Life circle delimiting method, system and device and storage medium

Abstract

The invention discloses a life circle defining method, system and device and a storage medium. The method comprises the following steps: acquiring mobile phone signaling data and residential quarter AOI data; according to the mobile phone signaling data, identifying an individual living anchor point, and based on the residential quarter AOI data, determining a function type of a grid of a visited destination of the individual; and based on the function type, extracting activity places of collective regular access, and delimiting a space range of a life circle. The method is beneficial to improving the accuracy of life circle delimiting, and can be widely applied to the technical field of urban space planning.

Description

Technical Field

[0001] The present invention relates to the technical field of urban space planning, and in particular to a method, system, device and storage medium for delineating a living circle. Background Art

[0002] The concept of providing nearby services to all residents has become increasingly popular in recent years, particularly in megacities with rapidly changing population structures and significant spatial disparity. Mobile phone signaling data, with its wide coverage, real-time availability, and relatively low cost, is widely used in transportation planning, urban planning, socioeconomic analysis, and other fields, demonstrating significant potential in capturing the patterns of human activity.

[0003] Existing technical solutions for planning using mobile phone signaling data are mainly based on the processing of individual data, lack consideration of the regularity of collective activities, and are unable to form large-scale data sets, resulting in inaccurate delineation of the spatial scope of the living circle. Summary of the Invention

[0004] The purpose of the present invention is to provide a more accurate living circle delineation method, system, device and storage medium.

[0005] On one hand, this application provides a method for delineating a living circle, comprising: obtaining mobile phone signaling data and residential area area (AOI) data; identifying anchor points in an individual's daily life based on the mobile phone signaling data, and determining the functional type of the grid of the individual's visited destination based on the residential area AOI data; and extracting the collectively regularly visited activity locations based on the functional type, and delineating the spatial scope of the living circle. This application is conducive to improving the accuracy of living circle delineation.

[0006] Optionally, based on the functional type of the grid visited by the individual, the activity locations that the group regularly visits are extracted, and the spatial scope of the living circle is delineated, including:

[0007] Determine a basic time period; the basic time period is the minimum number of days that can reflect the daily activity patterns of the group;

[0008] Extracting the mobile phone signaling data based on the basic time period to determine the activity location visited by the group regularly;

[0009] The activity locations are aggregated using a minimum polygon algorithm to define the spatial scope of the living circle.

[0010] Optionally, determine a basic time period, including:

[0011] Determine the probability density function of each individual visiting the activity location;

[0012] determining an individual time period based on the probability density function;

[0013] According to the individual time periods, the basic time period that can reflect the daily activity patterns of the group is determined.

[0014] Optionally, extracting the mobile phone signaling data based on the basic time period to determine the activity location that is regularly visited by the group includes:

[0015] Extracting mobile phone signaling data for several consecutive basic time periods;

[0016] aggregating the mobile phone signaling data by individual and destination grids to determine individual travel trajectories;

[0017] All the individual travel trajectories are screened to determine the destination grids under different set visit rates.

[0018] Optionally, aggregating the activity locations by a minimum polygon algorithm to define the spatial scope of the living circle includes:

[0019] Aggregating the destination grids under the set visit rate using a minimum polygon algorithm to obtain an aggregated polygonal spatial range;

[0020] The aggregated polygonal spatial range is superimposed and compared with the city grid to determine the spatial range of the living circle under different visit rates, which is presented as a continuous grid.

[0021] Optionally, the identification method of the present application further includes:

[0022] Deduplication, data cleaning, and preprocessing of the mobile phone signaling data;

[0023] The residential area AOI data is deduplicated, spatially corrected, and normalized.

[0024] Optionally, identifying anchor points in the individual's daily life based on the mobile phone signaling data, and determining the functional type of the destination grid visited by the individual based on the residential area AOI data, includes:

[0025] The grids where the residence time at the visited places is greater than or equal to the first duration are selected as the anchor points of individual daily life;

[0026] If the grid corresponding to the individual's daily life anchor point with the longest dwell time within the first preset time is located in the residential area AOI, the corresponding grid is determined to be the individual's residence;

[0027] If the individual is a permanent resident, the grid corresponding to the individual's daily life anchor point with the longest residence time within the second preset time is determined as the individual's workplace;

[0028] If the dwelling time of the individual visiting the first grid is greater than or equal to the second duration, the first grid is not a residence and the first grid is not a workplace, and the first grid is determined to be the activity place of the individual.

