CN109842896B - Grid value evaluation method and device - Google Patents
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Abstract
The embodiment of the invention provides a grid value evaluation method and device. The method comprises the following steps: acquiring a plurality of MR data of each cell in a preset area range, an ARPU value of each user terminal in the preset area range, the total traffic of each user terminal and the traffic of each user terminal in each cell in a preset time period; according to the ARPU value of each user terminal, the total traffic and the traffic in each cell, respectively calculating the corresponding ARPU value of each user terminal in each cell; determining a plurality of MR data corresponding to each user terminal in each cell according to the triple information carried by the MR data, and determining the grids to which the MR data belong; and calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid. The device is used for executing the method. The method and the device provided by the embodiment of the invention improve the grid value evaluation accuracy.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a grid value evaluation method and device.
Background
With the increasingly fierce competition of the mobile market, the problem of wide coverage is basically solved, but when Voice Over LTE (VOLTE) and Video services based on IMS are developed in a large scale, the problem of deep coverage is increasingly fragmented, accurate identification is difficult, construction cost is high, and operators often give priority to high-value areas when planning and constructing base stations, so that research on evaluation methods of high-value areas by people is more and more concerned by people.
With the improvement of the requirement of positioning accuracy, the evaluation range of the high-value area is refined from the previous coarse-grained grids and base station levels to smaller grid levels, so that the value quantification of the grid levels becomes a new trend. Under the prior art, the evaluation of grid value generally includes that according to data provided by a Customer Relationship Management (CRM), a provincial Business Operation Support System (BOSS), Customer service, operation analysis and other systems, according to dimensions such as User group attributes, service usage, Customer service complaints, Average income Per User (ARPU), and the like, a weight predetermined by an expert scoring method is adopted, individual comprehensive values of users are calculated through weighted summation, then according to a proportion of communication traffic of users in a certain cell to all communication traffic of users, the individual comprehensive values of users are distributed to the cell, according to an association Relationship between grids and cells, the individual comprehensive values distributed by the cell are distributed to each grid covered by the cell, and finally the individual comprehensive values distributed by each cell in each grid are superposed, and obtaining the comprehensive value of the grid. The individual comprehensive value allocated to each cell is distributed to each covered grid evenly, so that the value of each grid covered by one cell is the same, the actual value of each grid cannot be reflected, and the accuracy of grid value evaluation is greatly influenced.
Therefore, it is an important issue to be solved in the industry at present to provide a grid value evaluation method to improve the accuracy of grid value evaluation.
Disclosure of Invention
Aiming at the defects in the prior art, the embodiment of the invention provides a grid value evaluation method and a grid value evaluation device.
In one aspect, an embodiment of the present invention provides a grid value evaluation method, including:
acquiring a plurality of MR data of each cell in a preset area range, an ARPU value of each user terminal in the preset area range, the total traffic of each user terminal and the traffic of each user terminal in each cell in a preset time period; the MR data carries triplet information;
according to the ARPU value of each user terminal, the total traffic and the traffic in each cell, respectively calculating the corresponding ARPU value of each user terminal in each cell;
determining a plurality of MR data corresponding to each user terminal in each cell according to the triple information, and determining a grid to which each MR data belongs;
and calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value.
In another aspect, an embodiment of the present invention provides a grid value evaluation apparatus, including:
an obtaining unit, configured to obtain, in a preset time period, multiple MR data of each cell within a preset area range, an ARPU value of each user terminal within the preset area range, a total traffic of each user terminal, and a traffic of each user terminal in each cell; the MR data carries triplet information;
a calculating unit, configured to calculate, according to the ARPU value of each user terminal, the total traffic volume, and the traffic volume in each cell, an ARPU value corresponding to each user terminal in each cell;
a processing unit, configured to determine, according to the triplet information, multiple MR data corresponding to each user terminal in each cell, and determine a grid to which each MR data belongs;
and the evaluation unit is used for calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value.
In another aspect, an embodiment of the present invention provides an electronic device, including a processor, a memory, and a bus, where:
the processor and the memory complete mutual communication through a bus;
the processor may invoke a computer program in memory to perform the steps of the above-described method.
In yet another aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the above-mentioned method.
