Methods, systems, equipment, and media for generating candidate sites in weak coverage areas

Abstract

This invention discloses a method, system, device, and medium for generating candidate sites for weak coverage areas. The method first rasterizes the spatial data and demand data within the target area to a grid with a preset precision, constructing a multi-dimensional feature vector. Weak coverage grids are then extracted from the grid based on the multi-dimensional feature vector and a coverage strength threshold. Weak coverage areas are then extracted based on these grids. A comprehensive governance priority index is calculated based on the geometric characteristics of the weak coverage areas, and each weak coverage area is ranked by governance priority. Based on the ranking results, weak coverage areas to be addressed are determined. Finally, candidate sites are generated for these areas based on the existing candidate base station distribution and coverage improvement entropy. Using this invention can reduce the overlap between candidate sites and existing base station coverage, improve the coverage coverage filling effect, and enhance the rationality of site layout, effectively solving the problem of weak network coverage.

Description

Technical Field

[0001] This invention relates to the field of processing technology, and in particular to a method, system, device and medium for generating candidate sites in weak coverage areas. Background Technology

[0002] Due to factors such as geographical complexity, building obstruction, and uneven base station distribution, weak coverage areas inevitably exist in networks—areas where the signal strength received by terminals is below the threshold required for normal communication. Weak coverage areas directly impact call quality, data transmission rates, and user experience, making them a critical issue that network optimization urgently needs to address. Existing methods for selecting candidate sites for weak coverage areas typically involve the following steps: First, weak coverage locations are identified using measurement reports collected through drive tests or network management systems; second, network planning engineers prioritize weak coverage areas based on experience, determining areas to be addressed first; finally, candidate sites are selected within or around the weak coverage areas using a combination of manual experience or simple geographic information system tools to fill in the gaps in coverage.

[0003] However, the selection of existing candidate sites relies heavily on subjective human judgment, without taking into account the spatial layout constraints of existing base station distribution, and without using quantitative indicators to evaluate the coverage improvement effect of candidate sites. This easily leads to problems such as high overlap between candidate sites and existing base station coverage, poor blind spot filling effect, or unreasonable site layout that cannot effectively cover weak coverage core areas. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device, and medium for generating candidate sites in weak coverage areas, which can reduce the overlap between candidate sites and existing base station coverage, improve the blind spot filling effect and the rationality of site layout, and effectively solve the problem of weak network coverage.

[0005] To achieve the above objectives, the present invention provides a method for generating candidate sites in weak coverage areas, comprising: The spatial data and demand data within the target area are rasterized to a grid with a preset precision, and a multi-dimensional feature vector is constructed for each grid. Weakly covered grids are extracted from the grid based on the multidimensional feature vector and the preset coverage intensity threshold, and weakly covered regions are extracted based on the weakly covered grids. For each weak coverage area, a comprehensive governance priority index is calculated based on the geometric characteristics of the weak coverage area; Based on the comprehensive governance priority index, the governance priorities of each weak coverage area are ranked, and based on the governance priority ranking results, the weak coverage areas to be governed are determined. For the weak coverage areas to be addressed, candidate sites are generated based on the existing candidate base station distribution and coverage improvement entropy.

[0006] Optionally, the step of generating candidate sites for the weak coverage area to be addressed, based on the existing candidate base station distribution and coverage improvement entropy, includes: A mask of prohibited construction areas is constructed based on the geographic information data of the target area to obtain feasible candidate areas; Within the feasible candidate area, a Voronoi map is constructed based on the location distribution of existing base stations, and coverage blind spots within the weak coverage area to be addressed are identified. Initial candidate sites are generated in the intersection area between the Voronoi map boundary of the coverage blind spot and the weak coverage area to be addressed. Calculate the coverage improvement entropy of each initial candidate site, select the initial candidate site with the largest coverage improvement entropy as the preferred candidate site, and continue until the preset constraints are met, then terminate the candidate site generation and output the candidate site list.

[0007] Optionally, the coverage improvement entropy is the difference between the proportion of base station coverage area after adding candidate sites as new base stations and the proportion of base station coverage area before adding the candidate sites.

[0008] Optionally, the preset constraints include minimum coverage gain constraint, maximum number of candidate sites constraint, and spacing constraint between candidate sites.

[0009] Optionally, the geometric features of the weak coverage area include the area, concavity / convexity, boundary tortuosity coefficient, and minimum distance from the center point of the weak coverage area to the existing site.

[0010] Optionally, the step of calculating a comprehensive governance priority index for each weak coverage area based on the geometric characteristics of the weak coverage area includes: The coverage gap strength is calculated based on the area of ​​the weak coverage area and the average signal strength. Calculate the business value based on the user density and complaint density in the weak coverage area; The difficulty of remediation is calculated based on the boundary tortuosity coefficient, concavity and convexity of the weak coverage area, and the minimum distance from the center point of the area to the existing site. The comprehensive governance priority index of the weak coverage area is calculated by weighting and summing the coverage gap intensity, business value, and governance difficulty.