[0029] On the other hand, an embodiment of the present invention provides a system for defining a living circle, the system comprising:

[0030] The first module is used to obtain mobile phone signaling data and residential area AOI data;

[0031] The second module is used to identify anchor points in the individual's daily life based on the mobile phone signaling data, and determine the functional type of the destination grid visited by the individual based on the residential area AOI data;

[0032] The third module is used to extract the activity locations that are visited regularly by the group based on the functional types and to define the spatial scope of the living circle.

[0033] On the other hand, an embodiment of the present invention provides a device for defining a living circle, the device comprising:

[0034] at least one processor;

[0035] at least one memory for storing at least one program;

[0036] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method for defining a living circle.

[0037] On the other hand, an embodiment of the present invention provides a storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to implement the above-mentioned method for delineating a living circle.

[0038] The method provided by an embodiment of the present invention includes: obtaining mobile phone signaling data and residential area area (AOI) data; identifying anchor points in an individual's daily life based on the mobile phone signaling data; and determining the functional type of the grid of the individual's visited destination based on the residential area AOI data; and based on the functional type, extracting the collectively regularly visited activity locations and delineating the spatial scope of the living circle. This application is conducive to improving the accuracy of living circle delineation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present invention or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1 A flow chart of an embodiment of a method for defining a living circle provided by the present invention;

[0041] Figure 2 A flow chart of another embodiment of the method for defining a living circle provided by the present invention;

[0042] Figure 3 A schematic diagram of the process of inferring the city grid function type corresponding to the destination visited by an individual provided by the present invention;

[0043] Figure 4 A schematic diagram of the process of screening collective regularly visited locations and demarcating the spatial scope of a community life circle provided by the present invention;

[0044] Figure 5 A schematic diagram of the process of demarcating the spatial scope of a community life circle under different visit rates provided by the present invention;

[0045] Figure 6 A schematic structural diagram of an embodiment of the living circle demarcation system provided by the present invention;

[0046] Figure 7 This is a structural schematic diagram of an embodiment of the living circle demarcation device provided by the present invention. DETAILED DESCRIPTION

[0047] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0048] The concept of providing nearby services to all residents has become increasingly popular in recent years, especially in megacities with rapidly changing population structures and significant spatial disparity. Mobile phone signaling data, with its wide coverage, real-time availability, and relatively low cost, has been widely used in transportation planning, urban planning, socioeconomic analysis, and other fields, demonstrating great potential in capturing the regularity of people's activities. However, existing approaches to measuring urban community life circles using mobile phone signaling data suffer from two issues: First, existing community life circle measurements are mostly based on individual-scale travel data and lack a collective description of activity spaces. The spatial extent of a community life circle is formed by the collective behavior of residents living in the same location, and its measurement requires the aggregation of individual travel behaviors. Second, although mobile phone signaling data records a series of mobile phone users' activity locations, these activities do not always occur regularly. Therefore, when aggregating individual-level activity spaces into collective-level activity spaces, it is necessary to filter out those locations that residents visit randomly or sporadically, extract the regularity of community residents' collective behavior, and thus identify the spatial extent of the community life circle.

[0049] Meanwhile, some studies using traditional questionnaires and GPS data to study residents' travel behavior are costly, and the data collected often fails to fully capture residents' activities. Furthermore, the data samples only include a small number of people, making it difficult to infer collective activity patterns based on this small amount of data. Existing studies using mobile phone signaling data to explore residents' travel behavior and activity spaces often overlook temporal rhythms, failing to fully explore the periodicity, repetitiveness, and regularity of habitual behavior. Range deviations and characteristic anomalies caused by residents' sporadic and random behaviors are not effectively identified. When aggregating individual residents' travel trajectories into collective travel trajectories, it is necessary to filter out places that are not regularly visited by the collective. Overall, existing research has yet to develop a systematic and mature technical solution for defining the functional types of places visited by urban residents and the scope of community living circles.

