CN112153568B

CN112153568B - Wi-Fi identification and binding method, device and equipment based on service scene

Info

Publication number: CN112153568B
Application number: CN202010890164.1A
Authority: CN
Inventors: 黄舟; 孙翔; 段航
Original assignee: Hanhai Information Technology Shanghai Co Ltd
Current assignee: Hanhai Information Technology Shanghai Co Ltd
Priority date: 2020-08-28
Filing date: 2020-08-28
Publication date: 2022-08-30
Anticipated expiration: 2040-08-28
Also published as: CN112153568A

Abstract

The invention provides a Wi-Fi identification method, a binding method, a device, electronic equipment and a storage medium based on a service scene, wherein the identification method comprises the following steps: acquiring service requests sent by a plurality of users with the service purpose of the same building, and sending a Wi-Fi list around each service request, planar polygon data of the building and a plurality of pieces of track data responding to the service requests; identifying the identification information of the building where the Wi-Fi sending the service request is located in each Wi-Fi list; processing each piece of track data in the plurality of pieces of track data, and selecting an area with the largest number of key points of each piece of track data as a two-dimensional Gaussian distribution center point; determining a building with the shortest boundary distance from the two-dimensional Gaussian distribution center point to the plane polygon data of the building as a target building; and binding the identification information of the building where the Wi-Fi sending the service request is positioned with the target building to obtain the Wi-Fi of the target building.

Description

Wi-Fi identification and binding method, device and equipment based on service scene

Technical Field

The invention relates to a network technology, in particular to a Wi-Fi identification method based on a service scene, a Wi-Fi binding method based on the service scene, a device, an electronic device and a storage medium.

Background

With the development of each service at present, the demand for the scene positioning capability is more and more strong. When the longitude and latitude of the user are obtained, the real scene where the user is located, that is, the building where the user is located, such as a supermarket, a community, a market, a building and the like, needs to be confirmed. In the related technology, indoor and outdoor judgment is continuously carried out by moving Wi-Fi signal receiving equipment, after a user enters a building, the Wi-Fi signal intensity is obtained by detecting signals of wireless routers in the building at different positions, and the distance between the equipment and each wireless router is calculated, so that the longitude and the latitude and the height of each wireless router are calculated. And then performing cluster analysis on the stored position data of all the wireless routers, determining each class as an independent building to be numbered according to the result of the cluster analysis, and storing the characteristics of the buildings. And then, when the user requests, comparing the building characteristics according to the information of the surrounding routers, and finishing the identification of the building.

However, in the related art, in order to complete the identification of the building with the building as a dimension, building features (latitude and longitude, height, and the like of the wireless router) must be manually collected inside the building. Thereby increasing labor costs. Furthermore, the position of the newly added router needs to be estimated by depending on information such as longitude and latitude, Wi-Fi signal strength and the like during indoor positioning, and due to the fact that the indoor satellite positioning result is greatly deviated due to factors such as being shielded by a building and the like, signals of the wireless router are also influenced by the building structure, the router model and the like, and Wi-Fi in actual life may frequently change, such as closing of a shop in a market, newly added shop, moving of Wi-Fi and the like, which can cause the change of the whole Wi-Fi environment, and accordingly inaccurate positioning is caused.

Therefore, how to identify Wi-Fi of a building in a real service scene and accurately position the building by using the Wi-Fi is a technical problem to be solved at present.

Disclosure of Invention

The invention provides a Wi-Fi identification method based on a service scene, a Wi-Fi binding method based on the service scene, a device, electronic equipment and a storage medium, which are used for at least solving the technical problem that in the related technology, due to the fact that a building is used as a dimension, information of a plurality of different wireless routers is collected through movement of mobile equipment in the building to establish building characteristics, and when the Wi-Fi environment changes, positioning is inaccurate. The technical scheme of the invention is as follows:

according to a first aspect of the embodiments of the present invention, a Wi-Fi identification method based on a service scenario is provided, which includes:

acquiring service requests sent by a plurality of users of the same building with service purposes, a Wi-Fi list around the service requests when each service request is sent, planar polygon data of the building and a plurality of pieces of track data responding to the service requests;

identifying the identification information of the building where the Wi-Fi in each Wi-Fi list sending the service request is located; processing each piece of track data in the plurality of pieces of track data, and selecting an area with the largest number of key points in each piece of track data as a two-dimensional Gaussian distribution center point;

determining a building with the shortest boundary distance from the two-dimensional Gaussian distribution center point to the plane polygon data of the building as a target building;

and binding the identification information of the building where the Wi-Fi sending the service request is located with the target building to obtain the Wi-Fi of the target building.

Optionally, the processing each piece of trajectory data in the plurality of pieces of trajectory data, and selecting the region with the largest number of key points in each piece of trajectory data as the two-dimensional gaussian distribution center point includes:

preprocessing each piece of track data in the plurality of pieces of track data to obtain key point data of the plurality of tracks;

and aggregating the key point data of the plurality of tracks, and selecting the area with the largest number of key points as a two-dimensional Gaussian distribution center point.

Optionally, the preprocessing each of the plurality of pieces of trajectory data to obtain the key point data of the plurality of trajectories includes:

selecting track points meeting preset conditions from each piece of track data;

judging whether each track point is an abnormal track point, if so, extracting a normal track point which is in front of the abnormal track point and adjacent to the abnormal track point;

and deleting the abnormal track points from the track points to obtain normal track points, and using the normal track points as track key points in the corresponding track data.

Optionally, the aggregating the key point data of the plurality of tracks, and selecting the region with the largest number of key points as the two-dimensional gaussian distribution center point, includes:

acquiring a geohash block where the longitude and latitude of a track key point of each track data are located;

mapping the track key points in each track data to corresponding geohash blocks;

counting the number of key points in each geohash block;

extracting a geohash block where all track key points are located, wherein all the extracted geohash blocks form a two-dimensional rectangular plane;

respectively taking the numerical values of two sides of the two-dimensional rectangular plane as an x axis and a y axis, and taking the number of the key points in the geohash block as the numerical value of a z axis to establish a three-dimensional coordinate system;

determining mixed two-dimensional Gaussian distribution of the number distribution of key points of the fitting track in the three-dimensional coordinate system;

determining a central area of a central point of the mixed two-dimensional Gaussian distribution on the two-dimensional rectangular plane;

and selecting the area with the maximum number of the track key points in the central area as a two-dimensional Gaussian distribution central point.

