CN108734501B - Mobile positioning platform - Google Patents
Mobile positioning platform Download PDFInfo
- Publication number
- CN108734501B CN108734501B CN201710280894.8A CN201710280894A CN108734501B CN 108734501 B CN108734501 B CN 108734501B CN 201710280894 A CN201710280894 A CN 201710280894A CN 108734501 B CN108734501 B CN 108734501B
- Authority
- CN
- China
- Prior art keywords
- user
- data
- statistics
- merchant
- information
- 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
- 238000004458 analytical method Methods 0.000 claims abstract description 27
- 230000007613 environmental effect Effects 0.000 claims abstract description 13
- 230000033001 locomotion Effects 0.000 claims abstract description 13
- 238000011156 evaluation Methods 0.000 claims description 23
- 238000010586 diagram Methods 0.000 claims description 18
- 238000007619 statistical method Methods 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 4
- 230000003068 static effect Effects 0.000 claims description 4
- 238000012546 transfer Methods 0.000 claims description 3
- 230000000284 resting effect Effects 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 7
- 238000001514 detection method Methods 0.000 description 14
- 238000000034 method Methods 0.000 description 14
- 230000008569 process Effects 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 6
- 239000002537 cosmetic Substances 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 239000008358 core component Substances 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 239000000306 component Substances 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000004308 statistical model calculation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0261—Targeted advertisements based on user location
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a mobile positioning platform for carrying out data statistics according to a user position. The platform calculates user position coordinates according to environmental characteristic information reported by the user terminal, and judges a user behavior mode according to the motion state information reported by the user terminal, the user position coordinates, the mobile phone use state and the electronic map information; further, data statistics and analysis based on the user location and corrected by the user behavior pattern data are performed. By implementing the invention, accurate and reliable statistical data and analysis results can be provided for mobile marketing, accurate advertising and market management, and data support is provided for the industries to develop personalized marketing according to consumer shopping preferences, improve advertising effect and improve market management efficiency.
Description
Technical Field
The invention relates to the field of mobile positioning data processing, in particular to a mobile positioning management platform for carrying out business data statistical analysis according to mobile user position data.
Background
Consumer shopping habits and consumption preferences are of great significance to merchants. Firstly, a merchant can develop planned personalized marketing aiming at the consumption preference of specific consumers, thereby improving the effectiveness of marketing activities and improving consumer experience; and secondly, carrying out statistical analysis according to behavior record data in the shopping process of a large number of consumers to obtain consumption preference data of consumer groups, and providing guidance and decision basis for business activities such as business regulation operation strategies, marketing plan making, advertisement delivery target determining and the like for merchants.
In recent years, with the vigorous development of internet technology, electronic commerce is prominent in the military, and online shopping has become a frequently used shopping approach for people. In the field of electronic commerce, personalized information pushing according to consumer habits is widely adopted. For example, chinese patent CN106294882a proposes a data mining method and apparatus, which uses big data of visitor accessing website to analyze visitor's behavior and value information, and provides help for internet merchants to adjust marketing strategy, optimize personalized recommendation mechanism and promote user experience. However, the current practical application technology means is simpler, and usually, the website of the electronic commerce records the information of browsing the webpage and purchasing the commodity by the consumer, and then the information of the related commodity is directionally pushed to the consumer, so that the personalized marketing effect is achieved.
For the physical retail industry, because it is far more difficult to acquire the consumption preference data of the consumer group than the network electronic commerce, some statistical analysis can be performed only by means of a questionnaire or a final consumption record, and it is difficult to comprehensively grasp the consumption habit data of the consumer group. In recent years, the industry is also attempting to use the mobile internet technology to perform the attempt work, for example, chinese patent CN106330690a discloses a method for performing people statistics based on a map function of a communication software application, which can perform people statistics for a specific place by using the map function in the instant messaging software, so that a merchant is convenient to perform people statistics for consumers. Chinese patent CN104820863A proposes a consumer portrait generation method and device, which uses consumer mobile phone to access to a WiFi network of a mall to obtain the position change and residence time information of the consumer, so as to carry out portrait of shopping preference of the consumer, and provide data support for personalized marketing of the mall.
In summary, in the market at present, both the e-commerce industry and the physical retail industry, some technical means have been used to perform data statistics and analysis on shopping habits and preferences of consumers, so as to provide support for developing personalized marketing. However, the currently used technical scheme is simpler, and the statistical characteristics of the consumer groups and the individual characteristics of each consumer individual cannot be comprehensively mastered. For example, e-commerce typically pushes relevant merchandise information simply based on a recent shopping record of a consumer; the above patent CN106330690a only counts the number of people using a certain instant messaging application software in a specific place; the above patent CN104820863a provides a premise that the consumer mobile phone must be connected to a mall WiFi system, and the consumer habit analysis is not completely accurate only according to the consumer location information, residence time and associated merchant, and the obtained portrait is not complete.
On the other hand, the mobile phone of the user is used for detecting the surrounding environment characteristic information and reporting the surrounding environment characteristic information to the positioning platform, the detection information reported by the user is compared with the database information, and the position coordinates of the user, particularly the accurate position of the user in an indoor place, can be determined through a matching algorithm. A solution for indoor positioning according to the characteristics of wireless signals around the user is proposed in patent CN103024659 a.
