CN110730432B - Proximity user identification method, terminal and readable storage medium - Google Patents
Proximity user identification method, terminal and readable storage medium Download PDFInfo
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Abstract
The application discloses a method, a terminal and a computer readable storage medium for identifying adjacent users, wherein the MR characteristic coefficients of each user are obtained by obtaining the MR characteristic information of each resident cell and calculating according to the MR characteristic information of each resident cell; calculating MR characteristic similarity according to the MR characteristic information and the MR characteristic coefficient; according to the preset grouping condition and the MR characteristic similarity, the optimal adjacent user grouping is calculated and obtained, the indoor adjacent user identification is realized, and the user identification range is expanded, so that the indoor wireless network quality can be monitored by utilizing the group characteristics of the adjacent users, namely, the communication problem of the indoor network is judged and alarmed according to the communication index change of the adjacent users.
Description
Technical Field
The present application relates to the field of wireless communication technologies, and in particular, to a method, a terminal, and a readable storage medium for identifying neighboring users based on a group of resident users.
Background
At present, more than 70% of services of the mobile internet occur indoors, and indoor scenes become key areas for network optimization and service application. The existing indoor user identification is used for positioning an indoor position of a single user at a specific moment, namely based on user wireless network connection or user WIFI network and Bluetooth equipment connection, the identification and positioning of the indoor user can be realized only after field positioning fingerprint data is collected through the support of indoor hardware equipment. Therefore, the current indoor user identification method has high hardware requirement conditions, and the obtained single user data is not representative.
Disclosure of Invention
The present application mainly aims to provide a method, a terminal and a computer storage medium for identifying a neighboring user, and aims to solve the technical problems that an indoor user identification method in the prior art is high in hardware requirement condition and obtained single user data is not representative.
In order to achieve the above object, an embodiment of the present application provides a neighboring user identification method, where the neighboring user identification method includes the following steps:
acquiring resident cell MR characteristic information of each user, and calculating and acquiring an MR characteristic coefficient of each user according to the MR characteristic information of each resident cell;
calculating MR characteristic similarity according to the MR characteristic information and the MR characteristic coefficient;
and calculating to obtain the optimal adjacent user grouping according to the preset grouping condition and the MR characteristic similarity.
Optionally, the step of acquiring MR characteristic information of each residential cell of each user includes:
acquiring MR data of the resident cell working in daytime for each preset number of days of the user as first MR data;
acquiring MR data of the resident residential community of each user at night for preset days as second MR data;
and screening the MR data of each user resident cell in a preset time period based on the first MR data and the second MR data.
Optionally, after the step of screening MR data of each user residential cell in a preset time period, the method includes:
based on the MR data of each user resident cell, sequentially taking each user resident cell as a main service cell, taking each cell except the resident cell in the MR data of the resident cell as an adjacent cell, and acquiring the field intensity of the main service cell, the average field intensity of the field intensity of each adjacent cell, the identification number of each main service cell and the identification number of each adjacent cell;
screening the communication reporting frequency according to the preset screening condition of the communication reporting frequency of each adjacent cell to obtain the frequency of the selected adjacent cell, wherein the preset screening condition comprises a preset reporting frequency ranking and a preset frequency threshold;
and taking the average field intensity and the selected neighbor cell frequency as MR characteristic information of each resident cell.
Optionally, after the step of using the average field strength and the selected neighboring cell frequency as MR characteristic information of each of the residential cells, the method includes:
normalizing each average field intensity to obtain a field intensity coefficient;
normalizing the frequency of each selected adjacent cell to obtain a frequency coefficient;
and taking the field intensity coefficient and the frequency coefficient as the MR characteristic coefficient of each user.
Optionally, the step of calculating the MR feature similarity according to the MR feature information and the MR feature coefficient includes:
acquiring the MR characteristic information of two resident users in the same preset cell;
if the MR characteristic information of the two resident users has the same adjacent cell identification number, extracting the MR characteristic coefficients of the two resident users so as to obtain the MR characteristic similarity of the two resident users through calculation of a preset first algorithm;
if the MR characteristic information of the two resident users does not have the same adjacent cell identification number, extracting the MR characteristic coefficients of the two resident users so as to calculate and obtain the MR characteristic similarity of the two resident users through a preset second algorithm.
