CN111641917A - System and method for counting passenger flow of shopping mall store - Google Patents
System and method for counting passenger flow of shopping mall store Download PDFInfo
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- CN111641917A CN111641917A CN202010409016.3A CN202010409016A CN111641917A CN 111641917 A CN111641917 A CN 111641917A CN 202010409016 A CN202010409016 A CN 202010409016A CN 111641917 A CN111641917 A CN 111641917A
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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- 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
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- 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
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
Abstract
The invention discloses a system and a method for shopping mall store passenger flow statistics, wherein the system comprises an active room branch base station, a positioning engine, a data analysis engine, an SAAS server and a cloud platform, the active room branch base station is in communication connection with the positioning engine, the positioning engine is in communication connection with the data analysis engine, the data analysis engine is in communication connection with the SAAS server, and the SAAS server is in communication connection with the cloud platform; the invention has the advantages that: and according to the real-time position information of the user, counting the passenger flow of each area in the market, thereby realizing reasonable operation of each area in the market.
Description
Technical Field
The invention relates to the field of retail management, in particular to a system and a method for counting the passenger flow of shopping malls.
Background
Market passenger flow statistics (shopping flow statistics) is to accurately count the number of people passing in and out of each entrance in real time by installing passenger flow statistics equipment in an operation area, so that a market is managed scientifically according to data, and a targeted marketing means is adopted according to the passenger flow of each store.
Effective business management has become an important factor in the success or failure of business marketing today when business competition is becoming increasingly intense. The business model gradually changes from the traditional seat business to the extremely initiative business, and puts higher requirements on business managers: the system has the advantages that the system can quickly respond to the weak change of the market in the shortest time, has market predictability, saves the commercial operation cost to the maximum extent, and improves the scientificity of daily operation decision of a market, the comfort of shopping environment, the rationality of human resource allocation and the like. The biggest leading person of market rules is a commodity purchaser, namely a customer, how to scientifically and effectively analyze the passenger flow in time and space and make operation decisions quickly and timely becomes the key to the success of marketing modes of commerce and retail industry.
By counting the passenger flow at different time intervals, managers can increase the number of workers in the peak period of the passenger flow, thereby improving the service quality and further increasing the sales; and staff is reduced in idle time, and the waste of staff is avoided. Through continuous passenger flow statistics every day, the passenger flow change rule of one day, one week, one month and one year can be obtained, so that managers can accurately plan future activities and determine time, manpower, inventory ordering amount and the like. Through the comparison of passenger flow, the promoted activities are effectively evaluated, and marketing and sales promotion investment return are effectively evaluated. Through the passenger flow statistics of different floors and different regions, managers can count the attraction rate and the busyness of each region, so that the berths are reasonably distributed, and the sales volume is increased.
Chinese patent application publication No. CN105574497A discloses a system and a method for counting the number of people in a mall based on face detection, wherein the system for counting the number of people in the mall based on face detection comprises a shooting terminal in the mall for sending a photo of a customer who shoots through an entrance of the mall; and the passenger flow volume counting server is used for carrying out face recognition on all the photos, recognizing the face information of each photo, generating a face recognition result and similarity according to a temporary population library, a key population library and the face information which are established in advance, preferentially processing the photos with high similarity in the face recognition result, counting customers, generating the number of the shopping malls, and obtaining the passenger flow volume. The beneficial effects of the invention application are as follows: the number of the people in the market is counted, real-time, visual and accurate passenger flow volume data are provided for managers, and more efficient management and organization work is facilitated. However, the method only counts the passenger flow of the shopping mall, and cannot count the passenger flow of each area in the shopping mall according to the real-time position information of the user, so that the reasonable operation of each area of the shopping mall cannot be realized.
Disclosure of Invention
The technical problem to be solved by the invention is that the system and the method for counting the passenger flow of the shopping mall in the prior art can not count the passenger flow of each area in the shopping mall according to the real-time position information of the user, so that the problem of rationalized operation of each area in the shopping mall can not be realized.
