CN114201482A - Dynamic population distribution statistical method and device, electronic equipment and readable storage medium - Google Patents
Dynamic population distribution statistical method and device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a population dynamic distribution statistical method, which comprises the following steps: the method comprises the steps of obtaining a spatial view of a target place, calculating a syntactic form index in the spatial view by using a preset spatial syntactic model, constructing a population area map according to the syntactic form index, obtaining population position data in the target place, carrying out data cleaning on the population position data to obtain standard position data, constructing a population track map in the target place according to the standard position data, and comparing the population area map with a superposed region in the population track map to obtain a population distribution statistical result. Furthermore, the invention relates to blockchain techniques, the demographics may be stored in nodes of the blockchain. The invention also provides a population dynamic distribution statistical method and device, electronic equipment and a computer readable storage medium. The invention can solve the problem of inaccurate demographic distribution result.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a population dynamic distribution statistical method, a population dynamic distribution statistical device, electronic equipment and a computer readable storage medium.
Background
The dynamic population distribution is an important research topic and has important significance in scenes such as city planning, post-disaster analysis and the like. However, population density and development change have a lot of uncertainties, previous population dynamic analysis is generally counted according to administrative regions, the spatial resolution is low, and meanwhile, due to the fact that the traditional urban population distribution research data acquisition speed is low, data updating is delayed, the quantity and quality of data are difficult to guarantee, and the population distribution counting result is inaccurate.
Disclosure of Invention
The invention provides a population dynamic distribution statistical method, a device, equipment and a storage medium, and mainly aims to solve the problem of inaccurate population distribution statistical result.
In order to achieve the above object, the present invention provides a demographic method, including:
acquiring a space view of a target place, calculating a syntactic form index in the space view by using a preset space syntactic model, and constructing a population area map according to the syntactic form index;
acquiring population position data in the target place, and performing data cleaning on the population position data to obtain standard position data;
constructing a population trajectory map within the target site according to the standard location data;
and comparing the overlapping areas in the population area graph and the population track graph to obtain a population distribution statistical result.
Optionally, the calculating a syntactic morphology index in the spatial view by using a preset spatial syntactic model includes:
constructing a space structure of each space in the target site according to the space view;
obtaining a contour view of each space in the target site based on the space structure, wherein each contour in the contour view is called a space element;
and calculating the integration indexes of all the space elements in the outline view according to a preset integration index calculation formula.
Optionally, the calculating an integration indicator of each spatial element in the profile view according to a preset integration indicator calculation formula includes:
taking each space element in the outline view as a space node;
calculating the average depth value of all the spatial nodes;
and calculating the aggregation degree of the average depth value by using the integration index calculation formula, and taking the aggregation degree as the integration index.
Optionally, the calculating an average depth value of all the spatial nodes includes:
sequentially selecting one node from all the space nodes as a target node;
calculating the minimum connecting step number from the target node to the unselected space node, and taking the minimum connecting step number as the depth value of the target node;
and summarizing the depth values of the target nodes to obtain a total depth value, and obtaining the average depth value based on a preset average depth value calculation formula and the total depth value.
Optionally, the data cleaning of the population location data to obtain standard location data includes:
performing data anomaly detection and data missing value detection on the population position data, and removing position data with detection data anomaly or detection data missing values to obtain initial removed data;
and carrying out data deduplication processing on the initial eliminated data to obtain the standard position data.
Optionally, the constructing a population trajectory graph within the target site according to the standard location data includes:
calculating a population stagnation point in the target place by using a preset behavior algorithm according to the standard position data;
and according to the density of the population stopping points, rendering the area in the target place by using the marking color set to obtain the population track graph.
Optionally, the calculating, according to the standard location data, a population stopping point in the target site by using a preset behavior algorithm includes:
sequencing the position track points in the standard position data according to time to obtain a track point sequence;
sequentially selecting one track point in the track point sequence as a target track point, and traversing the track points which are not selected in the track point sequence according to the time sequence;
if the distance from the traversed track point to the target track point is smaller than or equal to a preset distance threshold value, determining the traversed track point as an adjacent point of the target track point, and determining the last traversed track point as a critical track point of the target track point when the distance from the traversed track point to the target track point is larger than the preset distance threshold value;
judging whether the time interval from the critical track point to the target track point is greater than a preset time threshold value or not;
if the time interval from the critical track point to the target track point is not greater than a preset time threshold, selecting the next track point as the target track point, and returning to the step of traversing the unselected track points in the track point sequence according to the time sequence;
if the time interval from the critical track point to the target track point is greater than a preset time threshold, clustering the target track point, the adjacent points of the target track point and the critical track point of the target track point to obtain a dwell point;
and summarizing the stop points to obtain the population stop points in the target place.
