A base station site selection method, system, device and storage medium

Abstract

The application discloses a base station site selection method, system, device and storage medium. The base station site selection method comprises the following steps: calculating a 5G user migration probability based on a grid user density, a grid reference signal receiving power, a cell traffic and user information; calculating a 5G user activity index based on the grid user density, the grid reference signal receiving power, the cell traffic and the user information; calculating a competition demand imbalance index value based on a first user occupancy rate, a first grid 5G coverage rate, a second user occupancy rate and a second grid 5G coverage rate; calculating a user dissatisfaction imbalance index value, a competition synergy coefficient value, an active demand compensation factor and a migration coverage gap ratio based on the 5G user migration probability, the 5G user activity index and the competition demand imbalance index value, so as to calculate a closeness score; and performing base station site selection based on the closeness score, thereby improving the efficiency and resource utilization of the base station site selection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of base station site selection, and in particular to a base station site selection method, system, device and storage medium. BACKGROUND

[0002] With the large-scale commercialization of 5G networks and the advance layout of 6G technology, mobile communication systems are continuously evolving towards ultra-dense networking and intelligent collaboration. In the traditional base station site selection process, simulation prediction is usually carried out based on a wireless propagation model, and combined with wireless quality indicators, historical traffic volume peaks and user complaint heat maps collected by the operator system, parameters such as static coverage radius and capacity threshold are manually set to complete site planning. In the 4G low-frequency network environment, this method can still meet the corresponding requirements with experience rules.

[0003] Currently, when facing complex scenarios such as 5G millimeter wave high-frequency band penetration loss increase and 6G non-ground network three-dimensional networking, the current base station site selection method mainly relies on the wireless network key indicators and complaint data of the operator, which can only reflect the network status of the operator, and lacks integration of competitor data. Moreover, the current base station site selection method relies too much on past experience and lacks dynamic analysis in the time dimension. SUMMARY

[0004] The present application aims to at least solve the technical problems existing in the prior art. To this end, the present application proposes a base station site selection method, system, device and storage medium, which can integrate multi-modal data, realize objective sorting and real-time adjustment of base station site selection, and improve the efficiency and resource utilization of base station site selection.

[0005] In a first aspect of the present application, a base station site selection method is provided, comprising the following steps:

[0006] Obtaining first grid data and second grid data of a target area, wherein the first grid data includes grid user density, grid reference signal received power, cell traffic, first user occupancy rate and first grid 5G coverage rate of a base station site selection operator, and the second grid data includes second user occupancy rate and second grid 5G coverage rate of other competing operators;

[0007] Calculating 5G user migration probability based on the grid user density, grid reference signal received power and cell traffic; calculating 5G user activity index based on the grid user density, grid reference signal received power and cell traffic;

[0008] Calculating a competition demand imbalance index value based on the first user occupancy rate, the first grid 5G coverage rate, the second user occupancy rate and the second grid 5G coverage rate;

[0009] calculating a user dissatisfaction imbalance index value based on the 5G user migration probability and the competition demand imbalance index value; calculating a competition synergy coefficient value based on the 5G user migration probability and the competition demand imbalance index value; calculating an active demand compensation factor based on the 5G user activity index and the competition demand imbalance index value; and calculating a migration coverage gap ratio value based on the 5G user migration probability and the competition demand imbalance index value;

[0010] calculating a closeness score based on the user dissatisfaction imbalance index value, the competition synergy coefficient value, the active demand compensation factor, and the migration coverage gap ratio value; and performing base station site selection based on the closeness score.

[0011] According to the control method, at least the following beneficial effects are achieved:

[0012] The method obtains first grid data and second grid data of a target area, wherein the first grid data includes grid user density, grid reference signal received power, cell traffic, first user occupancy rate, and first grid 5G coverage rate of a base station site selection operator, and the second grid data includes second user occupancy rate and second grid 5G coverage rate of other competing operators. The method improves data accuracy and diversity by fusing multiple data sources. The 5G user migration probability is calculated based on grid user density, grid reference signal received power, and cell traffic. The 5G user activity index is calculated based on grid user density, grid reference signal received power, and cell traffic. The competition demand imbalance index value is calculated based on first user occupancy rate, first grid 5G coverage rate, second user occupancy rate, and second grid 5G coverage rate. The user dissatisfaction imbalance index value is calculated based on 5G user migration probability and competition demand imbalance index value. The competition synergy coefficient value is calculated based on 5G user migration probability and competition demand imbalance index value. The active demand compensation factor is calculated based on 5G user activity index and competition demand imbalance index value. The migration coverage gap ratio value is calculated based on 5G user migration probability and competition demand imbalance index value. The method realizes objective ranking and real-time adjustment of base station site selection. The closeness score is calculated based on user dissatisfaction imbalance index value, competition synergy coefficient value, active demand compensation factor, and migration coverage gap ratio value. Base station site selection is performed based on the closeness score, improving the efficiency and resource utilization rate of base station site selection.

[0013] According to some embodiments of the present application, the 5G user migration probability is calculated based on the grid user density, the grid reference signal received power, and the cell traffic, including:

[0014] obtaining a historical user 5G migration ratio and a grid 5G coverage rate;

[0015] calculating a cell grid weight based on the grid user density and the grid reference signal received power by the following formula:

[0016]

[0017] in, For the first Cell grid weight of the c-th cell in the nth grid To preset the receive power factor, To preset the user density coefficient, For the first Normalized grid reference signal received power of each grid. For the first The received power of the grid reference signal for each grid cell. For the first Raster user density per grid cell Let c be the set of all grid cells covered by the c-th cell. For the first Raster user density per grid cell For the first Normalized grid reference signal received power of each grid. For the first The received power of the grid reference signal for each grid;

[0018] The grid flow is calculated based on the cell grid weight and the cell flow using the following formula:

[0019]

[0020] in, For the first The grid traffic of the c-th cell in the grid. Let c be the cell traffic of the c-th cell;

[0021] The historical user 5G migration ratio, the grid 5G coverage, and the grid traffic are input into a trained LSTM-Transformer model for prediction to obtain the 5G user migration probability. The grid traffic includes grid 5G traffic and grid 4G traffic.

[0022] According to some embodiments of this application, the calculation of the 5G user activity index based on the grid user density, grid reference signal received power, and cell traffic includes:

[0023] The historical user 5G migration ratio, the grid 5G coverage rate and the grid traffic are input into the trained LSTM-Transformer model for prediction to obtain the original value of grid 5G user activity.

[0024] The 5G user activity index is calculated based on the original 5G user activity value of the grid using the following formula:

[0025]

[0026] wherein, is a 5G user activity index, is a grid 5G user activity raw value, is a minimum value in the grid 5G user activity raw value, is a maximum value in the grid 5G user activity raw value.