[0050] The present invention aims to make up for the shortcomings of the existing technology and proposes a technical method for measuring community living circles using mobile phone signaling data. It aggregates individual behaviors according to their place of residence to reflect the spatial heterogeneity of different areas of the city, and accurately captures and identifies the collective regular travel behavior of urban residents and the spatial scope of their community living circles.

[0051] The following describes in detail the method and system for defining a living circle according to an embodiment of the present invention.

[0052] Reference Figure 1 In an embodiment of the present invention, a method for defining a living circle is provided, which mainly includes the following steps:

[0053] S100: Acquire mobile phone signaling data and residential area AOI data;

[0054] S200: identifying anchor points in the individual's daily life based on mobile phone signaling data, and determining the functional type of the individual's visited destination grid based on residential area area of interest data;

[0055] S300: Based on the functional type, extract the activity locations that are regularly visited by the group and define the spatial scope of the living circle.

[0056] Mobile phone signaling data refers to mobile phone signaling data from terminals owned by individuals / residents within a defined area. Individual life anchor points are the actual locations where residents move or stay. Functional types represent the purpose of the grid, such as residence, workplace, or activity venue. Based on the functional type of each individual's visit to the grid within the area, we filter / extract the locations that are regularly visited by the group. Based on these regularly visited locations, we then define the spatial scope of the living circle.

[0057] Optionally, based on the functional type of the grid visited by the individual, the activity locations that the group regularly visits are extracted, and the spatial scope of the living circle is delineated, including:

[0058] Determine the basic time period; the basic time period is the minimum number of days that can reflect the daily activities of the group;

[0059] Extract mobile phone signaling data based on basic time periods to determine the locations of activities that are regularly visited by groups;

[0060] Activity locations are aggregated using the minimum polygon algorithm to define the spatial scope of the living circle.

[0061] The basic time period in this application is determined based on the activity patterns of all individuals in the study area.

[0062] Optionally, determine a basic time period, including:

[0063] Determine the probability density function of each individual visiting the activity location;

[0064] Based on the probability density function, determine the individual time period;

[0065] Determine the basic time period based on the individual time period.

[0066] In this application, the individual time period can be determined based on the KL divergence indicator, and the basic time period is determined based on the individual time periods of all individuals.

[0067] Optionally, extracting mobile phone signaling data based on a basic time period to determine the location of activities that are regularly visited by the group includes:

[0068] Extracting mobile phone signaling data for several consecutive basic time periods;

[0069] Mobile phone signaling data is aggregated by individual and destination grids to determine individual travel trajectories;

[0070] All individual travel trajectories are screened to determine the destination grids under different set visit rates.

[0071] This application aggregates destination grids under different set visit rates into activity locations that are visited collectively and regularly.

[0072] Optionally, the activity locations are aggregated using a minimum polygon algorithm to define the spatial scope of the living circle, including:

[0073] The destination grids under the set visit rate are aggregated using the minimum polygon algorithm to obtain the aggregated polygonal spatial range;

[0074] The spatial scope of the aggregated polygons is superimposed and compared with the urban grid to determine the spatial scope of the living circle, which is presented as a continuous grid.

[0075] Optionally, the identification method of the present application further includes:

[0076] Deduplication, data cleaning, and pre-processing of mobile phone signaling data;

[0077] The AOI data of residential areas are deduplicated, spatially corrected, and normalized.

[0078] This application performs normalization processing on the residential area AOI data after deduplication and spatial position correction, such as eliminating the residential area AOI with an area smaller than one grid unit; and integrating the retained residential area AOI into several complete grids.

[0079] Optionally, identifying anchor points in the individual's daily life based on the mobile phone signaling data, and determining the functional type of the destination grid visited by the individual based on the residential area AOI data, includes:

[0080] The grids where the residence time at the visited places is greater than or equal to the first duration are selected as the anchor points of individual daily life;

[0081] If the grid with the longest dwell time within the first preset time corresponding to the individual's daily life anchor point is located in the residential area AOI, the corresponding grid is determined to be the individual's residence;

[0082] If the individual is a permanent resident, the grid corresponding to the individual's daily life anchor point with the longest residence time within the second preset time period is determined as the individual's workplace;

[0083] If the dwelling time of the individual visiting the first grid is greater than or equal to the second duration, the first grid is not a residence and the first grid is not a workplace, and the first grid is determined to be the activity place of the individual.