In order to solve the above problem, the present invention further provides a demographic distribution apparatus, comprising:
the population area map building module is used for acquiring a spatial view of a target place, calculating a syntactic form index in the spatial view by using a preset spatial syntactic model, and building a population area map according to the syntactic form index;
the position data cleaning module is used for acquiring population position data in the target place and cleaning the population position data to obtain standard position data;
the population track map building module is used for building a population track map in the target place according to the standard position data;
and the population distribution statistical module is used for comparing the overlapping areas in the population area graph and the population track graph to obtain a population distribution statistical result.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one computer program; and
a processor executing the computer program stored in the memory to implement the demographic method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the above-mentioned method for demographic distribution.
The method calculates the syntactic form indexes in the space view through the space syntactic model, constructs the population area graph according to the syntactic form indexes, can obtain an area with strong theoretically aggregated population, constructs the population locus graph in a target place according to standard position data, can obtain an actual population aggregated area, and can obtain a more accurate population distribution statistical result by comparing the population area graph and the population locus graph. Therefore, the dynamic population distribution statistical method, the dynamic population distribution statistical device, the electronic equipment and the computer-readable storage medium can solve the problem that the population distribution statistical result is inaccurate.
Drawings
FIG. 1 is a flow chart illustrating a method for demographic distribution according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a demographic apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device implementing the method for demographic distribution according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a population dynamic distribution statistical method. The execution subject of the demographic distribution method includes, but is not limited to, at least one of the electronic devices that can be configured to execute the method provided by the embodiments of the present application, such as a server, a terminal, and the like. In other words, the demographics method may be performed by software installed in the terminal device or the server device, or hardware, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a flowchart of a demographic method according to an embodiment of the present invention is shown. In this embodiment, the demographic method includes:
s1, obtaining a space view of the target place, calculating a syntactic form index in the space view by using a preset space syntactic model, and constructing a population area map according to the syntactic form index.
In the embodiment of the present invention, the spatial view may be a plan view, and the target location may be a street, a building, a city, or the like. For example, a plan view of the home palace is taken. The space syntax model is used for displaying the relation between the space and the population activities through a series of measurable quantitative syntax indexes.
In an optional embodiment of the present invention, the syntactic morphology index may be an integration index, and the integration index reflects accessibility and convenience of the spatial unit and the ability of gathering and attracting population, thereby reflecting the centrality of one unit space relative to the rest of the unit spaces.
Specifically, the calculating the syntactic morphology index in the spatial view by using a preset spatial syntactic model includes:
constructing a space structure of each space in the target site according to the space view;
obtaining a contour view of each space in the target site based on the space structure, wherein each contour in the contour view is called a space element;
and calculating the integration indexes of all the space elements in the outline view according to a preset integration index calculation formula.
In the embodiment of the present invention, for example, the target site is city a, and each space is a building, a street, a river, and the like in city a.
In an embodiment of the present invention, the outline view may be an axial plan view of a building, and for a plan view of a city, the road network in the city is represented by the axis passing through all streets with the longest minimum, so that the whole road network is divided into a series of smaller spaces, and then the streets are expressed as a non-directional and non-weighted graph. For example, an axis line graph is drawn according to a space syntax by adopting an insert Axwomen6 of ArcGIS, and the level relation of rivers, bridges, different roads and other important buildings are divided into spaces to obtain an axis plane graph.
In detail, the calculating the integration index of each spatial element in the profile view according to a preset integration index calculation formula includes:
taking each space element in the outline view as a space node;
calculating the average depth value of all spatial nodes;
and calculating the aggregation degree of the average depth value by using the integration index calculation formula, and taking the aggregation degree as the integration index.
In the embodiment of the present invention, the depth value is an important intermediate variable for calculating the integration index, and refers to the minimum number of connection steps (shortest distance) from a certain spatial element to all other spatial elements, and an average depth value MD is usually usediAnd (4) showing.
Specifically, the calculating an average depth value of all the spatial nodes includes:
sequentially selecting one node from all the space nodes as a target node;
calculating the minimum connecting step number from the target node to the unselected space node, and taking the minimum connecting step number as the depth value of the target node;
and summarizing the depth values of the target nodes to obtain a total depth value, and obtaining the average depth value based on a preset average depth value calculation formula and the total depth value.
