CN111078816B - Position-based analysis method, device, terminal and storage medium - Google Patents
Position-based analysis method, device, terminal and storage medium Download PDFInfo
- Publication number
- CN111078816B CN111078816B CN201911299145.5A CN201911299145A CN111078816B CN 111078816 B CN111078816 B CN 111078816B CN 201911299145 A CN201911299145 A CN 201911299145A CN 111078816 B CN111078816 B CN 111078816B
- Authority
- CN
- China
- Prior art keywords
- information corresponding
- group
- population type
- attribute information
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a position-based analysis method, a position-based analysis device, a terminal and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: and acquiring a plurality of groups of access identifiers in a preset specified range from the network behavior monitoring database according to the position service information corresponding to the access identifiers in the network behavior monitoring database, determining attribute information corresponding to each group of access identifiers according to each group of access identifiers, and analyzing crowd constitution in the preset specified range according to the attribute information corresponding to the plurality of groups of access identifiers. Based on the location service information and the monitoring database, a plurality of groups of access identifications in a preset specified range are obtained, attribute information corresponding to each group of access identifications is determined, and according to the attribute information, the crowd constitution in the preset specified range is analyzed, so that unnecessary human resource waste is reduced, and the efficiency of analyzing and counting the crowd in the preset specified range is improved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a location-based analysis method, a location-based analysis device, a location-based terminal, and a storage medium.
Background
The personnel information in one area is counted, so that the personnel constitution condition in the area can be known more clearly, and reference basis can be provided for the site selection of some storefronts, so that the personnel information in the appointed area is more and more necessary to count.
In the related art, a person in a designated area can be visited and investigated by an investigator, and information of the person in the designated area is counted.
However, in the related art, by the visit and investigation of the investigator, the information of the personnel in the designated area is manually counted, which wastes unnecessary human resources and reduces the efficiency of counting the information of the personnel in the designated area.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a position-based analysis method, a position-based analysis device, a position-based analysis terminal and a position-based storage medium, so that the problems that unnecessary human resources are wasted and efficiency in counting personnel information in a specified area is reduced due to the fact that investigation and investigation are performed by investigation personnel and the personnel information in the specified area are counted manually in the related art are solved.
In order to achieve the above purpose, the technical scheme adopted by the embodiment of the invention is as follows:
in a first aspect, an embodiment of the present invention provides a location-based analysis method, including:
acquiring a plurality of groups of access identifiers in a preset designated range from a network behavior monitoring database according to position service information corresponding to the access identifiers in the network behavior monitoring database;
determining attribute information corresponding to each group of access identifiers according to each group of access identifiers;
and analyzing the crowd constitution of the preset appointed range according to the attribute information corresponding to the multiple groups of access identifiers.
Further, each set of access identities includes: a device identifier and the occurrence time of the device identifier;
the determining the attribute information corresponding to each group of access identifiers according to each group of access identifiers comprises the following steps:
determining the population type corresponding to the equipment identifier according to the appearance duration of the equipment identifier;
and determining attribute information corresponding to the population type as attribute information corresponding to each group of access identifiers according to the population type.
Further, the determining, according to the occurrence duration of the device identifier, the population type corresponding to the device identifier includes:
if the appearance duration of the equipment identifier is greater than or equal to the preset duration, determining that the population type corresponding to the equipment identifier is a resident population type;
and if the occurrence time of the equipment identifier is smaller than the preset time, determining that the population type corresponding to the equipment identifier is the floating population type.
Further, each group of access identities further includes: an IP address; the determining the population type corresponding to the equipment identifier according to the appearance duration of the equipment identifier comprises the following steps:
determining the IP attribute of each group of access identifiers according to the IP address;
and determining the population type corresponding to the equipment identifier under the IP attribute according to the occurrence time of the equipment identifier under the IP attribute.
Further, the determining, according to the population type, the attribute information corresponding to the population type as the attribute information corresponding to each group of access identifiers includes:
according to the population type, determining population attributes corresponding to the population type and interest tags corresponding to the population type; the attribute information corresponding to each group of access identifiers comprises: the demographic attributes and the interest tags.
Further, the demographic attributes include at least one of: gender, age, academic, consumption level;
the interest tag includes at least one of: mother and infant labels, car labels and cosmetic labels.