[0011] Optionally, the step of prioritizing the governance of each weak coverage area based on the comprehensive governance priority index, and determining the weak coverage areas to be addressed based on the governance priority ranking results, includes: Based on the comprehensive governance priority index of each weak coverage area and the preset multi-level threshold, the weak coverage areas are ranked in terms of governance priority; wherein, the multi-level threshold is calibrated based on the average value and standard deviation of the comprehensive governance priority index of all weak coverage areas. Priority ordering is adjusted for weak coverage areas that meet preset user density and complaint density thresholds; Based on the priority ranking results, identify the weak coverage areas that need to be addressed.

[0012] To achieve the above objectives, the present invention also provides a candidate site generation system for weak coverage areas, comprising: The data rasterization module is used to rasterize the spatial data and required data within the target area to a grid with a preset precision, and to construct a multi-dimensional feature vector for each grid. The weak coverage area identification module is used to extract weak coverage grids from the grid based on the multidimensional feature vector and a preset coverage intensity threshold, and to extract weak coverage areas based on the weak coverage grids. The governance priority index calculation module is used to calculate the comprehensive governance priority index for each weak coverage area based on the geometric characteristics of the weak coverage area. The weak coverage area identification module is used to sort the governance priorities of each weak coverage area based on the comprehensive governance priority index, and to determine the weak coverage area to be governed based on the governance priority sorting results. The candidate site generation module is used to generate candidate sites for the weak coverage area to be addressed, based on the existing candidate base station distribution and coverage improvement entropy.

[0013] To achieve the above objectives, the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the weak coverage area candidate site generation method as described above.

[0014] To achieve the above objectives, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the weak coverage area candidate site generation method as described above.

[0015] Compared with existing technologies, this invention provides a method, system, device, and medium for generating candidate sites for weak coverage areas. This method first extracts weak coverage areas by rasterizing the target area space and demand data and constructing a multi-dimensional feature vector. Then, it calculates a comprehensive governance priority index based on the geometric features of the weak coverage areas to achieve objective priority ranking, overcoming the reliance on subjective human judgment in existing technologies. Simultaneously, it incorporates spatial layout constraints based on the existing base station distribution when generating candidate sites and uses coverage improvement entropy as a quantitative indicator to evaluate the coverage improvement effect of candidate sites. This reduces the overlap between candidate sites and existing base station coverage, improves the blind spot filling effect, and enhances the rationality of site layout, effectively solving the problem of weak network coverage. Attached Figure Description

[0016] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for generating candidate sites in a weak coverage area according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a candidate site generation system for weak coverage areas provided in an embodiment of the present invention; Figure 3 This is a structural block diagram of a terminal device provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 , Figure 1 This is a flowchart of a method for generating candidate sites in weak coverage areas provided by an embodiment of the present invention. Figure 1 As shown, the method for generating candidate sites for weak coverage areas includes steps S1 to S5: Step S1: Rasterize the spatial data and required data within the target area to a grid with a preset precision to obtain the multidimensional feature vector of each grid. In one optional embodiment, spatial data is basic data reflecting the current network status and geographical characteristics of the target area, including the geographical coordinates of existing base stations. Signal acquisition points Data such as the reference signal received power RSRP(i,j) of grid (i,j), the signal-to-interference-plus-noise ratio SINR(i,j) of grid (i,j), the reference signal received quality RSRQ(i,j) of grid (i,j), the terrain elevation data Elev(i,j), and the area boundary AdminID(i,j).

[0020] Demand data is key data reflecting the business value and user perception in the target area, including user location. User density (UseDen(i,j)), service traffic data (traffic distribution (Traffic(i,j)), downlink throughput (Throughput(i,j)), packet loss rate (PLR(i,j)), call duration (CallTime(i,j)), complaint points) And complaint density data (ComplaintDen(i,j)).

[0021] In step S1, a 30m×30m high-precision grid can be selected to construct the basic grid network of the target area as a unified spatial reference. The basic grid is denoted as the set. .

[0022] In the construction of the grid, the projection can use WGS84 coordinates and UTM coordinates for isometric projection, which can ensure the distance of the grid; the overall range of the grid is defined by the regional boundary of the target area, ensuring that subsequent governance analysis does not exceed the management scope; all subsequent access data are aligned to the most recent 7-day time window to ensure the timeliness of the data and avoid the problem of mismatch between historical data and the current network status.

[0023] Then, all spatial and demand data are rasterized into this framework to eliminate vector-raster heterogeneity. After rasterization, each raster (i,j) corresponds to a set of standardized multidimensional feature vectors. These vectors aggregate multidimensional information such as spatial, signal, business, and user perception of the raster, in the following form: ; in, This indicates the distance from the grid to the nearest base station, reflecting the current weakness of the coverage; RSRP, SINR, and RSRQ are the average signal quality indicators within the grid; Elev is the average elevation of the grid; Admin is the area identifier to which the grid belongs; UserDen, Traffic, Troughput, PLR, and CallTime are the user density and service performance indicators within the grid; ComplaintDen is the density of user complaints within the grid.

[0024] Step S2: Extract weakly covered grid cells from the grid based on the multidimensional feature vector and the preset coverage intensity threshold, and extract weakly covered regions based on the weakly covered grid cells; First, based on a preset coverage strength threshold, weak coverage is determined for each grid (i,j) generated in step S1, and a binary weak coverage grid matrix is ​​constructed. For example, in mobile communication networks, areas with a reference signal received power (RSRP) ≤ -110dBm are typically defined as weak coverage areas. In this embodiment, this threshold is used to construct a weak coverage grid identification function, as follows: ; Here, RSRP(i,j) represents the signal quality feature in the multidimensional feature vector of grid (i,j). WeakCover(i,j)=1 indicates that the grid is a weakly covered grid, and WeakCover(i,j)=0 indicates that it is a normally covered grid. Based on this, continuous signal strength data can be converted into discrete binary grids to achieve preliminary identification of weakly covered areas.

