A method for selecting regional value center points of communication networks

Abstract

The present disclosure aims to provide a method for selecting a value center point of a communication network area, comprising the following steps: S1: obtaining basic information: obtaining basic information of a research area, the basic information including computer room information, base station information and broadband information; S2: preprocessing basic information: performing the following preprocessing on the basic information obtained by S1; S3: respectively calculating the business scores and structural scores of all benchmark points obtained by S2, and finally obtaining the comprehensive value score corresponding to each benchmark point and then sorting them; S4: determining each value center point one by one according to each benchmark point sorted by S3 and its corresponding comprehensive value score, until the number of determined value center points reaches an estimated number M; S5: matching each value center point determined by S4 with the computer room, determining a plan for constructing the computer room, and providing a reference for the operator to determine whether the planning plan is reasonable through the matching degree between the computer room and the value center point in the operator's computer room planning plan.

Description

Technical Field

[0001] The present disclosure relates to the field of wireless communication technology, and in particular to a method for selecting a value center point of a communication network area. Background Art

[0002] With the rapid development of communication networks, operators are faced with complex network environments and diversified user needs. The coexistence of multiple network standards of 2 / 3 / 4 / 5G, and the superposition of dual-sided business demands for home broadband and government and enterprise dedicated lines have led to increasing network construction and operating costs. In this context, operators have successively carried out a series of network optimization work such as reducing frequency and withdrawing networks and streamlining architecture. In the process of optimization and adjustment, how to analyze the key areas of network coverage that carry a large number of users and services, identify the value center point, and build a minimalist network architecture at the optimal cost has an important impact on operators to reduce TCO costs, improve network quality, and promote business development. Summary of the invention

[0003] In response to the above technical problems in the related technology, the present disclosure proposes a method for selecting a value center point of a communication network area, which can overcome the above shortcomings of the prior art.

[0004] To achieve the above technical objectives, the technical solution of the present disclosure is implemented as follows:

[0005] The present disclosure aims to provide a method for selecting a value center point of a communication network region, comprising the following steps:

[0006] S1 Obtain basic information: Obtain basic information of the study area, including computer room information, base station information, and broadband information;

[0007] Base station information includes: base station latitude and longitude information, and the average daily traffic of the base station in the past month;

[0008] Broadband information includes: longitude and latitude information of home broadband communities, longitude and latitude information of government and enterprise buildings with dedicated lines, the number of online users of home broadband communities and the number of online users of government and enterprise dedicated lines in the last day;

[0009] The information of the computer room includes: the longitude and latitude of the computer room, the ownership of the computer room, the area of ​​the computer room, the distance and maximum load of the power supply to the computer room, the transmission resources of the computer room, and whether there are any dangerous sources around the computer room;

[0010] S2 preprocesses basic information: The basic information obtained in S1 is preprocessed as follows:

[0011] S2.1: Determine the attribution scenario of the study area. The attribution scenario refers to the specific area type of the study area (which can be urban area, county town, township, rural area, etc.);

[0012] S2.2: According to the scenario of the study area, set the corresponding analysis radius r (specifically: urban area - 300m, county town - 500m, township - 800m, rural area - 1500m);

[0013] S2.3 Positioning and parameter calculation: A point in the study area is selected as the reference point 0, and then 5 circles are made with the reference point 0 as the center and r, 2r, 3r, 4r, and 5r as the radius, that is, the radiation area of ​​the reference point 0 is divided into 5 layers, and the number of base stations BS in each layer, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base station F, and the sum of broadband users B are counted respectively;

[0014] Then, based on the reference point 0, several points in several relative directions of the reference point 0 are selected as reference points and numbered in sequence. Then, with each of the selected reference points as the center of the circle, five circles are made with reference to the reference point 0 and with the radii of r, 2r, 3r, 4r, and 5r, respectively. The number of base stations BS, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base station F, and the sum of broadband users B are counted for each layer of each reference point.

[0015] Repeat the above method until the new point exceeds the boundary of the study area, indicating that the benchmark point has reached the limit. Then, all benchmark points and their corresponding base station numbers BS, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base station F, and the sum of broadband users B are obtained.

