CN113810850A

CN113810850A - Method for creating positioning database and electronic equipment

Info

Publication number: CN113810850A
Application number: CN202110921506.6A
Authority: CN
Inventors: 杨伟; 刘增军; 龚卫林; 黄鹏飞
Original assignee: Honor Device Co Ltd
Current assignee: Honor Device Co Ltd
Priority date: 2021-08-11
Filing date: 2021-08-11
Publication date: 2021-12-17
Anticipated expiration: 2041-08-11
Also published as: WO2023016052A1; CN113810850B

Abstract

The application provides a method for creating a positioning database and electronic equipment, and relates to the technical field of positioning. The problems that fingerprint data for creating the positioning database is insufficient and the precision of the positioning database is insufficient are solved. The specific scheme is as follows: the first device receiving first crowdsourcing data from each second device; the first equipment determines a first coordinate of the first track point in a preset coordinate system; the first equipment calibrates the distance and the direction of the second track point relative to the first track point according to the first coordinate of the first track point; the first equipment determines a second coordinate of the second track point in the preset coordinate system according to the first coordinate and the calibrated distance and direction; and the first equipment constructs a positioning database according to the first coordinate, the second coordinate and the fingerprint data respectively corresponding to the first track point and the second track point.

Description

Method for creating positioning database and electronic equipment

Technical Field

The present application relates to the field of positioning technologies, and in particular, to a method for creating a positioning database and an electronic device.

Background

With the advent of large indoor locations (e.g., large hospitals, superstores, etc.), the market demand for indoor location services has increased. Of course, the premise for realizing the indoor positioning service is to establish a corresponding positioning database.

In the related art, when the location database is established, a professional is required to collect fingerprint data at a designated location point, for example, a wireless fidelity (Wi-Fi) signal or a bluetooth signal is collected at a fixed point, so that each fingerprint data corresponds to an absolute coordinate of the designated location point. However, the manual collection method is limited by the labor cost, and the collected fingerprint data is limited. Thus, the positioning accuracy of the established positioning database is also low.

Disclosure of Invention

The application provides a method for creating a positioning database and electronic equipment, which enable a user entering an indoor place to participate in a fingerprint data acquisition process of the indoor place in a crowdsourcing mode to acquire more fingerprint data in the indoor place. And more fingerprint data are utilized, so that the positioning accuracy of the established positioning database is improved.

In order to achieve the purpose, the technical scheme is as follows:

in a first aspect, a method for creating a location database provided by an embodiment of the present application is applied to a first device, where the first device is communicatively connected to a plurality of second devices located in an indoor location, and the method includes: the first device receiving first crowdsourcing data from each of the second devices; the first crowdsourcing data comprises a first track point, a second track point and fingerprint data corresponding to the first track point and the second track point respectively; the first track point and the second track point are track points on a first displacement track generated by the movement of a user in the indoor place, and the fingerprint data is used for indicating electromagnetic information of corresponding positions of the track points in the indoor place; the first device determines a first coordinate of the first track point in a preset coordinate system; the first device calibrates the distance and the direction of the second track point relative to the first track point according to the first coordinate of the first track point; the first equipment determines a second coordinate of the second track point in the preset coordinate system according to the first coordinate and the calibrated distance and direction; the first equipment constructs a positioning database according to the first coordinate, the second coordinate and the fingerprint data corresponding to the first track point and the second track point respectively, wherein the positioning database comprises the corresponding relation between the first coordinate and the second coordinate and the corresponding fingerprint data.

Understandably, first crowdsourcing data includes track point and fingerprint data, and the second equipment only needs the user who carries the second equipment to normally walk in indoor place when gathering first crowdsourcing data can. In other words, the second device collects the first crowdsourcing data, which may be done without the user's perception. Gather first crowdsourcing data without perception, not only do benefit to more second equipment and join in the collection of first crowdsourcing data, can also effectively reduce the human cost who gathers.

For a piece of first crowd-sourced data, the first device may first determine first coordinates of a first track point. And calibrating the distance and the direction of the second track point relative to the first track point by using the first coordinate, thereby obtaining a second coordinate of the second track point under a preset coordinate system. In this way, all the track points in the first crowdsourcing data can be accurately projected to the preset coordinate system. And determining the corresponding relation between a plurality of coordinate points and the fingerprint data in a preset coordinate system by matching with the corresponding relation between the track points and the fingerprint data.

In this way, in the case where the first device obtains a plurality of first crowdsourcing data, the first device may determine fingerprint data corresponding to more coordinate points in the preset coordinate system. Based on more coordinate points and fingerprint data, a more accurate positioning database can be established. By utilizing the positioning database, the actual position of the user in the preset coordinate system can be accurately identified, and the indoor positioning accuracy is improved.

In some possible embodiments, the first crowdsourcing data further comprises: a first area name corresponding to the first track point; the preset coordinate system is a world coordinate system; the first coordinate is a first absolute coordinate under the world coordinate system; the first device determines a first coordinate of the first track point in a preset coordinate system, and the method includes: the first equipment determines a first absolute coordinate of the first track point under the world coordinate system according to the first area name and a preconfigured POI (point of interest) database; wherein the POI database includes absolute coordinates corresponding to different area names.

In the above embodiment, the world coordinate system is used as the preset coordinate system, and thus, the association between the fingerprint data and the real world is determined. The positioning database established in this way is more convenient for positioning in the real world. In addition, a first absolute coordinate of the first track point in the world coordinate system is determined from the POI database by using a first area name corresponding to the first track point. The complexity of an algorithm for determining the first absolute coordinate is simplified, and the efficiency of determining the first absolute coordinate is improved.

In some possible embodiments, the POI database further comprises: area areas corresponding to different area names; the first device determines a first absolute coordinate of the first track point under the world coordinate system according to the first area name and a preconfigured POI (point of interest) database, and the method comprises the following steps: the first equipment queries a second absolute coordinate and a region area corresponding to the first region name from the POI database according to the first region name; and under the condition that the inquired area of the region does not exceed a preset area threshold, the first device determines the inquired second absolute coordinate to be the first absolute coordinate.

In the above embodiment, when the first track point belongs to the area with the smaller area, the first absolute coordinate of the first track point is determined quickly, so that the problem that the found first absolute coordinate is not accurate enough is solved.

In some possible embodiments, the determining, by the first device, a first absolute coordinate of the first track point in the world coordinate system according to the first area name and a preconfigured POI database, further includes: under the condition that the inquired area of the region exceeds the preset area threshold, the first device acquires second crowdsourcing data and third crowdsourcing data; the second crowdsourcing data comprises third track points and corresponding second area names, the third crowdsourcing data comprises fourth track points and fifth track points, the fourth track points are matched with the fingerprint data corresponding to the first track points, and the fifth track points are matched with the fingerprint data corresponding to the third track points; the first equipment queries a third absolute coordinate corresponding to the second area name from the POI database according to the second area name; the first device obtains a track distance between the fourth track point and the fifth track point; and the first equipment performs linear fitting according to the second absolute coordinate, the third absolute coordinate and the track distance to determine the first absolute coordinate.

When the area is too large, the absolute coordinates searched by the POI database cannot accurately indicate the real position of the first track point. In the above embodiment, the second absolute coordinates queried from the POI database are calibrated by matching a plurality of crowdsourcing data, so as to obtain the first absolute coordinates accurately indicating the real position of the first track point.

In some possible embodiments, the first displacement trajectory comprises at least two of the first trajectory points; the first device calibrates the distance and the direction of the second track point relative to the first track point according to the first coordinate of the first track point, and the calibration method includes: and the first equipment calibrates the distance between the initial direction of the first displacement track and the track point contained in the first displacement track according to the first coordinates of at least two first track points and by combining a Graph optimization SLAM model to obtain a second displacement track.

In the embodiment, the Graph SLAM model is used, and at least two first coordinates are matched, so that the initial direction and distance of the second track point relative to the first track point are calibrated, and the second displacement track capable of accurately indicating the real displacement condition of the user is obtained.

In some possible embodiments, the first displacement trajectory comprises one of the first trajectory points; the first device calibrates the distance and the direction of the second track point relative to the first track point according to the first coordinate of the first track point, and the calibration method includes: the first device acquires a third track; the fingerprint data of at least one track point in the third track is matched with the fingerprint data of the first track point or the second track point; a fourth coordinate of at least one track point in the third track under the preset coordinate system is determined; the first equipment splices the first displacement track and the third track to obtain a combined track; and the first equipment calibrates the initial direction of the combined track and the distance between track points contained in the combined track according to the first coordinate and the fourth coordinate by combining a Graph SLAM model to obtain a second displacement track.

In the above embodiment, by combining the tracks, it is ensured that the track processed by the Graph SLAM model has at least two track points with known coordinates in the preset coordinate system. Therefore, after the Graph SLAM model is calibrated, a second displacement track capable of accurately indicating the real displacement condition of the user is obtained.

In some possible embodiments, before the first device determines the second coordinate of the second track point in the preset coordinate system according to the first coordinate and the calibrated distance and direction, the method further includes: and the first equipment determines the calibrated distance and direction according to the second displacement track.

In the above embodiment, the obtained second displacement trajectory is used to replace the first displacement trajectory, so as to solve the problems that the initial direction of the first displacement trajectory has a large error and the distance between the trajectory points is not accurate enough. Thus, the accuracy of the obtained second coordinates of the second trace points is ensured.

In some possible embodiments, after the location database is created, the method further comprises: the first device optimizes the location database using a Graph SLAM model.

In the above embodiment, the Graph SLAM model is used to perform averaging processing on the coordinates indicating the same position point in the positioning database, so as to improve the accuracy of the positioning database.

In a second aspect, an embodiment of the present application provides a method for creating a location database, where the method is applied to a second device located in an indoor location, and the second device is in communication connection with a first device, and the method includes: the second equipment responds to the detected first event and collects a first track point and corresponding fingerprint data; the fingerprint data is used for indicating electromagnetic information of corresponding positions of track points in the indoor places; the first event comprises receiving an operation for indicating code scanning or identifying a passing landmark position point; after the detected first event, the second equipment acquires a second track point and corresponding fingerprint data; the first track point and the second track point are track points on a first displacement track generated by the movement of the user in the indoor place; the second device sending first crowdsourcing data to the first device; the first crowdsourcing data comprises: the fingerprint identification device comprises a first track point, a second track point and fingerprint data corresponding to the first track point and the second track point respectively; the first crowdsourcing data is used for the first device to determine a first coordinate of the first track point in a preset coordinate system and a second coordinate of the second track point in the preset coordinate system, and a positioning database is constructed; the positioning database comprises a corresponding relation among the first coordinate, the second coordinate and corresponding fingerprint data.

In the above embodiment, the second device initiates acquisition of the track points and fingerprint data in response to the first event. The acquisition process may be performed without the perception of the user. The starting track point and the fingerprint data are collected without perception, influence on a user is avoided, and available data can be provided for creating a positioning database.

In some possible embodiments, the first event is receiving an operation indicating a code scan, the first crowdsourcing data further includes a first area name corresponding to the first track point, and the method further includes: the second equipment responds to the first event and acquires a display interface containing a scanning result; the second equipment performs character recognition on the display interface; and the second equipment determines the second equipment as the first area name according to the recognized text information.

In some possible embodiments, the first event is identification of a passing landmark location point, the first crowdsourcing data further comprises a first area name corresponding to the first track point, the method further comprising: the second device determines that the second device passes through the landmark position point by using a scene recognition model; the second equipment acquires landmark names corresponding to the landmark position points; the second device determines the landmark name as the first area name.

In some possible embodiments, the first event further comprises capturing a first image comprising displayed items in an indoor location; the second device comprises placing area names corresponding to different displayed articles; the first crowdsourcing data further comprises a first area name corresponding to the first track point, the method further comprising: the second equipment inquires the name of a placement area corresponding to the displayed article; the second device determines the name of the placement area as the first area name.