Optionally, the step of calculating to obtain an optimal neighbor user group according to a preset group condition and the MR feature similarity includes:
determining the number of users of an effective user group according to a preset first parameter;
and determining the number of the optimal user grouping users from the number of the users of the effective user group according to a preset second parameter.
Optionally, after the step of determining the optimal user number of the user group from the number of the users in the effective user group, the method includes:
calculating the MR feature similarity between every two users in the optimal user grouping range;
traversing all the MR feature similarities, and acquiring the minimum feature similarity and the maximum feature similarity of the selected range according to hierarchical clustering;
taking the minimum feature similarity as an initial value of a single connection coefficient, and obtaining the single connection coefficient meeting the optimal user grouping through iterative computation;
and taking the maximum feature similarity as an initial value of the full-connection coefficient, and obtaining the full-connection coefficient meeting the optimal user grouping through iterative computation.
Optionally, after the step of obtaining an optimal neighbor user group by calculation according to a preset grouping condition and the MR feature similarity, the method includes:
identifying a group of adjacent users according to the optimal adjacent user group, and giving each user identification number;
filling according to the real position information of each user and each identification number to obtain a real position grid distribution map of each user;
and based on the real position grid distribution map, combining the optimal adjacent user group, performing color rendering according to the user group, and verifying the nearest user group effect.
The present application further provides a terminal, the terminal including: a memory, a processor and a proximity user identification program stored on the memory and executable on the processor, the proximity user identification program when executed by the processor implementing the steps of the proximity user identification method as described above.
The present application further provides a computer storage medium having stored thereon a proximity user identification program which, when executed by a processor, implements the steps of the proximity user identification method as described above.
In the optimal adjacent user grouping process, resident cell MR characteristic information of each user is firstly obtained, and MR characteristic coefficients of each user are obtained through calculation according to the resident cell MR characteristic information; then, calculating MR characteristic similarity according to the MR characteristic information and the MR characteristic coefficient; and finally, calculating to obtain the optimal adjacent user group according to the preset group condition and the MR characteristic similarity. The method and the device identify the nearby users based on the resident user group, so that the group characteristics of the nearby users can be utilized to monitor the indoor wireless network quality, and the indoor network fault is alarmed. Meanwhile, the method can be applied to associated service promotion aiming at adjacent user groups, and has great application value.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic diagram of a hardware structure of an optional terminal according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a method for identifying neighboring users according to an embodiment of the present invention;
FIG. 3 is a detailed flowchart of step S10 in FIG. 2;
FIG. 4 is a schematic diagram of additional process steps added after step S13 in FIG. 3;
FIG. 5 is a schematic diagram of additional process steps added after step S16 in FIG. 4;
FIG. 6 is a detailed flowchart of step S20 in FIG. 2;
FIG. 7 is a detailed flowchart of step S30 in FIG. 2;
FIG. 8 is a schematic diagram of additional process steps added after step S32 in FIG. 7;
FIG. 9 is a schematic diagram of additional process steps added after step S30 in FIG. 2;
FIG. 10 is a schematic diagram illustrating a grid distribution of real locations of users in the method for identifying neighboring users of the present application;
fig. 11 is a schematic diagram illustrating a rendering effect of a group of verified nearest users in the method for identifying a neighboring user according to the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
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.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of description of the present application, and have no specific meaning by themselves. Thus, "module", "component" or "unit" may be used mixedly.
As shown in fig. 1, fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present application.
The terminal in the embodiment of the application can be a fixed terminal, such as an internet of things intelligent device, and comprises an intelligent air conditioner, an intelligent lamp, an intelligent power supply, an intelligent router and other intelligent homes; the system can also be a mobile terminal, and comprises a smart phone, a wearable networking AR/VR device, a smart sound box, an automatic driving automobile and other networking equipment.
As shown in fig. 1, the architecture of the proximity subscriber identity system includes nodes and servers, and the device structure thereof may include: a processor 1001, such as a CPU, a memory 1005, and a communication bus 1002. The communication bus 1002 is used for realizing connection communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the proximity user identification system may further include a user interface, a network interface, a camera, RF (Radio Frequency) circuitry, a sensor, audio circuitry, a WiFi module, and the like. The user interface may include a Display screen (Display), touch screen, camera (including AR/VR devices), etc., and the optional user interface may also include a standard wired interface, a wireless interface. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface, bluetooth interface, probe interface, 3G/4G/5G networking communication interface, etc.).
