts that the saturation degree is reached, planning in advance to inform a nearby base station, and actively introducing other temporary access mobile terminal connection applications into other nearby base stations;
4. the busy and idle degrees of the base station every day are judged, whether the base station is expanded and a new fixed or temporary base station is deployed nearby or surrounding base stations are temporarily closed and removed is determined, the utilization rate of the base station is improved, and dynamic and self-adaptive intelligent operation and scheduling of communication equipment are realized.
Compared with the prior art, the invention has the following obvious advantages and beneficial effects:
(1) the method is based on base station connection information in call ticket data, provides call ticket information mining call prediction and dynamic base station access method based on K nearest neighbor algorithm, and predicts and prepares intervention application (access quantity) of a relatively fixed number of a user in advance by mining base station connection data in call ticket big data and based on base station capacity and connection prediction, so that a telecom operator can save cost and make full use of the existing built base station, can better serve the mobile user, and improve user experience.
(2) The dynamic scheduling of the base station is guided, and the relative fixed access numbers are classified based on the relevant difference information quoting K nearest neighbor algorithm, so that a base station dynamic scheduling mode which not only fully utilizes each base station device, saves the control cost, but also ensures better base station connection and telephone communication experience of users is realized.
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Fig. 1 is a flow chart of a call bill information mining call prediction and dynamic base station access method based on a K nearest neighbor algorithm.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
The flow chart of the method of the invention is shown in figure 1 and comprises the following steps:
1. starting from the communication habits of people and the working rules of base stations in different areas and at different time points, searching fixed call information (or telephone access volume) of the base stations at certain time points in a period of time and the maximum mobile terminal access volume which can be borne by the base stations in the call bill big data;
2. for the regular telephone numbers, taking statistical information such as the call time point, the access continuity, the variance of different call times and the like as characteristics, accurately classifying each number based on the K nearest neighbor algorithm, and the specific flow is as follows:
given a training set of data, each training set of data is already classified.
Setting an initial test data DtCalculating DtEuclidean distances to all data in the training set and sorting.
Selecting training set separation DtK training set data closest to.
Comparing K training set data, and selecting the classification type with the most occurrence in the training set data, wherein the classification type is the final test data DtClassification of (3).
In the subsequent operation, different scheduling means and flow control rules are adopted for different classified numbers, the conversation and the like are accurately predicted, when the time point or the time period is about to be reached, the saturation degree of the base station is checked, and according to the situation, a space is reserved for the class numbers of the mobile terminals which are relatively fixedly connected with the base station;
3. when the base station predicts that the saturation degree is reached, planning in advance to inform a nearby base station, and actively introducing other temporary access mobile terminal connection applications into other nearby base stations;
4. the busy and idle degrees of the base station every day are judged, whether the base station is expanded and a new fixed or temporary base station is deployed nearby or surrounding base stations are temporarily closed and removed is determined, the utilization rate of the base station is improved, and dynamic and self-adaptive intelligent operation and scheduling of communication equipment are realized.

Claims (1)

1.一种基于K最近邻算法的话单信息挖掘通话预测、动态基站接入方法，其特征在于，包括如下步骤：1. a call prediction, dynamic base station access method based on bill information mining based on K nearest neighbor algorithm, is characterized in that, comprises the steps: 步骤1、在话单大数据中寻找出基站一段时间内，时间点相对规律性的、固定的通话信息，以及该基站所能承担的最大移动终端接入量；Step 1. Find out the relatively regular and fixed call information of the base station within a period of time, and the maximum mobile terminal access amount that the base station can undertake in the big data of the bill; 步骤2、对上述时间点相对规律性的、固定的通话信息，以其通话时间点、接入连续性、不同通话时间方差作为统计信息，基于K最近邻算法对每一号码进行精确分类；当将要到达该时间点或时间段时，检查该基站的饱和程度，为那些相对固定的连接该基站的移动终端类别号码预留空间；Step 2. For the relatively regular and fixed call information at the above-mentioned time points, use the call time point, access continuity, and variance of different call times as statistical information, and accurately classify each number based on the K-nearest neighbor algorithm; when When the time point or time period is about to arrive, check the saturation level of the base station and reserve space for the relatively fixed class numbers of mobile terminals connected to the base station; 步骤3、当基站预测将达到饱和程度时，提前规划通知附近基站，将其他的临时接入移动终端连接申请主动导入其他附近基站；Step 3. When the base station predicts that it will reach the saturation level, plan and notify nearby base stations in advance, and actively import other temporary access mobile terminal connection applications to other nearby base stations; 步骤4、判断该基站每天的繁忙、空闲程度，决定是否为该基站扩容以及在附近部署新的固定或临时性基站，或将周围基站暂时关闭、拆撤。Step 4: Judge the busyness and idleness of the base station every day, and decide whether to expand the base station and deploy a new fixed or temporary base station nearby, or temporarily shut down or dismantle the surrounding base stations.
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