[0027] According to some embodiments of the present application, the calculating the competition demand imbalance index value based on the first user occupancy rate, the first grid 5G coverage rate, the second user occupancy rate and the second grid 5G coverage rate comprises:

[0028] obtaining a user reporting times and a user number of a base station site selection operator;

[0029] calculating a grid active demand value based on the user reporting times and the user number;

[0030] calculating a grid competition coverage gap value based on the first grid 5G coverage rate and the second grid 5G coverage rate;

[0031] calculating a grid competition user gap value based on the first user occupancy rate and the second user occupancy rate;

[0032] calculating the coverage gap by the following formula:

[0033]

[0034] wherein, is a coverage gap, is a grid competition coverage gap value, is a minimum value in the grid competition coverage gap value, is a maximum value in the grid competition coverage gap value;

[0035] calculating the user gap by the following formula:

[0036]

[0037] wherein, is a user gap, is a maximum value in the grid competition user gap value, is a minimum value in the grid competition user gap value, is a grid competition user gap value;

[0038] calculating the active demand degree by the following formula:

[0039]

[0040] wherein, is an active demand degree, is a grid active demand value, is a minimum value in the grid active demand value, is a maximum value in the grid active demand value;

[0041] The coverage gap weight value is calculated by the following formula:

[0042]

[0043] wherein, is a coverage gap weight value;

[0044] The user gap weight value is calculated based on the grid active demand value by the following formula:

[0045]

[0046] wherein, is a user gap weight value;

[0047] The active demand degree weight value is calculated by the following formula:

[0048]

[0049] wherein, is an active demand degree weight value;

[0050] The competitive demand imbalance index value is calculated based on the coverage gap, the user gap, the active demand degree, the coverage gap weight value, the user gap weight value and the active demand degree weight value by the following formula:

[0051]

[0052]

[0053] wherein, is a competitive demand imbalance index value, is a synergy penalty term.

[0054] According to some embodiments of the present application, the user dissatisfaction imbalance index value is calculated based on the 5G user migration probability and the competitive demand imbalance index value; a competitive synergy coefficient value is calculated based on the 5G user migration probability and the competitive demand imbalance index value; an active demand compensation factor is calculated based on the 5G user activity index and the competitive demand imbalance index value; a migration coverage gap ratio value is calculated based on the 5G user migration probability and the competitive demand imbalance index value, comprising:

[0055] Obtaining a grid user complaint frequency;

[0056] Calculating the user dissatisfaction imbalance index value based on the grid user complaint frequency, the 5G user migration probability and the competition demand imbalance index value by the following formula:

[0057]

[0058] wherein, is the user dissatisfaction imbalance index value, is the grid user complaint frequency, is the 5G user migration probability;

[0059] Calculating the competition synergy coefficient value based on the 5G user migration probability and the competition demand imbalance index value by the following formula:

[0060]

[0061] wherein, is the competition synergy coefficient value;

[0062] Calculating the active demand compensation factor based on the 5G user activity index and the competition demand imbalance index value by the following formula:

[0063]

[0064] wherein, is the active demand compensation factor, is a preset denominator coefficient;

[0065] Calculating the migration coverage gap ratio value based on the 5G user migration probability and the competition demand imbalance index value by the following formula:

[0066]

[0067] wherein, is the migration coverage gap ratio value.

[0068] According to some embodiments of the present application, the closeness score is calculated based on the user dissatisfaction imbalance index value, the competition synergy coefficient value, the active demand compensation factor and the migration coverage gap ratio value; and the base station site selection is performed based on the closeness score, comprising:

[0069] Data normalizing the user dissatisfaction imbalance index value by the following formula to obtain a first index:

[0070]

[0071] wherein, is the first index, The minimum value among the user dissatisfaction imbalance index values. The maximum value among the user dissatisfaction imbalance index values;

[0072] The second indicator is obtained by standardizing the competitive synergy coefficient value using the following formula:

[0073]

[0074] Where I is the total number of grid cells. Let i be the competition and cooperation coefficient value of the i-th grid. The average value of the competition and collaboration coefficient. The standard deviation of the competition and synergy coefficient value. This is the second indicator;

[0075] The active demand compensation factor is standardized using the following formula to obtain the third indicator:

[0076]

[0077] in, This is the third indicator;

[0078] The migration coverage gap ratio is normalized using the following formula to obtain the fourth indicator:

[0079]

[0080] in, This is the fourth indicator;

[0081] Based on the first indicator, the second indicator, the third indicator, and the fourth indicator, the corresponding weight of each indicator is calculated using the following formula:

[0082]

[0083] in, The weight of the j-th indicator in the i-th grid. This represents the j-th indicator of the i-th grid. When j equals 1, it corresponds to the first indicator; when j equals 2, it corresponds to the second indicator; when j equals 3, it corresponds to the third indicator; and when j equals 4, it corresponds to the fourth indicator.

[0084] Based on the weight of the aforementioned indicator, the information entropy corresponding to the indicator is calculated using the following formula:

[0085]

[0086] in, Let be the information entropy corresponding to the j-th indicator;

[0087] The index corresponding weight is calculated based on the index corresponding information entropy by the following formula:

[0088]

[0089] wherein, is the index weight of the jth index, and J is the total number of indexes;

[0090] The closeness score is calculated based on the first index, the second index, the third index, the fourth index, and the index corresponding weight; and base station site selection is performed based on the closeness score.

[0091] According to some embodiments of the present application, the closeness score is calculated based on the first index, the second index, the third index, the fourth index, and the index corresponding weight; and base station site selection is performed based on the closeness score, including:

[0092] The corresponding index ideal value is calculated based on the first index, the second index, the third index, the fourth index, and the index corresponding weight by the following formula:

[0093]

[0094] wherein, is the corresponding index ideal value of the jth index of the ith grid;

[0095] The maximum value of the first index of the ith grid, the maximum value of the second index of the ith grid, and the maximum value of the fourth index of the ith grid are obtained, and the three maximum values are taken as the positive index ideal solution;

[0096] The positive distance is calculated based on the positive index ideal solution by the following formula:

[0097]

[0098] wherein, is the positive distance of the ith grid, is the jth positive index ideal solution;

[0099] The minimum value of the third index of the ith grid is obtained, and is taken as the negative index ideal solution;

[0100] The negative distance is calculated based on the negative index ideal solution by the following formula:

[0101]

[0102] wherein, is the negative distance of the ith grid, is the ideal solution of the jth negative index;

[0103] The closeness score is calculated based on the positive distance and the negative distance by the following formula:

[0104]

[0105] wherein, is the closeness score of the ith grid;

[0106] The closeness scores are arranged in descending order to obtain a candidate base station deployment priority list;

[0107] Base station site selection is performed based on the candidate base station deployment priority list.

[0108] In a second aspect of the present application, a base station site selection system is provided, which comprises:

[0109] A data acquisition module is configured to acquire first grid data and second grid data of a target area, wherein the first grid data comprises grid user density, grid reference signal received power, cell traffic, first user occupancy rate and first grid 5G coverage rate of a base station site selection operator, and the second grid data comprises second user occupancy rate and second grid 5G coverage rate of other competing operators;

[0110] A 5G user data calculation module is configured to calculate 5G user migration probability based on the grid user density, the grid reference signal received power and the cell traffic, and to calculate 5G user activity index based on the grid user density, the grid reference signal received power and the cell traffic;

[0111] A competition demand imbalance index value calculation module is configured to calculate competition demand imbalance index value based on the first user occupancy rate, the first grid 5G coverage rate, the second user occupancy rate and the second grid 5G coverage rate;

[0112] An index calculation module is configured to calculate user dissatisfaction imbalance index value based on the 5G user migration probability and the competition demand imbalance index value, to calculate competition synergy coefficient value based on the 5G user migration probability and the competition demand imbalance index value, to calculate active demand compensation factor based on the 5G user activity index and the competition demand imbalance index value, and to calculate migration coverage gap ratio based on the 5G user migration probability and the competition demand imbalance index value;

[0113] A base station site selection module is configured to calculate closeness score based on the user dissatisfaction imbalance index value, the competition synergy coefficient value, the active demand compensation factor and the migration coverage gap ratio, and to perform base station site selection based on the closeness score.