[0025] Because the attenuation of mobile communication signals has spatial continuity, weak coverage gratings typically form continuous connected regions rather than discrete isolated points. To avoid misclassifying discrete weak coverage gratings as independent governance units, this embodiment of the invention employs the eight-neighborhood connectivity method to perform connectivity analysis on the weak coverage gratings and extract continuous weak coverage region polygons.

[0026] Specifically, the generated binary weakly covered raster matrix is ​​first used as input. The eight-neighbor connected region labeling algorithm (which simultaneously considers the adjacency relationships of the raster in the top, bottom, left, right and four diagonal directions) is used to label the connectivity of all weakly covered rasters with a value of 1. Each connected weakly covered region is assigned a unique region ID, thus obtaining the labeling matrix and the total number of connected regions.

[0027] Then, for each connected region ID, the label matrix is ​​traversed, and the raster coordinates of all cells labeled with that ID are extracted to form the raster set of the m-th weakly covered region. The raster coordinates of each connected region are converted into vector polygons, resulting in a set of all continuous weakly covered regions within the target area. , ,..., ,..., }

[0028] It is worth noting that this embodiment uses an eight-neighborhood connection method instead of the traditional four-neighborhood connection method in order to better fit the spatial distribution characteristics of mobile communication signals, avoid misjudging weak coverage grids with oblique connections as multiple independent areas, and ensure that the extracted weak coverage areas are consistent with the actual network coverage.

[0029] To comprehensively characterize the scale, shape, and spatial location characteristics of weak coverage areas, embodiments of the present invention describe each weak coverage area... Calculate the following geometric characteristic parameters.

[0030] First, calculate the weak coverage area. area : ; in, In this embodiment, the grid side length is determined. .

[0031] Next, the concavity / convexity of the weakly covered area is calculated. Concavity / convexity is used to characterize the shape regularity of weakly covered areas, reflecting the degree of fragmentation of the area. The calculation method is as follows: ; in, It is a region The convex hull area (i.e., the area of ​​the smallest convex polygon that encloses the weakly covered region). ; The vertices of the convex hull are: ; That is, the vertices close in a counter-clockwise order. It should be noted that concavity / convexity... The value range is [0,1]. The closer the value is to 0, the closer the region is to a convex polygon and the more regular the shape. The closer the value is to 1, the more concave and broken parts the region has and the more irregular the shape.

[0032] Next, the complexity of the weakly covered area boundary is calculated, reflecting the degree of tortuosity of the boundary relative to a standard circle, using the boundary tortuosity coefficient. : ; in, Indicates the region The perimeter of the outline can be calculated by counting the number of grid edges at the outer boundary of the statistical region: It should be noted that the boundary tortuosity coefficient... The value of is greater than 1. The closer the value is to 1, the closer the boundary of the region is to a circle and the more regular the shape. The larger the value, the more tortuous and complex the boundary is, and the more difficult it is to predict the occlusion and attenuation of the signal at the boundary.

[0033] Finally, calculate the center point of the region. Minimum distance to existing stations : ; Where K is the total number of existing base stations in the target area. The geographic coordinates of the k-th base station; the center point of the region. It is a business-weighted in-center, which can be weighted by business traffic or complaint density, so that the regional center is set in a weak coverage area, and is calculated by the average coordinates of all grids in the weak coverage area: , ; It should be noted that, It is used to reflect the spatial relationship between weak coverage areas and existing base stations, and to characterize the degree of weakness of the current network coverage in that area.

[0034] Step S3: For each weak coverage area, calculate the comprehensive governance priority index based on the geometric characteristics of the weak coverage area; In one optional embodiment, the step of calculating the comprehensive governance priority index based on the geometric characteristics of each weak coverage area includes steps S301 to S304: Step S301: Calculate the coverage gap strength based on the area of ​​the weak coverage area and the average signal strength; Step S302: Calculate the service value based on the user density and complaint density of the weak coverage area; Step S303: Calculate the difficulty of governance based on the boundary tortuosity coefficient, concavity and convexity of the weak coverage area and the minimum distance from the center point of the area to the existing site; Step S304: Calculate the comprehensive governance priority index of the weak coverage area by weighted summation of the coverage gap intensity, business value, and governance difficulty.

[0035] It should be noted that the quantitative approach to prioritizing weak coverage area governance adopted in this embodiment of the invention is based on the core logic of "problem severity × business value ÷ governance difficulty," and achieves scientific ranking by constructing a Comprehensive Governance Priority Index (GPI). First, the physical problem scale is measured by the coverage gap intensity, and then weighted and amplified by combining the area area with the deviation from the average RSRP. Second, a business value coefficient is introduced to integrate user density, traffic, complaints, and other demand characteristics to reflect the importance of network usage in the area. Finally, governance difficulty (such as terrain complexity and site accessibility) is considered to avoid blind investment.