Further, the analyzing the crowd composition in the preset specified range according to the attribute information corresponding to the multiple groups of access identifiers includes:
and analyzing the personnel number and/or the personnel duty ratio corresponding to each attribute information in the preset designated range according to the attribute information corresponding to the multiple groups of access identifiers.
In a second aspect, an embodiment of the present invention further provides a location-based analysis apparatus, including:
the acquisition module is used for acquiring a plurality of groups of access identifiers in a preset specified range from the network behavior monitoring database according to the position service information corresponding to the access identifiers in the network behavior monitoring database;
the determining module is used for determining attribute information corresponding to each group of access identifiers according to each group of access identifiers;
and the analysis module is used for analyzing the crowd composition in the preset specified range according to the attribute information corresponding to the multiple groups of access identifiers.
Further, each set of access identities includes: a device identifier and the occurrence time of the device identifier;
the determining module is further configured to determine a population type corresponding to the device identifier according to an occurrence duration of the device identifier; and determining attribute information corresponding to the population type as attribute information corresponding to each group of access identifiers according to the population type.
Further, the determining module is further configured to determine that the population type corresponding to the device identifier is a resident population type if the occurrence duration of the device identifier is greater than or equal to a preset duration; and if the occurrence time of the equipment identifier is smaller than the preset time, determining that the population type corresponding to the equipment identifier is the floating population type.
Further, each group of access identities further includes: an IP address; the determining module is further configured to determine an IP attribute of each group of access identifiers according to the IP address; and determining the population type corresponding to the equipment identifier under the IP attribute according to the occurrence time of the equipment identifier under the IP attribute.
Further, the determining module is further configured to determine, according to the population type, a population attribute corresponding to the population type and an interest tag corresponding to the population type; the attribute information corresponding to each group of access identifiers comprises: the demographic attributes and the interest tags.
Further, the demographic attributes include at least one of: gender, age, academic, consumption level; the interest tag includes at least one of: mother and infant labels, car labels and cosmetic labels.
Further, the analysis module is further configured to analyze the number of people and/or the duty ratio of people corresponding to each attribute information in the preset specified range according to the attribute information corresponding to the multiple groups of access identifiers.
In a third aspect, an embodiment of the present invention further provides a terminal, including: a memory storing a computer program executable by the processor, and a processor implementing the method of any one of the first aspects when the processor executes the computer program.
In a fourth aspect, an embodiment of the present invention further provides a storage medium, where a computer program is stored, where the computer program is read and executed to implement the method according to any one of the first aspects.
The beneficial effects of the invention are as follows: the embodiment of the invention provides a position-based analysis method, which comprises the steps of acquiring a plurality of groups of access identifiers in a preset designated range from a network behavior monitoring database according to position service information corresponding to the access identifiers in the network behavior monitoring database, determining attribute information corresponding to each group of access identifiers according to each group of access identifiers, and analyzing crowd constitution in the preset designated range according to the attribute information corresponding to the plurality of groups of access identifiers. Based on the location service information and the monitoring database, a plurality of groups of access identifications in a preset specified range are obtained, attribute information corresponding to each group of access identifications is determined, and according to the attribute information, the crowd constitution in the preset specified range is analyzed, so that unnecessary human resource waste is reduced, and the efficiency of analyzing and counting the crowd in the preset specified range is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a location-based analysis method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a location-based analysis method according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a location-based analysis method according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a location-based analysis method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a location-based analysis device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention.
According to the position-based analysis method provided by the embodiment of the invention, the execution subject can be a terminal, for example, can be a computer device such as a computer, a mobile phone, a tablet computer and the like, and the embodiment of the invention is not particularly limited.
Fig. 1 is a flow chart of a location-based analysis method according to an embodiment of the present invention, as shown in fig. 1, where the method includes:
s101, according to the position service information corresponding to the access identification in the network behavior monitoring database, acquiring a plurality of groups of access identifications in a preset specified range from the network behavior monitoring database.
The network behavior monitoring database may include a plurality of groups of access identifiers and location service information corresponding to the access identifiers.
In some embodiments, a user may input a specified range in the terminal, the terminal may determine a preset specified range according to an input operation of the user, traverse each location service information to determine a plurality of location service information within the preset specified range, and determine an access identifier corresponding to each location service information, so as to obtain a plurality of groups of access identifiers within the preset specified range.
