Method, device and equipment for identifying depopulated area and storage medium

Abstract

The invention provides an unmanned area identification method and device, equipment and a storage medium. The method comprises the following steps: dividing a target identification area into a plurality of grids, calculating the resident point density of each grid, and determining target resident points according to the resident point density of each grid; randomly selecting a residence point from the target residence points as a head residence point, taking a grid where the head residence point is located as a head grid, taking the head grid as a current grid, finding a plurality of residence points through depth-first search based on the grids, and connecting the head residence point and the searched residence points into a boundary curve; and judging whether the quantity characteristics of the residence points contained in the boundary curve meet a preset condition, and if so, determining the boundary curve as a depopulated area boundary curve. The method has the advantages of being high in unmanned area recognition accuracy, low in calculation complexity and free of manual participation.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to a method, apparatus, device and storage medium for identifying an uninhabited area. Background Art

[0002] Uninhabited area identification plays a vital role in safety and early warning. Uninhabited areas typically refer to sparsely populated areas with harsh geographical conditions or inaccessible transportation, such as mountainous areas and deserts. In these areas, factors such as harsh climates and the prevalence of wild animals can pose a threat to human life. Therefore, uninhabited area identification can demarcate early warning areas and transmit information via communication networks or dedicated emergency communication facilities, enabling people to be aware of potential dangers and risks before entering uninhabited areas. This is crucial for ensuring personal safety. Overall, this technology is of great significance in safety and early warning, as well as emergency communications.

[0003] Currently, research on uninhabited areas (UNINs) is still in its early stages. Traditional manual methods for identifying uninhabited areas are highly subjective, prone to errors and omissions, and their accuracy needs to be improved. Therefore, with the rapid development of data analysis and artificial intelligence technologies, machine learning and deep learning-based methods are beginning to be applied to uninhabited areas.

[0004] However, existing methods for identifying uninhabited areas based on machine learning and deep learning still have the following defects: First, it is difficult to collect characteristic information of uninhabited areas, and it is impossible to obtain sufficiently accurate and comprehensive data, resulting in the inability to accurately determine uninhabited areas; second, due to the lack of data and training samples, the accuracy of identifying uninhabited areas is low. Summary of the Invention

[0005] The present application provides an uninhabited area recognition method, apparatus, device and storage medium, which are used to solve the technical problems in the prior art such as difficulty in collecting feature information of uninhabited areas, lack of data and training samples, and low accuracy in uninhabited area recognition.

[0006] In a first aspect, the present application provides a method for identifying an uninhabited area, the method comprising:

[0007] Get the user's dwell point data;

[0008] Dividing the target recognition area into a plurality of grids, calculating the dwell point density of each grid, and determining the dwell points contained in the grids whose dwell point density is less than or equal to a preset density threshold as target dwell points;

[0009] Randomly selecting one of the target residence points as the head residence point, taking the grid where the head residence point is located as the head grid, taking the head grid as the current grid, and performing a depth-first search step, the depth-first search step comprising: taking the current grid as the center grid, searching for the residence points contained in the grids surrounding the current grid, and taking the grid where the searched residence point is located as the current grid;

[0010] The depth-first search step is executed cyclically until a tail dwelling point is found, the spacing between the grid where the tail dwelling point is located and the head grid is less than a preset grid spacing threshold, and the number of dwelling points searched by the depth-first search step is greater than 2, then the depth-first search step is stopped;

[0011] Connecting the head residence point and the residence points searched by the depth-first search step to form a boundary curve;

[0012] It is determined whether the quantity characteristic of the stationary points contained in the boundary curve meets a preset condition, and if so, the boundary curve is determined to be a no-man's-land boundary curve.

[0013] In the above-mentioned preferred technical solution of the method for identifying an uninhabited area, the method of taking the current grid as the center grid, searching for the stationary point contained in the grids surrounding the current grid, and using the grid where the searched stationary point is located as the current grid includes:

[0014] Taking the current grid as the center grid, search the grids from the 1st grid to the Nth grid around the current grid in sequence, where n is a preset search threshold. If the residence point is found in the Nth grid around the current grid, the grid where the searched residence point is located is used as the current grid.