[0084] In this application, the first duration is the threshold for identifying an individual's life anchor point; the first preset time is non-working hours at night. Permanent residents can be identified by whether the number of days they have lived in a fixed residence within a period of time meets the corresponding threshold. The second preset time is working hours on weekdays. The second duration is the residence threshold for identifying individual activities. The first grid in this application refers to one of the grids among all grids and does not limit the number of grids.

[0085] The following is a detailed description of the living circle demarcation method provided by this application using a specific embodiment:

[0086] The purpose of the present invention is to provide a method for accurately identifying the functional types of actual activity locations of urban residents and delineating the spatial scope of community living circles based on mobile phone signaling data.

[0087] Based on large-scale mobile phone signaling data in cities, this paper proposes a four-step workflow for accurately identifying the functional types of urban residents' actual activity locations and the spatial scope of their community life circles: S1. Preprocessing of mobile phone signaling data and residential area area of interest (AOI) data; S2. Identification of anchor points for individual residents' daily activities; S3. Inference of the functional types of individual life anchor points; S4. Screening of activity locations that are regularly visited by the group and demarcating the spatial scope of the community life circle ( Figure 2 ).

[0088] The basic steps of the present invention are as follows:

[0089] S1. Preprocessing of mobile phone signaling data and residential area AOI data:

[0090] S11. Mobile phone signaling data acquisition and preprocessing: Obtain mobile phone signaling data and mobile phone user attribute information within the study area from the operator, including user ID, residence location latitude and longitude, residence time, user travel time, etc. Through data cleaning, remove drift data, duplicate data, and ping-pong data in the mobile phone signaling data;

[0091] S12. Acquisition and preprocessing of residential area AOI data: Use web crawler tools to collect AOI data of all residential areas within the study area from Baidu Maps and perform the following processing:

[0092] (1) In GIS, the “Delete Identical Items” tool was used to delete duplicate AOI data. Then, the “Remove Overlapping Items (Multiple)” tool was used, and the “Grid” method was selected in the tool to deal with the boundary overlap problem in the AOI data. Finally, a residential area AOI geospatial dataset without overlap and duplication was obtained.

[0093] (2) Considering that the extraction accuracy of mobile phone signaling data is 150m×150m, and some residential AOIs may be smaller than the unit extraction accuracy, it is necessary to use the "Create Fishnet" tool in GIS to convert the study area and the residential AOI dataset processed in the previous step into a 150m×150m grid, and then eliminate the residential AOIs with an area smaller than one grid unit. At the same time, for residential AOIs with an area larger than one grid unit, when dividing the grid, the part larger than half of the grid area is considered to occupy one grid, and the part smaller than half is eliminated to ensure that all residential AOIs are divided into complete grids.

[0094] S2. Anchor point identification of individual residents’ daily activities:

[0095] Mobile phone signaling data can update the location of mobile phone users by actively capturing signals or network events. However, due to the influence of "over-sampling or under-sampling signals", some captured spatial locations may not be the actual activity locations of mobile phone users. It is necessary to set a minimum residence time for users at the visited locations to identify the actual activity locations of residents. This is called "individual life anchor point" in the present invention, and a residence time of at least 30 minutes (i.e., the first duration in this application) is used as the threshold for identifying individual life anchor points.

