CN111372193A - Method and device for accurately positioning activity area of user in rest period - Google Patents
Method and device for accurately positioning activity area of user in rest period Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- H—ELECTRICITY
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
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- H—ELECTRICITY
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- H04W4/06—Selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS]; Services to user groups; One-way selective calling services
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Abstract
The invention provides a method and a device for accurately positioning an activity area of a user in a rest period, wherein the method comprises the steps of identifying a push message generated by using a life service type APP on a mobile terminal of the user; if the push message generated by the use of the living service APP on the mobile terminal of the user can be identified, effective position information is screened out from the push message, and an activity area of the user in the rest period is obtained; if the push message generated by the use of the living service APP on the user mobile terminal cannot be identified, WiFi information connected or scanned by the mobile terminal in the latest rest period and a GPS position report point of the user mobile terminal in the latest rest period are obtained, longitude and latitude coordinates of a WiFi hotspot and longitude and latitude coordinates of the mobile terminal are obtained, and the activity area of the user in the rest period is calculated through a pre-trained activity area prediction model. According to the invention, the activity areas of the user in the rest period are obtained in different modes, so that the positioning and obtaining of the activity areas of the user in the rest period are more accurate.
Description
Technical Field
The invention relates to the technical field of position positioning, in particular to a method and a device for accurately positioning an activity area of a user in a rest period.
Background
With the rise of artificial intelligence, mobile internet and internet of things, big data becomes bigger and bigger, and infinite imagination and commercial application value are brought. However, the big data is abstract, and can be better applied by the traditional industry only by matching the big data with the point in space where the object exists and the geographical location information, so that the value of the big data is really exerted. Nowadays, the application field of the big location data is very wide, such as accurate marketing, commercial point selection layout, city planning, comprehensive treatment and the like based on the big location data.
The existing technologies for acquiring big data of a position are also more and more abundant: besides the GIS, the mobile phone can also be captured by means of mobile phone base station signaling, WiFi connection, IP addresses and the like; even more and more APPs will collect location information by way of guiding user authorization.
However, the existing positioning mode for the activity area of the user in the rest period is single, and the problem of low positioning precision exists.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: accurate positioning of active areas during a user's rest period.
In order to solve the technical problems, the invention adopts the technical scheme that: a method for accurately positioning an active area of a user in a rest period comprises the following steps,
s20, identifying push messages generated by the use of the life service APP on the mobile terminal of the user;
if the push message generated by the use of the living service type APP on the mobile terminal of the user can be identified, executing step S30;
s30, effective position information is screened out from the push message, and an activity area of the user in the rest period is obtained;
if the push message generated by the use of the life service type APP on the mobile terminal of the user cannot be identified, executing steps S40-S70;
s40, acquiring WiFi information connected or scanned by the mobile terminal in the latest rest period and GPS position report points of the user mobile terminal in the latest rest period;
s60, acquiring longitude and latitude coordinates of a WiFi hotspot and longitude and latitude coordinates of the mobile terminal according to WiFi information connected or scanned by the mobile terminal in the latest rest period and GPS position report points of the mobile terminal in the latest rest period;
and S70, calculating the activity area of the user in the rest period according to the longitude and latitude coordinates of the WiFi hotspot and the longitude and latitude coordinates of the mobile terminal through a pre-trained activity area prediction model.
Further, the step S30 specifically includes,
s31, collecting push messages generated by the latest living service APP on the mobile terminal of the user and arranging the push messages into a message document;
s32, screening effective position information from the message document by keyword matching by using a text recognition algorithm;
and S33, inquiring the longitude and latitude coordinates of the activity area of the user in the rest period according to the effective position information.
Further, the step S40 is followed by a step,
and S50, judging whether the obtained WiFi information and GPS position report points are missing or not, and if so, filling up the missing data by using the modes of the WiFi information and the GPS position report points respectively.
Further, in the step S60, the acquiring of the longitude and latitude coordinates of the WiFi hotspot specifically includes,
and sending the bssid and ssid data of the WiFi hotspot to a position server for retrieval and query to obtain the longitude and latitude coordinates of the WiFi hotspot.
