Density analysis method and device, resident area determination method and device, electronic equipment and storage medium

Abstract

The invention provides a density analysis method and device, a resident area determination method and device, electronic equipment and a storage medium. The density analysis method comprises the following steps: acquiring a position coordinate and a signal coverage radius of each base station in a target area; calculating the density adjustment weight of each base station based on the signal coverage radius of each base station; then bandwidth parameters are determined based on the position coordinates and the density adjustment weight of each base station; and finally, constructing a probability density estimation function of the target area based on a preset kernel function, the number of the base stations in the target area, the bandwidth parameter and the density adjustment weight and the position coordinate of each base station. According to the invention, the signal coverage radius of each base station of the target area is introduced in the construction process of the probability density estimation function, so that the accuracy of density estimation of any position of the target area by the probability density estimation function can be ensured; and an inaccurate density estimation result caused by assimilation processing of a heterogeneous base station and the like in the prior art is avoided.

Description

Technical Field

[0001] The present invention relates to the field of big data, and in particular, to a density analysis method and device, a dwelling area determination method and device, an electronic device, and a storage medium. Background Art

[0002] With the large-scale deployment of the fifth-generation mobile communication technology (5G) and the pre-research promotion of the sixth-generation mobile communication technology (6G), the mobile communication network shows a development trend of heterogeneity and ultra-densification. As a key infrastructure of the radio access network, the spatial distribution density of base stations directly affects network capacity, service quality, and user perception experience. Especially in emerging application scenarios such as smart cities and vehicle-to-everything (V2X) networks, accurate base station distribution density estimation has become an important technical basis for network planning and optimization, user behavior analysis, and urban computing.

[0003] The existing base station density estimation methods in the prior art treat heterogeneous base stations equally, which essentially violates the physical layer characteristics of the mobile communication network, resulting in inaccurate density estimation results and systematic biases. Summary of the Invention

[0004] The purpose of the present invention is to provide a density analysis method and device, a dwelling area determination method and device, an electronic device, and a storage medium to improve the problems existing in the prior art.

[0005] Embodiments of the present invention may be implemented as follows:

[0006] In a first aspect, the present invention provides a density analysis method, including:

[0007] Obtaining the position coordinates and signal coverage radius of each base station in a target area;

[0008] Calculating the density adjustment weight of each base station based on the signal coverage radius of each base station respectively;

[0009] Determining a bandwidth parameter based on the position coordinates and density adjustment weight of each base station;

[0010] Constructing a probability density estimation function of the target area based on a preset kernel function, the number of base stations in the target area, the bandwidth parameter, the density adjustment weight of each base station, and the position coordinates; the probability density estimation function is used to determine the base station distribution density at any position in the target area.

[0011] Optionally, the step of determining the bandwidth parameter based on the position coordinates and density adjustment weight of each base station includes:

[0012] Performing weighted averaging on the position coordinates of all base stations based on the density adjustment weight of each base station to obtain a weighted average center point of the position coordinates of all base stations;

[0013] Calculate the interval distance from each base station to the weighted average center point, and filter out the median value among all the interval distances to obtain the target interval distance;

[0014] Determine the bandwidth parameter based on the sum of the density adjustment weights of all base stations, the preset bandwidth constant, and the target interval distance.

[0015] Optionally, the calculation formula for the bandwidth parameter is:

[0016]

[0017] In the formula, h represents the bandwidth parameter, A represents the preset bandwidth constant, D m represents the target interval distance, and s represents the sum of the density adjustment weights of all base stations;

[0018] The calculation formula for the probability density estimation function is:

[0019]

[0020] In the formula, (x, y) represents the position coordinates of any position in the target area, f(x, y) represents the base station distribution density at (x, y); n represents the number of base stations in the target area, S i represents the density adjustment weight of the i-th base station in the target area, K i represents the density contribution weight of the i-th base station in the target area, dist i represents the distance between (x, y) and the i-th base station in the target area.

[0021] In a second aspect, the present invention provides a method for determining a residence area, including:

[0022] Obtain the residence information of the user in the target area during a specified period, where the residence information includes the position coordinates of at least one residence point;

[0023] Input the position of each residence point into the probability density estimation function respectively to obtain the base station distribution density at each residence point; the probability density estimation function is constructed by the density analysis method described in the first aspect above;

[0024] Determine the residence area of the user around each residence point based on the base station distribution density at each residence point, the preset density value set, and the magnification value set.