[0016] S2.4: Based on the study area, the belonging scenario and the area of ​​the study area, preliminarily calculate the estimated number of all value centers M, M = the area of ​​the study area / (π*(5r)2) + 1;

[0017] S3: Calculate the business scores and structural scores of all benchmarks obtained in S2 respectively, and finally obtain the comprehensive value score corresponding to each benchmark and sort them;

[0018] S4: According to the benchmark points sorted in S3 and their corresponding comprehensive value scores, determine the value center points one by one until the number of determined value center points reaches the estimated number M;

[0019] S5: According to the matching of each value center point determined in S4 with the computer room, a plan for constructing the computer room is determined, and the matching degree between the computer room and the value center point in the operator's computer room planning plan is used to provide a reference for the operator to determine whether the planning plan is reasonable. S5 can be implemented using existing technology.

[0020] Preferably, the specific steps of S2.3 are:

[0021] S2.3.1: Select any point in the study area as reference point 0, whose point number is 0. Take reference point 0 as the center and make 5 circles with radius r, 2r, 3r, 4r, and 5r respectively, and divide the radiation area of ​​reference point 0 into 5 layers; count the number of base stations in each layer: BS (i, j), the average single-station access distance between the base station and the computer room connected to it: D (i, j), the sum of the average daily traffic of the base station: F (i, j), and the sum of broadband users: B (i, j); where i and j correspond to the point number and the number of layers respectively, that is, i=0, 1≦j≦5;

[0022] S2.3.2: First, taking reference point 0 as the basis, four points at a distance r above, below, left, and right of reference point 0 are respectively selected as four reference points, and the point numbers are recorded as 1, 2, 3, and 4, i.e., reference points 1, 2, 3, and 4;

[0023] S2.3.3: Take reference points 1, 2, 3, and 4 as the center of the circle and r, 2r, 3r, 4r, and 5r as the radius to make 5 circles respectively, and divide the radiation area of ​​reference points 1, 2, 3, and 4 into 5 layers; for reference points 1, 2, 3, and 4, respectively count the number of base stations in each layer: BS (i, j), the average access distance between the base station and the uplink room: D (i, j), the sum of the average daily traffic of the base station: F (i, j), and the sum of broadband users: B (i, j); where i is the point number, j is the layer number, 1≦i≦4, 1≦j≦5;

[0024] S2.3.4: Then, based on reference points 1, 2, 3, and 4, four points at a distance r from each other in the upper, lower, left, and right directions of reference points 1, 2, 3, and 4 are respectively selected as four reference points, and duplicate points are eliminated. The obtained points are numbered in sequence as 5, 6, 7, 8, ...K, i.e., reference points 5, 6, 7, 8, ...K;

[0025] S2.3.5: Take reference points 5, 6, 7, 8, ...K as the center of the circle and r, 2r, 3r, 4r, 5r as the radius to make 5 circles respectively, and divide the radiation area of ​​reference points 5, 6, 7, 8, ...K into 5 layers; for reference points 5, 6, 7, 8, ...K, count the number of base stations in each layer: BS (i, j), the average access distance between the base station and the uplink room: D (i, j), the sum of the average daily traffic of the base station: F (i, j), and the sum of broadband users: B (i, j); where i is the point number, j is the layer number, 5≦i≦K, 1≦j≦5;

[0026] S2.3.6: Finally, based on points 5, 6, 7, 8, ...K, refer to S2.3.4~S2.3.5 for iteration until the newly determined points exceed the boundary of the study area. The point numbers of all the determined benchmark points are recorded as {0, 1, 2, ...N}, where N is the largest point number;

[0027] S2.3.7: According to the four dimensions of the number of base stations BS, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base stations F, and the sum of broadband users B, the data of the benchmark points {0, 1, 2, ...N} are normalized to obtain the base station density score BSS (i, j), base station access score DS (i, j), base station service score FS (i, j), and broadband service score BS (i, j) of each layer of each benchmark point; where i is the point number, j is the layer number, 0≦i≦N, 1≦j≦5.

[0028] Preferably, in S2.3.7, for the data of the base station access score DS (i, j), due to the difference between the data of "the average single-station access distance D between the base station and the computer room connected to it" and other three-dimensional data, the data is inversely calculated and then normalized.

