CN113112069B - Population distribution prediction method, population distribution prediction system and electronic equipment - Google Patents
Population distribution prediction method, population distribution prediction system and electronic equipment Download PDFInfo
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Abstract
The invention relates to the technical field of population prediction, in particular to a population distribution prediction method, a population distribution prediction system and electronic equipment. The population distribution prediction method comprises the following steps: collecting positioning information of users in an area to be predicted, and splitting the positioning information of which the positioning heat is greater than a preset value into a plurality of sample points associated with coordinates; clustering the positioning information of the users in the area to be predicted by combining a preset clustering radius threshold value and a preset sample threshold value to obtain clustering sample points; determining the boundary and the range of a core area of the area to be predicted based on the clustering sample points of the same category; and calculating the positioning heat of the core area to estimate the population of the core area. The core area with concentrated users is found out according to the positioning information, and the number of potential customer groups is evaluated by estimating the specific population number of the core area, so that reference can be provided for the development of market business. The population distribution prediction system and the electronic equipment are used for further implementing the population distribution prediction method.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of population prediction, in particular to a population distribution prediction method, a population distribution prediction system and electronic equipment.
[ background of the invention ]
At present, the traditional population prediction or statistical method is mainly performed manually or related data is obtained from an official party, and for a common enterprise, the population number of a specific area is directly related to the market scale of the enterprise, so that it is very important for the common enterprise to accurately predict the actual active population number of the corresponding area, and the control accuracy of the enterprise on the market potential is directly determined. However, the existing population prediction method is not suitable for common enterprises, which also results in that the common enterprises cannot rapidly and accurately acquire the potential market scale of the corresponding area in many cases, and the information is relatively lagged when the market is laid out, which is not beneficial to adjusting the market planning in time.
In view of this, the present application is specifically made.
[ summary of the invention ]
The invention provides a population distribution prediction method, a population distribution prediction system and electronic equipment, aiming at overcoming the technical problems of poor universality, low acquisition speed and information lag existing in the existing population prediction method.
The invention provides a population distribution prediction method for providing reference for the potential of enterprises to enter a new market so as to better input resources, which comprises the following steps: providing an area to be predicted, collecting positioning information of users in the area to be predicted, wherein the positioning information at least comprises positioning heat, and splitting the positioning information of which the positioning heat is greater than a preset value into a plurality of sample points associated with coordinate information; the equally dividing the positioning information with the positioning heat degree larger than the preset value into a plurality of sample points with the same coordinates comprises the following steps: dividing the region to be predicted into a plurality of sub-regions, and marking the coordinates of each sub-region; calculating the number of people in each sub-area by utilizing the positioning heat occupation ratio of the sub-areas, and defining a temporary metering unit n to split the areas with the number larger than that of the temporary metering unit n in each sub-area; marking the coordinate of each temporary measurement unit n, wherein the coordinate of n is the same as that of the sub-area where the n is located, and taking the temporary measurement unit n as a temporary sample of clustering processing; based on a clustering method, in combination with coordinate information, a preset clustering radius threshold and a preset sample threshold, clustering positioning information of users in an area to be predicted to obtain clustering sample points, wherein the clustering sample points comprise people number information and coordinate information; determining the boundary and the range of at least one core area of the area to be predicted based on the clustering sample points of the same category; and calculating the positioning heat degree of the at least one core area so as to estimate the population of the at least one core area.
Preferably, when splitting the positioning information with the positioning heat greater than the preset value, splitting the positioning information with the positioning heat greater than the preset value into a plurality of sample points with the same coordinates in an equal amount; and/or during clustering, clustering the sample points of the positioning information of the user by using a DBSCN clustering method and combining the coordinate information according to a preset clustering radius threshold and a preset sample threshold so as to obtain clustered sample points.