[0015] In the preferred technical solution of the above-mentioned method for identifying an uninhabited area, determining whether the quantity characteristic of the stationary points contained in the boundary curve meets a preset condition, and if so, determining the boundary curve as an uninhabited area boundary curve, includes:

[0016] Among the grids inside the boundary curve, determining a grid having a number of the dwell points greater than a preset number threshold as a marked grid;

[0017] The ratio of the marked grid to the grid inside the boundary curve is calculated, and it is determined whether the ratio is less than a preset ratio threshold. If so, the boundary curve is determined to be a no-man's-land boundary curve.

[0018] In the preferred technical solution of the above-mentioned method for identifying an uninhabited area, after determining whether the number characteristic of the stationary points contained in the boundary curve meets a preset condition and if so, determining the boundary curve as an uninhabited area boundary curve, the method further includes:

[0019] Smoothing the no-man's land boundary curve to obtain a smoothed no-man's land boundary curve;

[0020] Determine the stationary point through which the smoothed no-man's land boundary curve passes as a boundary stationary point, and determine a base station associated with the boundary stationary point based on signaling data associated with the boundary stationary point;

[0021] If the base station associated with the border residence point receives the signaling data sent by the mobile terminal, it sends alarm information to the mobile terminal.

[0022] In the preferred technical solution of the above-mentioned no-man's land identification method, the smoothing of the no-man's land boundary curve to obtain the smoothed no-man's land boundary curve includes:

[0023] The no-man's land boundary curve is smoothed using cubic B-spline interpolation to obtain a smoothed no-man's land boundary curve.

[0024] In the preferred technical solution of the above-mentioned no-man's land identification method, the use of cubic B-spline interpolation to smooth the no-man's land boundary curve to obtain the smoothed no-man's land boundary curve includes:

[0025] Get the stationary points P0, P1..., P on the no-man's land boundary curve n-1 The latitude and longitude coordinate data, where P i =(x i ,y i ) are the latitude and longitude coordinates of the station point on the no-man’s land boundary curve;

[0026] Set the longitude and latitude coordinates of the starting node and the ending node, use the starting node and the ending node as control points, and calculate the longitude and latitude coordinates of the boundary nodes according to the interpolation coordinate calculation formula. The interpolation coordinate calculation formula is:

[0027]

[0028] Among them, B x (t) is the longitude coordinate value of the boundary node, B y (t) is the latitude coordinate value of the boundary node, x i P i The longitude coordinate value, y i P i The latitude coordinate value of is the interpolation basis function in the longitude direction, is the interpolation basis function in the latitude direction, and t is m equally spaced numbers selected between [0,1];

[0029] The boundary nodes are marked according to their longitude and latitude coordinate values, and the boundary nodes are connected to form the smoothed no-man's land boundary curve.

[0030] In the preferred technical solution of the above-mentioned method for identifying an uninhabited area, the step of calculating the density of the resident points in each grid includes:

[0031] The dwell point density is calculated according to the dwell point density calculation formula, which is:

[0032]

[0033] Wherein, ρ is the density of the resident points, n is the number of the resident points in a single grid, and a is the side length of the grid.

[0034] In a second aspect, the present application provides a device for identifying an uninhabited area, the device comprising:

[0035] A data acquisition module is used to obtain the user's residence point data;

[0036] Dwell point screening module for:

[0037] Dividing the target recognition area into a plurality of grids, calculating the dwell point density of each grid, and determining the dwell points contained in the grids whose dwell point density is less than or equal to a preset density threshold as target dwell points;

[0038] Boundary search module for:

[0039] Randomly selecting one of the target residence points as the head residence point, taking the grid where the head residence point is located as the head grid, taking the head grid as the current grid, and performing a depth-first search step, the depth-first search step comprising: taking the current grid as the center grid, searching for the residence points contained in the grids surrounding the current grid, and taking the grid where the searched residence point is located as the current grid;

[0040] The depth-first search step is executed cyclically until a tail dwelling point is found, the spacing between the grid where the tail dwelling point is located and the head grid is less than a preset grid spacing threshold, and the number of dwelling points searched by the depth-first search step is greater than 2, then the depth-first search step is stopped;

[0041] a curve generating module, configured to connect the head dwell point and the dwell points found in the depth-first search step into a boundary curve;

[0042] Judgment module, used to:

[0043] It is determined whether the quantity characteristic of the stationary points contained in the boundary curve meets a preset condition, and if so, the boundary curve is determined to be a no-man's-land boundary curve.