The location service information may be LBS (Location Based Services, location-based service) information, and the location information included in the LBS information may be longitude, latitude, or the like.
S102, determining attribute information corresponding to each group of access identifiers according to each group of access identifiers.
The attribute information corresponding to each group of access identifier may be a population type corresponding to the access identifier, a population attribute, an interest feature, etc. corresponding to the access identifier, or a population type, a population attribute, an interest feature, etc. corresponding to the access identifier.
Each set of access identities may include an access identity and characteristic information corresponding to the access identity.
In some embodiments, the terminal may analyze the feature information corresponding to the access identifier included in each group of access identifiers, and determine attribute information corresponding to each group of access identifiers. The terminal may also obtain attribute information corresponding to each group of access identifiers from a monitoring database or other preset databases based on the access identifiers included in each group of access identifiers, which is not particularly limited in the embodiment of the present invention.
S103, analyzing the crowd constitution of a preset appointed range according to the attribute information corresponding to the multiple groups of access identifiers.
In the embodiment of the invention, the terminal can adopt a preset analysis algorithm, analyze the crowd constitution of a preset designated range according to the attribute information corresponding to the multiple groups of access identifiers to obtain an analysis result, and display the analysis result to the user, so that the user can intuitively know the crowd constitution condition, the characteristics and the like of the preset designated range according to the analysis result.
In summary, the embodiment of the present invention provides a location-based analysis method, according to location service information corresponding to access identifiers in a network behavior monitoring database, multiple groups of access identifiers in a preset specified range are obtained from the network behavior monitoring database, according to each group of access identifiers, attribute information corresponding to each group of access identifiers is determined, and then, according to the attribute information corresponding to the multiple groups of access identifiers, crowd composition in the preset specified range is analyzed. Based on the location service information and the monitoring database, a plurality of groups of access identifications in a preset specified range are obtained, attribute information corresponding to each group of access identifications is determined, and according to the attribute information, the crowd constitution in the preset specified range is analyzed, so that unnecessary human resource waste is reduced, and the efficiency of analyzing and counting the crowd in the preset specified range is improved.
Optionally, each group of access identities includes: the device identifier and the occurrence duration of the device identifier, where the device identifier may be represented by a device ID (Identity document, unique code), and fig. 2 is a schematic flow chart of a location-based analysis method provided by an embodiment of the present invention, as shown in fig. 2, where S102 may include:
s201, determining population types corresponding to the equipment identifiers according to the occurrence time of the equipment identifiers.
The occurrence duration of the device identifier may be: the device identity appears for a period of time under the corresponding location information.
In a possible implementation manner, the terminal may have a preset duration, and the terminal may determine a size relationship between the occurrence duration of each device identifier and the preset duration, obtain a determination result, and determine a population type corresponding to the device identifier according to the determination result.
S202, determining attribute information corresponding to the population type as attribute information corresponding to each group of access identifiers according to the population type.
Wherein the population types may include multiple categories, each of which may have a corresponding at least one device identification.
In one possible implementation manner, the terminal may determine attribute information corresponding to each device identifier in each population type, and then determine attribute information corresponding to each group of access identifiers.
In addition, when the terminal inputs the designated range, the user can also input target attribute information, the terminal can respectively determine the target attribute information corresponding to each equipment identifier in each population type, and then the terminal can determine the target attribute information corresponding to each group of access identifiers.
It should be noted that, the terminal may determine the attribute information corresponding to each device identifier from the monitoring database, or may determine the attribute information corresponding to each device identifier from other databases, which is not limited in particular by the embodiment of the present invention.
Optionally, fig. 3 is a flow chart of a location-based analysis method according to an embodiment of the present invention, as shown in fig. 3, in S201, determining, according to an occurrence duration of the device identifier, a population type corresponding to the device identifier may include:
s301, if the occurrence time of the equipment identifier is greater than or equal to a preset time, determining that the population type corresponding to the equipment identifier is a resident population type.
S302, if the appearance duration of the equipment identifier is smaller than the preset duration, determining that the population type corresponding to the equipment identifier is the floating population type.
Population types may include resident population types and floating population types, among others.
It should be noted that, the terminal may create a resident population list, and when the population type corresponding to the device identifier is a resident population type, the terminal may add the device identifier to the resident population list.