[0036] Based on this, the weak coverage area in the embodiments of the present invention The General Governance Priority Index (GPI) is calculated as follows: ; in, The larger the value, the weaker the coverage area. The higher the governance priority index; This represents the weighting coefficient of the corresponding indicator, satisfying... It can be adjusted according to different scenarios; for example, in urban scenarios, the weight of business value can be increased. In suburban scenarios, the intensity weight of coverage gaps can be increased. Resource-scarce scenarios can increase the difficulty of governance; adjustment coefficient weighting. .

[0037] The strength of the cover gap is expressed by the following formula: ; in, This represents the area of ​​m weakly covered regions. This represents the total area of ​​the weakly covered region; Indicates weak coverage area Average RSRP value within; Indicates weak coverage area The weak coverage threshold within the area is -110dBm in this embodiment; Indicates weak coverage area The weak coverage determination threshold and signal strength adjustment range are specified in the embodiments of the present invention. (i.e., the weak coverage range of -110dBm to -150dBm). This represents the signal quality adjustment coefficient, and a value of 0.5 is recommended in this embodiment.

[0038] The business value is represented by the following formula: ; in, This indicates a weak coverage area. Average user density For the region The total number of grid cells within; Indicates weak coverage area The average complaint density; This is the user density weighting coefficient, with a value range of [0,1].

[0039] The difficulty of governance is expressed by the following formula: ; in, Indicates weak coverage area The unevenness; Indicates weak coverage area The minimum distance from the center point of the region to the nearest existing base station; Indicates all areas with weak coverage The maximum value, ; This represents the terrain complexity weighting coefficient, with a value range of [0,1]. In this embodiment, a value of 0.6 is recommended.

[0040] It should be noted that the weights of the above indicators are not arbitrarily selected empirical values, but are derived based on the following three-layer logical deduction, with terrain complexity as the weighting coefficient. For example: 1. Statistical regression based on historical engineering data (data support) Through retrospective analysis of over 1,200 actual landfill projects in mountainous and hilly areas of a certain province over the past three years, the inventors discovered: Among the many factors influencing the difficulty of station construction (terrain slope, accessibility, difficulty of power supply, and difficulty of land coordination), terrain slope (which directly determines tower height, foundation engineering volume, and feasibility of construction machinery access) has the highest contribution. Then, a multiple linear regression model was used to analyze the correlation between "actual construction period / cost" and "each influencing factor." The results show that the standardized regression coefficient of the terrain complexity factor is approximately 0.58~0.62. Therefore, a value of 0.6 is a mathematical fit result of the strong correlation between historical actual construction costs and terrain characteristics, and can most accurately reflect the dominant role of terrain in the difficulty of governance.

[0041] 2. Scene sensitivity analysis based on the "barrel effect" (logical support) The difficulty of governance aims to identify the "toughest nuts to crack." In weak coverage governance scenarios: non-terrain factors (such as distance from the site) can usually be solved by adding transmission equipment or fine-tuning parameters, and their difficulty increases linearly. Terrain factors (such as high mountains and deep valleys) often have a non-linear blocking effect: once the slope exceeds a threshold (such as 25°), conventional construction methods fail, and special processes (such as cableway transportation and high pile foundations) must be adopted, leading to an exponential increase in costs.

[0042] 3. Weighting Logic: If the weight is too low (e.g., <0.4), the blocking effect of complex terrain will be underestimated, leading to the priority planning of sites that are "theoretically feasible but actually impossible to construct"; if the weight is too high (e.g., >0.8), the preferred option that is farther away but has flat terrain may be completely ignored. 0.6 is a balance point that highlights the decisive blocking role of terrain (the dominant factor) while retaining room for adjustment of other factors (such as distance).

[0043] Step S4: Based on the comprehensive governance priority index, sort the governance priorities of each weak coverage area, and based on the governance priority sorting results, determine the weak coverage areas to be governed. Step S4 constructs an adaptive three-level priority division mechanism based on the Comprehensive Governance Priority Index (GPI) of each weak coverage area calculated in step S3, and introduces core area business protection rules to solve the defects of poor scenario adaptability and lagging governance of core business areas caused by the use of fixed thresholds to divide priorities in the existing weak coverage governance scheme.

[0044] Specifically, step S4 includes steps S401 to S403: Step S401: Based on the comprehensive governance priority index of each weak coverage area and the preset multi-level threshold, sort the weak coverage areas by governance priority; wherein, the multi-level threshold is calibrated based on the average value and standard deviation of the comprehensive governance priority index of all weak coverage areas. Step S402: Priority order correction is performed on weak coverage areas that meet the preset user density threshold and complaint density threshold; Step S403: Based on the priority ranking results, determine the weak coverage areas to be addressed.