[0044] In a third aspect, the present application provides a device for identifying an unmanned area, the device comprising: a memory, and a processor communicatively connected to the memory;

[0045] The memory stores computer-executable instructions;

[0046] The processor executes the computer-executable instructions stored in the memory to implement the above-mentioned method for identifying an uninhabited area.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the above-mentioned method for identifying an uninhabited area.

[0048] The present application provides a method, apparatus, device, and storage medium for identifying an uninhabited area, which have the following technical effects:

[0049] First, since the number of visitors to the border of an uninhabited area is relatively small, the density of user residence points is low. Therefore, by identifying the density of grid residence points, we can accurately find the residence points at the border of an uninhabited area or the border of a densely populated area. At the same time, the residence point data can be obtained through trajectory clustering based on the signaling data between user mobile terminals and base stations. Signaling data is relatively easy to obtain, and sufficient residence point data can improve the accuracy of uninhabited area boundary identification.

[0050] Second, this application uses a depth-first search strategy based on the divided grid to find closed boundary curves. It adopts an unsupervised learning method with simple algorithm logic. It eliminates the shortcomings of complex deep learning or machine learning algorithms, such as poor interpretability and long training time, and saves time for training and parameter adjustment.

[0051] 3. No manual search is required, and there is little reliance on human resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0053] Figure 1 This is a flow chart of a method for identifying an uninhabited area provided in an embodiment of the present application;

[0054] Figure 2 This is a schematic diagram of the depth-first search steps provided in an embodiment of the present application;

[0055] Figure 3 This is a schematic diagram of the search area provided in the embodiment of the present application;

[0056] Figure 4 This is a flow chart of a method for determining whether a boundary curve is a no-man's-land boundary curve provided in an embodiment of the present application;

[0057] Figure 5 This is a flow chart of a no-man's-land boundary warning method provided by an embodiment of the present application;

[0058] Figure 6 This is a schematic diagram of an uninhabited area identification device provided in an embodiment of the present application;

[0059] Figure 7 This is a schematic diagram of an unmanned area identification device provided in an embodiment of the present application.

[0060] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0061] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0062] In the existing technology, the methods of identifying uninhabited areas based on machine learning and deep learning still have the following defects: first, it is difficult to collect characteristic information of uninhabited areas, and it is impossible to obtain sufficiently accurate and comprehensive data, resulting in the inability to accurately determine uninhabited areas; second, due to the lack of data and training samples, the accuracy of identifying uninhabited areas is low.

[0063] In response to the problems of the existing technology, the technical concept of this application is as follows: obtain the user's residence point data, divide the target recognition area into several grids, determine the residence points in the grids where the residence point density is less than or equal to the preset density threshold as the target residence points, randomly select a residence point from the target residence points as the head residence point, and use the grid where the head residence point is located as the current grid to start a depth-first search: search for residence points in the grids around the current grid with the current grid as the center. If a new residence point is found, the grid where the new residence point is located is used as the new current grid, and the residence points in the grids around the new current grid are searched. The depth-first search is performed cyclically until a residence point is found, and the distance between the grid where the residence point is located and the grid where the head residence point is located is less than the preset grid spacing threshold. The depth-first search is stopped, and the head residence point and all the residence points found in the search step are connected to form a boundary curve. Then, the number of residence points contained in the boundary curve is determined to determine whether the boundary curve is a no-man's land boundary curve.

[0064] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0065] In a possible embodiment of the present application, a method for identifying an uninhabited area is provided, which can be applied to a server. Figure 1 This is a flow chart of a method for identifying an uninhabited area provided by an embodiment of the present application. Figure 1 As shown, the method includes:

[0066] S101, obtaining the user's residence point data;

[0067] Specifically, user dwelling point data can be obtained through trajectory clustering and fitting based on the signaling data exchanged between the user's mobile terminal and the base station. Dwelling points are locations where users are likely to stay. Because uninhabited areas are characterized by a low number of visitors to their boundaries, using dwelling point data to identify uninhabited areas has a high degree of accuracy.