[0025] Optionally, the step of determining the residence area of the user around each residence point based on the base station distribution density at each residence point, the preset density value set, and the magnification value set includes:

[0026] Generate a dwell radius magnification diagram according to the density value set and the magnification value set; the dwell radius magnification diagram reflects the corresponding relationship between the distribution density and the magnification value.

[0027] For each of the dwell points, look up the target magnification value corresponding to the base station distribution density at the dwell point from the dwell radius magnification diagram.

[0028] Use a circular area centered at the position coordinates of the dwell point and with a target dwell radius equal to the product of the target magnification value and the preset dwell radius as the dwell area of the user around the dwell point.

[0029] Optionally, the density value set includes K density values, and the magnification value set includes K magnification values; the step of generating a dwell radius magnification diagram according to the density value set and the magnification value set includes:

[0030] Starting from the first density value in the density value set, successively use the k-th density value in the density value set as the abscissa and the k-th value in the magnification value set as the ordinate to determine control points, so as to obtain K control points, where k = 1, 2, 3,..., K.

[0031] Based on all the control points, determine the parametric equation of the Bessel curve; the range of change of the independent variable of the parametric equation is 0 - 1, and the dependent variables are the distribution density and the magnification value.

[0032] Adjust the value of the independent variable of the parametric equation to generate a dwell radius magnification diagram.

[0033] In a third aspect, the present invention provides a density analysis device, including:

[0034] A base station data acquisition module, which acquires the position coordinates and signal coverage radius of each base station in the target area.

[0035] A weight calculation module, which is used to calculate the density adjustment weight of each base station respectively based on the signal coverage radius of each base station.

[0036] A bandwidth calculation module, which is used to determine the bandwidth parameter based on the position coordinates and density adjustment weight of each base station.

[0037] A construction module, which is used to construct a probability density estimation function of the target area based on a preset kernel function, the number of base stations in the target area, the bandwidth parameter, the density adjustment weight and position coordinates of each base station; the probability density estimation function is used to determine the base station distribution density at any position in the target area.

[0038] Fourth aspect, the present invention provides a dwelling area determination device, comprising:

[0039] A dwelling acquisition module, configured to acquire the dwelling information of a user in the target area within a specified period, where the dwelling information includes the position coordinates of at least one dwelling point;

[0040] A density estimation module, configured to input the position of each of the dwelling points into a probability density estimation function respectively to obtain the base station distribution density at each of the dwelling points; the probability density estimation function is constructed by the density analysis method described in the first aspect above;

[0041] A region determination module, configured to determine the dwelling area around each dwelling point of the user based on the base station distribution density at each dwelling point, a preset density value set, and a magnification value set.

[0042] Fifth aspect, the present invention provides an electronic device, comprising: a memory and a processor, where the memory stores a software program, and when the electronic device runs, the processor executes the software program to implement: the density analysis method described in the first aspect above, or, the dwelling area determination method described in the second aspect above.

[0043] Sixth aspect, the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements: the density analysis method described in the first aspect above, or, the dwelling area determination method described in the second aspect above.

[0044] Compared with the prior art, the embodiments of the present invention provide a density analysis method and device, a dwelling area determination method and device, an electronic device, and a storage medium. The density analysis method is as follows: obtaining the position coordinates and signal coverage radius of each base station in the target area; then calculating the density adjustment weight of each base station respectively based on the signal coverage radius of each base station; then determining the bandwidth parameter based on the position coordinates and density adjustment weight of each base station; finally, constructing a probability density estimation function of the target area based on a preset kernel function, the number of base stations in the target area, the bandwidth parameter, the density adjustment weight of each base station, and the position coordinates. The probability density estimation function is used to determine the base station distribution density at any position in the target area. Since the signal coverage radius of each base station in the target area is introduced in the construction process of the probability density estimation function of the present invention, the accuracy of using the probability density estimation function for density estimation can be ensured, and the density estimation result inaccurate caused by equalizing heterogeneous base stations as in the prior art can be avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0046] Figure 1 It is a schematic flow chart of a density analysis method provided by an embodiment of the present invention.

[0047] Figure 2 It is a base station density distribution map of a target area provided by an embodiment of the present invention.

[0048] Figure 3 It is a schematic flow chart of a method for determining a residence area provided by an embodiment of the present invention.

[0049] Figure 4 It is a residence radius magnification diagram provided by an embodiment of the present invention.

[0050] Figure 5 It is a schematic diagram of a residence situation provided by an embodiment of the present invention.