[0114] The system obtains first grid data and second grid data of a target area, wherein the first grid data includes grid user density, grid reference signal receiving power, cell traffic, first user occupancy rate and first grid 5G coverage rate of a base station site selection operator, and the second grid data includes second user occupancy rate and second grid 5G coverage rate of other competing operators. The application improves data accuracy and diversity by fusing multi-source data, calculates 5G user migration probability based on grid user density, grid reference signal receiving power and cell traffic, calculates 5G user activity index based on grid user density, grid reference signal receiving power and cell traffic, calculates competition demand imbalance index value based on first user occupancy rate, first grid 5G coverage rate, second user occupancy rate and second grid 5G coverage rate, calculates user dissatisfaction imbalance index value based on 5G user migration probability and competition demand imbalance index value, calculates competition synergy coefficient value based on 5G user migration probability and competition demand imbalance index value, calculates active demand compensation factor based on 5G user activity index and competition demand imbalance index value, and calculates migration coverage gap ratio based on 5G user migration probability and competition demand imbalance index value. The application realizes objective sorting and real-time adjustment of base station site selection, calculates closeness score based on user dissatisfaction imbalance index value, competition synergy coefficient value, active demand compensation factor and migration coverage gap ratio, and performs base station site selection based on the closeness score, thereby improving the efficiency and resource utilization rate of base station site selection.

[0115] In a third aspect, the application provides a base station site selection electronic device, comprising at least one control processor and a memory connected in communication with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to perform the base station site selection method described above.

[0116] In a fourth aspect, the application provides a computer-readable storage medium, which stores computer-executable instructions for causing a computer to perform the base station site selection method described above.

[0117] It should be noted that the beneficial effects of the second to fourth aspects of the application and the prior art are the same as those of the above-mentioned base station site selection system and the prior art, which will not be described here.

[0118] Additional aspects and advantages of the application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0119] The above and / or additional aspects and advantages of the application will become apparent and be readily appreciated from the following description, including the appended drawings, wherein:

[0120] Figure 1 is a flow chart of a base station site selection method according to an embodiment of the present application;

[0121] Figure 2 is a structural schematic diagram of an embodiment of a base station site selection system provided by the present application;

[0122] Figure 3 is a structural schematic diagram of an embodiment of an electronic device provided by the present application. DETAILED DESCRIPTION

[0123] The embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary only, and are only used to explain the present application, and cannot be understood as a limitation of the present application.

[0124] In the description of the present application, if there is a description to first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the sequence of indicated technical features.

[0125] In the description of the present application, it is to be understood that the orientation description, such as up, down, etc., indicates the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element indicated must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present application.

[0126] In the description of the present application, it is to be noted that, unless otherwise explicitly limited, the words such as setting, installing, connecting, etc. should be broadly understood, and the person skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical solution.

[0127] With the large-scale commercialization of 5G networks and the advance layout of 6G technology, mobile communication systems are continuously evolving towards ultra-dense networking and intelligentized collaboration. In the traditional base station site selection process, simulation prediction is usually carried out according to the wireless propagation model, and combined with the wireless quality indicators collected by the operator system, the historical traffic peak and the user complaint heat map, the station planning is completed by manually setting parameters such as static coverage radius and capacity threshold. In the 4G low frequency network environment, this method can still meet the corresponding requirements with experience rules.

[0128] Currently, when facing the complex scenarios of 5G millimeter wave high frequency band penetration loss increasing sharply and 6G non-ground network three-dimensional networking, the current base station site selection method mainly relies on the wireless network key indicators and complaint data of the operator, which can only reflect the network status of the operator, and lacks the integration of the competitive data of the friend operator. Moreover, the current base station site selection method excessively relies on past experience and lacks dynamic analysis in the time dimension.

[0129] To solve the above technical defects, the embodiments of the present application provide a base station site selection method, system, device and storage medium.

[0130] Please refer to Figure 1 , which is a flowchart of a base station site selection method provided by the embodiments of the present application. The method is applied to an electronic device, which can be a server or the like. As shown in Figure 1 , the base station site selection method comprises:

[0131] Step S101, acquiring first grid data and second grid data of a target area, wherein the first grid data comprises grid user density, grid reference signal received power, cell traffic, first user occupancy rate and first grid 5G coverage rate of a base station site selection operator, and the second grid data comprises second user occupancy rate and second grid 5G coverage rate of other competitive operators;

[0132] Step S102, calculating 5G user migration probability based on grid user density, grid reference signal received power and cell traffic; calculating 5G user activity index based on grid user density, grid reference signal received power and cell traffic;

[0133] Step S103, calculating competitive demand imbalance index value based on first user occupancy rate, first grid 5G coverage rate, second user occupancy rate and second grid 5G coverage rate;

[0134] Step S104, calculating user dissatisfaction imbalance index value based on 5G user migration probability and competitive demand imbalance index value; calculating competitive synergy coefficient value based on 5G user migration probability and competitive demand imbalance index value; calculating active demand compensation factor based on 5G user activity index and competitive demand imbalance index value; calculating migration coverage gap ratio based on 5G user migration probability and competitive demand imbalance index value;

[0135] Step S105, calculating closeness score based on user dissatisfaction imbalance index value, competitive synergy coefficient value, active demand compensation factor and migration coverage gap ratio; and performing base station site selection based on the closeness score.

[0136] Specifically, the first grid data and the second grid data are derived from operator OMC data and third party OTT data.

[0137] This method acquires first and second grid data of the target area. The first grid data includes grid user density, grid reference signal received power, cell traffic, first user occupancy rate, and first grid 5G coverage rate of the base station site selection operator. The second grid data includes the second user occupancy rate and second grid 5G coverage rate of other competing operators. This application improves data accuracy and diversity by fusing multi-source data. It calculates the 5G user migration probability based on grid user density, grid reference signal received power, and cell traffic; it calculates the 5G user activity index based on grid user density, grid reference signal received power, and cell traffic; and it calculates the 5G user activity index based on the first user occupancy rate, the first grid 5G coverage rate, and the second user occupancy rate. The second grid 5G coverage rate calculates the competition demand imbalance index value, and the user dissatisfaction imbalance index value is calculated based on the 5G user migration probability and the competition demand imbalance index value. The competition coordination coefficient value is calculated based on the 5G user migration probability and the competition demand imbalance index value. The active demand compensation factor is calculated based on the 5G user activity index and the competition demand imbalance index value. The migration coverage gap ratio is calculated based on the 5G user migration probability and the competition demand imbalance index value. This application realizes objective ranking and real-time adjustment of base station site selection, calculates proximity score based on the user dissatisfaction imbalance index value, the competition coordination coefficient value, the active demand compensation factor, and the migration coverage gap ratio; and performs base station site selection based on the proximity score, improving the efficiency and resource utilization of base station site selection.