Preferably, splitting the positioning information with the positioning heat greater than the preset value into a plurality of sample points with the same coordinate equally comprises: dividing a region to be predicted into a plurality of sub-regions, and marking the coordinates of each sub-region; calculating the number of people in each sub-region by utilizing the positioning heat occupation ratio of the sub-region, and defining a temporary metering unit n to split the regions with the number larger than that of the temporary metering unit n in each sub-region; and marking the coordinate of each temporary measurement unit n, wherein the coordinate of n is the same as that of the sub-area where the n is located, and the temporary measurement unit n is used as a temporary sample of clustering processing.
Preferably, when the positioning information of the user in the area to be predicted is collected, the positioning information is GPS positioning information, Beidou positioning information or mobile phone signaling positioning information.
Preferably, the rule of determining at least one core region of the area to be predicted comprises: and combining administrative division boundaries of the city with clustered sample points in the category to determine boundaries and ranges of the core area.
Preferably, after the cluster sample points are obtained by the clustering process, the noise point removal is performed on the obtained cluster sample points.
Preferably, the population distribution prediction method further comprises: after at least one core area of the area to be predicted is determined, acquiring people stream data of the core area, and determining the proportion of a target customer group.
Preferably, when people stream data of the core area are obtained, the people stream data comprise intersection monitoring videos, and the intersection monitoring videos are analyzed through video image recognition based on screening conditions to obtain the proportion of target customer groups.
In order to further solve the above technical problem, the present invention also provides a population distribution prediction system, including: the system comprises an information collection module, a clustering processing module, a core area analysis module and a population calculation module; the information collection module is used for collecting positioning information of users in an area to be predicted, and splitting the positioning information of which the positioning heat is greater than a preset value into a plurality of sample points associated with coordinates; the equally dividing the positioning information with the positioning heat degree larger than the preset value into a plurality of sample points with the same coordinates comprises the following steps: dividing the region to be predicted into a plurality of sub-regions, and marking the coordinates of each sub-region; calculating the number of people in each sub-area by utilizing the positioning heat proportion of the sub-areas, and defining a temporary metering unit n to split the areas, the number of which is greater than that of the temporary metering unit n, in each sub-area; marking the coordinate of each temporary measurement unit n, wherein the coordinate of n is the same as that of the sub-area where the n is located, and taking the temporary measurement unit n as a temporary sample of clustering processing; the clustering processing module is used for clustering the positioning information of the user by combining a preset clustering radius threshold value and a preset sample threshold value to obtain a clustering sample point; the core area analysis module is used for combining the clustering sample points to obtain the boundary and the range of the core area of the area to be predicted; and the population calculating module is used for calculating the positioning heat of the core area so as to estimate the population of the core area.
In order to further solve the above technical problem, the present invention also provides an electronic device, comprising: a memory and a processor; the memory stores a computer program arranged to perform the above population distribution prediction method when run; the processor is arranged to execute the above described population distribution prediction method by means of a computer program.
Compared with the prior art, the technical scheme provided by the invention has the beneficial effects that:
1. the population distribution prediction method carries out clustering processing according to the positioning information of the users, so that a core area with concentrated users is found out, and the number of potential customer groups is evaluated by estimating the specific population number of the core area, so that reference can be provided for developing market services. The core area is equivalent to the area with the most concentrated consumption capacity in the whole area to be predicted, and can be used for providing reference for market business work of commercial activities. In addition, the method can be used for evaluating population distribution situations and population distribution change trends in the whole region to be predicted, so as to be used as a reference for population management work of relevant government departments and a reference for region development planning.
2. When the positioning information with the positioning heat degree larger than the preset value is split, the positioning information with the positioning heat degree larger than the preset value is equally split into a plurality of sample points with the same coordinate, and the reliability of the clustering structure is greatly improved.
3. The number of people of each sub-region is calculated by utilizing the positioning heat proportion of the sub-region, the temporary metering unit n is defined to represent the number of people of each sub-region, and the number of people is tender enough to represent the positioning heat, so that the population scale condition is reflected more visually, the reasonable splitting of the sample points is facilitated, and the method has positive significance for improving the accuracy and reliability of clustering.