[0096] S3. Infer the functional type of individual life anchor points: Based on mobile phone signaling data and user travel behavior characteristics, determine the corresponding functional type of the urban grid where the resident has visited and stayed ( Figure 3 );

[0097] In the previous step, individual life anchor points were identified. For most people, the functions of their anchor points mainly include residence, work, and other important activity locations. Therefore, the present invention, based on the grids of the residents' actual activity locations, combines the individual's visit time information to infer the corresponding anchor point function type. Since all locations are presented at the grid level, no specific location information is revealed. At the same time, the original dataset assigns unique anonymous IDs to different users and only includes travel between different grids (Table 1), so no personal privacy information is leaked. The urban grids corresponding to individual life anchor points have the following three functional types:

[0098] A. Residence: The grid where the individual spent the longest time at a certain location at night (between 9 pm and 8 am the next day, i.e., the first preset time in this application), and the grid is located within the residential area AOI. Grids outside the residential area AOI are directly eliminated;

[0099] B. Workplace: After identifying the place of residence, first identify mobile phone users who have lived in a fixed place of residence for at least 10 days in a month and determine them as permanent residents of the city. In this invention, only the movement trajectories of permanent residents are retained; then the grid where these permanent residents spend the longest time during the day on weekdays (9:00 am to 5:00 pm on weekdays, i.e., the second preset time in this application) is determined to be the resident's workplace;

[0100] C. Activity venues: In addition to the above functional types, urban grids visited by permanent residents and staying for no less than 30 minutes (i.e., the second duration in this application) are identified as activity venues.

[0101]

[0102] Table 1

[0103] S4. Screening the activity places that are visited regularly by the group and defining the spatial scope of the community life circle: Based on the activity places determined in the previous step, screen and extract the activity places that are visited regularly by the group, and then define the spatial scope of the urban community life circle ( Figure 4 , Figure 5 );

[0104] S41. Capturing the minimum number of days that accurately reflects the group's daily activity patterns: The number of locations visited by individuals increases over time. Individuals with complex activity patterns will visit a higher number of locations, and this growth will be more stable. Therefore, to more accurately screen the locations that the group regularly visits and thus define the boundaries of the community life circle, it is necessary to capture the minimum number of days that accurately reflect the group's daily activity patterns. This serves as the basic time period for the study, and then, based on this time period, identify all locations that the group regularly visits.

[0105] The present invention uses the KL divergence index (Kullback–Leibler divergence indexes) to calculate the minimum number of days required to reflect the collective daily activity patterns of residents. Existing studies have confirmed that the KL divergence of each individual will slowly converge over time. The convergence time of individuals with complex activity patterns will be longer than that of individuals with less complex activity patterns, and the uncertainty will also be higher. Since determining the minimum number of days for residents' daily activity events through the KL divergence index is already a relatively mature technical method, the present invention only combines this research topic and describes the key steps therein, specifically:

[0106] S411. Calculate the probability density function of individual residents visiting a certain activity location: Calculate the probability of each individual resident visiting a certain grid cell within time t, the formula is:

[0107]

[0108] Where N i (t) represents the cumulative number of times an individual visits grid unit i within time t, N A (t) represents the cumulative number of individual visits to the Class A grid in the study area within time t, and the ratio of the two is recorded as the probability density function;

[0109] S412. Calculate the KL divergence index, the formula is:

[0110]

[0111] Where, f t (i) and f t-1 (i) represents the probability density function values for two consecutive days, t and t-1, respectively. The KL divergence metric measures the information loss when the model selected at time t-1 is used to approximate the model at time t. This information loss gradually converges to zero as time t increases. This property allows us to determine the time t at which the information loss ceases and the time at which the distribution converges to zero, and use this as the minimum number of days required to capture the daily activity patterns of an individual.

[0112] S413. Calculate the minimum number of days that reflect the daily activity pattern of the group: Based on the above steps, calculate the minimum number of days corresponding to all individuals, and then take the arithmetic mean as the minimum number of days required to reflect the daily activity pattern of the group.

[0113] S42, screening the places visited by the group regularly: the average minimum number of days reflecting the group's daily activity patterns calculated based on the KL divergence index in S41 is used as the basic time period for screening the places visited by the group regularly. The specific steps are:

[0114] S421, data preparation: prepare mobile phone signaling data for two consecutive basic time periods according to the format of Table 1;

[0115] S422, extracting individual travel trajectories: For the data in S421 above, aggregate individual travels according to user ID and travel destination grid to obtain individual resident travel trajectories;

[0116] S423, data aggregation: Using the residential area AOI grid as the basic unit, aggregate individual travel trajectory data and count all relevant information about the destination grids visited by groups living in the same community, including the number of destination grids, the frequency of destination visits, and the number of permanent residents in the residential area AOI (based on the urban permanent residents identified in S3, count the total number of permanent residents corresponding to each residential area AOI grid);

[0117] S424. Filtering Regularly Visited Locations: First, filter out the destination grids that have been visited by residents from the same residential area (AOI) within two consecutive basic time periods. Then, calculate the visit rate for each destination grid: visit rate = visit frequency / residential area's permanent population. Finally, based on the calculated visit rates, filter out destination grids with visit rates of 2%, 5%, and 10%, respectively, as regularly visited locations.