[0068] S102, dividing the target recognition area into a number of grids, calculating the dwell point density of each grid, and determining the dwell points contained in the grids whose dwell point density is less than or equal to a preset density threshold as target dwell points;

[0069] It should be noted that since the number of visitors to the no-man's-land boundary is relatively small, the density of user residence points is relatively low. Based on this characteristic, in order to reduce the computational complexity of subsequent processes, it is necessary to filter out areas with relatively dense residence points. The remaining residence points are likely to be residence points at the no-man's-land boundary.

[0070] Optionally, calculate the dwell point density for each grid cell, including:

[0071] The dwell point density is calculated according to the dwell point density calculation formula. The dwell point density calculation formula is:

[0072]

[0073] Where ρ is the density of the dwell points, n is the number of dwell points in a single grid, and a is the side length of the grid.

[0074] S103, randomly selecting a dwell point from the target dwell points as the head dwell point, taking the grid where the head dwell point is located as the head grid, taking the head grid as the current grid, and performing a depth-first search step;

[0075] Specifically, the depth-first search step includes: taking the current grid as the center grid, searching for the stationary points contained in the grids surrounding the current grid, and taking the grid where the searched stationary points are located as the current grid.

[0076] S104, looping through the depth-first search step until a tail dwelling point is found, the spacing between the grid where the tail dwelling point is located and the head grid is less than a preset grid spacing threshold, and the number of dwelling points found in the depth-first search step is greater than 2, then stopping the depth-first search step;

[0077] Figure 2 This is a schematic diagram of the depth-first search steps provided in the embodiment of the present application. Figure 2 Steps S103 and S104 of this embodiment are described in detail. Figure 2 As shown, the direction of the arrow indicates the order of searching for the resident points, starting from the head resident point, with the grid where the head resident point is located as the current grid, searching for other resident points in the search area of ​​the head resident point (the nine-square grid area centered on the current grid), after searching for the second resident point, with the grid where the second resident point is located as the current grid, searching for other resident points in the search area of ​​the second resident point (the nine-square grid area centered on the current grid), searching for the third resident point, with the grid where the third resident point is located as the current grid, searching for other resident points in the search range of the third resident point (the nine-square grid area centered on the current grid), and finally finding the fourth resident point, which falls in the search area of ​​the head resident point and is less than the preset grid spacing threshold. At the same time, the number of resident points found by the depth-first search is greater than 2, then the depth-first search step is stopped, and the fourth resident point is the tail resident point, as shown in FIG. Figure 2 As shown, the head dwell point to the fourth dwell point are connected to form a boundary curve. It should be noted that, Figure 2For convenience, all search areas are in a nine-square grid. In actual applications, the size of the search area is variable. For example, if no other dwelling points are found in the nine-square grid search area, the search range is expanded to 25 squares. To avoid the search area being too large, a search area threshold needs to be set.

[0078] It can be seen that this embodiment searches for closed boundary curves based on the depth-first search method, which does not require training samples, thus avoiding the problem of low recognition accuracy in unmanned areas due to lack of training samples in the prior art.

[0079] S105, connecting the head stationary point and the stationary points searched in the depth-first search step to form a boundary curve;

[0080] S106: Determine whether the quantity characteristic of the stationary points contained in the boundary curve meets the preset conditions:

[0081] If yes, then S107, the boundary curve is determined as the no-man's land boundary curve;

[0082] If not, S108 , the boundary curve is determined as a densely populated area boundary curve.

[0083] It should be noted that since the identified boundary curve may be the boundary of an uninhabited area or the boundary of a densely populated area (also with a small number of user visits), it is necessary to determine whether it is an uninhabited area boundary curve based on the number characteristics of the stationary points inside the boundary curve.

[0084] The technical effects of this embodiment are:

[0085] First, since the number of visitors to the border of an uninhabited area is relatively small, the density of user residence points is low. Therefore, by identifying the density of grid residence points, we can accurately find the residence points at the border of an uninhabited area or the border of a densely populated area. At the same time, the residence point data can be obtained through trajectory clustering based on the signaling data between user mobile terminals and base stations. Signaling data is relatively easy to obtain, and sufficient residence point data can improve the accuracy of uninhabited area boundary identification.

[0086] Second, this application uses a depth-first search strategy based on the divided grid to find closed boundary curves. It adopts an unsupervised learning method with simple algorithm logic. It eliminates the shortcomings of complex deep learning or machine learning algorithms, such as poor interpretability and long training time, and saves time for training and parameter adjustment.

