Method, device, storage medium and electronic device for estimating resident population distribution

Abstract

The present application provides a method, device, storage medium and electronic device for estimating the distribution of the number of residents, which relate to the field of big data. In the method, the electronic device obtains a variety of population distribution data in the study area, wherein each type of population distribution data corresponds to a local area in the study area; based on the multiple population distribution data, the population density of each of the multiple plots in the study area is obtained; the study area is divided into study sub-areas; based on the population density of each plot and the number of residents in the study sub-area, the number of residents in each plot in the neighborhood of the study sub-area is obtained. In this way, high-precision and high-frequency population data are provided through multi-source population distribution data.

Description

Technical Field

[0001] The present application relates to the field of big data, and more specifically, to a method, device, storage medium and electronic device for estimating the distribution of resident population. Background Art

[0002] The number of residents is the basis for land supply and supporting facilities layout, and is also a basic element of national economic development. The structure and quality of the population even directly determine the potential for regional development. Whether it is the construction of new urbanization or the planning and layout of national land space, reliable population data is indispensable. In addition, with the development of digitalization and information technology, smart city construction has achieved remarkable achievements, but the calculation of high-precision population data has always faced the problems of high cost, low coverage, and long cycle.

[0003] Traditional means of obtaining population data are mainly obtained through questionnaires and census data, and also through image acquisition through cameras. Among them, although the census method has the highest accuracy, the real-time nature of the data is relatively poor due to the long interval between censuses; in addition, the census requires the active cooperation of the subjects, otherwise there will be certain subjective errors. The image acquisition method usually requires the use of a large number of high-definition cameras to ensure full coverage of the monitoring range, and then based on the real-time acquisition of the target area video stream based on the camera and transmit it to the central processing system, the image processing software is used to pre-process the received video stream, including denoising, contrast enhancement, etc., and then the human body detection algorithm is used to detect pedestrians in the video frame and extract pedestrian features (such as contours, positions, etc.), and then the tracking algorithm is used to track the movement trajectory of pedestrians in continuous video frames according to pedestrian features, and the pedestrians entering and leaving the monitoring area are counted to complete population statistics. However, this method actually requires a lot of hardware and time costs.

[0004] Therefore, there is an urgent need for a low-cost, high-precision, and high-frequency method for estimating the number of resident population. Summary of the invention

[0005] In order to overcome at least one of the deficiencies in the prior art, the present application provides a method, device, storage medium and electronic device for estimating the distribution of residential population, specifically including:

[0006] In a first aspect, the present application provides a method for estimating the distribution of resident population, the method comprising:

[0007] Acquire a plurality of population distribution data in a study area, wherein each type of population distribution data corresponds to a local area in the study area;

[0008] According to the plurality of population distribution data, obtaining the population density of the plurality of plots in the study area;

[0009] dividing the study area into study sub-areas;

[0010] According to the population density of each of the plots and the number of residents in the research sub-area, the number of residents in each of the plots in the neighborhoods of the research sub-area is obtained.

[0011] In combination with the optional implementation manner of the first aspect, the multiple population distribution data include building layer data, population survey data and night light data.

[0012] In conjunction with an optional implementation manner of the first aspect, the study sub-area includes a plurality of sub-blocks, and obtaining the number of residents of each type of land parcel in the neighborhood of the study sub-area according to the population density of each type of land parcel and the number of residents in the study sub-area includes:

[0013] According to the area of ​​each sub-block and the population density of the land parcel to which it belongs, the population proportion of each sub-block is obtained;

[0014] Obtain the number of residents in the study sub-area;

[0015] According to the population proportion of each sub-block and the number of residents, the number of residents in each type of plot in the neighborhood of the research sub-area is obtained.

[0016] In combination with the optional implementation manner of the first aspect, obtaining the population proportion of each sub-block according to the area of ​​each sub-block and the population density of the corresponding plot includes:

[0017] Obtaining the distributed population of each sub-block according to the area of ​​each sub-block and the population density of the land parcel to which it belongs;

[0018] According to the distributed population of each of the sub-blocks, the total population in the research sub-area is obtained;

[0019] According to the distributed population of each sub-block and the total population, the population proportion of each sub-block is obtained.