In some possible embodiments, the method further comprises: the second device collects first fingerprint data; the second device sending the first fingerprint data to the first device; the second equipment receives the first positioning coordinates fed back by the first equipment; the first positioning coordinate is a coordinate matched with the first fingerprint data in the positioning database.

In some possible embodiments, the method further comprises: the second equipment receives the positioning database sent by the first equipment; the second device collects first fingerprint data; the second equipment inquires a matched first positioning coordinate from the positioning database according to the first fingerprint data; the second equipment acquires at least one second positioning coordinate from the positioning database according to the first positioning coordinate; the first positioning coordinate and each second positioning coordinate correspond to a displacement track; the second equipment acquires second fingerprint data; and the second equipment determines a matched third positioning coordinate from the second positioning coordinate according to the second fingerprint data.

In the above embodiment, it is improved that all fingerprint data in the location database needs to be traversed for each location, and the location efficiency is improved.

In a third aspect, an electronic device (for example, the first device described above) provided in an embodiment of the present application includes one or more processors and a memory; the memory coupled with the processor, the memory to store computer program code, the computer program code comprising computer instructions, which, when executed by the one or more processors, the one or more processors to receive first crowdsourcing data from each of the second devices; the first crowdsourcing data comprises a first track point, a second track point and fingerprint data corresponding to the first track point and the second track point respectively; the first track point and the second track point are track points on a first displacement track generated by the movement of a user in the indoor place, and the fingerprint data is used for indicating electromagnetic information of corresponding positions of the track points in the indoor place; determining a first coordinate of the first track point in a preset coordinate system; according to the first coordinate of the first track point, calibrating the distance and the direction of the second track point relative to the first track point; determining a second coordinate of the second track point in the preset coordinate system according to the first coordinate and the calibrated distance and direction; and constructing a positioning database according to the first coordinate, the second coordinate and the fingerprint data respectively corresponding to the first track point and the second track point, wherein the positioning database comprises the corresponding relation between the first coordinate and the second coordinate and the corresponding fingerprint data.

In some possible embodiments, the first crowdsourcing data further comprises: a first area name corresponding to the first track point; the preset coordinate system is a world coordinate system; the first coordinate is a first absolute coordinate under the world coordinate system; the one or more processors further to: determining a first absolute coordinate of the first track point under the world coordinate system according to the first area name and a preconfigured POI (point of interest) database; wherein the POI database includes absolute coordinates corresponding to different area names.

In some possible embodiments, the POI database further comprises: area areas corresponding to different area names; the one or more processors further to: according to the first area name, inquiring a second absolute coordinate and an area corresponding to the first area name from the POI database; and under the condition that the inquired area of the region does not exceed a preset area threshold, determining the inquired second absolute coordinate as the first absolute coordinate.

In some possible embodiments, the one or more processors are further configured to: under the condition that the inquired area of the region exceeds the preset area threshold, acquiring second crowdsourcing data and third crowdsourcing data; the second crowdsourcing data comprises third track points and corresponding second area names, the third crowdsourcing data comprises fourth track points and fifth track points, the fourth track points are matched with the fingerprint data corresponding to the first track points, and the fifth track points are matched with the fingerprint data corresponding to the third track points; according to the second area name, inquiring a third absolute coordinate corresponding to the second area name from the POI database; obtaining the distance between the fourth track point and the fifth track point; and performing linear fitting according to the second absolute coordinate, the third absolute coordinate and the track distance to determine the first absolute coordinate.

In some possible embodiments, the first displacement trajectory comprises at least two of the first trajectory points; the one or more processors further to: and calibrating the distance between the initial direction of the first displacement track and the track point contained in the first displacement track according to the first coordinates of at least two first track points and by combining a Graph optimization SLAM model to obtain a second displacement track.

In some possible embodiments, the first displacement trajectory comprises one of the first trajectory points; the one or more processors further to: acquiring a third track; the fingerprint data of at least one track point in the third track is matched with the fingerprint data of the first track point or the second track point; a fourth coordinate of at least one track point in the third track under the preset coordinate system is determined; splicing the first displacement track and the third track to obtain a combined track; and calibrating the initial direction of the first track of the combined track and the distance between track points contained in the first track of the combined track by combining a Graph SLAM model according to the first coordinate and the fourth coordinate to obtain a second displacement track.

In some possible embodiments, before determining the second coordinate of the second trajectory point in the preset coordinate system according to the first coordinate and the calibrated distance and direction, the one or more processors are further configured to: and determining the calibrated distance and direction according to the second displacement track.

In some possible embodiments, after the location database is created, the one or more processors are further configured to: and optimizing the positioning database by utilizing a Graph SLAM model.

In a fourth aspect, an electronic device (for example, the second device described above) provided in an embodiment of the present application includes one or more processors and a memory; the memory coupled to the processor, the memory to store computer program code, the computer program code comprising computer instructions, which, when executed by the one or more processors, cause the one or more processors to:

collecting a first track point and corresponding fingerprint data in response to a detected first event; the fingerprint data is used for indicating electromagnetic information of corresponding positions of track points in the indoor places; the first event comprises receiving an operation for indicating code scanning or identifying a passing landmark position point;

after the detected first event, acquiring a second track point and corresponding fingerprint data; the first track point and the second track point are track points on a first displacement track generated by the movement of the user in the indoor place;

sending first crowdsourcing data to the first device; the first crowdsourcing data comprises: the fingerprint identification device comprises a first track point, a second track point and fingerprint data corresponding to the first track point and the second track point respectively;

the first crowdsourcing data is used for the first device to determine a first coordinate of the first track point in a preset coordinate system and a second coordinate of the second track point in the preset coordinate system, and a positioning database is constructed; the positioning database comprises a corresponding relation among the first coordinate, the second coordinate and corresponding fingerprint data.

In some possible embodiments, the first event is receipt of an operation to indicate a code sweep, the first crowdsourcing data further includes a first area name corresponding to the first track point, and the one or more processors are further to:

responding to a first event, and acquiring a display interface containing a scanning result;

performing character recognition on the display interface;

and determining the first area name according to the identified text information.

In some possible embodiments, the first event is identification of a passing landmark location point, the first crowdsourcing data further comprises a first area name corresponding to the first track point, the one or more processors are further to:

determining that the electronic equipment passes through the landmark position point by using a scene recognition model;

acquiring a landmark name corresponding to the landmark position point;

determining the landmark name as the first area name.

In some possible embodiments, the first event further comprises capturing a first image comprising displayed items in an indoor location; the electronic equipment comprises placing area names corresponding to different displayed articles; the first crowdsourcing data further comprises a first area name corresponding to the first track point, the one or more processors further to:

inquiring the name of a placing area corresponding to the displayed article;

determining the name of the placement area as the first area name.

In some possible embodiments, the one or more processors are further configured to:

collecting first fingerprint data;

sending the first fingerprint data to the first device;

receiving a first positioning coordinate fed back by the first equipment; the first positioning coordinate is a coordinate matched with the first fingerprint data in the positioning database.

receiving the positioning database sent by the first device;

collecting first fingerprint data;

inquiring a matched first positioning coordinate from the positioning database according to the first fingerprint data;

acquiring at least one second positioning coordinate from the positioning database according to the first positioning coordinate; the first positioning coordinate and each second positioning coordinate correspond to a displacement track; collecting second fingerprint data; and determining a matched third positioning coordinate from the second positioning coordinate according to the second fingerprint data.

In a fifth aspect, a computer storage medium provided in an embodiment of the present application includes computer instructions, which, when executed on an electronic device (e.g., the first device described above), cause the electronic device to perform the method described in the first aspect and possible embodiments thereof; alternatively, the computer instructions, when executed on an electronic device (e.g. the second device described above), cause the electronic device to perform the method described in the second aspect and its possible embodiments described above.

In a sixth aspect, the present application provides a computer program product, which, when run on the above-mentioned electronic device (e.g. the above-mentioned first device), causes the electronic device to perform the method described in the above-mentioned first aspect and its possible embodiments; alternatively, the computer program product, when run on an electronic device (e.g. the second device described above), causes the electronic device to perform the method described in the second aspect and its possible embodiments described above.

It is understood that the electronic device, the computer-readable storage medium and the computer program product provided in the foregoing aspects are all applied to the corresponding method provided above, and therefore, the beneficial effects achieved by the electronic device, the computer-readable storage medium and the computer program product provided in the foregoing aspects can refer to the beneficial effects in the corresponding method provided above, and are not described herein again.

Drawings

Fig. 1 is an exemplary diagram of creating a location database in the related art;

FIG. 2 is a schematic diagram of a positioning system provided in an embodiment of the present application;

fig. 3 is a diagram illustrating a server structure according to an embodiment of the present application;

fig. 4 is a diagram illustrating a structure of a mobile phone according to an embodiment of the present application;

FIG. 5 is an exemplary diagram of collected fingerprint data and displacement traces provided by an embodiment of the present application;

FIG. 6 is a second exemplary diagram of fingerprint data and displacement traces according to the present disclosure;

fig. 7A is a diagram illustrating a display interface of a mobile phone according to an embodiment of the present application;

FIG. 7B is a flowchart illustrating steps of a method according to an embodiment of the present application;

FIG. 8 is an exemplary diagram for determining absolute coordinates of a trace point provided by an embodiment of the present application;

FIG. 9 is a diagram illustrating an example of the distance between the absolute coordinates of the store and the track point d according to an embodiment of the present disclosure;

fig. 10 is an exemplary diagram for determining a track distance between a track point b and a track point f by using crowdsourcing data 4 according to an embodiment of the present application;

FIG. 11 is a diagram illustrating an example of calibrating a displacement trace according to an embodiment of the present disclosure;

FIG. 12 is an exemplary diagram of integration of multiple available paths provided by embodiments of the present application;

FIG. 13 is a second flowchart illustrating steps of a method according to an embodiment of the present invention;

fig. 14 is an exemplary diagram of a chip system provided in an embodiment of the present application.

Detailed Description

Embodiments of the present embodiment will be described in detail below with reference to the accompanying drawings.

With the advent of large indoor locations (e.g., large hospitals, superstores, etc.), the demand for indoor location services by users has also increased.

However, due to the influence of the building wall, when a user in an indoor location uses a conventional positioning technology (such as a satellite positioning technology) to perform positioning, problems such as positioning drift easily occur. To improve this problem, indoor positioning techniques have been developed. The indoor positioning technology is different from the satellite positioning technology in that: the realization of the indoor positioning technology requires the pre-establishment of a corresponding positioning database.

The location database may include a plurality of location points and corresponding fingerprint data in an indoor location. The fingerprint data may be used to indicate an electromagnetic environment in which the corresponding location point is located. Understandably, there are differences in the electromagnetic environment at different location points. Therefore, when the user is in the indoor place, the actual position of the user in the indoor place can be determined only by comparing the fingerprint data of the current position with the fingerprint data in the positioning database.

Therefore, after the accurate positioning database is created for the indoor place, the accurate indoor positioning service can be provided for the user. In the related art, the positioning database needs to be generated by measuring in an indoor place by a professional.

To illustrate, a location database is created for a mall. First, a professional needs to select a plurality of measurement points 102 in a mall map 101 shown in fig. 1 (a). Understandably, there is a corresponding relationship between the map coordinate system of the market map and the world coordinate system (also called absolute coordinate system). Thus, each test point 102 corresponds to a true location within the store, i.e., has corresponding absolute coordinates. The professional then needs to go to the real location indicated by each measurement point 102 in turn for fingerprint data acquisition. Thus, according to the corresponding relationship between the measuring point 102 and the fingerprint data, the fingerprint data corresponding to a plurality of absolute coordinates in the mall map 101 is determined, and a positioning database corresponding to the mall is constructed. For example, in the case where the test point 102 indicated by the absolute coordinate 1 acquires the fingerprint data 1 and the test point 102 indicated by the absolute coordinate 2 acquires the fingerprint data 2, the constructed location database includes a correspondence relationship between the absolute coordinate 1 and the fingerprint data 1 and a correspondence relationship between the absolute coordinate 2 and the fingerprint data 2.