[0051] Figure 6 It is a schematic structural diagram of a density analysis device provided by an embodiment of the present invention.

[0052] Figure 7 It is a schematic structural diagram of a device for determining a residence area provided by an embodiment of the present invention.

[0053] Figure 8 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, rather than all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the accompanying drawings here can be arranged and designed in various different configurations.

[0055] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0056] It should be noted that like reference numerals and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0057] It should be noted that, without conflict, the features in the embodiments of the present invention can be combined with each other.

[0058] In the prior art, the mainstream method for estimating the base station density is to use kernel density estimation, which will establish a two-dimensional probability density distribution function based on a unified signal coverage radius. However, in actual network deployment, different base stations have significant heterogeneous characteristics: from macro base stations (Macro Cell) with a coverage radius of several kilometers, to small base stations (SmallCell) with a coverage range of hundreds of meters, and then to millimeter-wave micro base stations (Micro Cell), the physical property differences of different types of base stations result in an order-of-magnitude difference in their effective coverage ranges. The existing base station density estimation method based on a unified signal coverage radius equalizes heterogeneous base stations, which essentially violates the physical layer characteristics of the mobile communication network, leading to inaccurate density estimation results and systematic biases.

[0059] Based on the discovery of the above technical problems, the inventors have proposed the following technical solutions through creative labor to solve or improve the above problems. It should be noted that the defects existing in the above prior art solutions are all the results obtained by the inventors through practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of the present application below for the above problems should all be the contributions made by the inventors to the present application during the invention creation process, and should not be understood as the technical content known to those skilled in the art.

[0060] An embodiment of the present invention provides a density analysis method. During the process of constructing a probability density estimation function for a target area, the signal coverage of each base station in the target area is introduced, which can ensure the accuracy of the base station distribution density at any position in the target area calculated using the probability density estimation function and conform to the actual base station distribution law.

[0061] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a density analysis method provided by an embodiment of the present invention. The execution subject of this method can be an electronic device, and the electronic device can be, but is not limited to, computing devices such as smart phones, personal notebooks, personal computers, servers, etc. This density analysis method includes the following steps S101 to S104.

[0062] S101. Obtain the position coordinates and signal coverage radius of each base station in the target area.

[0063] In this embodiment, the target area can be a geographical area such as multiple cities, one city, or one district in a city specified by the user, or it can be a partially geographical area randomly delimited by the user. There are several public mobile communication base stations (hereinafter simply referred to as base stations) in the target area, and it is necessary to obtain the position coordinates (i.e., longitude and latitude coordinates) and signal coverage radius of each base station.

[0064] S102. Calculate the density adjustment weights of each base station based on the signal coverage radius of each base station respectively.

[0065] In actual situations, the relationship between the signal coverage radius of a base station and the actual distribution of base stations is as follows: If most base stations with a large signal coverage radius are used in an area, then the distribution of base stations in this area is relatively sparse, belonging to a base station sparse area; while if most base stations with a small signal coverage radius are used in an area, then the distribution of base stations in this area is relatively dense, belonging to a base station dense area.

[0066] In this embodiment, for each base station in the target area, the density adjustment weight of this base station is calculated based on the signal coverage radius of this base station. The density adjustment weight reflects the contribution degree of the signal coverage radius of this base station to the density near this base station.

[0067] S103. Determine the bandwidth parameter based on the position coordinates and density adjustment weight of each base station.

[0068] In the kernel density estimation method, the bandwidth parameter is a parameter used to control the smoothing range of the kernel function, which determines the "resolution" of the kernel density estimation, that is, the scale of spatial analysis.

[0069] In this embodiment, the density adjustment weight of each base station is also used when determining the bandwidth parameter to ensure that the obtained bandwidth parameter is a relatively balanced value with respect to the signal coverage radii of each base station in the target area.

[0070] S104. Construct a probability density estimation function for the target area based on a preset kernel function, the number of base stations in the target area, the bandwidth parameter, the density adjustment weight and position coordinates of each base station.

[0071] In this embodiment, the probability density estimation function can be used to determine the base station distribution density at any position in the target area.