[0020] In conjunction with the optional implementation manner of the first aspect, obtaining the number of residents in the study sub-area includes:

[0021] Acquire signaling data of each user in the study area within a preset time period;

[0022] Determining the residence of each user based on the signaling data of each user;

[0023] The number of residents in the research sub-area is determined based on the residence of each user.

[0024] In conjunction with an optional implementation manner of the first aspect, a plurality of base stations are distributed in the study area, and dividing the study area into study sub-areas includes:

[0025] Obtaining the location of each base station in the study area;

[0026] The position of each base station is used as a control point of a Thiessen polygon to divide the study area into study sub-areas.

[0027] In combination with the optional implementation manner of the first aspect, the method further includes:

[0028] Determining roads in the study sub-area;

[0029] A plurality of sub-blocks excluding the road are determined from the research sub-area.

[0030] In a second aspect, the present application further provides a device for estimating the distribution of resident population, the device comprising:

[0031] A population density module, used for acquiring a variety of population distribution data in a study area, wherein each type of population distribution data corresponds to a local area in the study area;

[0032] The population density module is further used to obtain the population density of each of the various plots in the study area according to the various population distribution data;

[0033] A residential population module for dividing the study area into study sub-areas;

[0034] The residential population module is used to obtain the number of residents of each type of land parcel in the neighborhood of the research sub-area based on the population density of each type of land parcel and the number of residents in the research sub-area.

[0035] In combination with an optional implementation manner of the second aspect, the multiple population distribution data include building layer data, population survey data and night light data.

[0036] In conjunction with the optional implementation manner of the second aspect, the research sub-area includes a plurality of sub-blocks, and the residential population module is further specifically used for:

[0037] According to the area of ​​each sub-block and the population density of the land parcel to which it belongs, the population proportion of each sub-block is obtained;

[0038] Obtain the number of residents in the study sub-area;

[0039] According to the population proportion of each sub-block and the number of residents, the number of residents in each type of plot in the neighborhood of the research sub-area is obtained.

[0040] In conjunction with the optional implementation manner of the second aspect, the residential population module is further specifically used for:

[0041] Obtaining the distributed population of each sub-block according to the area of ​​each sub-block and the population density of the land parcel to which it belongs;

[0042] According to the distributed population of each of the sub-blocks, the total population in the research sub-area is obtained;

[0043] According to the distributed population of each sub-block and the total population, the population proportion of each sub-block is obtained.

[0044] In conjunction with the optional implementation manner of the second aspect, the residential population module is further specifically used for:

[0045] Acquire signaling data of each user in the study area within a preset time period;

[0046] Determining the residence of each user based on the signaling data of each user;

[0047] The number of residents in the research sub-area is determined based on the residence of each user.

[0048] In conjunction with the optional implementation manner of the second aspect, a plurality of base stations are distributed in the study area, and the residential population module is further specifically used for:

[0049] Obtaining the location of each base station in the study area;

[0050] The position of each base station is used as a control point of a Thiessen polygon to divide the study area into study sub-areas.

[0051] In conjunction with the optional implementation manner of the second aspect, the residential population module is further used for:

[0052] Determining roads in the study sub-area;

[0053] A plurality of sub-blocks excluding the road are determined from the research sub-area.

[0054] In a third aspect, the present application also provides a storage medium, which is a computer-readable storage medium, and which stores a computer program. When the computer program is executed by a processor, the method for estimating the distribution of the number of resident population is implemented.

[0055] In a fourth aspect, the present application also provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for estimating the distribution of the number of resident population is implemented.

[0056] Compared with the prior art, this application has the following beneficial effects:

[0057] The present application provides a method, device, storage medium and electronic device for estimating the distribution of the number of residents. In the method, the electronic device obtains a variety of population distribution data in the study area, wherein each type of population distribution data corresponds to a local area in the study area; based on the multiple population distribution data, the population density of each of the multiple plots in the study area is obtained; the study area is divided into study sub-areas; based on the population density of each plot and the number of residents in the study sub-area, the number of residents in each plot in the neighborhood of the study sub-area is obtained. In this way, high-precision and high-frequency population data are provided through multi-source population distribution data. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 A flow chart of a method for estimating the number of resident population distribution provided in an embodiment of the present application;