It will be appreciated by those skilled in the art that the proximity subscriber identification system architecture shown in fig. 1 does not constitute a limitation of the proximity subscriber identification system and may include more or less components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, and a proximity user identification program. The operating system is a program that manages and controls the hardware and software resources of the proximity subscriber identification system, supporting the operation of the proximity subscriber identification program as well as other software and/or programs. The network communication module is used to enable communication between the various components within the memory 1005, as well as with other hardware and software in the proximity subscriber identification system.
In the proximity subscriber identity system shown in fig. 1, the processor 1001 is configured to execute a proximity subscriber identity program stored in the memory 1005, and implement the following steps:
acquiring resident cell MR characteristic information of each user, and calculating and acquiring an MR characteristic coefficient of each user according to the MR characteristic information of each resident cell;
calculating MR characteristic similarity according to the MR characteristic information and the MR characteristic coefficient;
and calculating to obtain the optimal adjacent user grouping according to the preset grouping condition and the MR characteristic similarity.
Further, the processor 1001 may call the proximity user identification program stored in the memory 1005, and also perform the following operations:
acquiring MR data of the resident cell working in daytime for each preset number of days of the user as first MR data;
acquiring MR data of the resident residential community of each user at night for preset days as second MR data;
and screening the MR data of each user resident cell in a preset time period based on the first MR data and the second MR data.
Further, the processor 1001 may call the proximity user identification program stored in the memory 1005, and also perform the following operations:
based on the MR data of each user resident cell, sequentially taking each user resident cell as a main service cell, taking each cell except the resident cell in the MR data of the resident cell as an adjacent cell, and acquiring the field intensity of the main service cell, the average field intensity of the field intensity of each adjacent cell, the identification number of each main service cell and the identification number of each adjacent cell;
screening the communication reporting frequency according to the preset screening condition of the communication reporting frequency of each adjacent cell to obtain the frequency of the selected adjacent cell, wherein the preset screening condition comprises a preset reporting frequency ranking and a preset frequency threshold;
and taking the average field intensity and the selected neighbor cell frequency as MR characteristic information of each resident cell.
Further, the processor 1001 may call the proximity user identification program stored in the memory 1005, and also perform the following operations:
normalizing each average field intensity to obtain a field intensity coefficient;
normalizing the frequency of each selected adjacent cell to obtain a frequency coefficient;
and taking the field intensity coefficient and the frequency coefficient as the MR characteristic coefficient of each user.
Further, the processor 1001 may call the proximity user identification program stored in the memory 1005, and also perform the following operations:
acquiring the MR characteristic information of two resident users in the same preset cell;
if the MR characteristic information of the two resident users has the same adjacent cell identification number, extracting the MR characteristic coefficients of the two resident users so as to obtain the MR characteristic similarity of the two resident users through calculation of a preset first algorithm;
if the MR characteristic information of the two resident users does not have the same adjacent cell identification number, extracting the MR characteristic coefficients of the two resident users so as to calculate and obtain the MR characteristic similarity of the two resident users through a preset second algorithm.
Further, the processor 1001 may call the proximity user identification program stored in the memory 1005, and also perform the following operations:
determining the number of users of an effective user group according to a preset first parameter;
and determining the number of the optimal user grouping users from the number of the users of the effective user group according to a preset second parameter.
Further, the processor 1001 may call the proximity user identification program stored in the memory 1005, and also perform the following operations:
calculating the MR feature similarity between every two users in the optimal user grouping range;
traversing all the MR feature similarities, and acquiring the minimum feature similarity and the maximum feature similarity of the selected range according to hierarchical clustering;
taking the minimum feature similarity as an initial value of a single connection coefficient, and obtaining the single connection coefficient meeting the optimal user grouping through iterative computation;
and taking the maximum feature similarity as an initial value of the full-connection coefficient, and obtaining the full-connection coefficient meeting the optimal user grouping through iterative computation.
Further, the processor 1001 may call the proximity user identification program stored in the memory 1005, and also perform the following operations:
identifying a group of adjacent users according to the optimal adjacent user group, and giving each user identification number;
filling according to the real position information of each user and each identification number to obtain a real position grid distribution map of each user;
and based on the real position grid distribution map, combining the optimal adjacent user group, performing color rendering according to the user group, and verifying the nearest user group effect.
Based on the hardware structure, various embodiments of the method for identifying the adjacent user are provided.