For example, a set of access identities may include: the device identifier A1, and the occurrence duration T1 corresponding to the A1, and the other group of access identifiers may include: the device identifier A2 and the occurrence duration T2 corresponding to the device identifier A2 may be T0, where if T1 is greater than T0, A1 is a resident population type, and if T2 is less than T0, A2 is a resident population type.
Optionally, each group of access identities further includes: fig. 4 is a flow chart of a location-based analysis method according to an embodiment of the present invention, as shown in fig. 4, in S201, the determining, according to the occurrence duration of the device identifier, a population type corresponding to the device identifier may further include:
s401, determining the IP attribute of each group of access identifiers according to the IP address.
The network behavior monitoring database may be an advertisement monitoring log, where the advertisement monitoring log may include: device identification, duration of occurrence of device identification, and IP address, etc. Of course, the network behavior monitoring database may be other logs including the device identifier, the occurrence duration of the device identifier, and the IP address, which is not particularly limited in the embodiment of the present invention.
It should be noted that, the terminal may determine, according to the IP address, that the IP attribute of each group of access identifiers belongs to a preset IP, where the preset IP may include a residential IP, a school IP, and an enterprise IP.
S402, determining the population type corresponding to the equipment identifier under the IP attribute according to the occurrence time of the equipment identifier under the IP attribute.
In some embodiments, when the IP attribute is a preset IP, the terminal may determine whether the occurrence duration of the device identifier under the IP attribute meets a preset time condition, if so, may determine that the population type corresponding to the device identifier under the IP attribute is a resident population type, and if not, may determine that the population type corresponding to the device identifier under the IP attribute is a floating population type.
In addition, the residence IP, the school IP and the enterprise IP can all have corresponding preset time lengths, and the preset time lengths corresponding to the residence IP, the school IP and the enterprise IP can be the same or different. If the occurrence time of the device identifier under the IP attribute is longer than or equal to the preset time, the population type corresponding to the device identifier under the IP attribute is the resident population type, and if the occurrence time of the device identifier under the IP attribute is shorter than the preset time, the population type corresponding to the device identifier under the IP attribute is the floating population type.
For example, the preset time periods corresponding to the residence IP, the school IP and the enterprise IP can be 3 days, the appearance time period of the equipment identifier under the residence IP can be 1 day, the appearance time period of the equipment identifier under the school IP can be 3 days, the appearance time period of the equipment identifier under the enterprise IP can be 6 days, the population types of the equipment identifiers under the residence IP are floating population types, and the population types of the equipment identifiers under the school IP and the equipment identifiers under the enterprise IP are resident population types.
In the embodiment of the invention, the IP attribute of each group of access identifiers is determined according to the IP address, and the population type corresponding to the device identifier under the IP attribute is determined according to the occurrence time of the device identifier under the IP attribute, so that the population type can be determined more accurately.
Optionally, in S202, the process of determining, according to the population type, attribute information corresponding to the population type as attribute information corresponding to each group of access identifiers may include: and determining population attributes corresponding to the population types and interest tags corresponding to the population types according to the population types.
Wherein, the attribute information corresponding to each group of access identifiers may include: population attributes and interest tags.
In one possible implementation, the terminal may determine the population attribute and the interest tag corresponding to each device identifier in each population type, respectively, and then may determine the population attribute and the interest tag corresponding to each group of access identifiers.
Optionally, the demographic attributes include at least one of: gender, age, academy, consumption level, interest tags include at least one of: mother and infant labels, car labels and cosmetic labels.
Of course, demographic attributes may also include: wedding, height, weight, etc., the interest tag may also include: game tags, sports tags, travel tags, financial tags, etc., which are not particularly limited by the embodiments of the present invention.
Optionally, in S103, the process of analyzing the crowd configuration in the preset specified range according to the attribute information corresponding to the multiple groups of access identifiers may further include: and analyzing the number of people and/or the duty ratio of the people corresponding to each attribute information in the preset designated range according to the attribute information corresponding to the multiple groups of access identifiers.
Each group of access identifiers can comprise equipment identifiers, and the terminal can analyze the number of people and/or the occupancy rate of people corresponding to each attribute information in a preset designated range according to attribute information corresponding to a plurality of groups of access identifiers based on population types corresponding to the equipment identifiers.