[0029] Preferably, the specific steps of S3 are:

[0030] S3.1: traverse all reference points 0, 1, 2, 3...N, initialize i=0, that is, select reference point 0, and then continue S3.2;

[0031] S3.2: Calculate the business score YW(i), structure score JG(i), and comprehensive value score V(i) of benchmark point i, specifically according to the following formula;

[0032] YW(i)=∑FS(i,j)*k(j)+∑BS(i,j)*k(j); (Formula 1);

[0033] JG(i) = ∑BSS(i, j)*k(j)+ ∑DS(i, j)*k(j); (Formula 2);

[0034] V(i) = YW(i) + JG(i); (Formula 3);

[0035] In the above formula: i is the point number of the current reference point, 0≦i≦N; j is the layer number of the reference point, 1≦j≦5;

[0036] YW(i)--business score of benchmark point i;

[0037] JG(i) - structural score of benchmark point i;

[0038] V(i) – the comprehensive value score of benchmark point i;

[0039] k(j)--weight adjustment coefficient, representing the weight of each layer from 1 to 5, with values ​​of 1, 0.8, 0.6, 0.4, and 0.2 respectively, used to calculate the business score corresponding to each point;

[0040] S3.3: If i≧N, it means that all reference points have been traversed, and then continue to S3.4; otherwise, increase the value of i by 1, that is, select the next reference point in order, and return to S3.2;

[0041] S3.4: Finally, sort all benchmarks in descending order of their comprehensive value scores.

[0042] Preferably, the specific steps of S4 are:

[0043] S4.1: Select the benchmark point with the largest comprehensive value score and record it as value center point 1;

[0044] S4.2: Value center point 1 is recorded as the current value center point T, where T is the point number of the current value center point. T is initialized to 1, indicating that the iteration starts from value center point 1;

[0045] S4.3: Calculate the distance between the current value center point T and other reference points, and remove other reference points whose distance from the current value center point T is less than 5r;

[0046] S4.4: Increase the value of T by 1, and select the one with the largest comprehensive value score from the remaining benchmark points as the value center point T, and indicate that the current value center point T has also been iterated;

[0047] S4.5: If T≧M, it means that the number of selected value centers has reached the estimated number M, and the iteration should end and go to S4.6; otherwise, it means that the iteration should continue and return to S4.3;

[0048] S4.6: Save and record the selected value center points 1, 2...T.

[0049] Preferably, the specific steps of S5 are:

[0050] S5.1: Select all existing computer rooms in the study area as the basis for selecting architecture computer rooms;

[0051] S5.2: Comprehensively evaluate all existing equipment rooms in terms of property rights, space, power support, transmission resources, etc., and reject those that do not meet the requirements.

[0052] S5.3: Match the distance between the existing computer room that meets the preliminary judgment conditions and each value center point selected in S4. If the distance between the existing computer room and a value center point is less than r, the judgment is reasonable;

[0053] S5.4: Preset a threshold value based on the actual situation of the study area. If the proportion of reasonable computer rooms in the planning scheme is higher than the threshold value, then the planning scheme of the computer room architecture is reasonable; if the proportion is lower than the threshold value, then the planning scheme of the computer room architecture is unreasonable, and it is necessary to refer to the value center points to adjust the location of the existing computer room.

[0054] Preferably, the threshold value is ≥ 60%. In actual operation, the threshold value may fluctuate up or down according to the situation, but shall not be less than 60%.

[0055] Preferably, in S2.1, the types of the attribution scenarios include urban areas, county towns, towns, and rural areas.

[0056] Preferably, in S2.2, a corresponding analysis radius r is set for the study area according to its belonging scenario, specifically: urban area - 300m, county town - 500m, township - 800m, rural area - 1500m.

[0057] Beneficial effects of the present disclosure: Compared with the prior art, the present disclosure starts from the two aspects of fixed and mobile business dimensions and the structural dimension of base station layout. By taking the reference point as the center and extending outward layer by layer with the preset value as the radius, the data such as base station scale, base station business volume, base station access distance, and broadband user volume are established with regional value in a quantifiable corresponding relationship. At the same time, it can intuitively reflect the value concentration degree of the point, and conduct comprehensive analysis and calculation of big data on this basis to accurately obtain the value center point of the communication network area. Compared with the traditional network value prediction method, it abandons the practice of taking base station as a single dimension, adds structural dimension and broadband business dimension, collects and analyzes data at any point in the target area, and presets the scenario differentiation model for the analysis method according to different scenarios, which improves the accuracy and rationality of the prediction results and provides a high-value reference for network streamlining of operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 The present invention provides an overall flow chart of a method for selecting a value center point in a communication network region.