[0087] 3. No manual search is required, and there is little reliance on human resources.

[0088] In one possible embodiment of the present application, a depth-first search method is provided, the method comprising:

[0089] Taking the current grid as the center grid, search the grids from the 1st grid to the Nth grid around the current grid in turn, where N is the preset search threshold. If a residence point is found in the nth grid around the current grid, the grid where the searched residence point is located will be used as the current grid.

[0090] Specifically, first search for the station point in the first grid around the current grid. If no station point is found, search for the station point in the second grid around the current grid, and so on. Figure 3 is a schematic diagram of the search area provided in the embodiment of the present application, Figure 3 Shows the specific range from the first grid to the second grid around the current grid.

[0091] In a possible embodiment of the present application, a method is provided for determining whether a boundary curve is a no-man's-land boundary curve. Figure 4 This is a flow chart of a method for determining whether a boundary curve is a no-man's-land boundary curve provided in an embodiment of the present application. Figure 4 As shown, the method includes:

[0092] S401: Among the grids inside the boundary curve, a grid having a number of stationary points greater than a preset threshold is determined as a marked grid;

[0093] S402: Calculate the ratio of the marked grid to the grid inside the boundary curve, and determine whether the ratio is less than a preset ratio threshold. If so, determine the boundary curve as a no-man's-land boundary curve.

[0094] It should be noted that if the number of dwell points in a grid is greater than the preset threshold, it means that the grid has a large number of visitors. If the proportion of marked grids is too high, it means that the boundary curve is a densely populated area.

[0095] This embodiment uses a statistical analysis method to identify whether a boundary curve is a no-man's land boundary curve or a densely populated area boundary curve, with low computational complexity and high reliability.

[0096] In a possible embodiment of the present application, a method for warning the boundary of an uninhabited area is provided. Figure 5 This is a flow chart of a method for warning the boundary of an uninhabited area provided by an embodiment of the present application. Figure 5 As shown, the method includes:

[0097] S501, smoothing the no-man's land boundary curve to obtain a smoothed no-man's land boundary curve;

[0098] Since the no-man's-land boundary curve searched out according to the aforementioned embodiment may not be smooth, it is necessary to smooth the no-man's-land boundary in order to reduce the influence of noise points.

[0099] Optionally, the no-man's-land boundary curve is smoothed by cubic B-spline interpolation to obtain a smoothed no-man's-land boundary curve, including:

[0100] Get the stationary points P0, P1..., P on the no-man's land boundary curve n-1 The latitude and longitude coordinate data, where P i =(x i ,y i ) are the latitude and longitude coordinates of the station point on the no-man’s land boundary curve;

[0101] Set the latitude and longitude coordinates of the start and end nodes. Use the start and end nodes as control points and calculate the latitude and longitude coordinates of the boundary nodes according to the interpolation coordinate calculation formula. The interpolation coordinate calculation formula is:

[0102]

[0103] Among them, B x (t) is the longitude coordinate value of the boundary node, B y (t) is the latitude coordinate value of the boundary node, x i P i The longitude coordinate value, y i P i The latitude coordinate value of is the interpolation basis function in the longitude direction, is the interpolation basis function in the latitude direction, and t is m equally spaced numbers selected between [0,1];

[0104] Boundary nodes are marked according to their longitude and latitude coordinates, and are connected to form a smoothed no-man's-land boundary curve.

[0105] S502: Determine a station point through which the smoothed no-man's land boundary curve passes as a boundary station point, and determine a base station associated with the boundary station point based on signaling data associated with the boundary station point;

[0106] Since the residence point in this application is obtained based on the signaling data exchanged between the user mobile terminal and the base station, the signaling data associated with the boundary residence point can be used to know which base stations the user interacts with by signaling when at the boundary residence point, that is, the base stations associated with the boundary residence point.

[0107] S503: If the base station associated with the border residency point receives signaling data sent by the mobile terminal, it sends an alarm message to the mobile terminal.

[0108] In this embodiment, a warning message is sent to a user who is about to enter the boundary of the no-man's land, thereby preventing the user from accidentally entering the no-man's land.