For example, the number of device identifications belonging to the resident population type may be a, i.e., the number of persons belonging to the resident population type is a, the number of device identifications belonging to the floating population type may be B, i.e., the number of persons belonging to the floating population type is B, the number of persons having the car label is C among the persons of the resident population type, the number of persons having the car label is D among the persons of the floating population type, the terminal may count the duty ratio C/a of the car personnel in the resident population, and the duty ratio D/B of the car personnel in the floating population.
In the embodiment of the invention, the terminal can also determine the area of the preset specified range according to the preset specified range, and count the total number of people in the preset specified range, so that the population density of the preset specified range can be calculated, and the personnel distribution data and the like of each age group in the preset specified range can be determined to assist the banking enterprises to determine the addresses of the business network points.
In addition, the terminal can also determine the proportion and the number of females in a preset designated range according to the sex attribute in the population attribute, and determine the number of people interested in the cosmetic product higher according to the cosmetic label in the interest label, so as to assist the cosmetic merchant to select the site in the preset designated range.
In summary, the embodiment of the present invention provides a location-based analysis method, according to location service information corresponding to access identifiers in a network behavior monitoring database, multiple groups of access identifiers in a preset specified range are obtained from the network behavior monitoring database, according to each group of access identifiers, attribute information corresponding to each group of access identifiers is determined, and then, according to the attribute information corresponding to the multiple groups of access identifiers, crowd composition in the preset specified range is analyzed. Based on the location service information and the monitoring database, a plurality of groups of access identifications in a preset specified range are obtained, attribute information corresponding to each group of access identifications is determined, and according to the attribute information, the crowd constitution in the preset specified range is analyzed, so that unnecessary human resource waste is reduced, the efficiency of analyzing and counting the crowd in the preset specified range is improved, statistics of personnel structures in the preset specified range can be realized, and statistical information can be provided for shop site selection.
Fig. 5 is a schematic structural diagram of a location-based analysis device according to an embodiment of the present invention, where, as shown in fig. 5, the device may include:
the obtaining module 501 is configured to obtain, from the network behavior monitoring database, a plurality of groups of access identifiers within a preset specified range according to location service information corresponding to the access identifiers in the network behavior monitoring database;
a determining module 502, configured to determine attribute information corresponding to each group of access identifiers according to each group of access identifiers;
and the analysis module 503 is configured to analyze the crowd composition in the preset specified range according to the attribute information corresponding to the multiple groups of access identifiers.
Optionally, each group of access identities includes: a device identifier, a duration of occurrence of the device identifier;
the determining module 502 is further configured to determine, according to an occurrence duration of the device identifier, a population type corresponding to the device identifier; and determining attribute information corresponding to the population type as attribute information corresponding to each group of access identifiers according to the population type.
Optionally, the determining module 502 is further configured to determine that the population type corresponding to the device identifier is a resident population type if the occurrence duration of the device identifier is greater than or equal to a preset duration; if the appearance duration of the equipment identifier is smaller than the preset duration, determining that the population type corresponding to the equipment identifier is the floating population type.
Optionally, each group of access identities further includes: an IP address; the determining module 502 is further configured to determine an IP attribute of each group of access identifiers according to the IP address; and determining the population type corresponding to the equipment identifier under the IP attribute according to the appearance duration of the equipment identifier under the IP attribute.
Optionally, the determining module 502 is further configured to determine, according to the population type, a population attribute corresponding to the population type and an interest tag corresponding to the population type; the attribute information corresponding to each group of access identifiers comprises: population attributes and interest tags.
Optionally, the demographic attributes include at least one of: gender, age, academic, consumption level; the interest tag includes at least one of: mother and infant labels, car labels and cosmetic labels.
Optionally, the analysis module 503 is further configured to analyze the number of people and/or the duty ratio of people corresponding to each attribute information in the preset specified range according to attribute information corresponding to multiple groups of access identifiers.
The foregoing apparatus is used for executing the method provided in the foregoing embodiment, and its implementation principle and technical effects are similar, and are not described herein again.
The above modules may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more microprocessors (digital singnal processor, abbreviated as DSP), or one or more field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), or the like. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Fig. 6 is a schematic structural diagram of a terminal according to an embodiment of the present invention, where the terminal may be a computing device with a data processing function.
The terminal comprises: processor 601, memory 602.