[0060] Figure 2 A population density comparison chart of various plots provided in the embodiments of the present application;

[0061] Figure 3 A schematic diagram of a research sub-area and sub-block provided in an embodiment of the present application;

[0062] Figure 4 A schematic diagram comparing the prediction results provided in the embodiment of the present application with the actual survey results;

[0063] Figure 5 A schematic diagram of the structure of a device for estimating the number of resident population distribution provided in an embodiment of the present application;

[0064] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0066] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0067] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0068] In the description of the present application, the term "comprises", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0069] Based on the above statement, as introduced in the background technology, there are many problems with traditional means of obtaining population data. Therefore, there is an urgent need for a low-cost, high-precision, and high-frequency method for estimating the number of resident population.

[0070] Based on the discovery of the above technical problems, the inventors have proposed the following technical solutions to solve or improve the above problems through creative work. It should be noted that the defects in the solutions in the above prior art are the results obtained by the inventors after practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed in the embodiments of the present application for the above problems below should all be the contributions made by the inventors to the present application in the process of invention and creation, and should not be understood as technical contents known to those skilled in the art.

[0071] In view of this, this embodiment provides a method for estimating the distribution of the number of residents. In this method, an electronic device obtains a variety of population distribution data in a study area, wherein each type of population distribution data corresponds to a local area in the study area; based on the multiple population distribution data, the population density of each of the multiple plots in the study area is obtained; the study area is divided into study sub-areas; based on the population density of each plot and the number of residents in the study sub-area, the number of residents in each plot in the neighborhood of the study sub-area is obtained. In this way, high-precision and high-frequency population data are provided through multi-source population distribution data.

[0072] It is worth noting that for the scheme involved in this application, the data related to user privacy is explicitly notified before being collected and generated during the execution of the embodiments of this application, and is authorized by the user. At the same time, for the personal information involved in the embodiments of this application, the location of the storage device is in compliance with the laws and regulations of the country / region where the above-mentioned data-related behaviors occur, and the related behaviors include but are not limited to: authorization, generation, use, storage, etc. It can be understood that the population distribution data, user signaling data and other information used to implement this scheme, the location of the storage device is in compliance with the laws and regulations of the country / region where the above-mentioned data-related behaviors occur, and the related behaviors include but are not limited to: authorization, generation, use, storage, etc.

[0073] In addition, it should be noted that the electronic device for implementing the method for estimating the distribution of the number of resident population may be, but is not limited to, a mobile terminal, a tablet computer, a laptop computer, a desktop computer, a server, etc. The server may be a single server or a server group. The server group may be centralized or distributed (for example, the server may be a distributed system). In some embodiments, the server may be local or remote relative to the user terminal. In some embodiments, the server may be implemented on a cloud platform; as an example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, etc., or any combination thereof. In some embodiments, the server may be implemented on an electronic device having one or more components.

[0074] To make the solution provided by this embodiment clearer, Figure 1 Each step of the method is described in detail. However, it should be understood that the operations of the flowchart may not be implemented in order, and steps without logical contextual relationships may be reversed in order or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowchart, or remove one or more operations from the flowchart under the guidance of the content of this application. Figure 1 As shown, the method includes:

[0075] S1, obtain various population distribution data in the study area.

[0076] Among them, each type of population distribution data corresponds to a local area in the study area. In an optional implementation, the multiple population distribution data include building layer data, population survey data and night light data. It can be understood that the above three types of data reflect the population distribution in the study area from different angles. The building layer data provides the physical properties and uses of the building, which helps to estimate the population size inside the building; the population survey data is obtained through field surveys and provides direct population size and distribution information; the night light data uses light intensity to indirectly reflect population activities, especially in the absence of other data, it can be used as a supplementary means. These three types of data work together to estimate the population distribution in the study area more comprehensively and accurately. The following is a detailed description of these three types of data:

[0077] Building layer data, for local areas with complete building layers, obtaining building layer data refers to the specific information of each building in the local area, including the building's geographical location, base area, building height, total building area, etc. These data can be used to estimate the population size in each building. Specifically, building layer data usually contains detailed parameters for each building, such as the building base area (i.e., the projected area of ​​the building on the ground), building height (the height from the ground to the roof), total building area (the total area of ​​all floors inside the building), etc. In addition, it may also include information such as the purpose of the building (such as residential, commercial, office, etc.), the vacancy rate of the building (i.e., the proportion of the unused part to the total area), etc.