4. The boundary and the range of the core area are determined by combining the administrative division boundary of the city and the clustering sample points in the category, so that the division of the core area is adaptive to the division of the administrative division, the core area is more conveniently managed in a unified manner, and the management disorder is avoided.
5. After the clustering processing, the noise point removal is carried out on the obtained clustering sample points, so that the reliability of the core area confirmation can be further improved.
6. By analyzing the human flow data in the core area, the occupation ratio condition of the target customer group can be counted, so that the approximate number of the target customer group can be estimated, the control precision of the input amount when the shared bicycle is input can be greatly improved, excessive input is avoided, the control precision of the actual target customer is greatly improved, the over-estimation market potential is avoided, and the market development risk is reduced.
[ description of the drawings ]
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a population distribution prediction method provided in embodiment 1 of the present invention;
fig. 2 is a schematic flowchart of step S1 of the population distribution prediction method according to embodiment 1 of the present invention;
fig. 3 is a flowchart illustrating step S5 of the population distribution prediction method according to embodiment 1 of the present invention;
fig. 4 is a schematic diagram illustrating a population distribution prediction system according to embodiment 2 of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to embodiment 3 of the present invention;
fig. 6 is a schematic structural diagram of a computer system of a terminal device/server for implementing an embodiment of the present invention.
Description of reference numerals: 1-a population distribution prediction system; 11-an information collection module; 12-a cluster processing module; 13-core region analysis module; 14-population calculation module; 8-an electronic device; 81-a memory; 82-a processor; 800-a computer system; 801-Central Processing Unit (CPU); 802-memory (ROM); 803-RAM; 804-a bus; 805-I/O interfaces; 806-an input section; 807-an output section; 808-a storage portion; 809 — a communication section; 810-a driver; 811-removable media.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures.
It should be understood that as used herein, a "system," "device," "unit," "module," and/or "module" and the like is a method for distinguishing different components, elements, components, parts, or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to or removed from these processes.
Example 1
Referring to fig. 1, the present embodiment provides a population distribution prediction method, which includes the following steps:
s1, providing an area to be predicted, collecting positioning information of users in the area to be predicted, wherein the positioning information at least comprises a positioning heat, and splitting the positioning information of which the positioning heat is greater than a preset value into a plurality of sample points associated with coordinates;
s2, based on a clustering method, combining a preset clustering radius threshold value and a preset sample threshold value, clustering the positioning information of the users in the area to be predicted to obtain clustering sample points;
s3, determining the boundary and the range of at least one core area of the area to be predicted based on the clustering sample points of the same category; and
and S4, calculating the positioning heat degree of the at least one core area to estimate the population of the at least one core area.
The population distribution prediction method carries out clustering processing according to the positioning information of the users, so that a core area with concentrated users is found out, and the number of potential customer groups is evaluated by estimating the specific population number of the core area, so that reference can be provided for developing market services.
It can be understood that the population distribution prediction method may be used to find a core area, so as to display a range with more active population activities and more concentrated population in the area to be predicted, where the core area is equivalent to an area with the most concentrated consumption capacity in the entire area to be predicted, and may be used to provide a reference for market business work of business activities. In addition, the method can be used for evaluating population distribution situations and population distribution change trends in the whole region to be predicted, so as to be used as a reference for population management work of relevant government departments and a reference for region development planning.
In step S1, when splitting the positioning information with the positioning heat greater than the preset value, the area to be predicted is the area where we need to find out the core area and perform the estimation of the potential user group, and generally may be selected according to administrative divisions, for example: the present invention is not limited to a single county, a single city, a single district, etc., and the boundaries and the ranges of the regions may be artificially divided according to actual needs, rather than being divided according to the existing administrative divisions. The dividing mode of the region to be predicted can be flexibly selected.