The invention solves the technical problems through the following technical means: a system for counting the passenger flow of a shop in a shopping mall comprises an active room sub base station, a positioning engine, a data analysis engine, an SAAS server and a cloud platform, wherein the active room sub base station is in communication connection with the positioning engine;
the active indoor sub-base station is used for reporting mobile phone positioning signal information based on the remote radio frequency unit to a positioning engine in real time;
the positioning engine is used for acquiring the position information of the user based on the map according to the reported mobile phone positioning signal information and reporting the position information to the data analysis engine at intervals of preset time; the position information comprises a user mobile phone IP address, a map coordinate and a timestamp;
the data analysis engine is used for mapping the user identification in the mobile hadoop cluster and mapping the IP of the user mobile phone into a mobile phone number;
the data analysis engine is also used for analyzing various statistical indexes by using the position information; the various statistical indexes comprise store passenger flow, floor passenger flow, market passenger flow and user residence time;
and the SAAS server is used for acquiring various statistical data from the data analysis engine and presenting the various statistical data on the cloud platform.
The invention obtains the position information of a user based on a map through a positioning engine, reports the position information to a data analysis engine at preset time intervals, the data analysis engine maps the mobile phone IP of the user into a mobile phone number, the SAAS server obtains various statistical data from the data analysis engine, presents the various statistical data on a cloud platform, and counts the passenger flow of each area in a shopping mall according to the real-time position information of the user, thereby realizing the reasonable operation of each area in the shopping mall.
Preferably, the data analysis engine is further configured to:
step 301: taking half an hour as a time partition, creating an IP partition table, and recording IP addresses of all user mobile phones of the time partition;
step 302: starting a Spark task every half hour, inquiring the corresponding relation between an IP address and a mobile phone number in a signaling partition table of a corresponding time partition according to the IP address of a user mobile phone and the ID of an active indoor partition base station, and outputting the inquiry result to an IP-mobile phone number mapping table;
step 303: and replacing the user mobile phone IP in the position information reported by the positioning engine with a mobile phone number.
Preferably, the data analysis engine is further configured to:
step 401: planning a market map, identifying different area types in the map, and setting different residence time thresholds aiming at the different area types;
step 402: mapping the coordinates into areas according to the position information reported by the positioning engine, and accumulating the time of the same area by using the timestamp to obtain the residence time of the user in the area; according to the condition that the signal is discontinuous, subtracting the starting time from the ending time to be used as the residence time of the user in the area according to the corrected area information;
step 403: aiming at the problem of floor jump, taking half a minute as a time window, taking the floor with the longest accumulated time of the time window as a resident floor in the time window, and correcting data of other floors except the resident floor in the time window into data of a similar region of the resident floor;
step 404: for the problem of regional oscillation, data of half a minute before and after the data of the regional oscillation are taken, the residence time of each region within 1 minute is accumulated, and the region with the longest residence time is taken as the actual residence region of the user in the time period;
step 405: after acquiring the area where the user is located and the corresponding residence time, judging whether the residence time of the user in a certain area exceeds the residence time threshold, if the residence time of the user in the certain area exceeds the residence time threshold of the area, considering that the user enters the area, otherwise, considering that the user does not enter the area;
step 406: and counting the passenger flow of each area in a preset time period according to the user track table and residence time thresholds corresponding to different area types, thereby obtaining the shop passenger flow table, the floor passenger flow table and the market passenger flow table.
Preferably, the preset time interval is 2-4 seconds.
The invention also provides a method for counting the passenger flow of the shopping mall stores, which comprises the following steps:
the method comprises the following steps: the active indoor sub-base station reports mobile phone positioning signal information based on the remote radio frequency unit to a positioning engine in real time;
step two: the positioning engine acquires the position information of the user based on the map according to the reported mobile phone positioning signal information, and reports the position information to the data analysis engine at intervals of preset time; the position information comprises a user mobile phone IP address, a map coordinate and a timestamp;
step three: the data analysis engine maps the user identification in the mobile hadoop cluster and maps the IP of the user mobile phone into a mobile phone number;
step four: the data analysis engine analyzes various statistical indexes by using the position information; the various statistical indexes comprise store passenger flow, floor passenger flow, market passenger flow and user residence time;
step five: the SAAS server acquires various types of statistical data from the data analysis engine and presents the various types of statistical data on the cloud platform.
Preferably, the third step includes:
step 301: taking half an hour as a time partition, creating an IP partition table, and recording IP addresses of all user mobile phones of the time partition;
step 302: starting a Spark task every half hour, inquiring the corresponding relation between an IP address and a mobile phone number in a signaling partition table of a corresponding time partition according to the IP address of a user mobile phone and the ID of an active indoor partition base station, and outputting the inquiry result to an IP-mobile phone number mapping table;
step 303: and replacing the user mobile phone IP in the position information reported by the positioning engine with a mobile phone number.
Preferably, the fourth step includes:
step 401: planning a market map, identifying different area types in the map, and setting different residence time thresholds aiming at the different area types;
step 402: mapping the coordinates into areas according to the position information reported by the positioning engine, and accumulating the time of the same area by using the timestamp to obtain the residence time of the user in the area; according to the condition that the signal is discontinuous, subtracting the starting time from the ending time to be used as the residence time of the user in the area according to the corrected area information;
step 403: aiming at the problem of floor jump, taking half a minute as a time window, taking the floor with the longest accumulated time of the time window as a resident floor in the time window, and correcting data of other floors except the resident floor in the time window into data of a similar region of the resident floor;
step 404: for the problem of regional oscillation, data of half a minute before and after the data of the regional oscillation are taken, the residence time of each region within 1 minute is accumulated, and the region with the longest residence time is taken as the actual residence region of the user in the time period;
step 405: after acquiring the area where the user is located and the corresponding residence time, judging whether the residence time of the user in a certain area exceeds the residence time threshold, if the residence time of the user in the certain area exceeds the residence time threshold of the area, considering that the user enters the area, otherwise, considering that the user does not enter the area;
step 406: and counting the passenger flow of each area in a preset time period according to the user track table and residence time thresholds corresponding to different area types, thereby obtaining the shop passenger flow table, the floor passenger flow table and the market passenger flow table.
Preferably, the preset time interval is 2-4 seconds.
The invention has the advantages that:
(1) the invention obtains the position information of a user based on a map through a positioning engine, reports the position information to a data analysis engine at preset time intervals, the data analysis engine maps the mobile phone IP of the user into a mobile phone number, the SAAS server obtains various statistical data from the data analysis engine, presents the various statistical data on a cloud platform, and counts the passenger flow of each area in a shopping mall according to the real-time position information of the user, thereby realizing the reasonable operation of each area in the shopping mall.
(2) The invention maps the IP of the user mobile phone into the mobile phone number based on the matching process of the sliding window, reduces the consumption of big data resources in the hadoop cluster, and completes the matching of the real user under the condition of limited big data resources. The IP + the base station ID + the time partition are used for accurate matching, so that the matching accuracy is improved, and the condition that the same IP is matched with different mobile phone numbers is avoided.
(3) The invention provides a corresponding solution for the situation of discontinuous signals, the problem of floor jump and the problem of regional oscillation, realizes the real positioning of the user track and ensures the accuracy of the passenger flow analysis data.
Drawings
FIG. 1 is a block diagram of a system for providing statistics of the passenger flow of a store in a mall according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a user identifier mapping process in a method for shopping mall store passenger flow statistics according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a process of analyzing various statistical indexes by using location information by a data analysis engine in the method for counting the passenger flow of the shopping mall store according to the embodiment of the present invention;
fig. 4 is a flowchart of a method for shopping mall store passenger flow statistics according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. 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.
Example 1
As shown in fig. 1, a system for shopping mall store passenger flow statistics includes an active room sub base station 1, a positioning engine 2, a data analysis engine 3, an SAAS server 4, and a cloud platform 5, where the active room sub base station 1 and the positioning engine 2 are in communication connection through an SCTP protocol. The positioning engine 2 is in communication connection with the data analysis engine 3 through an AMQP protocol. The data analysis engine 3 is in communication connection with the SAAS server 4 through an HTTP protocol. The SAAS server 4 is in communication connection with the cloud platform 5 through an HTTP protocol.
The active indoor sub-base station 1 is used for reporting mobile phone positioning signal information based on a remote radio frequency unit to the positioning engine 2 in real time; the active indoor sub-base station 1 reports mobile phone positioning signal information based on remote radio frequency units, wherein the mobile phone positioning signal information is obtained by the prior art and can be a field intensity triangulation algorithm, a TDOA algorithm or a fingerprint positioning algorithm, the field intensity triangulation algorithm is mainly based on a triangulation positioning principle, namely, a measured target point and two reference points with known coordinates can form a triangle, the distance and the coordinates of the target point can be found out by calculating the length of a reference edge in the triangle and measuring the angle formed by the two reference points and the target point, the remote radio frequency units are known reference points, and the mobile phone is the target point. The TDOA algorithm is a key technique in mobile phone location, and it performs location estimation on signal sources by detecting the time difference of arrival of signals at different remote radio unit base stations. The time difference between the arrival of the same signal at two remote rf units determines the hyperbola in which a mobile phone is located, and the location of the mobile phone, i.e., the mobile phone, can be obtained by measuring the two hyperbolas, which can be referred to in "statistical model of arrival time and time difference error in TDOA location" in the section 29 of the university of Chongqing (Nature science edition). The fingerprint positioning algorithm is specifically referred to chinese patent publication No. CN103442430A, and discloses a fingerprint positioning method and a server, which divide a positioning coverage area into a plurality of sub-areas in advance according to a building pattern, and establish a fingerprint database according to fingerprint information of known position reference points of each sub-area under various traffic conditions. In the actual positioning process, firstly, a matched fingerprint database is determined, then the current fingerprint information is compared with the fingerprint information in the determined fingerprint database, a plurality of reference points with the most similar fingerprint information are selected, the positions of the selected reference points are weighted and averaged to be used as the current position of the terminal, and the positioning is completed. The active indoor sub-base station 1 adopts Lampsite or Zhongxing QCell, and the remote radio frequency unit adopts pRRU or Pico RRU.
The positioning engine 2 is used for acquiring the position information of the user based on the map according to the reported mobile phone positioning signal information and reporting the position information to the data analysis engine 3 at preset time intervals; the position information comprises a user mobile phone IP address, a map coordinate and a timestamp; the preset time interval is 2-4 seconds, and 2 seconds are adopted in the embodiment. The positioning engine 2 has rich API interfaces, supports UDP, TCP, HTTP, HTTPS and other protocol types, and meets the development requirements of various communication interfaces. Positioning engine 2 can adopt the 2 software of positioning engine that huaxing intelligence was controlled, and its positioning engine 2 can realize showing personnel's position, personnel's name, looks over real-time orbit, historical loading orbit, shows the state of base station label etc..
The data analysis engine 3 is used for mapping the user identification in the mobile hadoop cluster and mapping the user mobile phone IP into a mobile phone number; the format of the user location information reported by the positioning engine 2 is as follows: IP address, map coordinates (x, y, z), timestamp. The IP address is the identifier of the access network allocated by the core network to each mobile phone user, and the user may change when leaving or re-accessing the network, and meanwhile, the IP addresses allocated by different core networks may be the same. That is, a handset may have different IP addresses at different time periods. If only the IP address is used for calculating the passenger flow, the passenger flow data is inaccurate, and only the IP address can not correspond to a real user portrait. The identification mapping principle is then used to solve the problem. The identification mapping principle is that an operator mobile signaling table is used, IP and mobile phone numbers are matched in the mobile signaling table, but the data volume of the signaling table is huge, and if the IP and mobile phone numbers are directly matched, the resource consumption is huge, so that the method adopts a matching mode based on a time sliding window, and simultaneously, the IP + base station ID is used as a keyword for carrying out accurate matching during matching, so that the same IP is prevented from being matched with wrong mobile phone numbers. As shown in fig. 3, the specific process is as follows:
step 301: taking half an hour as a time partition, creating an IP partition table, and recording IP addresses of all user mobile phones of the time partition; for example, in fig. 3, a time partition is set to be a half hour, and an IP partition table is created for each time partition, and the IP partition table includes a plurality of IP partition tables.