[0045] For example, a three-tiered dynamic categorization mechanism is established based on the calculated Comprehensive Governance Priority Index (GPI) to avoid regional governance imbalances caused by fixed thresholds. To avoid regional governance imbalances caused by fixed thresholds, a three-tier priority allocation mechanism is constructed based on GPI values, dividing weak coverage areas into high, medium, and low priority levels. The calculation formula is as follows: ; in, This represents the m-th weak coverage area. Priority level; This indicates a high-priority threshold, with an initial value of 0.75. This indicates a low-priority threshold, with an initial value of 0.35. However, the aforementioned static threshold is prone to failure in different scenarios (e.g., the overall GPI in urban areas is too high, while the overall GPI in suburban areas is too low; a fixed threshold will cause a large number of urban areas to be misclassified as high priority and weakly covered suburban areas to be underestimated). Therefore, this embodiment introduces a dynamic calibration mechanism based on the statistical characteristics of regional GPI to achieve scenario-adaptive adjustment of the threshold: ; ; in, This represents the average GPI across all weakly covered areas. This represents the standard deviation of GPI for all areas with weak coverage. This indicates a high priority coefficient, and a value of 1.0 to 1.5 is recommended; specifically, 1.0 is recommended for urban scenarios and 1.5 for suburban scenarios. This indicates a low priority coefficient, and a value of 0.8 to 1.2 is recommended; 0.8 for urban scenarios and 1.2 for suburban scenarios.

[0046] It is worth noting that, through this mechanism, the threshold can be dynamically adjusted according to the overall GPI distribution of different target areas, thereby ensuring that the priority division always adapts to the current scenario and avoids the limitations of static thresholds.

[0047] In addition, to prevent GPI from being underestimated in some core areas with high user density and concentrated complaints due to signal quality not reaching the "extremely weak" level, mandatory rules for business protection can be set to rigidly adjust the priority of core areas: ; in, This represents the critical user density threshold, typically set at 80 people / km² in urban areas and 30 people / km² in suburban areas. This represents the critical complaint density threshold, typically set at 0.5 complaints / km² / month.

[0048] After this operation, even if the GPI of a weakly covered area does not reach the high priority threshold, as long as its user density and complaint density both exceed the critical value, it will be forcibly promoted to high priority. This ensures that core areas with concentrated user pain points are given priority in governance, and prevents user experience issues from being ignored.

[0049] Finally, after completing the priority level classification and core area correction, a structured priority list of weak coverage areas is output, and the governance time limit requirements for different levels of areas are clearly defined, providing a clear basis for the governance order in the subsequent generation of candidate sites. High-priority areas: These areas require candidate site planning and coverage improvement assessments to be completed within 7 days, and will be given priority in the allocation of governance resources. Medium priority areas: These areas require site planning to be completed within 15 to 30 days, with remediation proceeding in batches. Low priority areas: These areas are included in the long-term planning database and their coverage can be reviewed quarterly, with the governance plan dynamically adjusted based on network changes.

[0050] Step S5: For the weak coverage area to be addressed, generate candidate sites based on the existing candidate base station distribution and coverage improvement entropy.

[0051] In an optional embodiment, step S5 includes steps S501 to S503: Step S501: Construct a no-building area mask based on the geographic information data of the target area to obtain feasible candidate areas; Step S502: Within the feasible candidate area, construct a Voronoi map based on the location distribution of existing base stations, identify coverage blind spots in the weak coverage area to be addressed, and generate initial candidate sites in the intersection area between the Voronoi map boundary of the coverage blind spot and the weak coverage area to be addressed. Step S503: Calculate the coverage improvement entropy of each initial candidate site, select the initial candidate site with the largest coverage improvement entropy as the preferred candidate site, until the preset constraints are met, terminate the candidate site generation, and output the candidate site list.

[0052] For example, to ensure the feasibility and signal coverage effectiveness of the generated candidate sites, a spatial constraint model for the candidate sites can be constructed first, and the spatial location of the candidate sites can be initially screened from two dimensions: exclusion of prohibited areas and calculation of antenna line of sight.

[0053] Based on GIS data of the target area (including terrain slope, surface type, administrative boundaries, etc.), a no-construction mask is constructed to exclude areas unsuitable for base station construction. The rules for determining the no-construction grid are as follows: ; in, Represents a grid Areas designated as prohibited from construction are excluded from the pool of candidate sites. Represents grid The terrain slope is calculated using the collected elevation data Elev(i,j); Represents grid Surface type, It indicates that the construction of base stations is prohibited in areas such as water surfaces and nature reserves.

[0054] This rule allows for the rapid elimination of grids that do not meet the construction requirements, thus narrowing down the range of candidate sites.

[0055] Meanwhile, to ensure that the signal from the candidate sites can effectively cover weak coverage areas, the antenna field of view of each candidate site can be analyzed based on a digital elevation model (DEM) to calculate the signal strength (x, y) of the candidate site for the weak coverage area. The visible coverage ratio is calculated using the following formula: ; in, This represents the visible coverage ratio of the candidate site (x, y); For visibility identification, determine the grid. For each candidate site address (x, y), it is marked as visible if it is visible, otherwise it is marked as 0. Indicates weak coverage area The total number of grid cells within.

[0056] The higher the visible coverage ratio, the less the signal of the candidate site is affected by terrain obstruction, and the stronger the coverage effectiveness.

[0057] Furthermore, within the spatially constrained area, a method combining Voronoi diagrams and coverage improvement entropy is used to automatically identify blind spots and generate optimal candidate sites, maximizing the blind spot filling effect.

[0058] First, a Voronoi diagram of existing base stations is constructed based on the geographical distribution of existing base stations within the target area. The target area is divided into multiple Thiessen polygons. The distance from all points within each polygon to the corresponding base station is less than the distance to other base stations, thereby automatically identifying weak areas covered by existing base stations, i.e. blind spots.