[0109] In a possible embodiment of the present application, a device for identifying an uninhabited area is provided. Figure 6 This is a schematic diagram of an unmanned area identification device provided in an embodiment of the present application. Figure 6 As shown, the device 60 includes:

[0110] Data acquisition module 601, used to obtain user's residence point data;

[0111] The residence point screening module 602 is used to:

[0112] Divide the target recognition area into a number of grids, calculate the dwell point density of each grid, and determine the dwell points contained in the grids whose dwell point density is less than or equal to a preset density threshold as target dwell points;

[0113] The boundary search module 603 is used to:

[0114] A station point is randomly selected from the target station points as the head station point, the grid where the head station point is located is used as the head grid, the head grid is used as the current grid, and a depth-first search step is performed. The depth-first search step includes: using the current grid as the center grid, searching for station points contained in grids around the current grid, and using the grid where the searched station point is located as the current grid;

[0115] The depth-first search step is executed cyclically until a tail dwelling point is found, the distance between the grid where the tail dwelling point is located and the head grid is less than the preset grid spacing threshold, and the number of dwelling points found in the depth-first search step is greater than 2, then the depth-first search step is stopped;

[0116] A curve generating module 604 is used to connect the head dwell point and the dwell points found in the depth-first search step into a boundary curve;

[0117] The judgment module 605 is used to:

[0118] It is determined whether the quantity characteristic of the stationary points contained in the boundary curve meets the preset conditions. If so, the boundary curve is determined to be a no-man's land boundary curve.

[0119] In a possible embodiment of the present application, a device for identifying an uninhabited area is provided. Figure 7 This is a schematic diagram of an unmanned area identification device provided in an embodiment of the present application. Figure 7 As shown, the device 70 includes: a memory 701, a processor 702 and an interactive interface 703 that are communicatively connected to the memory 701, and the memory 701, the processor 702 and the interactive interface 703 are connected via a bus 704;

[0120] The memory 701 stores computer-executable instructions;

[0121] The processor 702 executes the computer-executable instructions stored in the memory 701 to implement the above-mentioned method for identifying an unmanned area.

[0122] In a possible embodiment of the present application, a computer-readable storage medium is provided, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the above-mentioned no-man's land identification method.

[0123] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0124] It should be further noted that, although the various steps in the flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0125] It should be understood that the above-described device embodiments are merely illustrative, and the device of the present application may also be implemented in other ways. For example, the division of units / modules in the above-described embodiments is merely a logical functional division, and actual implementations may employ other division methods. For example, multiple units, modules, or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0126] In addition, unless otherwise specified, the functional units / modules in the various embodiments of the present application may be integrated into a single unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The aforementioned integrated units / modules may be implemented in the form of hardware or software program modules.

[0127] If the integrated unit / module is implemented in hardware, the hardware may be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor may be any appropriate hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC. Unless otherwise specified, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0128] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0129] In the above embodiments, the description of each embodiment has its own focus. For parts not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0130] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0131] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for identifying an uninhabited area, characterized in that: The method comprises: Get the user's dwell point data; Dividing the target recognition area into a plurality of grids, calculating the dwell point density of each grid, and determining the dwell points contained in the grids whose dwell point density is less than or equal to a preset density threshold as target dwell points; Randomly selecting one of the target residence points as the head residence point, taking the grid where the head residence point is located as the head grid, taking the head grid as the current grid, and performing a depth-first search step, the depth-first search step comprising: taking the current grid as the center grid, searching for the residence points contained in the grids surrounding the current grid, and taking the grid where the searched residence point is located as the current grid; The depth-first search step is executed cyclically until a tail dwelling point is found, the spacing between the grid where the tail dwelling point is located and the head grid is less than a preset grid spacing threshold, and the number of dwelling points searched by the depth-first search step is greater than 2, then the depth-first search step is stopped; Connecting the head residence point and the residence points searched by the depth-first search step to form a boundary curve; It is determined whether the quantity characteristic of the stationary points contained in the boundary curve meets a preset condition, and if so, the boundary curve is determined to be a no-man's-land boundary curve.

2. The method according to claim 1, characterized in that The step of searching for the stationary point contained in the grids surrounding the current grid with the current grid as the center grid, and using the grid where the searched stationary point is located as the current grid includes: Taking the current grid as the center grid, search the grids from the 1st grid to the Nth grid around the current grid in sequence, where N is a preset search threshold. If the residence point is found in the nth grid around the current grid, the grid where the searched residence point is located is used as the current grid.