Aiming at the market demand and the defects of the prior art, the invention provides the mobile positioning platform capable of carrying out business data statistics analysis according to the mobile user position data, which can provide more accurate and complete consumer shopping habit and preference statistics data for entity business and provide technical support for entity business to develop online and offline combined personalized marketing.
Disclosure of Invention
The invention provides a mobile positioning platform for carrying out data statistics based on a user position, which is characterized by comprising the following components:
1) The positioning engine unit is in charge of calculating the position coordinates of the user according to the environmental characteristic information reported by the user terminal;
2) The user behavior pattern analysis unit is responsible for judging a user behavior pattern according to the user motion state information and the user mobile phone use state information reported by the user terminal and combining the user position coordinates and the electronic map information;
3) A database unit for storing user data, merchant data and map data; the user data comprises a user positioning result, a user behavior mode and a user statistical result, the merchant data comprises a merchant name, a position coordinate, a merchant area, a merchant type and a merchant statistical result, and the map data comprises a floor layout structure, positions and occupied ranges of all shops, names of all shops, public setting positions and indoor road network information;
4) A statistical analysis engine unit is responsible for performing data statistics and analysis based on the user location and corrected by the user behavior pattern data.
The environmental characteristic information reported by the user terminal refers to one type of characteristic information or a combination of a plurality of types of characteristic information which has position correlation and changes along with the position change, and the environmental characteristic information comprises peripheral wireless signal strength and identity information of an information source; the positioning engine searches a positioning database according to the surrounding environment characteristic information reported by the user terminal, and calculates the position of the user through a matching algorithm; the positioning result comprises building information of the user, and position coordinates on the floor and floor map, wherein the building information comprises building names, addresses, GPS or Beidou coordinates.
The user behavior mode refers to the current behavior characteristics of the user and the degree of interest in nearby shops/commodities, wherein the degree is characterized by an interest evaluation value, and the current behavior characteristics of the user comprise commodity browsing, commodity selecting, resting waiting, catering, leisure, entertainment and walking.
The user behavior pattern analysis unit judges the behavior pattern of the user, and the result is written into the user database:
a) Judging whether the user is in a walking state according to the user movement state information, if so, further judging whether the step frequency of the user exceeds a threshold value, wherein the threshold value is set to different values according to the gender and age of the user, and if so, setting the behavior characteristic of the user as walking, and the interest evaluation value is 0; if the threshold value is not exceeded, setting the behavior characteristic of the user as browsing goods, and turning to the following step E); if the user is not walking, the step B) is carried out;
b) Further inquiring the current position of the user when the user is in a non-walking state such as static state or occasional shaking state, judging whether the user is in a dining or leisure and entertainment store, if so, setting the behavior characteristics of the user as dining and leisure and entertainment, and setting the interest evaluation value as 1; otherwise, turning to the following step C);
c) Judging whether the user uses the mobile phone according to the mobile phone gesture and the use state of the user, if not, setting the behavior characteristics of the user as selecting goods, and turning to the following step E); if the user is using the mobile phone, the step D) is carried out;
d) Judging whether the user is in a passenger flow channel or a rest area according to the current position of the user, if so, setting the behavior characteristics of the user as rest waiting, and setting the interest evaluation value as 0; otherwise, turning to the following step E);
e) According to different transfer conditions, user interest evaluation values with values in the range of 0-1 are given according to the following principles: the interest of not using the mobile phone is higher than that of using the mobile phone, and the interest of stationary or occasionally shaking is higher than that of walking; the lower the step frequency, the higher the interest.
The data statistics analysis engine periodically operates based on the data statistics and analysis corrected by the user behavior mode, and each time the data statistics analysis engine grabs the original data of the user positioning result to be counted, namely the original data accumulated from the last end to the current start; the statistical period and the timing start time are determined by system configuration parameters.
The periodically operated user location based and user behavior pattern corrected data statistics and analysis comprises the steps of writing the results into a database:
i) Capturing the effective positioning result to be counted of each user from the user database in turn, and carrying out statistics of the following ii) -iii);
ii) statistical residence time metadata: multiplying the number of effective positioning results of the user in the same market by a positioning period, and taking the result as the residence time of the user in the market in the statistical period; likewise calculating residence times at each floor of the mall; storing the user residence time metadata statistics results in a user database, wherein the user residence time metadata statistics results comprise the user ID, the market ID, the entering market and the leaving time, the residence time in the market and the residence time in each floor;
iii) Statistics of user attention metadata: for each effective positioning result, calculating the distance D between the position coordinates of the commercial tenant and the position coordinates of the user in the commercial tenant on the same floor, and enclosing if D is less than or equal to R, wherein R=a×R0, R0 is set by the working parameters of the system, the size of the system is positively related to the average area S0 of the commercial tenant on the floor, and a=S/S0, S is the area of the commercial tenant; inquiring the interest evaluation value of the user at the moment, and storing the interest evaluation value serving as the attention degree metadata of the user for the surrounding merchants in a user database;
iV) counting accumulated value of attention of commercial tenant: and summarizing the attention metadata of all users in the current statistical period, and counting the accumulated values of the attention of the users, which are obtained by all merchants in the current statistical period.