The memory 602 is used for storing a program, and the processor 601 calls the program stored in the memory 602 to execute the above-described method embodiment. The specific implementation manner and the technical effect are similar, and are not repeated here.
Optionally, the present invention also provides a program product, such as a computer readable storage medium, comprising a program for performing the above-described method embodiments when being executed by a processor.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units 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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (english: processor) to perform some of the steps of the methods according to the embodiments of the invention. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
Claims (8)
1. A method of location-based analysis, comprising:
acquiring a plurality of groups of access identifiers in a preset designated range from a network behavior monitoring database according to position service information corresponding to the access identifiers in the network behavior monitoring database;
determining attribute information corresponding to each group of access identifiers according to each group of access identifiers;
analyzing the crowd constitution of the preset appointed range according to the attribute information corresponding to the multiple groups of access identifiers;
the access identities of each group comprise: a device identifier and the occurrence time of the device identifier;
the determining the attribute information corresponding to each group of access identifiers according to each group of access identifiers comprises the following steps:
determining the population type corresponding to the equipment identifier according to the appearance duration of the equipment identifier;
according to the population type, determining attribute information corresponding to the population type as attribute information corresponding to each group of access identifiers;
the access identities of each group further comprise: an IP address; the determining the population type corresponding to the equipment identifier according to the appearance duration of the equipment identifier comprises the following steps:
determining the IP attribute of each group of access identifiers according to the IP address;
determining a population type corresponding to the equipment identifier under the IP attribute according to the occurrence time of the equipment identifier under the IP attribute, wherein the population type comprises: resident population type and floating population type.
2. The method of claim 1, wherein the determining the population type corresponding to the device identifier according to the occurrence duration of the device identifier comprises:
if the appearance duration of the equipment identifier is greater than or equal to the preset duration, determining that the population type corresponding to the equipment identifier is a resident population type;
and if the occurrence time of the equipment identifier is smaller than the preset time, determining that the population type corresponding to the equipment identifier is the floating population type.
3. The method of claim 1, wherein the determining, according to the population type, the attribute information corresponding to the population type as the attribute information corresponding to each group of access identities includes:
according to the population type, determining population attributes corresponding to the population type and interest tags corresponding to the population type; the attribute information corresponding to each group of access identifiers comprises: the demographic attributes and the interest tags.
4. A method according to claim 3, wherein the demographic attributes include at least one of: gender, age, academic, consumption level;
the interest tag includes at least one of: mother and infant labels, car labels and cosmetic labels.
5. The method according to any one of claims 1-4, wherein analyzing the crowd constitution of the preset specified range according to the attribute information corresponding to the plurality of groups of access identifiers includes:
and analyzing the personnel number and/or the personnel duty ratio corresponding to each attribute information in the preset designated range according to the attribute information corresponding to the multiple groups of access identifiers.
6. A location-based analysis device, comprising:
the acquisition module is used for acquiring a plurality of groups of access identifiers in a preset specified range from the network behavior monitoring database according to the position service information corresponding to the access identifiers in the network behavior monitoring database;
the determining module is used for determining attribute information corresponding to each group of access identifiers according to each group of access identifiers;
the analysis module is used for analyzing the crowd constitution of the preset appointed range according to the attribute information corresponding to the multiple groups of access identifiers;
the access identities of each group comprise: a device identifier and the occurrence time of the device identifier;
the determining module is further configured to determine a population type corresponding to the device identifier according to an occurrence duration of the device identifier; according to the population type, determining attribute information corresponding to the population type as attribute information corresponding to each group of access identifiers;
the access identities of each group further comprise: an IP address; the determining module is further configured to determine an IP attribute of each group of access identifiers according to the IP address; determining a population type corresponding to the equipment identifier under the IP attribute according to the occurrence time of the equipment identifier under the IP attribute, wherein the population type comprises: resident population type and floating population type.
7. A terminal, comprising: a memory and a processor, the memory storing a computer program executable by the processor, the processor implementing the method of any of the preceding claims 1-5 when the computer program is executed.