The positioning information of the user includes, but is not limited to, GPS positioning information, beidou positioning information, and mobile phone signaling positioning information, and optionally, in this embodiment, the positioning information of the user adopts GPS positioning information. The positioning information of the user can be collected through the shared bicycle app, and can also be collected through other common software.
In order to facilitate the clustering process and further improve the accuracy of the clustering process, in step S1, the positioning information with the positioning heat greater than the preset value is equally divided into a plurality of sample points with the same coordinates, and the sample points are used for clustering process. For example, if the location heat is very high at a certain position in the area to be predicted, the number of people representing the position is very large, and if the position is wholly used as a point for clustering, the size of the point is too large, so that the reliability of clustering analysis is reduced. Therefore, the positioning information with the positioning heat degree larger than the preset value is split.
Referring to fig. 2, in order to further improve convenience in practical application and improve accuracy of splitting the positioning information, before splitting, the positioning heat is converted into the corresponding number of people, so as to control the scale of each clustering sample in the clustering process more accurately. Specifically, step S1 includes the following steps:
s11, dividing the region to be predicted into a plurality of sub-regions, and marking the coordinates of each sub-region;
s12, calculating the number of people in each sub-region by utilizing the positioning heat ratio of the sub-region, and defining a temporary metering unit n to split the regions with the number larger than that of the temporary metering unit n in each sub-region; and
and S13, marking the coordinate of each temporary measurement unit n, wherein the coordinate of n is the same as that of the sub-region where the n is located, and taking the temporary measurement unit n as a temporary sample of the clustering process.
For example, the area to be predicted is divided into 3000 sub-areas with the same size, the location heat percentage of the first sub-area is 1%, and the total population number in the entire area to be predicted is 100w, so that the population number in the first sub-area is 100w × 15% — 1 w. If 1w is directly used as a sample point for clustering, the population of the sample point is more than that of other sample points, and the accuracy of clustering is reduced. In order to guarantee the efficiency and accuracy of clustering processing, the preset value of positioning heat splitting can be converted into the number of population, and 100 people are set, so that the 1w people in the first sub-area can be split into 100 sample points, each sample point contains 100 people, meanwhile, in order to facilitate statistics, temporary metering units n can be introduced, each temporary metering unit n represents 100 people, and the total population in the first sub-area is also 100 n. Each temporary measurement unit n can be regarded as a sample point split from the first sub-region, and since the 100 temporary sample points n are split from the first sub-region, the coordinates of the 100 temporary sample points n are the same as the coordinates of the first sub-region. When the clustering process is performed, the 100 temporary sample points n are used to replace the first sub-area to participate in the clustering process. The other sub-regions are split in a similar manner.
It should be noted that, when the splitting operation is performed, if the number of people in a certain sub-area cannot be split into n on average, for example, the number of people in a certain sub-area is 1035, 35 people remain after splitting into 10 n, and since 35 people do not constitute n enough, the remaining 35 people are removed and are not counted in the sample points of the clustering process. However, if the total number of people in a certain sub-area is less than 10 and cannot form an n, the number of people in the sub-area is not taken as a sample point of the clustering process, the sub-area is removed, and the sub-area does not need to be considered when the clustering process is performed. Since the number of the sample points is very small, the influence of the sample points on the clustering process is very small, the referential is not large, and the forced introduction can interfere the confirmation of the core area, so that the sample points are removed to improve the reliability of the determination of the core area.
It should be noted that the preset value for locating the splitting of the heat degree and the specific numerical value of the temporary measurement unit n can be flexibly adjusted according to the actual situation, and if the total number of people in the area to be predicted is large, the numerical value of the temporary measurement unit n can be considered to be increased. If the distribution of the positioning heat degrees in the areas to be predicted is concentrated and the positioning heat degree proportion of each area is balanced, the preset value for splitting the positioning heat degrees can be increased. But is not limited thereto.