According to a second aspect of the embodiments of the present invention, there is provided a Wi-Fi binding method based on a service scenario, including:

when detecting that a user sends a service request, acquiring Wi-Fi for sending the service request; wherein the service request comprises: an indoor positioning request, a service delivery request or a rendering request of a building and a point of interest (POI) during map display;

searching whether a building corresponding to the Wi-Fi exists in a database;

and if the building corresponding to the Wi-Fi exists, binding the Wi-Fi with the building so as to provide building-level route navigation for the service request.

Optionally, the method further includes:

if the building corresponding to the Wi-Fi does not exist in the database, acquiring a service destination address for sending the service request;

determining a target building corresponding to the Wi-Fi according to the destination address of the server;

and binding the Wi-Fi with the determined target building so as to provide building-level route navigation for the service request.

Optionally, the determining, according to the service destination address, a target building corresponding to the Wi-Fi includes:

acquiring service requests sent by a plurality of other users of the same building where the service destination address is located, and a Wi-Fi list around the service request when each service request is sent; boundary information of all building data, and a plurality of pieces of trajectory data in response to the configuration request;

identifying building address information where each Wi-Fi in each Wi-Fi list is located;

aggregating the key point data of the plurality of tracks, and selecting the area with the largest number of key points as a two-dimensional Gaussian distribution center point;

judging that the building with the shortest boundary distance from the two-dimensional Gaussian distribution center point to the building data is a target building;

and binding the Wi-Fi of the service request with the target building to obtain the Wi-Fi of the target building.

According to a third aspect of the embodiments of the present invention, there is provided a Wi-Fi recognition apparatus based on a service scenario, including:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for executing the acquisition of service requests sent by a plurality of users of the same building for service purpose, and the Wi-Fi lists around the service requests, the planar polygon data of the building and a plurality of track data responding to the service requests when each service request is sent;

the identification module is used for identifying the identification information of the building where the Wi-Fi sending the service request is located in each Wi-Fi list;

the processing module is used for processing each piece of track data in the plurality of pieces of track data, and selecting the area with the largest number of key points in each piece of track data as a two-dimensional Gaussian distribution center point;

the first determining module is used for determining the building with the shortest boundary distance from the two-dimensional Gaussian distribution center point to the plane polygon data of the building as a target building;

and the binding module is used for binding the identification information of the building where the Wi-Fi sending the service request is located with the target building to obtain the Wi-Fi of the target building.

Optionally, the processing module includes:

the preprocessing module is used for preprocessing each piece of track data in the plurality of pieces of track data to obtain key point data of the plurality of tracks;

and the aggregation processing module is used for performing aggregation processing on the key point data of the plurality of tracks and selecting the area with the largest number of key points as a two-dimensional Gaussian distribution center point.

Optionally, the preprocessing module includes:

the first selection module is used for selecting track points meeting preset conditions from each piece of track data;

the judging module is used for judging whether each track point is an abnormal track point;

the first extraction module is used for extracting a normal track point which is in front of the abnormal track point and is adjacent to the abnormal track point when the judgment module judges that the track point is the abnormal track point;

and the deleting module is used for deleting the abnormal track points from the track points to obtain normal track points, and using the normal track points as track key points in the corresponding track data.

Optionally, the aggregating process includes:

the second acquisition module is used for acquiring a geohash block in which the longitude and latitude of the track key point of each piece of track data are located;

the mapping module is used for mapping the track key points in each piece of track data to the corresponding geohash block;

the statistical module is used for counting the number of key points in each geohash;

the second extraction module is used for extracting the geohash blocks where all the track key points are located, and all the extracted geohash blocks form a two-dimensional rectangular plane;

the establishing module is used for respectively taking the numerical values of the two sides of the two-dimensional rectangular plane as an x axis and a y axis and taking the number of the key points in the geohash block as the numerical value of a z axis to establish a three-dimensional coordinate system;

the second determination module is used for determining the mixed two-dimensional Gaussian distribution of the number distribution of the key points of the fitting track in the three-dimensional coordinate system;

a third determining module, configured to determine a central area of a central point of the mixed two-dimensional gaussian distribution on the two-dimensional rectangular plane;

and the second selection module is used for selecting the two-dimensional Gaussian distribution central point with the largest number of the track key points in the central area.

According to a fourth aspect disclosed in the embodiments of the present invention, there is provided a Wi-Fi binding apparatus based on a service scenario, including:

the first acquisition module is used for acquiring Wi-Fi for sending a service request when detecting that a user sends the service request; wherein the service request comprises: an indoor positioning request, a service delivery request or a rendering request of a building and a point of interest (POI) during map display;

the searching module is used for searching whether a building corresponding to the Wi-Fi is stored in a database;

the first binding module is used for binding the Wi-Fi with the building when the searching module finds the building corresponding to the Wi-Fi, so that building-level route navigation is provided for the service request.

Optionally, the apparatus further comprises:

the second obtaining module is used for obtaining a service destination address for sending the service request when the searching module does not search the building corresponding to the Wi-Fi in the database;

the determining module is used for determining a target building corresponding to the Wi-Fi according to the destination address of the server;

and the second binding module is used for binding the Wi-Fi with the target building determined by the determination module so as to provide building-level route navigation for the service request.

Optionally, the determining module includes:

a third obtaining module, configured to obtain service requests sent by multiple other users in the same building where the service destination address is located, and a Wi-Fi list around the service request when the service request is sent; boundary information of all building data, and a plurality of pieces of trajectory data in response to the configuration request;

the identification module is used for identifying the building address information where each Wi-Fi in each Wi-Fi list is located;

and the building determining module is used for determining the building with the shortest boundary distance from the two-dimensional Gaussian distribution center point to the building data as a target building.

According to a fifth aspect provided by an embodiment of the present invention, there is provided an electronic apparatus, including:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement a traffic scenario based Wi-Fi recognition method as described above or a traffic scenario based Wi-Fi binding method as described above.

According to a fifth aspect provided by an embodiment of the present invention, there is provided a storage medium, wherein instructions, when executed by a processor of an electronic device, enable the electronic device to execute the Wi-Fi recognition method based on service scenario or the Wi-Fi binding method based on service scenario described above.