The periodically operated user location based and user behavior pattern corrected data statistics and analysis further comprises: counting the frequency of a user visiting a certain market, the residence time length of each time and the start-stop time of each residence according to the user residence time metadata, and distinguishing user types, wherein the user types comprise common consumers and market staff; actively filtering non-consumer users when the user attention metadata and merchant attention accumulated value are counted.
The location platform further includes generating statistics specifying a mall, floor, merchant type, consumer group based on the statistics metadata based on the user location and corrected by the user behavior pattern, the statistics being performed according to a specified time period.
The statistics of the appointed mall, the floors, the merchant types and the consumer groups comprise average residence time of the consumers in the mall and average residence time of the consumers in each floor, user attention ranks counted according to different merchant types, shopping interest preference of each individual consumer, shopping interest preference counted according to the user groups, mall passenger flow thermodynamic diagram and mall passenger flow line diagram.
The platform further comprises a service interface unit which is responsible for providing a data query interface and a service data synchronization interface for the third party service platform; the data query interface provides real-time position information query of the third-party service platform user, and position-based user related statistical information and merchant related statistical information query; and the business data synchronization interface is responsible for completing the merchant information synchronization between the platform and the third-party business platform.
By implementing the invention, accurate and reliable statistical data and analysis results can be provided for mobile marketing, accurate advertising and market management, and data support is provided for the industries to develop personalized marketing according to consumer shopping preferences, improve advertising effect and improve market management efficiency.
Other features and advantages of the present invention will become more apparent from the following detailed description of embodiments of the present invention, which is to be read in connection with the accompanying drawings.
Drawings
FIG. 1 is a network architecture of an application scenario of the present invention;
FIG. 2 is a functional block diagram of one embodiment of the present invention;
FIG. 3 is a schematic diagram of a mobile terminal coordinate system;
FIG. 4 is a flow chart of a positioning platform analyzing user behavior patterns in an embodiment of the invention;
FIG. 5 is a functional block diagram of a positioning platform in an embodiment of the invention;
fig. 6 is a schematic diagram of a mall thermodynamic diagram statistics according to an embodiment of the present invention.
Detailed Description
The following describes in detail the embodiments of the present invention with reference to the drawings.
As shown in fig. 1, an application scenario of the present invention is that a user mobile phone 101 accesses a mobile network through a mobile base station 100, uses a data service to access the internet, logs in to a service platform 104 on a network side, and uses a mobile internet service provided by the service platform, where the service is provided by a mobile application 102A in the user mobile phone. As one of the core components of the present invention, a location detection SDK (Software Development Kit ) 102B is embedded in the mobile application 102A, and is responsible for collecting and reporting information required by the location platform 103 to the latter. The positioning platform 103 deployed on one side of the internet is another core component of the present invention, and its main function is to determine the real-time position coordinates and behavior patterns of the user according to the information reported by 102B, and further perform data statistics and analysis. Those skilled in the art will readily understand that: by adopting the network architecture, the positioning detection SDK 102B can acquire the real-time positioning result of the positioning platform 103, and the service platform 104 can also acquire the real-time position and statistical data of the user from the positioning platform 103, so that the mobile application 102A and the service platform 104 can easily realize LBS (Location Based Service) service based on the user position and personalized marketing service based on the statistics of the user position data; because of the loosely coupled architecture, the application of the present invention is not particularly limited to the mobile application 102A and the service platform 104, and the location detection SDK 102B may be simultaneously embedded into various mobile applications 102A for operation, and the location platform may also provide user location and statistics query services to various service platforms in a cloud service manner.
A functional block diagram of one embodiment of the present invention is shown in fig. 2. The positioning detection SDK 102B comprises functional units such as environment characteristic information detection, motion state detection, mobile phone state detection and the like; the positioning platform 103 comprises functional units such as a positioning engine, a user behavior pattern analysis, a database, a data statistics analysis engine and the like.
In the positioning detection SDK 102B, an environmental characteristic information detection unit periodically collects environmental characteristic information around a user according to working parameters issued by the positioning platform 103, and reports the environmental characteristic information to the positioning platform 103 as a basis of positioning calculation; the motion state detection unit calculates the real-time motion state of the mobile phone, such as static, occasional swing, walking, step frequency and step accumulation and the like, according to the data of the acceleration sensor in the mobile phone 101, and the real-time motion state information of the mobile phone is also reported to the positioning platform 103 at regular time as a basis for judging the interested degree of the user in the nearby shops/shops; the mobile phone state detection unit obtains mobile phone posture information according to the acceleration sensor and the electronic compass data in the mobile phone 101, and can obtain the current use condition of the mobile phone by using the API provided by the mobile phone operation system, for example, whether the screen is in an active state, and the mobile phone posture and the use condition information are also reported to the positioning platform 103 at regular time as a basis for analyzing the user behavior mode.
The above-mentioned environmental characteristic information of the user's surroundings refers to one type of characteristic information or a combination of several types of characteristic information having a position correlation and varying with a position, such as the surrounding wireless signal strength and the identity information of the information source proposed in the invention patent CN103024659 a.