In an optional embodiment of the present invention, the preset average depth value calculation formula is as follows:
wherein the numerator represents the total depth value of the spatial node i, dijIndicating the depth values i to j (j ≠ i), i.e. the minimum number of connection steps i to j, the sum of the minimum number of connection steps i to all other nodes is the total depth value of i, n is the total emptyThe number of nodes between, n-1, indicates that at most n-1 spatial nodes are connected to i.
Further, the calculating the clustering degree of the average depth values by using the integrated index calculation formula includes:
calculating a relative asymmetry value of the average depth value using the integrated index calculation formula;
and performing reciprocal processing on the relative asymmetry value to obtain the aggregation degree.
In an optional embodiment of the present invention, the relative asymmetry value is calculated by the following formula:
wherein RA isiIs the relative asymmetry value.
Usually, with RAiRepresents the integration level I of the spatial node IiSo as to conform to the conventional habit of "the larger the value and the greater the integration level", namely:
in the embodiment of the invention, the integration index IiIs one of the most common and effective syntactic indexes in urban space research, and characterizes the aggregation or dispersion degree between one space element and all other spaces or partial spaces within a few steps (usually 3 steps) away from the space element.
In an embodiment of the present invention, the constructing a population area map according to the syntactic morphology index includes:
according to the index size of the integrated index, carrying out color filling on the space elements in the outline view according to a preset labeling color set;
and summarizing the space elements filled with the colors to obtain the population area graph.
In an optional embodiment of the present invention, because the integration index reflects accessibility, convenience, and ability of gathering and attracting population of the spatial unit, generally, the greater the integration index is greater than 1, the stronger the gathering ability of the space is, and conversely, the weaker the gathering ability of the space is, different colors may be used for labeling, for example, a spatial region with the largest integration index is filled with red, a spatial region with the smallest integration index is filled with blue, and the ability of gathering population in the target site can be clearly and intuitively seen.
And S2, acquiring the population position data in the target place, and performing data cleaning on the population position data to obtain standard position data.
In the embodiment of the invention, the population position data can be acquired from a Location Based Service (LBS) platform, the position data in the LBS platform is user anonymous geographical position data, the data is event trigger data, is triggered when a user uses a positioning request of the mobile internet, and is from various APPs, namely instant position data formed by events of logging, searching, sending and receiving information, pushing and the like of the user in various APPs.
In detail, the data cleaning of the population position data to obtain standard position data includes:
performing data anomaly detection and data missing value detection on the population position data, and removing position data with detection data anomaly or detection data missing values to obtain initial removed data;
and carrying out data deduplication processing on the initial eliminated data to obtain the standard position data.
In the embodiment of the invention, whether the population position data has a missing value or not can be detected through a mismap function missing function, if the population position data has no missing value, processing is not carried out, if the population position data has the missing value, removing is carried out, meanwhile, a single-side test method and a double-side test method can be used for carrying out data anomaly detection on the population position data, if the population position data has no abnormal value, processing is not carried out, and if the population position data has the abnormal value, removing is carried out.
In an optional embodiment of the present invention, the performing data deduplication processing on the initial eliminated data to obtain the standard position data includes:
calculating a distance value of any two data in the initial eliminated data by using a preset distance formula, if the distance value is smaller than a preset distance threshold, deleting any one data, and if the distance value is larger than or equal to the distance threshold, simultaneously keeping the two data;
the preset distance formula is as follows:
wherein d represents the distance value between any two data in the initial culling data, and w1jAnd w2jRepresenting any two data in the initial culled data.
In an optional embodiment of the present invention, the preset distance threshold may be 0.1.
And S3, constructing a population track graph in the target place according to the standard position data.
In the embodiment of the invention, the population track graph can be a population thermodynamic diagram, the colors of the map are rendered through the density of the stop points, the denser the population distribution is represented by red, the less the population distribution is represented by blue, and the distribution characteristics of the population on a continuous spatial scale can be quickly and accurately obtained through the thermodynamic diagram.
In detail, the building of the population trajectory graph within the target site according to the standard location data includes:
calculating a population stagnation point in the target place by using a preset behavior algorithm according to the standard position data;
and according to the density of the population stopping points, rendering the area in the target place by using the marking color set to obtain the population track graph.
In the embodiment of the invention, the preset behavior algorithm can be an ST-DBSCAN algorithm, and the ST-DBSCAN algorithm identifies the position data in the target place and generates new stopping point data according to the point characteristics of the behavior track.