According to a sixth aspect of embodiments of the present invention, there is provided a computer program product according to instructions of which, when executed by a processor of an electronic device, cause the electronic device to perform the service scenario based Wi-Fi identification method as described above or the service scenario based Wi-Fi binding method as described above.

The technical scheme provided by the embodiment of the invention at least has the following beneficial effects:

in the embodiment of the invention, firstly, service requests sent by a plurality of users with the service purpose of the same building are obtained, a Wi-Fi list around each service request is sent, planar polygon data of the building and a plurality of pieces of track data responding to the service requests are obtained; then, identifying the identification information of the building where the Wi-Fi sending the service request is located in each Wi-Fi list; processing each piece of track data in the plurality of pieces of track data, and selecting an area with the largest number of key points in each piece of track data as a two-dimensional Gaussian distribution center point; then, determining the building with the shortest boundary distance from the two-dimensional Gaussian distribution center point to the plane polygon data of the building as a target building; and finally, binding the identification information of the building where the Wi-Fi sending the service request is located with the target building to obtain the Wi-Fi of the target building. That is to say, in the embodiment of the present invention, focusing is performed on a home delivery scene, using Wi-Fi carried by a buyer service request and multiple pieces of trajectory data of multiple deliverers, determining a target building through comprehensive judgment such as trajectory key point extraction and fitting of two-dimensional gaussian distribution in the trajectory data, and binding identification information of the building where the Wi-Fi that sent the service request is located with the target building, thereby implementing mining of the building Wi-Fi. The invention does not need extra data collection work and directly uses the existing service data and track data. Indoor and outdoor judgment on positioning points of the mobile equipment is not needed, positioning precision is improved, and positioning error risks are reduced.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention and are not to be construed as limiting the invention.

Fig. 1 is a flowchart of a Wi-Fi recognition method based on a service scenario according to an embodiment of the present invention.

Fig. 2 is a schematic diagram of an original trajectory point of a distributor according to an embodiment of the present invention.

Fig. 3 is a schematic diagram of deleting an abnormal track point from an original track point according to an embodiment of the present invention.

Fig. 4 is a schematic diagram of a track key point according to an embodiment of the present invention.

Fig. 5 is a schematic diagram of a map divided into multiple grids by a geohash with a length of 9 according to an embodiment of the present invention.

Fig. 6 is a schematic diagram of establishing a three-dimensional coordinate system according to an embodiment of the present invention.

Fig. 7 is a flowchart of a Wi-Fi binding method based on a service scenario according to an embodiment of the present invention.

Fig. 8 is another flowchart of a Wi-Fi binding method based on a service scenario according to an embodiment of the present invention.

Fig. 9 is a block diagram of a Wi-Fi recognition apparatus based on a service scenario according to an embodiment of the present invention.

Fig. 10 is a block diagram of a Wi-Fi binding apparatus based on a service scenario according to an embodiment of the present invention.

Fig. 11 is a block diagram of an electronic device according to an embodiment of the present invention.

Fig. 12 is a block diagram of an apparatus for Wi-Fi identification based on a service scenario or Wi-Fi binding based on a service scenario according to an embodiment of the present invention.

Detailed Description

In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.

Fig. 1 is a flowchart illustrating a Wi-Fi recognition method based on a service scenario, which is used in a terminal as shown in fig. 1, and according to an exemplary embodiment, includes the following steps:

in step 101, acquiring service requests sent by a plurality of users with the service purpose of the same building, a Wi-Fi list around the time of sending each service request, planar polygon data of the building, and a plurality of pieces of track data responding to the service requests;

in step 102, identifying the identification information of the building where the Wi-Fi in each Wi-Fi list sending the service request is located; processing each piece of track data in the plurality of pieces of track data, and selecting an area with the largest number of key points in each piece of track data as a two-dimensional Gaussian distribution center point;

in step 103, determining a building with the shortest boundary distance from the center point of the two-dimensional Gaussian distribution to the plane polygon data of the building as a target building;

in step 104, the identification information of the building where the Wi-Fi that sent the service request is located is bound with the target building, so as to obtain the Wi-Fi of the target building.

The Wi-Fi identification method based on the service scene can be applied to terminals, servers and the like without limitation, and the terminal implementation equipment can be electronic equipment such as a smart phone, a notebook computer, a tablet computer and the like without limitation.

The following describes in detail specific implementation steps of a Wi-Fi identification method based on a service scenario according to an embodiment of the present invention with reference to fig. 1.

First, step 101 is executed to obtain service requests sent by a plurality of users with service purposes in the same building, and a Wi-Fi list around each service request, planar polygon data of the building, and a plurality of pieces of track data responding to the service requests.

In this step, the service purpose is a destination of the service, for example, a building or the like whose delivery destination is the same. That is, it is necessary to obtain service requests sent by a plurality of users having the same delivery destination and a plurality of pieces of trajectory data of a plurality of configurators responding to the service requests, and of course, it is also necessary to obtain a Wi-Fi list around when each service request is sent, and each Wi-Fi list may include one or more Wi-Fi. Specifically, in this step, three types of data need to be acquired, and for convenience of description, the service request in this embodiment is a delivery request, but in practical application, the service request is not limited to this.

The first type of data is data associated with the buyer. Including the Wi-Fi listing information around when the buyer initiated the delivery request. Specifically, for example, in a scenario where a user orders take a take-out in building a, the user requests positioning when placing an order, and therefore when a take-out order request initiated by the user is detected in the background, Wi-Fi information around the take-out order request sent by the user can be acquired. It should be noted that the present embodiment generally selects those requests that originate from inside the building.

The second type of data is trajectory data associated with a plurality of dispatchers. That is, each distributor forms a piece of track data in the process of distributing for the buyer, and the track data generally consists of a GPS positioning point and has the attributes of positioning longitude and latitude, precision, speed, time and the like. In this embodiment, a plurality of pieces of trajectory data of the deliverer who delivers goods to the same destination are selected.

The third type of data is data relating to the building in which the buyer is located. The building-related data is two-dimensional plane data of the building on a map, that is, only the fence attribute related to the present invention in the building-related data, but of course, the building-related data may also include a height attribute, and thus the height attribute is not related to the present invention and will not be described herein. The fence attribute refers to boundary information of a building and is a plane polygon formed by longitude and latitude points. In the invention, the acquired building attributes are combined with other information (such as Wi-Fi for initiating a delivery request, trajectory data of a deliverer and the like) and applied to each service scene.