[0072] The density analysis method provided by the embodiment of the present invention obtains the position coordinates and signal coverage radius of each base station in the target area; then calculates the density adjustment weight of each base station based on the signal coverage radius of each base station; then determines the bandwidth parameter based on the position coordinates and density adjustment weight of each base station; finally, constructs a probability density estimation function of the target area based on a preset kernel function, the number of base stations in the target area, the bandwidth parameter, the density adjustment weight and position coordinates of each base station. Since the present invention introduces the signal coverage radius of each base station in the target area during the construction of the probability density estimation function, it can ensure the accuracy of the probability density estimation function for density estimation at any position in the target area, and avoid inaccurate density estimation results caused by the homogenization of heterogeneous base stations as in the prior art.

[0073] In an alternative implementation, the process of "determining the bandwidth parameter based on the position coordinates and density adjustment weight of each base station" in step S103 may include the following sub-steps:

[0074] S1031. Based on the density adjustment weight of each base station, perform a weighted average on the position coordinates of all base stations to obtain the weighted average center point of the position coordinates of all base stations.

[0075] Assume that the position coordinates of the i-th base station in the target area are (x i , y i ), and the density adjustment weight of the i-th base station is represented as S i , then the position coordinates of the weighted average center point are as follows:

[0076]

[0077] In the formula, n represents the number of base stations in the target area.

[0078] S1032. Calculate the interval distance from each base station to the weighted average center point, and screen out the median of all interval distances to obtain the target interval distance.

[0079] S1033. Determine the bandwidth parameter based on the sum of the density adjustment weights of all base stations, a preset bandwidth constant, and the target interval distance.

[0080] Optionally, the calculation formula of the bandwidth parameter may be:

[0081]

[0082] In the formula, h represents the bandwidth parameter, A represents a preset bandwidth constant set by the user according to the spatial range of the target area. D m represents the target interval distance, which reflects the concentration degree of the distribution of all base stations in the target area. Represents the sum of the density adjustment weights of all base stations.

[0083] Optionally, the weight adjustment function for calculating the density adjustment weight of the base station can be:

[0084]

[0085] In the formula, e is the natural constant, and r i is the signal coverage radius of the i-th base station in the target area.

[0086] It can be understood that the signal coverage ranges between different types of base stations are the same. For example, the signal coverage range of a 3G base station is 5 kilometers, the signal coverage range of a 4G base station is 3 kilometers, and the signal coverage range of a 5G base station is 0.3 kilometers. When r i = 0.3, S i ≈ 1.561; when r i = 3, S i ≈ 2.589; when r i = 5, S i ≈ 2.700. That is: when r i is larger, the value of S i is close to the natural constant e.

[0087] If the signal coverage radius of the base station is directly used as the density adjustment weight, it is easy to cause the estimated value of the base station distribution density in the actually sparse base station area to be too large and distorted, which is obviously unreasonable. Therefore, compared with directly using the signal coverage radius of the base station as the density adjustment weight, the base station A of 3G and 4G with a larger signal coverage radius has a relatively large density adjustment weight, while the base station A of 5G with a smaller signal coverage radius has a relatively small density adjustment weight, which will not cause density evaluation distortion.

[0088] Optionally, the calculation formula of the probability density estimation function is:

[0089]

[0090] In the formula, (x, y) represents the position coordinates of any position in the target area, and f(x, y) represents the base station distribution density at (x, y); S(r i ) represents the density adjustment weight of the i-th base station in the target area, and K i represents the density contribution weight of the i-th base station in the target area, and dist i represents the distance between the point (x, y) and the i-th base station in the target area.

[0091] Among them, K i is calculated through the kernel function. In this embodiment, the adopted kernel function is:

[0092]

[0093] Among them, is the standardized distance.

[0094] For the probability density estimation function constructed above, it can be applied to but not limited to the following aspects:

[0095] 1. Base station layout optimization: By analyzing the density distribution of base stations, identify areas with excessive or insufficient coverage, optimize the base station layout, and avoid resource waste or coverage blind spots.

[0096] 2. User behavior analysis: Areas with high base station density are usually areas with frequent user activities. User behavior patterns can be further analyzed in combination with user data; or the peak network traffic areas can be predicted through density distribution, and resource allocation can be done in advance.

[0097] 3. Urban planning and infrastructure construction: Areas with high base station density may be commercial centers, transportation hubs or densely populated areas, providing a reference for urban planning; strengthen the construction of infrastructure such as power and optical fibers in high-density areas to support the operation of base stations.

[0098] 4. Emergency management and disaster response: After natural disasters (such as earthquakes, floods), evaluate the damage of the network through the base station density distribution, quickly restore communication; deploy temporary base stations in low-density areas to ensure emergency communication needs.