According to the grid value evaluation method and device provided by the embodiment of the invention, a plurality of MR data of each cell in a preset area range and an ARPU value of each user terminal in the preset area range, the total traffic of each user terminal and the traffic of each user terminal in each cell are obtained in a preset time period; the MR data carries triple information, and the corresponding ARPU values of the user terminals in the cells are respectively calculated according to the ARPU values of the user terminals, the total traffic of the user terminals and the traffic of the user terminals in the cells; determining a plurality of MR data corresponding to each user terminal in each cell according to the triple information carried by the MR data, determining a grid to which each MR data belongs, then calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value, thereby improving the grid value evaluation accuracy.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a grid value evaluation method according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a grid value evaluation apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an entity apparatus of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a grid value evaluation method according to an embodiment of the present invention, and as shown in fig. 1, the embodiment provides a grid value evaluation method including:
s101, acquiring a plurality of MR data of each cell in a preset area range, an ARPU value of each user terminal in the preset area range, total traffic of each user terminal and traffic of each user terminal in each cell in a preset time period; the MR data carries triplet information;
specifically, the grid value evaluation device obtains a plurality of Measurement Report (MR) data of each cell within a preset area range and an Average Revenue Per User (ARPU) value of each User terminal within the preset area range, a total traffic of each User terminal and a traffic of each User terminal within each cell within a preset time period; the MR data carries triplet information. Wherein, the total traffic of a certain user terminal is the sum of the traffic of the user terminal in each cell, and the total traffic and the traffic in each cell can be represented by the number of generated call tickets, and can also be represented by other parameters; the preset time period and the preset area range can be set and adjusted according to actual conditions, and are not specifically limited herein; the triplet information includes a Mobility Management Entity Group Identifier (MMEGI), a mobility management Entity number (MMEC), and a unique Identifier of the user terminal on a mobility management Entity side S1 interface (Mobile management Entity S1 Application Protocol UE ID, MME S1AP UE ID), where the MMEGI is unique within a Public Land Mobile Network (PLMN); MMEC is unique within one MME group.
S102, respectively calculating the corresponding ARPU value of each user terminal in each cell according to the ARPU value of each user terminal, the total traffic and the traffic in each cell;
specifically, the apparatus calculates the ARPU value corresponding to each ue in each cell according to the ARPU value of each ue, the total traffic volume, and the traffic volume in each cell, that is, the ARPU value of each ue is distributed to each cell according to the ratio of the traffic volume in each cell to the total traffic volume, that is, the ARPU value corresponding to each ue in each cell.
S103, determining a plurality of MR data corresponding to each user terminal in each cell according to the triple information, and determining a grid to which each MR data belongs;
specifically, the apparatus obtains an association relationship between the triplet information and an IMSI corresponding to each user terminal, determines a user terminal corresponding to each MR data of each cell according to the triplet information carried in each MR data of each cell and the association relationship between the triplet information and the IMSI corresponding to each user terminal, determines multiple MR data corresponding to each user terminal in each cell according to the user terminal corresponding to the MR data of each cell, and determines a grid to which each MR data belongs. It should be noted that the grid is obtained by dividing the preset region range according to a preset side length, the grid may be a grid of 50 meters × 50 meters, or a grid of other sizes, and the grid may be divided and adjusted specifically according to an actual situation, which is not specifically limited herein; the grid to which the MR data belongs refers to a grid to which the geographic position of the reported MR data is within the area range of the grid, the device can determine the grid to which the MR data belongs by using an MR positioning method based on a fingerprint database, and can also determine the grid to which the MR data belongs by performing MR positioning in other ways, and the device can be specifically set and adjusted according to actual conditions, and is not specifically limited here, and the MR positioning method based on the fingerprint database is consistent with the steps in the prior art, and is not described here again.
S104, calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value.
Specifically, the apparatus calculates the ARPU value of each MR data corresponding to each user terminal in each cell according to the ARPU value corresponding to each user terminal in each cell and the MR data, then calculates the ARPU value of each grid according to the ARPU value of each MR data corresponding to each user terminal in each cell and the grid to which the user terminal belongs, and evaluates the value of each grid according to the ARPU value.
In the grid value evaluation method provided by the embodiment of the present invention, by acquiring multiple MR data of each cell within a preset area range, an ARPU value of each user terminal within the preset area range, a total traffic of each user terminal, and a traffic of each user terminal in each cell within a preset time period, according to the ARPU value of each user terminal, the total traffic, and the traffic in each cell, an ARPU value corresponding to each user terminal in each cell is calculated respectively; determining a plurality of MR data corresponding to each user terminal in each cell according to the triple information carried by the MR data, determining the grid to which each MR data belongs, then calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value, thereby improving the grid value evaluation accuracy.