In this way, after entering the store, the user uses the terminal device (e.g. mobile phone) to collect the fingerprint data in the environment, such as fingerprint data 3. As shown in fig. 1 (b), it is determined from the positional database that the fingerprint data 3 matches the fingerprint data 2. Thereby, it is positioned that the user is located near the absolute coordinate 2. Of course, there may be a relatively large error between the actual position of the user and the absolute coordinates 2. The error decreases as the number of test points 102 increases.

In other words, the more test points are selected in the process of constructing the positioning database, the smaller the error between the actual position and the positioned absolute coordinate is. However, manual measurement is time and labor consuming, and as more and more indoor places need to be located, the location accuracy of the location database is limited by manpower cost. In addition, since the electromagnetic environment in an indoor location may change, the updating efficiency of the positioning database obtained by manual measurement is low.

In order to solve the problems, the application provides a method for creating a positioning database, and users entering an indoor place are made to participate in a data acquisition process in a crowdsourcing mode, so that displacement tracks of the users in the indoor place and corresponding fingerprint data are obtained. Understandably, after the user enters the indoor place, the activity route is relatively random. By utilizing a crowdsourcing mode, a large number of users participate in the data acquisition process, so that the users are not excessively influenced, and displacement tracks and corresponding fingerprint data which are distributed all over indoor places can be obtained. And then, analyzing displacement tracks and fingerprint data acquired by a large number of users to determine the fingerprint data of a large number of position points in the indoor place and establishing a corresponding positioning database. Therefore, the positioning accuracy of the established positioning database is improved.

The above-described method for creating the positioning database may be applied to a positioning system as shown in fig. 2, for example. The above-described positioning system includes a device 1 (which may also be referred to as a first device) and a device 2 (which may also be referred to as a second device) that has accepted a crowdsourcing task. The crowdsourcing task may be a task participating in fingerprint data collection of an indoor location.

The device 2 is in communication connection with the device 1. The wireless connection between the device 1 and the device 2 may be established through a global system for mobile communications (GSM), General Packet Radio Service (GPRS), code division multiple access (code division multiple access, CDMA), Wideband Code Division Multiple Access (WCDMA), time division code division multiple access (TD-SCDMA), Long Term Evolution (LTE), bluetooth, wireless fidelity (Wi-Fi), NFC, voice over Internet protocol (VoIP), and a communication protocol supporting a network slice architecture.

By way of example, the device 1 may be embodied as a server, a tablet Computer, a television (also referred to as a smart screen, a smart television or a large screen device), a notebook Computer, an Ultra-mobile Personal Computer (UMPC), a handheld Computer, or other electronic device with data processing capability, for example, a device with Pedestrian Dead Reckoning (PDR) capability, which is not limited in any way by the embodiments of the present application.

Further, the device 2 may be a mobile device having an inertial measurement unit and an electromagnetic environment acquisition function, such as a mobile phone, a tablet Computer, a notebook Computer, an Ultra-mobile Personal Computer (UMPC), a handheld Computer, a netbook, a Personal Digital Assistant (PDA), a wearable electronic device, a virtual reality device, and the like, which are not limited in this embodiment.

In the present embodiment, the portable device 2 may be carried by a user into an indoor location (e.g., a mall shown in fig. 2). The device 2 collects real-time inertial information that can be used to indicate the displacement trajectory of the user in an indoor location without the user's perception.

Illustratively, inertial information such as acceleration, angular velocity, magnetism, and pressure of the user during traveling may be sensed by a Sensor (Sensor) in the device 2. The inertial information acquired at each time point of the device 2 is processed, for example, by PDR, to determine a corresponding trace point. Meanwhile, the track points corresponding to the plurality of acquisition time points can form a displacement track indicating the user in the indoor place according to the acquisition time sequence.

In some embodiments, the device 2 may also detect whether the user is approaching a preset location point. The absolute coordinates corresponding to the preset position points can be determined. For example, the device 2 may determine whether the user is approaching the preset location point by detecting whether an event corresponding to the predetermined location occurs during the process of collecting the inertial information.

When determining the route preset position point, the device 2 may mark the inertia information acquired at this time, thereby indicating that the trajectory point corresponding to the inertia information and the preset position point are at the same position. In other words, a displacement trajectory path preset position point indicating the user is implemented.

In addition, in the embodiment of the present application, the device 2 may also collect fingerprint data. The fingerprint data can be synchronously acquired with the inertia information, so that the track point indicated by the inertia information is aligned with the fingerprint data on a time axis. That is, there is a correspondence between the acquired fingerprint data and the trace points.

The fingerprint data can be used for distinguishing electromagnetic environments of different acquisition position points. For example, fingerprint data may be indicated by the acquired radio signals. The radio signal may include one or a combination of a Wireless-Fidelity (Wi-Fi) signal, a bluetooth signal, a base station signal, a frequency modulation signal, and the like.

Understandably, in an indoor location, the types of radio signals that may be collected at different location points may differ. Therefore, the types of radio signals included in the fingerprint data of different location points are also different. For example, when the device 2 collects a Wi-Fi signal and a bluetooth signal at the location point 1, it may be determined that the fingerprint data corresponding to the location point 1 includes the Wi-Fi signal and the bluetooth signal. The device 2 collects the base station signal and the bluetooth signal at the location point 2, and then determines that the fingerprint data corresponding to the location point 2 includes the base station signal and the bluetooth signal.

Therefore, the corresponding fingerprint data are determined by utilizing the types of the radio signals which can be acquired at different acquisition position points, and the electromagnetic environments of the different acquisition position points can be distinguished based on the fingerprint data.

It will also be appreciated that different location points may differ in strength, frequency, etc. of the acquired radio signals in addition to the type of radio signals that may be acquired.

Take Wi-Fi signals as an example. When different Wi-Fi signals cover different location points, Media Access Control (MAC) fields, Service Set Identifiers (SSIDs), boot time (boot), and center Frequency (Frequency) of the Wi-Fi signals collected at the different location points may also be different. In addition, even different position points covered by the same Wi-Fi signal have different Received Signal Strength Indication (RSSI) values due to the difference of distance from the router.

It can be seen that when a Wi-Fi signal is included in the fingerprint data, the Wi-Fi signal may be characterized by one or a combination of the MAC field, RSSI, boost, center Frequency, and the like. Thus, the electromagnetic environments of different acquisition location points are more accurately distinguished.

Take the base station signal as an example. When different location points are covered by different base station signals, the resident cell identifiers collected at different location points are different, for example, bootime, that is, identifiers such as a start time of a cell, a Mobile Country Code (MCC), a mobile operator code (MNC), a Location Area Code (LAC), a cell ID, a network type (RAT) of the cell, and a channel number (ChannelNumber) of the cell are different. Similarly, even at different locations covered by the same base station signal, the corresponding base station RSSI varies due to the distance to the base station.

It can be seen that when the base station signal is included in the fingerprint data, the base station signal may be characterized by one or a combination of bootime, MCC, MNC, LAC, cell ID, network access control (RAT) of the cell, ChannelNumber, and the like. Thus, the electromagnetic environments of different acquisition location points are more accurately distinguished.

Take bluetooth signal as an example. The bluetooth signals corresponding to different location points are different, so the bluetooth names, bluetooth addresses and the like of the bluetooth signals which can be collected by different location points are different.

It can be seen that when a bluetooth signal is included in the fingerprint data, the bluetooth signal may be characterized by one or a combination of a bluetooth name, a bluetooth address, and the like. Thus, the electromagnetic environments of different acquisition location points are more accurately distinguished.

Take the example of frequency modulated signal. The strength of the frequency modulation signal detected by different position points at the same frequency point may be different. It can be seen that when the fingerprint data includes a frequency modulation signal, the frequency modulation signal may be indicated by the frequency modulation signal strength of the designated frequency point. Thus, the electromagnetic environments of different acquisition location points are more accurately distinguished.

In the embodiment of the present application, the data indicating the displacement trajectory and the corresponding fingerprint data acquired by the device 2 are summarized by the device 1. In this way, the device 1 can determine the displacement tracks of different users and the fingerprint data corresponding to each track point in the displacement tracks.

Then, the device 2 determines a correspondence between a plurality of absolute coordinates and the fingerprint data according to the plurality of displacement trajectories and the corresponding fingerprint data.

For example, in the case that the displacement trajectory approaches at least two preset position points, the device 1 may determine absolute coordinates of at least two trajectory points in the displacement trajectory according to the preset position points. And calibrating the relative position between each track point in the displacement track based on the absolute coordinates of the two track points. And then, determining absolute coordinates of all calibrated track points in the displacement track. In this way, the apparatus 1 can determine a correspondence between a plurality of absolute coordinates and fingerprint data.

Further illustratively, in the case where the displacement trajectory approaches a preset position point, the device 1 may determine a known trajectory intersecting the displacement trajectory, wherein the known trajectory includes at least one trajectory point for which absolute coordinates have been determined. And determining the absolute coordinates of at least two track points in the displacement track according to the intersection point between the known track and the displacement track. And calibrating the relative position between each track point in the displacement track based on the absolute coordinates of the two track points. And then, determining absolute coordinates of all calibrated track points in the displacement track. In this way, the device 1 can in turn determine a correspondence between a plurality of absolute coordinates and the fingerprint data. Like this, alright in order to construct the accurate positioning database in location according to the corresponding relation between a large amount of absolute coordinate and the fingerprint data.

In addition, the device 2 will continuously and long-term provide the collected data to the device 1, ensuring that the created location database can be updated in time, ensuring accuracy, even if the electromagnetic environment in the indoor location changes.

In some embodiments, different indoor locations may correspond to different apparatuses 1, and of course, different indoor locations may correspond to the same apparatus 1. The examples of the present application are not limited to these.

In the following, a server is taken as an example of the device 1 in the positioning system, and fig. 3 shows a schematic structural diagram of the server.

As shown in fig. 3, the server may include: a processor 310, a memory 320, a communication module 330, and the like.

The processor 310, the memory 320 and the communication module 340 are electrically connected to each other directly or indirectly to implement data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.

Among other things, processor 310 may include one or more processing units, such as: the processor 310 may include an Application Processor (AP), a modem processor, a Graphics Processing Unit (GPU), an Image Signal Processor (ISP), a controller, a memory, a video codec, a Digital Signal Processor (DSP), a baseband processor, and/or a neural-Network Processing Unit (NPU), etc. The different processing units may be separate devices or may be integrated into one or more processors.

The controller may be a neural hub and a command center of the server. The controller can generate an operation control signal according to the instruction operation code and the timing signal to complete the control of instruction fetching and instruction execution.

A memory may also be provided in the processor 310 for storing instructions and data. In some embodiments, the memory in the processor 310 is a cache memory. The memory may hold instructions or data that have just been used or recycled by the processor 310. If the processor 310 needs to reuse the instruction or data, it can be called directly from the memory. Avoiding repeated accesses reduces the latency of the processor 310, thereby increasing the efficiency of the system.

The Memory 320 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 320 is used for storing programs or data.

The communication module 340 is used for communication connection with other devices through the server and for transceiving data through the network.

It should be understood that the architecture shown in fig. 3 is merely a schematic diagram of a server, which may also include more or fewer components than shown in fig. 3, or have a different configuration than shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.

Further exemplarily, a mobile phone is taken as the device 2 in the positioning system for example, and fig. 4 shows a schematic structural diagram of the server.