[0118] S43. Delineate the scope of the community life circle formed under different visit rates: Based on the minimum polygon algorithm and the collective regularly visited destination grids with different visit rates screened out in S42, delineate the spatial scope of the community life circle under the corresponding visit rate. Since the minimum polygon algorithm is a mature and public technology, the present invention will not go into details here. The selection of this algorithm is mainly based on the following two considerations: First, the previous steps have screened out many random or accidentally visited places (these places may be spatial outliers). After filtering, the remaining destination grids are confirmed to be collective and regularly visited places, and should all be included in the collective daily activity space; secondly, an important advantage of the minimum polygon algorithm is that it can cover all places, thereby generating relatively accurate boundaries.

[0119] This application uses large-scale mobile phone signaling data to accurately identify anchor points in the daily activities of individual urban residents. It then effectively identifies and categorizes the functional types of urban grids corresponding to these anchor points, combining activity dwell time and travel behavior characteristics. Based on the KL divergence metric, it accurately captures the minimum number of days that reflect regular collective activity, providing an accurate basic time period for precisely defining the scope of community life circles. It also precisely extracts the collectively regularly visited locations of urban residents, quantifies the visit rates of their destination grids, and then, using a minimum polygon algorithm, accurately extracts and delineates the scope of community life circles at different visit rates.

[0120] While many existing technologies exist for identifying residents' travel behaviors and community life circles based on mobile phone signaling data, no mature solution exists for identifying and extracting the specific functional types of community life circles and activity grids visited by large-scale urban residents. Therefore, this solution is innovative and scientific in its identification content. By combining large-scale mobile phone signaling data, this paper proposes a method for finely categorizing the functional types of individual life anchor points of permanent urban residents, as well as a technical method for accurately extracting and demarcating the scope of urban residents' community life circles. This allows for a scientific analysis of the collective travel behaviors and activity spaces of urban residents.

[0121] This application screens out the actual activity places of permanent urban residents by setting a threshold for residence time, and analyzes the travel trajectories of permanent residents. It effectively eliminates the interference of "over-sampling or under-sampling signals" in the research objects, and accurately identifies the real activity places of residents, that is, individual life anchor points. Combined with the judgment of residents' monthly residence behavior, the three functional types corresponding to the individual life anchor points of residents are effectively divided. By calculating the KL divergence index, the minimum number of days required to reflect the collective regular travel activities of residents is accurately captured, providing an accurate basic time period for screening collective regular visit places. On the basis of identifying and screening the activity places that urban residents visit collectively and regularly, the minimum polygon algorithm is used to accurately extract and delineate the spatial scope of the community life circle under different visit rates.

[0122] Secondly, refer to the attached Figure 6 A system for defining a living circle according to an embodiment of the present invention is described. The system specifically includes:

[0123] The first module 310 is used to obtain mobile phone signaling data and residential area AOI data;

[0124] The second module 320 is used to identify the individual's life anchor point based on the mobile phone signaling data, and determine the functional type of the individual's visited destination grid based on the residential area AOI data;

[0125] The third module 330 is used to extract the activity locations that are regularly visited by the group based on the functional type, and to define the spatial scope of the living circle.

[0126] It can be seen that the contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0127] Reference Figure 7 , an embodiment of the present invention provides a living circle delineation device, the device comprising:

[0128] at least one processor 410;

[0129] at least one memory 420, for storing at least one program;

[0130] When the at least one program is executed by the at least one processor 410 , the at least one processor 410 implements the living circle delineation method.