On the basis of the foregoing embodiment, further, the determining, according to the triplet information, a plurality of MR data corresponding to each user terminal in each cell includes:
acquiring an association relation between the triple information and IMSI corresponding to each user terminal;
determining a user terminal corresponding to each MR data of each cell according to triple information carried in each MR data of each cell and an incidence relation between the triple information and the IMSI corresponding to each user terminal;
and determining a plurality of MR data corresponding to each user terminal in each cell according to the user terminal corresponding to the MR data of each cell.
Specifically, when the user terminal and the MME establish initial connection, the MME allocates corresponding triplet information, and when the user terminal performs any service, the MME platform associates the IMSI corresponding to the user terminal with the triplet information, and when the triplet information corresponding to the user terminal is updated, the MME platform simultaneously updates the association relationship between the IMSI corresponding to the user terminal and the triplet information, thereby ensuring that the relationship between the user terminal and the triplet information is uniquely corresponding at the same time. Based on information security considerations, the MR data reported by the user terminal does not contain the IMSI information of the user, but carries the triple information corresponding to the user terminal, the device obtains the association relationship between the triple information and the IMSI corresponding to each user terminal from the MME platform, and obtains the IMSI corresponding to the user terminal reporting each MR data by matching the triple information carried in the MR data reported by the user terminal with the association relationship, that is, the IMSI backfill is performed on the MR data, so that the user terminal corresponding to each MR data of each cell can be obtained, and then a plurality of MR data corresponding to each user terminal in each cell can be determined according to the user terminal corresponding to the MR data of each cell.
On the basis of the foregoing embodiment, further, the calculating, according to the ARPU value of each ue, the total traffic volume, and the traffic volume in each cell, the ARPU value corresponding to each ue in each cell includes:
according to the formula: b isi,j＝Ai×wWii,,jjCalculating the corresponding ARPU value of the ith user terminal in the preset area in the jth cell; wherein, Bi,jIs the corresponding ARPU value, A, of the ith user terminal in the jth celliIs the ARPU value, w, of the ith user terminali,jFor the traffic of the ith user terminal in the jth cell in the preset time period, Wi,jAnd the traffic of the ith user terminal in the preset area in the preset time period is set.
On the basis of the foregoing embodiment, further, the calculating an ARPU value of each grid according to the ARPU value, the MR data, and the grid to which each MR data belongs corresponding to each user terminal in each cell includes:
calculating the ARPU value of each MR data corresponding to each user terminal in each cell according to the ARPU value corresponding to each user terminal in each cell and the number of the MR data;
and calculating the ARPU value of each grid according to the ARPU value of each MR data corresponding to each user terminal in each cell and the grid to which the user terminal belongs.
Specifically, the apparatus calculates the ARPU value of each MR data corresponding to each user terminal in each cell according to the ARPU value corresponding to each user terminal in each cell and the number of the MR data corresponding to each user terminal in each cell, that is, the ARPU value corresponding to each user terminal in each cell is equally distributed to the MR data corresponding to each user terminal in each cell, and then calculates the ARPU value corresponding to each cell in each grid according to the ARPU value of each MR data corresponding to each user terminal in each cell and the grid to which the user terminal belongs, and takes the sum of the ARPU values corresponding to each cell in one grid as the ARPU value of the grid.
On the basis of the foregoing embodiment, further, the calculating, according to the ARPU value and the number of the multiple MR data corresponding to each user terminal in each cell, an ARPU value of each MR data corresponding to each user terminal in each cell includes:
according to the formula:
calculating the ARPU value of each MR data corresponding to the ith user terminal in the jth cell in the preset area; wherein, bi,jARPU value, B of each MR data corresponding to ith user terminal in jth celli,jIs the corresponding ARPU value, M, of the ith user terminal in the jth celli,jAnd the number of the MR data corresponding to the ith user terminal in the jth cell is obtained.
On the basis of the foregoing embodiment, further, the calculating an ARPU value of each grid according to an ARPU value of each MR data corresponding to each user terminal in each cell and a grid to which the user terminal belongs includes:
respectively calculating the corresponding ARPU values of the cells in the grids according to the ARPU values of the MR data corresponding to the user terminals in the cells and the grids to which the MR data belong;
and taking the sum of the corresponding ARPU values of the cells in one grid as the ARPU value of the grid.