Referring to fig. 2, a first embodiment of the proximity user identification method of the present application provides a proximity user identification method, where the method includes:
step S10, resident cell MR characteristic information of each user is obtained, and MR characteristic coefficients of each user are obtained through calculation according to the resident cell MR characteristic information;
the resident cell refers to a communication cell occupied by the physical position of a user and the mobile phone communication reporting place for a long time, and comprises two working types of a working place and a residential place; if the user A is in work in the B-technology park and lives in the C apartment in a certain time period, such as 5 months, the mobile communication reporting place of the user A is the B-technology park and the C apartment, and the B-technology park and the C apartment are used as resident cells of the user A. The resident cell MR characteristic information includes three parts of information of a user mobile phone identification number, a residence point type and a resident cell ECI (resident cell identification number). The MR characteristic coefficient refers to a coefficient for characterizing the MR characteristics of a user, and comprises a field intensity coefficient and a frequency coefficient.
Step S20, calculating MR characteristic similarity according to the MR characteristic information and the MR characteristic coefficient;
the MR characteristic similarity refers to the distance between two users obtained by respectively calculating the field intensity and the frequency in the MR characteristic coefficients of the two users.
And step S30, calculating to obtain the optimal adjacent user grouping according to the preset grouping condition and the MR characteristic similarity.
The preset grouping condition refers to the limitation requirement of the number of users in a group grouped by adjacent users; firstly, obtaining the number of users through a preset grouping condition; then sequentially calculating the distance between every two users in the enclosure to obtain MR feature similarity; and finally, performing iterative operation to obtain the available optimal adjacent user group.
In this embodiment, first, resident cell MR feature information of each user is obtained, and an MR feature coefficient of each user is obtained by calculation according to the resident cell MR feature information; then, calculating MR characteristic similarity according to the MR characteristic information and the MR characteristic coefficient; and finally, calculating to obtain the optimal adjacent user group according to the preset group condition and the MR characteristic similarity. The method and the device identify the nearby users based on the resident user group, so that the group characteristics of the nearby users can be utilized to monitor the indoor wireless network quality, and the indoor network fault is alarmed. Meanwhile, the method can be applied to associated service promotion aiming at adjacent user groups, and has great application value.
Further, in another embodiment of the proximity user identification method according to the present application, referring to fig. 3, step S10 includes:
step S11, acquiring MR data of a resident working cell of each user in daytime for preset days as first MR data;
the resident cells comprise a daytime work resident cell and a night resident cell; the resident cell working in the daytime refers to a cell occupied by a user in a working place for a long time in the daytime, and the resident cell living in the evening refers to a cell occupied by the user in a residential place for a long time at night; the preset number of days refers to a certain number of representative dates, and mainly aims to enlarge the statistical time range and reduce the error of the obtained statistical data, if a certain user goes out for one month to the field, if the user goes for one month for statistics, the required data of the user resident cell cannot be obtained, and therefore, the time dimension needs to be lengthened, for example, three months are used for statistics; the MR data comprises a user mobile phone identification number, a main service cell identification number, a neighbor cell identification number, an average field intensity and a neighbor cell reporting frequency.
Step S12, acquiring MR data of resident residential districts of each user at night for preset days as second MR data;
after the MR data of the residential community working in daytime for the preset days of each user is acquired as the first MR data, the MR data of the residential community living in evening for the preset days of each user is acquired as the second MR data in the same way. According to the work and rest habits of modern human beings, a bright time period is generally used for working in a workplace, and a dark time period is generally returned to a residence place for preparation for sleeping, so that the data of different residential districts of users in the daytime and at night are respectively counted, and the accuracy of the obtained MR data of the residential districts is improved.
And step S13, screening the MR data of each user residential cell within a preset time period based on each first MR data and each second MR data.
In order to further improve the representativeness of the obtained MR data and improve the accuracy of data analysis, the first MR data and the second MR data obtained from the steps S11 and S12 are selected for screening; the preset time period comprises a day working time period and a night residence time period, wherein the day working time period is 9: 00-12: 00 and 14: 00-17: 00, residence time period at night is 19: 00-24: 00 and 0: 00-6: 00. namely, the screening period from the first MR data is 9: 00-12: 00 and 14: 00-17: 00, a screening period from the second MR data is 19: 00-24: 00 and 0: 00-6: 00.