As shown in fig. 4, the device 2 (e.g., a mobile phone) may include: the mobile communication device includes a processor 210, an external memory interface 220, an internal memory 221, a Universal Serial Bus (USB) interface 230, a charging management module 240, a power management module 241, a battery 242, an antenna 1, an antenna 2, a mobile communication module 250, a wireless communication module 260, an audio module 270, a speaker 270A, a receiver 270B, a microphone 270C, an earphone interface 270D, a sensor module 280, a button 290, a motor 291, an indicator 292, a camera 293, a display 294, and a Subscriber Identity Module (SIM) card interface 295.

The sensor module 280 may include a pressure sensor, a gyroscope sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a distance sensor, a proximity light sensor, a fingerprint sensor, a temperature sensor, a touch sensor, an ambient light sensor, a bone conduction sensor, and the like.

It is to be understood that the structure illustrated in the present embodiment does not constitute a specific limitation to the mobile phone. In other embodiments, the handset may include more or fewer components than shown, or combine certain components, or split certain components, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

Processor 210 may include one or more processing units, such as: the processor 210 may include an Application Processor (AP), a modem processor, a Graphics Processing Unit (GPU), an Image Signal Processor (ISP), a controller, a memory, a video codec, a Digital Signal Processor (DSP), a baseband processor, and/or a neural-Network Processing Unit (NPU), etc. The different processing units may be separate devices or may be integrated into one or more processors.

The controller can be the neural center and the command center of the mobile phone. The controller can generate an operation control signal according to the instruction operation code and the timing signal to complete the control of instruction fetching and instruction execution.

A memory may also be provided in processor 210 for storing instructions and data. In some embodiments, the memory in the processor 210 is a cache memory. The memory may hold instructions or data that have just been used or recycled by processor 210. If the processor 210 needs to use the instruction or data again, it can be called directly from the memory. Avoiding repeated accesses reduces the latency of the processor 210, thereby increasing the efficiency of the system.

In some embodiments, processor 210 may include one or more interfaces. The interface may include an integrated circuit (I2C) interface, an integrated circuit built-in audio (I2S) interface, a Pulse Code Modulation (PCM) interface, a universal asynchronous receiver/transmitter (UART) interface, a Mobile Industry Processor Interface (MIPI), a general-purpose input/output (GPIO) interface, a Subscriber Identity Module (SIM) interface, and/or a Universal Serial Bus (USB) interface, etc.

It should be understood that the connection relationship between the modules in this embodiment is only an exemplary illustration, and does not constitute a limitation on the structure of the mobile phone. In other embodiments, the mobile phone may also adopt different interface connection manners or a combination of multiple interface connection manners in the above embodiments.

The charge management module 240 is configured to receive a charging input from a charger. The charger may be a wireless charger or a wired charger. The charging management module 240 can also supply power to the mobile phone through the power management module 241 while charging the battery 242.

The power management module 241 is used to connect the battery 242, the charging management module 240 and the processor 210. The power management module 241 receives input from the battery 242 and/or the charging management module 240, and provides power to the processor 210, the internal memory 221, the external memory, the display 294, the camera 293, and the wireless communication module 260. In some embodiments, the power management module 241 and the charging management module 240 may also be disposed in the same device.

The wireless communication function of the mobile phone can be realized by the antenna 1, the antenna 2, the mobile communication module 250, the wireless communication module 260, the modem processor, the baseband processor, and the like. In some embodiments, the antenna 1 of the handset is coupled to the mobile communication module 250 and the antenna 2 is coupled to the wireless communication module 260, such that the handset can communicate with a network and other devices, such as a wearable device, through wireless communication techniques.

The

antennas

1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in the handset may be used to cover a single or multiple communication bands. Different antennas can also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 250 may provide a solution including wireless communication of 2G/3G/4G/5G, etc. applied to a mobile phone. The mobile communication module 250 may include at least one filter, a switch, a power amplifier, a Low Noise Amplifier (LNA), and the like. The mobile communication module 250 may receive the electromagnetic wave from the antenna 1, filter, amplify, etc. the received electromagnetic wave, and transmit the electromagnetic wave to the modem processor for demodulation.

The mobile communication module 250 may also amplify the signal modulated by the modem processor, and convert the signal into electromagnetic wave through the antenna 1 to radiate the electromagnetic wave. In some embodiments, at least some of the functional modules of the mobile communication module 250 may be disposed in the processor 210. In some embodiments, at least some of the functional modules of the mobile communication module 250 may be disposed in the same device as at least some of the modules of the processor 210.

The wireless communication module 260 may provide solutions for wireless communication applied to a mobile phone, including WLAN (e.g., wireless fidelity, Wi-Fi) network, Bluetooth (BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), Infrared (IR), and the like.

The GNSS may include a beidou satellite navigation system (BDS), a Global Positioning System (GPS), a global navigation satellite system (GLONASS), a quasi-zenith satellite system (QZSS), and/or a Satellite Based Augmentation System (SBAS).

The wireless communication module 260 may be one or more devices integrating at least one communication processing module. The wireless communication module 260 receives electromagnetic waves via the antenna 2, performs frequency modulation and filtering processing on electromagnetic wave signals, and transmits the processed signals to the processor 210. The wireless communication module 260 may also receive a signal to be transmitted from the processor 210, frequency-modulate and amplify the signal, and convert the signal into electromagnetic waves via the antenna 2 to radiate the electromagnetic waves.

The mobile phone implements the display function through the GPU, the display screen 294, and the application processor. The GPU is a microprocessor for image processing, and is connected to the display screen 294 and an application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 210 may include one or more GPUs that execute program instructions to generate or alter display information.

The display screen 294 is used to display images, video, and the like. The display screen 294 includes a display panel.

The mobile phone can implement a shooting function through the ISP, the camera 293, the video codec, the GPU, the display screen 294, the application processor, and the like. The ISP is used to process the data fed back by the camera 293. The camera 293 is used to capture still images or video. In some embodiments, the handset may include 1 or N cameras 293, N being a positive integer greater than 1.

The external memory interface 220 may be used to connect an external memory card, such as a Micro SD card, to extend the storage capability of the mobile phone. The external memory card communicates with the processor 210 through the external memory interface 220 to implement a data storage function. For example, files such as music, video, etc. are saved in an external memory card.

Internal memory 221 may be used to store computer-executable program code, including instructions. The processor 210 executes various functional applications of the cellular phone and data processing by executing instructions stored in the internal memory 221. For example, in the present embodiment, the processor 210 may execute instructions stored in the internal memory 221, and the internal memory 221 may include a program storage area and a data storage area.

The storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required by at least one function, and the like. The data storage area can store data (such as audio data, a phone book and the like) created in the use process of the mobile phone. In addition, the internal memory 221 may include a high-speed random access memory, and may further include a nonvolatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash memory (UFS), and the like.

It is to be understood that the illustrated structure of the embodiments of the present application does not constitute a specific limitation to the mobile phone. In other embodiments of the present application, the handset may include more or fewer components than shown, or combine certain components, or split certain components, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The methods in the following embodiments may be implemented in a device having the above hardware structure. The method provided by the embodiment of the present application is described below by taking the device 1 as a server and the device 2 as a mobile phone to create a location database suitable for mall location.

In the embodiment of the present application, the establishment of the positioning database can be divided into a data acquisition phase and a data processing phase. The data acquisition stage can be realized by a mobile phone which receives crowdsourcing tasks.

In some embodiments, handsets entering an indoor venue may each have accepted crowdsourcing tasks by default. In other embodiments, after the mobile phone receives an instruction from the user to participate in data collection, it is determined that the crowdsourcing task is accepted.

Illustratively, in the event that the user activates the purchased handset, the handset may display a prompt message 1. The prompt information 1 is used for inquiring whether to participate in a data acquisition stage of constructing a positioning database. In this way, after receiving the determination operation of the user, it is determined that the cell phone has accepted the crowd-sourced task for all indoor places.

Further, for example, the mobile phone may display the prompt message 2 before the user enters the indoor location. The prompt information 2 is used for inquiring whether the user collects and uploads displacement tracks and fingerprint data in the indoor place. In this way, after receiving the determination operation of the user, it is determined that the handset has accepted the crowdsourcing task for the indoor location.

In the implementation process, the mode of triggering the mobile phone to display the prompt message 2 can be determined according to the actual conditions of different indoor places.

For example, in a scenario where a broadcast device is disposed at an entrance of an indoor location (e.g., a smart billboard, a smart sign, etc. is disposed at an entrance of an indoor location). The broadcasting apparatus may transmit crowdsourcing request information for the indoor location. Thus, when the user carries the mobile phone and passes through the broadcasting equipment, the mobile phone can receive the crowdsourcing request information and display the prompt message 2.

For another example, an indoor location may need to scan a location code to enter the scene. After the mobile phone of the user scans the place code, the place information corresponding to the indoor place can be obtained. And when the mobile phone inquires that the indoor place is the place needing to establish the positioning database according to the place information, the mobile phone can be triggered to display the prompt information 2.

Of course, in any scenario, after the mobile phone displays the prompt message 2, it may be determined that the mobile phone has accepted the crowdsourcing task according to the operation determined by the user instruction.

The mobile phone receiving the crowdsourcing task can execute the task of collecting the displacement track and the corresponding fingerprint data after entering the corresponding indoor place along with the user.

In some embodiments, the mobile phone may determine whether an indoor location has been entered before performing the displacement trace and fingerprint data acquisition.

As a possible way, the handset determines whether the handset enters an indoor location according to satellite positioning technology. For example, when the mobile phone detects that the distance between the current position and the entrance of the indoor place is smaller than the set value, and the inertial measurement unit of the mobile phone detects that the movement direction faces the inside of the indoor place, it can be determined that the mobile phone enters the indoor place. For another example, when the mobile phone detects that the distance between the current location and the entrance of the indoor location is smaller than the set value, and the signal strength of a Global Positioning System (GPS) received by the mobile phone is lower than a set strength threshold, it can be determined that the mobile phone enters the indoor location.

As another possible way, the mobile phone may determine whether the mobile phone enters the indoor location according to whether the mobile phone receives data sent by a broadcasting device (e.g., an intelligent sign, an intelligent billboard, an indoor navigation robot, etc. in the indoor location). For example, the information carrying the indoor location identifier is received, and it is determined that the user has entered the indoor location.

As another possible way, the mobile phone may also determine whether the mobile phone enters the indoor location according to whether the location code of the indoor location is scanned. For example, the location code scanned by the mobile phone can obtain the corresponding location information. And when the place information indicates that the current place is an indoor place, judging that the mobile phone enters the indoor place.

As another possible way, the mobile phone determines whether the mobile phone enters an indoor place by identifying a geo-fence. For example, the cell phone identifies a geofence of an indoor location, and determines that the cell phone has entered the indoor location with the user.

In addition, the combination of the modes can be adopted to judge whether the mobile phone enters an indoor scene.

In the embodiment of the application, after the mobile phone is determined to enter an indoor scene, the mobile phone starts to acquire inertial information used for indicating a displacement track and corresponding fingerprint data. In some embodiments, the manner in which the mobile phone collects the inertial information and the corresponding fingerprint data may include the following:

first, as shown in fig. 5, fingerprint data is periodically collected from the time when the mobile phone enters the indoor location until the mobile phone is detected to leave the indoor location.

In some embodiments, the mobile phone may refer to the description of the previous embodiments to determine whether to enter an indoor location. After the mobile phone is determined to enter the indoor place, the mobile phone can collect fingerprint data according to a preset time period. The collected fingerprint data can be one or a combination of MAC field, SSID, Boottime, RSSI, Frequency and the like of Wi-Fi signals. The collected fingerprint data can also be one or the combination of the Bluetooth name, the Bluetooth address and the like of the Bluetooth signal. The collected fingerprint data may also be a base station signal or a frequency modulation signal, and the like, which is not specifically limited in this embodiment of the application.