Specifically, the calculating the population stagnation point in the target site by using a preset behavior algorithm according to the standard position data includes:
sequencing the position track points in the standard position data according to time to obtain a track point sequence;
sequentially selecting one track point in the track point sequence as a target track point, and traversing the track points which are not selected in the track point sequence according to the time sequence;
if the distance from the traversed track point to the target track point is smaller than or equal to a preset distance threshold value, determining the traversed track point as an adjacent point of the target track point, and determining the last traversed track point as a critical track point of the target track point when the distance from the traversed track point to the target track point is larger than the preset distance threshold value;
judging whether the time interval from the critical track point to the target track point is greater than a preset time threshold value or not;
if the time interval from the critical track point to the target track point is not greater than a preset time threshold, selecting the next track point as the target track point, and returning to the step of traversing the unselected track points in the track point sequence according to the time sequence;
if the time interval from the critical track point to the target track point is greater than a preset time threshold, clustering the target track point, the adjacent points of the target track point and the critical track point of the target track point to obtain a dwell point;
and summarizing all the stop points to obtain the population stop points in the target place.
In an optional embodiment of the present invention, the distance threshold may be set to S meters, and the time threshold may be set to T2 minutes; sequencing all track points according to a time sequence, judging whether the distance between the next track point On +1 and the track point On is less than S meters or not for one track point On, if so, taking the next track point On +1 and the track point On as an adjacent point of On, continuing the above operation, and if the distance between the next track point On +1 and the track point On is greater than a threshold value S meters, namely, stopping searching, calculating the time interval between the track point On + m and the track point On, and if the time interval is greater than T2 minutes, recording all the track points from On to On + m; clustering all the obtained track points On to On + m to generate stop points, wherein the longitude and latitude of the stop points are the mean values of the longitude and latitude of all the track points On to On + m, the time tn of positioning the track point On is the starting time of the action point, and the time tn + m of positioning the track point On + m is the ending time of the action point. For example, according to the ST-DBSCAN algorithm, a user stays within a range of 100 meters for 15 minutes to be regarded as a stop point in an area, a thermodynamic diagram for making the stop point renders the color of a map through the density of the stop point, the denser the population distribution is represented by red, the less the population distribution is represented by blue, and the distribution characteristics of the population on a continuous spatial scale are quickly and accurately obtained through the thermodynamic diagram.
And S4, comparing the overlapping areas in the population area graph and the population track graph to obtain a population distribution statistical result.
In the embodiment of the invention, a population area graph obtained through a space syntax model can obtain an area (R1) with strong population clustering capability in a target place according to syntax form indexes, the distribution condition of a population in the space is analyzed by utilizing a big data technology to obtain an actual population clustering area (R2), R1 is a theoretical population distribution clustering area, R2 is an actual population distribution clustering area, and the problems of whether the two areas are overlapped, which areas are overlapped and the like are discovered through comparative analysis of the theoretical population distribution clustering area and the actual population distribution clustering area, so that the accuracy of a population dynamic statistical result is improved.
In the embodiment of the present invention, the repetition area may be determined by the color filled in the population area map and the population trace map, for example, an area in which the same area in the population area map and the population trace map is filled with blue is the repetition area.
For example, comparing a population area map obtained by a space language syntax model with an LBS big data population trajectory map, cities can be divided into three categories: 1. the space accessibility is good and the population is highly gathered; 2. spatial accessibility is good but the degree of population is general; 3. the space accessibility is general, the population clustering degree is general, the urban area global clustering capability is analyzed, and suggestions are provided for realizing the optimized development of the urban population center.
In another alternative embodiment of the present invention, the population density index can be calculated by the demographic distribution:
wherein POP represents the total number of population in the area, and S represents the total area of the area.
In the embodiment of the invention, for example, in the insurance field, the population density index (Pd) in units of people/square kilometer in different areas can be calculated to refine the population index, the refined population is applied to calculation of a catastrophic disaster index (physical exposure of a disaster object: population density index), the later-stage disaster risk evaluation is perfected, and meanwhile, data support is further provided for underwriting by combining insurance data.
The method calculates the syntactic form indexes in the space view through the space syntactic model, constructs the population area graph according to the syntactic form indexes, can obtain an area with strong theoretically aggregated population, constructs the population locus graph in a target place according to standard position data, can obtain an actual population aggregated area, and can obtain a more accurate population distribution statistical result by comparing the population area graph and the population locus graph. Therefore, the population dynamic distribution statistical method provided by the invention can solve the problem of inaccurate population distribution statistical result.
Fig. 2 is a functional block diagram of a demographic apparatus according to an embodiment of the present invention.