In this embodiment, MR data of a working residential cell in daytime and MR data of a residential cell in evening for each user for a preset number of days are acquired, and then based on these data, MR data of each user residential cell in a preset time period is screened, so as to prepare for subsequently acquiring MR characteristic information of the user residential cell.
Further, in another embodiment of the proximity user identification method according to the present application, referring to fig. 4, after step S13, the method includes:
step S14, based on the MR data of each user resident cell, sequentially taking each user resident cell as a main service cell, taking each cell except the resident cell in the MR data of the resident cell as an adjacent cell, and acquiring the average field intensity of the main service cell and each adjacent cell, each main service cell identification number and each adjacent cell identification number;
a mobile phone can receive signals of a plurality of network element cells, a cell providing service is called a main service cell, and a cell providing alternative service is called a neighboring cell. The average field intensity refers to an average value obtained by calculating the field intensity of a single user main service cell and the field intensity of each adjacent cell of the user in turn, and the number of the adjacent cells corresponds to the number of the average field intensity one by one.
Step S15, according to the preset screening condition of the communication reporting frequency of each neighboring cell, screening the communication reporting frequency to obtain the frequency of the selected neighboring cell, wherein the preset screening condition comprises a preset reporting frequency ranking and a preset frequency threshold;
the reporting frequency preset screening condition refers to a condition set for screening out MR data (such as leaving seats and the like) reported by a user at a non-stationary position; acquiring the reporting frequency FREQ of user neighbor data according to a frequency calculation formulaiCalculating the frequency FREQ of reporting the data of the user neighbor celliThe formula of (1) is as follows:
after calculating the frequency of each cell, extracting the frequency of occurrence FREQiIn the first 6 cells and satisfies FREQi≥FREQmWherein FREQmIs a threshold for frequency. For example, the MR characteristic values of the final individual users are output as follows:
and step S16, taking the average field intensity and the selected adjacent cell frequency as the MR characteristic information of each resident cell.
For example, if there are 3 neighboring cells (neighboring cells for short) of a certain user, namely, neighboring cell 1, neighboring cell 2, and neighboring cell 3, the average field intensity E1 of the main service field intensity E and the field intensity of neighboring cell 1, and the frequency of neighboring cell 1, can be obtained through step S14 and step S15; average field intensity E2 of field intensity of the main clothes and field intensity of the adjacent region 2, and frequency of the adjacent region 2; the average field intensity E3 of the main clothing field intensity and the field intensity of the adjacent region 3, and the frequency of the adjacent region 3, the main clothing field intensity E, the average field intensity E1, the average field intensity E2, the average field intensity E3, the frequency of the adjacent region 1, the frequency of the adjacent region 2, and the frequency of the adjacent region 3 are MR characteristic information.
In this embodiment, after accumulating multiple days and screening MR data of each user's residential cell in a specific time period, the neighbor cell selection frequency is screened according to the neighbor cell reporting frequency, and finally, the obtained average field strength and the selected neighbor cell frequency are used as MR characteristic information of each residential cell.
Further, in another embodiment of the proximity user identification method according to the present application, with reference to fig. 5, after step S16, the method includes:
step S17, normalizing each average field intensity to obtain a field intensity coefficient;
step S18, normalizing the frequency of each selected adjacent cell to obtain a frequency coefficient;
and step S19, taking the field intensity coefficient and the frequency coefficient as the MR characteristic coefficient of each user.
Each average field strength and each selected adjacent frequency refer to two MR characteristic values obtained in the step S16; that is, the user resident cell MR characteristic value includes the average field strength and the adjacent cell frequency, because in the characteristic value list, RSRP (field strength, reference signal received power) and FREQ (occurrence frequency) have different dimensions, data standardization processing is needed to solve the comparability problem between data indexes, that is, the normalized average field strength and each selected adjacent cell frequency become (0, 1) values, the normalization method is to convert the dimensional data into dimensionless data expression, the field strength and frequency are converted into the range of (0, 1) according to the linear function normalization (Min-Max scaling) formula, wherein, the normalization formula is as follows:
wherein X is a numerical value before normalization, XnormIs a normalized numerical value, XmaxIs the maximum value, X, in the sample dataminIs the minimum value in the sample data; respectively normalizing the average field intensity and the adjacent region frequency, namely substituting the normalized field intensity and the adjacent region frequency into the normalization formula to obtain the field intensity coefficient RSRPnormFrequency-sum coefficient FREQnormThe field strength coefficient and the frequency coefficient are the MR characteristic coefficient of each user.