In addition, the mobile phone can acquire own inertia information while acquiring fingerprint data. For example, the inertia information acquired at each acquisition time point can be calculated from the PDR to obtain a corresponding trajectory point. And the plurality of track points can form a displacement track according to the sequence of acquisition. Wherein each inertial information may correspond to a time stamp for the time of acquisition of the inertial information. Likewise, the fingerprint data also corresponds to a time stamp indicating the time of acquisition of the fingerprint data. Thus, by using the time stamp, the mobile phone can align the inertia information with the fingerprint data, thereby determining the corresponding relationship between the trajectory point indicated by the inertia information and the fingerprint data.

Of course, in other possible embodiments, every time inertial information is collected, a corresponding fingerprint data may be collected. Thus, the correspondence between the locus points indicated by the inertia information and the fingerprint data is determined.

In the embodiment of the application, when the mobile phone leaves an indoor place, the collection of the fingerprint data is stopped. As an example, the handset determines whether the handset is away from an indoor location based on satellite positioning technology. For example, if the GPS signal received by the mobile phone returns to normal and the satellite positioning technology is used to determine that the mobile phone is located at an exit of an indoor location, it can be determined that the mobile phone is away from the indoor location. As another example, the mobile phone may also determine whether the mobile phone leaves the indoor location by identifying a geo-fence. For example, the cell phone does not recognize the geofence of the indoor location, determining that the cell phone has left the indoor location with the user.

Therefore, the fingerprint data can be continuously collected from the time when the mobile phone enters the indoor place to the time when the mobile phone leaves the indoor place. Therefore, the number of the collected fingerprint data is increased, and the subsequent establishment of a positioning database is facilitated.

In the second mode, as shown in fig. 6 (a), fingerprint data is periodically collected from the time the mobile phone enters an indoor location until a predetermined trigger event is detected, and the mobile phone suspends the collection of fingerprint data.

In some embodiments, the triggering event may be the handset detecting that the handset has stopped moving and connecting to a router in an indoor location, for example, connecting to a Wi-Fi router provided in a store in a mall.

In other embodiments, the triggering event may be the mobile phone passing through a LandMark location point (LandMark) in an indoor location, such as an elevator, a corner, an escalator, an entrance/exit, a unique checkout counter on each floor in a mall, and the like. For example, the AI model in the mobile phone may be used to identify an actual scene, and determine whether the mobile phone passes through a landmark location point according to the identification result. And when the mobile phone is determined to pass through the landmark position point, determining that the mobile phone detects a trigger event. Further exemplarily, when the landmark location point is an elevator, the trigger event may be detected by sensing that the user takes the elevator by the mobile phone. As another example, where the landmark location is a toilet, the triggering event may be a sound from a cell phone recognizing a flush of water in the toilet. As another example, the landmark location point is an escalator, and the trigger event may be a mobile phone acquiring that an included angle between the movement direction and the horizontal plane exceeds a threshold. For another example, the trigger event may be that fingerprint data acquired by the mobile phone in real time matches fingerprint data corresponding to the landmark location point.

In other embodiments, the trigger event may also be a cell phone scan for area codes in an indoor location. A plurality of area codes may be set in an indoor location. Each area code corresponds to a space in an indoor location. The mobile phone can acquire the absolute coordinates of the current space by scanning the area code.

For example, each store in a shopping mall may correspond to an area code, and after the user arrives at the store, the user may scan the corresponding area code using a mobile phone to obtain absolute coordinates of the store where the user enters.

For another example, each office in a hospital may correspond to an area code, and after the user arrives at the office, the user may scan the corresponding area code using a mobile phone to obtain absolute coordinates of the office where the user arrives.

For another example, each display area of the museum corresponds to one area code, and after the user reaches the display area, the user can scan the area code to obtain the explanation content and the absolute coordinates corresponding to the display area.

In addition, different trigger events can be set according to the characteristics of different indoor places.

For example, when the indoor location is a hospital, the trigger event may be that the mobile phone scans a code before the consulting room to check in, the mobile phone uses an electronic social security card to realize card swiping, or the mobile phone stays at the same location for more than a specified time.

Also exemplarily, when the indoor place is a shopping mall, the triggering event may be a mobile phone code scanning payment, a mobile phone scanning order, a mobile phone code scanning connection Wi-Fi, a mobile phone obtaining a picture of a commodity displayed in the shopping mall by photographing, a price of the commodity searched by the mobile phone, or a purchase order function of an application program enabled by the mobile phone.

In this embodiment of the present application, the trigger event may be detected by using an Artificial Intelligence (AI) algorithm preset in the mobile phone.

In addition, after the mobile phone suspends fingerprint data collection, if the mobile phone detects a starting event, the fingerprint data collection is restarted periodically until a set triggering event is detected again.

In some embodiments, the start event may be: the handset reverts to a dormant state (e.g., the handset is off or the handset is locked) and the handset changes from a stationary state to a moving state. In other words, the cell phone may restart the collection of fingerprint data in case the user pauses playing the cell phone and continues to walk in the indoor place.

In other embodiments, the start event may be: the distance between the current position of the mobile phone and the track point 1 exceeds a distance threshold value 1. The inertia information collected by the mobile phone when the trigger event occurs can be used for indicating the track point 1. In other words, the handset may restart the collection of fingerprint data in the event that the user has left the trigger event venue.

Therefore, in the second mode, the fingerprint data collection is suspended or restarted by using the strategy, so that abundant fingerprint data in indoor places can be obtained, and the energy consumption of the mobile phone can be reduced. The long-term occupation of the mobile phone resources by behaviors such as fingerprint data acquisition and the like is improved, and the influence on the normal use of the mobile phone by a user is improved to a certain extent.

In a third mode, as shown in (b) in fig. 6, when the mobile phone detects a set trigger event in an indoor location, the mobile phone starts to periodically collect fingerprint data until the collection duration reaches a set time threshold.

In some embodiments, the trigger event may refer to the trigger event in the second embodiment, and will not be described herein. For example, the time threshold is 2 minutes, and the mobile phone periodically collects fingerprint data within 2 minutes after scanning payment in the store.

Therefore, the time for collecting the fingerprint data by the mobile phone can be effectively reduced and the energy saving of the mobile phone is improved.

Of course, besides the above three modes, the mobile phone may also adopt other modes to collect fingerprint data and data for indicating displacement trajectory. For example, after the mobile phone enters an indoor place, the mobile phone can randomly start to collect the inertia information and the fingerprint data under the condition that the mobile phone moves, and stop collecting when the collection time reaches a set time threshold.

In some embodiments, the mobile phone may determine the manner of collecting the fingerprint data and the inertial information from the above manners according to the configuration operation of the user. In other embodiments, the mobile phone may also acquire the fingerprint data and the inertial information by default in a first mode, a second mode or a third mode.

In addition, the mobile phone is located in an indoor place, and can collect calibration information besides collecting fingerprint data and data for indicating a displacement track. The calibration information corresponds to an absolute coordinate in a world coordinate system and can be used for determining the absolute coordinate of the track point 1. That is, the calibration information can be used to determine the corresponding position of the trace point 1 in the real space.

In some embodiments, the handset may perform the acquisition of calibration information in response to a triggering event. The collected calibration information may be a name of an area of the real space, such as a name of a shop or a name of a consulting room. Understandably, in an indoor place, the area names corresponding to different areas may be different. Meanwhile, in an indoor place, absolute coordinates corresponding to different areas can be predetermined. Therefore, the corresponding absolute coordinates can be determined according to the acquired area names.

In some embodiments, the manner in which calibration information is collected may be different for different types of trigger events.

As an example, a partial type of triggering event may trigger the cell phone to display an interface containing a name of an area (e.g., a store name, a consulting room name). Therefore, for the type of trigger event, the mobile phone can capture an interface displaying the area name, and identify the area name serving as the calibration information from the interface.

For example, in the scenario that the triggering event is scanning payment, after the mobile phone detects code scanning payment, the mobile phone may display an interface 701 as shown in (a) in fig. 7A, where the interface 701 is used to indicate that the code scanning payment is successful. In addition, the interface 701 includes the name of the checkout store, such as the a buttercup store. Since code-scan payments typically occur near checkout stores, it may be determined that the cell phone is located near the checkout store when a code-scan payment is detected. At this time, the handset may capture the interface 701 and recognize "a milky tea shop" from the interface 701 as calibration information.

In addition, trigger events like scanning a desktop code order, purchasing a purchase order in a consumer application, etc. may also trigger the handset to display an interface including the name of the store. After the trigger event is detected, the mobile phone can capture an interface with a shop name displayed, and the shop name is identified from the interface and used as calibration information.

For another example, when the trigger event is the check-in of the front scanning code in the consulting room, the mobile phone can display an interface indicating that the check-in is successful. The interface includes the name of the office that was successfully checked in. It can thus be determined that the handset is now located near the office, so the handset can capture the interface and identify the office name from the interface as the calibration information.

For another example, when the triggering event is connecting a router, in the case that a router provided by a connected merchant is detected, the mobile phone may display an interface 702 as shown in (B) in fig. 7A, where the interface 702 includes a router named "a milky tea shop," a router of "B cinema," and the like. In the interface 702, the identifier 703 and the identifier 704 are displayed on the router corresponding to the milky tea shop a. The mark 703 indicates that the router signal of the a milky tea shop is full, and the mark 704 indicates that the mobile phone has accessed the router of the a milky tea shop. From this it can be determined that the handset is located in the vicinity of the a milky tea shop, in which case the handset determines "a milky tea shop" as the calibration information based on the logo 703, the logo 704 and "a milky tea shop" recognized from the interface 702.

As another example, after some types of triggering events occur, the handset may collect ambient voice data containing the area name. Therefore, the calibration information corresponding to the trigger event can also be environmental audio data. For example, after the mobile phone is in a store and the mobile phone scans the code to pay, the mobile phone collects 10 yuan for the A milk tea shop. In this case, the mobile phone can determine that the milk tea store A is the calibration information from the '10 yuan received by the milk tea store A' through a voice recognition technology. For another example, after the mobile phone is in a shopping mall and the mobile phone scans the code and is connected with the Wi-Fi, the mobile phone acquires 'welcome a milky tea shop', and in this case, the mobile phone can determine the 'welcome a milky tea shop' as the calibration information from the 'welcome a milky tea shop' through a voice recognition technology.

As an example, after a partial type of triggering event occurs, the cell phone may capture an image containing the displayed items in an indoor location, such as referred to as a first image. Since the display area of the article can be predetermined, the mobile phone can determine the corresponding area name as the calibration information by identifying the article.

For example, when the trigger event is to take a picture of a commodity by a mobile phone to check the price, the mobile phone recognizes that the commodity is the clothes of the brand C, and the clothes of the brand C are sold by the clothes store of the brand C uniformly, so that the 'clothes store of the brand C' can be determined as the calibration information.

In another example, when the mobile phone detects that the trigger event is passing a landmark location point, the mobile phone may use the identified landmark name as corresponding calibration information. For example, if the mobile phone AI recognizes that the mobile phone passes through an elevator, the landmark name "elevator" is used as the designation information.

In addition, after the mobile phone detects a trigger event, a situation that the calibration information is not acquired may also occur. For example, after the mobile phone scans the payment, the interface displaying the shop name is not captured, so that the calibration information is not obtained. For another example, the router connected to the mobile phone is not named by the store name, so after the mobile phone is connected to the router provided by the store, the interface including the store name is not acquired, and further, the calibration information and the like are not acquired.

Under the condition that the calibration information is not acquired, the trace point 1 corresponding to the trigger event can be regarded as a common trace point, and corresponding fingerprint data is synchronously recorded.

Under the condition that the mobile phone acquires the fingerprint data, the data for indicating the displacement track and the calibration information, the fingerprint data, the data for indicating the displacement track and the calibration information can be sent to the server. Under the condition that the mobile phone collects the fingerprint data and the data for indicating the displacement track, the fingerprint data and the data for indicating the displacement track can be sent to the server.