The demographic distribution apparatus 100 of the present invention may be installed in an electronic device. According to the implemented functions, the demographic distribution device 100 may include a demographic map building module 101, a location data cleansing module 102, a demographic trajectory map building module 103, and a demographic distribution module 104. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the population area map building module 101 is configured to obtain a spatial view of a target place, calculate a syntactic form index in the spatial view by using a preset spatial syntactic model, and build a population area map according to the syntactic form index;
the position data cleaning module 102 is configured to acquire population position data in the target site, and perform data cleaning on the population position data to obtain standard position data;
the population track map building module 103 is configured to build a population track map in the target site according to the standard location data;
the demographic distribution module 104 is configured to compare the demographic area map with a coincidence area in the demographic trajectory map to obtain a demographic distribution result.
In detail, the demographic distribution apparatus 100 includes the following modules:
the method comprises the steps of firstly, obtaining a space view of a target place, calculating a syntactic form index in the space view by using a preset space syntactic model, and constructing a population area map according to the syntactic form index.
In the embodiment of the present invention, the spatial view may be a plan view, and the target location may be a street, a building, a city, or the like. For example, a plan view of the home palace is taken. The space syntax model is used for displaying the relation between the space and the population activities through a series of measurable quantitative syntax indexes.
In an optional embodiment of the present invention, the syntactic morphology index may be an integration index, and the integration index reflects accessibility and convenience of the spatial unit and the ability of gathering and attracting population, thereby reflecting the centrality of one unit space relative to the rest of the unit spaces.
Specifically, the calculating the syntactic morphology index in the spatial view by using a preset spatial syntactic model includes:
constructing a space structure of each space in the target site according to the space view;
obtaining a contour view of each space in the target site based on the space structure, wherein each contour in the contour view is called a space element;
and calculating the integration indexes of all the space elements in the outline view according to a preset integration index calculation formula.
In the embodiment of the present invention, for example, the target site is city a, and each space is a building, a street, a river, and the like in city a.
In an embodiment of the present invention, the outline view may be an axial plan view of a building, and for a plan view of a city, the road network in the city is represented by the axis passing through all streets with the longest minimum, so that the whole road network is divided into a series of smaller spaces, and then the streets are expressed as a non-directional and non-weighted graph. For example, an axis line graph is drawn according to a space syntax by adopting an insert Axwomen6 of ArcGIS, and the level relation of rivers, bridges, different roads and other important buildings are divided into spaces to obtain an axis plane graph.
In detail, the calculating the integration index of each spatial element in the profile view according to a preset integration index calculation formula includes:
taking each space element in the outline view as a space node;
calculating the average depth value of all spatial nodes;
and calculating the aggregation degree of the average depth value by using the integration index calculation formula, and taking the aggregation degree as the integration index.
In the embodiment of the present invention, the depth value is an important intermediate variable for calculating the integration index, and refers to the minimum number of connection steps (shortest distance) from a certain spatial element to all other spatial elements, and an average depth value MD is usually usediAnd (4) showing.
Specifically, the calculating an average depth value of all the spatial nodes includes:
sequentially selecting one node from all the space nodes as a target node;
calculating the minimum connecting step number from the target node to the unselected space node, and taking the minimum connecting step number as the depth value of the target node;
and summarizing the depth values of the target nodes to obtain a total depth value, and obtaining the average depth value based on a preset average depth value calculation formula and the total depth value.
In an optional embodiment of the present invention, the preset average depth value calculation formula is as follows:
wherein the numerator represents the total depth value of the spatial node i, dijAnd the depth values of i to j (j ≠ i) are represented, namely the minimum connecting step number of i to j, the sum of the minimum connecting step numbers of i to all other nodes is the total depth value of i, n is the total number of spatial nodes, and n-1 indicates that at most n-1 spatial nodes are connected with i.
Further, the calculating the clustering degree of the average depth values by using the integrated index calculation formula includes:
calculating a relative asymmetry value of the average depth value using the integrated index calculation formula;
and performing reciprocal processing on the relative asymmetry value to obtain the aggregation degree.
In an optional embodiment of the present invention, the relative asymmetry value is calculated by the following formula:
wherein RA isiIs the relative asymmetry value.
Usually, with RAiRepresents the integration level I of the spatial node IiSo as to conform to the conventional habit of "the larger the value and the greater the integration level", namely:
in the embodiment of the invention, the integration index IiIs one of the most common and effective syntactic indexes in urban space research, and characterizes the aggregation or dispersion degree between one space element and all other spaces or partial spaces within a few steps (usually 3 steps) away from the space element.
In an embodiment of the present invention, the constructing a population area map according to the syntactic morphology index includes:
according to the index size of the integrated index, carrying out color filling on the space elements in the outline view according to a preset labeling color set;
and summarizing the space elements filled with the colors to obtain the population area graph.