[0078] Population survey data can be used to supplement local areas where there is no building layer or the building layer data is incomplete and there are conditions for survey. That is, by randomly selecting some plots for field surveys, the survey ratio can be based on a sampling rate of 1‰ or lower, and the number of residents on these plots is recorded. In this way, relatively accurate population distribution data can be obtained even without building data.

[0079] Night light data refers to the light brightness information on the earth's surface at night collected through satellite remote sensing technology. In this embodiment, for local areas where field surveys cannot be conducted, especially in the absence of building layer data or where field surveys cannot be conducted, night light data is used to estimate the population in the local area. Specifically, it is assumed that within the same plot of land, the higher the light brightness, the higher the population density. By statistically calculating the light brightness of the plot corresponding to each land type, the light brightness value of each land type can be obtained, and then used as the weight for subsequent high-precision population data calculation. It should be noted that light brightness is easily affected by external factors such as street lighting, so it is necessary to eliminate these interfering factors, especially the light brightness of roads and transportation facilities. In this way, the population density can be estimated more accurately, thereby improving the accuracy of the overall population distribution data.

[0080] Based on the introduction of various population distribution data in the above embodiments, continue to refer to Figure 1 The method for estimating the distribution of the number of residents provided in this embodiment also includes:

[0081] S2, based on various population distribution data, obtain the population density of various plots in the study area.

[0082] In this embodiment, combined with the data of the third national land survey, the study area is divided into types of plots such as urban residential land, rural homestead land, higher education land, commercial service facility land, government and group land, science, education, culture and health land, highway land, railway land, industrial land, logistics and warehousing land, and public utility land.

[0083] For example, continuing to take the building layer data as an example, the population density value of each land type can be calculated by the following expression:

[0084]

[0085] In the formula, Indicates The population density of the plot, For the The number of plots corresponding to the planting plots, Number one of the plots; It represents the The area of ​​the plot; Indicates The number of buildings contained in the plot, Indicates a specific building number; To indicate the The building base area corresponding to each building, is the total building height, For the floor height, is the vacancy rate, The building type is 0 if it is under construction or has not been completed, and 1 if it has been completed. Per capita according to statistics The land area of ​​this type can be obtained by looking up the statistical yearbook or other relevant data. Therefore, the meaning of this expression is that by Each plot in the plot Each building within Estimate population size and sum to calculate average population density.

[0086] Of course, considering that not all local areas in the study area have building layers and complete data, when calculating the population density of each plot, it is also necessary to combine the population size and area of ​​each plot estimated by other population distribution data to obtain the population density of each plot. Figure 2 As shown, for the study area of ​​this embodiment, the population density of each type of land is the highest in urban residential land, because this is the main area where residents live; rural residential land is second, although the residential density is lower, there is still a certain number of residents; other land types such as commercial land and industrial land have lower population densities, mainly because these areas are mainly used for work and production activities, and have fewer residents. Overall, areas with higher population density are usually where residents live in large numbers.

[0087] Based on the above embodiments, the population density of various plots is introduced. Figure 1 , the method provided in this embodiment also includes:

[0088] S3, divide the study area into study sub-areas.

[0089] In this implementation, there are multiple base stations distributed in the study area. Since the coverage of the base station is clear, this embodiment divides the study area into several polygons based on the location of the base station. Any point in each polygon is closest to its controlling base station, so as to more accurately count and allocate the number of residents within the coverage of each base station, and combine the local market share of the operator and the mobile phone penetration rate to calculate the number of residents after the expansion. This method can better match the actual population distribution and improve the accuracy of the estimation.

[0090] Therefore, as an optional implementation, the electronic device may obtain the position of each base station in the study area; use the position of each base station as a control point of a Thiessen polygon, and divide the study area into study sub-areas.