It should be noted that the embodiment of the present invention is based on big data, and a large amount of such data is acquired instead of a delivery request + the data of the trajectory of the deliverer. Specifically, when a plurality of delivery destinations are the same, the destinations are considered to be the same building, and WI-FI mining is performed based on a large amount of trajectory data.

Secondly, step 102 is executed, identification information of a building where the Wi-Fi sending the service request is located in each Wi-Fi list is identified, each piece of track data is processed, and the area with the largest number of key points in each piece of track data is selected as a two-dimensional Gaussian distribution center point.

In this step, the background may identify identification Information (ID) of a building where each Wi-Fi in each Wi-Fi list is located, that is, by monitoring network information of the user, address information of the user according to the network, that is, information of the building where the user is located may be obtained. For example, what street and how many buildings in what area building a is, etc. The specific identification process is well known in the art and will not be described herein.

In this step, each piece of trajectory data in the plurality of pieces of trajectory data is processed, and the region with the largest number of key points in each piece of trajectory data is selected as the two-dimensional gaussian distribution center point, where the specific processing procedure includes:

preprocessing each piece of track data in the plurality of pieces of track data to obtain key point data of the plurality of tracks; and aggregating the key point data of the plurality of tracks, and selecting the area with the largest number of key points as a two-dimensional Gaussian distribution center.

In the embodiment, each of the plurality of pieces of trajectory data is formed using a plurality of continuous positioning points given by GPS satellites. For example, after a distributor enters a building, the satellite information may not be obtained due to the shielding of the building, and thus a positioning result may not be obtained. In addition, when the distributor approaches to the building, the positioning result generally has a large drift due to the multipath effect of the signal, and the confidence of the positioning result is also reduced. As shown in fig. 2, fig. 2 is a schematic diagram of an original trajectory point of a distributor according to an embodiment of the present invention, and as shown in fig. 2, before the distributor enters a certain cell and after the distributor leaves the certain cell, the trajectory of the distributor is relatively smooth and relatively matched with a road network. However, after the dispatcher enters the cell, the positioning points are distributed in a messy way due to the multipath effect and are not already in the track. In the embodiment of the invention, before the data is used, the data corresponding to the original track point is processed, and the purpose of processing is to extract the normal track point adjacent to the abnormal track point.

In this step, the pretreatment process may include:

the background selects track points meeting preset conditions from each piece of track data; then, judging whether each track point is an abnormal track point, if so, extracting a normal track point which is in front of the abnormal track point and is adjacent to the abnormal track point; and then deleting the abnormal track points from the track points to obtain normal track points, and taking the normal track points as track key points in the corresponding track data. The track points in the invention are usually GPS positioning points, and the GPS positioning points have the following attributes: positioning longitude and latitude, precision, speed and time. The method specifically comprises the following steps:

1) firstly, selecting track points meeting preset conditions from each piece of track data, wherein the preset conditions at least comprise one of the following conditions, and the precision between two continuous GPS positioning points is smaller than a threshold value g; the distance between the two GPS positioning points is smaller than a threshold d, the time difference between the two GPS positioning points is smaller than a threshold t, and the variance between the average speed between the two GPS positioning points and the first n average speeds does not exceed a threshold delta. And n is the number of GPS positioning points of each piece of track data. Of course, in practical applications, the method is not limited to this, and other GPS positioning points meeting preset conditions may also be adaptively included. The invention refers to the track formed by the track points as a compliant track. Wherein the compliance trajectory is divided into a forward compliance trajectory and a reverse compliance trajectory. Firstly, determining a forward compliance track, recording the last GPS positioning point p as a candidate key point, and setting a forward counter countp to be 0. The backward compliance trace is set to null and the backward counter countn is 0. Continuing to judge the next GPS positioning point p;

2) and continuously judging whether the next GPS positioning point p meets the preset condition. If the GPS positioning point p meets the preset condition in the step 1), the GPS positioning point p is called as a normal track point, and the step 3) is carried out. If the GPS positioning point p does not meet the preset condition in 1), the GPS positioning point p is called as an abnormal track point, a forward counter is +1, and the current candidate key point is set as a determined key point, and the step 4) is carried out;

3) if the forward counter is smaller than the threshold c1, adding the GPS positioning point p into the forward track, setting the GPS positioning point p as a new candidate key point, setting the current reverse track as empty and the reverse counter as 0, continuously judging the next GPS positioning point p, and entering the step 2); otherwise, entering step 4);

4) calculating a reverse track taking the current next GPS positioning point p as an end point, if a reverse compliance track appears, selecting the end point of the forward compliance track and the start point of the reverse compliance track as a pair of key points of the track, regarding the reverse compliance track as a forward direction, emptying the reverse track and a reverse counter, continuing the next GPS positioning point p, and re-entering the step 2) until the whole track is finished; if a reverse compliance track with p as a key point does not appear, adding p into the reverse track, reversing the counter to be +1, judging the next GPS positioning point p again, and entering the step 2);

5) and repeating the process until all the key points are judged.

6) And after the judgment is finished, namely when the whole track is finished, extracting all key points to serve as a subsequent excavation basis. Namely, extracting normal track points which are in front of the abnormal track points and are adjacent to the abnormal track points;

7) deleting the abnormal track points from the track points (namely the original track points) to obtain normal track points; specifically, as shown in fig. 3, a schematic diagram for deleting an abnormal track point from an original track point is provided in the embodiment of the present invention.

8) And taking the normal track points as track key points in the corresponding track data. Specifically, as shown in fig. 4, a schematic diagram of a track key point provided in an embodiment of the present invention is shown.

As can be seen from fig. 2, fig. 3 and fig. 4, fig. 2 shows the original trajectory without any processing, and it can be seen that some sharp abnormal trajectory points appear in the middle; FIG. 3 shows the trace after the abnormal trace points are deleted, and it can be seen that the trace is much smoother; the normal track points marked by two circles shown in fig. 4 are found key points of the track.