In this embodiment, the average field strength and the frequency of the neighboring cells are respectively normalized by a normalization method, so as to obtain the MR characteristic coefficient of each user: field intensity coefficient RSRPnormFrequency-sum coefficient FREQnorm。
Further, in another embodiment of the method for identifying a proximity user according to the present application, referring to fig. 6, step S20 includes:
step S21, acquiring MR characteristic information of two resident users in the same preset cell;
step S22, if the MR characteristic information of the two resident users has the same adjacent cell identification number, extracting the MR characteristic coefficients of the two resident users, and calculating by a preset first algorithm to obtain the MR characteristic similarity of the two resident users;
step S23, if the MR feature information of the two resident users does not have the same adjacent cell identification number, extracting the MR feature coefficients of the two resident users, and calculating by a preset second algorithm to obtain the MR feature similarity of the two resident users.
Distance measures (Distance) are used to measure the Distance that an individual has in space, with greater Distance indicating greater variability between individuals. Euclidean distance is a commonly used distance definition, referring to the true distance between two points in m-dimensional space, and for multidimensional vectors a ═ a1, a2, … …, An and B ═ B1, B2, … …, Bn, the euclidean distance is calculated as follows:
wherein the content of the first and second substances,
the sum of the distances of the user adjacent cell pairs is obtained, and n is the number of the user adjacent cell pairs.
The characteristic values of the resident users comprise measured adjacent region ECI of the signal, field intensity and frequency of occurrence, the values become (0, 1) after normalization, two resident users in the same cell are extracted, MR characteristics of the two resident users are extracted, distance calculation is carried out aiming at adjacent region data of the two sides, the adjacent region pair distance of the two users can be obtained through the method, then the user MR characteristic distance can be calculated through the adjacent region pair distance, and two conditions are separated, and the formula is as follows:
case 1: the neighbor cell lists of the two users both have the signal of the cell, and the first algorithm is used for calculating the MR characteristic distance of the users, and the specific formula is as follows:
case 2: one of the two parties does not have the cell signal, and the second algorithm is used for calculating the MR characteristic distance of the user, and the specific formula is as follows:
in the embodiment, based on the Euclidean distance model, the MR feature similarity of the users is calculated, and the higher the similarity is, the closer the actual distance between the two users is.
Further, in another embodiment of the proximity user identification method according to the present application, referring to fig. 7, step S30 includes:
step S31, determining the number of users of the effective user group according to a preset first parameter;
and step S32, determining the number of the optimal user grouping users from the number of the users of the effective user group according to the preset second parameter.
In this embodiment, the optimal user group needs to satisfy the preset first parameter α and the preset second parameter β:
GroupUsermax≤β
wherein: the group user is the number of users belonging to an effective user group (the number of people in the group is more than or equal to 5), the Alluser is all users, and alpha is the optimal number of people in the effective user group; GroupUsermaxThe number of users in the user group with the largest number of people; beta is the optimal number of people in the user group with the most people.
Further, in another embodiment of the method for identifying a proximity user according to the present application, referring to fig. 8, after step S32, the method includes:
step S33, calculating the MR feature similarity between every two users in the optimal user grouping range;
the MR feature similarity between every two users is the distance between the MR feature coefficients (clusters), and the method for calculating the distance between the clusters is as follows:
each cluster is a set and therefore some distance from the set needs to be calculated. For example, given clusters Ci and Cj, the distance can be calculated in 3 ways:
minimum distance: dmin(Ci,Cj)＝minx∈Ci,z∈Cjdist(x,z)
Maximum distance: dmax(Ci,Cj)＝maxx∈Ci,z∈Cjdist(x,z)
Average distance:
the minimum distance is determined by the closest sample of the two clusters and the maximum distance is determined by the farthest sample of the two clusters.
Step S34, traversing the MR feature similarity, and obtaining the minimum feature similarity and the maximum feature similarity of the selected range according to hierarchical clustering;
traversal refers to making one visit to each node in the tree (or graph) in turn along a search route. The operation performed by the access node depends on the specific application problem, and the specific access operation may be to check the value of the node, update the value of the node, and the like. Is differentIn the traversal mode, the order of accessing the nodes is different. Traversing the distances among all users to obtain the Distance as the minimum value in the Distance matrixminDistance is the maximum value in the Distance matrixmax。
Hierarchical clustering tries to divide a data set at different levels, and the idea is to regard each sample in the data set as an initial clustering cluster, then find out two clusters closest to the two clusters to merge, and repeat the steps continuously until a preset clustering number or a certain condition is reached.