[0131] Similarly, the contents of the above method embodiments are applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0132] An embodiment of the present invention further provides a computer-readable storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to execute the above-mentioned method for delineating a living circle.

[0133] Similarly, the contents of the above method embodiments are applicable to the present storage medium embodiment. The functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0134] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0135] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art will be able to implement the present invention set forth in the claims using ordinary skill without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0136] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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. The computer software product is stored in a storage medium and includes several programs for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0137] The logic and / or steps represented in a flowchart or otherwise described herein, for example, may be considered as an ordered list of executable programs for implementing the logical functions, and may be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can retrieve and execute a program from a program execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, a program execution system, apparatus, or device.

[0138] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0139] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable program execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0140] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0141] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0142] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for defining a living circle, characterized in that: The method comprises the following steps: Obtain mobile phone signaling data and residential area AOI data; identifying anchor points in the individual's daily life based on the mobile phone signaling data, and determining the functional type of the destination grid visited by the individual based on the residential area AOI data; Based on the functional types, the activity locations that are regularly visited by the group are extracted, and the spatial scope of the living circle is delineated.

2. The method for defining a living area according to claim 1, wherein: Based on the functional types of the grids visited by the individuals, the activity locations that the group regularly visits are extracted, and the spatial scope of the living circle is delineated, including: Determine a basic time period; the basic time period is the minimum number of days that can reflect the daily activity patterns of the group; Extracting the mobile phone signaling data based on the basic time period to determine the activity location visited by the group regularly; The activity locations are aggregated using a minimum polygon algorithm to define the spatial scope of the living circle.

3. The method for defining a living circle according to claim 2, wherein: Determining the basic time period includes: Determine the probability density function of each individual visiting the activity location; determining an individual time period based on the probability density function; The basic time period is determined based on the individual time periods.

4. The method for defining a living area according to claim 2, wherein: Extracting the mobile phone signaling data based on the basic time period to determine a location of an activity visited regularly by a group includes: Extracting mobile phone signaling data for several consecutive basic time periods; aggregating the mobile phone signaling data by individual and destination grids to determine individual travel trajectories; All the individual travel trajectories are screened to determine the destination grids under different set visit rates.

5. The method for defining a living area according to claim 3, wherein: The activity locations are aggregated using the minimum polygon algorithm to define the spatial scope of the living circle, including: The destination grids under the set visit rate are aggregated using the minimum polygon algorithm to obtain the aggregated polygonal spatial range; The aggregated polygonal spatial range is superimposed and compared with the urban grid to determine the spatial range of the living circle under different visit rates and present it as a continuous grid.

6. The method for defining a living area according to claim 1, wherein: The method further comprises: Deduplication, data cleaning, and preprocessing of the mobile phone signaling data; The residential area AOI data is deduplicated, spatially corrected, and normalized.

7. The method for defining a living zone according to claim 1, wherein: Identifying anchor points in the individual's daily life based on the mobile phone signaling data, and determining the functional type of the individual's visited destination grid based on the residential area AOI data, including: The grids where the residence time at the visited places is greater than or equal to the first duration are selected as the anchor points of individual daily life; If the grid corresponding to the individual's daily life anchor point with the longest dwell time within the first preset time is located in the residential area AOI, the corresponding grid is determined to be the individual's residence; If the individual is a permanent resident, the grid corresponding to the individual's daily life anchor point with the longest residence time within the second preset time is determined as the individual's workplace; If the dwelling time of the individual visiting the first grid is greater than or equal to the second duration, the first grid is not a residence and the first grid is not a workplace, and the first grid is determined to be the activity place of the individual.

8. A living circle demarcation system, characterized in that: The system comprises: The first module is used to obtain mobile phone signaling data and residential area AOI data; The second module is used to identify anchor points in the individual's daily life based on the mobile phone signaling data, and determine the functional type of the destination grid visited by the individual based on the residential area AOI data; The third module is used to extract the activity locations that are visited regularly by the group based on the functional types and to define the spatial scope of the living circle.

9. A living circle defining device, characterized in that: The device comprises: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the living circle delineation method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to implement the living circle demarcation method according to any one of claims 1 to 7 when executed by the processor.

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