In step S2, a DBSCN clustering method is used, and the sample points of the positioning information of the user are clustered according to a preset clustering radius threshold and a preset sample threshold in combination with the coordinate information, so as to obtain the clustered sample points. The obtained clustering sample points contain the information of the number of people and the coordinate information, so that the core area of the area to be predicted is determined conveniently.
In order to improve the reliability of confirming the core region, in step S2, noise point removal is further performed on the obtained cluster sample points.
In step S3, according to the obtained clustered sample points, sub-regions corresponding to clustered sample points based on the same category are divided into the same core region, so that each core region in the region to be predicted can be obtained.
In order to further improve the rationality of the core area range, the rule for acquiring the core area includes: the administrative division boundaries of the city and the clustered sample points within the category are combined to determine the boundaries and extent of the core area. That is, when determining the range of the core area, the boundary and the range of the core area may be corrected and refined by combining with the administrative boundary of the actual city, so as to facilitate management of the core area. For example, the sub-areas are all artificially divided, some sub-areas may be located at the boundary of two areas to include partial ranges of the two areas, and just this sub-area is the outermost side of the core area, so that when the core area is finally determined, in order to facilitate management of the core area, the boundary of the core area may be determined according to the original administrative division boundary, and the population of the corresponding sub-area is entirely divided into the sub-areas where the sub-areas are located. Of course, when the area to be evaluated is divided into a plurality of sub-areas, the factors of the administrative division can be directly considered in the division of the sub-areas.
It is noted that in the determination of the boundary and extent of the at least one core area, factors that may be considered in combination in addition to the administrative district boundary of the city may be: road distribution, river distribution, obstacle distribution, consumption area distribution (e.g., business trips, scenic spots, other entertainment venues, etc.), office area distribution, residential area distribution, public transportation site distribution, other infrastructure distribution (e.g., hospitals, schools, government departments, etc.), government policies, competitor layouts, other field operating conditions (e.g., cell phone network signals, shelter distribution, actual operating income, etc.), and is not limited thereto.
In step S4, since the positioning heat has been converted into the population number in the above, when estimating the total population number in the core area, the number of people gathered in all the sample points in the core area is only required to be added. Of course, if the positioning heat is not converted into the population number, but the heat value of the positioning heat or the percentage of the positioning heat is used for clustering, the positioning heat percentage of the whole core area needs to be calculated by using the heat value of the positioning heat or the percentage of the positioning heat, and then the population number in the core area is estimated according to the total population number in the area to be evaluated.
Referring to fig. 3, in order to further improve the accuracy of controlling the client group and actually find the scale of the target client group, the population distribution prediction method further includes step S5: and after the at least one core area of the area to be predicted is determined, acquiring the people flow data of the core area, and determining the proportion of the target customer group. The population in the core area is classified by analyzing the artifact data to determine the size of the actual target customer that meets the requirements. For example: not all of the population in the core area is our target customers before the shared bike is released, only healthy, age-appropriate populations will be counted as our target customer population. By analyzing the human flow data in the core area, the proportion condition of the target customer group can be counted, so that the approximate number of the target customer group can be estimated, the control precision of the throwing amount when people throw the sharing bicycle can be greatly improved, and excessive throwing is avoided.
In order to improve the estimation accuracy of the target client group, in step S5, the people stream data includes intersection monitoring videos, and the intersection monitoring videos are analyzed through video image recognition based on the screening condition to obtain the occupation ratio of the target client group. Through video image recognition, information such as approximate age range, limb health and the like can be judged, so that the actual proportion of a target customer group can be determined conveniently.
It should be noted that the population distribution prediction method can also be used for market potential evaluation in other fields, or for evaluating the size of a consumer group in a new market, so as to serve as a prospect analysis reference before entering the market. And is not limited thereto.
Example 2
Referring to fig. 4, the present embodiment provides a population distribution predicting system 1, where the population distribution predicting system 1 includes: the system comprises an information collection module 11, a clustering processing module 12, a core area analysis module 13 and a population calculation module 14.