[0059] To maximize the blind spot coverage, in Voronoi edges and weak coverage areas Initial candidate sites are generated within the intersection area, which represents natural blind spots in the existing coverage. Adding a new base station in these areas yields the highest coverage improvement benefit. To quantify the coverage improvement effect of each initial candidate site, this embodiment introduces a coverage improvement entropy ΔH, which is the difference between the percentage of base station coverage area after adding a candidate site and the percentage of base station coverage area before adding the candidate site. By comparing the changes in coverage distribution before and after adding a new base station, the coverage improvement value of the candidate sites is evaluated.

[0060] Coverage improves entropy The calculation formula is as follows: ; The formula indicates that the point with the largest ΔH is selected as the candidate site. The larger the ΔH value, the more significant the improvement in the uniformity of the coverage distribution after adding the candidate site, and the better the blind spot filling effect. This represents the percentage of the original coverage area of ​​the K base stations; This indicates the percentage of the coverage area after the addition of K+1 base stations.

[0061] The coverage area of ​​the K base stations is statistically calculated based on actual signal propagation simulation using a high-precision geographic grid. The specific calculation is as follows: (1) Basic grid Forming a grid set The default grid size is 50×50, and the grid area is... =2500 square meters, the total area is the planned area. .

[0062] (2) Criteria for determining effective coverage For any grid To determine whether a location is effectively covered by a set of K base stations, the following two conditions must be met simultaneously: Condition A (Signal Strength): The maximum reference signal received power (RSRP) received by this grid from at least one base station in the set is greater than a set threshold ThRSRP (e.g., -110dBm).

[0063] Condition B (Signal Quality / Signal-to-Noise Ratio): The signal-to-interference-plus-noise ratio (SINR) of the primary serving cell of this grid is greater than the set threshold ThSINR (e.g., -3dB), excluding interference areas where there is a signal but cannot be demodulated.

[0064] If grid If both conditions A and B are met, it is marked as a valid covered grid (state value). =1), otherwise it is an uncovered or weakly covered raster ( =0).

[0065] (3) The coverage area of ​​K base stations ( The sum of the areas of all valid coverage grid cells: ; Coverage percentage (Rcov) is defined as: .

[0066] Based on the above calculations, the coverage area ratio before and after the addition of a new base station can be obtained, and then the coverage improvement entropy ΔH can be calculated. The initial candidate site with the largest ΔH is selected as the preferred candidate site.

[0067] Furthermore, to ensure that the signal from the candidate site can accurately cover the weak coverage area, the antenna height, azimuth angle, and horizontal beamwidth can be calculated based on the spatial characteristics and terrain conditions of the weak coverage area to optimize the signal propagation direction and coverage range.

[0068] Antenna height needs to be determined by combining the minimum communication distance from the candidate site to the weak coverage area and the terrain slope. Different calculation models are used for flat terrain and complex terrain to ensure that the signal can overcome terrain obstruction and reach the weak coverage area. ; in, Indicates the recommended antenna height; Indicates the distance from candidate sites to areas with weak coverage. Minimum communication distance at the center point; This represents the average terrain slope of the poorly covered area. This indicates flat terrain; the rest are considered complex terrain.

[0069] It should be noted that the calculation logic of this model is as follows: the farther the distance, the higher the antenna height is required to overcome path loss; in complex terrain, there is more obstruction, so a higher base height and a lower distance coefficient are used to balance coverage requirements and construction costs.

[0070] The antenna azimuth needs to be pointed in the direction of the highest user density within the weak coverage area to maximize perceived user benefits. This requires calculating the weak coverage area. Directional axis: ; in, This is the pointing azimuth angle of the antenna's main lobe; The horizontal beamwidth (BW) is determined by the angle of the weakly covered area. Sure: ; Among them, 1.2 To reserve margin for edge coverage and avoid insufficient edge coverage due to terrain obstruction.

[0071] After completing all the above calculations, a structured list of candidate sites is generated. For example, the list may include information such as area identifier, priority level, candidate site coordinates, antenna parameters, coverage gain, construction cost and estimated construction period, providing a basis for subsequent site planning.

[0072] Meanwhile, to avoid generating invalid candidate sites, the embodiments of the present invention set the following boundary conditions and termination rules: (1) Minimum coverage gain constraint: When the coverage improvement entropy of a single preferred candidate site is... If the value is less than 0.08, it indicates that the coverage improvement effect at this point is insufficient to offset the construction costs, and the candidate site generation process for this area will be terminated.

[0073] Single candidate site coverage gain Generation will terminate when the value is less than 0.08; (2) Maximum number of candidate sites constraint: The maximum number of candidate sites allowed to be generated in each weak coverage area is: ; in, Indicates weak coverage area The total area; 15000 m² is the average effective coverage area of ​​a single base station; therefore, dividing the total area by the average effective coverage area per station yields the theoretical lower limit of the number of sites required; rounding up and adding 1 ensures that even in smaller areas there is at least one candidate site, and taking the minimum value of 5 limits the maximum number to avoid resource waste.