Specifically, according to the grid to which each MR data corresponding to each user terminal in each cell belongs, the MR data corresponding to each user terminal in each cell included in each grid is determined, the ARPU value corresponding to each cell in each grid is calculated according to the ARPU value of the MR data corresponding to each user terminal in each cell included in each grid, and then the sum of the ARPU values corresponding to each cell in one grid is taken as the ARPU value of the grid, so that the ARPU value of each grid in the preset area is calculated respectively.
On the basis of the foregoing embodiment, further, the calculating, according to the ARPU value of each MR data corresponding to each user terminal in each cell and the grid to which the MR data belongs, the ARPU value corresponding to each cell in each grid includes:
according to the formula:
calculating the corresponding ARPU value of the jth cell in the kth grid; wherein Q isj,kCorresponding ARPU value, N, in kth grid for jth cellp,j,kThe number of MR data which is corresponding to the p-th user terminal in the j-th cell and belongs to the k-th grid is n, the total number of the user terminals in the j-th cell is b'p,jIs the ARPU value, b 'of each MR data corresponding to the p-th user terminal in the j cell'p,j∈{bi,jL is the total number of the user terminals in the preset area, p is greater than or equal to 1 and less than or equal to n, i is greater than or equal to 1 and less than or equal to L, and n is less than or equal to L.
Correspondingly, the taking the sum of the ARPU values corresponding to the cells in one grid as the ARPU value of the grid includes:
according to the formula:
calculating an ARPU value of a kth grid; wherein, PkIs the ARPU value, Q, of the k-th gridj,kIs the corresponding ARPU value of the jth cell in the kth grid, m is the total number of the cells corresponding to the kth grid, and j is more than or equal to 1 and less than or equal to m.
In the grid value evaluation method provided by the embodiment of the present invention, by acquiring multiple MR data of each cell within a preset area range, an ARPU value of each user terminal within the preset area range, a total traffic of each user terminal, and a traffic of each user terminal in each cell within a preset time period, according to the ARPU value of each user terminal, the total traffic, and the traffic in each cell, an ARPU value corresponding to each user terminal in each cell is calculated respectively; determining a plurality of MR data corresponding to each user terminal in each cell according to the triple information carried by the MR data, determining the grid to which each MR data belongs, then calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value, thereby improving the grid value evaluation accuracy.
Fig. 2 is a schematic structural diagram of a grid value evaluation device according to an embodiment of the present invention, and as shown in fig. 2, the embodiment of the present invention provides a grid value evaluation device, including: an acquisition unit 201, a calculation unit 202, a processing unit 203 and an evaluation unit 204, wherein:
the acquiring unit 201 is configured to acquire, in a preset time period, multiple MR data of each cell within a preset area range, an ARPU value of each user terminal within the preset area range, a total traffic of each user terminal, and a traffic of each user terminal in each cell; the MR data carries triplet information; the calculating unit 202 is configured to calculate, according to the ARPU value of each ue, the total traffic volume, and the traffic volume in each cell, an ARPU value corresponding to each ue in each cell; the processing unit 203 is configured to determine, according to the triplet information, multiple corresponding MR data of each user terminal in each cell, and determine a grid to which each MR data belongs; the evaluation unit 204 is configured to calculate an ARPU value of each grid according to the ARPU value, the MR data, and the grid to which each MR data belongs corresponding to each user terminal in each cell, and evaluate a value of each grid according to the ARPU value.
The grid value evaluation device provided by the embodiment of the invention calculates the corresponding ARPU value of each user terminal in each cell according to the ARPU value, the total traffic and the traffic of each user terminal in each cell by acquiring a plurality of MR data of each cell in a preset area range, the ARPU value of each user terminal in the preset area range, the total traffic of each user terminal and the traffic of each user terminal in each cell in a preset time period; determining a plurality of MR data corresponding to each user terminal in each cell according to the triple information carried by the MR data, determining the grid to which each MR data belongs, then calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value, thereby improving the grid value evaluation accuracy.
Optionally, the processing unit 203 is specifically configured to obtain an association relationship between the triplet information and the IMSI corresponding to each user terminal; determining a user terminal corresponding to each MR data of each cell according to triple information carried in each MR data of each cell and an incidence relation between the triple information and the IMSI corresponding to each user terminal; and determining a plurality of MR data corresponding to each user terminal in each cell according to the user terminal corresponding to the MR data of each cell.