In an optional embodiment of the present invention, because the integration index reflects accessibility, convenience, and ability of gathering and attracting population of the spatial unit, generally, the greater the integration index is greater than 1, the stronger the gathering ability of the space is, and conversely, the weaker the gathering ability of the space is, different colors may be used for labeling, for example, a spatial region with the largest integration index is filled with red, a spatial region with the smallest integration index is filled with blue, and the ability of gathering population in the target site can be clearly and intuitively seen.
And step two, acquiring population position data in the target place, and performing data cleaning on the population position data to obtain standard position data.
In the embodiment of the invention, the population position data can be acquired from a Location Based Service (LBS) platform, the position data in the LBS platform is user anonymous geographical position data, the data is event trigger data, is triggered when a user uses a positioning request of the mobile internet, and is from various APPs, namely instant position data formed by events of logging, searching, sending and receiving information, pushing and the like of the user in various APPs.
In detail, the data cleaning of the population position data to obtain standard position data includes:
performing data anomaly detection and data missing value detection on the population position data, and removing position data with detection data anomaly or detection data missing values to obtain initial removed data;
and carrying out data deduplication processing on the initial eliminated data to obtain the standard position data.
In the embodiment of the invention, whether the population position data has a missing value or not can be detected through a mismap function missing function, if the population position data has no missing value, processing is not carried out, if the population position data has the missing value, removing is carried out, meanwhile, a single-side test method and a double-side test method can be used for carrying out data anomaly detection on the population position data, if the population position data has no abnormal value, processing is not carried out, and if the population position data has the abnormal value, removing is carried out.
In an optional embodiment of the present invention, the performing data deduplication processing on the initial eliminated data to obtain the standard position data includes:
calculating a distance value of any two data in the initial eliminated data by using a preset distance formula, if the distance value is smaller than a preset distance threshold, deleting any one data, and if the distance value is larger than or equal to the distance threshold, simultaneously keeping the two data;
the preset distance formula is as follows:
wherein d represents the distance value between any two data in the initial culling data, and w1jAnd w2jRepresenting any two data in the initial culled data.
In an optional embodiment of the present invention, the preset distance threshold may be 0.1.
And thirdly, constructing a population track graph in the target place according to the standard position data.
In the embodiment of the invention, the population track graph can be a population thermodynamic diagram, the colors of the map are rendered through the density of the stop points, the denser the population distribution is represented by red, the less the population distribution is represented by blue, and the distribution characteristics of the population on a continuous spatial scale can be quickly and accurately obtained through the thermodynamic diagram.
In detail, the building of the population trajectory graph within the target site according to the standard location data includes:
calculating a population stagnation point in the target place by using a preset behavior algorithm according to the standard position data;
and according to the density of the population stopping points, rendering the area in the target place by using the marking color set to obtain the population track graph.
In the embodiment of the invention, the preset behavior algorithm can be an ST-DBSCAN algorithm, and the ST-DBSCAN algorithm identifies the position data in the target place and generates new stopping point data according to the point characteristics of the behavior track.
Specifically, the calculating the population stagnation point in the target site by using a preset behavior algorithm according to the standard position data includes:
sequencing the position track points in the standard position data according to time to obtain a track point sequence;
sequentially selecting one track point in the track point sequence as a target track point, and traversing the track points which are not selected in the track point sequence according to the time sequence;
if the distance from the traversed track point to the target track point is smaller than or equal to a preset distance threshold value, determining the traversed track point as an adjacent point of the target track point, and determining the last traversed track point as a critical track point of the target track point when the distance from the traversed track point to the target track point is larger than the preset distance threshold value;
judging whether the time interval from the critical track point to the target track point is greater than a preset time threshold value or not;
if the time interval from the critical track point to the target track point is not greater than a preset time threshold, selecting the next track point as the target track point, and returning to the step of traversing the unselected track points in the track point sequence according to the time sequence;
if the time interval from the critical track point to the target track point is greater than a preset time threshold, clustering the target track point, the adjacent points of the target track point and the critical track point of the target track point to obtain a dwell point;
and summarizing all the stop points to obtain the population stop points in the target place.