Step 302: starting a Spark task every half hour, inquiring the corresponding relation between an IP address and a mobile phone number in a signaling partition table of a corresponding time partition according to the IP address of a user mobile phone and the ID of an active room partition base station 1, and outputting the inquiry result to an IP-mobile phone number mapping table; with continued reference to fig. 3, in the figure, one mobile signaling partition table takes 10 minutes, i.e. one third of the time partition, and one time partition includes 3 mobile signaling partition tables arranged according to time. Starting the Spark task every half hour, and simultaneously inquiring the 3 mobile signaling partition tables. The mobile signaling partition table contains information such as the IP address of the user mobile phone, the ID of the active indoor partition base station 1, the number of the mobile phone and the like, the corresponding relation between the IP address and the number of the mobile phone can be inquired according to the IP address of the user mobile phone and the ID of the active indoor partition base station 1, and the corresponding relation is stored in a form of a table (an IP-mobile phone number mapping table) to generate a first IP-mobile phone number mapping table which is convenient to check. And after half an hour is finished, entering the next time partition, starting a Spark task for the next time, repeating the process to generate a second IP-mobile phone number mapping table, and finally generating a plurality of IP-mobile phone number mapping tables.
Step 303: and replacing the user mobile phone IP in the position information reported by the positioning engine 2 with a mobile phone number.
The data analysis engine 3 is also used for analyzing various statistical indexes by using the position information; the various statistical indexes comprise store passenger flow, floor passenger flow, market passenger flow and user residence time; the format of the user location information reported by the positioning engine 2 is as follows: the user identification, the map coordinates (x, y, z) and the timestamp are reported every 2 seconds, and the reported coordinates are the coordinates calculated according to the real-time signals of the mobile phone of the user. Due to the characteristic of wireless positioning, the reported positioning data has the problems of jumping, oscillation, hollowing and the like in different degrees, such as: oscillating back and forth between different areas, the last time in area A and the next time in area B; jumping among different floors, wherein the first time is in the 1 st floor, and the next time is in the 3 rd floor; when the 4G signal is not good, data loss may occur, for example, half a minute of the elevator car has no positioning signal reported. How to determine the real track of the user and whether to enter the store is a difficult point in the practical analysis. As shown in fig. 4, the specific processing procedure is as follows:
step 401: planning a market map, identifying different area types in the map, and setting different residence time thresholds aiming at the different area types; the threshold for store-in is set to 20 seconds.
Step 402: mapping the coordinates into areas according to the position information reported by the positioning engine 2, and accumulating the time of the same area by using the timestamp to obtain the residence time of the user in the area; according to the condition that the signal is discontinuous, subtracting the starting time from the ending time to be used as the residence time of the user in the area according to the corrected area information;
step 403: aiming at the problem of floor jump, taking half a minute as a time window, taking the floor with the longest accumulated time of the time window as a resident floor in the time window, and correcting data of other floors except the resident floor in the time window into data of a similar region of the resident floor;
step 404: for the problem of regional oscillation, data of half a minute before and after the data of the regional oscillation are taken, the residence time of each region within 1 minute is accumulated, and the region with the longest residence time is taken as the actual residence region of the user in the time period;
step 405: after acquiring the area where the user is located and the corresponding residence time, judging whether the residence time of the user in a certain area exceeds the residence time threshold, if the residence time of the user in the certain area exceeds the residence time threshold of the area, considering that the user enters the area, otherwise, considering that the user does not enter the area;
step 406: and counting the passenger flow of each area in a preset time period according to the user track table and residence time thresholds corresponding to different area types, thereby obtaining the shop passenger flow table, the floor passenger flow table and the market passenger flow table.
And the SAAS server 4 is used for acquiring various types of statistical data from the data analysis engine 3 and presenting the various types of statistical data on the cloud platform 5.
According to the technical scheme, the system for counting the passenger flow of the shop in the market acquires the position information of a user based on a map through the positioning engine 2, reports the position information to the data analysis engine 3 at preset time intervals, the data analysis engine 3 maps the mobile phone IP of the user into a mobile phone number, the SAAS server 4 acquires various statistical data from the data analysis engine 3, presents the various statistical data on the cloud platform 5, counts the passenger flow of each area in the market according to the real-time position information of the user, and achieves reasonable operation of each area in the market.