[0060] Figure 2 This is a schematic diagram of the hierarchical structure of the point epitaxy provided by the present invention.

[0061] Figure 3 A schematic diagram of the architecture room determination process provided by the present invention. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure.

[0063] like Figure 1-3 As shown, in order to facilitate the understanding of the above technical solutions of the present disclosure, the above technical solutions of the present disclosure are described in detail below through specific usage methods.

[0064] The method for selecting a value center point of a communication network area disclosed in the present disclosure comprises the following steps:

[0065] S1 Obtain basic information: Obtain basic information of the study area, including computer room information, base station information, and broadband information;

[0066] Base station information includes: base station latitude and longitude information, and the average daily traffic of the base station in the past month;

[0067] Broadband information includes: longitude and latitude information of home broadband communities, longitude and latitude information of government and enterprise buildings with dedicated lines, the number of online users of home broadband communities and the number of online users of government and enterprise dedicated lines in the last day;

[0068] The information of the computer room includes: the longitude and latitude of the computer room, the ownership of the computer room, the area of ​​the computer room, the distance and maximum load of the power supply to the computer room, the transmission resources of the computer room, and whether there are any dangerous sources around the computer room;

[0069] S2 preprocesses basic information: The basic information obtained in S1 is preprocessed as follows:

[0070] S2.1: Determine the attribution scenario of the study area. The attribution scenario refers to the specific area type of the study area (which can be urban area, county town, township, rural area, etc.);

[0071] S2.2: According to the scenario of the study area, set the corresponding analysis radius r (specifically: urban area - 300m, county town - 500m, township - 800m, rural area - 1500m);

[0072] S2.3 Positioning and parameter calculation: A point in the study area is selected as the reference point 0, and then 5 circles are made with the reference point 0 as the center and r, 2r, 3r, 4r, and 5r as the radius, that is, the radiation area of ​​the reference point 0 is divided into 5 layers, and the number of base stations BS in each layer, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base station F, and the sum of broadband users B are counted respectively;

[0073] Then, based on the reference point 0, several points in several relative directions of the reference point 0 are selected as reference points and numbered in sequence. Then, with each of the selected reference points as the center of the circle, five circles are made with reference to the reference point 0 and with the radii of r, 2r, 3r, 4r, and 5r, respectively. The number of base stations BS, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base station F, and the sum of broadband users B are counted for each layer of each reference point.

[0074] Repeat the above method until the new point exceeds the boundary of the study area, indicating that the benchmark point has reached the limit. Then, all benchmark points and their corresponding base station numbers BS, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base station F, and the sum of broadband users B are obtained.

[0075] S2.4: Based on the study area, the belonging scenario and the area of ​​the study area, preliminarily calculate the estimated number of all value centers M, M = the area of ​​the study area / (π*(5r)2) + 1;

[0076] S3: Calculate the business scores and structural scores of all benchmarks obtained in S2 respectively, and finally obtain the comprehensive value score corresponding to each benchmark and sort them;

[0077] S4: According to the benchmark points sorted in S3 and their corresponding comprehensive value scores, determine the value center points one by one until the number of determined value center points reaches the estimated number M;

[0078] S5: According to the matching of each value center point determined in S4 with the computer room, a plan for constructing the computer room is determined, and the matching degree between the computer room and the value center point in the operator's computer room planning plan is used to provide a reference for the operator to determine whether the planning plan is reasonable. S5 can be implemented using existing technology.

[0079] In one embodiment, the specific steps of S2.3 are:

[0080] S2.3.1: Select any point in the study area as reference point 0, whose point number is 0. Take reference point 0 as the center and make 5 circles with radius r, 2r, 3r, 4r, and 5r respectively, and divide the radiation area of ​​reference point 0 into 5 layers; count the number of base stations in each layer: BS (i, j), the average single-station access distance between the base station and the computer room connected to it: D (i, j), the sum of the average daily traffic of the base station: F (i, j), and the sum of broadband users: B (i, j); where i and j correspond to the point number and the number of layers respectively, that is, i=0, 1≦j≦5;

[0081] S2.3.2: First, taking reference point 0 as the basis, four points at a distance r above, below, left, and right of reference point 0 are respectively selected as four reference points, and the point numbers are recorded as 1, 2, 3, and 4, i.e., reference points 1, 2, 3, and 4;

[0082] S2.3.3: Take reference points 1, 2, 3, and 4 as the center of the circle and r, 2r, 3r, 4r, and 5r as the radius to make 5 circles respectively, and divide the radiation area of ​​reference points 1, 2, 3, and 4 into 5 layers; for reference points 1, 2, 3, and 4, respectively count the number of base stations in each layer: BS (i, j), the average access distance between the base station and the uplink room: D (i, j), the sum of the average daily traffic of the base station: F (i, j), and the sum of broadband users: B (i, j); where i is the point number, j is the layer number, 1≦i≦4, 1≦j≦5;

[0083] S2.3.4: Then, based on reference points 1, 2, 3, and 4, four points at a distance r from each other in the upper, lower, left, and right directions of reference points 1, 2, 3, and 4 are respectively selected as four reference points, and duplicate points are eliminated. The obtained points are numbered in sequence as 5, 6, 7, 8, ...K, i.e., reference points 5, 6, 7, 8, ...K;

[0084] S2.3.5: Take reference points 5, 6, 7, 8, ...K as the center of the circle and r, 2r, 3r, 4r, 5r as the radius to make 5 circles respectively, and divide the radiation area of ​​reference points 5, 6, 7, 8, ...K into 5 layers; for reference points 5, 6, 7, 8, ...K, count the number of base stations in each layer: BS (i, j), the average access distance between the base station and the uplink room: D (i, j), the sum of the average daily traffic of the base station: F (i, j), and the sum of broadband users: B (i, j); where i is the point number, j is the layer number, 5≦i≦K, 1≦j≦5;

[0085] S2.3.6: Finally, based on points 5, 6, 7, 8, ...K, refer to S2.3.4~S2.3.5 for iteration until the newly determined points exceed the boundary of the study area. The point numbers of all the determined benchmark points are recorded as {0, 1, 2, ...N}, where N is the largest point number;

[0086] S2.3.7: According to the four dimensions of the number of base stations BS, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base stations F, and the sum of broadband users B, the data of the benchmark points {0, 1, 2, ...N} are normalized to obtain the base station density score BSS (i, j), base station access score DS (i, j), base station service score FS (i, j), and broadband service score BS (i, j) of each layer of each benchmark point; where i is the point number, j is the layer number, 0≦i≦N, 1≦j≦5.

[0087] In one embodiment, in S2.3.7, for the data of the base station access score DS (i, j), due to the difference between the data of "the average single-station access distance D between the base station and the computer room connected to it" and other three-dimensional data, the data is inversely calculated and then normalized.

[0088] In one embodiment, the specific steps of S3 are:

[0089] S3.1: traverse all reference points 0, 1, 2, 3...N, initialize i=0, that is, select reference point 0, and then continue S3.2;

[0090] S3.2: Calculate the business score YW(i), structure score JG(i), and comprehensive value score V(i) of benchmark point i, specifically according to the following formula;

[0091] YW(i)=∑FS(i,j)*k(j)+∑BS(i,j)*k(j); (Formula 1);

[0092] JG(i) = ∑BSS(i, j)*k(j)+ ∑DS(i, j)*k(j); (Formula 2);

[0093] V(i) = YW(i) + JG(i); (Formula 3);

[0094] In the above formula: i is the point number of the current reference point, 0≦i≦N; j is the layer number of the reference point, 1≦j≦5;

[0095] YW(i)--business score of benchmark point i;

[0096] JG(i) - structural score of benchmark point i;

[0097] V(i) – the comprehensive value score of benchmark point i;

[0098] k(j)--weight adjustment coefficient, representing the weight of each layer from 1 to 5, with values ​​of 1, 0.8, 0.6, 0.4, and 0.2 respectively, used to calculate the business score corresponding to each point;

[0099] S3.3: If i≧N, it means that all reference points have been traversed, and then continue to S3.4; otherwise, increase the value of i by 1, that is, select the next reference point in order, and return to S3.2;

[0100] S3.4: Finally, sort all benchmarks in descending order of their comprehensive value scores.

[0101] In one embodiment, the specific steps of S4 are:

[0102] S4.1: Select the benchmark point with the largest comprehensive value score and record it as value center point 1;

[0103] S4.2: Value center point 1 is recorded as the current value center point T, where T is the point number of the current value center point. T is initialized to 1, indicating that the iteration starts from value center point 1;

[0104] S4.3: Calculate the distance between the current value center point T and other reference points, and remove other reference points whose distance from the current value center point T is less than 5r;

[0105] S4.4: Increase the value of T by 1, and select the one with the largest comprehensive value score from the remaining benchmark points as the value center point T, and indicate that the current value center point T has also been iterated;

[0106] S4.5: If T≧M, it means that the number of selected value centers has reached the estimated number M, and the iteration should end and go to S4.6; otherwise, it means that the iteration should continue and return to S4.3;

[0107] S4.6: Save and record the selected value center points 1, 2...T.

[0108] In one embodiment, the specific steps of S5 are:

[0109] S5.1: Select all existing computer rooms in the study area as the basis for selecting architecture computer rooms;

[0110] S5.2: Comprehensively evaluate all existing equipment rooms in terms of property rights, space, power support, transmission resources, etc., and reject those that do not meet the requirements.

[0111] S5.3: Match the distance between the existing computer room that meets the preliminary judgment conditions and each value center point selected in S4. If the distance between the existing computer room and a value center point is less than r, the judgment is reasonable;

[0112] S5.4: Preset a threshold value based on the actual situation of the study area. If the proportion of reasonable computer rooms in the planning scheme is higher than the threshold value, then the planning scheme of the computer room architecture is reasonable; if the proportion is lower than the threshold value, then the planning scheme of the computer room architecture is unreasonable, and it is necessary to refer to the value center points to adjust the location of the existing computer room.

[0113] In one embodiment, the threshold value is ≥ 60%. In actual operation, the threshold value may fluctuate up or down according to the situation, but shall not be less than 60%.

[0114] In one embodiment, in S2.1, the types of the belonging scenarios include urban areas, county towns, towns, and rural areas.

[0115] In a certain embodiment, in S2.2, a corresponding analysis radius r is set for the study area according to the belonging scenario, specifically: urban area - 300m, county town - 500m, township - 800m, rural area - 1500m.

[0116] In summary, through the unique design of the present disclosure, compared with the prior art, the present disclosure starts from the two aspects of fixed and mobile business dimensions, and the structural dimension of base station layout, and establishes a quantifiable correspondence between data such as base station scale, base station business volume, base station access distance, and broadband user volume and regional value by taking the reference point as the center and extending outward layer by layer with the preset value as the radius. At the same time, it can intuitively reflect the value concentration degree of the point, and conduct comprehensive analysis and calculation of big data on this basis to accurately obtain the value center point of the communication network area. Compared with the traditional network value prediction method, it abandons the practice of taking the base station as a single dimension, adds the structural dimension and broadband business dimension, collects and analyzes data at any point in the target area, and presets the scenario differentiation model for the analysis method according to different scenarios, which improves the accuracy and rationality of the prediction results and provides a high-value reference for the network streamlining of operators.

[0117] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for selecting a value center point of a communication network region, characterized in that: The following steps are involved: S1 Obtain basic information: Obtain basic information of the study area, including computer room information, base station information, and broadband information; Base station information includes: base station latitude and longitude information, and the average daily traffic of the base station in the past month; Broadband information includes: longitude and latitude information of home broadband communities, longitude and latitude information of government and enterprise buildings with dedicated lines, the number of online users of home broadband communities and the number of online users of government and enterprise dedicated lines in the last day; The information of the computer room includes: the longitude and latitude of the computer room, the ownership of the computer room, the area of ​​the computer room, the distance and maximum load of the power supply to the computer room, the transmission resources of the computer room, and whether there are any dangerous sources around the computer room; S2 preprocesses basic information: The basic information obtained in S1 is preprocessed as follows: S2.1: Determine the attribution scenario of the study area, where the attribution scenario represents the specific area type of the study area; S2.2: According to the belonging scenario of the study area, set the corresponding analysis radius r for it; S2.3 Positioning and parameter calculation: A point in the study area is selected as the reference point 0, and then 5 circles are made with the reference point 0 as the center and r, 2r, 3r, 4r, and 5r as the radius, that is, the radiation area of ​​the reference point 0 is divided into 5 layers, and the number of base stations BS in each layer, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base station F, and the sum of broadband users B are counted respectively; Then, based on the reference point 0, several points in several relative directions of the reference point 0 are selected as reference points and numbered in sequence. Then, with each of the selected reference points as the center of the circle, five circles are made with reference to the reference point 0 and with the radii of r, 2r, 3r, 4r, and 5r, respectively. The number of base stations BS, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base station F, and the sum of broadband users B are counted for each layer of each reference point. Repeat the above method until the new point exceeds the boundary of the study area, indicating that the benchmark point has reached the limit. Then, all benchmark points and their corresponding base station numbers BS, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base station F, and the sum of broadband users B are obtained. The specific steps of S2.3 are: S2.3.1: Select any point in the study area as reference point 0, whose point number is 0. Take reference point 0 as the center and make 5 circles with radius r, 2r, 3r, 4r, and 5r respectively, and divide the radiation area of ​​reference point 0 into 5 layers; count the number of base stations in each layer: BS (i, j), the average single-station access distance between the base station and the computer room connected to it: D (i, j), the sum of the average daily traffic of the base station: F (i, j), and the sum of broadband users: B (i, j); where i and j correspond to the point number and the number of layers respectively, that is, i=0, 1≦j≦5; S2.3.2: First, taking reference point 0 as the basis, four points at a distance r above, below, left, and right of reference point 0 are respectively selected as four reference points, and the point numbers are recorded as 1, 2, 3, and 4, i.e., reference points 1, 2, 3, and 4; S2.3.3: Take reference points 1, 2, 3, and 4 as the center of the circle and r, 2r, 3r, 4r, and 5r as the radius to make 5 circles respectively, and divide the radiation area of ​​reference points 1, 2, 3, and 4 into 5 layers; for reference points 1, 2, 3, and 4, respectively count the number of base stations in each layer: BS (i, j), the average access distance between the base station and the uplink room: D (i, j), the sum of the average daily traffic of the base station: F (i, j), and the sum of broadband users: B (i, j); where i is the point number, j is the layer number, 1≦i≦4, 1≦j≦5; S2.3.4: Then, based on reference points 1, 2, 3, and 4, four points at a distance r from each other in the upper, lower, left, and right directions of reference points 1, 2, 3, and 4 are respectively selected as four reference points, and duplicate points are eliminated. The obtained points are numbered in sequence as 5, 6, 7, 8, ...K, i.e., reference points 5, 6, 7, 8, ...K; S2.3.5: Take reference points 5, 6, 7, 8, ...K as the center of the circle and r, 2r, 3r, 4r, 5r as the radius to make 5 circles respectively, and divide the radiation area of ​​reference points 5, 6, 7, 8, ...K into 5 layers; for reference points 5, 6, 7, 8, ...K, count the number of base stations in each layer: BS (i, j), the average access distance between the base station and the uplink room: D (i, j), the sum of the average daily traffic of the base station: F (i, j), and the sum of broadband users: B (i, j); where i is the point number, j is the layer number, 5≦i≦K, 1≦j≦5; S2.3.6: Finally, based on points 5, 6, 7, 8, ...K, refer to S2.3.4~S2.3.5 for iteration until the newly determined points exceed the boundary of the study area. The point numbers of all the determined benchmark points are recorded as {0, 1, 2, ...N}, where N is the largest point number; S2.3.7: According to the four dimensions of the number of base stations BS, the average single-station access distance D between the base station and the computer room connected to it, the sum of the average daily traffic of the base station F, and the sum of the broadband users B, the data of the benchmark points {0, 1, 2, ... N} are normalized respectively to obtain the base station density score BSS (i, j), base station access score DS (i, j), base station service score FS (i, j), and broadband service score BS (i, j) of each layer of each benchmark point; where i is the point number, j is the layer number, 0≦i≦N, 1≦j≦5; S2.4: Based on the research area, the belonging scenario and the area of ​​the research area, preliminarily calculate the estimated number of all value centers M, M = the area of ​​the research area / (π*(5r) 2 )+1; S3: Calculate the business scores and structural scores of all benchmarks obtained in S2 respectively, and finally obtain the comprehensive value score corresponding to each benchmark and sort them; The specific steps of S3 are: S3.1: traverse all reference points 0, 1, 2, 3...N, initialize i=0, that is, select reference point 0, and then continue S3.2; S3.2: Calculate the business score YW(i), structure score JG(i), and comprehensive value score V(i) of benchmark point i, specifically according to the following formula; YW(i)=∑FS(i,j)*k(j)+∑BS(i,j)*k(j); (Formula 1); JG(i) = ∑BSS(i, j)*k(j)+ ∑DS(i, j)*k(j); (Formula 2); V(i) = YW(i) + JG(i); (Formula 3); In the above formula: i is the point number of the current reference point, 0≦i≦N; j is the layer number of the reference point, 1≦j≦5; YW(i)--business score of benchmark point i; JG(i) - structural score of benchmark point i; V(i) – the comprehensive value score of benchmark point i; k(j)--weight adjustment coefficient, representing the weight of each layer from 1 to 5, with values ​​of 1, 0.8, 0.6, 0.4, and 0.2 respectively, used to calculate the business score corresponding to each point; S3.3: If i≧N, it means that all reference points have been traversed, and then continue to S3.4; otherwise, increase the value of i by 1, that is, select the next reference point in order, and return to S3.2; S3.4: Finally, sort all benchmarks in descending order of their comprehensive value scores; S4: According to the benchmark points sorted in S3 and their corresponding comprehensive value scores, determine the value center points one by one until the number of determined value center points reaches the estimated number M; S5: According to the matching of each value center point determined in S4 with the computer room, determine the plan for the computer room structure, and provide a reference for the operator to determine whether the planning plan is reasonable through the matching degree between the computer room structure and the value center point in the operator's computer room structure planning plan.

2. The selection method according to claim 1, characterized in that: In S2.3.7, the data of the base station access score DS (i, j) is reciprocated and then normalized.

3. The selection method according to claim 1, characterized in that: The specific steps of S4 are: S4.1: Select the benchmark point with the largest comprehensive value score and record it as value center point 1; S4.2: Value center point 1 is recorded as the current value center point T, where T is the point number of the current value center point. T is initialized to 1, indicating that the iteration starts from value center point 1; S4.3: Calculate the distance between the current value center point T and other reference points, and remove other reference points whose distance from the current value center point T is less than 5r; S4.4: Increase the value of T by 1, and select the one with the largest comprehensive value score from the remaining benchmark points as the value center point T, and indicate that the current value center point T has also been iterated; S4.5: If T≧M, it means that the number of selected value centers has reached the estimated number M, and the iteration should end and go to S4.6; otherwise, it means that the iteration should continue and return to S4.3; S4.6: Save and record the selected value center points 1, 2...T.

4. The selection method according to claim 3, characterized in that: The specific steps of S5 are: S5.1: Select all existing computer rooms in the study area as the basis for selecting architecture computer rooms; S5.2: Comprehensively assess the property rights, space, power support, and transmission resources of all existing equipment rooms, and directly reject those that do not meet the requirements; S5.3: Match the distance between the existing computer room that meets the preliminary judgment conditions and each value center point selected in S4. If the distance between the existing computer room and a value center point is less than r, the judgment is reasonable; S5.4: Preset a threshold value based on the actual situation of the study area. If the proportion of reasonable computer rooms in the planning scheme is higher than the threshold value, then the planning scheme of the computer room architecture is reasonable; if the proportion is lower than the threshold value, then the planning scheme of the computer room architecture is unreasonable, and it is necessary to refer to the value center points to adjust the location of the existing computer room.

5. The selection method according to claim 4, characterized in that: The threshold value is ≥ 60%.

6. The selection method according to claim 1, characterized in that: In S2.1, the types of the belonging scenarios include urban areas, county towns, towns, and rural areas.

7. The selection method according to claim 6, characterized in that: In S2.2, the corresponding analysis radius r is set according to the belonging scenario of the study area, specifically: urban area - 300m, county town - 500m, township - 800m, rural area - 1500m.

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