[0074] (3) Spacing constraints between candidate sites: The Euclidean distance between any two candidate sites must be greater than the maximum communication radius. 70%, that is, greater than This constraint can prevent co-channel interference caused by candidate sites being too close together, thus ensuring signal quality.

[0075] It is worth noting that by using spatial constraint modeling, Voronoi diagram blind spot identification, coverage improvement entropy assessment, and intelligent optimization of antenna parameters, the entire process of candidate site generation can be automated, which can solve the problems of low efficiency, strong subjectivity, and lack of comprehensive consideration of multi-dimensional constraints in traditional manual site selection.

[0076] In summary, the proposed method for generating candidate sites for weak coverage areas, provided by this invention, extracts continuous weak coverage areas by rasterizing the target area space and demand data and constructing a multi-dimensional feature vector. Then, it calculates a comprehensive governance priority index based on the geometric features of the weak coverage areas to achieve objective priority ranking, overcoming the reliance on subjective human judgment in existing technologies. Furthermore, by combining the existing base station distribution with spatial layout constraints during candidate site generation and using coverage improvement entropy as a quantitative indicator to evaluate the coverage improvement effect of candidate sites, it can reduce the overlap between candidate sites and existing base station coverage, improve the blind spot filling effect and the rationality of site layout, effectively solving the problem of weak network coverage.

[0077] Based on the above method items, the present invention provides corresponding system items embodiments.

[0078] See Figure 2 , Figure 2 This is a structural block diagram of a candidate site generation system for weak coverage areas provided in an embodiment of the present invention. The candidate site generation system for weak coverage areas includes: The data rasterization module 21 is used to rasterize the spatial data and required data within the target area to a grid with a preset precision, and to construct a multi-dimensional feature vector for each grid. The weak coverage area identification module 22 is used to extract weak coverage grids from the grid according to the multi-dimensional feature vector and the preset coverage intensity threshold, and to extract weak coverage areas based on the weak coverage grids. The governance priority index calculation module 23 is used to calculate the comprehensive governance priority index for each weak coverage area based on the geometric characteristics of the weak coverage area. The weak coverage area identification module 24 is used to sort the governance priorities of each weak coverage area based on the comprehensive governance priority index, and determine the weak coverage area to be governed based on the governance priority sorting result. The candidate site generation module 25 is used to generate candidate sites for the weak coverage area to be addressed, based on the existing candidate base station distribution and coverage improvement entropy.

[0079] In one optional embodiment, the candidate site generation module 25 is configured to: A mask of prohibited construction areas is constructed based on the geographic information data of the target area to obtain feasible candidate areas; Within the feasible candidate area, a Voronoi map is constructed based on the location distribution of existing base stations, and coverage blind spots within the weak coverage area to be addressed are identified. Initial candidate sites are generated in the intersection area between the Voronoi map boundary of the coverage blind spot and the weak coverage area to be addressed. Calculate the coverage improvement entropy of each initial candidate site, select the initial candidate site with the largest coverage improvement entropy as the preferred candidate site, and continue until the preset constraints are met, then terminate the candidate site generation and output the candidate site list.

[0080] In one optional embodiment, the governance priority index calculation module 23 is used for: The coverage gap strength is calculated based on the area of ​​the weak coverage area and the average signal strength. Calculate the business value based on the user density and complaint density in the weak coverage area; The difficulty of remediation is calculated based on the boundary tortuosity coefficient, concavity and convexity of the weak coverage area, and the minimum distance from the center point of the area to the existing site. The comprehensive governance priority index of the weak coverage area is calculated by weighting and summing the coverage gap intensity, business value, and governance difficulty.

[0081] In an optional embodiment, the weak coverage area identification module 24 is used to: Based on the comprehensive governance priority index of each weak coverage area and the preset multi-level threshold, the weak coverage areas are ranked in terms of governance priority; wherein, the multi-level threshold is calibrated based on the average value and standard deviation of the comprehensive governance priority index of all weak coverage areas. Priority ordering is adjusted for weak coverage areas that meet preset user density and complaint density thresholds; Based on the priority ranking results, identify the weak coverage areas that need to be addressed.

[0082] It should be noted that the weak coverage area candidate site generation system provided in this embodiment of the invention is used to execute all the process steps of the weak coverage area candidate site generation method in the above embodiment. The working principle and beneficial effect of the two are one-to-one, so they will not be described again.

[0083] This invention also provides a terminal device, such as... Figure 3 The diagram shown is a structural block diagram of a preferred embodiment of a terminal device provided by the present invention. The terminal device includes a processor 31, a memory 32, and a computer program stored in the memory 32 and configured to be executed by the processor 31. When the processor 31 executes the computer program, it implements the weak coverage area candidate site generation method as described in any of the above embodiments.

[0084] In addition, embodiments of the present invention also provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the weak coverage area candidate site generation method as described in any of the above embodiments.

[0085] When the processor 31 executes the computer program, it implements the steps in the above-described embodiment of the weak coverage area candidate site generation method, for example... Figure 1 All steps of the weak coverage area candidate site generation method shown. Alternatively, when the processor 31 executes the computer program, it implements the functions of each module in the above-described weak coverage area candidate site generation system embodiment, for example... Figure 2 The functions of each module in the weak coverage area candidate site generation system are shown.

[0086] Preferably, the computer program can be divided into one or more modules / units, which are stored in the memory 32 and executed by the processor 31 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device.