Optionally, the calculating unit 202 is specifically configured to:
calculating the corresponding ARPU value of the ith user terminal in the preset area in the jth cell; wherein, Bi,jIs the corresponding ARPU value, A, of the ith user terminal in the jth celliIs the ARPU value, w, of the ith user terminali,jFor the traffic of the ith user terminal in the jth cell in the preset time period, Wi,jAnd the traffic of the ith user terminal in the preset area in the preset time period is set.
Optionally, the evaluation unit 204 is specifically configured to calculate, according to the ARPU value corresponding to each user terminal in each cell and the number of the multiple MR data, an ARPU value of each MR data corresponding to each user terminal in each cell; and calculating the ARPU value of each grid according to the ARPU value of each MR data corresponding to each user terminal in each cell and the grid to which the user terminal belongs.
Optionally, the processing unit 203 is specifically configured to:
calculating the ARPU value of each MR data corresponding to the ith user terminal in the jth cell in the preset area; wherein, bi,jARPU value, B of each MR data corresponding to ith user terminal in jth celli,jIs the corresponding ARPU value, M, of the ith user terminal in the jth celli,jAnd the number of the MR data corresponding to the ith user terminal in the jth cell is obtained.
Optionally, the processing unit 203 is specifically configured to calculate, according to the ARPU value of each MR data corresponding to each user terminal in each cell and the grid to which the MR data belongs, the ARPU value corresponding to each cell in each grid; and taking the sum of the corresponding ARPU values of the cells in one grid as the ARPU value of the grid.
Optionally, the processing unit 203 is specifically configured to:
calculating the corresponding ARPU value of the jth cell in the kth grid; wherein Q isj,kCorresponding ARPU value, N, in the kth grid for the jth cellp,j,kThe number of MR data which is corresponding to the p-th user terminal in the j-th cell and belongs to the k-th grid is n, the total number of the user terminals in the j-th cell is b'p,jIs the ARPU value, b 'of each MR data corresponding to the p-th user terminal in the j cell'p,j∈{bi,j1,2 … L, wherein L is the total number of the user terminals in the preset area, p is greater than or equal to 1 and less than or equal to n, i is greater than or equal to 1 and less than or equal to L, and n is less than or equal to L; according to the formula:
calculating an ARPU value of a kth grid; wherein, PkIs the ARPU value, Q, of the k-th gridj,kIs the corresponding ARPU value of the jth cell in the kth grid, m is the total number of the cells corresponding to the kth grid, and j is more than or equal to 1 and less than or equal to m.
The embodiment of the grid value evaluation apparatus provided by the present invention may be specifically configured to execute the processing flows of the above method embodiments, and the functions thereof are not described herein again, and refer to the detailed description of the above method embodiments.
Fig. 3 is a schematic structural diagram of an entity apparatus of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device may include: a processor (processor)301, a memory (memory)302, and a bus 303, wherein the processor 301 and the memory 302 communicate with each other via the bus 303. The processor 301 may call the computer program in the storage 302 to perform the methods provided by the method embodiments described above, including for example: acquiring a plurality of MR data of each cell in a preset area range, an ARPU value of each user terminal in the preset area range, the total traffic of each user terminal and the traffic of each user terminal in each cell in a preset time period; the MR data carries triplet information; according to the ARPU value of each user terminal, the total traffic and the traffic in each cell, respectively calculating the corresponding ARPU value of each user terminal in each cell; determining a plurality of MR data corresponding to each user terminal in each cell according to the triple information, and determining a grid to which each MR data belongs; and calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer can execute the methods provided by the above method embodiments, for example, the method includes: acquiring a plurality of MR data of each cell in a preset area range, an ARPU value of each user terminal in the preset area range, the total traffic of each user terminal and the traffic of each user terminal in each cell in a preset time period; the MR data carries triplet information; the MR data carries triplet information; according to the ARPU value of each user terminal, the total traffic and the traffic in each cell, respectively calculating the corresponding ARPU value of each user terminal in each cell; determining a plurality of MR data corresponding to each user terminal in each cell according to the triple information, and determining a grid to which each MR data belongs; and calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores a computer program, where the computer program causes the computer to execute the method provided by the foregoing method embodiments, for example, the method includes: acquiring a plurality of MR data of each cell in a preset area range, an ARPU value of each user terminal in the preset area range, the total traffic of each user terminal and the traffic of each user terminal in each cell in a preset time period; the MR data carries triplet information; the MR data carries triplet information; according to the ARPU value of each user terminal, the total traffic and the traffic in each cell, respectively calculating the corresponding ARPU value of each user terminal in each cell; determining a plurality of MR data corresponding to each user terminal in each cell according to the triple information, and determining a grid to which each MR data belongs; and calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value.