In some examples, the data indicating the displacement trajectory may be acquired inertial information. In other examples, the data indicating the displacement trajectory may also include the trajectory point corresponding to the inertial information. That is, in this example, after the mobile phone collects the inertial information, the mobile phone may also calculate the track point corresponding to the inertial information by using the PDR.

As an implementation manner, as shown in fig. 7B, the method provided by the embodiment of the present application may include:

s101, the mobile phone detects a first event.

In some embodiments, the first event may be the trigger event mentioned in the previous embodiments.

S102, the mobile phone responds to the detected first event and collects a first track point and corresponding fingerprint data.

In some embodiments, the mobile phone collects the first track point with the first event as a trigger, so that the first track point can indicate the place of the first event. And collecting corresponding fingerprint data while collecting the first track point.

S103, after the detected first event, the mobile phone collects a second track point and corresponding fingerprint data.

In some embodiments, the acquisition time point of the second trajectory point may be subsequent to the first trajectory point. The first track point and the second track point can be determined through inertia information collected by the mobile phone and can be used for indicating a displacement track sensed by the mobile phone.

In other embodiments, the second trace point may be collected first until the first event is detected, and the first trace point is collected.

And S104, the mobile phone sends the first track point, the second track point and the corresponding fingerprint data to the server.

In some embodiments, the first track point, the second track point, and the fingerprint data corresponding to the first track point and the second track point respectively may form first crowdsourcing data collected by the mobile phone.

In some embodiments, the first crowdsourcing data further comprises: a first area name corresponding to the first track point. Wherein the first area name may be a name of an occurrence place of the first event. For example, when the first event is a card payment event, the first area name may be a store name. For another example, when the first event is a doctor's office report, the first area name may be a doctor's office name.

In other embodiments, the handset sends to the server some raw data collected, such as inertial information indicating track points, fingerprint data, and a first area name. Therefore, after the server receives the original data, the track points can be determined according to the inertia information, and therefore the first crowdsourcing data from the mobile phone is determined.

In some embodiments, the server determines crowdsourcing data corresponding to the mobile phone according to the data sent by the mobile phone.

Illustratively, the crowdsourcing data includes a plurality of trace points indicating a displacement trace of the handset, corresponding fingerprint data, and calibration data. For example, the crowdsourcing data 1 shown in table 1 below:

TABLE 1

Tracing point	Fingerprint data	Calibration information
			Track point a	Fingerprint data a	Is free of
Track point b	Fingerprint data b	Calibration information a

In table 1, the trace points a and the trace points b are arranged according to the time sequence of acquisition, and can be used for indicating the displacement trace of the mobile phone. In addition, table 1 shows that the mobile phone acquires fingerprint data a at a track point a and fingerprint data b at a track point b. The mobile phone detects a trigger event at the track point b, and acquires the calibration information a after the trigger event is detected.

Further illustratively, the crowd-sourced data includes a plurality of trace points indicating a displacement trace of the handset and corresponding fingerprint data. For example, crowd sourced data 2 shown in table 2 below:

TABLE 2

Tracing point	Fingerprint data
		Track point c	Fingerprint data c
Track point d	Fingerprint data d
		Trace point e	Fingerprint data e

In table 2, the trace points c, d, and e are arranged according to the time sequence of acquisition, and can be used to indicate the displacement trace of the mobile phone. In addition, the mobile phone collects fingerprint data c at the track point c, collects fingerprint data d at the track point d, and collects fingerprint data e at the track point e. In addition, table 2 shows that the handset does not detect a trigger event on the displacement trajectory.

In some embodiments, when the mobile phone sends a plurality of pieces of inertial information to the server instead of sending the trace points, the server may further calculate the trace point corresponding to each piece of inertial information by using the PDR, and determine the corresponding relationship between the trace point and the fingerprint data and the calibration information based on the corresponding relationship between the inertial information and the fingerprint information and the calibration information.

The server may obtain multiple pieces of crowd-sourced data after a large number of users randomly move about the same indoor location with their cell phones (or other types of devices 2). In this way, the server may enter the data processing stage based on the multiple pieces of crowd-sourced data.

In the data processing stage, the server may determine, according to crowdsourcing data belonging to the same indoor location, correspondence between a plurality of absolute position points in the indoor location and fingerprint data. Wherein an absolute position point is a position point having absolute coordinates.

The following describes a process in which the server determines correspondence between a plurality of absolute position points in the indoor location and the fingerprint data, taking the indoor location as an example of a mall.

In some embodiments, the measured location points and corresponding fingerprint data may be included in a server. The measured location point is a location point with known absolute coordinates in a shopping mall. For example, the absolute coordinates and the position points of the corresponding fingerprint data are calibrated in advance. For convenience of description, the absolute coordinates of the measured position points are referred to as absolute coordinates 3, and the fingerprint data acquired at the absolute coordinates 3 is also referred to as fingerprint data 4.

In some embodiments, the server may utilize fingerprint data 4 to compare against fingerprint data included in various pieces of crowd-sourced data. When the fingerprint data 4 matches the fingerprint data 5 in the crowd-sourced data, the track point 2 corresponding to the fingerprint data 5 is determined from the crowd-sourced data. The server determines that the track point 2 and the measured position point are the same point in space, and then the absolute coordinate of the track point 2 is determined accordingly.

For example, the server records that the absolute coordinates a of the cashier of a shopping mall correspond to the fingerprint data 4. In addition, the crowd-sourced data 2 shown in table 2 may indicate a displacement trajectory 801 as shown in fig. 8, and a trajectory point c, a trajectory point d, a trajectory point e, and the like are included in the displacement trajectory 801. When the crowd-sourced data 2 indicates that the fingerprint data d of the track point d matches the fingerprint data 4, it is determined that the track point d matches the fingerprint data 4. In this way, the server can determine the absolute coordinates a as the absolute coordinates of the track point d.

In other embodiments, the server may further include a correspondence between landmark names and absolute coordinates. Thus, when the calibration information in the crowdsourcing data is the landmark name, the absolute coordinates of the track point corresponding to the calibration information can be determined.

In other embodiments, the server may also include a point of interest (POI) database. The POI database includes the store name and the location related information of the store.

For example, the POI data corresponding to the POI database in the 6 th building of the city united states department store, as shown in table 3 below:

TABLE 3

In some embodiments, in the case that the crowdsourcing data includes the calibration information, the server may further determine the absolute coordinates of at least one trajectory point in the crowdsourcing data according to the calibration information and the POI database. For example, for the crowdsourcing data 1 shown in table 1, the absolute coordinates of the trajectory point b can be determined using the calibration information a.

For example, in the case where the designation information a is "a milky tea shop" in table 1, the absolute coordinates of "a milky tea shop" are queried to be (x1, y1) through the POI, so that the server can determine that the absolute coordinates of the track point b are also (x1, y 1).

In addition, in the case that the POI database includes POI data of multiple shopping malls, since the same shop name may exist in different shopping malls, the server may query multiple pieces of POI data from the POI database according to the calibration information.

For example, in the case where a plurality of stores have a milky tea shop, the POI data searched from the POI database according to "milky tea shop" is shown in table 4:

TABLE 4

At this time, in order to determine the absolute coordinates of the trajectory point b in the crowdsourcing data 1 shown in table 1, the judgment may be performed by combining the geo-fence collected when the mobile phone enters the shopping mall. For example, if the cell phone recognizes the geo-fence "seat a of city of great conway city, city of Sichuan" before collecting the original data for crowdsourcing data 1, the absolute coordinate of the trajectory point b in the crowdsourcing data 1 may be determined to be (x1, y 1). Similarly, before the mobile phone collects the original data of the crowdsourcing data 1, the geo-fence of "city of the city, the jinjiang province and the business market" is identified, and then the absolute coordinate of the track point b in the crowdsourcing data 1 can be determined to be (x5, y 5).

It will be appreciated that the absolute coordinates of the store in the POI database are typically the geometric center of the store, and the handset detects that there is a distance between the actual location of the trigger event (e.g., track point b) and the geometric center of the store. For example, as shown in fig. 9, a buttercup of a buttercup store is located in area 901, and then track point b may be located within area 901. Clearly, there is a distance d between the track point b and the geometric center 902 of the store. The larger the store area, the larger the spacing d.

Therefore, in other embodiments, in order to improve the accuracy of the determined track point b, after the absolute coordinates, that is, (x1, y1) are searched from the POI database by using the calibration information a of the crowdsourcing data 1, the absolute coordinates of the track point b are recorded as (x1+ δ 1, y1+ γ 1), where δ 1 and γ 1 are undetermined parameters with undetermined values. Therefore, the server can fit the values of the delta 1 and the gamma 1 by combining other crowdsourcing data, and the accurate absolute coordinate of the track point b is obtained.

As an example, the process of fitting the values of δ 1 and γ 1 is as follows:

s1, obtain the crowdsourcing data with the calibration information b, such as called second crowdsourcing data. Wherein, the shop indicated by the calibration information b is different from the shop indicated by the calibration information a. For example, the calibration information b is a second area name.

Illustratively, the crowdsourcing data 3 with the calibration information b may be as shown in table 5 below:

TABLE 5

Tracing point	Fingerprint data	Calibration information
			Track point f	Fingerprint data f	Calibration information b
Trace point g	Fingerprint data g	Is free of

The calibration information B is "B cinema", and the absolute coordinates of the B cinema are determined to be (x2, y2) by querying the POI database, for example, referred to as third absolute coordinates. At this time, the absolute coordinates (x2+ δ 2, y2+ γ 2) of the locus point f are also recorded. The trace point f may also be referred to as a third trace point.

S2, crowd sourced data having both fingerprint data f and fingerprint data b, such as referred to as third crowd sourced data, is acquired. For example, crowdsourcing data 4 shown in table 6 below:

TABLE 6

Tracing point	Fingerprint data
		Track point h	Fingerprint data b
Track point i	Fingerprint data f

As shown in fig. 10, the crowd-sourced data 4 indicates a displacement trajectory a passing through a trajectory point h (e.g., referred to as a fourth trajectory point) and a trajectory i (e.g., referred to as a fifth trajectory point). Wherein, the fingerprint data that track point h corresponds matches with the fingerprint data that track point b corresponds, is fingerprint data b, promptly, track point h and track point b are the same point. Meanwhile, the fingerprint data corresponding to the track point i is matched with the fingerprint data corresponding to the track point f, and the fingerprint data is the fingerprint data f, namely, the track point i and the track point f are the same. That is, the displacement trajectory indicated by the crowdsourcing data acquired at S2 can be regarded as passing through the trajectory point f and the trajectory point b at the same time.

And S3, acquiring a track distance m1 between the track point f and the track point b.

In some embodiments, from the crowd-sourced data 4, a straight-line distance between the trajectory point h and the trajectory point i is calculated as the trajectory distance m1 based on the PDR.

In addition, in the case where a plurality of crowdsourcing data having both fingerprint data f and fingerprint data b is acquired. If different crowdsourcing data indicates that different displacement tracks pass through the track point f and the track point b, a plurality of linear distances are calculated by combining PDR according to a plurality of crowdsourcing data. The server may determine the trajectory distance m1 from the plurality of straight-line distances. For example, the average of the plurality of straight-line distances is defined as the trajectory distance m 1. S4, then constructing an equation to be solved according to the absolute coordinates of (x1+ delta 1, y1+ gamma 1), (x2+ delta 2, y2+ gamma 2) and the track distance m 1. For example, the equation to be solved is

And S5, acquiring crowdsourcing data with the calibration information c. The shop indicated by the calibration information c is different from the shop indicated by the calibration information a and the shop indicated by the calibration information b.