The information collection module 11 is configured to collect positioning information of users in an area to be predicted, and split the positioning information with a positioning heat greater than a preset value into a plurality of sample points associated with coordinates.
The clustering processing module 12 is configured to perform clustering processing on the positioning information of the user by combining a preset clustering radius threshold and a preset sample threshold to obtain a clustering sample point.
The core area analysis module 13 is configured to obtain a boundary and a range of the core area of the area to be predicted by combining the clustering sample points. And
the population calculating module 14 is used for calculating the positioning heat of the core area to estimate the population of the core area.
Example 3
Referring to fig. 5, the present embodiment provides an electronic device 8, including: a memory 81 and a processor 82. The memory 81 stores a computer program arranged to perform the population distribution prediction method of embodiment 1 when run. The processor 82 is arranged to execute the population distribution prediction method of embodiment 1 by means of a computer program.
Referring now to FIG. 6, a block diagram of a computer system 800 suitable for use with a terminal device/server implementing an embodiment of the present invention is shown. The terminal device/server shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the computer system 800 includes a Central Processing Unit (CPU)801 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for the operation of the system 800 are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including components such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.
According to an embodiment of the present disclosure, the processes described above with reference to the flow charts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program performs the above-described functions defined in the method of the present invention when executed by the Central Processing Unit (CPU) 801. It should be noted that the computer readable medium of the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present invention may be implemented by software or hardware. As another aspect, the present invention also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not assembled into the device.
The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to perform the steps of: s1, providing an area to be predicted, collecting positioning information of a user, and splitting the positioning information of which the positioning heat is greater than a preset value into a plurality of sample points associated with coordinates; s2, based on a clustering method, combining a preset clustering radius threshold value and a preset sample threshold value, clustering the positioning information of the user to obtain clustering sample points; s3, combining the clustering sample points to obtain the boundary and the range of the core area of the area to be predicted; and S4, calculating the positioning heat of the core area to estimate the population of the core area.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
1. the population distribution prediction method carries out clustering processing according to the positioning information of the users, so that a core area with concentrated users is found out, and the number of potential customer groups is evaluated by estimating the specific population number of the core area, so that reference can be provided for developing market services. The core area is equivalent to the area with the most concentrated consumption capacity in the whole area to be predicted, and can be used for providing reference for market business work of commercial activities. In addition, the method can be used for evaluating population distribution situations and population distribution change trends in the whole region to be predicted, so as to be used as a reference for population management work of relevant government departments and a reference for region development planning.
2. When the positioning information with the positioning heat degree larger than the preset value is split, the positioning information with the positioning heat degree larger than the preset value is split into a plurality of sample points with the same coordinate, and the reliability of the clustering structure is greatly improved.
3. The number of people of each sub-region is calculated by utilizing the positioning heat proportion of the sub-region, the temporary metering unit n is defined to represent the number of people of each sub-region, and the number of people is tender enough to represent the positioning heat, so that the population scale condition is reflected more visually, the reasonable splitting of the sample points is facilitated, and the method has positive significance for improving the accuracy and reliability of clustering.
4. The boundary and the range of the core area are determined by combining the administrative division boundary of the city and the clustering sample points in the category, so that the division of the core area is adaptive to the division of the administrative division, the core area is more conveniently managed in a unified manner, and the management disorder is avoided.
5. After the clustering processing, the noise point removal is carried out on the obtained clustering sample points, so that the reliability of the core area confirmation can be further improved.