8. A storage medium having stored thereon a computer program which, when read and executed, implements the method of any of the preceding claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911299145.5A CN111078816B (en) | 2019-12-16 | 2019-12-16 | Position-based analysis method, device, terminal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911299145.5A CN111078816B (en) | 2019-12-16 | 2019-12-16 | Position-based analysis method, device, terminal and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111078816A CN111078816A (en) | 2020-04-28 |
| CN111078816B true CN111078816B (en) | 2023-08-25 |
Family
ID=70314916
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911299145.5A Active CN111078816B (en) | 2019-12-16 | 2019-12-16 | Position-based analysis method, device, terminal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111078816B (en) |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1729447A1 (en) * | 2005-06-03 | 2006-12-06 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Method and monitoring system for sample-analysis of data comprising a multitude of data packets |
| WO2007086684A1 (en) * | 2006-01-26 | 2007-08-02 | Nhn Corporation | Method and system for calculating advertising-fee of local advertising information |
| CN101340606A (en) * | 2007-07-04 | 2009-01-07 | 中国移动通信集团公司 | Method and system for obtaining population information |
| CN102893300A (en) * | 2010-03-15 | 2013-01-23 | 尼尔森（美国）有限公司 | Methods and apparatus for integrating volumetric sales data, media consumption information, and geographic -demographic data to target advertisements |
| CN104361658A (en) * | 2014-09-30 | 2015-02-18 | 北京锐安科技有限公司 | Method and device for detecting population information of each place in region |
| WO2015076714A1 (en) * | 2013-11-22 | 2015-05-28 | Telefonaktiebolaget L M Ericsson (Publ) | Centralised capability discovery |
| CN106096631A (en) * | 2016-06-02 | 2016-11-09 | 上海世脉信息科技有限公司 | A kind of recurrent population's Classification and Identification based on the big data of mobile phone analyze method |
| CN106682931A (en) * | 2015-11-11 | 2017-05-17 | 北京国双科技有限公司 | Marketing information display method and device |
| CN107133318A (en) * | 2017-05-03 | 2017-09-05 | 北京市交通信息中心 | A kind of population recognition methods based on mobile phone signaling data |
| CN107801203A (en) * | 2017-11-13 | 2018-03-13 | 毛国强 | The evaluation method and its system of the density of population and mobility based on multi-data fusion |
| CN108566648A (en) * | 2017-12-29 | 2018-09-21 | 福建福诺移动通信技术有限公司 | A kind of resident population judgment method in the region based on carrier data |
| CN109189949A (en) * | 2018-08-03 | 2019-01-11 | 江苏省城市规划设计研究院 | A kind of population distribution calculation method |
| CN109362041A (en) * | 2018-12-18 | 2019-02-19 | 成都方未科技有限公司 | A kind of population space-time distributional analysis method based on big data |
| CN109918459A (en) * | 2019-01-28 | 2019-06-21 | 同济大学 | A kind of city mid-scale view real population statistical method based on mobile phone signaling |
| CN110019626A (en) * | 2017-12-21 | 2019-07-16 | 腾讯科技（深圳）有限公司 | A kind of the determination method and relevant apparatus of population distribution information |
| CN110046174A (en) * | 2019-03-07 | 2019-07-23 | 特斯联（北京）科技有限公司 | A kind of population migration analysis method and system based on big data |
| CN110443738A (en) * | 2019-07-17 | 2019-11-12 | 浙江大华技术股份有限公司 | Floating population's register method and system and relevant device |
| CN110503453A (en) * | 2019-07-05 | 2019-11-26 | 平安银行股份有限公司 | Customer-action analysis method, apparatus, computer equipment and storage medium |
-
2019
- 2019-12-16 CN CN201911299145.5A patent/CN111078816B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1729447A1 (en) * | 2005-06-03 | 2006-12-06 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Method and monitoring system for sample-analysis of data comprising a multitude of data packets |
| WO2007086684A1 (en) * | 2006-01-26 | 2007-08-02 | Nhn Corporation | Method and system for calculating advertising-fee of local advertising information |
| CN101340606A (en) * | 2007-07-04 | 2009-01-07 | 中国移动通信集团公司 | Method and system for obtaining population information |
| CN102893300A (en) * | 2010-03-15 | 2013-01-23 | 尼尔森（美国）有限公司 | Methods and apparatus for integrating volumetric sales data, media consumption information, and geographic -demographic data to target advertisements |