[0099] In an optional example, the probability density estimation function constructed above can be used to generate a base station density distribution map of the target area. For example, Figure 2 is a base station density distribution map of a target area determined by using the probability density estimation function constructed above. Among them, Figure 2 The base station distribution density is represented by colors from low to high as: blue, yellow, orange, red, and there are also transitional colors between these four colors. It can be seen from this that Figure 2 The orange-red area of is the base station dense area, while the blue area is the base station sparse area.

[0100] In another optional example, Table (1) below shows the basic information of 9 base stations in a target area A:

[0101] Table (1)

[0102]

[0103]

[0104] Based on the longitudes and latitudes of the 9 base stations in Table (1), the base station distribution densities at 13 location points in the target area A are calculated using the probability density estimation function based on the present invention, as shown in Table (2) below:

[0105] Table (2)

[0106] Position point Longitude Latitude Base station distribution density P1 116.0480606 29.46127146 5.11246099 P2 116.053968 29.46127146 5.11246099 P3 116.0598754 29.46127146 5.11246099 P4 116.0657828 29.46127146 5.11246099 P5 116.0716902 29.46127146 5.11246099 P6 116.0775976 29.46127146 5.11246099 P7 116.0835049 29.46127146 7.668691485 P8 116.0894123 29.46127146 7.668691485 P9 116.0953197 29.46127146 7.668691485 P10 116.1012271 29.46127146 7.668691485 P11 116.1071345 29.46127146 7.668691485 P12 116.1130419 29.46127146 0.000102249 P13 116.1189493 29.46127146 0.000102249

[0107] It should be noted that the above examples are only for illustration and are not limited here.

[0108] Optionally, the following introduces a process of optimizing the user's residence information based on the above-constructed probability density estimation function.

[0109] Please refer to Figure 3 , Figure 3 which is a schematic flowchart of a method for determining a residence area provided by an embodiment of the present invention. The execution subject of this method can be an electronic device, and the electronic device can be, but is not limited to, computing devices such as smartphones, personal notebooks, personal computers, servers, etc. The method for determining a residence area includes the following steps S201 to S203:

[0110] S201. Obtain the residence information of the user in the target area within a specified period.

[0111] In this embodiment, the residence information can be input by the user or obtained by immediate calculation. The residence information can reflect the travel and residence situation of the user within a specified period, and may include at least the position coordinates of one residence point. The specified period can be a period of time within a specific day, a specific day, a specific week, etc., and is not limited here.

[0112] S202. Input the position of each residence point into the probability density estimation function respectively to obtain the base station distribution density at each residence point.

[0113] In this embodiment, for each residence point, inputting the residence point into the above-constructed probability density estimation function can determine the base station distribution density at the residence point.

[0114] S203. Determine the residence area of the user around each residence point based on the base station distribution density at each residence point, a preset density value set, and a magnification value set.

[0115] In this embodiment, the residence area around the determined residence point is the estimated activity area of the user at the residence point to achieve travel analysis of the user.

[0116] In the prior art, when analyzing the user's stay, a fixed radius is usually used as the stay radius around each stay point of the user. The stay area determined in this way is inaccurate. However, for the stay area determination method provided by the embodiments of the present invention, by using the probability density estimation function of the newly constructed target area to determine the base station distribution density at each stay point of the user, and then respectively using the base station distribution density at each stay point, in combination with the preset density value set and magnification value set, to determine the stay area at each stay point. In this way, the obtained stay area is more in line with the actual stay situation of the user.

[0117] Optionally, the sub-steps of "determining the stay area of the user around each stay point based on the base station distribution density at each stay point, the preset density value set, and the magnification value set" in step S203 may include S2031 to S2033.

[0118] S2031. Generate a stay radius magnification map according to the density value set and the magnification value set.

[0119] In this embodiment, the stay radius magnification map reflects the corresponding relationship between the distribution density and the magnification value. The density value set may include K preset density values, and the magnification value set may include K preset magnification values. Therefore, the sub-steps of step S2031 may include S20311 to S20313:

[0120] S20311. Starting from the first density value in the density value set, sequentially use the k-th density value in the density value set as the abscissa and the k-th in the magnification value set as the ordinate to determine the control points, so as to obtain K control points, where k = 1, 2, 3,..., K;

[0121] S20312. Based on all the control points, determine the parametric equation of the Bessel curve;

[0122] Among them, the value range of the independent variable t of the parametric equation is [0, 1], and the dependent variables are the distribution density and the magnification value.