The crowdsourcing data with the calibration information c, which may be referred to as crowdsourcing data 5, is shown in table 7 below:

TABLE 7

Tracing point	Fingerprint data	Calibration information
			Trace point j	Fingerprint data j	Is free of
Track point k	Fingerprint data k	Calibration information c

The calibration information C is "C clothes shop", and the absolute coordinates of the C clothes shop are determined to be (x3, y3) by querying the POI database, and at this time, the absolute coordinates of the track point k are also recorded (x3+ δ 3, y3+ γ 3).

And S6, acquiring other crowdsourcing data passing through the track point f and the track point k at the same time, and acquiring a track distance m2 between the track point f and the track point k.

In some embodiments, the principle of S6 can be referred to as S2 and S3, which are not described herein.

S7, then constructing an equation to be solved according to the absolute coordinates of (x3+ delta 3, y3+ gamma 3), (x2+ delta 2, y2+ gamma 2) and the track distance m 2. For example, the equation to be solved is

The above steps describe a process of determining two to-be-solved modes, and at least ten to-be-solved equations are determined based on other crowdsourced data according to the principle of the above steps. Then, all the modes to be solved are solved simultaneously. Thus, the values of the fixed parameters of delta 1, delta 2, gamma 1 and gamma 2 are determined.

In this manner, not only the absolute coordinates (x1+ δ 1, y1+ γ 1) of the locus point b but also the absolute coordinates (x2+ δ 2, y2+ γ 2) of the locus point f, the absolute coordinates (x3+ δ 3, y3+ γ 3) of the locus point k, and the like can be determined.

In other embodiments, the server may determine the absolute coordinates of the track point corresponding to the calibration information in the above manner of combining with other crowdsourcing data when the area of the store indicated by the calibration information exceeds a preset area. And when the area of the shop indicated by the calibration information does not exceed the preset area, using the absolute coordinates of the shop inquired in the POI database as the absolute coordinates of the track point corresponding to the calibration information. Thus, the advantages of the two modes are fully combined.

In this way, the server can determine the absolute coordinates of one or more trajectory points in the crowd-sourced data. The trace points for which absolute coordinates are determined may also be referred to as known trace points. The server can utilize known track points, calibrate the relative position relation (such as direction and distance) between other track points and known track points in crowdsourcing data, and solve the problems that the initial direction of the displacement track calculated by the PDR is wrong, and the distance evaluation between the track points has errors and the like. After the relative position relation among a plurality of track points is calibrated, the absolute coordinates of other track points can be determined by using the absolute coordinates of the known track points.

In some embodiments, in the case that the same crowdsourcing data includes at least two known track points, the known track points in the crowdsourcing data can be used to calibrate the relative position relationship between other track points and the known track points in the crowdsourcing data. For example, crowdsourcing data 6 is as follows in table 8:

TABLE 8

The above-described crowdsourcing data 6 indicates a displacement trajectory 1101 shown in (a) in fig. 11, the displacement trajectory 1101 including a trajectory point m, a trajectory point n, a trajectory point o, and a trajectory point r.

And the server determines the absolute coordinate m of the track point m according to the calibration information m and determines the absolute coordinate r of the track point r according to the calibration information r. In this way, the server can calibrate the relative direction between the trajectory point m and the trajectory point n, i.e., calibrate the initial direction of the displacement trajectory 1101, using the direction between the absolute coordinate m and the absolute coordinate r. After the initial orientation calibration, the other trajectory points in displacement trajectory 1101 are also adjusted with respect to the orientation of trajectory point m. The positional relationship between the adjusted plurality of trace points is shown in fig. 11 (b).

After the initial orientation calibration, the relative position relationship between other trace points in the crowdsourcing data 6 is also accurate. In addition, the actual unit step length of the user when walking can be determined by using the actual distance between the absolute coordinate m and the absolute coordinate r. For example, the PDR determines that it has traveled 6m from track point m to track point r based on a standard step size (e.g., 0.6 meters per step), whereas, if the actual distance between absolute coordinate m and absolute coordinate r is 5m, then the actual unit step size for the user can be determined to be 0.5 meters. After the actual unit step length of the user is determined, the displacement distance between any two track points in the corrected displacement track 1101 is determined according to the actual unit step length.

After the above-described calibration process is performed on the displacement trajectory 1101, a displacement trajectory 1102 shown in fig. 11 (b) is obtained. Therefore, the server can determine the absolute coordinates of other track points based on the absolute coordinates of the known track points.

In other embodiments, when the crowdsourcing data includes at least two known trajectory points, the displacement trajectory indicated by the crowdsourcing data and the absolute coordinates of the known trajectory points may be input into a Graph optimization (Graph SLAM) model, so as to obtain the absolute coordinates of other trajectory points in the displacement trajectory. It will be appreciated that the Graph SLAM model can also be used to calibrate the direction and distance between trace points in a displacement trace.

In other embodiments, in the case that there is only one known track point in the crowdsourcing data, the relative position relationship of the plurality of track points in the crowdsourcing data may be calibrated in cooperation with other crowdsourcing data having known track points. Therefore, absolute coordinates corresponding to more track points are determined.

As an example, for crowd-sourced data with only one known track point, the calibration process of the relative position relationship between the track points is as follows:

and A1, splicing the crowdsourcing data only containing one known track point (namely, the track point with determined absolute coordinates) with other crowdsourcing data with at least one known track point to obtain a combined track with at least two known track points.

Taking crowdsourcing data 1 as an example, the absolute coordinates of the trace point b are determined by the calibration information a, but the absolute coordinates of the trace point a are unknown. At this time, the absolute coordinates of the trajectory point d in the crowd-sourced data 2 are also determined. Meanwhile, the server includes crowdsourcing data 7, as shown in table 9:

TABLE 9

Tracing point	Fingerprint data
		Trace point q	Fingerprint data a
Track point w	Track point w
		Track point y	Fingerprint data c

As shown in (a) in fig. 12, the trajectory point q of the crowd-sourced data 7 and the trajectory point a in the crowd-sourced data 1 have matching fingerprint data and can be regarded as the same position point. The track point y of the crowd-sourced data 7 and the track point c of the crowd-sourced data 2 have matched fingerprint data and can be regarded as the same position point.

At this time, the server stitches the displacement trajectories indicated by the crowdsourcing data 1, the crowdsourcing data 7 and the crowdsourcing data 2 into one combined trajectory using the similarity of the fingerprints, as shown in (b) in fig. 12. The resulting combined track thus has two known track points.

A2, processing a combined track with two known track points by using a Graph SLAM (Graph SLAM) model to obtain absolute coordinates of all track points in the combined track.

As another example, the server includes crowdsourcing data 8, as shown in table 10:

watch 10

Tracing point	Fingerprint data
		Track point z	Fingerprint data a
Track point x	Fingerprint data x

The track point z of the crowdsourcing data 8, the track point a in the crowdsourcing data 1 and the track point q in the crowdsourcing data 7 have matched fingerprint data and can be regarded as the same point. In other words, the displacement trajectories indicated by the crowdsourcing data 1, the crowdsourcing data 7 and the crowdsourcing data 8 meet at the same point, which may be referred to as a meeting point. Under the scene, if the track point w, the track point x and the track point b are all known track points, determining a plurality of paths to be selected 1 passing through the absolute coordinate b by using the absolute coordinate b of the track point b. The method for determining the candidate route 1 comprises the following steps: and determining the possible positions of the track points a in different initial directions by using the absolute coordinates b as base points, and then determining the path 1 to be selected according to the possible positions of the track points a and the absolute coordinates b.

And similarly, determining a plurality of paths to be selected 2 passing through the absolute coordinates w by using the absolute coordinates w of the track points w. And determining a plurality of paths to be selected 3 passing through the absolute coordinate x by using the absolute coordinate x of the track point x.

In some embodiments, the candidate route 1, the candidate route 2, and the candidate route 3 may be further screened in combination with a shopping mall map, so as to screen out candidate routes that are not passable in the shopping mall map. It should be noted that each track point in the path to be selected corresponds to an absolute coordinate. And then, determining the intersection points of the screened candidate path 1, the candidate path 2 and the candidate path 3 and corresponding absolute coordinates.

In addition, when a plurality of junction points are determined, a shopping mall map can be combined to screen out some junction points which cannot exist. For example, screen out points that intersect outdoors, screen out points that intersect in a suspended area, and the like.

Thus, at least two known track points can be determined in the three displacement tracks. For a displacement track with at least two known track points, the server can determine the absolute coordinates of other track points in the displacement track by using a Graph SLAM model.

In some embodiments, the server may also establish a correspondence between the absolute coordinates and the fingerprint data using the track points in the crowdsourcing data as links. In this way, crowd-sourced data in which the absolute coordinates of the trace points are determined may also indicate the absolute trajectory of a bar made up of a plurality of absolute coordinates. Of course, each absolute coordinate in the absolute track also corresponds to fingerprint data. Thus, a location database can be constructed using the absolute tracks and the corresponding fingerprint data.

As an example, the location database may be composed of a plurality of tables. Each table includes a column indicating absolute coordinates and a column indicating fingerprint data, with a one-to-one correspondence between the absolute coordinates and the fingerprint data. Absolute coordinates in the same table may constitute an absolute track. Thus, the established positioning database not only comprises the corresponding relation between the position points and the fingerprint data, but also comprises the corresponding relation between the paths and the fingerprint data.

Since multiple users walk the same actual path in the same mall, multiple absolute tracks indicating the same actual path may be included in the location database created by the server.

In some embodiments, the server obtains multiple absolute tracks indicating the same actual path. Illustratively, the server may determine a plurality of absolute traces indicative of the same actual path from the fingerprint data to which the absolute traces are paired. For example, two absolute traces may be determined to indicate the same actual path if there is a pairwise match between the fingerprint data for the two absolute traces.

Then, the server may process the acquired plurality of absolute tracks using the Graph SLAM model to obtain a corrected track indicating the actual path. The Graph SLAM model can be used for averaging a plurality of absolute tracks. In the process of processing the absolute track by using the Graph SLAM model, the absolute coordinate of the absolute track can be corrected, but the fingerprint data corresponding to the absolute coordinate is not changed. Therefore, the corrected absolute coordinates also correspond to the fingerprint data. And updating a plurality of absolute tracks indicating the same actual path in the positioning database by using the corrected tracks and the corresponding fingerprint data, thereby realizing the optimization of the positioning database.

In the embodiment of the present application, the location database includes a corresponding relationship between absolute coordinates in a mall and fingerprint data. Thus, the process of navigating the mobile phone by using the positioning database can be as follows: when the mobile phone acquires a fingerprint data, the mobile phone traverses the positioning database by using the fingerprint data to search the corresponding absolute coordinate, thereby realizing positioning.

In further embodiments, the location database further comprises a correspondence between absolute trajectories and fingerprint data. Thus, the process of navigating the mobile phone by using the positioning database can be as follows:

after the mobile phone acquires the first fingerprint data (such as the first fingerprint data), the corresponding absolute coordinates, such as the first positioning coordinates, are searched from the positioning database by using the fingerprint data. And determining an absolute track corresponding to the searched absolute coordinates, such as an absolute track 1. In this way, when the mobile phone acquires the second fingerprint data, the second fingerprint data (for example, referred to as second fingerprint data) is matched with the fingerprint data corresponding to other coordinate points (for example, referred to as second positioning coordinates) in the absolute track 1, so as to quickly determine the corresponding absolute coordinates, that is, the third positioning coordinates corresponding to the second fingerprint data. Therefore, the situation that all fingerprint data collected by the mobile phone need to be compared through the traversing positioning database is avoided, and the accuracy and the timeliness of indoor positioning are improved.

As an example, after acquiring the fingerprint data, the mobile phone may send the fingerprint data to the server, and the server performs positioning using the positioning database, and feeds back the positioning result to the mobile phone.