| WO2015076714A1 (en) * | 2013-11-22 | 2015-05-28 | Telefonaktiebolaget L M Ericsson (Publ) | Centralised capability discovery |
| CN104361658A (en) * | 2014-09-30 | 2015-02-18 | 北京锐安科技有限公司 | Method and device for detecting population information of each place in region |
| CN106682931A (en) * | 2015-11-11 | 2017-05-17 | 北京国双科技有限公司 | Marketing information display method and device |
| CN106096631A (en) * | 2016-06-02 | 2016-11-09 | 上海世脉信息科技有限公司 | A kind of recurrent population's Classification and Identification based on the big data of mobile phone analyze method |
| CN107133318A (en) * | 2017-05-03 | 2017-09-05 | 北京市交通信息中心 | A kind of population recognition methods based on mobile phone signaling data |
| CN107801203A (en) * | 2017-11-13 | 2018-03-13 | 毛国强 | The evaluation method and its system of the density of population and mobility based on multi-data fusion |
| CN110019626A (en) * | 2017-12-21 | 2019-07-16 | 腾讯科技（深圳）有限公司 | A kind of the determination method and relevant apparatus of population distribution information |
| CN108566648A (en) * | 2017-12-29 | 2018-09-21 | 福建福诺移动通信技术有限公司 | A kind of resident population judgment method in the region based on carrier data |
| CN109189949A (en) * | 2018-08-03 | 2019-01-11 | 江苏省城市规划设计研究院 | A kind of population distribution calculation method |
| CN109362041A (en) * | 2018-12-18 | 2019-02-19 | 成都方未科技有限公司 | A kind of population space-time distributional analysis method based on big data |
| CN109918459A (en) * | 2019-01-28 | 2019-06-21 | 同济大学 | A kind of city mid-scale view real population statistical method based on mobile phone signaling |
| CN110046174A (en) * | 2019-03-07 | 2019-07-23 | 特斯联（北京）科技有限公司 | A kind of population migration analysis method and system based on big data |
| CN110503453A (en) * | 2019-07-05 | 2019-11-26 | 平安银行股份有限公司 | Customer-action analysis method, apparatus, computer equipment and storage medium |
| CN110443738A (en) * | 2019-07-17 | 2019-11-12 | 浙江大华技术股份有限公司 | Floating population's register method and system and relevant device |
Non-Patent Citations (1)
| Title |
|---|
| 基于GPRS的农村人口管理系统设计;高海侠;傅伟;;湖北农业科学(第13期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111078816A (en) | 2020-04-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11893593B2 (en) | Sales prediction systems and methods | |
| US11887132B2 (en) | Processor systems to estimate audience sizes and impression counts for different frequency intervals | |
| CN104254865A (en) | Empirical expert determination and question routing system and method | |
| CN110300084B (en) | IP address-based portrait method and apparatus, electronic device, and readable medium | |
| CN108833453A (en) | A kind of method and apparatus determined using account | |
| CN108399564A (en) | Credit-graded approach and device | |
| US20210097556A1 (en) | Data processing | |
| CN108269107A (en) | User information processing method and processing device | |
| CN113569162A (en) | Data processing method, device, equipment and storage medium | |
| CN111612085A (en) | Method and device for detecting abnormal point in peer-to-peer group | |
| CN112862544A (en) | Object information acquisition method and device and storage medium | |
| CN111078816B (en) | Position-based analysis method, device, terminal and storage medium | |
| CN112184332A (en) | Method, device and equipment for pushing activity information | |
| CN111182465A (en) | Method and device for determining terminal affiliation | |
| CN116861107A (en) | Business content display method, device, equipment, medium and product | |
| CN111768219A (en) | Advertisement crowd experiment method, device and storage medium | |
| CN113408817B (en) | Traffic distribution method, device, equipment and storage medium | |
| US10999697B2 (en) | Intelligent geofence provisioning | |
| CN111125272B (en) | Regional characteristic acquisition method, regional characteristic acquisition device, computer equipment and medium | |
| US11550859B2 (en) | Analytics system entity resolution | |
| CN110737693A (en) | Data mining processing method, device, equipment and computer readable storage medium | |
| CN117972096B (en) | Method and system for processing interaction message of social platform | |
| CN117033745B (en) | Method, system, equipment and storage medium for identifying cheating object | |
| CN110399399A (en) | Method, apparatus, electronic equipment and the storage medium of customer analysis | |
| CN116796076B (en) | Service recommendation method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |