CN116249071A - Scenic spot population density determining method and device, electronic equipment and medium - Google Patents

Scenic spot population density determining method and device, electronic equipment and medium Download PDF

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CN116249071A
CN116249071A CN202211648442.8A CN202211648442A CN116249071A CN 116249071 A CN116249071 A CN 116249071A CN 202211648442 A CN202211648442 A CN 202211648442A CN 116249071 A CN116249071 A CN 116249071A
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徐学锋
周佩雷
陈春锋
杨蕊
朱国栋
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China Telecom Corp Ltd
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    • G06Q50/10—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
    • H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
    • 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
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    • 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
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Abstract

The embodiment of the invention provides a method and a device for determining population density of scenic spots, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a plurality of user data collected by Wi-Fi probes deployed at all scenic spots; according to the user data and the probe positions of the Wi-Fi probes, determining the user positions of the users corresponding to the user data and the acquisition time corresponding to the user positions; and determining population densities of all scenic spots at different moments according to the user positions and the acquisition time. Compared with a method for calculating the people flow density condition after face recognition by adopting a camera, the embodiment of the invention is not limited by the mounting position of the Wi-Fi probe; and the population density can be determined only based on the user data, other information is not required to be additionally identified, and the problem of low accuracy in identifying some objects is solved.

Description

Scenic spot population density determining method and device, electronic equipment and medium
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method, an apparatus, an electronic device, and a medium for determining population density of a scenic spot.
Background
In recent years, the growing contradiction between travel demands and the rarity of travel resources is gradually highlighted, so that tourist attractions are crowded into a normal state. Scenic spot density is an index that measures the extent to which the travel industry affects or is located in socioeconomic life in a particular area.
The space bearing capacity of the tourist resource refers to the number of tourists which can be accommodated in a tourist area in a scenic spot at a certain period (for example, one day) on the premise of ensuring the quality of the tourist resource and the tourist quality of the tourist. Tourist attractions are usually composed of a plurality of scenic spots (spots), and the mobility of tourists is fully considered. The formula is fully considered in the general rule of travel planning:
Figure BDA0004010829850000011
wherein C is the total space capacity of scenic spot day, X i Is the sightseeing area of the ith scenic spot; y is Y i The tourist at the ith scenic spot is suitable for the tourist area, namely the basic space standard value occupied by each tourist is averaged; t is the effective open time of each day of the scenic spot, T is the average tour time required by each tourist to complete all tour activities, D i The instantaneous guest capacity for the ith attraction; z is the daily turnover rate of the scenic spot.
The current crowded density calculation generally adopts a camera to carry out face recognition, and then calculates the people flow density, but the current crowded density calculation is limited by the installation position of the camera and the accuracy rate of object recognition.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus, electronic device and storage medium for determining a population density of a attraction that overcomes or at least partially solves the foregoing, comprising:
a method of determining a population density of a attraction, the method comprising:
acquiring a plurality of user data collected by Wi-Fi probes deployed at all scenic spots;
determining user positions of a plurality of users corresponding to the user data and acquisition time corresponding to the user positions according to the user data and probe positions of the Wi-Fi probes;
and determining population densities of all scenic spots at different moments according to the user positions and the acquisition time.
Optionally, the determining, according to the plurality of user data and probe positions of each Wi-Fi probe, user positions of a plurality of users corresponding to the plurality of user data and acquisition times corresponding to each user position includes:
analyzing target user data to obtain signal strength indication (RSSI) received by a target, target acquisition time for collecting the target user data, and target equipment codes of a target Wi-Fi probe for collecting the target user data;
Acquiring a target probe position corresponding to the target equipment code;
determining a target relative distance of a target user corresponding to target user data relative to the target Wi-Fi probe according to the target RSSI;
and determining the target user position of the target user at the target acquisition time according to the target relative distance and the target probe position.
Optionally, the parsing the target user data includes:
analyzing a target frame head part from the target user data, and determining a target instruction type corresponding to the target user data according to the target frame head part;
and when the target instruction type is the data instruction type, analyzing a target frame load part analyzed from the target user data.
Optionally, after parsing the target frame payload portion, the method further includes:
caching the target RSSI, the target acquisition time and the target equipment code into a cache area;
and when the target RSSI, the target acquisition time and the storage time of the target equipment code in the cache area exceed preset time, executing the step of determining the target relative distance of the target user corresponding to the target user data relative to the target Wi-Fi probe according to the target RSSI.
Optionally, the determining, according to the target RSSI, a target relative distance between a target user corresponding to target user data and the target Wi-Fi probe includes:
determining a target propagation loss according to the target RSSI;
and determining the target relative distance according to the target propagation loss.
Optionally, the target probe position includes a target probe longitude and latitude, and the determining, according to the target relative distance and the target probe position, a target user position of the target user at the target acquisition time includes:
determining a target user position of the target user at the target acquisition time according to the target relative distance and the target probe longitude and latitude; the target user location includes a target user longitude and latitude.
Optionally, the determining the target user position of the target user at the target acquisition time according to the target relative distance and the target probe longitude and latitude includes:
determining a target distance interval in which the target relative distance is located according to a preset segmentation rule; the target distance interval corresponds to a preset conversion relation between longitude and latitude and distance;
And determining the longitude and latitude of the target user when the target user is at the target acquisition time according to the conversion relation corresponding to the target distance interval and the longitude and latitude of the target probe.
The embodiment of the invention also provides a device for determining the population density of the scenic spots, which comprises the following steps:
the acquisition module is used for acquiring a plurality of user data collected by Wi-Fi probes deployed at all scenic spots;
the first determining module is used for determining the user positions of a plurality of users corresponding to the user data and the acquisition time corresponding to the user positions according to the user data and the probe positions of the Wi-Fi probes;
and the second determining module is used for determining population densities of all scenic spots at different moments according to the user positions and the acquisition time.
Optionally, the first determining module is configured to parse the target user data to obtain a signal strength indicator RSSI received by a target, a target acquisition time for collecting the target user data, and a target device code of a target Wi-Fi probe for collecting the target user data; acquiring a target probe position corresponding to the target equipment code; determining a target relative distance of a target user corresponding to target user data relative to the target Wi-Fi probe according to the target RSSI; and determining the target user position of the target user at the target acquisition time according to the target relative distance and the target probe position.
Optionally, the first determining module is configured to analyze a target frame header portion from the target user data, and determine a target instruction type corresponding to the target user data according to the target frame header portion; and when the target instruction type is the data instruction type, analyzing a target frame load part analyzed from the target user data.
Optionally, the apparatus further comprises:
and the caching module is used for caching the target RSSI, the target acquisition time and the target equipment code into a caching area after analyzing the target frame load part.
The first determining module is configured to determine, according to the target RSSI, a target relative distance of a target user corresponding to target user data with respect to the target Wi-Fi probe when the target RSSI, the target acquisition time, and a storage duration of the target device code in the cache area exceed a preset duration.
Optionally, the first determining module is configured to determine a target propagation loss according to the target RSSI; and determining the target relative distance according to the target propagation loss.
Optionally, the target probe position includes a target probe longitude and latitude, and the first determining module is configured to determine a target user position of the target user at the target acquisition time according to the target relative distance and the target probe longitude and latitude; the target user location includes a target user longitude and latitude.
Optionally, the first determining module is configured to determine, according to a preset segmentation rule, a target distance interval in which the target relative distance is located; the target distance interval corresponds to a preset conversion relation between longitude and latitude and distance; and determining the longitude and latitude of the target user when the target user is at the target acquisition time according to the conversion relation corresponding to the target distance interval and the longitude and latitude of the target probe.
The embodiment of the invention also provides electronic equipment, which comprises a processor, a memory and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the method for determining the population density of scenic spots when being executed by the processor.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the method for determining the population density of the scenic spots when being executed by a processor.
The embodiment of the invention has the following advantages:
in the embodiment of the invention, a plurality of user data collected by Wi-Fi probes deployed at all scenic spots are acquired; according to the user data and the probe positions of the Wi-Fi probes, determining the user positions of the users corresponding to the user data and the acquisition time corresponding to the user positions; and determining population densities of all scenic spots at different moments according to the user positions and the acquisition time. Compared with a method for calculating the people flow density condition after face recognition by adopting a camera, the embodiment of the invention is not limited by the mounting position of the Wi-Fi probe; and the population density can be determined only based on the user data, other information is not required to be additionally identified, and the problem of low accuracy in identifying some objects is solved.
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In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1a is a flow chart of steps of a method for determining population density of a attraction according to an embodiment of the present invention;
FIG. 1b is a schematic diagram of a method of determining residence time in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of steps of another method for determining population density of attractions according to one embodiment of the present invention;
FIG. 3 is a schematic diagram of a system for implementing a method for determining population density of a attraction according to an embodiment of the present invention;
FIG. 4 is a diagram of a data model of an embodiment of the present invention;
FIG. 5 is a schematic diagram of the locations of a Wi-Fi probe and a terminal according to an embodiment of the invention;
FIG. 6 is a schematic diagram of the location of another Wi-Fi probe and terminal of an embodiment of the invention;
FIG. 7 is a data set of an embodiment of the present invention;
FIG. 8 is a visual thermodynamic diagram of an embodiment of the invention;
Fig. 9 is a schematic structural diagram of a device for determining population density of scenic spots according to an embodiment of the invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to realize statistics of the visit population densities of different scenic spots in a scenic spot, one or more Wi-Fi probes can be arranged at the different scenic spots in advance; the one or more Wi-Fi probes may collect user data of a user visiting the attraction by acquiring signals sent by the device of the user visiting the attraction; the population density of the sights may then be determined based on the collected user data.
Compared with a method for calculating people flow density by adopting a camera to carry out face recognition later, the method for determining the people flow density of the scenic spots, provided by the embodiment of the invention, is based on the fact that Wi-Fi probes are not limited by installation positions any more, and can determine the people flow density only by user data, and the problem of low accuracy in identifying some objects does not exist because other information is not required to be identified additionally.
In addition, it should be noted that the one or more Wi-Fi probes are set after being recorded by the relevant departments; and the Wi-Fi probe collecting and using the user data is also performed after the user agrees.
Referring to fig. 1a, a step flow chart of a method for determining population density of scenic spots according to an embodiment of the invention may include the following steps:
step 101, acquiring a plurality of user data collected by Wi-Fi probes deployed at all scenic spots.
The user data may be user data generated by the Wi-Fi probe based on a signal sent/received by the user equipment when the user equipment performs data interaction with the Wi-Fi probe (or a signal transceiver such as a base station).
In practical application, when the visit population density of each scenic spot in a scenic spot needs to be determined, a plurality of user data collected by Wi-Fi probes can be acquired from Wi-Fi probes deployed at each scenic spot.
As an example, the frequency of acquiring the user data may be set according to a preset frequency, for example: acquired once an hour, or acquired in real time, so as to update the visit population density of each scenic spot in real time.
As another example, the setting position of the Wi-Fi probe may be set according to the actual situation; in order to ensure that the density of the visiting population of each scenic spot can be accurately determined, the density can be set according to the signal range of the Wi-Fi probes, so that the signal range of all Wi-Fi probes can cover any position of all scenic spots, and in order to realize that a user terminal can enter the signal range of one Wi-Fi probe from the other Wi-Fi probe in a seamless manner when the user moves, the embodiment of the invention is not limited in this way.
In practical application, the Wi-Fi probe can collect user data as long as the user equipment enters the signal range of the Wi-Fi probe; therefore, the method for determining the population density of the scenic spots provided by the embodiment of the invention is not limited by the installation position of the monitoring equipment.
Step 102, determining user positions of a plurality of users corresponding to the user data and acquisition time corresponding to the user positions according to the user data and probe positions of the Wi-Fi probes.
After obtaining a plurality of user data, the probe positions of the Wi-Fi probes can be obtained again; the probe location of the Wi-Fi probe may be the location of the Wi-Fi probe, which may be pre-collected and stored at the time of deployment of the Wi-Fi probe.
After the probe positions of the Wi-Fi probes and the plurality of user data are obtained, the user positions of the users corresponding to each user data can be determined according to the plurality of user data and the probe positions of the Wi-Fi probes, so that the user positions of the plurality of users corresponding to the plurality of user data can be determined.
When the user positions corresponding to the user positions are determined, the acquisition time corresponding to the user positions can be determined; that is, the time at which the Wi-Fi probe collects user data, the collection time may be used to indicate when the user enters a attraction; from two acquisition times that are adjacent one another, it is also possible to determine when the user left the spot, as shown in fig. 1b, and the length of residence in the spot and when the user left the spot can be determined based on two adjacent entry times.
Step 103, according to the user position and the acquisition time, determining population densities of all scenic spots at different moments.
After determining the user locations corresponding to the plurality of users and the acquisition time corresponding to each user location, the population density of each attraction at different moments may be determined based on the acquisition time and the user locations.
Specifically, it may be determined which attraction the user is at based on the user location; then, based on the acquisition time, determining when the user is at the attraction; based on the location information and the acquisition time of a plurality of users, the density of the visiting population of each scenic spot at different moments can be counted.
In the embodiment of the invention, a plurality of user data collected by Wi-Fi probes deployed at all scenic spots are acquired; according to the user data and the probe positions of the Wi-Fi probes, determining the user positions of the users corresponding to the user data and the acquisition time corresponding to the user positions; and determining population densities of all scenic spots at different moments according to the user positions and the acquisition time. Compared with a method for calculating the people flow density condition after face recognition by adopting a camera, the embodiment of the invention is not limited by the mounting position of the Wi-Fi probe; and the population density can be determined only based on the user data, other information is not required to be additionally identified, and the problem of low accuracy in identifying some objects is solved.
Referring to fig. 2, a flowchart illustrating steps of another method for determining population density of a sight spot according to an embodiment of the present invention may include the following steps:
step 201, acquiring a plurality of user data collected by Wi-Fi probes deployed at all scenic spots.
In practical application, when the visit population density of each scenic spot in a scenic spot needs to be determined, a plurality of user data collected by Wi-Fi probes can be acquired from Wi-Fi probes deployed at each scenic spot.
Step 202, analyzing the target user data to obtain a signal strength indication RSSI of target reception, a target acquisition time for collecting the target user data, and a target device code of a target Wi-Fi probe for collecting the target user data.
The target user data may be any one of the plurality of collected user data.
After obtaining the target user data, the target user data may be parsed to obtain a target RSSI (Received Signal Strength Indication ) recorded therein, a target acquisition time to collect the target user data, and a target device code of a target Wi-Fi probe to collect the target user data; the target device encoding may uniquely identify the target Wi-Fi probe.
In an embodiment of the present invention, the step of parsing the target user data may be implemented as the following sub-steps:
and step 11, analyzing a target frame head part from the target user data, and determining a target instruction type corresponding to the target user data according to the target frame head part.
First, a target frame header portion may be first analyzed from target user data, and a target instruction type corresponding to the target user data may be determined from the target frame header portion.
And a sub-step 12 of analyzing the target frame load part analyzed from the target user data when the target instruction type is the data instruction type.
If the target instruction type is the data instruction type, the target frame payload portion analyzed from the target user data may be parsed to obtain a target RSSI, a target acquisition time for collecting the target user data, and a target device code for a target Wi-Fi probe for collecting the target user data.
In another embodiment of the present invention, after parsing the target frame payload portion, the method may further include the following steps:
caching the target RSSI, the target acquisition time and the target equipment code into a cache area; when the target RSSI, the target acquisition time and the storage time of the target equipment code in the cache area exceed the preset time, the step of determining the target relative distance of the target user corresponding to the target user data relative to the target Wi-Fi probe according to the target RSSI is executed.
In practical application, in order to avoid the influence of the overlarge data amount on the data processing efficiency, after analyzing the target frame load part to obtain the target RSSI, the target acquisition time and the target equipment code, the analyzed data can be cached first; the data is then processed later according to the preset mechanism.
Specifically, the target RSSI, the target acquisition time, and the target device code may be cached in a cache area; then, the storage time of the data in the cache area is counted.
When the storage time of the data stored in the cache area exceeds a preset time, the data can be continuously processed; specifically, the step of determining the target relative distance of the target user corresponding to the target user data with respect to the target Wi-Fi probe according to the target RSSI may be continuously performed, which is not limited in the embodiment of the present invention.
Step 203, obtaining the target probe position corresponding to the target equipment code.
After the target equipment code is obtained, the target probe position of the target Wi-Fi probe corresponding to the target equipment code can be obtained; the target probe location may be pre-coded for the target device.
And 204, determining target propagation loss according to the target RSSI.
After the target RSSI is obtained, a target propagation loss may be determined based on the target RSSI.
Specifically, loss Los of electric wave propagation in free space is:
Los＝32.44+20lgD(Km)+20lgF(MHz) (1)
where Los is propagation loss in dBm; d is distance in Km; f is the operating frequency in MHz.
The Los theoretical calculation formula is:
los=transmit power+|receive sensitivity| (2)
However, the actual wireless communication is affected by various external factors, such as loss caused by the atmosphere, the blocking object, multipath, etc., and the approximate communication distance can be calculated by taking the reference value of the loss into the above equation. The actual Los value can therefore be calculated according to the following formula:
los=receive sensitivity+lna gain+tx power+antenna Gain-atmospheric attenuation (3)
However, specific data such as atmospheric attenuation cannot be obtained in an actual scene, so that the value of Los can be obtained through reverse calculation of the RSSI, and the calculation formula of the RSSI is as follows:
RSSI = transmit power + antenna gain at the transmitting end-propagation loss (Los) +antenna gain at the receiving end (4)
dBm＝10xlog(mW)。
Step 205, determining the target relative distance according to the target propagation loss.
After the target propagation loss is obtained, a target relative distance can be calculated based on the target propagation loss; specifically, the target relative distance may be calculated based on the formula (1).
After the target relative distance is obtained, verification can be performed based on the following formula:
d＝10^((abs(RSSI)-A)/(10*n)) (5)
wherein: d is the calculated distance (unit: m); RSSI is the received signal strength in decibel milliwatts (units: dbm); a is the signal intensity when the transmitting end and the receiving end are separated by 1 meter; n is the ambient attenuation factor.
Step 206, the target probe position comprises the longitude and latitude of the target probe, and the target user position of the target user at the target acquisition time is determined according to the target relative distance and the target probe longitude and latitude; the target user location includes the target user longitude and latitude.
After the target relative distance is obtained, the longitude and latitude of the target user where the target user is located can be calculated based on the target relative distance and the target probe position of the target Wi-Fi probe.
Specifically, the target probe position may include a target probe longitude and latitude; after the target relative distance is obtained, the longitude and latitude of the target user at the target acquisition time can be calculated based on the longitude and latitude of the target probe and the target relative distance.
In an embodiment of the present invention, the longitude and latitude of the target user can be determined by the following substeps:
step 21, determining a target distance interval in which the target relative distance is located according to a preset segmentation rule; the target distance interval corresponds to a preset conversion relation between longitude and latitude and distance.
First, a preset segmentation rule may be preset, for example: the relative distance is divided into five distance intervals of 0-1m, 1-10 m, 10-100 m, 100-1000 m and 1000-10000 m; then, after the target relative distance is obtained, a target distance section in which the target relative distance is located can be determined.
The conversion relationship between longitude and latitude and distance may be set for different distance intervals, for example:
in the case of equal latitudes:
longitude every 0.00001 degree, distance difference about 1 meter; longitude every 0.0001 degree, distance about 10 meters apart; longitude every 0.001 degrees, the distances differ by about 100 meters; longitude every 0.01 degree, distance is about 1000 meters apart;
every 0.1 degree, the distances differ by about 10000 meters.
In the case where the longitudes are equal:
the latitude is separated by about 1.1 meter at intervals of 0.00001 degrees; the latitude varies by about 11 meters every 0.0001 degree; the latitude is about 111 meters apart at 0.001 degree intervals; the latitude is about 1113 meters apart at intervals of 0.01 degrees; the latitude was separated by about 11132 meters every 0.1 degrees.
And step 22, determining the longitude and latitude of the target user at the target acquisition time according to the conversion relation corresponding to the target distance interval and the longitude and latitude of the target probe.
In practical application, the longitude and latitude of the target user at the target acquisition time can be calculated through the corresponding conversion relation of the target distance interval and the longitude and latitude of the target probe.
Step 207, determining population densities of all scenic spots at different moments according to the user positions and the acquisition time.
After determining the user locations corresponding to the plurality of users and the acquisition time corresponding to each user location, the population density of each attraction at different moments may be determined based on the acquisition time and the user locations.
Specifically, it may be determined which attraction the user is at based on the user location; then, based on the acquisition time, determining when the user is at the attraction; based on the location information and the acquisition time of a plurality of users, the density of the visiting population of each scenic spot at different moments can be counted.
As an example, the density of the visiting population of each sight spot may be displayed in a map based on the determined longitude and latitude of the user corresponding to each user, so that the staff may know the actual number of the visiting persons of each sight spot more clearly, so that the staff may make corresponding countermeasures, which is not limited by the embodiment of the present invention.
In the embodiment of the invention, a plurality of user data collected by Wi-Fi probes deployed at all scenic spots are acquired; analyzing the target user data to obtain signal strength indication (RSSI) received by a target, target acquisition time for collecting the target user data and target equipment codes of a target Wi-Fi probe for collecting the target user data; acquiring a target probe position corresponding to a target equipment code; determining target propagation loss according to the target RSSI; determining a target relative distance according to the target propagation loss; the target probe position comprises the longitude and latitude of the target probe, and the target user position of the target user at the target acquisition time is determined according to the target relative distance and the target probe longitude and latitude; the target user position comprises the longitude and latitude of the target user; and determining population densities of all scenic spots at different moments according to the user positions and the acquisition time. Compared with a method for calculating the people flow density condition after face recognition by adopting a camera, the embodiment of the invention is not limited by the mounting position of the Wi-Fi probe; and the population density can be determined only based on the user data, other information is not required to be additionally identified, and the problem of low accuracy in identifying some objects is solved.
For further explanation of the above-described method for determining population density of a attraction, the following is exemplified:
as shown in fig. 3, the method may be implemented based on a system, which may include a metadata acquisition unit, a Wi-Fi probe data analysis unit, a real-time distance calculation unit, a concentration calculation unit, a real-time difference calculation unit, and a terminal visual analysis unit.
The metadata acquisition unit is used for acquiring user data.
The Wi-Fi probe data analysis unit includes MAC (Media Access Control, medium access control) data protocol analysis, and the like.
The real-time distance calculating unit calculates the directional distance length between the terminal and the Wi-Fi probe according to the analyzed data through a constructed formula.
The concentration calculation unit comprises a collection of orientation distances between a plurality of terminals and Wi-Fi probe time, so that the density is obtained according to a fixed-length mode.
The real-time calculation time difference unit comprises the contents such as the time trend of the distance change between the terminal and the Wi-Fi probe.
The terminal visual analysis unit is used for displaying the live condition of the density of the sightseeing population.
The Wi-Fi probe data analysis unit comprises MAC data protocol analysis and analysis work aiming at frame data and frame load of data; specifically:
1. The processing flow of the unit comprises analysis of the MAC data protocol collected by the Wi-Fi probe. After collecting the data from the Wi-Fi probe, the server performs actions such as analysis, processing and storage.
2. Analyzing the frame header part to obtain an instruction type, analyzing the instruction, and analyzing the frame load when the instruction is the data instruction type; if it is other instruction, reply the answer message.
3. The frame load analysis work analyzes 190 bytes in total of the frame load part, and 190 bytes of contents such as equipment number, terminal MAC, acquisition time, RSSI, hot SSID (Service Set Identifier ), hot MAC, longitude and latitude coordinates, place number and the like are analyzed.
4. And the data processing is carried out, the content of the collected effective frame load is cached, when the content reaches 30 seconds, the cached data is sent to the data processing service, and the data processing service sinks to the storage unit.
The real-time distance calculating unit comprises RSSI data which are analyzed in a data analysis link, acquires the value and a corresponding MAC address from the storage unit, calculates according to a distance formula, and stores the data after obtaining an effective distance; specifically:
1. The corresponding MAC address and RSSI data value are obtained from the storage unit, one-to-one association relation is established by binding the MAC address and the RSSI data value, the corresponding association relation is stored in the cache, and a data model is established. The data model diagram is shown in figure 4.
2. And taking out the corresponding relation data of the MAC and the RSSI in the cache, and initializing the data, wherein the RSSI value is expanded by 10 times when the data is transmitted, so that the data needs to be divided by 10 to obtain the original value.
3. And (3) finishing initialization of the preparation data in the step (2), and calculating distance data.
4. Binding the Distance data into Distance parameters in the terminal model to form a complete terminal model portrait, and finishing data storage.
Referring to fig. 5, for a plurality of user data collected by a Wi-Fi probe 501, the RSSI corresponding to each user data is determined, so as to obtain a position relationship diagram (as in fig. 6) between the Wi-Fi probe 501 and the terminal 502 corresponding to different RSSI.
The concentration calculating unit comprises content such as concentration measurement and calculation according to the terminal data model.
Specifically:
1. and acquiring a terminal model data set from the cache data, and converting the data model into a JSON data format. The granularity reaches a single termination model.
2. The latitude and longitude of the device are set when the Wi-Fi probe is set, so that the same latitude or the same longitude can be known according to the direction of the directional antenna. Dividing the Distance parameters in the terminal model data set into five Distance sections of 0-1, 1-10, 10-100, 100-1000 and 1000-10000 according to longitude and latitude rules, and incorporating the Distance parameters in the terminal model data set into corresponding section sections according to the segmentation rules to form a new data set.
3. And (3) calculating longitude and latitude values of each terminal point position according to the following different calculation rules for the data set group obtained in the step (2). The rules are as follows:
a) In the case of equal latitudes:
longitude every 0.00001 degree, distance difference about 1 meter; every 0.0001 degree, the distances differ by about 10 meters; every 0.001 degree, the distances differ by about 100 meters; every 0.01 degree, the distances differ by about 1000 meters;
every 0.1 degree, the distances differ by about 10000 meters.
b) In the case where the longitudes are equal:
the latitude is separated by about 1.1 meter at intervals of 0.00001 degrees; every 0.0001 degree, the distances differ by about 11 meters; every 0.001 degree, the distances differ by about 111 meters; every 0.01 degree, the distances differ by about 1113 meters; every 0.1 degree, the distances differ by about 11132 meters.
And obtaining the longitude and latitude values of each terminal point position through the calculation rule, and storing the corresponding longitude and latitude values into corresponding fields in the terminal model.
The real-time calculation time difference unit comprises the contents of terminal association analysis, stay time length influence analysis and the like among the Wi-Fi probes. Specifically:
1. after the terminal data are processed by the unit, the terminal data have complete data structures and parameters, and indexes for processing the association analysis of the terminal include: the device number is the unique identification code of the Wi-Fi probe device, different device number records are formed by deploying a plurality of Wi-Fi probes, and a single data set is formed for the same terminal MAC, as shown in figure 7.
2. By arranging the entry time in the associated data set of the terminal MAC in ascending order, and then calculating the time length of the entry time among the data records, the residence time length in a certain equipment scene is formed, and seamless switching is realized as far as possible in the deployment depending on the equipment.
3. The data result in the step 2 is classified according to the hours to form terminal dense condition data of each time period, and meanwhile, the terminal dense condition data are displayed in a visual thermodynamic diagram mode, as shown in the figure 8; wherein the abscissa represents time, the ordinate represents scenic spot identification, and the number in the square represents density.
It should be noted that, for simplicity of description, the method embodiments are shown as a series of acts, but it should be understood by those skilled in the art that the embodiments are not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts are not necessarily required by the embodiments of the invention.
Referring to fig. 9, a schematic structural diagram of a determining device for population density of a scenic spot according to an embodiment of the invention may include the following modules:
an acquisition module 901, configured to acquire a plurality of user data collected by Wi-Fi probes deployed at each scenic spot;
the first determining module 902 is configured to determine, according to the plurality of user data and probe positions of each Wi-Fi probe, user positions of a plurality of users corresponding to the plurality of user data, and acquisition times corresponding to the user positions;
a second determining module 903, configured to determine population densities of the scenic spots at different moments according to the user location and the acquisition time.
In an alternative embodiment of the present invention, the first determining module 902 is configured to parse the target user data to obtain a signal strength indication RSSI of target reception, a target acquisition time for collecting the target user data, and a target device code of a target Wi-Fi probe for collecting the target user data; acquiring a target probe position corresponding to a target equipment code; determining a target relative distance of a target user corresponding to target user data relative to a target Wi-Fi probe according to the target RSSI; and determining the target user position of the target user at the target acquisition time according to the target relative distance and the target probe position.
In an alternative embodiment of the present invention, a first determining module 902 is configured to analyze a target frame header portion from target user data, and determine a target instruction type corresponding to the target user data according to the target frame header portion; and when the target instruction type is the data instruction type, analyzing the target frame load part analyzed from the target user data.
In an alternative embodiment of the present invention, the apparatus further comprises:
the buffer module is used for buffering the target RSSI, the target acquisition time and the target equipment code into a buffer area after analyzing the target frame load part;
The first determining module 902 is configured to determine, according to the target RSSI, a target relative distance of a target user corresponding to the target user data with respect to the target Wi-Fi probe when the target RSSI, the target acquisition time, and the storage duration of the target device code in the cache area exceed a preset duration.
In an alternative embodiment of the present invention, a first determining module 902 is configured to determine a target propagation loss according to a target RSSI; and determining the relative distance of the targets according to the propagation loss of the targets.
In an alternative embodiment of the present invention, the target probe position includes a target probe longitude and latitude, and the first determining module 902 is configured to determine, according to the target relative distance and the target probe longitude and latitude, a target user position of the target user at the target acquisition time; the target user location includes the target user longitude and latitude.
In an alternative embodiment of the present invention, a first determining module 902 is configured to determine, according to a preset segmentation rule, a target distance interval in which a target relative distance is located; the target distance interval corresponds to a preset conversion relation between longitude and latitude and distance; and determining the longitude and latitude of the target user when the target user is at the target acquisition time according to the conversion relation corresponding to the target distance interval and the longitude and latitude of the target probe.
In the embodiment of the invention, a plurality of user data collected by Wi-Fi probes deployed at all scenic spots are acquired; according to the user data and the probe positions of the Wi-Fi probes, determining the user positions of the users corresponding to the user data and the acquisition time corresponding to the user positions; and determining population densities of all scenic spots at different moments according to the user positions and the acquisition time. Compared with a method for calculating the people flow density condition after face recognition by adopting a camera, the embodiment of the invention is not limited by the mounting position of the Wi-Fi probe; and the population density can be determined only based on the user data, other information is not required to be additionally identified, and the problem of low accuracy in identifying some objects is solved.
The embodiment of the invention also provides electronic equipment, which comprises a processor, a memory and a computer program stored on the memory and capable of running on the processor, wherein the computer program is executed by the processor to realize the method for determining the population density of the scenic spots.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the method for determining the population density of the scenic spots when being executed by a processor.
For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
The above detailed description of a method, apparatus, electronic device and storage medium for determining population density of scenic spots provides, and specific examples are applied to illustrate the principles and embodiments of the present invention, and the above examples are only used to help understand the method and core idea of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims (10)

1. A method for determining population density of a attraction, the method comprising:
acquiring a plurality of user data collected by Wi-Fi probes deployed at all scenic spots;
determining user positions of a plurality of users corresponding to the user data and acquisition time corresponding to the user positions according to the user data and probe positions of the Wi-Fi probes;
and determining population densities of all scenic spots at different moments according to the user positions and the acquisition time.
2. The method of claim 1, wherein determining the user locations of the plurality of users corresponding to the plurality of user data and the acquisition time corresponding to each user location based on the plurality of user data and the probe locations of each Wi-Fi probe comprises:
analyzing target user data to obtain signal strength indication (RSSI) received by a target, target acquisition time for collecting the target user data, and target equipment codes of a target Wi-Fi probe for collecting the target user data;
acquiring a target probe position corresponding to the target equipment code;
determining a target relative distance of a target user corresponding to target user data relative to the target Wi-Fi probe according to the target RSSI;
And determining the target user position of the target user at the target acquisition time according to the target relative distance and the target probe position.
3. The method of claim 2, wherein parsing the target user data comprises:
analyzing a target frame head part from the target user data, and determining a target instruction type corresponding to the target user data according to the target frame head part;
and when the target instruction type is the data instruction type, analyzing a target frame load part analyzed from the target user data.
4. A method according to claim 3, wherein after parsing the target frame payload portion, the method further comprises:
caching the target RSSI, the target acquisition time and the target equipment code into a cache area;
and when the target RSSI, the target acquisition time and the storage time of the target equipment code in the cache area exceed preset time, executing the step of determining the target relative distance of the target user corresponding to the target user data relative to the target Wi-Fi probe according to the target RSSI.
5. The method of claim 2, wherein determining, according to the target RSSI, a target relative distance of a target user corresponding to target user data with respect to the target Wi-Fi probe comprises:
determining a target propagation loss according to the target RSSI;
and determining the target relative distance according to the target propagation loss.
6. The method of claim 2, wherein the target probe location comprises a target probe longitude and latitude, and wherein determining the target user location of the target user at the target acquisition time based on the target relative distance and the target probe location comprises:
determining a target user position of the target user at the target acquisition time according to the target relative distance and the target probe longitude and latitude; the target user location includes a target user longitude and latitude.
7. The method of claim 6, wherein determining the target user location of the target user at the target acquisition time based on the target relative distance and the target probe longitude and latitude comprises:
determining a target distance interval in which the target relative distance is located according to a preset segmentation rule; the target distance interval corresponds to a preset conversion relation between longitude and latitude and distance;
And determining the longitude and latitude of the target user when the target user is at the target acquisition time according to the conversion relation corresponding to the target distance interval and the longitude and latitude of the target probe.
8. A device for determining population density of a attraction, the device comprising:
the acquisition module is used for acquiring a plurality of user data collected by Wi-Fi probes deployed at all scenic spots;
the first determining module is used for determining the user positions of a plurality of users corresponding to the user data and the acquisition time corresponding to the user positions according to the user data and the probe positions of the Wi-Fi probes;
and the second determining module is used for determining population densities of all scenic spots at different moments according to the user positions and the acquisition time.
9. An electronic device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing a method of determining the population density of a attraction as claimed in any one of claims 1 to 7.
10. A computer readable storage medium, having stored thereon a computer program which when executed by a processor implements a method of determining a population density of attractions according to one of the claims 1 to 7.
CN202211648442.8A 2022-12-21 2022-12-21 Scenic spot population density determining method and device, electronic equipment and medium Pending CN116249071A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116630100A (en) * 2023-07-25 2023-08-22 珠海大横琴泛旅游发展有限公司 Travel data processing method, device, equipment and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116630100A (en) * 2023-07-25 2023-08-22 珠海大横琴泛旅游发展有限公司 Travel data processing method, device, equipment and storage medium
CN116630100B (en) * 2023-07-25 2024-01-30 珠海大横琴泛旅游发展有限公司 Travel data processing method, device, equipment and storage medium

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