As another example, the mobile phone may also obtain the location database from the server, so that the mobile phone may determine the location result from the location database according to the fingerprint data collected in real time.

As an implementation manner, the method provided in this embodiment may also be applied to a second device, such as a server.

As shown in fig. 13, the above method may include the steps of:

s201, the server receives first crowdsourcing data from each mobile phone.

In some embodiments, the first crowdsourcing data includes a first track point, a second track point, and fingerprint data corresponding to the first track point and the second track point respectively. The first track point and the second track point belong to a first displacement track, namely, the mobile phone senses the movement track.

S202, the server determines a first coordinate of the first track point in a preset coordinate system.

In some embodiments, the preset coordinate system may be a world coordinate system or a preselected map coordinate system. For convenience of description, the preset coordinate system is a world coordinate system, and the first coordinate is an absolute coordinate.

As one example, the similarity of the fingerprint data may be utilized in conjunction with known track points (e.g., track points of known absolute coordinates) to determine a first coordinate of a first track point, i.e., a first absolute coordinate.

As another example, a first coordinate, e.g., a first absolute coordinate, of the first track point may also be determined by using calibration information (e.g., a first area name) corresponding to the first track point in conjunction with the POI database. And in the process of determining the first absolute coordinate of the first track point by combining the POI database, the absolute coordinate directly inquired from the POI database by using the first area name is called as a second absolute coordinate. When the area corresponding to the first area name does not exceed the preset area threshold, the second absolute coordinate can be directly used as the first absolute coordinate. When the area corresponding to the first area name exceeds the preset area threshold, the first absolute coordinate is further determined according to the second absolute coordinate and by combining with other crowdsourcing data, and the determination principle may refer to the description in the foregoing embodiment, which is not described herein again.

And S203, the server calibrates the distance and the direction of the second track point relative to the first track point according to the first coordinate of the first track point.

In some embodiments, the calibrated second trajectory point and the first trajectory point belong to a second displacement trajectory, and the second displacement trajectory can more accurately indicate the real movement trajectory of the mobile phone.

As an example, when there are at least two first trace points with determined first coordinates on the first displacement trace, the Graph SLAM model may be directly utilized to process the first displacement trace, so as to obtain an adjusted second displacement trace.

As another example, when there is only one first track point of the determined first coordinate on the first displacement track, the server acquires the third track. The fingerprint data of at least one track point in the third track is matched with the fingerprint data of the first track point or the second track point; and determining a fourth coordinate of at least one track point in the third track under a preset coordinate system. And splicing the first displacement track and the third track to obtain a combined track. In this way, according to the first coordinate and the fourth coordinate, a Graph SLAM model is combined, the initial direction of the combined track and the distance between track points contained in the combined track are calibrated, and a second displacement track is obtained.

And S204, the server determines a second coordinate of the second track point in a preset coordinate system according to the first coordinate and the calibrated distance and direction.

S205, the server constructs a positioning database according to the first coordinate, the second coordinate and the fingerprint data corresponding to the first track point and the second track point respectively.

In addition, an embodiment of the present application further provides an electronic device, which may include: a memory and one or more processors. The memory is coupled to the processor. The memory is for storing computer program code comprising computer instructions. The processor, when executing the computer instructions, may cause the electronic device to perform the steps performed by the handset or the server in the above embodiments. Of course, the electronic device includes, but is not limited to, the above-described memory and one or more processors. For example, the structure of the electronic device may refer to the structure of the server shown in fig. 3. For another example, the structure of the electronic device may be the structure of a mobile phone shown in fig. 4.

The embodiment of the present application further provides a chip system, which can be applied to the electronic device in the foregoing embodiments. As shown in FIG. 14, the system-on-chip includes at least one processor 2201 and at least one interface circuit 2202. The processor 2201 may be a processor in the electronic device described above. The processor 2201 and the interface circuit 2202 may be interconnected by wires. The processor 2201 may receive and execute computer instructions from the memory of the electronic device described above via the interface circuit 2202. The computer instructions, when executed by the processor 2201, may cause the electronic device to perform the steps performed by the cell phone in the embodiments described above. Of course, the chip system may further include other discrete devices, which is not specifically limited in this embodiment of the present application.

Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.

Each functional unit in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or make a contribution to the prior art, or all or part of the technical solutions may be implemented in the form of a software product stored in a storage medium and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a processor to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: flash memory, removable hard drive, read only memory, random access memory, magnetic or optical disk, and the like.

The above description is only a specific implementation of the embodiments of the present application, but the scope of the embodiments of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the embodiments of the present application should be covered by the scope of the embodiments of the present application. Therefore, the protection scope of the embodiments of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for creating a location database, the method being applied to a first device, the first device being communicatively connected to a plurality of second devices located in indoor locations, the method comprising:

the first device receiving first crowdsourcing data from each of the second devices; the first crowdsourcing data comprises a first track point, a second track point and fingerprint data corresponding to the first track point and the second track point respectively; the first track point and the second track point are track points on a first displacement track generated by the movement of a user in the indoor place, and the fingerprint data is used for indicating electromagnetic information of corresponding positions of the track points in the indoor place;

the first device determines a first coordinate of the first track point in a preset coordinate system;

the first device calibrates the distance and the direction of the second track point relative to the first track point according to the first coordinate of the first track point;

the first equipment determines a second coordinate of the second track point in the preset coordinate system according to the first coordinate and the calibrated distance and direction;

the first equipment constructs a positioning database according to the first coordinate, the second coordinate and the fingerprint data corresponding to the first track point and the second track point respectively, wherein the positioning database comprises the corresponding relation between the first coordinate and the second coordinate and the corresponding fingerprint data.

2. The method of claim 1, wherein the first crowdsourcing data further comprises: a first area name corresponding to the first track point; the preset coordinate system is a world coordinate system; the first coordinate is a first absolute coordinate under the world coordinate system;

the first device determines a first coordinate of the first track point in a preset coordinate system, and the method includes:

the first equipment determines a first absolute coordinate of the first track point under the world coordinate system according to the first area name and a preconfigured POI (point of interest) database; wherein the POI database includes absolute coordinates corresponding to different area names.

3. The method of claim 2, wherein the POI database further comprises: area areas corresponding to different area names;

the first device determines a first absolute coordinate of the first track point under the world coordinate system according to the first area name and a preconfigured POI (point of interest) database, and the method comprises the following steps:

the first equipment queries a second absolute coordinate and a region area corresponding to the first region name from the POI database according to the first region name;

and under the condition that the inquired area of the region does not exceed a preset area threshold, the first device determines the inquired second absolute coordinate to be the first absolute coordinate.

4. The method of claim 3, wherein the first device determines a first absolute coordinate of the first track point in the world coordinate system based on the first area name and a preconfigured point of interest (POI) database, further comprising:

under the condition that the inquired area of the region exceeds the preset area threshold, the first device acquires second crowdsourcing data and third crowdsourcing data; the second crowdsourcing data comprises third track points and corresponding second area names, the third crowdsourcing data comprises fourth track points and fifth track points, the fourth track points are matched with the fingerprint data corresponding to the first track points, and the fifth track points are matched with the fingerprint data corresponding to the third track points;

the first equipment queries a third absolute coordinate corresponding to the second area name from the POI database according to the second area name;

the first device obtains a track distance between the fourth track point and the fifth track point;

and the first equipment performs linear fitting according to the second absolute coordinate, the third absolute coordinate and the track distance to determine the first absolute coordinate.

5. The method of claim 1, wherein the first displacement trajectory comprises at least two of the first trajectory points;

the first device calibrates the distance and the direction of the second track point relative to the first track point according to the first coordinate of the first track point, and the calibration method includes:

and the first equipment calibrates the distance between the initial direction of the first displacement track and the track point contained in the first displacement track according to the first coordinates of at least two first track points and by combining a Graph optimization SLAM model to obtain a second displacement track.

6. The method of claim 1, wherein said first displacement trajectory comprises one of said first trajectory points;

the first device acquires a third track; the fingerprint data of at least one track point in the third track is matched with the fingerprint data of the first track point or the second track point; a fourth coordinate of at least one track point in the third track under the preset coordinate system is determined;

the first equipment splices the first displacement track and the third track to obtain a combined track;

and the first equipment calibrates the initial direction of the combined track and the distance between track points contained in the combined track according to the first coordinate and the fourth coordinate by combining a Graph SLAM model to obtain a second displacement track.

7. The method of claim 5 or 6, wherein before the first device determines the second coordinates of the second trajectory point in the predetermined coordinate system based on the first coordinates and the calibrated distance and direction, the method further comprises:

and the first equipment determines the calibrated distance and direction according to the second displacement track.

8. The method of claim 1, wherein after the database creation, the method further comprises:

the first device optimizes the location database using a Graph SLAM model.

9. A method for creating a location database, the method being applied to a second device located at an indoor location, the second device being communicatively connected to a first device, the method comprising:

the second equipment responds to the detected first event and collects a first track point and corresponding fingerprint data; the fingerprint data is used for indicating electromagnetic information of corresponding positions of track points in the indoor places; the first event comprises receiving an operation for indicating code scanning or identifying a passing landmark position point;

after the detected first event, the second equipment acquires a second track point and corresponding fingerprint data; the first track point and the second track point are track points on a first displacement track generated by the movement of the user in the indoor place;

the second device sending first crowdsourcing data to the first device; the first crowdsourcing data comprises: the fingerprint identification device comprises a first track point, a second track point and fingerprint data corresponding to the first track point and the second track point respectively;

10. The method of claim 9, wherein the first event is receipt of an operation to indicate a code sweep, the first crowdsourcing data further comprises a first area name corresponding to the first track point, the method further comprising:

the second equipment responds to the first event and acquires a display interface containing a scanning result;

the second equipment performs character recognition on the display interface;

and the second equipment determines the second equipment as the first area name according to the recognized text information.

11. The method of claim 9, wherein the first event is identification of a passing landmark location point, the first crowdsourcing data further comprises a first area name corresponding to the first track point, the method further comprising:

the second device determines that the second device passes through the landmark position point by using a scene recognition model;

the second equipment acquires landmark names corresponding to the landmark position points;

the second device determines the landmark name as the first area name.

12. The method of claim 9, wherein the first event further comprises capturing a first image comprising a displayed item in an indoor location; the second device comprises placing area names corresponding to different displayed articles; the first crowdsourcing data further comprises a first area name corresponding to the first track point, the method further comprising:

the second equipment inquires the name of a placement area corresponding to the displayed article;

the second device determines the name of the placement area as the first area name.

13. The method of claim 9, wherein the method further comprises:

the second device collects first fingerprint data;

the second device sending the first fingerprint data to the first device;

the second equipment receives the first positioning coordinates fed back by the first equipment; the first positioning coordinate is a coordinate matched with the first fingerprint data in the positioning database.

14. The method of claim 9, wherein the method further comprises:

the second equipment receives the positioning database sent by the first equipment;

the second device collects first fingerprint data;

the second equipment inquires a matched first positioning coordinate from the positioning database according to the first fingerprint data;

the second equipment acquires at least one second positioning coordinate from the positioning database according to the first positioning coordinate; the first positioning coordinate and each second positioning coordinate correspond to a displacement track;

the second equipment acquires second fingerprint data;

and the second equipment determines a matched third positioning coordinate from the second positioning coordinate according to the second fingerprint data.

15. An electronic device, characterized in that the electronic device comprises one or more processors and memory; the memory coupled with the processor, the memory for storing computer program code, the computer program code comprising computer instructions, which when executed by the one or more processors, cause the one or more processors to perform the method of any one of claims 1-8; or for performing the method of any one of claims 9-14.

16. A computer storage medium comprising computer instructions that, when executed on an electronic device, cause the electronic device to perform the method of any one of claims 1-8; or cause the electronic device to perform the method of any of claims 9-14.