[0123] S20313. Adjust the value of the independent variable of the parametric equation to generate a stay radius magnification map.

[0124] In this embodiment, for the independent variable t of the parametric equation, every time a value of the independent variable t is determined from its value range [0, 1], a set of values of the distribution density and the magnification value can be determined. Repeating this several times, several sets of values of the distribution density and the magnification value can be obtained, and then a stay radius magnification map can be generated. For the mapping curve in this stay radius magnification map, the horizontal axis can be the distribution density and the vertical axis can be the magnification value, and vice versa.

[0125] In an alternative example, assume that the set of density values density and the set of magnification values Rho are as follows:

[0126] Density = {5.5, 5.4, 4.6, 3.4, 3.20, 2.60, 1.9, 1.65, 1.35, 1}

[0127] Rho = {1.0, 1.26, 1.45, 1.6, 1.92, 2.08, 2.3, 2.4, 2.5, 3}

[0128] Then, based on the set of density values density and the set of magnification values Rho, 10 control points can be determined. Based on these 10 control points, a mapping curve as shown in Figure 4 can be obtained. This mapping curve is actually obtained by fitting a number of points represented by several sets of values of distribution density and magnification values. This example is only for illustration, and the embodiments of the present invention do not limit the set of density values and the set of magnification values.

[0129] S2032. For each residence point, look up the target magnification value corresponding to the base station distribution density at the residence point from the residence radius magnification diagram.

[0130] S2033. Use the circular area with the position coordinates of the residence point as the center and the product of the target magnification value and the preset residence radius as the target residence radius as the residence area of the user around the residence point.

[0131] For example, please refer to Figure 5 , assuming that the residence information of the user includes the position coordinates of 3 residence points (red points A, B, C) and the position coordinates of multiple other passing points (green points). If the position coordinates of residence point A are input into the probability density estimation function constructed above, and the base station distribution density at residence point A obtained is 3.5, then from Figure 4 , the target magnification value corresponding to residence point A can be found and determined to be 1.75. If the fixed preset residence radius is 0.5 km, then the residence radius r at residence point A = 1.75 × 0.5 = 0.875 km. Then, it can be determined that the circular area with residence point A as the center and r = 0.875 km as the radius is the residence area 1 of the user around residence point A. Similarly, the residence area 2 of the user around residence point B and the residence area 3 of the user around residence point C can be determined.

[0132] This example is only for illustration and is not limited here.

[0133] To execute the corresponding steps in the above method embodiments and various possible implementation manners, the following gives an implementation manner of a density analysis device and a residence area determination device.

[0134] Please refer to Figure 6, Figure 6 shows a schematic structural diagram of a density analysis device provided by an embodiment of the present invention. The density analysis device 200 includes: a base station data acquisition module 210, a weight calculation module 220, a bandwidth calculation module 230, and a construction module 240. Among them:

[0135] The base station data acquisition module 210 acquires the position coordinates and signal coverage radius of each base station in the target area;

[0136] The weight calculation module 220 is configured to calculate the density adjustment weight of each base station based on the signal coverage radius of each base station respectively;

[0137] The bandwidth calculation module 230 is configured to determine a bandwidth parameter based on the position coordinates and density adjustment weight of each base station;

[0138] The construction module 240 is configured to construct a probability density estimation function of the target area based on a preset kernel function, the number of base stations in the target area, the bandwidth parameter, the density adjustment weight and position coordinates of each base station; the probability density estimation function is used to determine the base station distribution density at any position in the target area.

[0139] Optionally, the bandwidth calculation module 230 may specifically be configured to: perform weighted averaging on the position coordinates of all base stations based on the density adjustment weight of each base station to obtain a weighted average center point of the position coordinates of all base stations; calculate the distance between each base station and the weighted average center point, and screen out the median of all the distances to obtain a target distance; determine the bandwidth parameter based on the sum of the density adjustment weights of all base stations, a preset bandwidth constant, and the target distance.

[0140] Please refer to Figure 8 , Figure 8 shows a schematic structural diagram of a residence area determination device provided by an embodiment of the present invention. The residence area determination device 400 includes: a residence acquisition module 410, a density estimation module 420, and a region determination module 430. Among them:

[0141] The residence acquisition module 410 is configured to acquire the residence information of a user in the target area during a specified period, and the residence information includes the position coordinates of at least one residence point;

[0142] The density estimation module 420 is configured to input the position of each residence point into the probability density estimation function respectively to obtain the base station distribution density at each residence point; the probability density estimation function is constructed by the density analysis method introduced above;

[0143] The region determination module 430 is configured to determine the residence area of the user around each residence point based on the base station distribution density at each residence point, a preset density value set, and a magnification value set.