In the hierarchical clustering of aggregates, two standard methods for determining the distance between clusters are single linkage (single linkage) and complete linkage (complete linkage). And single connection, namely calculating the distance between the most similar two samples in each pair of clusters, and combining the clusters to which the two samples with the shortest distance belong. Fully connected, and the least similar samples (farthest distance) distributed in the two clusters are found through comparison, so that the combination of the clusters is completed.
Step S35, using the minimum feature similarity as the initial value of the single connection coefficient, and obtaining the single connection coefficient meeting the optimal user grouping through iterative computation;
and step S36, obtaining the full-connection coefficient meeting the optimal user grouping by iterative computation by taking the maximum feature similarity as an initial value of the full-connection coefficient.
The key factors for determining user group clustering include two factors: single connection coefficient (distance), full connection coefficient (distance complete). Setting DistancesingleHas an initial value of Distancemin，DistancecompleteHas an initial value of DistancemaxCalculating the optimal coefficient Distance meeting two conditions of optimal user grouping through iterative operationsingle、Distancecomplete。
In this embodiment, first, the MR feature similarity between every two users in the selected range is calculated and obtained; then traversing each MR feature similarity to obtain the minimum feature similarity and the maximum feature similarity in the selected range; then, the minimum feature similarity is used as an initial value of the single connection coefficient, and the single connection coefficient meeting the optimal user grouping is obtained through iterative computation; and finally, obtaining the full-connection coefficient meeting the optimal user grouping by using the maximum characteristic similarity as an initial value of the full-connection coefficient through iterative computation.
Further, in another embodiment of the method for identifying a proximity user according to the present application, referring to fig. 9, after step S30, the method includes:
step S40, identifying the adjacent user groups according to the optimal adjacent user groups, and giving each user identification number;
and extracting user data under a certain floor for verification test. For example, through the clustering algorithm, the users are clustered, the groups of nearby users are identified, and ID identifiers are assigned, with the following results:
GroupA:AA,AB,AD,AE,AG,BA,BB,BC,BD,BE,BF,BG,CA,CB,CC,CD,CE,CF
GroupB:DC,DD,DF,EC,EE,FC,FD,FE,FG,GC,GD,GE,GF,GG,HG
GroupC:FF,HC,HD,HE,HF,ID,IE,JA,JB,JD,JF
GroupD:IC
wherein AA and AB … are the identification numbers of the users.
Step S50, filling according to the real position information and each identification number of each user, and obtaining the real position grid distribution map of each user;
and dividing the plane area into 7-by-10 grid areas, filling according to the real position information and the identification number of the user, and acquiring a grid distribution map of the real position of the user, wherein the real position refers to fig. 10.
And step S60, based on the real position grid distribution map, combining the optimal adjacent user grouping, performing color rendering according to the user group, and verifying the nearest user grouping effect.
The real position grid distribution diagram refers to a related diagram of the distribution of the physical positions of the users. Here, the first color is represented by (1), the 2 nd color is represented by (2), the third color is represented by (3), and the fourth color is represented by (4), and the rendering effect is described with reference to fig. 11.
In the embodiment, firstly, adjacent user groups are identified according to the optimal adjacent user groups, and each user identification number is given; filling according to the real position information and the identification numbers of the users to obtain the real position grid distribution map of the users; and finally, based on the real position grid distribution diagram, combining with the optimal adjacent user grouping, performing color rendering according to the user group, and verifying the nearest user grouping effect, namely realizing the group identification and effect verification of the adjacent users.
The present application further provides a terminal, the terminal including: a memory, a processor and a proximity user identification program stored on the memory and executable on the processor, the proximity user identification program when executed by the processor implementing the steps of the proximity user identification method described above.
The present application also provides a computer readable storage medium having stored thereon a proximity user identification program, which when executed by a processor implements the steps of the proximity user identification method described above.