Performing aggregation processing on the key point data of the plurality of tracks, and selecting an area with the largest number of key points as a two-dimensional Gaussian distribution center point; the method specifically comprises the following steps:

acquiring a geohash block where the longitude and latitude of a track key point of each track data are located; mapping the track key points in each track data to corresponding geohash blocks; counting the number of key points in each geohash; extracting a geohash block where all track key points are located, wherein all the extracted geohash blocks form a two-dimensional rectangular plane; respectively taking two sides of the two-dimensional rectangular plane as an x axis and a y axis, and taking the number of key points in the geohash block as a z axis to establish a three-dimensional coordinate system; determining mixed two-dimensional Gaussian distribution of the number distribution of key points of the fitting track in the three-dimensional coordinate system; determining a central area of a central point of the mixed two-dimensional Gaussian distribution on the two-dimensional rectangular plane; and selecting the two-dimensional Gaussian distribution center point with the maximum number of the track key points in the center area.

That is to say, in this step, the track key points are aggregated specifically by the following method:

1) firstly, a geohash block where the longitude and latitude of a track key point are located is obtained. The geohash block is a general technology for geographically representing a rectangular geographic plane space, and may be represented by a string of characters, as shown in fig. 5, where fig. 5 is a schematic diagram of dividing a map into multiple grids by using a geohash with a length of 9 according to an embodiment of the present invention. Wherein, the geohash block with the character length of 9 is about a square with the side length of 5 meters, the invention selects the example that the geohash with the character length of 9 divides the map into a plurality of grids.

It should be noted that GeoHash is a method for converting two-dimensional longitude and latitude into a character string, and in general, the more the prefix matching of the character string, the closer the distance. As shown in fig. 5, the GeoHash strings of 9 beijing regions, WX4ER, WX4G2, WX4G3, etc., are shown, and each string represents a rectangular region. That is, all points (longitude and latitude coordinates) in the rectangular area share the same GeoHash character string, so that privacy can be protected (only approximate area positions are represented instead of specific points), and caching is easier. Different coding lengths represent different range intervals, and the longer the character string, the more precise the range is represented. The character strings are similar in representation distance, so that the POI information nearby can be inquired by prefix matching of the character strings.

2) And aiming at a plurality of pieces of track data of the same destination, after mapping the track key points in each piece of track data to the geohash block, counting the number of key points in each geohash block.

3) In this case, the two-dimensional planar map may be regarded as a large mesh divided by a plurality of geohash blocks. Intercepting a part of the geohash block containing all track key points to form a two-dimensional rectangular plane, respectively taking values of two side lengths of the two-dimensional rectangular plane as an x axis and a y axis, and taking the number of the key points in the geohash block as a value of a z axis to establish a three-dimensional coordinate system, wherein the established three-dimensional coordinate system is shown in fig. 6, and fig. 6 is a schematic diagram for establishing the three-dimensional coordinate system provided by the embodiment of the invention.

4) In the three-dimensional coordinate system, a mixed two-dimensional gaussian distribution fitting the distribution of the number of the key points of the trajectory is found, and an example of the two-dimensional gaussian distribution is shown in fig. 6. And determining a region which is closer to the central point of the two-dimensional Gaussian distribution in the xy plane (the two-dimensional map divided by the geohash block) as a central region according to the threshold k. For example, the vertical projection point of the two-dimensional gaussian distribution center point on the xy plane is (x1, y1), and a plane C (x, y) is determined, where the plane C (x, y) satisfies:

and | x-x1| ≦ k and | y-y1| ≦ k.

And selecting a region with the maximum number of the central regions C including the key points of the tracks as a two-dimensional Gaussian distribution central point, namely a target two-dimensional Gaussian distribution central point, in all the two-dimensional Gaussian distributions.

The obtained target two-dimensional gaussian distribution center geohash, plane area C (x, y), is used for the determination of the target building in the following steps.

Thirdly, executing step 103, and determining the building with the shortest boundary distance corresponding to the planar polygon data from the geohash to the building as a target building;

in this step, in step 101, the plane polygon data (which may also be referred to as building data) acquired from the building only includes the fence and height attributes. In step 102, a two-dimensional gaussian distribution center geohash is calculated for a destination building, and in this step, only a building closest to the geohash needs to be determined, that is, the target building. The specific determination process is well known to those skilled in the art and will not be described herein.

Finally, step 104 is executed to bind the identification information of the building where the Wi-Fi sending the service request is located with the target building, so as to obtain the Wi-Fi of the target building.

In this step, after the target building is obtained, when the delivery destinations requested by the buyer are the same (the buyer can determine that the delivery destinations are the same building), the Wi-Fi at the time of initiating the service request may be bound to the target building, that is, the Wi-Fi of the building is determined.

Further, the corresponding relation between the buildings corresponding to the Wi-Fi can be stored in a database.

In the embodiment of the invention, firstly, service requests sent by a plurality of users with the service purposes of the same building are obtained, a surrounding Wi-Fi list is sent when each service request is sent, planar polygon data of the building and a plurality of pieces of track data responding to the service requests are obtained; then, identifying the identification information of the building where the Wi-Fi sending the service request is located in each Wi-Fi list; processing each piece of track data in the plurality of pieces of track data, and selecting an area with the largest number of key points in each piece of track data as a two-dimensional Gaussian distribution center point; then, determining the building with the shortest boundary distance from the two-dimensional Gaussian distribution center point to the corresponding plane polygon data of the building as a target building; and finally, binding the identification information of the building where the Wi-Fi sending the service request is located with the target building to obtain the Wi-Fi of the target building. That is to say, in the embodiment of the present invention, focusing is performed on a home delivery scene, using Wi-Fi carried by a buyer service request and multiple pieces of trajectory data of multiple deliverers, determining a target building through comprehensive judgment such as trajectory key point extraction and fitting of two-dimensional gaussian distribution in the trajectory data, and binding identification information of the building where the Wi-Fi that sent the service request is located with the target building, thereby implementing mining of the building Wi-Fi. The invention does not need extra data collection work and directly uses the existing business data and track data. Indoor and outdoor judgment on positioning points of the mobile equipment is not needed, positioning precision is improved, and positioning error risks are reduced.

Referring to fig. 7, a flowchart of a Wi-Fi binding method based on a service scenario provided in an embodiment of the present invention is shown, where the method includes:

step 701: when detecting that a user sends a service request, acquiring Wi-Fi for sending the service request; wherein the service request comprises: an indoor positioning request, a service request or a rendering request of a building and a point of interest (POI) during map display;

step 702: judging whether a building corresponding to the Wi-Fi is stored in a database;

in the step, whether a building corresponding to the Wi-Fi is stored in a database is inquired in the future, if the building corresponding to the Wi-Fi is stored in the database, the building is directly bound, and if the building corresponding to the Wi-Fi is not stored in the database, the building corresponding to the Wi-Fi is determined again, and the corresponding relation of the building is stored in the database.

Step 703: and if the building corresponding to the Wi-Fi exists, binding the Wi-Fi with the building so as to provide building-level route navigation for the service request.

In the embodiment of the invention, after the Wi-Fi of the excavated building is excavated, the method can be used for a plurality of projects, and the following description will be given by taking the application to three projects as an example:

1) indoor positioning items: when a user requests positioning, the user carries a Wi-Fi list, a building where each Wi-Fi in the Wi-Fi list is located is identified, then a target building where the user is located is calculated according to the technical scheme provided in the embodiment, then the WiFi which initiates the positioning request and the target building are bound, and a more accurate positioning result is provided for the WiFi, such as direct positioning to a specific building in a cell.

2) Path planning applied to the dispatchers: the process is similar to the application in 1), when a distributor distributes goods for buyers, whether a building corresponding to the Wi-Fi is stored in a database is judged; if the Wi-Fi is in the building, the Wi-Fi and the building are directly bound, accurate route navigation of the destination building level is provided for a distributor, and the distribution efficiency of the distributor is improved.

3) The method is applied to rendering of buildings and points of interest (POI) in map display: when a map is displayed, the following may occur. POI such as "a period of 3 blocks" do not perfectly fit with its corresponding building. When viewed by a user, the user experience is greatly influenced. If after the relationship mining from Wi-Fi to the building is realized, the relationship mining from the POI to the building can be realized by combining with other works such as portrayal from Wi-Fi to the POI, the relationship mining from the POI to the building can be realized by taking Wi-Fi as a bridge, the POI coordinates can be corrected, the rendering is supported to be more accurate, and the upstream service can be supported by the more accurate POI coordinates.

Referring to fig. 8, another flowchart of a Wi-Fi binding method based on a service scenario according to an embodiment of the present invention is shown, where the method includes:

step 801: when detecting that a user sends a service request, acquiring Wi-Fi for sending the service request; wherein the service request comprises: an indoor positioning request, a service request or a rendering request of a building and a point of interest (POI) during map display;

step 802: searching whether a building corresponding to the Wi-Fi exists in a database; if yes, go to step 803; otherwise, go to step 804;

step 803: binding the Wi-Fi with the building to provide building-level route navigation for the service request;

step 804: acquiring a service destination address for sending the service request;

step 805: determining a target building corresponding to the Wi-Fi according to the destination address of the server;

in the step, firstly, service requests sent by a plurality of other users of the same building where the service destination address is located and a surrounding Wi-Fi list when each service request is sent are obtained; boundary information of all building data, and a plurality of pieces of trajectory data in response to the configuration request; identifying building address information where each Wi-Fi in each Wi-Fi list is located; preprocessing each piece of track data in the plurality of pieces of track data to obtain key point data of the plurality of tracks; aggregating the key point data of the plurality of tracks, and selecting the area with the largest number of key points as a two-dimensional Gaussian distribution center point; judging that the building with the shortest boundary distance from the two-dimensional Gaussian distribution center point to the building data is a target building; and binding the Wi-Fi of the service request with the target building to obtain the Wi-Fi of the target building.

The specific implementation process of each step is detailed in the implementation process of the corresponding step in the above method embodiment, and is not described herein again.

Step 806: and binding the Wi-Fi with the determined target building so as to provide building-level route navigation for the service request.

In the embodiment of the invention, when a user is detected to send a service request, the Wi-Fi sending the service request is obtained, and then whether a building corresponding to the Wi-Fi exists in a database is judged; if the Wi-Fi exists in the positioning position, the Wi-Fi is directly bound with the building, otherwise, the target building corresponding to the Wi-Fi is directly determined, namely, the embodiment of the invention directly uses the existing service data and track data without extra data collection work, and does not need to carry out indoor and outdoor judgment on the positioning point of the mobile equipment, thereby improving the positioning precision and reducing the risk of positioning errors.

It is noted that while for simplicity of explanation, the method embodiments are shown as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may occur in other orders or concurrently with other steps in accordance with the invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.

Fig. 9 is a block diagram of a Wi-Fi recognition apparatus based on a service scenario according to an embodiment of the present invention. Referring to fig. 9, the apparatus includes: a first obtaining module 901, an identifying module 902, a processing module 903, a first determining module 904 and a binding module 905. Wherein,

the first obtaining module 901 is configured to execute obtaining service requests sent by multiple users of the same building for a service purpose, and a Wi-Fi list around the time of sending each service request, planar polygon data of the building, and multiple pieces of trajectory data in response to the service requests;

the identifying module 902 is configured to identify identification information of a building where the Wi-Fi that sends the service request is located in each Wi-Fi list;

the processing module 903 is configured to process each piece of trajectory data in the plurality of pieces of trajectory data, and select a region with the largest number of key points in each piece of trajectory data as a two-dimensional gaussian distribution center point;

the first determining module 904 is configured to determine, as a target building, a building with a shortest boundary distance from the center point of the two-dimensional gaussian distribution to a corresponding boundary of the planar polygon data of the building;

the binding module 905 is configured to bind the identification information of the building where the Wi-Fi that sent the service request is located with the target building, so as to obtain the Wi-Fi of the target building.

Optionally, in another embodiment, on the basis of the above embodiment, the processing module includes: a pre-processing module and an aggregation processing module, wherein,

the aggregation processing module is used for performing aggregation processing on the key point data of the plurality of tracks and selecting the area with the largest number of key points as a two-dimensional Gaussian distribution center point.

Optionally, in another embodiment, on the basis of the foregoing embodiment, the preprocessing module includes: a first selection module, a judgment module, a first extraction module and a deletion module, wherein,

the first extraction module is used for extracting a normal track point which is in front of the abnormal track point and adjacent to the abnormal track point when the judgment module judges that the track point is the abnormal track point;

Optionally, in another embodiment, on the basis of the above embodiment, the aggregation process includes: a second obtaining module, a mapping module, a statistical module, a second extracting module, an establishing module, a second determining module, a third determining module and a second selecting module, wherein,

the second obtaining module is used for obtaining a geohash block in which the longitude and latitude of the track key point of each piece of track data are located;

the establishing module is used for establishing a three-dimensional coordinate system by taking the numerical values of two sides of the two-dimensional rectangular plane as an x axis and a y axis respectively and taking the number of key points in the geohash block as the numerical value of a z axis;

the second determining module is used for determining the mixed two-dimensional Gaussian distribution of the number distribution of the key points of the fitting track in the three-dimensional coordinate system;

the third determining module is configured to determine a central area of a central point of the mixed two-dimensional gaussian distribution on the two-dimensional rectangular plane;

the second selection module is used for selecting the two-dimensional Gaussian distribution central point with the largest number of the track key points in the central area.

An embodiment of the present invention further provides a Wi-Fi binding apparatus based on a service scenario, a schematic structural diagram of which is shown in fig. 10, where the apparatus includes: a first obtaining module 1001, a lookup module 1002, and a first binding module 1003, wherein,

the first obtaining module 1001 is configured to, when it is detected that a user sends a service request, obtain Wi-Fi that sends the service request; wherein the service request comprises: an indoor positioning request, a service delivery request or a rendering request of a building and a point of interest (POI) during map display;

the searching module 1002 is configured to search a database for whether a building corresponding to the Wi-Fi exists;

the first binding module 1003 is configured to bind the Wi-Fi with the building when the search module finds the building corresponding to the Wi-Fi, so as to provide building-level route navigation for the service request.

Optionally, in another embodiment, on the basis of the above embodiment, the apparatus further includes: a first binding module, a determining module, and a second binding module, wherein,

the first determining module is used for determining a target building corresponding to the Wi-Fi according to the destination address of the server;

the second binding module is used for binding the Wi-Fi with the target building determined by the determining module so as to provide building-level route navigation for the service request.

Optionally, in another embodiment, on the basis of the foregoing embodiment, the first determining module includes: a third acquisition module, an identification module, a processing module, and a building determination module, wherein,

the third obtaining module is configured to obtain service requests sent by multiple other users in the same building where the service destination address is located, and a Wi-Fi list around the service request when each service request is sent; boundary information of all building data, and a plurality of pieces of trajectory data in response to the configuration request;

the identification module is used for identifying the building address information of each Wi-Fi in each Wi-Fi list;

the processing module is used for processing each piece of track data in the plurality of pieces of track data, and selecting an area with the largest number of key points in each piece of track data as a two-dimensional Gaussian distribution center point;

the building determining module is used for determining the building with the shortest boundary distance from the two-dimensional Gaussian distribution center point to the building data as a target building.

An embodiment of the present invention further provides an electronic device, including:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement a traffic scenario based Wi-Fi recognition method as described above or to implement a traffic scenario based Wi-Fi binding method as described above.

Embodiments of the present invention further provide a storage medium, where instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the above Wi-Fi recognition method based on a service scenario or implement the above Wi-Fi binding method based on a service scenario.

With regard to the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and reference may be made to part of the description of the embodiment of the method for relevant points, and the description will not be described in detail here.

In an exemplary embodiment, there is also provided a storage medium comprising instructions, such as a memory comprising instructions, executable by a processor of an apparatus to perform the above method. Alternatively, the storage medium may be a non-transitory computer readable storage medium, which may be, for example, a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

Fig. 11 is a block diagram of an electronic device 1100 according to an embodiment of the invention. For example, the electronic device 1100 may be a mobile terminal or a server, and in the embodiment of the present invention, the electronic device is described as an example of a mobile terminal. For example, the electronic device 1100 can be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.

Referring to fig. 11, electronic device 1100 may include one or more of the following components: processing components 1102, memory 1104, power components 1106, multimedia components 1108, audio components 1110, input/output (I/O) interfaces 1112, sensor components 1114, and communication components 1116.

The processing component 1102 generally controls the overall operation of the electronic device 1100, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 1102 may include one or more processors 1120 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 1102 may include one or more modules that facilitate interaction between the processing component 1102 and other components. For example, the processing component 1102 may include a multimedia module to facilitate interaction between the multimedia component 1108 and the processing component 1102.

The memory 1104 is configured to store various types of data to support operation at the device 1100. Examples of such data include instructions for any application or method operating on the electronic device 1100, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 1104 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power component 1106 provides power to the various components of the electronic device 1100. The power components 1106 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 1100.

The multimedia component 1108 includes a screen that provides an output interface between the electronic device 1100 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1108 includes a front facing camera and/or a rear facing camera. The front-facing camera and/or the rear-facing camera can receive external multimedia data when the device 1100 is in an operational mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 1110 is configured to output and/or input audio signals. For example, the audio component 1110 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 1100 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 1104 or transmitted via the communication component 1116. In some embodiments, the audio assembly 1110 further includes a speaker for outputting audio signals.

The I/O interface 1112 provides an interface between the processing component 1102 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 1114 includes one or more sensors for providing various aspects of state assessment for the electronic device 1100. For example, the sensor assembly 1114 may detect the open/closed status of the device 1100, the relative positioning of components, such as a display and keypad of the electronic device 1100, the sensor assembly 1114 may also detect a change in the position of the electronic device 1100 or a component of the electronic device 1100, the presence or absence of user contact with the electronic device 1100, orientation or acceleration/deceleration of the electronic device 1100, and a change in the temperature of the electronic device 1100. Sensor assembly 1114 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. The sensor assembly 1114 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1114 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 1116 is configured to facilitate wired or wireless communication between the electronic device 1100 and other devices. The electronic device 1100 may access a wireless network based on a communication standard, such as Wi-Fi, a carrier network (e.g., 2G, 3G, 4G, or 5G), or a combination thereof. In an exemplary embodiment, the communication component 1116 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1116 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 1100 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described illustrated service scenario-based Wi-Fi recognition method or service scenario-based Wi-Fi binding method.

In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 1104 comprising instructions, executable by the processor 1120 of the electronic device 1100 to perform the above-illustrated business scenario-based Wi-Fi identification method or business scenario-based Wi-Fi binding method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

In an exemplary embodiment, there is also provided a computer program product, the instructions of which, when executed by the processor 1120 of the electronic device 1100, cause the electronic device 1100 to perform the above-illustrated service scenario-based Wi-Fi identification method based on or service scenario-based Wi-Fi binding method.