[0138] In some embodiments, the 5G user migration probability is calculated based on grid user density, grid reference signal received power, and cell traffic, including:

[0139] Step S201: Obtain the historical user 5G migration ratio and grid 5G coverage;

[0140] Step S202: Calculate the cell grid weight based on the grid user density and the grid reference signal received power using the following formula:

[0141]

[0142] in, For the first Cell grid weight of the c-th cell in the nth grid To preset the receive power factor, To preset the user density coefficient, For the first Normalized grid reference signal received power of each grid. For the first The received power of the grid reference signal for each grid cell. For the first Raster user density per grid cell Let c be the set of all grid cells covered by the c-th cell. For the first Raster user density per grid cell For the first Normalized grid reference signal received power of each grid. For the first The received power of the grid reference signal for each grid;

[0143] Step S203: Calculate the grid traffic based on the cell grid weight and cell traffic using the following formula:

[0144]

[0145] in, For the first The grid traffic of the c-th cell in the grid. Let c be the cell traffic of the c-th cell;

[0146] Step S204: Input the historical user 5G migration ratio, grid 5G coverage and grid traffic into the trained LSTM-Transformer model for prediction to obtain the 5G user migration probability. Among them, grid traffic includes grid 5G traffic and grid 4G traffic.

[0147] This application implements a joint weighting rule for user density and RSRP from the cell level to the grid level. This framework can effectively break down barriers between different data sources, providing comprehensive and accurate data support for global decision-making.

[0148] In some embodiments, the 5G user activity index is calculated based on grid user density, grid reference signal received power, and cell traffic, including:

[0149] Step S301: Input the historical user 5G migration ratio, grid 5G coverage and grid traffic into the trained LSTM-Transformer model for prediction to obtain the original value of grid 5G user activity.

[0150] Step S302: Calculate the 5G user activity index based on the original value of 5G user activity in the grid using the following formula:

[0151]

[0152] in, The 5G user activity index This represents the original value of 5G user activity in the grid. This is the minimum value among the original values ​​of 5G user activity in the grid. This is the maximum value among the original values ​​of 5G user activity in the grid.

[0153] The application calculates the 5G user activity index in real time, so as to closely match the dynamic changes of the actual network, and make the decision always in the optimal state.

[0154] In some embodiments, the competitive demand imbalance index value is calculated based on the first user occupancy rate, the first grid 5G coverage rate, the second user occupancy rate and the second grid 5G coverage rate, including:

[0155] Step S401, obtaining the user reporting times and the number of users of the base station site operator;

[0156] Step S402, calculating the grid active demand value based on the user reporting times and the number of users;

[0157] Step S403, calculating the grid competitive coverage gap value based on the first grid 5G coverage rate and the second grid 5G coverage rate;

[0158] Step S404, calculating the grid competitive user gap value based on the first user occupancy rate and the second user occupancy rate;

[0159] Step S405, calculating the coverage gap through the following formula:

[0160]

[0161] Wherein, the coverage gap is, the grid competitive coverage gap value is, the minimum value in the grid competitive coverage gap value is, the maximum value in the grid competitive coverage gap value is;

[0162] Step S406, calculating the user gap through the following formula:

[0163]

[0164] Wherein, the user gap is, the maximum value in the grid competitive user gap value is, the minimum value in the grid competitive user gap value is, the grid competitive user gap value is;

[0165] Step S407, calculating the active demand degree through the following formula:

[0166]

[0167] Wherein, the active demand degree is, the grid active demand value is, the minimum value in the grid active demand value is, is the maximum value in the grid active demand value;

[0168] Step S408, calculate the coverage gap weight value by the following formula:

[0169]

[0170] wherein, is the coverage gap weight value;

[0171] Step S409, calculate the user gap weight value by the following formula:

[0172]

[0173] wherein, is the user gap weight value;

[0174] Step S410, calculate the active demand degree weight value by the following formula:

[0175]

[0176] wherein, is the active demand degree weight value;

[0177] Step S411, calculate the competitive demand imbalance index value based on the coverage gap, the user gap, the active demand degree, the coverage gap weight value, the user gap weight value and the active demand degree weight value by the following formula:

[0178]

[0179]

[0180] wherein, is the competitive demand imbalance index value, is the synergy penalty term.

[0181] The application calculates the competitive demand imbalance index value in real time, so as to closely match the dynamic changes of the actual network, and keep the decision in the optimal state at all times.

[0182] In some embodiments, the user dissatisfaction imbalance index value is calculated based on the 5G user migration probability and the competitive demand imbalance index value; the competitive synergy coefficient value is calculated based on the 5G user migration probability and the competitive demand imbalance index value; the active demand compensation factor is calculated based on the 5G user activity index and the competitive demand imbalance index value; the migration coverage gap ratio is calculated based on the 5G user migration probability and the competitive demand imbalance index value, including:

[0183] Step S501, obtain the grid user complaint times;

[0184] Step S502, calculating a user dissatisfaction imbalance index value based on the grid user complaint number, the 5G user migration probability and the competition demand imbalance index value through the following formula:

[0185]

[0186] wherein, is the user dissatisfaction imbalance index value, is the grid user complaint number, is the 5G user migration probability;

[0187] Step S503, calculating a competition synergy coefficient value based on the 5G user migration probability and the competition demand imbalance index value through the following formula:

[0188]

[0189] wherein, is the competition synergy coefficient value;

[0190] Step S504, calculating an active demand compensation factor based on the 5G user activity index and the competition demand imbalance index value through the following formula:

[0191]

[0192] wherein, is the active demand compensation factor, is a preset denominator coefficient;

[0193] Step S505, calculating a migration coverage gap ratio value based on the 5G user migration probability and the competition demand imbalance index value through the following formula:

[0194]

[0195] wherein, is the migration coverage gap ratio value.

[0196] In some embodiments, a closeness score is calculated based on the user dissatisfaction imbalance index value, the competition synergy coefficient value, the active demand compensation factor and the migration coverage gap ratio value; and base station site selection is performed based on the closeness score, including:

[0197] Step S601, data normalizing the user dissatisfaction imbalance index value through the following formula to obtain a first item index:

[0198]

[0199] wherein, is the first item index, is the minimum value in the user dissatisfaction imbalance index value, The maximum value in the user dissatisfaction imbalance index value is obtained;

[0200] In step S602, the competition synergy coefficient value is data standardized by the following formula to obtain a second index:

[0201]

[0202] wherein I is the total number of grids, is the competition synergy coefficient value of the i-th grid, is the mean value of the competition synergy coefficient value, is the standard deviation of the competition synergy coefficient value, is the second index;

[0203] In step S603, the active demand compensation factor is data standardized by the following formula to obtain a third index:

[0204]

[0205] wherein, is the third index;

[0206] In step S604, the migration coverage gap ratio is data normalized by the following formula to obtain a fourth index:

[0207]

[0208] wherein, is the fourth index;

[0209] In step S605, the index corresponding proportion is calculated based on the first index, the second index, the third index and the fourth index by the following formula:

[0210]

[0211] wherein, is the index corresponding proportion of the j-th index of the i-th grid, is the j-th index of the i-th grid. When j is equal to 1, it corresponds to the first index; when j is equal to 2, it corresponds to the second index; when j is equal to 3, it corresponds to the third index; when j is equal to 4, it corresponds to the fourth index;

[0212] In step S606, the index corresponding information entropy is calculated based on the index corresponding proportion by the following formula:

[0213]

[0214] wherein, is the index corresponding information entropy of the j-th index;

[0215] Step S607, calculating the index corresponding weight based on the index corresponding information entropy through the following formula:

[0216]

[0217] wherein, is the index weight of the jth index, and J is the total number of indexes;

[0218] Step S608, calculating the closeness score based on the first index, the second index, the third index, the fourth index and the index corresponding weight; and performing base station site selection based on the closeness score.