Example 2
As shown in fig. 4, corresponding to embodiment 1 of the present invention, embodiment 2 of the present invention further provides a method for shopping mall store passenger flow statistics, where the method includes:
step S1: the active indoor sub-base station 1 reports mobile phone positioning signal information based on a remote radio frequency unit to a positioning engine 2 in real time;
step S2: the positioning engine 2 acquires the position information of the user based on the map according to the reported mobile phone positioning signal information, and reports the position information to the data analysis engine 3 at preset time intervals; the position information comprises a user mobile phone IP address, a map coordinate and a timestamp; the preset time interval is 2-4 seconds, and 2 seconds are adopted in the embodiment.
Step S3: the data analysis engine 3 maps the user identification in the mobile hadoop cluster and maps the user mobile phone IP into a mobile phone number; the format of the user location information reported by the positioning engine 2 is as follows: IP address, map coordinates (x, y, z), timestamp. The IP address is the identifier of the access network allocated by the core network to each mobile phone user, and the user may change when leaving or re-accessing the network, and meanwhile, the IP addresses allocated by different core networks may be the same. That is, a handset may have different IP addresses at different time periods. If only the IP address is used for calculating the passenger flow, the passenger flow data is inaccurate, and only the IP address can not correspond to a real user portrait. The identification mapping principle is then used to solve the problem. The identification mapping principle is that an operator mobile signaling table is used, IP and mobile phone numbers are matched in the mobile signaling table, but the data volume of the signaling table is huge, and if the IP and mobile phone numbers are directly matched, the resource consumption is huge, so that the method adopts a matching mode based on a time sliding window, and simultaneously, the IP + base station ID is used as a keyword for carrying out accurate matching during matching, so that the same IP is prevented from being matched with wrong mobile phone numbers. As shown in fig. 3, the specific process is as follows:
step 301: taking half an hour as a time partition, creating an IP partition table, and recording IP addresses of all user mobile phones of the time partition; for example, in fig. 3, a time partition is set to be a half hour, and an IP partition table is created for each time partition, and the IP partition table includes a plurality of IP partition tables.
Step 302: starting a Spark task every half hour, inquiring the corresponding relation between an IP address and a mobile phone number in a signaling partition table of a corresponding time partition according to the IP address of a user mobile phone and the ID of an active room partition base station 1, and outputting the inquiry result to an IP-mobile phone number mapping table; with continued reference to fig. 3, in the figure, one mobile signaling partition table takes 10 minutes, i.e. one third of the time partition, and one time partition includes 3 mobile signaling partition tables arranged according to time. Starting the Spark task every half hour, and simultaneously inquiring the 3 mobile signaling partition tables. The mobile signaling partition table contains information such as the IP address of the user mobile phone, the ID of the active indoor partition base station 1, the number of the mobile phone and the like, the corresponding relation between the IP address and the number of the mobile phone can be inquired according to the IP address of the user mobile phone and the ID of the active indoor partition base station 1, and the corresponding relation is stored in a form of a table (an IP-mobile phone number mapping table) to generate a first IP-mobile phone number mapping table which is convenient to check. And after half an hour is finished, entering the next time partition, starting a Spark task for the next time, repeating the process to generate a second IP-mobile phone number mapping table, and finally generating a plurality of IP-mobile phone number mapping tables.
Step 303: and replacing the user mobile phone IP in the position information reported by the positioning engine 2 with a mobile phone number.