Fig. 12 is a block diagram of an apparatus 1200 for service scenario-based Wi-Fi identification or service scenario-based Wi-Fi binding according to an embodiment of the present invention. For example, the apparatus 1200 may be provided as a server. Referring to fig. 12, the apparatus 1200 includes a processing component 1222 that further includes one or more processors, and memory resources, represented by memory 1232, for storing instructions, such as application programs, that are executable by the processing component 1222. The application programs stored in memory 1232 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1222 is configured to execute instructions to perform the above-described methods.

The apparatus 1200 may also include a power supply component 1226 configured to perform power management of the apparatus 1200, a wired or wireless network interface 1250 configured to connect the apparatus 1200 to a network, and an input output (I/O) interface 1258. The apparatus 1200 may operate based on an operating system stored in the memory 1232, such as Windows Server, MacOSXTM, UnixTM, LinuxTM, FreeBSDTM, or the like.

Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims

1. A Wi-Fi identification method based on service scenes is characterized by comprising the following steps:

acquiring service requests sent by a plurality of users with the service purpose of the same building, a surrounding Wi-Fi list when each service request is sent, planar polygon data of the building and a plurality of pieces of track data responding to the service requests;

identifying the identification information of the building where the Wi-Fi sending the service request is located in each Wi-Fi list; processing each piece of track data in the plurality of pieces of track data to obtain a geohash block in which the longitude and latitude of a track key point of each piece of track data are located;

counting the number of key points in each geohash block;

extracting the geohash blocks where all the track key points are located, wherein all the extracted geohash blocks form a two-dimensional rectangular plane;

respectively taking the numerical values of two sides of the two-dimensional rectangular plane as an x axis and a y axis, and taking the number of key points in the geohash block as the numerical value of a z axis to establish a three-dimensional coordinate system;

selecting the area with the largest number of the track key points in the central area as a two-dimensional Gaussian distribution central point;

the key points are normal track points adjacent to the abnormal track points;

2. The Wi-Fi recognition method according to claim 1, wherein the processing each of the plurality of pieces of trajectory data comprises:

and preprocessing each piece of track data in the plurality of pieces of track data to obtain track key points of the plurality of pieces of track data.

3. The Wi-Fi recognition method based on a service scenario according to claim 2, wherein the preprocessing each of the plurality of pieces of trajectory data to obtain trajectory key points of the plurality of pieces of trajectory data comprises:

selecting track points meeting preset conditions from each piece of track data;

4. A Wi-Fi binding method based on service scene is characterized by comprising the following steps:

searching whether a building corresponding to the Wi-Fi exists in a database;

if the building corresponding to the Wi-Fi exists, the Wi-Fi is bound with the building so as to provide building-level route navigation for the service request;

the method further comprises the following steps:

acquiring service requests sent by a plurality of other users of the same building where the service destination address is located, and a Wi-Fi list around the service request when each service request is sent; boundary information of all building data, and a plurality of pieces of trajectory data responding to the configuration request;

acquiring a geohash block where the longitude and latitude of a track key point of each piece of track data are located;

mapping the track key points in each track data to the corresponding geohash block;

counting the number of key points in each geohash block;

binding the Wi-Fi of the service request with the target building to obtain the Wi-Fi of the target building;

5. The Wi-Fi binding method based on a traffic scenario as claimed in claim 4, wherein the method further comprises:

6. A Wi-Fi recognition apparatus based on a service scenario, comprising:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for executing the acquisition of service requests sent by a plurality of users of the same building for service purpose, and peripheral Wi-Fi lists, planar polygon data of the building and a plurality of track data responding to the service requests when each service request is sent;

the binding module is used for binding the identification information of the building where the Wi-Fi sending the service request is located with the target building to obtain the Wi-Fi of the target building;

the processing module comprises:

the mapping module is used for mapping the track key points in each piece of track data to the corresponding geohash blocks;

the statistical module is used for counting the number of key points in each geohash block;

and the second selection module is used for selecting the area with the largest number of the track key points in the central area as a two-dimensional Gaussian distribution central point.

7. The Wi-Fi recognition device based on traffic scenarios of claim 6, wherein the processing module comprises:

and the preprocessing module is used for preprocessing each piece of track data in the plurality of pieces of track data to obtain track key points of the plurality of pieces of track data.

8. The Wi-Fi recognition device based on traffic scenario of claim 7, wherein the preprocessing module comprises:

9. A Wi-Fi binding apparatus based on a service scenario, comprising:

the first binding module is used for binding the Wi-Fi with the building when the searching module finds the building corresponding to the Wi-Fi so as to provide building-level route navigation for the service request;

the device further comprises:

a second binding module for binding the Wi-Fi with the target building determined by the determination module so as to provide building-level route navigation for the service request;

the determining module comprises:

a third obtaining module, configured to obtain service requests sent by multiple other users in the same building where the service destination address is located, and a Wi-Fi list around the service request when each service request is sent; boundary information of all building data, and a plurality of pieces of trajectory data responding to the configuration request;

the processing module is used for processing each piece of track data in the plurality of pieces of track data to obtain a geohash block in which the longitude and latitude of a track key point of each piece of track data are located; mapping the track key points in each track data to corresponding geohash blocks; counting the number of key points in each geohash block; extracting a geohash block where all track key points are located, wherein all the extracted geohash blocks form a two-dimensional rectangular plane; respectively taking the numerical values of two sides of the two-dimensional rectangular plane as an x axis and a y axis, and taking the number of the key points in the geohash block as the numerical value of a z axis to establish a three-dimensional coordinate system; determining mixed two-dimensional Gaussian distribution of the number distribution of key points of the fitting track in the three-dimensional coordinate system; determining a central area of a central point of the mixed two-dimensional Gaussian distribution on the two-dimensional rectangular plane; selecting the area with the largest number of the track key points in the central area as a two-dimensional Gaussian distribution central point;

10. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the traffic scenario based Wi-Fi recognition method of any of claims 1 to 3 or the traffic scenario based Wi-Fi binding method of any of claims 4 to 5.

11. A storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform a service scenario based Wi-Fi recognition method of any one of claims 1 to 3 or a service scenario based Wi-Fi binding method of any one of claims 4 to 5.