Further, before step S20, a step is included,
s10, carrying out weight parameter training on the prediction model by using the sample data to obtain an activity region prediction model, wherein the prediction model is as follows:
wherein, the value range of n is [1,10], (wlng, wlat) is the longitude and latitude coordinate of WiFi, ww is the weight based on WiFi; (glng, glat) is the longitude and latitude coordinates of the GPS, and gw is the weight based on the GPS.
A device for accurately positioning the activity area of a user in a rest period comprises,
the push message identification module is used for identifying push messages generated by using the living service APP on the mobile terminal of the user;
if the push message generated by the use of the living service APP on the user mobile terminal can be identified, the mobile terminal goes to an active area acquisition module;
the active area acquisition module is used for screening effective position information from the push message and acquiring an active area of the user in a rest period;
if the push message generated by the use of the life service APP on the user mobile terminal cannot be identified, the WiFi information and GPS position report point acquisition module is switched to;
the WiFi information and GPS position report point acquisition module is used for acquiring WiFi information connected or scanned by the mobile terminal in the latest rest period and GPS position report points of the user mobile terminal in the latest rest period;
the longitude and latitude acquisition module is used for acquiring longitude and latitude coordinates of WiFi hotspots and longitude and latitude coordinates of the mobile terminal according to WiFi information connected or scanned by the mobile terminal of the user in the latest rest period and GPS position report points of the mobile terminal of the user in the latest rest period;
and the activity area calculation module is used for calculating the activity area of the user in the rest period according to the longitude and latitude coordinates of the WiFi hotspot and the longitude and latitude coordinates of the mobile terminal through a pre-trained activity area prediction model.
Further, the active area acquiring module comprises,
the push message sorting unit is used for collecting push messages generated by the latest living service APP on the mobile terminal of the user and sorting the push messages into a message document;
the position information screening unit is used for screening effective position information from the information document through keyword matching by utilizing a text recognition algorithm;
and the activity area query unit is used for querying the longitude and latitude coordinates of the activity area of the user in the rest period according to the effective position information.
Furthermore, the WiFi information and GPS position report point acquisition module also comprises a GPS position report point acquisition module,
and the data filling module is used for judging whether the obtained WiFi information and GPS position report points are missing or not, and if so, filling the missing data by using the modes of the WiFi information and the GPS position report points respectively.
Further, the longitude and latitude acquisition module comprises,
and the longitude and latitude conversion unit is used for sending the bssid and ssid data of the WiFi hotspot to the position server for retrieval and query to obtain the longitude and latitude coordinates of the WiFi hotspot.
Further, before the pushing the message identification module, the method further comprises,
the model training module is used for carrying out weight parameter training on the prediction model by using the sample data to obtain an activity area prediction model, and the prediction model is as follows:
wherein, the value range of n is [1,10], (wlng, wlat) is the longitude and latitude coordinate of WiFi, ww is the weight based on WiFi; (glng, glat) is the longitude and latitude coordinates of the GPS, and gw is the weight based on the GPS.
The invention has the beneficial effects that: the method directly obtains the activity area of the user in the rest period by identifying the push message generated by the use of the life service type APP on the mobile terminal of the user, obtains WiFi information connected or scanned by the mobile terminal in the nearest rest period and a GPS position report point of the user mobile terminal in the nearest rest period when the push message generated by the use of the life service type APP on the mobile terminal of the user cannot be identified, and calculates the activity area of the user in the rest period through a pre-trained activity area prediction model. The activity areas of the user in the rest period are acquired in a corresponding mode under different conditions, so that the positioning acquisition of the activity areas of the user in the rest period is more accurate.
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The following detailed description of the invention refers to the accompanying drawings.
FIG. 1 is a flowchart of a method for accurately locating an active area of a user during a rest period according to an embodiment of the present invention;
FIG. 2 is a block diagram of an apparatus for accurately locating an active area of a user during a rest period according to an embodiment of the present invention;
FIG. 3 is a schematic block diagram of a computer apparatus of an embodiment of the present invention.