The detection and calculation technology of the motion state of the mobile phone has many specific applications in the industry, such as a smart watch, a body-building and step-counting application in a bracelet, and many walking body-building APP in Android and iOS markets. And thus, for brevity, detailed descriptions of specific implementations thereof will not be provided herein.
The mobile phone posture information comprises a azimuth angle, a pitching angle and a rolling angle of the mobile phone. As is readily understood by those skilled in the art, the three angles are defined by the angle of the projection of the mobile phone y-axis on the horizontal plane deviating from the north direction, the rotation angle of the mobile phone around the x-axis, and the rotation angle of the mobile phone around the y-axis; the acquisition of these three angles is also a common technology in the industry, for example, in the Android system, the values of these three angles can be obtained through the method getOrientation () provided by the system. When a user uses the mobile phone in normal vertical screen, the pitch angle of the mobile phone is generally between 0 and 90 degrees, and the rolling angle is smaller; if the user uses the mobile phone on the transverse screen, the pitch angle is generally small, and the roll angle is larger. For ease of understanding, fig. 3 shows a schematic diagram of the mobile phone coordinate system, where the xy plane is the horizontal plane and the z axis points to the sky.
In the positioning platform 103, the positioning engine unit searches a positioning database according to the surrounding environment characteristic information reported by the positioning detection SDK 102B, and calculates the position of the user through a matching algorithm. When the user is in an indoor environment, the positioning result comprises building information, floors and position coordinates on a floor map where the user is located. The building information comprises building names, addresses, GPS or Beidou coordinates and the like. The user behavior pattern analysis unit intelligently judges according to the user motion state, the mobile phone gesture and the mobile phone use condition information reported by the SDK 102B and combining the user position information and the electronic map information to obtain the current behavior characteristics of the user and the degree of interest in nearby shops/commodities, wherein the degree is represented by an interest evaluation value. The intelligent judgment process is as shown in fig. 4:
a) Judging whether the user is in a walking state according to the user movement state information, if so, further judging whether the step frequency of the user exceeds a threshold value, wherein the threshold value can be set to different values according to the gender and age of the user, and if so, setting the interest evaluation value of the user to be 0 when the user walks through the position; if the threshold value is not exceeded, the user is slowly walking through the position, and the user is likely to be in the process of browsing the merchant/commodity, and the target merchant/commodity is likely to be further selected, and the process goes to the following step 5); if the user is not walking, the following step 2) is carried out;
b) Further inquiring the current position of the user when the user is in a non-walking state such as static state or occasional shaking state, judging whether the user is in a restaurant or recreational store, if so, indicating that the user may be eating or recreational consuming, and setting the interest evaluation value of the user as 1; otherwise, turning to the following step 3);
c) Judging whether the user uses the mobile phone according to the mobile phone gesture and the use state of the user, if not, indicating that the user is likely to watch and select the target merchant/commodity carefully, and then switching to the following step 5); if the user is using the mobile phone, the following step 4) is performed;
d) Judging whether the user is in a passenger flow channel or a rest area according to the current position of the user, if so, indicating that the user is in a rest and waiting state, and setting the interest evaluation value of the user to be 0; otherwise, go to the following step 5);
e) According to different transfer conditions, giving user interest evaluation values with values in the range of 0-1, wherein the principle is that the interest of the user who does not use the mobile phone is higher than that of the user who uses the mobile phone, and the interest of the user who is stationary or accidentally shakes is higher than that of the user who walks; the slower the walking speed (lower the step frequency) the higher the interest.
The data statistics and analysis engine captures the original data from the database, obtains the statistical metadata through statistical model calculation, and further collects and analyzes to obtain statistical results.
Taking a mall retail data statistical analysis as an example, FIG. 5 further provides a functional block diagram of the positioning platform 103 of the present invention. The positioning engine stores the positioning result of the user into a user database to be checked, and each result record comprises information such as user identity, positioning result detail, positioning time and the like. The user behavior pattern data is also stored in a user database to be checked, and each record comprises information such as user identity identification, position information, behavior characteristics, interest evaluation value, time labels and the like. The merchant database stores information such as names, index ID numbers, merchant categories, store areas, position coordinates of stores on an electronic map and the like of merchants in a mall. For computer processing, the location coordinates in the technical field of electronic maps generally refer to the centroid coordinates of map blocks occupied by shops. The map database stores floor electronic maps of various shops in the service range of the system, wherein the floor electronic maps comprise floor layout structures, various shop positions and occupied ranges, various shop names, public setting positions of washrooms/elevators/escalators, indoor road networks and the like. The data statistics and analysis engine regularly captures the positioning result to be counted in the user database according to routine tasks of system configuration, combines the related user behavior mode data, map data and merchant data, processes the original data to be counted into statistical metadata, and then assembles the metadata into a final statistical result according to statistical items and requirements. The statistical metadata and the summarized final statistical result are stored in a statistical result database. The positioning platform 103 further comprises a service interface unit, which provides a data query interface and a service data synchronization interface for the third party service platform 104 shown in fig. 1. The service platform 104 can inquire the real-time position information of the user through the data inquiry interface, and the user related statistical information and the merchant related statistical information based on the position; the merchant information synchronization with the positioning platform 103 can be accomplished through a business data synchronization interface. It is easy for those skilled in the art to understand that the layout structure of the internal storey shops is relatively stable or less variable for a shop, and the business of each bunk can be adjusted frequently according to the operation condition. The third party service platform serving as a service directly facing the mall, the merchant and the consumer user is more sensitive to the change condition of the merchant in the service range of the third party service platform, so that the positioning platform 103 can synchronously acquire the update information of the merchant from the third party service platform 104 in time by providing the data synchronization function, and the data statistics analysis quality of the third party service platform is ensured.
The process of grabbing the original data by the data statistics analysis engine to process the original data into statistics metadata periodically and regularly operates, and grabbing the original data to be counted each time, namely, the original data accumulated from the last end to the current start. The statistical period and timing start-up time may be configured by system parameters such as 24 per day: and starting at 00 times, and carrying out statistics once. The detailed steps of each statistical effort are as follows:
1) capturing the effective positioning result to be counted of each user from a user database in turn, and carrying out statistics of the following 2) -3);
2) Statistical residence time metadata: multiplying the number of valid positioning results of the same mall by a positioning period, the result being the residence time of the user in the mall during the statistical period; the residence time at each floor of the mall can also be calculated; storing the user residence time metadata statistics results in a user database, wherein the user residence time metadata statistics results comprise information such as the user ID, the market ID, the entering market and leaving time, residence time in the market, residence time in each floor and the like;
3) Statistics of user attention metadata: for each effective positioning result, calculating the distance D between the position coordinates of the commercial tenant and the position coordinates of the user in the commercial tenant on the same floor, and enclosing if D is less than or equal to R, wherein R=axR0, R0 is set by the working parameters of the system, the size of the system is positively correlated with the average area S0 of the commercial tenant on the floor, namely, the larger the average area of the commercial tenant on the floor is, the larger R0 is, the smaller the opposite is, and a=S0/S0, and S is the area of the commercial tenant; inquiring the interest evaluation value of the user at the moment, and storing the interest evaluation value serving as the attention degree metadata of the user for the surrounding merchants in a user database;
4) Counting accumulated value of attention of merchants: and summarizing the attention metadata of all users in the current statistical period, and counting the accumulated values of the attention of the users, which are obtained by all merchants in the current statistical period.
Among the above statistical metadata, the user residence time metadata may be used to further distinguish user types. For example, the frequency of visiting a certain market, the residence time length of each residence, the fluctuation of the start-stop time of each residence and the like can be counted through the market residence time metadata of the user, and the statistics results can be compared to easily distinguish whether the user is a common consumer visiting the market or a staff of the market. Because for a consumer who is shopping in a common market, the visit frequency is not too high (such as not more than 4 times per week), the average residence time is not too long (such as not more than 5 hours), and the time fluctuation of each start and stop is large and has no obvious rule; for market staff, the visit frequency is high and has strong regularity, each residence time is long and regular, and each start and stop time has obvious regularity. After the real consumer users are identified, the non-consumer users can be actively filtered when the user attention metadata and the merchant attention accumulated value are counted, so that the counted data is more real and reliable.
Because in the field of mobile internet, other registration information provided by users except for information related to personal identity, account number, contact information and the like of shopping payment cannot be verified through closed loop, the application cannot distinguish whether the users in a mall are ordinary consumers or mall operators. If the statistics are made indiscriminately, it is obvious that many unrealistic interference data are introduced. For example, a salesman at a cosmetic counter, if indistinguishable, must indicate that she is most interested in cosmetics and in brands sold at her counter based on statistics of her location; the scheme can more accurately reflect the shopping interest preference of the user by filtering the data of the user in the self-working market and counting the data of the user serving as the common consumer in other markets.
In addition, in modern shops, particularly in department store commodity areas, hard partitions such as walls, doors and the like are not arranged among merchants, and no matter which technology always has certain error in judging the position of a user, so that the user's interest in the merchant is judged by judging that the user is in the store of the merchant, and a lot of error data are often introduced. Further, even if the user is outside the store, it is not stated that he is not interested in the store. If he is standing to watch the display of goods in the shop window, he is actually interested, but not yet to the extent that he has entered the shop to pick goods. Conversely, if a user is walking through a store, even if it is "inside" the store for a period of time, it does not indicate that he is interested in shopping at that store. The invention can better reflect shopping interests and preferences of users by selecting the surrounding merchants through the distance threshold value positively related to the area of the shops and correcting the user behavior pattern data.
In summary, compared with the method that the user interest degree of the merchant is judged by accumulating the stay time of the user in a store, the user interest degree metadata corrected by the user type filtering and the behavior pattern data is more accurate and reliable, and the interest degree of the user to the merchant can be reflected better; the accumulated value of the merchant attention degree counted according to the method can better reflect the attention degree of the merchant to the consumer, and the data of the accumulated value is more true and reliable. In addition, in the scheme of the invention, the users who consume in catering, leisure and entertainment places are particularly picked out to be treated independently, and the follow-up statistics comparison of the attention degree of different types of merchants is facilitated. This is to take into account that there is no comparability between different types of merchants together, because the long duration of consumer consumption within a restaurant, recreational, or recreational merchant, a direct comparison of the time a consumer spends in a chinese restaurant and the time a cosmetic counter spends, does not reflect the consumer's interest preferences in both.
The statistics metadata can be further generated according to requirements, statistics according to markets, floors, merchant types and consumer groups can be performed according to a specified time period. Such as average residence time of consumers in a mall and average residence time of each floor in a certain time period, ranking according to user attention degree counted by different merchant types, shopping interest preference of each individual consumer, shopping interest preference counted by user groups, and the like. The commercial value of these statistics is self-evident, e.g., mobile marketing operators may develop personalized marketing for the shopping interest preferences of individual users; advertisers can deliver accurate advertisements according to shopping interest preferences of user groups; the store operator can adjust the store business state combination and the store layout according to the data such as the merchant attention statistics, the average residence time of the consumers and the like. In addition, the original data and the statistical metadata of the user positioning result can be further processed into more statistical results. Such as generating a mall traffic distribution thermodynamic diagram, counting traffic lines, etc. The passenger flow thermodynamic diagram is that positioning results of all users in the mall in a time period are overlaid on a floor electronic map and mapped into a highlight color representing passenger flow density on the electronic map, and the greater the overlaid user density of a certain area is, the darker and brighter the color of the area is. When the statistical time period is small enough, a real-time thermodynamic diagram is obtained. Fig. 6 is an example of a mall floor thermodynamic diagram. And drawing the track of the positioning result of each user in the residence period of the mall according to the time sequence, and analyzing the passenger flow line of the mall by superposing the tracks of a large number of users in the same mall. With the thermodynamic diagram of passenger flow and the line statistics data, the mall operators can optimize the operation efficiency according to the thermodynamic diagram and the line statistics data, such as adjusting the position of the mall, setting different rentals in unit area according to different positions of the mall, and the like.
In the embodiment of the invention, the functions of detecting the environmental characteristic information by the mobile phone of the user, reporting the position information acquired by the positioning platform, detecting the motion state of the user, detecting the use state of the mobile phone and the like are integrated in an SDK development kit. The advantages of the scheme are obvious, the number of users and the position data acquisition efficiency can be rapidly increased by embedding the SDK package in different LBS applications, and the statistical data based on the positions of the users can be effectively accumulated, so that the business value is generated. Of course, those skilled in the art will readily appreciate that the above embodiments do not preclude the function of carrying the SDK through a separate APP.
Many variations and modifications of the above-described data statistics method and system may be made without departing from the scope and spirit of the invention, as will be apparent to those of ordinary skill in the art. For example, in the embodiment of the present invention, the user handset accesses the Internet through the mobile base station by using the data service, and may also access the Internet through any wireless network providing Internet access service, such as a WiFi network, a WiMAX network, a mobile communication network, and an evolution network (LTE, 5G) of a wireless local area network. The invention is also suitable for counting advertisement effect, especially show window advertisement effect in indoor environment, and can give out interest evaluation value of the advertisement by combining walking speed and mobile phone use condition of the user, and the attraction degree of the advertisement to the user can be obtained by counting for a period of time.
The description of the present invention has been presented for purposes of illustration and is not intended to be exhaustive or limited to the invention in the form disclosed. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. The scope of the invention is defined by the appended claims.
Claims (7)
1. A mobile positioning platform, wherein the platform performs data statistics based on user location, the platform comprising:
1) A positioning engine unit for calculating the position coordinates of the user according to the environmental characteristic information reported by the user terminal, wherein the environmental characteristic information reported by the user terminal refers to one type of characteristic information or a combination of a plurality of types of characteristic information which has position correlation and changes along with the position change, and the environmental characteristic information comprises the surrounding wireless signal intensity and the identity information of the information source; the positioning engine unit searches a positioning database according to the environmental characteristic information reported by the user terminal, and calculates the position of the user through a matching algorithm; the positioning result comprises building information of the user, a floor and position coordinates on a floor map, wherein the building information comprises building names and addresses;
2) A user behavior pattern analysis unit for judging a user behavior pattern according to user motion state information and user mobile phone use state information reported by a user terminal and combining user position coordinates and electronic map information, wherein the user behavior pattern refers to the current behavior characteristics of a user and the degree of interest to nearby shops/commodities, and the degree is represented by an interest evaluation value; the current behavior characteristics of the user comprise commodity browsing, commodity selecting, resting and waiting, dining, leisure and entertainment and walking, and the judging of the behavior mode of the user specifically comprises the following steps that the result is written into a user database:
a) Judging whether the user is in a walking state according to the user movement state information, if so, further judging whether the step frequency of the user exceeds a threshold value, wherein the threshold value is set to different values according to the gender and age of the user, if so, setting the behavior characteristic of the user as walking, setting the interest evaluation value as 0, and ending the judgment; if the threshold value is not exceeded, setting the behavior characteristic of the user as browsing goods, and turning to the following step E); if the user is not walking, the step B) is carried out;
b) If the user is in a non-walking state and comprises static or occasional shaking, further inquiring the current position of the user, judging whether the user is in a restaurant or leisure and entertainment store, if so, setting the behavior characteristics of the user as restaurant and leisure and entertainment, setting the interest evaluation value as 1, and ending the judgment; otherwise, turning to the following step C);
c) Judging whether the user uses the mobile phone according to the mobile phone gesture and the use state of the user, if not, setting the behavior characteristics of the user as selecting goods, and turning to the following step E); if the user is using the mobile phone, the step D) is carried out;
d) Judging whether the user is in a passenger flow channel or a rest area according to the current position of the user, if so, setting the behavior characteristics of the user as rest waiting, setting the interest evaluation value as 0, and ending the judgment; otherwise, turning to the following step E);
e) According to different transfer conditions, user interest evaluation values with values in the range of 0-1 are given according to the following principles: the interest of not using the mobile phone is higher than that of using the mobile phone, and the interest of stationary or occasionally shaking is higher than that of walking; the lower the step frequency, the higher the interest;
3) A database unit for storing user data, merchant data and map data; the user data comprises a user positioning result, a user behavior mode and a user statistical result, the merchant data comprises a merchant name, a position coordinate, a merchant area, a merchant type and a merchant statistical result, and the map data comprises a floor layout structure, positions and occupied ranges of all shops, names of all shops, public setting positions and indoor road network information;
4) A statistical analysis engine unit is responsible for performing data statistics and analysis based on the user location and corrected by the user behavior pattern data.
2. The mobile positioning platform according to claim 1, wherein the statistical analysis engine unit periodically operates in a timing mode based on the user position and corrected by the user behavior pattern data, and each time, the user positioning result original data to be counted, that is, the original data accumulated from the last end to the current start is grabbed; the statistical period and the timing start time are determined by system configuration parameters.
3. Mobile positioning platform according to any of claims 1-2, characterized in that said data statistics and analysis based on user position and corrected by user behavior pattern data comprises the steps of writing the results into a database:
i) Capturing the effective positioning result to be counted of each user from the user database in turn, and carrying out statistics of the following ii) -iii);
ii) statistical residence time metadata: multiplying the number of effective positioning results of the user in the same market by a positioning period, and taking the result as the residence time of the user in the market in the statistical period; likewise calculating residence times at each floor of the mall; storing the user residence time metadata statistics results in a user database, wherein the user residence time metadata statistics results comprise the user ID, the market ID, the entering market and the leaving time, the residence time in the market and the residence time in each floor;
iii) Statistics of user attention metadata: for each effective positioning result, calculating the distance D between the position coordinates of the commercial tenant and the position coordinates of the user in the commercial tenant on the same floor, and enclosing if D is less than or equal to R, wherein R=a×R0, R0 is set by the working parameters of the system, the size of the system is positively related to the average area S0 of the commercial tenant on the floor, and a=S/S0, S is the area of the commercial tenant; inquiring the interest evaluation value of the user at the moment, and storing the interest evaluation value serving as the attention degree metadata of the user for the surrounding merchants in a user database;
iv) counting accumulated value of attention of commercial tenant: and summarizing the attention metadata of all users in the current statistical period, and counting the accumulated values of the attention of the users, which are obtained by all merchants in the current statistical period.
4. A mobile positioning platform as defined in claim 3 wherein said periodically running user location based and user behavior pattern data corrected data statistics and analysis further comprises: counting the frequency of a user visiting a certain market, the residence time length of each residence time and the start-stop time of each residence through the user residence time metadata, and distinguishing user types, wherein the user types comprise common consumers and market staff; actively filtering non-consumer users when the user attention metadata and merchant attention accumulated value are counted.
5. The mobile positioning platform of claim 3 wherein the data statistics and analysis based on user location and corrected by user behavior pattern data further comprises generating statistics for specified malls, floors, merchant types, consumer groups based on the statistics metadata, the statistics being performed for specified time periods.
6. The mobile positioning platform of claim 5, wherein the statistics of the designated mall, floor, merchant type, consumer group include average residence time of the consumer at the mall and average residence time of the consumer at each floor, user attention ranking separately counted by different merchant types, shopping interest preference of each individual consumer, shopping interest preference counted by user group, mall passenger flow thermodynamic diagram, mall passenger flow line diagram.