[0219] The application improves the real-time performance of the decision-making process through the dynamic weight adjustment mechanism, and can better cope with complex and variable network environment and market demand.

[0220] In some embodiments, the closeness score is calculated based on the first index, the second index, the third index, the fourth index and the index corresponding weight; and the base station site selection is performed based on the closeness score, including:

[0221] Step S701, calculating the corresponding index ideal value based on the first index, the second index, the third index, the fourth index and the index corresponding weight through the following formula:

[0222]

[0223] wherein, is the corresponding index ideal value of the jth index of the ith grid;

[0224] Step S702, obtaining the maximum value of the first index of the ith grid, the maximum value of the second index of the ith grid and the maximum value of the fourth index of the ith grid, and taking the three maximum values as the positive index ideal solution;

[0225] Step S703, calculating the positive distance based on the positive index ideal solution through the following formula:

[0226]

[0227] wherein, is the positive distance of the ith grid, is the jth positive index ideal solution;

[0228] Step S704, obtaining the minimum value of the third index of the ith grid, and taking it as the negative index ideal solution;

[0229] Step S705, calculating the negative distance based on the negative index ideal solution through the following formula:

[0230]

[0231] wherein, is the negative distance of the i-th grid, is the j-th negative index ideal solution;

[0232] Step S706, based on the positive distance and the negative distance, calculate the closeness score by the following formula:

[0233]

[0234] wherein, is the closeness score of the i-th grid;

[0235] Step S707, arrange the closeness scores in descending order to obtain a candidate base station deployment priority list;

[0236] Step S708, base on the candidate base station deployment priority list to perform base station site selection.

[0237] The present application quantitatively evaluates the advantages and disadvantages of the candidate area, and provides scientific guidance for the accurate placement of resources according to the evaluation results, effectively reduces the cost expenditure brought by the traditional trial and error method, and improves the efficiency and success rate of resource allocation and placement.

[0238] Specifically, in order to facilitate the understanding of those skilled in the art, a set of best embodiments is provided as follows:

[0239] I. Data acquisition:

[0240] Obtain the first grid data and the second grid data of the target area, wherein the first grid data includes the grid user density of the base station site operator, the grid reference signal receiving power, the cell traffic, the first user occupancy rate and the first grid 5G coverage rate, and the second grid data includes the second user occupancy rate and the second grid 5G coverage rate of other competitors;

[0241] II. 5G user index calculation:

[0242] Calculate the 5G user migration probability based on the grid user density, the grid reference signal receiving power and the cell traffic; calculate the 5G user activity index based on the grid user density, the grid reference signal receiving power and the cell traffic, specifically:

[0243] Calculate the 5G user migration probability based on the grid user density, the grid reference signal receiving power and the cell traffic, including:

[0244] Obtain the historical user 5G migration ratio and the grid 5G coverage rate;

[0245] Calculate the cell grid weight based on the grid user density and the grid reference signal receiving power by the following formula:

[0246]

[0247] wherein, is a cell grid weight of the cth cell of the nth grid, is a preset receiving power coefficient, is a preset user density coefficient, is a standardized grid reference signal receiving power of the nth grid, is a grid reference signal receiving power of the nth grid, is a grid user density of the nth grid, is a set of all grids covered by the cth cell, is a grid user density of the nth grid, is a standardized grid reference signal receiving power of the nth grid, is a grid reference signal receiving power of the nth grid; The grid traffic is calculated based on the cell grid weight and the cell traffic by the following formula: wherein, is a grid traffic of the cth cell of the nth grid,

[0248] is a cell traffic of the cth cell;

[0249]

[0250] The historical user 5G migration ratio, the grid 5G coverage rate and the grid traffic are input into the trained LSTM-Transformer model for prediction to obtain a 5G user migration probability, wherein the grid traffic includes a grid 5G traffic and a grid 4G traffic. The historical user 5G migration ratio, the grid 5G coverage rate and the grid traffic are input into the trained LSTM-Transformer model for prediction to obtain a grid 5G user activity original value. The 5G user activity index is calculated based on the grid 5G user activity original value by the following formula:

[0251]

[0252]

[0253] The 5G user activity index is calculated based on the grid 5G user activity original value by the following formula:

[0254]

[0255] wherein, is a 5G user activity index, is a grid 5G user activity original value,​​​​​​​ is the minimum value in the grid 5G user activity raw value, is the maximum value in the grid 5G user activity raw value.

[0256] III. Competition demand imbalance index value calculation:

[0257] The competition demand imbalance index value is calculated based on the first user occupancy rate, the first grid 5G coverage rate, the second user occupancy rate and the second grid 5G coverage rate, specifically:

[0258] Obtain the number of user reports and the number of users of the base station site operator;

[0259] Calculate the grid active demand value based on the number of user reports and the number of users;

[0260] Calculate the grid competitive coverage gap value based on the first grid 5G coverage rate and the second grid 5G coverage rate;

[0261] Calculate the grid competitive user gap value based on the first user occupancy rate and the second user occupancy rate;

[0262] Calculate the coverage gap by the following formula:

[0263]

[0264] Wherein, is the coverage gap, is the grid competitive coverage gap value, is the minimum value in the grid competitive coverage gap value, is the maximum value in the grid competitive coverage gap value;

[0265] Calculate the user gap by the following formula:

[0266]

[0267] Wherein, is the user gap, is the maximum value in the grid competitive user gap value, is the minimum value in the grid competitive user gap value, is the grid competitive user gap value;

[0268] Calculate the active demand degree by the following formula:

[0269]

[0270] Wherein, is the active demand degree, is the grid active demand value, is the minimum value in the grid active demand value, is the maximum value in the active demand values of the grid;

[0271] The coverage gap weight value is calculated by the following formula:

[0272]

[0273] wherein, is the coverage gap weight value;

[0274] The user gap weight value is calculated by the following formula:

[0275]

[0276] wherein, is the user gap weight value;

[0277] The active demand degree weight value is calculated by the following formula:

[0278]

[0279] wherein, is the active demand degree weight value;

[0280] The competitive demand imbalance index value is calculated based on the coverage gap, the user gap, the active demand degree, the coverage gap weight value, the user gap weight value and the active demand degree weight value by the following formula:

[0281]

[0282]

[0283] wherein, is the competitive demand imbalance index value, is the synergy penalty term.

[0284] IV. Index value calculation:

[0285] The user dissatisfaction imbalance index value is calculated based on the 5G user migration probability and the competitive demand imbalance index value; the competitive synergy coefficient value is calculated based on the 5G user migration probability and the competitive demand imbalance index value; the active demand compensation factor is calculated based on the 5G user activity index and the competitive demand imbalance index value; the migration coverage gap ratio value is calculated based on the 5G user migration probability and the competitive demand imbalance index value, specifically:

[0286] The grid user complaint times are obtained;

[0287] The user dissatisfaction imbalance index value is calculated based on the grid user complaint times, the 5G user migration probability and the competitive demand imbalance index value by the following formula:

[0288]

[0289] wherein, is a user dissatisfaction imbalance index value, is a grid user complaint number, is a 5G user migration probability;

[0290] a competition synergy coefficient value is calculated based on the 5G user migration probability and the competition demand imbalance index value through the following formula:

[0291]

[0292] wherein, is the competition synergy coefficient value;

[0293] an active demand compensation factor is calculated based on the 5G user activity index and the competition demand imbalance index value through the following formula:

[0294]

[0295] wherein, is the active demand compensation factor, is a preset denominator coefficient;

[0296] a migration coverage gap ratio is calculated based on the 5G user migration probability and the competition demand imbalance index value through the following formula:

[0297]

[0298] wherein, is the migration coverage gap ratio.

[0299] Five, base station site selection:

[0300] a closeness score is calculated based on the user dissatisfaction imbalance index value, the competition synergy coefficient value, the active demand compensation factor and the migration coverage gap ratio; and base station site selection is performed based on the closeness score, specifically:

[0301] the user dissatisfaction imbalance index value is data normalized through the following formula to obtain a first index:

[0302]

[0303] wherein, is the first index, is a minimum value in the user dissatisfaction imbalance index value, is a maximum value in the user dissatisfaction imbalance index value;

[0304] the competition synergy coefficient value is data standardized through the following formula to obtain a second index:

[0305]

[0306] wherein I is the total number of grids, is the competition synergy coefficient value of the i-th grid, is the average value of the competition synergy coefficient value, is the standard deviation of the competition synergy coefficient value, is the second index;

[0307] The active demand compensation factor is data standardized by the following formula to obtain the third index:

[0308]

[0309] wherein, is the third index;

[0310] The migration coverage gap ratio is data normalized by the following formula to obtain the fourth index:

[0311]

[0312] wherein, is the fourth index;

[0313] The index corresponding proportion is calculated based on the first index, the second index, the third index and the fourth index by the following formula:

[0314]

[0315] wherein, is the index corresponding proportion of the j-th index of the i-th grid, is the j-th index of the i-th grid. When j is equal to 1, it corresponds to the first index; when j is equal to 2, it corresponds to the second index; when j is equal to 3, it corresponds to the third index; when j is equal to 4, it corresponds to the fourth index;

[0316] The index corresponding information entropy is calculated based on the index corresponding proportion by the following formula:

[0317]

[0318] wherein, is the index corresponding information entropy of the j-th index;

[0319] The index corresponding weight is calculated based on the index corresponding information entropy by the following formula:

[0320]

[0321] wherein, is the index weight of the j-th index, and J is the total number of indexes;

[0322] The corresponding index ideal value of the first index, the second index, the third index, the fourth index and the index corresponding weight is calculated by the following formula:

[0323]

[0324] Wherein, is the corresponding index ideal value of the jth index of the ith grid;

[0325] The maximum value of the first index of the ith grid, the maximum value of the second index of the ith grid and the maximum value of the fourth index of the ith grid are obtained, and the three maximum values are taken as the positive index ideal solution;

[0326] The positive distance is calculated based on the positive index ideal solution by the following formula:

[0327]

[0328] Wherein, is the positive distance of the ith grid, is the jth positive index ideal solution;

[0329] The minimum value of the third index of the ith grid is obtained, and it is taken as the negative index ideal solution;

[0330] The negative distance is calculated based on the negative index ideal solution by the following formula:

[0331]

[0332] Wherein, is the negative distance of the ith grid, is the jth negative index ideal solution;

[0333] The closeness score is calculated based on the positive distance and the negative distance by the following formula:

[0334]

[0335] Wherein, is the closeness score of the ith grid;

[0336] The closeness score is arranged in descending order to obtain a candidate base station deployment priority list;

[0337] The base station site selection is carried out based on the candidate base station deployment priority list.

[0338] In addition, refer to Figure 2In one embodiment of the present application, a base station site selection system is provided, comprising a data acquisition module 1100, a 5G user data calculation module 1200, a competition demand imbalance index value calculation module 1300, an index calculation module 1400, and a base station site selection module 1500, wherein:

[0339] The data acquisition module 1100 is configured to acquire first grid data and second grid data of a target area, wherein the first grid data comprises grid user density, grid reference signal received power, cell traffic, first user occupancy rate, and first grid 5G coverage rate of a base station site selection operator, and the second grid data comprises second user occupancy rate and second grid 5G coverage rate of other competing operators;

[0340] The 5G user data calculation module 1200 is configured to calculate 5G user migration probability based on grid user density, grid reference signal received power, and cell traffic; and calculate 5G user activity index based on grid user density, grid reference signal received power, and cell traffic;

[0341] The competition demand imbalance index value calculation module 1300 is configured to calculate a competition demand imbalance index value based on first user occupancy rate, first grid 5G coverage rate, second user occupancy rate, and second grid 5G coverage rate;

[0342] The index calculation module 1400 is configured to calculate a user dissatisfaction imbalance index value based on 5G user migration probability and competition demand imbalance index value; calculate a competition synergy coefficient value based on 5G user migration probability and competition demand imbalance index value; calculate an active demand compensation factor based on 5G user activity index and competition demand imbalance index value; and calculate a migration coverage gap ratio based on 5G user migration probability and competition demand imbalance index value;

[0343] The base station site selection module 1500 is configured to calculate a closeness score based on the user dissatisfaction imbalance index value, the competition synergy coefficient value, the active demand compensation factor, and the migration coverage gap ratio; and perform base station site selection based on the closeness score.

[0344] The system obtains first grid data and second grid data of a target area, wherein the first grid data includes grid user density, grid reference signal receiving power, cell traffic, first user occupancy rate and first grid 5G coverage rate of a base station site selection operator, and the second grid data includes second user occupancy rate and second grid 5G coverage rate of other competing operators. The application improves data accuracy and diversity by fusing multi-source data, calculates 5G user migration probability based on grid user density, grid reference signal receiving power and cell traffic, calculates 5G user activity index based on grid user density, grid reference signal receiving power and cell traffic, calculates competition demand imbalance index value based on first user occupancy rate, first grid 5G coverage rate, second user occupancy rate and second grid 5G coverage rate, calculates user dissatisfaction imbalance index value based on 5G user migration probability and competition demand imbalance index value, calculates competition synergy coefficient value based on 5G user migration probability and competition demand imbalance index value, calculates active demand compensation factor based on 5G user activity index and competition demand imbalance index value, and calculates migration coverage gap ratio based on 5G user migration probability and competition demand imbalance index value. The application realizes objective sorting and real-time adjustment of base station site selection, calculates closeness score based on user dissatisfaction imbalance index value, competition synergy coefficient value, active demand compensation factor and migration coverage gap ratio, and performs base station site selection based on the closeness score, thereby improving the efficiency and resource utilization rate of base station site selection.

[0345] It should be noted that the system embodiment and the method embodiment described above are based on the same inventive concept, and therefore the related content of the method embodiment described above is also applicable to the system embodiment, which will not be described here.

[0346] Figure 3 A hardware structure diagram of base station site selection provided by the embodiment of the application is shown.

[0347] The base station site selection device can include a processor 301 and a memory 302 having computer program instructions stored therein.

[0348] Specifically, the processor 301 described above can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the application.

[0349] The memory 302 can include mass storage for data or instructions. As an example and not by way of limitation, the memory 302 can include a Hard Disk Drive (HDD), a floppy disk drive, a flash memory, a Compact Disc (CD) or other optical disk, a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 302 can include removable or non-removable (or fixed) media, where appropriate. Where appropriate, the memory 302 can be internal or external to the integrated gateway disaster recovery appliance. In particular embodiments, the memory 302 is non-volatile, solid-state memory.

[0350] In some embodiments, the memory 302 can include read-only memory (ROM), random-access memory (RAM), a disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software that, when executed (by one or more processors), is operable to

[0351] The processor 301 implements the base station siting method of any of the above embodiments by reading and executing computer program instructions stored in the memory 302.

[0352] In one example, the base station siting device can further include a communication interface 303 and a bus 310. Wherein, as shown, the processor 301, the memory 302, the communication interface 303 are connected through the bus 310 and complete the communication between each other. Figure 3

[0353] The communication interface 303 is mainly used to realize the communication between each module, device, unit and / or equipment in the embodiments of the present application.

[0354] ​Bus 310 includes hardware, software, or both, to couple components of the base station siting device to each other and to couple components to other components within the base station siting device. While bus 310 is shown for the sake of clarity as a single bus, bus 310 can include one or more buses operating together. Although bus 310 is shown as a single bus, it can include one or more buses operating together. In this context, bus 310 can include any suitable hardware, software, or both for coupling hardware, software, or both together as appropriate. While bus 310 is shown in the embodiments described herein as a single bus, it can include one or more buses operating together. In this context, bus 310 can include any suitable hardware, software, or both for coupling hardware, software, or both together as appropriate.

[0355] The base station siting device can perform the base station siting method in the embodiments of the present application based on the three-dimensional design model, thereby realizing the base station siting method and system described in combination Figure 1 and Figure 2 with the embodiments of the present application.

[0356] In addition, in combination with the base station siting method in the above embodiments, the embodiments of the present application can provide a computer storage medium to realize. The computer storage medium has computer program instructions stored thereon; the computer program instructions are executed by a processor to realize any one of the base station siting methods in the above embodiments.

[0357] It needs to be made clear that the present application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, detailed descriptions of known methods are omitted herein. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after understanding the spirit of the present application.

[0358] The functional blocks shown in the structural block diagrams above can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, and the like. When implemented in software, the elements of the present application are program or code segments that are used to perform the required tasks. The program or code segments can be stored in a machine-readable medium, or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. A "machine-readable medium" includes any medium that can store or transport information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. The code segments can be downloaded via computer networks such as the Internet, intranets, and the like.

[0359] It is also important to note that the examples mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the examples, or in an order different from the examples, or several steps can be performed simultaneously.

[0360] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer program instructions can also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other processing device to operate in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks. The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer program instructions can also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other processing device to operate in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0361] The above merely describes a specific implementation of the present application. Those skilled in the art can clearly understand the specific working processes of the system, modules and units described above for the convenience and brevity of description, and can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein again. It should be understood that the protection scope of the present application is not limited to this, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A base station siting method, characterized by, The base station site selection method comprises: Obtaining first grid data and second grid data of a target area, wherein the first grid data comprises grid user density, grid reference signal receiving power, cell traffic, first user occupancy rate and first grid 5G coverage rate of a base station site selection operator, and the second grid data comprises second user occupancy rate and second grid 5G coverage rate of other competing operators; Obtaining historical user 5G migration proportion and grid 5G coverage rate; Calculating cell grid weight based on the grid user density and the grid reference signal receiving power through the following formula: in, For the first Cell grid weight of the c-th cell in the nth grid To preset the receive power factor, To preset the user density coefficient, For the first Normalized grid reference signal received power of each grid. For the first The received power of the grid reference signal for each grid cell. For the first Raster user density per grid cell Let c be the set of all grid cells covered by the c-th cell. For the first Raster user density per grid cell For the first Normalized grid reference signal received power of each grid. For the first The received power of the grid reference signal for each grid; Calculating grid traffic based on the cell grid weight and the cell traffic through the following formula: wherein, is the grid flow for the cth cell of the grid, is the grid flow for the cth cell of the grid, is the cell flow for the cth cell. Inputting the historical user 5G migration proportion, the grid 5G coverage rate and the grid traffic into a trained LSTM-Transformer model for prediction to obtain 5G user migration probability, wherein the grid traffic comprises grid 5G traffic and grid 4G traffic; Inputting the historical user 5G migration proportion, the grid 5G coverage rate and the grid traffic into a trained LSTM-Transformer model for prediction to obtain grid 5G user activity original value; Calculating 5G user activity index based on the grid 5G user activity original value through the following formula: wherein, is the 5G user engagement index, is the grid 5G user engagement raw value, is the minimum value in the grid 5G user engagement raw value, is the maximum value in the grid 5G user engagement raw value; Calculating competition demand imbalance index value based on the first user occupancy rate, first grid 5G coverage rate, the second user occupancy rate and the second grid 5G coverage rate; Calculating user dissatisfaction imbalance index value based on the 5G user migration probability and the competition demand imbalance index value; calculating competition synergy coefficient value based on the 5G user migration probability and the competition demand imbalance index value; calculating active demand compensation factor based on the 5G user activity index and the competition demand imbalance index value; calculating migration coverage gap ratio value based on the 5G user migration probability and the competition demand imbalance index value; Calculating closeness score based on the user dissatisfaction imbalance index value, the competition synergy coefficient value, the active demand compensation factor and the migration coverage gap ratio value; and performing base station site selection based on the closeness score.

2. The method of claim 1, wherein, The calculation of the competition demand imbalance index value based on the first user occupancy rate, first grid 5G coverage rate, the second user occupancy rate and the second grid 5G coverage rate comprises: Obtaining user reporting times and user number of a base station site selection operator; Calculating grid active demand value based on the user reporting times and the user number; Calculating grid competing operator coverage gap value based on the first grid 5G coverage rate and the second grid 5G coverage rate; Calculating grid competing operator user gap value based on the first user occupancy rate and the second user occupancy rate; Calculating coverage gap through the following formula: wherein, is a coverage gap, is a grid pair coverage gap value, is a minimum of the grid pair coverage gap values, is a maximum of the grid pair coverage gap values; Calculating user gap through the following formula: wherein, is the user gap, is the maximum of the grid competitor user gap values, is the minimum of the grid competitor user gap values, is the grid competitor user gap value; Calculating active demand degree through the following formula: wherein, is an active demand degree, is a grid active demand value, is a minimum value among the grid active demand values, is a maximum value among the grid active demand values; Calculating coverage gap weight value through the following formula: wherein is a coverage gap weight value; Calculating user gap weight value through the following formula: wherein, is the user gap weight value; Calculating active demand degree weight value through the following formula: wherein, is an active demand degree weight value; calculating the competition demand imbalance index value based on the 5G user migration probability and the competition demand imbalance index value; wherein, is a competitive demand imbalance index value, is a synergistic penalty term.