Detailed Description
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, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, one embodiment of the present invention is: a method for accurately positioning an active area of a user in a rest period comprises the following steps,
s10, carrying out weight parameter training on the prediction model by using the sample data to obtain an activity region prediction model, wherein the prediction model is as follows:
wherein, the value range of n is [1,10], (wlng, wlat) is the longitude and latitude coordinate of WiFi, ww is the weight based on WiFi; (glng, glat) is the longitude and latitude coordinates of the GPS, and gw is the weight based on the GPS.
S20, identifying push messages generated by the use of the life service APP on the mobile terminal of the user;
if the push message generated by the use of the living service type APP on the mobile terminal of the user can be identified, executing step S30;
s30, effective position information is screened out from the push message, and an activity area of the user in the rest period is obtained;
specifically, the step S30 includes the following steps,
s31, collecting push messages generated by the latest living service APP on the mobile terminal of the user and arranging the push messages into a message document;
s32, screening effective position information from the message document by keyword matching by using a text recognition algorithm;
and S33, inquiring the longitude and latitude coordinates of the activity area of the user in the rest period according to the effective position information.
If the push message generated by the use of the life service type APP on the mobile terminal of the user cannot be identified, executing steps S40-S70;
s40, acquiring WiFi information connected or scanned by the mobile terminal in the latest rest period and GPS position report points of the user mobile terminal in the latest rest period;
and S50, judging whether the obtained WiFi information and GPS position report points are missing or not, and if so, filling up the missing data by using the modes of the WiFi information and the GPS position report points respectively.
S60, acquiring longitude and latitude coordinates of a WiFi hotspot and longitude and latitude coordinates of the mobile terminal according to WiFi information connected or scanned by the mobile terminal in the latest rest period and GPS position report points of the mobile terminal in the latest rest period;
specifically, the obtaining of the longitude and latitude coordinates of the WiFi hotspot sends the bssid and ssid data of the WiFi hotspot to a location server to retrieve and query to obtain the longitude and latitude coordinates of the WiFi hotspot.
And S70, calculating the activity area of the user in the rest period according to the longitude and latitude coordinates of the WiFi hotspot and the longitude and latitude coordinates of the mobile terminal through a pre-trained activity area prediction model.
The advancement of the scheme is as follows:
1. the invention has strong logic interpretability, no black box processing link and simple use;
2. the method obtains basic data from multiple dimensions, and has rich and reliable data sources and more accurate algorithm;
3. the method and the device utilize the behavior log generated by the mobile terminal of the user to jointly analyze the offline activity area of the user in the rest period from a plurality of angles of related living service type apps, WiFi connections and lbs report points in combination with effective weight parameters.
The present embodiment takes the analysis of the offline activity areas of two users, a and B, each during the rest period in the last month as an example to explain the specific logic and implementation of the present invention.
1. Training weight parameters
The prediction model is:
wherein, the value range of n is [1,10], (wlng, wlat) is the longitude and latitude coordinate of WiFi, ww is the weight based on WiFi; (glng, glat) is the longitude and latitude coordinates of the GPS, and gw is the weight based on the GPS.
The prediction model was weight parameter trained using sample data, ww and gw for each time period, with a sample size of 50,000,000, from samples of the activity region offline for the rest period in the last month. The initial value of each weight w is defined to be 0.1. The setting error is less than 100 meters, and the number of model iterations is 500. The final trained parameters are shown in the following table:
table 1, trained weight parameters:
2. basic data
Nowadays, every APP used online integrates an SDK. For the SDK provider, with the SDK, the installation and uninstallation behaviors of the APP on the mobile terminal of the user can be collected in the authorized range of the user, and the time length for opening the APP can be known, and the information such as WiFi, equipment information, advertisement logs and the like can be obtained at the time.
The embodiment of the invention needs to use three parts of data, namely push information generated by using a life service type APP on the user mobile terminal, WiFi information connected or scanned by the user mobile terminal in the rest period in the last month and GPS position report point of the user mobile terminal in the rest period in the last month.
The following table is A, B a presentation of relevant data for the user on the last day:
table 2, basic data presentation:
3. text recognition algorithm for extracting effective information
The function of this step is to directly acquire the offline activity area of the rest period of the user. As shown in table 2 above, the data is data of a day in the last month, and in this step, the algorithm of the present invention sorts the collected push messages generated by the lifestyle service type APP in the last month on the mobile terminal of the user into a document, and then screens out effective location information by keyword matching using a text recognition algorithm.