Step S4: the data analysis engine 3 analyzes various statistical indexes by using the position information; the various statistical indexes comprise store passenger flow, floor passenger flow, market passenger flow and user residence time; the format of the user location information reported by the positioning engine 2 is as follows: the user identification, the map coordinates (x, y, z) and the timestamp are reported every 2 seconds, and the reported coordinates are the coordinates calculated according to the real-time signals of the mobile phone of the user. Due to the characteristic of wireless positioning, the reported positioning data has the problems of jumping, oscillation, hollowing and the like in different degrees, such as: oscillating back and forth between different areas, the last time in area A and the next time in area B; jumping among different floors, wherein the first time is in the 1 st floor, and the next time is in the 3 rd floor; when the 4G signal is not good, data loss may occur, for example, half a minute of the elevator car has no positioning signal reported. How to determine the real track of the user and whether to enter the store is a difficult point in the practical analysis. As shown in fig. 4, the specific processing procedure is as follows:
step 401: planning a market map, identifying different area types in the map, and setting different residence time thresholds aiming at the different area types; the threshold for store-in is set to 20 seconds.
Step 402: mapping the coordinates into areas according to the position information reported by the positioning engine 2, and accumulating the time of the same area by using the timestamp to obtain the residence time of the user in the area; according to the condition that the signal is discontinuous, subtracting the starting time from the ending time to be used as the residence time of the user in the area according to the corrected area information;
step 403: aiming at the problem of floor jump, taking half a minute as a time window, taking the floor with the longest accumulated time of the time window as a resident floor in the time window, and correcting data of other floors except the resident floor in the time window into data of a similar region of the resident floor;
step 404: for the problem of regional oscillation, data of half a minute before and after the data of the regional oscillation are taken, the residence time of each region within 1 minute is accumulated, and the region with the longest residence time is taken as the actual residence region of the user in the time period;
step 405: after acquiring the area where the user is located and the corresponding residence time, judging whether the residence time of the user in a certain area exceeds the residence time threshold, if the residence time of the user in the certain area exceeds the residence time threshold of the area, considering that the user enters the area, otherwise, considering that the user does not enter the area;
step 406: and counting the passenger flow of each area in a preset time period according to the user track table and residence time thresholds corresponding to different area types, thereby obtaining the shop passenger flow table, the floor passenger flow table and the market passenger flow table.
Step S5: the SAAS server 4 acquires various types of statistical data from the data analysis engine 3, and presents the various types of statistical data on the cloud platform 5.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A system for counting the passenger flow of a store in a shopping mall is characterized by comprising an active room sub-base station, a positioning engine, a data analysis engine, an SAAS server and a cloud platform, wherein the active room sub-base station is in communication connection with the positioning engine, the positioning engine is in communication connection with the data analysis engine, the data analysis engine is in communication connection with the SAAS server, and the SAAS server is in communication connection with the cloud platform;
the active indoor sub-base station is used for reporting mobile phone positioning signal information based on the remote radio frequency unit to a positioning engine in real time;
the positioning engine is used for acquiring the position information of the user based on the map according to the reported mobile phone positioning signal information and reporting the position information to the data analysis engine at intervals of preset time; the position information comprises a user mobile phone IP address, a map coordinate and a timestamp;
the data analysis engine is used for mapping the user identification in the mobile hadoop cluster and mapping the IP of the user mobile phone into a mobile phone number;
the data analysis engine is also used for analyzing various statistical indexes by using the position information; the various statistical indexes comprise store passenger flow, floor passenger flow, market passenger flow and user residence time;
and the SAAS server is used for acquiring various statistical data from the data analysis engine and presenting the various statistical data on the cloud platform.
2. A system for store outlet flow statistics, as claimed in claim 1, wherein said data analysis engine is further configured to:
step 301: taking half an hour as a time partition, creating an IP partition table, and recording IP addresses of all user mobile phones of the time partition;
step 302: starting a Spark task every half hour, inquiring the corresponding relation between an IP address and a mobile phone number in a signaling partition table of a corresponding time partition according to the IP address of a user mobile phone and the ID of an active indoor partition base station, and outputting the inquiry result to an IP-mobile phone number mapping table;
step 303: and replacing the user mobile phone IP in the position information reported by the positioning engine with a mobile phone number.