Furthermore, the logic instructions in the memory 302 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A grid value evaluation method, comprising:
acquiring a plurality of MR data of each cell within a preset area range within a preset time period, and an average income per user ARPU value of each user terminal within the preset area range, the total traffic of each user terminal and the traffic of each user terminal in each cell; the MR data carries triplet information;
according to the ARPU value of each user terminal, the total traffic and the traffic in each cell, respectively calculating the corresponding ARPU value of each user terminal in each cell;
determining a plurality of MR data corresponding to each user terminal in each cell according to the triple information, and determining a grid to which each MR data belongs; the triplet information comprises a mobility management entity group identity MMEGI, a mobility management entity number MMEC and a unique identity MME S1AP UE ID of the user terminal on a mobility management entity side S1 interface;
and calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value.
2. The method of claim 1, wherein the determining a plurality of MR data corresponding to each user terminal in each cell according to the triplet information comprises:
acquiring an association relation between the triple information and IMSI corresponding to each user terminal;
determining a user terminal corresponding to each MR data of each cell according to triple information carried in each MR data of each cell and an incidence relation between the triple information and the IMSI corresponding to each user terminal;
and determining a plurality of MR data corresponding to each user terminal in each cell according to the user terminal corresponding to the MR data of each cell.
3. The method of claim 1, wherein the calculating the ARPU value of each user terminal in each cell according to the ARPU value of each user terminal, the total traffic volume and the traffic volume in each cell comprises:
according to the formula:
calculating the corresponding ARPU value of the ith user terminal in the preset area in the jth cell; wherein, Bi,jIs the corresponding ARPU value, A, of the ith user terminal in the jth celliIs the ARPU value, W, of the ith user terminali,jFor the total traffic of the ith user terminal in the preset area within the preset time period, wi,jAnd the traffic of the ith user terminal in the jth cell in the preset time period is used.
4. The method of claim 1, wherein the calculating the ARPU value of each cell according to the ARPU value, the MR data and the cell to which the MR data belongs corresponding to each ue in each cell comprises:
calculating the ARPU value of each MR data corresponding to each user terminal in each cell according to the ARPU value corresponding to each user terminal in each cell and the number of the MR data;
and calculating the ARPU value of each grid according to the ARPU value of each MR data corresponding to each user terminal in each cell and the grid to which the user terminal belongs.
5. The method of claim 4, wherein the calculating the ARPU value of each MR data corresponding to each user terminal in each cell according to the ARPU value corresponding to each user terminal in each cell and the number of the MR data comprises:
according to the formula:
calculating the ith user terminal in the preset area to be at the firstThe ARPU value of each corresponding MR data in the j cell; wherein, bi,jARPU value, B of each MR data corresponding to ith user terminal in jth celli,jIs the corresponding ARPU value, M, of the ith user terminal in the jth celli,jAnd the number of the corresponding MR data in the jth cell is the ith user terminal.
6. The method according to claim 5, wherein the calculating the ARPU value of each grid according to the ARPU value of each MR data corresponding to each user terminal in each cell and the grid to which the user terminal belongs comprises:
according to the ARPU values of the MR data corresponding to the user terminals in the cells and the grids to which the MR data belong, respectively calculating the corresponding ARPU values of the cells in the grids;
and taking the sum of the corresponding ARPU values of the cells in one grid as the ARPU value of the grid.
7. The method of claim 6, wherein the calculating the ARPU values of the cells in the grids according to the ARPU values of the MR data corresponding to the user terminals in the cells and the grids to which the user terminals belong respectively comprises:
according to the formula:
calculating the corresponding ARPU value of the jth cell in the kth grid; wherein Q isj,kCorresponding ARPU value, N, in the kth grid for the jth cellp,j,kThe number of MR data which is corresponding to the p-th user terminal in the j-th cell and belongs to the k-th grid is n, the total number of the user terminals in the j-th cell is b'p,jIs the ARPU value, b 'of each MR data corresponding to the p-th user terminal in the j cell'p,j∈{bi,j1,2 … L, wherein L is the total number of the user terminals in the preset area, p is greater than or equal to 1 and less than or equal to n, i is greater than or equal to 1 and less than or equal to L, and n is less than or equal to L;
correspondingly, the taking the sum of the ARPU values corresponding to the cells in one grid as the ARPU value of the grid includes:
according to the formula:
calculating an ARPU value of a kth grid; wherein, PkIs the ARPU value, Q, of the k-th gridj,kIs the corresponding ARPU value of the jth cell in the kth grid, m is the total number of the cells corresponding to the kth grid, and j is more than or equal to 1 and less than or equal to m.