In the embodiments of the method for identifying a neighboring user, the terminal and the readable storage medium of the present application, all technical features of the embodiments of the method for identifying a neighboring user are included, and the expanding and explaining contents of the specification are substantially the same as those of the embodiments of the method for identifying a neighboring user, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (9)
1. A method for identifying a neighboring user, the method comprising:
acquiring resident cell MR characteristic information of each user, and calculating and acquiring an MR characteristic coefficient of each user according to the MR characteristic information of each resident cell;
calculating MR characteristic similarity according to the MR characteristic information and the MR characteristic coefficient;
calculating to obtain an optimal adjacent user group according to a preset group condition and the MR characteristic similarity;
identifying a group of adjacent users according to the optimal adjacent user group, and giving each user identification number;
filling according to the real position information of each user and each identification number to obtain a real position grid distribution map of each user;
and based on the real position grid distribution map, combining the optimal adjacent user group, performing color rendering according to the user group, and verifying the nearest user group effect.
2. The method for identifying neighboring users as claimed in claim 1, wherein the step of obtaining MR characteristic information of each resident cell of each user comprises:
acquiring MR data of the resident cell working in daytime for each preset number of days of the user as first MR data;
acquiring MR data of the resident residential community of each user at night for preset days as second MR data;
and screening the MR data of each user resident cell in a preset time period based on the first MR data and the second MR data.
3. The method for identifying neighboring users as claimed in claim 2, wherein the step of filtering each of the MR data of the user-resident cells within a preset time period is followed by:
based on the MR data of each user resident cell, sequentially taking each user resident cell as a main service cell, taking each cell except the resident cell in the MR data of the resident cell as an adjacent cell, and acquiring the field intensity of the main service cell, the average field intensity of the field intensity of each adjacent cell, the identification number of each main service cell and the identification number of each adjacent cell;
screening the communication reporting frequency according to the preset screening condition of the communication reporting frequency of each adjacent cell to obtain the frequency of the selected adjacent cell, wherein the preset screening condition comprises a preset reporting frequency ranking and a preset frequency threshold;
and taking the average field intensity and the selected neighbor cell frequency as MR characteristic information of each resident cell.
4. The neighbor cell identification method of claim 3, wherein said step of using said average field strength and said selected neighbor cell frequency as MR signature information of each said resident cell is followed by:
normalizing each average field intensity to obtain a field intensity coefficient;
normalizing the frequency of each selected adjacent cell to obtain a frequency coefficient;
and taking the field intensity coefficient and the frequency coefficient as the MR characteristic coefficient of each user.
5. The method for identifying a neighboring user according to claim 4, wherein the step of calculating the MR feature similarity based on the MR feature information and the MR feature coefficient comprises:
acquiring the MR characteristic information of two resident users in the same preset cell;
if the MR characteristic information of the two resident users has the same adjacent cell identification number, extracting the MR characteristic coefficients of the two resident users so as to obtain the MR characteristic similarity of the two resident users through calculation of a preset first algorithm;
if the MR characteristic information of the two resident users does not have the same adjacent cell identification number, extracting the MR characteristic coefficients of the two resident users so as to calculate and obtain the MR characteristic similarity of the two resident users through a preset second algorithm.
6. The method for identifying neighboring users according to claim 5, wherein the step of calculating to obtain the optimal neighboring user group according to the preset grouping condition and the MR feature similarity comprises:
determining the number of users of an effective user group according to a preset first parameter;
and determining the number of the optimal user grouping users from the number of the users of the effective user group according to a preset second parameter.
7. The method of claim 6, wherein said step of determining an optimal user population for grouping users from the number of users in the active set of users is followed by the step of:
calculating the MR feature similarity between every two users in the optimal user grouping range;
traversing all the MR feature similarities, and acquiring the minimum feature similarity and the maximum feature similarity in a selected range according to hierarchical clustering;
taking the minimum feature similarity as an initial value of the single-connection coacervation hierarchical clustering coefficient, and obtaining the single-connection coacervation hierarchical clustering coefficient meeting the optimal user grouping through iterative computation;
and taking the maximum characteristic similarity as an initial value of the fully-connected coacervation hierarchical clustering coefficient, and obtaining the fully-connected coacervation hierarchical clustering coefficient meeting the optimal user grouping through iterative computation.
8. A terminal, characterized in that the terminal comprises: memory, a processor and a proximity user identification program stored on the memory and executable on the processor, the proximity user identification program when executed by the processor implementing the steps of the proximity user identification method according to any of claims 1 to 7.
9. A storage medium having stored thereon a proximity user identification program which, when executed by a processor, carries out the steps of the proximity user identification method according to any one of claims 1 to 7.
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