[0144] Optionally, the area determination module 430 can specifically be configured to: generate a dwell radius magnification map according to a density value set and a magnification value set; the dwell radius magnification map reflects the corresponding relationship between the distribution density and the magnification value; for each dwell point, find the target magnification value corresponding to the base station distribution density at the dwell point from the dwell radius magnification map; use a circular area centered on the position coordinates of the dwell point and with the product of the target magnification value and a preset dwell radius as the target dwell radius as the dwell area of the user around the dwell point.

[0145] Optionally, the density value set includes K density values, and the magnification value set includes K magnification values. The area determination module 430 can specifically be configured to: starting from the first density value in the density value set, sequentially use the k-th density value in the density value set as the abscissa and the k-th value in the magnification value set as the ordinate to determine control points, so as to obtain K control points, where k = 1, 2, 3,..., K; based on all the control points, determine the parametric equation of the Bézier curve; the range of change of the independent variable of the parametric equation is 0-1, and the dependent variables are the distribution density and the magnification value; adjust the value of the independent variable of the parametric equation to generate the dwell radius magnification map.

[0146] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the density analysis device 200 and the dwell area determination device 400 described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0147] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device 300 includes a processor 310, a memory 320, and a bus 330. The processor 310 is connected to the memory 320 through the bus 330.

[0148] The memory 320 can be used to store software programs. For example, the software program corresponding to the density analysis device 200 or the software program corresponding to the dwell area determination device 400 provided by an embodiment of the present invention. The processor 310 executes various functional applications and data processing by running the software program stored in the memory 320, so as to implement the density analysis method or the dwell area determination method provided by an embodiment of the present invention.

[0149] Among them, the memory 320 may be, but is not limited to: RAM (Random Access Memory), ROM (Read Only Memory), FLASH (Flash Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electric Erasable Programmable Read-Only Memory), etc.

[0150] The processor 310 may be an integrated circuit chip with signal processing capabilities. The processor 310 may be a general-purpose processor, including: CPU (Central Processing Unit), NP (Network Processor), SoC (System on Chip), etc.; it may also be: DSP (Digital Signal Processing), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0151] It can be understood that Figure 6 The structure shown is only for illustration, and the electronic device 300 may also include more or fewer components than those shown Figure 6 in the figure, or have a configuration different from that shown Figure 6 in the figure. Figure 6 Each component shown in the figure can be implemented by hardware, software, or a combination thereof.

[0152] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements the density analysis method or the residence area determination method disclosed in the above embodiments. The computer-readable storage medium may be, but is not limited to: USB flash drive, mobile hard disk, ROM, RAM, PROM, EPROM, EEPROM, FLASH disk, or optical disc, etc., various media that can store program codes.

[0153] In summary, the embodiments of the present invention provide a density analysis method and apparatus, a residence area determination method and apparatus, an electronic device, and a storage medium. The density analysis method is as follows: obtaining the position coordinates and signal coverage radii of each base station in a target area; then calculating the density adjustment weights of each base station respectively based on the signal coverage radii of the respective base stations; then determining the bandwidth parameter based on the position coordinates and density adjustment weights of each base station; and finally constructing a probability density estimation function for the target area based on a preset kernel function, the number of base stations in the target area, the bandwidth parameter, the density adjustment weights of each base station, and the position coordinates. The probability density estimation function is used to determine the base station distribution density at any position in the target area. Since the signal coverage radii of each base station in the target area are introduced in the construction process of the probability density estimation function of the present invention, the accuracy of using the probability density estimation function for density estimation can be ensured, and the inaccurate density estimation result caused by the equalization processing of heterogeneous base stations as in the prior art can be avoided.

[0154] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A density analysis method, characterized in that: include: Obtain the location coordinates and signal coverage radius of each base station in the target area; Calculating the density adjustment weight of each base station based on the signal coverage radius of each base station; Determine bandwidth parameters based on the location coordinates and density adjustment weights of each base station; Constructing a probability density estimation function of the target area based on a preset kernel function, the number of base stations in the target area, the bandwidth parameter, the density adjustment weight and the position coordinates of each base station; The probability density estimation function is used to determine the base station distribution density at any location in the target area.