3. A system for store outlet flow statistics, as claimed in claim 1, wherein said data analysis engine is further configured to:
step 401: planning a market map, identifying different area types in the map, and setting different residence time thresholds aiming at the different area types;
step 402: mapping the coordinates into areas according to the position information reported by the positioning engine, and accumulating the time of the same area by using the timestamp to obtain the residence time of the user in the area; according to the condition that the signal is discontinuous, subtracting the starting time from the ending time to be used as the residence time of the user in the area according to the corrected area information;
step 403: aiming at the problem of floor jump, taking half a minute as a time window, taking the floor with the longest accumulated time of the time window as a resident floor in the time window, and correcting data of other floors except the resident floor in the time window into data of a similar region of the resident floor;
step 404: for the problem of regional oscillation, data of half a minute before and after the data of the regional oscillation are taken, the residence time of each region within 1 minute is accumulated, and the region with the longest residence time is taken as the actual residence region of the user in the time period;
step 405: after acquiring the area where the user is located and the corresponding residence time, judging whether the residence time of the user in a certain area exceeds the residence time threshold, if the residence time of the user in the certain area exceeds the residence time threshold of the area, considering that the user enters the area, otherwise, considering that the user does not enter the area;
step 406: and counting the passenger flow of each area in a preset time period according to the user track table and residence time thresholds corresponding to different area types, thereby obtaining the shop passenger flow table, the floor passenger flow table and the market passenger flow table.
4. A system for store business passenger flow statistics according to claim 1, characterized in that said predetermined time interval is 2-4 seconds.
5. A method for mall store passenger flow statistics, the method comprising:
the method comprises the following steps: the active indoor sub-base station reports mobile phone positioning signal information based on the remote radio frequency unit to a positioning engine in real time;
step two: the positioning engine acquires the position information of the user based on the map according to the reported mobile phone positioning signal information, and reports the position information to the data analysis engine at intervals of preset time; the position information comprises a user mobile phone IP address, a map coordinate and a timestamp;
step three: the data analysis engine maps the user identification in the mobile hadoop cluster and maps the IP of the user mobile phone into a mobile phone number;
step four: the data analysis engine analyzes various statistical indexes by using the position information; the various statistical indexes comprise store passenger flow, floor passenger flow, market passenger flow and user residence time;
step five: the SAAS server acquires various types of statistical data from the data analysis engine and presents the various types of statistical data on the cloud platform.
6. A method as claimed in claim 5, wherein said step three comprises:
step 301: taking half an hour as a time partition, creating an IP partition table, and recording IP addresses of all user mobile phones of the time partition;
step 302: starting a Spark task every half hour, inquiring the corresponding relation between an IP address and a mobile phone number in a signaling partition table of a corresponding time partition according to the IP address of a user mobile phone and the ID of an active indoor partition base station, and outputting the inquiry result to an IP-mobile phone number mapping table;
step 303: and replacing the user mobile phone IP in the position information reported by the positioning engine with a mobile phone number.
7. A method of store outlet traffic statistics, as recited in claim 5, wherein said step four comprises:
step 401: planning a market map, identifying different area types in the map, and setting different residence time thresholds aiming at the different area types;
step 402: mapping the coordinates into areas according to the position information reported by the positioning engine, and accumulating the time of the same area by using the timestamp to obtain the residence time of the user in the area; according to the condition that the signal is discontinuous, subtracting the starting time from the ending time to be used as the residence time of the user in the area according to the corrected area information;
step 403: aiming at the problem of floor jump, taking half a minute as a time window, taking the floor with the longest accumulated time of the time window as a resident floor in the time window, and correcting data of other floors except the resident floor in the time window into data of a similar region of the resident floor;
step 404: for the problem of regional oscillation, data of half a minute before and after the data of the regional oscillation are taken, the residence time of each region within 1 minute is accumulated, and the region with the longest residence time is taken as the actual residence region of the user in the time period;
step 405: after acquiring the area where the user is located and the corresponding residence time, judging whether the residence time of the user in a certain area exceeds the residence time threshold, if the residence time of the user in the certain area exceeds the residence time threshold of the area, considering that the user enters the area, otherwise, considering that the user does not enter the area;
step 406: and counting the passenger flow of each area in a preset time period according to the user track table and residence time thresholds corresponding to different area types, thereby obtaining the shop passenger flow table, the floor passenger flow table and the market passenger flow table.
8. A method as claimed in claim 5, wherein said predetermined time interval is 2-4 seconds.
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