[0091] For example, the location data of all base stations in the study area can be collected, and then these base stations are used as control points, and the Thiessen polygons are constructed using spatial analysis tools (e.g., the Create Thiessen Polygons tool in ArcGIS software), so that any point in each Thiessen polygon is closest to its control base station, and farther away from other base stations. In this way, the study area can be divided into several Thiessen polygon faces, each of which represents the coverage of a base station.

[0092] In this implementation, each divided Thiessen polygon surface is used as a research sub-area. This allows for more accurate management and statistics of the number of residents within the coverage area of ​​each base station. Figure 3 As shown in the figure, a local area of ​​the study area is shown, and the local area is divided into multiple study sub-areas 11 using Thiessen polygons.

[0093] Continue to see Figure 1 , the method provided in this embodiment also includes:

[0094] S4. According to the population density of each plot and the number of residents in the study sub-area, the number of residents in each plot in the neighborhood of the study sub-area is obtained.

[0095] It should be understood that the research sub-area also includes multiple sub-blocks. The electronic device can determine the roads in the research sub-area; then, determine multiple sub-blocks other than the roads from the research sub-area.

[0096] For example, see Figure 3 In each of the study sub-areas 11 shown, the roads in the study sub-area 11 divide the study sub-area 11 into a plurality of neighborhoods. It is not difficult to see from the figure that some neighborhoods may be relatively complete neighborhoods, while some neighborhoods are partial areas of some complete neighborhoods. Figure 3 From the legend, we can see that each neighborhood may contain multiple sub-blocks, which may correspond to multiple land types, such as urban residential land, higher education land, etc. Each land type may also correspond to multiple spatial surfaces.

[0097] Based on the introduction of multiple sub-blocks in the research sub-region in the above embodiment, this embodiment also provides the following optional implementation of step S4:

[0098] S4-1, according to the area of ​​each sub-block and the population density of the land parcel to which it belongs, obtain the population proportion of each sub-block.

[0099] For example, the electronic device counts the types of plots and corresponding areas of the sub-blocks in each neighborhood involved in the sub-area, and estimates the population proportion of each sub-block in the sub-area under study based on the population density of each plot, and the calculation expression is:

[0100]

[0101] In the formula, Indicates that for In the study sub-area Neighborhoods, including The plot type is The area of ​​the sub-block, Indicates Population density of cultivated land, It means the The plot type is The population ratio between the population in the sub-block and the total population in the study sub-area.

[0102] S4-2, obtain the number of residents in the study sub-area.

[0103] This embodiment uses the mobile phone signaling data obtained by the base station to analyze the number of residents in the study sub-area at a relatively low cost, that is, the population living in the study sub-area for a long time. That is, the electronic device obtains the signaling data of each user in the study area within a preset time period; determines the residence of each user based on the signaling data of each user; and determines the number of residents in the study sub-area based on the residence of each user.

[0104] Exemplarily, the electronic device clusters the daily signaling data of each user according to the spatial location using the DBSCAN algorithm to identify the user's residence location and the start and end time of his / her residence. Generally speaking, the observation period is not less than 4 weeks. For residences with the same location but discontinuous time and residences in different locations, they are numbered and distinguished to obtain the user's residence information at the daily level. Furthermore, the electronic device adds up the time of all the user's residences during the evening period to obtain the location where the user stays the longest, and combines the number of days threshold (not less than 1 / 2 of the observation period, i.e. 2 weeks) to determine each user's residence for the month; finally, based on the population of the residence in the study sub-area, the number of residents in the study sub-area is obtained. The entire algorithm process can be represented by the following expression:

[0105]

[0106] In the formula, since the observation period is not less than 4 weeks, it is assumed that the observation period is 30 days, and the day threshold is 1 / 2 of the observation period. Therefore, the day threshold is 15 days, that is, , Indicates The number of the place where the user stayed at night is , for each stopover, calculate the duration of night stays in that month , and the number of days the stay occurs , For users The last place of residence.

[0107] Based on the residential location obtained from mobile phone signaling data, the number of residents in the study sub-area can be obtained by the following expression:

[0108]

[0109] In the formula, For the study sub-area The number of residents in For users Place of residence (here the value is 1), Represents the user Place of residence Is the sub-region being studied? If yes, its value is 1, if no, its value is 0. It represents the expansion coefficient obtained by combining the operator's market share, mobile phone penetration rate and valid SIM cards, that is, how many actual users are represented by 1 user, and is provided by the operator.