In this embodiment, by calculating the Msg of the user in the last month, we effectively recognize that the offline activity area of the user a in the rest period in the last month is "bayowa sey 45 # of yu-he west street, tong kyo city," and do not recognize the offline activity area of the user B in the rest period in the last month. Here, the location information of user a continues to be converted to latitude and longitude coordinates (116.6367826905,39.9009702775), which is the offline activity area of user a in the rest period in the last month. For user B, failing to identify his offline active area in the rest period in the last month at this step, the algorithm continues to the next step.
4. Basic data processing
As can be seen from table 2, the data for both WiFi and GPS are incomplete and missing. In normal data acquisition work, data loss can be caused due to various reasons such as unsmooth network, information omission and shutdown. Therefore, the missing data needs to be complementally completed to facilitate the calculation of the algorithm.
The algorithm defines the rest period of the user as 20:00-06:00, and the time period is divided into 10 stages according to hours in the specific implementation process. Because the algorithm uses the data of the latest month, the data of the user B in the rest period of the latest month is collected and then divided into 10 time periods, then the mode is calculated according to the data in each time period, and the data about WiFi and GPS is obtained by replacing the missing data with the mode, so that the problem of poor data quality such as data missing, report point position drift and the like is effectively solved. The specific calculation result for the user B in this embodiment is shown in the following table, and the data in each time period is the mode of the corresponding data in the time period of the latest month of the user B.
Table 3, mode of each time period:
as can be seen from the above table, data about WiFi, or its bssid and ssid, needs to be converted into longitude and latitude coordinates for easy calculation.
Each WiFi hotspot has a globally unique bssid, and generally a WiFi hotspot will not move for a period of time. The mobile terminal can scan and collect surrounding WiFi signals under the condition that the Wi-Fi is started, and the bssid and the ssid broadcasted by the WiFi hotspot can be obtained no matter whether the signals are encrypted or not, whether the signals are connected or not, even if the signal strength is not enough to be displayed in a wireless signal list. And the mobile terminal sends the acquired bssid and ssid data to a position server, the server retrieves the geographic position of the WiFi, and calculates the geographic position of the WiFi equipment and returns the geographic position to the mobile terminal of the user by combining the strength of each WiFi signal.
By the method, the ssid and the bssid of the WiFi connected to the user B in the rest period in the latest month in table 3 are converted into corresponding longitude and latitude coordinates, and specific results are shown in the following table.
Table 4, data normalization:
5. calculating the offline activity area of the rest period of the user:
according to the calculation formula and the trained weight parameters, offline position information (namely longitude and latitude coordinates in the table 4) of the user B in the rest period in the last month is input, and finally offline activity areas (114.6521877160,36.0455517565) of the user B in the rest period in the last month are calculated.
Therefore, the algorithm of the present invention ends the calculation process, and the offline activity areas of the user a and the user B in the rest period in the last month obtained by the present embodiment are: (116.6367826905,39.9009702775) and (114.6521877160, 36.0455517565).
As shown in fig. 2, another embodiment of the present invention is: a device for accurately positioning the activity area of a user in a rest period comprises,
the model training module is used for carrying out weight parameter training on the prediction model by using the sample data to obtain an activity area prediction model, and the prediction model is as follows:
wherein, the value range of n is [1,10], (wlng, wlat) is the longitude and latitude coordinate of WiFi, ww is the weight based on WiFi; (glng, glat) is the longitude and latitude coordinates of the GPS, and gw is the weight based on the GPS.
The push message identification module is used for identifying push messages generated by using the living service APP on the mobile terminal of the user;
if the push message generated by the use of the living service APP on the user mobile terminal can be identified, the mobile terminal goes to an active area acquisition module;
the active area acquisition module is used for screening effective position information from the push message and acquiring an active area of the user in a rest period;
specifically, the active area acquiring module includes,
the push message sorting unit is used for collecting push messages generated by the latest living service APP on the mobile terminal of the user and sorting the push messages into a message document;
the position information screening unit is used for screening effective position information from the information document through keyword matching by utilizing a text recognition algorithm;
and the activity area query unit is used for querying the longitude and latitude coordinates of the activity area of the user in the rest period according to the effective position information.