In an optional embodiment of the present invention, the distance threshold may be set to S meters, and the time threshold may be set to T2 minutes; sequencing all track points according to a time sequence, judging whether the distance between the next track point On +1 and the track point On is less than S meters or not for one track point On, if so, taking the next track point On +1 and the track point On as an adjacent point of On, continuing the above operation, and if the distance between the next track point On +1 and the track point On is greater than a threshold value S meters, namely, stopping searching, calculating the time interval between the track point On + m and the track point On, and if the time interval is greater than T2 minutes, recording all the track points from On to On + m; clustering all the obtained track points On to On + m to generate stop points, wherein the longitude and latitude of the stop points are the mean values of the longitude and latitude of all the track points On to On + m, the time tn of positioning the track point On is the starting time of the action point, and the time tn + m of positioning the track point On + m is the ending time of the action point. For example, according to the ST-DBSCAN algorithm, a user stays within a range of 100 meters for 15 minutes to be regarded as a stop point in an area, a thermodynamic diagram for making the stop point renders the color of a map through the density of the stop point, the denser the population distribution is represented by red, the less the population distribution is represented by blue, and the distribution characteristics of the population on a continuous spatial scale are quickly and accurately obtained through the thermodynamic diagram.
And step four, comparing the overlapping areas in the population area graph and the population trajectory graph to obtain a population distribution statistical result.
In the embodiment of the invention, a population area graph obtained through a space syntax model can obtain an area (R1) with strong population clustering capability in a target place according to syntax form indexes, the distribution condition of a population in the space is analyzed by utilizing a big data technology to obtain an actual population clustering area (R2), R1 is a theoretical population distribution clustering area, R2 is an actual population distribution clustering area, and the problems of whether the two areas are overlapped, which areas are overlapped and the like are discovered through comparative analysis of the theoretical population distribution clustering area and the actual population distribution clustering area, so that the accuracy of a population dynamic statistical result is improved.
In the embodiment of the present invention, the repetition area may be determined by the color filled in the population area map and the population trace map, for example, an area in which the same area in the population area map and the population trace map is filled with blue is the repetition area.
For example, comparing a population area map obtained by a space language syntax model with an LBS big data population trajectory map, cities can be divided into three categories: 1. the space accessibility is good and the population is highly gathered; 2. spatial accessibility is good but the degree of population is general; 3. the space accessibility is general, the population clustering degree is general, the urban area global clustering capability is analyzed, and suggestions are provided for realizing the optimized development of the urban population center.
In another alternative embodiment of the present invention, the population density index can be calculated by the demographic distribution:
wherein POP represents the total number of population in the area, and S represents the total area of the area.
In the embodiment of the invention, for example, in the insurance field, the population density index (Pd) in units of people/square kilometer in different areas can be calculated to refine the population index, the refined population is applied to calculation of a catastrophic disaster index (physical exposure of a disaster object: population density index), the later-stage disaster risk evaluation is perfected, and meanwhile, data support is further provided for underwriting by combining insurance data.
The method calculates the syntactic form indexes in the space view through the space syntactic model, constructs the population area graph according to the syntactic form indexes, can obtain an area with strong theoretically aggregated population, constructs the population locus graph in a target place according to standard position data, can obtain an actual population aggregated area, and can obtain a more accurate population distribution statistical result by comparing the population area graph and the population locus graph. Therefore, the population dynamic distribution statistical device provided by the invention can solve the problem of inaccurate population distribution statistical result.
Fig. 3 is a schematic structural diagram of an electronic device implementing a demographic method according to an embodiment of the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication interface 12 and a bus 13, and may further comprise a computer program, such as a demographic program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in the electronic device and various types of data, such as codes of a demographic program, etc., but also for temporarily storing data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., demographics, etc.) stored in the memory 11 and calling data stored in the memory 11.
The communication interface 12 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
The bus 13 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 13 may be divided into an address bus, a data bus, a control bus, etc. The bus 13 is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 3 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 3 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the electronic device may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The demographic program stored in the memory 11 of the electronic device is a combination of instructions that, when executed in the processor 10, enable:
acquiring a space view of a target place, calculating a syntactic form index in the space view by using a preset space syntactic model, and constructing a population area map according to the syntactic form index;
acquiring population position data in the target place, and performing data cleaning on the population position data to obtain standard position data;
constructing a population trajectory map within the target site according to the standard location data;
and comparing the overlapping areas in the population area graph and the population track graph to obtain a population distribution statistical result.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring a space view of a target place, calculating a syntactic form index in the space view by using a preset space syntactic model, and constructing a population area map according to the syntactic form index;
acquiring population position data in the target place, and performing data cleaning on the population position data to obtain standard position data;
constructing a population trajectory map within the target site according to the standard location data;
and comparing the overlapping areas in the population area graph and the population track graph to obtain a population distribution statistical result.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for demographic profiling, the method comprising:
acquiring a space view of a target place, calculating a syntactic form index in the space view by using a preset space syntactic model, and constructing a population area map according to the syntactic form index;
acquiring population position data in the target place, and performing data cleaning on the population position data to obtain standard position data;
constructing a population trajectory map within the target site according to the standard location data;
and comparing the overlapping areas in the population area graph and the population track graph to obtain a population distribution statistical result.
2. The method of demographic distribution in accordance with claim 1, wherein said calculating syntactic shape indicators in the spatial view using a preset spatial syntactic model comprises:
constructing a space structure of each space in the target site according to the space view;
obtaining a contour view of each space in the target site based on the space structure, wherein each contour in the contour view is called a space element;
and calculating the integration indexes of all the space elements in the outline view according to a preset integration index calculation formula.
3. The method as claimed in claim 2, wherein the calculating the integration index of each spatial element in the profile view according to a preset integration index calculation formula comprises:
taking each space element in the outline view as a space node;
calculating the average depth value of all the spatial nodes;
and calculating the aggregation degree of the average depth value by using the integration index calculation formula, and taking the aggregation degree as the integration index.
4. The method of demographic distribution as set forth in claim 3, wherein the calculating an average depth value for all of the spatial nodes comprises:
sequentially selecting one node from all the space nodes as a target node;
calculating the minimum connecting step number from the target node to the unselected space node, and taking the minimum connecting step number as the depth value of the target node;
and summarizing the depth values of the target nodes to obtain a total depth value, and obtaining the average depth value based on a preset average depth value calculation formula and the total depth value.
5. The method of demographic profiling of claim 1, wherein the data cleansing of the demographic location data to obtain standard location data comprises:
performing data anomaly detection and data missing value detection on the population position data, and removing position data with detection data anomaly or detection data missing values to obtain initial removed data;
and carrying out data deduplication processing on the initial eliminated data to obtain the standard position data.
6. The method of demographic profiling of claim 5, wherein the constructing a population trajectory map within the target site from the standard location data comprises:
calculating a population stagnation point in the target place by using a preset behavior algorithm according to the standard position data;
and according to the density of the population stopping points, rendering the area in the target place by using the marking color set to obtain the population track graph.
7. The method of claim 6, wherein calculating the population stopping point in the target site using a predetermined behavioral algorithm based on the standard location data comprises:
sequencing the position track points in the standard position data according to time to obtain a track point sequence;
sequentially selecting one track point in the track point sequence as a target track point, and traversing the track points which are not selected in the track point sequence according to the time sequence;
if the distance from the traversed track point to the target track point is smaller than or equal to a preset distance threshold value, determining the traversed track point as an adjacent point of the target track point, and determining the last traversed track point as a critical track point of the target track point when the distance from the traversed track point to the target track point is larger than the preset distance threshold value;
judging whether the time interval from the critical track point to the target track point is greater than a preset time threshold value or not;
if the time interval from the critical track point to the target track point is not greater than a preset time threshold, selecting the next track point as the target track point, and returning to the step of traversing the unselected track points in the track point sequence according to the time sequence;
if the time interval from the critical track point to the target track point is greater than a preset time threshold, clustering the target track point, the adjacent points of the target track point and the critical track point of the target track point to obtain a dwell point;
and summarizing the stop points to obtain the population stop points in the target place.
8. A demographic apparatus, comprising:
the population area map building module is used for acquiring a spatial view of a target place, calculating a syntactic form index in the spatial view by using a preset spatial syntactic model, and building a population area map according to the syntactic form index;
the position data cleaning module is used for acquiring population position data in the target place and cleaning the population position data to obtain standard position data;
the population track map building module is used for building a population track map in the target place according to the standard position data;
and the population distribution statistical module is used for comparing the overlapping areas in the population area graph and the population track graph to obtain a population distribution statistical result.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of demographic distribution as recited in any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of demographic distribution according to any one of claims 1 to 7.
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| CN115035720A (en) * | 2022-06-10 | 2022-09-09 | 翁敏 | Traffic road condition data acquisition and processing method and management system based on satellite positioning |
| CN116434446A (en) * | 2023-05-04 | 2023-07-14 | 北京国信华源科技有限公司 | Targeting early warning device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115035720A (en) * | 2022-06-10 | 2022-09-09 | 翁敏 | Traffic road condition data acquisition and processing method and management system based on satellite positioning |
| CN116434446A (en) * | 2023-05-04 | 2023-07-14 | 北京国信华源科技有限公司 | Targeting early warning device |
| CN116434446B (en) * | 2023-05-04 | 2024-03-12 | 北京国信华源科技有限公司 | Targeting early warning device |
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