2. The density analysis method according to claim 1, characterized in that: The step of determining the bandwidth parameter based on the location coordinates and density adjustment weight of each base station includes: Based on the density adjustment weight of each base station, the position coordinates of all base stations are weighted averaged to obtain the weighted average center point of the position coordinates of all base stations; Calculate the interval distance from each base station to the weighted average center point, and select the median of all interval distances to obtain the target interval distance; The bandwidth parameter is determined based on the sum of the density adjustment weights of all base stations, a preset bandwidth constant and the target spacing distance.

3. The density analysis method according to claim 2, characterized in that: The calculation formula of the bandwidth parameter is: Wherein, h represents the bandwidth parameter, A represents the preset bandwidth constant, and D m represents the target interval distance, and s represents the sum of the density adjustment weights of all base stations; The calculation formula of the probability density estimation function is: Where (x, y) represents the position coordinates of any position in the target area, f(x, y) represents the base station distribution density at (x, y); n represents the number of base stations in the target area, S i represents the density adjustment weight of the i-th base station in the target area, K i represents the density contribution weight of the ith base station in the target area, dist i represents the distance between (x, y) and the i-th base station in the target area.

4. A method for determining a resident area, characterized in that: The method comprises: Acquire the user's stay information in the target area within a specified period of time, wherein the stay information includes the location coordinates of at least one stay point; The position of each of the resident points is input into a probability density estimation function to obtain the base station distribution density at each of the resident points; the probability density estimation function is constructed by the density analysis method according to any one of claims 1 to 3; Based on the base station distribution density at each of the residence points, a preset density value set and a preset ratio value set, a residence area of ​​the user around each residence point is determined.

5. The method for determining a resident area according to claim 4, characterized in that: The step of determining the user's residence area around each residence point based on the base station distribution density, the preset density value set and the multiplier value set at each residence point includes: Generate a residence radius multiplier map according to the density value set and the multiplier value set; the residence radius multiplier map reflects the corresponding relationship between the distribution density and the multiplier value; For each of the resident points, finding a target multiplier value corresponding to the base station distribution density at the resident point from the resident radius multiplier map; A circular area with the position coordinates of the dwelling point as the center and the product of the target magnification value and the preset dwelling radius as the target dwelling radius is used as the dwelling area of ​​the user around the dwelling point.

6. The method for determining a resident area according to claim 5, characterized in that: The density value set includes K density values, and the ratio value set includes K ratio values; the step of generating a resident radius ratio map according to the density value set and the ratio value set includes: Starting from the first density value in the density value set, control points are determined with the kth density value in the density value set as the horizontal coordinate and the kth density value in the magnification value set as the vertical coordinate to obtain K control points, where k = 1, 2, 3, ..., K; Based on all the control points, a parametric equation of the Bezier curve is determined; the range of the independent variable of the parametric equation is 0-1, and the dependent variable is the distribution density and the magnification value; The values ​​of the independent variables of the parameter equation are adjusted to generate a residence radius ratio diagram.

7. A density analysis device, characterized in that: include: Base station data acquisition module, which obtains the location coordinates and signal coverage radius of each base station in the target area; A weight calculation module, used to calculate the density adjustment weight of each base station based on the signal coverage radius of each base station; A bandwidth calculation module, used to determine bandwidth parameters based on the location coordinates and density adjustment weight of each base station; A construction module, configured to construct a probability density estimation function of the target area based on a preset kernel function, the number of base stations in the target area, the bandwidth parameter, a density adjustment weight and a position coordinate of each base station; The probability density estimation function is used to determine the base station distribution density at any location in the target area.

8. A device for determining a resident area, characterized in that: include: A residence acquisition module, used to acquire the residence information of the user in the target area within a specified period of time, wherein the residence information includes the position coordinates of at least one residence point; A density estimation module, used to input the position of each of the resident points into a probability density estimation function to obtain the base station distribution density at each of the resident points; the probability density estimation function is constructed by the density analysis method according to any one of claims 1 to 3; The area determination module is used to determine the user's residence area around each residence point based on the base station distribution density at each residence point, a preset density value set and a multiplier value set.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a software program, and when the electronic device is running, the processor executes the software program to implement: the density analysis method as described in any one of claims 1-3, or the resident area determination method as described in any one of claims 4-6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements: the density analysis method according to any one of claims 1 to 3, or the resident area determination method according to any one of claims 4 to 6.

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