6. By analyzing the human flow data in the core area, the occupation ratio condition of the target customer group can be counted, so that the approximate number of the target customer group can be estimated, the control precision of the input amount when the shared bicycle is input can be greatly improved, excessive input is avoided, the control precision of the actual target customer is greatly improved, the over-estimation market potential is avoided, and the market development risk is reduced.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A method for predicting a population distribution for providing a reference for an enterprise's potential to enter a new market for better resource investment, comprising:
providing an area to be predicted, collecting positioning information of users in the area to be predicted, wherein the positioning information at least comprises positioning heat, and splitting the positioning information of which the positioning heat is greater than a preset value into a plurality of sample points associated with coordinate information; the equally dividing the positioning information with the positioning heat degree larger than the preset value into a plurality of sample points with the same coordinates comprises the following steps: dividing the area to be predicted into a plurality of sub-areas, and marking the coordinates of each sub-area; calculating the number of people in each sub-area by utilizing the positioning heat occupation ratio of the sub-areas, and defining a temporary metering unit n to split the areas with the number larger than that of the temporary metering unit n in each sub-area; marking the coordinate of each temporary measurement unit n, wherein the coordinate of n is the same as that of the sub-area where the n is located, and taking the temporary measurement unit n as a temporary sample of clustering processing;
based on a clustering method, in combination with coordinate information, a preset clustering radius threshold and a preset sample threshold, clustering positioning information of users in the area to be predicted to obtain clustering sample points, wherein the clustering sample points comprise people number information and coordinate information;
determining a boundary and a range of at least one core area of the area to be predicted based on the clustered sample points of the same category; and
and calculating the positioning heat degree of the at least one core area so as to estimate the population of the at least one core area.
2. The population distribution prediction method according to claim 1, wherein when splitting the positioning information having a positioning heat greater than a preset value, the positioning information having a positioning heat greater than the preset value is split equally into a plurality of sample points having the same coordinates; and/or
And during clustering, clustering the sample points of the positioning information of the user by using a DBSCN clustering method and combining coordinate information according to the preset clustering radius threshold and the preset sample threshold to obtain the clustered sample points.
3. The population distribution prediction method of claim 1, wherein when collecting positioning information of users in the area to be predicted, the positioning information is GPS positioning information, Beidou positioning information or mobile phone signaling positioning information.
4. The method of claim 1, wherein the rules for determining the at least one core region of the area to be predicted comprise: and combining administrative division boundaries of cities and clustering sample points in the categories to determine the boundary and the range of the core area.
5. The method of claim 1, wherein after the clustering sample points are obtained by the clustering process, noise point removal is performed on the obtained clustering sample points.
6. The method of claim 1, further comprising: and after the at least one core area of the area to be predicted is determined, acquiring the people flow data of the core area, and determining the proportion of a target customer group.
7. The method of claim 6, wherein the traffic data of the core region is obtained and includes intersection surveillance video, and the intersection surveillance video is analyzed by video image recognition based on screening conditions to obtain the proportion of the target customer group.
8. A system for predicting a population distribution, comprising:
the information collection module is used for collecting positioning information of users in the area to be predicted and splitting the positioning information with the positioning heat degree larger than a preset value into a plurality of sample points associated with coordinates; the equally dividing the positioning information with the positioning heat degree larger than the preset value into a plurality of sample points with the same coordinates comprises the following steps: dividing the region to be predicted into a plurality of sub-regions, and marking the coordinates of each sub-region; calculating the number of people in each sub-area by utilizing the positioning heat occupation ratio of the sub-areas, and defining a temporary metering unit n to split the areas with the number larger than that of the temporary metering unit n in each sub-area; marking the coordinate of each temporary metering unit n, wherein the coordinate of n is the same as that of the sub-area where the n is located, and taking the temporary metering unit n as a temporary sample of clustering processing;
the clustering processing module is used for clustering the positioning information of the user by combining a preset clustering radius threshold value and a preset sample threshold value to obtain clustering sample points;
the core area analysis module is used for combining the clustering sample points to obtain the boundary and the range of the core area of the area to be predicted; and
and the population calculating module is used for calculating the positioning heat of the core area so as to estimate the population of the core area.
9. An electronic device, comprising: a memory and a processor;
the memory stores a computer program arranged to perform, when executed, the method of population distribution prediction according to any of the claims 1-7;
the processor is arranged to perform the method of population distribution prediction according to any of claims 1-7 by means of the computer program.
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