3. The method of claim 2, wherein, calculating the user dissatisfaction imbalance index value based on the 5G user migration probability and the competition demand imbalance index value; calculating a competition synergy coefficient value based on the 5G user migration probability and the competition demand imbalance index value; calculating an active demand compensation factor based on the 5G user activity index and the competition demand imbalance index value; calculating a migration coverage gap ratio value based on the 5G user migration probability and the competition demand imbalance index value, including: obtaining the number of user complaints in the grid; calculating the user dissatisfaction imbalance index value based on the number of user complaints in the grid, the 5G user migration probability and the competition demand imbalance index value: wherein, is the user dissatisfaction imbalance index value, is the grid user complaint number, is the 5G user migration probability; calculating the competition synergy coefficient value based on the 5G user migration probability and the competition demand imbalance index value: wherein, is the competition synergy coefficient value; calculating the active demand compensation factor based on the 5G user activity index and the competition demand imbalance index value: wherein, is an active demand compensation factor, is a preset denominator coefficient; calculating the migration coverage gap ratio value based on the 5G user migration probability and the competition demand imbalance index value: wherein, is the migration coverage gap ratio.

4. The base station siting method of claim 1, wherein calculating the closeness score based on the user dissatisfaction imbalance index value, the competition synergy coefficient value, the active demand compensation factor and the migration coverage gap ratio value; and performing base station site selection based on the closeness score, including: data normalizing the user dissatisfaction imbalance index value to obtain a first index value through the following formula: wherein, is the first index, is the minimum value of the user dissatisfaction imbalance index value, is the maximum value of the user dissatisfaction imbalance index value; data standardizing the competition synergy coefficient value to obtain a second index value through the following formula: wherein I is the total number of grids, is the competition synergy coefficient value of the i-th grid, is the average value of the competition synergy coefficient value, is the standard deviation of the competition synergy coefficient value, is the second index; data standardizing the active demand compensation factor to obtain a third index value through the following formula: wherein is the third index; data normalizing the migration coverage gap ratio value to obtain a fourth index value through the following formula: wherein is the fourth index; calculating the corresponding proportion of the index based on the first index, the second index, the third index and the fourth index through the following formula: wherein, a corresponding proportion of the index of the jth indicator of the ith grid, is the jth indicator of the ith grid, j equals to 1 corresponds to the first indicator, j equals to 2 corresponds to the second indicator, j equals to 3 corresponds to the third indicator, j equals to 4 corresponds to the fourth indicator; calculating the corresponding information entropy of the index based on the corresponding proportion of the index through the following formula: wherein, is the information entropy of the indicator pair corresponding to the jth indicator. calculating the corresponding weight of the index based on the corresponding information entropy of the index through the following formula: wherein, Wj is the weight of the jth indicator, and J is the total number of indicators. calculating the closeness score based on the first index, the second index, the third index, the fourth index and the corresponding weight of the index; and performing base station site selection based on the closeness score.

5. The method of claim 4, wherein, calculating the closeness score based on the first index, the second index, the third index, the fourth index and the corresponding weight of the index; and performing base station site selection based on the closeness score, including: calculating the corresponding ideal value of the index based on the first index, the second index, the third index, the fourth index and the corresponding weight of the index through the following formula: wherein, is the corresponding index ideal value for the jth index of the ith grid. obtaining the maximum value of the first index of the i-th grid, the maximum value of the second index of the i-th grid and the maximum value of the fourth index of the i-th grid, and taking the three maximum values as the positive ideal solution of the index; Calculate a positive distance based on the positive index ideal solution by the following formula: wherein, is the forward distance for the i-th grid, is the j-th item of the forward index ideal solution; Obtain the minimum value of the third index of the i-th grid, and take it as a negative index ideal solution; Calculate a negative distance based on the negative index ideal solution by the following formula: wherein, is the negative distance for the i-th grid, is the j-th negative index ideal solution; Calculate the closeness score based on the positive distance and the negative distance by the following formula: wherein, is the closeness score for the ith grid. Arrange the closeness scores in descending order to obtain a candidate base station deployment priority list; Perform base station site selection based on the candidate base station deployment priority list.

6. A base station siting system, characterized by comprising: The base station site selection system comprises: A data acquisition module configured to acquire first grid data and second grid data of a target area, wherein the first grid data comprises grid user density, grid reference signal received power, cell traffic, first user occupancy rate and first grid 5G coverage rate of a base station site selection operator, and the second grid data comprises second user occupancy rate and second grid 5G coverage rate of other competing operators; A 5G user data calculation module configured to acquire historical user 5G migration proportion and grid 5G coverage rate; Calculate a cell grid weight based on the grid user density and the grid reference signal received power by the following formula: in, For the first Cell grid weight of the c-th cell in the nth grid To preset the receive power factor, To preset the user density coefficient, For the first Normalized grid reference signal received power of each grid. For the first The received power of the grid reference signal for each grid cell. For the first Raster user density per grid cell Let c be the set of all grid cells covered by the c-th cell. For the first Raster user density per grid cell For the first Normalized grid reference signal received power of each grid. For the first The received power of the grid reference signal for each grid; Calculate a grid traffic based on the cell grid weight and the cell traffic by the following formula: wherein, is the grid flow for the cth cell of the grid, is the grid flow for the cth cell of the grid, is the cell flow for the cth cell. Input the historical user 5G migration proportion, the grid 5G coverage rate and the grid traffic into a trained LSTM-Transformer model for prediction to obtain a 5G user migration probability, wherein the grid traffic comprises grid 5G traffic and grid 4G traffic; Input the historical user 5G migration proportion, the grid 5G coverage rate and the grid traffic into a trained LSTM-Transformer model for prediction to obtain a grid 5G user activity original value; Calculate the 5G user activity index based on the grid 5G user activity original value by the following formula: wherein, is the 5G user engagement index, is the grid 5G user engagement raw value, is the minimum value among the grid 5G user engagement raw values, is the maximum value among the grid 5G user engagement raw values; A competitive demand imbalance index value calculation module configured to calculate a competitive demand imbalance index value based on the first user occupancy rate, the first grid 5G coverage rate, the second user occupancy rate and the second grid 5G coverage rate; An index calculation module configured to calculate a user dissatisfaction imbalance index value based on the 5G user migration probability and the competitive demand imbalance index value; calculate a competitive synergy coefficient value based on the 5G user migration probability and the competitive demand imbalance index value; calculate an active demand compensation factor based on the 5G user activity index and the competitive demand imbalance index value; and calculate a migration coverage gap ratio based on the 5G user migration probability and the competitive demand imbalance index value; A base station site selection module configured to calculate a closeness score based on the user dissatisfaction imbalance index value, the competitive synergy coefficient value, the active demand compensation factor and the migration coverage gap ratio; and perform base station site selection based on the closeness score.

7. A base station site selection device, characterized in that, comprising at least one control processor and a memory communicatively connected to the at least one control processor; the memory storing instructions executable by the at least one control processor, the instructions being executed by the at least one control processor to enable the at least one control processor to perform a base station siting method as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer readable storage medium stores computer executable instructions for causing a computer to perform a base station siting method as claimed in any one of claims 1 to 5.

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