[0110] S4-3, based on the population proportion and the number of residents in each sub-block, the number of residents in each type of plot in the neighborhood of the study sub-area is obtained.

[0111] As Figure 3 As shown, each neighborhood may contain multiple sub-blocks, and these sub-blocks may correspond to multiple types of plots. It can be understood that there may be multiple sub-blocks belonging to the same type of plot in each neighborhood. After the number of residents in the study sub-area and the population proportion of each sub-block are known, the number of residents in each type of plot in the study sub-area can be obtained by the following expression:

[0112]

[0113]

[0114] In the formula, For the The number of residents in the study sub-area, Indicates that for In the study sub-area Neighborhoods, plot type No. The number of people living in each sub-block; Neighborhood Middle The number of people living in the plot.

[0115] Thus, in the above implementation, a solution is provided for estimating high-precision population distribution data by integrating mobile phone signaling, land use data from the third national land survey, and nighttime light data. Figure 4 As shown, the predicted results of the above embodiment for the resident population are compared with the actual survey results. The two are well consistent, and the correlation coefficient reaches 0.8. Therefore, the accuracy requirement can be met.

[0116] Based on the method for estimating the number of residents in this embodiment. This embodiment also provides a device for estimating the number of residents in this embodiment, which includes at least one software function module that can be stored in a memory or fixed in an electronic device in the form of software. The processor in the electronic device is used to execute the executable module stored in the memory. For example, the software function module and computer program included in the device. Please refer to Figure 5 , functionally speaking, the device may include:

[0117] A population density module 21 is used to obtain a variety of population distribution data in the study area, wherein each type of population distribution data corresponds to a local area in the study area;

[0118] The population density module 21 is further used to obtain the population density of various plots in the study area according to various population distribution data;

[0119] Residential population module 22, used to divide the study area into study sub-areas;

[0120] The residential population module 22 is used to obtain the number of residents of each type of land parcel in the neighborhood of the research sub-area based on the population density of each type of land parcel and the number of residents in the research sub-area.

[0121] In this embodiment, the population density module 21 is used to implement Figure 1 In steps S1 and S2, the residential population module 22 is used to implement Figure 1 Therefore, for the detailed description of each module, please refer to the specific implementation of the corresponding step. In addition, since the invention concept is the same as that of the above-mentioned method for estimating the number of resident population distribution, it means that the device for estimating the number of resident population distribution can also implement other steps or sub-steps of the method through the above-mentioned modules, which will not be described in detail in this embodiment.

[0122] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0123] It should also be understood that if the above implementation is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0124] Therefore, this embodiment further provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the method for estimating the distribution of the number of resident population provided in this embodiment is implemented. The storage medium can be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes.

[0125] This embodiment provides an electronic device for implementing a method for estimating the distribution of the number of resident population. Figure 6 As shown, the electronic device includes a processor 32 and a memory 31. In addition, the memory 31 stores a computer program, and the processor implements the method for estimating the distribution of the number of residents provided in this embodiment by reading and executing the computer program corresponding to the above implementation in the memory 31.

[0126] Continue to see Figure 6 The electronic device further includes a communication unit 33. The memory 31, the processor 32 and the communication unit 33 are electrically connected to each other directly or indirectly through a system bus 34 to achieve data transmission or interaction.

[0127] The memory 31 may be an information recording device based on any electronic, magnetic, optical or other physical principle, used to record execution instructions, data, etc. In some embodiments, the memory 31 may be, but is not limited to, a volatile memory, a non-volatile memory, a storage drive, etc.

[0128] In some embodiments, the volatile memory may be a random access memory (RAM); in some embodiments, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), a flash memory, etc.; in some embodiments, the storage drive may be a disk drive, a solid-state drive, any type of storage disk (such as a CD, a DVD, etc.), or a similar storage medium, or a combination thereof, etc.

[0129] The communication unit 33 is used to send and receive data through a network. In some embodiments, the network may include a wired network, a wireless network, a fiber optic network, a telecommunication network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, or a near field communication (NFC) network, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include a wired or wireless network access point, such as a base station and / or a network switching node, and one or more components of the service request processing system may be connected to the network through the access point to exchange data and / or information.