8. A grid value evaluation apparatus, comprising:
an obtaining unit, configured to obtain, in a preset time period, a plurality of MR data of each cell within a preset area range, and an average revenue ARPU per user value of each user terminal within the preset area range, a total traffic of each user terminal, and a traffic of each user terminal within each cell; the MR data carries triplet information;
a calculating unit, configured to calculate, according to the ARPU value of each user terminal, the total traffic volume, and the traffic volume in each cell, an ARPU value corresponding to each user terminal in each cell;
a processing unit, configured to determine, according to the triplet information, multiple MR data corresponding to each user terminal in each cell, and determine a grid to which each MR data belongs; the triplet information comprises a mobility management entity group identity MMEGI, a mobility management entity number MMEC and a unique identity MME S1AP UE ID of the user terminal on a mobility management entity side S1 interface;
and the evaluation unit is used for calculating the ARPU value of each grid according to the ARPU value corresponding to each user terminal in each cell, the MR data and the grid to which each MR data belongs, and evaluating the value of each grid according to the ARPU value.
9. An electronic device comprising a processor, a memory, and a bus, wherein:
the processor and the memory complete mutual communication through a bus;
the processor may invoke a computer program stored in the memory to perform the steps of the method of any of claims 1-7.
10. 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 according to any one of claims 1 to 7.
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| CN110784883B (en) * | 2019-11-01 | 2022-08-05 | 中国联合网络通信集团有限公司 | Base station construction evaluation method, device, equipment and storage medium |
| CN110839257B (en) * | 2019-11-14 | 2023-03-21 | 中国联合网络通信集团有限公司 | Network quality evaluation method, device and storage medium |
| CN112996011B (en) * | 2019-12-12 | 2022-12-16 | 中国移动通信集团吉林有限公司 | Cell value evaluation method, device, computer equipment and readable storage medium |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103731855A (en) * | 2012-10-11 | 2014-04-16 | 中国移动通信集团上海有限公司 | Method and device for screening high-data service hot spot cells |
| CN104980950A (en) * | 2014-04-03 | 2015-10-14 | 中国移动通信集团浙江有限公司 | Network optimization server, mobile device of realizing network optimization and system of realizing network optimization |
| CN105873079A (en) * | 2015-01-23 | 2016-08-17 | 中国移动通信集团浙江有限公司 | Network planning method and device |
| CN105898762A (en) * | 2015-01-26 | 2016-08-24 | 华为技术有限公司 | Base station optimization and deployment method and device |
| CN106817384A (en) * | 2015-11-27 | 2017-06-09 | 亿阳信通股份有限公司 | A kind of analysis method and system that behavior is accessed based on user's telecommunications |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014220694A (en) * | 2013-05-09 | 2014-11-20 | 日本電気株式会社 | Communication system, base station, and communication method |
| US20150327142A1 (en) * | 2014-05-08 | 2015-11-12 | Marta Martinez Tarradell | Apparatus, computer-readable medium, and method to support low complexity user equipment |
-
2017
- 2017-11-28 CN CN201711282662.2A patent/CN109842896B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103731855A (en) * | 2012-10-11 | 2014-04-16 | 中国移动通信集团上海有限公司 | Method and device for screening high-data service hot spot cells |
| CN104980950A (en) * | 2014-04-03 | 2015-10-14 | 中国移动通信集团浙江有限公司 | Network optimization server, mobile device of realizing network optimization and system of realizing network optimization |
| CN105873079A (en) * | 2015-01-23 | 2016-08-17 | 中国移动通信集团浙江有限公司 | Network planning method and device |
| CN105898762A (en) * | 2015-01-26 | 2016-08-24 | 华为技术有限公司 | Base station optimization and deployment method and device |
| CN106817384A (en) * | 2015-11-27 | 2017-06-09 | 亿阳信通股份有限公司 | A kind of analysis method and system that behavior is accessed based on user's telecommunications |
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