7. The mobile positioning platform of claim 6, further comprising a service interface unit for providing a data query interface and a service data synchronization interface to a third party service platform; the data query interface provides real-time position information query of the third-party service platform user, and position-based user related statistical information and merchant related statistical information query; and the business data synchronization interface is responsible for completing the merchant information synchronization between the platform and the third-party business platform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710280894.8A CN108734501B (en) | 2017-04-19 | 2017-04-19 | Mobile positioning platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710280894.8A CN108734501B (en) | 2017-04-19 | 2017-04-19 | Mobile positioning platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108734501A CN108734501A (en) | 2018-11-02 |
| CN108734501B true CN108734501B (en) | 2023-06-16 |
Family
ID=63934730
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710280894.8A Active CN108734501B (en) | 2017-04-19 | 2017-04-19 | Mobile positioning platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108734501B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109613470A (en) * | 2018-12-04 | 2019-04-12 | 拉扎斯网络科技（上海）有限公司 | Information processing method, device, storage medium and electronic equipment |
| CN109711890B (en) * | 2018-12-28 | 2022-12-09 | 珍岛信息技术（上海）股份有限公司 | User data processing method and system |
| CN111065052B (en) * | 2019-12-26 | 2020-11-24 | 广东星舆科技有限公司 | Positioning track-based behavior analysis method and smart watch |
| CN111641688B (en) * | 2020-05-20 | 2022-09-02 | 成都众树信息科技有限公司 | Member marketing system based on mobile signaling |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103402177A (en) * | 2013-08-02 | 2013-11-20 | 南京市海聚信息科技有限公司 | Information pushing system of WiFi (wireless fidelity) terminal and implementation method thereof |
| CA2823413A1 (en) * | 2013-08-13 | 2015-02-13 | Hamid Reza Kashefy | Method or system for simultaneous electronic commerce in multiple distinct web stores including electronic shopping center |
| CN106294534A (en) * | 2016-07-18 | 2017-01-04 | 中国银联股份有限公司 | A kind of user interest coupling supplying system and user interest coupling method for pushing |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7903029B2 (en) * | 1996-09-09 | 2011-03-08 | Tracbeam Llc | Wireless location routing applications and architecture therefor |
| US20050222906A1 (en) * | 2002-02-06 | 2005-10-06 | Chen Timothy T | System and method of targeted marketing |
| US8669884B2 (en) * | 2011-02-02 | 2014-03-11 | Mapquest, Inc. | Systems and methods for generating electronic map displays with points of-interest information |
| CN103024659B (en) * | 2011-09-22 | 2018-09-11 | 嘉兴高恒信息科技有限公司 | A kind of location method and system |
| US9628958B1 (en) * | 2013-03-15 | 2017-04-18 | Paul McBurney | User-controlled, smart device-based location and transit data gathering and sharing |
| CN104820863A (en) * | 2015-03-27 | 2015-08-05 | 北京智慧图科技有限责任公司 | Consumer portrait generation method and device |
| CN106330690A (en) * | 2016-11-22 | 2017-01-11 | 中青冠岳科技(北京)有限公司 | People counting method and system based on communication software map function |
-
2017
- 2017-04-19 CN CN201710280894.8A patent/CN108734501B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103402177A (en) * | 2013-08-02 | 2013-11-20 | 南京市海聚信息科技有限公司 | Information pushing system of WiFi (wireless fidelity) terminal and implementation method thereof |
| CA2823413A1 (en) * | 2013-08-13 | 2015-02-13 | Hamid Reza Kashefy | Method or system for simultaneous electronic commerce in multiple distinct web stores including electronic shopping center |
| CN106294534A (en) * | 2016-07-18 | 2017-01-04 | 中国银联股份有限公司 | A kind of user interest coupling supplying system and user interest coupling method for pushing |
Non-Patent Citations (1)
| Title |
|---|
| 信令大数据技术在精准营销中的应用;王浩宇;孙启明;胡凯;;北京邮电大学学报(社会科学版)(04);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108734501A (en) | 2018-11-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108734502B (en) | Data statistics method and system based on user position | |
| US8838586B2 (en) | Relevancy ranking for map-related search | |
| US9576295B2 (en) | Adjusting a process for visit detection based on location data | |
| US9723450B2 (en) | Inference pipeline system and method | |
| CN108734501B (en) | Mobile positioning platform | |
| US8972357B2 (en) | System and method for data collection to validate location data | |
| US8892461B2 (en) | Mobile device user behavior analysis and authentication | |
| US20120271715A1 (en) | System and method for the automatic delivery of advertising content to a consumer based on the consumer's indication of interest in an item or service available in a retail environment | |
| CA2764414C (en) | System and method of disseminating electronic content utilizing geographic and time granularities | |
| US9307356B2 (en) | User description based on a context of travel | |
| US20110093339A1 (en) | System and method for the service of advertising content to a consumer based on the detection of zone events in a retail environment | |
| US20080281687A1 (en) | Method for determining user interest in products and services for targeted advertising | |
| EP2476090A1 (en) | System and method for the service of advertising content to a consumer based on the detection of zone events in a retail environment | |
| EP2988473B1 (en) | Argument reality content screening method, apparatus, and system | |
| US20150278873A1 (en) | Local advertisement via mobile device | |
| CN110706014A (en) | Shopping mall store recommendation method, device and system | |
| CN109348417A (en) | Display methods, device, terminal and the storage medium of route | |
| JP2020537252A (en) | Systems and methods for predicting similar mobile devices | |
| CN110033296A (en) | A kind of data processing method and device | |
| US9967352B2 (en) | Communication system with location based services mechanism and method of operation thereof | |
| JP7272522B2 (en) | Data analysis device, data analysis system, data analysis method and data analysis program | |
| JP2019114047A (en) | Device, method, and program for processing information | |
| Kröckel et al. | Customer tracking and tracing data as a basis for service innovations at the point of sale | |
| US9672546B2 (en) | Accurate and instantaneous commodity pricing information system | |
| CN111898322B (en) | Data processing method and related equipment |
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 | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231130 Address after: Room 1106-A-01, Building 3, Gangcheng Plaza, No. 16 North Ring Road, Taicang Port Economic and Technological Development Zone, Suzhou City, Jiangsu Province, 215434 Patentee after: Suzhou Ruibo Technology Co.,Ltd. Address before: 314006 Room 201, Building 2, No. 382, Asia Pacific Road, Nanhu District, Jiaxing, Zhejiang Province Patentee before: JIAXING GAOHENG INFORMATION TECHNOLOGY CO.,LTD. |