[0130] The processor 32 may be an integrated circuit chip with signal processing capability, and the processor may include one or more processing cores (e.g., a single-core processor or a multi-core processor). By way of example only, the processor may include a central processing unit (CPU), an application specific integrated circuit (ASIC), an application specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), or a microprocessor, or any combination thereof.

[0131] Understandably, Figure 6The structure shown is for illustration only. The electronic device may also have Figure 6 More or fewer components than shown, or with Figure 6 Different configurations are shown. Figure 6 The components shown may be implemented in hardware, software or a combination thereof.

[0132] It should be understood that the apparatus and method disclosed in the above-mentioned embodiments can also be implemented in other ways. The apparatus embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the apparatus, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0133] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for estimating the distribution of resident population, characterized in that: The method comprises: Acquire a variety of population distribution data in the study area, wherein the variety of population distribution data includes building layer data, population survey data and night light data, and each type of population distribution data corresponds to a local area in the study area; According to the plurality of population distribution data, the population density of each of the plurality of plots in the study area is obtained, wherein the study area is distributed with a plurality of base stations; Obtaining the location of each base station in the study area; Using the position of each base station as a control point of a Thiessen polygon, the study area is divided into study sub-areas, wherein the study sub-areas include a plurality of sub-blocks; According to the area of ​​each sub-block and the population density of the land parcel to which it belongs, the distributed population of each sub-block is obtained; according to the distributed population of each sub-block, the total population in the research sub-area is obtained; according to the distributed population of each sub-block and the total population, the population proportion of each sub-block is obtained; Acquire signaling data of each user in the study area within a preset time period; Determining the residence of each user based on the signaling data of each user; Determine the number of residents in the study sub-area according to the residence of each user, wherein the roads in the study sub-area divide the study sub-area into a plurality of neighborhoods, each neighborhood includes a plurality of sub-blocks, and the sub-blocks correspond to a plurality of plot types; According to the population proportion of each sub-block and the number of residents, the number of residents of each block in the neighborhood of the research sub-area is obtained, and the expression is: In the formula, For the The number of residents in the study sub-area, It means the The plot type is The population ratio between the population in the sub-block and the total population in the study sub-area, Indicates that for In the study sub-area Neighborhoods, plot type No. The number of people living in each sub-block, Neighborhood Middle The number of people living in the plot.

2. A device for estimating the distribution of resident population, characterized in that: The device comprises: A population density module is used to obtain a variety of population distribution data in the study area, wherein the multiple population distribution data include building layer data, population survey data and night light data, and each type of population distribution data corresponds to a local area in the study area; The population density module is further used to obtain the population density of each of the various plots in the study area according to the various population distribution data, wherein the study area is distributed with a plurality of base stations; A residential population module is used to obtain the position of each base station in the study area; use the position of each base station as a control point of a Thiessen polygon to divide the study area into study sub-areas, wherein the study sub-areas include a plurality of sub-blocks; The residential population module is further used to obtain the distributed population of each sub-block according to the area of ​​each sub-block and the population density of the land parcel to which it belongs; obtain the total population in the research sub-area according to the distributed population of each sub-block; and obtain the population proportion of each sub-block according to the distributed population of each sub-block and the total population; Acquire signaling data of each user in the study area within a preset time period; Determining the residence of each user based on the signaling data of each user; Determine the number of residents in the study sub-area according to the residence of each user, wherein the roads in the study sub-area divide the study sub-area into a plurality of neighborhoods, each neighborhood includes a plurality of sub-blocks, and the sub-blocks correspond to a plurality of plot types; According to the population proportion of each sub-block and the number of residents, the number of residents of each block in the neighborhood of the research sub-area is obtained, and the expression is: In the formula, For the The number of residents in the study sub-area, It means the The plot type is The population ratio between the population in the sub-block and the total population in the study sub-area, Indicates that for In the study sub-area Neighborhoods, plot type No. The number of people living in each sub-block, Neighborhood Middle The number of people living in the plot.

3. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, the method for estimating the distribution of the number of resident population as claimed in claim 1 is implemented.

4. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for estimating the distribution of the number of resident population as claimed in claim 1 is implemented.

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