3. The method according to claim 1, characterized in that The determining whether the quantity characteristic of the stationary points contained in the boundary curve meets a preset condition, and if so, determining the boundary curve as a no-man's-land boundary curve, includes: Among the grids inside the boundary curve, determining a grid having a number of the dwell points greater than a preset number threshold as a marked grid; The ratio of the marked grid to the grid inside the boundary curve is calculated, and it is determined whether the ratio is less than a preset ratio threshold. If so, the boundary curve is determined to be a no-man's-land boundary curve.

4. The method according to claim 1, wherein After determining whether the quantity characteristic of the stationary points contained in the boundary curve satisfies a preset condition and determining the boundary curve as a no-man's-land boundary curve if so, the method further includes: Smoothing the no-man's land boundary curve to obtain a smoothed no-man's land boundary curve; Determine the stationary point through which the smoothed no-man's land boundary curve passes as a boundary stationary point, and determine a base station associated with the boundary stationary point based on signaling data associated with the boundary stationary point; If the base station associated with the border residence point receives the signaling data sent by the mobile terminal, it sends alarm information to the mobile terminal.

5. The method according to claim 4, characterized in that The step of smoothing the no-man's land boundary curve to obtain a smoothed no-man's land boundary curve includes: The no-man's land boundary curve is smoothed using cubic B-spline interpolation to obtain a smoothed no-man's land boundary curve.

6. The method according to claim 5, characterized in that The method of smoothing the no-man's land boundary curve using cubic B-spline interpolation to obtain a smoothed no-man's land boundary curve includes: Get the stationary points P0, P1..., P on the no-man's land boundary curve n-1 The latitude and longitude coordinate data, where P i =(x i ,y i ) are the latitude and longitude coordinates of the station point on the no-man’s land boundary curve; Set the longitude and latitude coordinates of the starting node and the ending node, use the starting node and the ending node as control points, and calculate the longitude and latitude coordinates of the boundary nodes according to the interpolation coordinate calculation formula. The interpolation coordinate calculation formula is: Among them, B x (t) is the longitude coordinate value of the boundary node, B y (t) is the latitude coordinate value of the boundary node, x i P i The longitude coordinate value, y i P i The latitude coordinate value of is the interpolation basis function in the longitude direction, is the interpolation basis function in the latitude direction, and t is m equally spaced numbers selected between [0,1]; The boundary nodes are marked according to their longitude and latitude coordinate values, and the boundary nodes are connected to form the smoothed no-man's land boundary curve.

7. The method according to any one of claims 1 to 6, characterized in that The calculating the resident point density of each grid comprises: The dwell point density is calculated according to the dwell point density calculation formula, which is: Wherein, ρ is the density of the resident points, n is the number of the resident points in a single grid, and a is the side length of the grid.

8. A device for identifying an uninhabited area, characterized in that: The device comprises: A data acquisition module is used to obtain the user's residence point data; Dwell point screening module for: Dividing the target recognition area into a plurality of grids, calculating the dwell point density of each grid, and determining the dwell points contained in the grids whose dwell point density is less than or equal to a preset density threshold as target dwell points; Boundary search module for: Randomly selecting one of the target residence points as the head residence point, taking the grid where the head residence point is located as the head grid, taking the head grid as the current grid, and performing a depth-first search step, the depth-first search step comprising: taking the current grid as the center grid, searching for the residence points contained in the grids surrounding the current grid, and taking the grid where the searched residence point is located as the current grid; The depth-first search step is executed cyclically until a tail dwelling point is found, the spacing between the grid where the tail dwelling point is located and the head grid is less than a preset grid spacing threshold, and the number of dwelling points searched by the depth-first search step is greater than 2, then the depth-first search step is stopped; a curve generating module, configured to connect the head dwell point and the dwell points found in the depth-first search step into a boundary curve; Judgment module, used for: It is determined whether the quantity characteristic of the stationary points contained in the boundary curve meets a preset condition, and if so, the boundary curve is determined to be a no-man's-land boundary curve.

9. An unmanned area identification device, characterized in that: The device includes: a memory, and a processor communicatively connected to the memory; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method for identifying an uninhabited area according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for identifying an uninhabited area according to any one of claims 1 to 7.

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