If the push message generated by the use of the life service APP on the user mobile terminal cannot be identified, the WiFi information and GPS position report point acquisition module is switched to;
the WiFi information and GPS position report point acquisition module is used for acquiring WiFi information connected or scanned by the mobile terminal in the latest rest period and GPS position report points of the user mobile terminal in the latest rest period;
and the data filling module is used for judging whether the obtained WiFi information and GPS position report points are missing or not, and if so, filling the missing data by using the modes of the WiFi information and the GPS position report points respectively.
The longitude and latitude acquisition module is used for acquiring longitude and latitude coordinates of WiFi hotspots and longitude and latitude coordinates of the mobile terminal according to WiFi information connected or scanned by the mobile terminal of the user in the latest rest period and GPS position report points of the mobile terminal of the user in the latest rest period; the longitude and latitude acquisition module comprises a longitude and latitude acquisition module,
and the longitude and latitude conversion unit is used for sending the bssid and ssid data of the WiFi hotspot to the position server for retrieval and query to obtain the longitude and latitude coordinates of the WiFi hotspot.
And the activity area calculation module is used for calculating the activity area of the user in the rest period according to the longitude and latitude coordinates of the WiFi hotspot and the longitude and latitude coordinates of the mobile terminal through a pre-trained activity area prediction model.
It should be noted that, as can be clearly understood by those skilled in the art, the specific implementation processes of the apparatus for accurately positioning the activity area of the user during the rest period and each unit may refer to the corresponding descriptions in the foregoing method embodiments, and for convenience and brevity of description, no further description is provided herein.
The above-mentioned means for accurately locating the active area of a user during a rest period may be implemented in the form of a computer program which is executable on a computer device as shown in fig. 3.
Referring to fig. 3, fig. 3 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a terminal or a server, where the terminal may be an electronic device with a communication function, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of a plurality of servers.
Referring to fig. 3, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 comprises program instructions that, when executed, cause the processor 502 to perform a method of accurately locating an active area of a user during a rest period.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 can execute a method for accurately locating the activity area of the user during the rest period.
The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 3 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation of the computer device 500 to which the present application may be applied, and that a particular computer device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The processor 502 is configured to run a computer program 5032 stored in the memory to implement a method for accurately locating an active area of a user during a rest period.
It should be understood that, in the embodiment of the present Application, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program comprises program instructions. The program instructions, when executed by a processor, cause the processor to perform a method of accurately locating an active area of a user during a rest period.
The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, which can store various computer readable storage media.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be merged, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for accurately positioning an activity area of a user in a rest period is characterized in that: comprises the following steps of (a) carrying out,
s20, identifying push messages generated by the use of the life service APP on the mobile terminal of the user;
if the push message generated by the use of the living service type APP on the mobile terminal of the user can be identified, executing step S30;
s30, effective position information is screened out from the push message, and an activity area of the user in the rest period is obtained;
if the push message generated by the use of the life service type APP on the mobile terminal of the user cannot be identified, executing steps S40-S70;
s40, acquiring WiFi information connected or scanned by the mobile terminal in the latest rest period and GPS position report points of the user mobile terminal in the latest rest period;
s60, acquiring longitude and latitude coordinates of a WiFi hotspot and longitude and latitude coordinates of the mobile terminal according to WiFi information connected or scanned by the mobile terminal in the latest rest period and GPS position report points of the mobile terminal in the latest rest period;
and S70, calculating the activity area of the user in the rest period according to the longitude and latitude coordinates of the WiFi hotspot and the longitude and latitude coordinates of the mobile terminal through a pre-trained activity area prediction model.
2. The method of accurately locating a user's rest period activity area of claim 1, wherein: the step S30 specifically includes the steps of,
s31, collecting push messages generated by the latest living service APP on the mobile terminal of the user and arranging the push messages into a message document;
s32, screening effective position information from the message document by keyword matching by using a text recognition algorithm;
and S33, inquiring the longitude and latitude coordinates of the activity area of the user in the rest period according to the effective position information.