[0087] The processor 31 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor 31 can be any conventional processor. The processor 31 is the control center of the terminal device, connecting various parts of the terminal device through various interfaces and lines.

[0088] The memory 32 mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., and the data storage area can store related data, etc. In addition, the memory 32 can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, and a flash card, etc., or the memory 32 can also be other volatile solid-state storage devices.

[0089] It should be noted that the aforementioned terminal devices may include, but are not limited to, processors and memory, as will be understood by those skilled in the art. Figure 3The structural block diagram shown is merely a structural example of the terminal device described above and does not constitute a limitation on the structure of the terminal device. The terminal device may include more or fewer components than shown, or combine certain components, or use different components.

[0090] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for generating candidate sites in weak coverage areas, characterized in that, include: The spatial data and demand data within the target area are rasterized to a grid with a preset precision, and a multi-dimensional feature vector is constructed for each grid. Weakly covered grids are extracted from the grid based on the multidimensional feature vector and the preset coverage intensity threshold, and weakly covered regions are extracted based on the weakly covered grids. For each weak coverage area, a comprehensive governance priority index is calculated based on the geometric characteristics of the weak coverage area; Based on the comprehensive governance priority index, the governance priorities of each weak coverage area are ranked, and based on the governance priority ranking results, the weak coverage areas to be governed are determined. For the weak coverage areas to be addressed, candidate sites are generated based on the existing candidate base station distribution and coverage improvement entropy.

2. The method for generating candidate sites for weak coverage areas as described in claim 1, characterized in that, The process of generating candidate sites for the weak coverage areas to be addressed, based on the existing candidate base station distribution and coverage improvement entropy, includes: A mask of prohibited construction areas is constructed based on the geographic information data of the target area to obtain feasible candidate areas; Within the feasible candidate area, a Voronoi map is constructed based on the location distribution of existing base stations, and coverage blind spots within the weak coverage area to be addressed are identified. Initial candidate sites are generated in the intersection area between the Voronoi map boundary of the coverage blind spot and the weak coverage area to be addressed. Calculate the coverage improvement entropy of each initial candidate site, select the initial candidate site with the largest coverage improvement entropy as the preferred candidate site, and continue until the preset constraints are met, then terminate the candidate site generation and output the candidate site list.

3. The method for generating candidate sites for weak coverage areas as described in claim 2, characterized in that, The coverage improvement entropy is the difference between the percentage of base station coverage area after adding candidate sites as new base stations and the percentage of base station coverage area before adding the candidate sites.

4. The method for generating candidate sites for weak coverage areas as described in claim 2, characterized in that, The preset constraints include minimum coverage gain constraint, maximum number of candidate sites constraint, and spacing constraint between candidate sites.

5. The method for generating candidate sites for weak coverage areas as described in claim 1, characterized in that, The geometric characteristics of the weak coverage area include the area, concavity / convexity, boundary curvature coefficient, and minimum distance from the center point of the weak coverage area to the existing site.

6. The method for generating candidate sites for weak coverage areas as described in claim 3, characterized in that, For each weak coverage area, the comprehensive governance priority index is calculated based on the geometric characteristics of the weak coverage area, including: The coverage gap strength is calculated based on the area of ​​the weak coverage area and the average signal strength. Calculate the business value based on the user density and complaint density in the weak coverage area; The difficulty of remediation is calculated based on the boundary tortuosity coefficient, concavity and convexity of the weak coverage area, and the minimum distance from the center point of the area to the existing site. The comprehensive governance priority index of the weak coverage area is calculated by weighting and summing the coverage gap intensity, business value, and governance difficulty.

7. The method for generating candidate sites for weak coverage areas as described in claim 1, characterized in that, The process of prioritizing the governance of each weak coverage area based on the comprehensive governance priority index, and determining the weak coverage areas to be addressed based on the governance priority ranking results, includes: Based on the comprehensive governance priority index of each weak coverage area and the preset multi-level threshold, the weak coverage areas are ranked in terms of governance priority; wherein, the multi-level threshold is calibrated based on the average value and standard deviation of the comprehensive governance priority index of all weak coverage areas. Priority ordering is adjusted for weak coverage areas that meet preset user density and complaint density thresholds; Based on the priority ranking results, identify the weak coverage areas that need to be addressed.

8. A candidate site generation system for weak coverage areas, characterized in that, include: The data rasterization module is used to rasterize the spatial data and required data within the target area to a grid with a preset precision, and to construct a multi-dimensional feature vector for each grid. The weak coverage area identification module is used to extract weak coverage grids from the grid based on the multidimensional feature vector and a preset coverage intensity threshold, and to extract weak coverage areas based on the weak coverage grids. The governance priority index calculation module is used to calculate the comprehensive governance priority index for each weak coverage area based on the geometric characteristics of the weak coverage area. The weak coverage area identification module is used to sort the governance priorities of each weak coverage area based on the comprehensive governance priority index, and to determine the weak coverage area to be governed based on the governance priority sorting results. The candidate site generation module is used to generate candidate sites for the weak coverage area to be addressed, based on the existing candidate base station distribution and coverage improvement entropy.

9. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the weak coverage area candidate site generation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the weak coverage area candidate site generation method as described in any one of claims 1 to 7.

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