3. The method of accurately locating a user's rest period activity area of claim 1, wherein: said step S40 is followed by a step,
and S50, judging whether the obtained WiFi information and GPS position report points are missing or not, and if so, filling up the missing data by using the modes of the WiFi information and the GPS position report points respectively.
4. A method of accurately locating areas of user's rest period activity according to claim 3, characterized by: in step S60, the acquiring of the longitude and latitude coordinates of the WiFi hotspot specifically includes,
and sending the bssid and ssid data of the WiFi hotspot to a position server for retrieval and query to obtain the longitude and latitude coordinates of the WiFi hotspot.
5. The method of accurately locating a user's rest period activity area of claim 4, wherein: prior to step S20, there is further included the step,
s10, carrying out weight parameter training on the prediction model by using the sample data to obtain an activity region prediction model, wherein the prediction model is as follows:
wherein, the value range of n is [1,10], (wlng, wlat) is the longitude and latitude coordinate of WiFi, ww is the weight based on WiFi; (glng, glat) is the longitude and latitude coordinates of the GPS, and gw is the weight based on the GPS.
6. The utility model provides a device of accurate location user's rest interval activity area which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the push message identification module is used for identifying push messages generated by using the living service APP on the mobile terminal of the user;
if the push message generated by the use of the living service APP on the user mobile terminal can be identified, the mobile terminal goes to an active area acquisition module;
the active area acquisition module is used for screening effective position information from the push message and acquiring an active area of the user in a rest period;
if the push message generated by the use of the life service APP on the user mobile terminal cannot be identified, the WiFi information and GPS position report point acquisition module is switched to;
the WiFi information and GPS position report point acquisition module is used for acquiring WiFi information connected or scanned by the mobile terminal in the latest rest period and GPS position report points of the user mobile terminal in the latest rest period;
the longitude and latitude acquisition module is used for acquiring longitude and latitude coordinates of WiFi hotspots and longitude and latitude coordinates of the mobile terminal according to WiFi information connected or scanned by the mobile terminal of the user in the latest rest period and GPS position report points of the mobile terminal of the user in the latest rest period;
and the activity area calculation module is used for calculating the activity area of the user in the rest period according to the longitude and latitude coordinates of the WiFi hotspot and the longitude and latitude coordinates of the mobile terminal through a pre-trained activity area prediction model.
7. The apparatus for accurately locating an area of user's rest period activity according to claim 6, wherein: the active area acquisition module comprises a module for acquiring the active area,
the push message sorting unit is used for collecting push messages generated by the latest living service APP on the mobile terminal of the user and sorting the push messages into a message document;
the position information screening unit is used for screening effective position information from the information document through keyword matching by utilizing a text recognition algorithm;
and the activity area query unit is used for querying the longitude and latitude coordinates of the activity area of the user in the rest period according to the effective position information.
8. The apparatus for accurately locating an area of user's rest period activity according to claim 6, wherein: also included after the WiFi information and GPS location reporting point acquisition module,
and the data filling module is used for judging whether the obtained WiFi information and GPS position report points are missing or not, and if so, filling the missing data by using the modes of the WiFi information and the GPS position report points respectively.
9. The apparatus for accurately locating an area of user's rest period activity according to claim 8, wherein: the longitude and latitude acquisition module comprises a longitude and latitude acquisition module,
and the longitude and latitude conversion unit is used for sending the bssid and ssid data of the WiFi hotspot to the position server for retrieval and query to obtain the longitude and latitude coordinates of the WiFi hotspot.
10. The apparatus for accurately locating an area of user's rest period activity according to claim 9, wherein: before the push message identification module, further comprising,
the model training module is used for carrying out weight parameter training on the prediction model by using the sample data to obtain an activity area prediction model, and the prediction model is as follows:
wherein, the value range of n is [1,10], (wlng, wlat) is the longitude and latitude coordinate of WiFi, ww is the weight based on WiFi; (glng, glat) is the longitude and latitude coordinates of the GPS, and gw is the weight based on the GPS.
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