CN106961671B

CN106961671B - Method and device for collecting indoor positioning data

Info

Publication number: CN106961671B
Application number: CN201610012385.2A
Authority: CN
Inventors: 李晴阳; 冯磊; 闻宏观; 刘贤胜; 曲文启
Original assignee: Autonavi Software Co Ltd
Current assignee: Alibaba China Co Ltd
Priority date: 2016-01-08
Filing date: 2016-01-08
Publication date: 2020-03-03
Anticipated expiration: 2036-01-08
Also published as: CN106961671A

Abstract

The application provides a method and a device for collecting indoor positioning data. The method comprises the following steps: acquiring an indoor map of an area to be acquired and road network data corresponding to the indoor map; generating an acquisition route comprising at least one path in the indoor map according to the road network data; receiving positioning information generated by at least one signal source device acquired at an acquisition point along the acquisition route; obtaining initial indoor positioning data according to the positioning information of each acquisition point and the position information of the acquisition points in the road network; and compiling the initial indoor positioning data to obtain the indoor positioning data. The method and the device improve the efficiency and accuracy of collecting indoor positioning data.

Description

Method and device for collecting indoor positioning data

Technical Field

The invention relates to the technical field of internet, in particular to a method and a device for collecting indoor positioning data and an indoor positioning method and device.

Background

At present, outdoor positioning technology is mature, positioning is mainly performed through a GPS, but because GPS signals can be shielded by buildings and the like, accurate indoor positioning cannot be achieved due to weak indoor GPS signals. The indoor positioning technology is still in research and experimental stages, and currently, mainly scans positioning information on the inner periphery of a building through terminal equipment, matches the positioning information with indoor positioning data in a preset indoor positioning database, and takes the position corresponding to the successfully matched indoor positioning data as the current position of the terminal equipment.

The indoor positioning data is mainly acquired through the following modes: the method comprises the following steps that field workers identify indoor roads according to an indoor plane graph, collect wifi information on the identified indoor roads and mark position points on the indoor plane graph; after the collection is completed, the collected indoor positioning data and the position points marked on the indoor plane graph are given to the field workers, and the collected indoor positioning data and the position points on the indoor plane graph are processed by the field workers to obtain the indoor positioning data.

According to the acquisition mode of the indoor positioning data, on one hand, after the outdoor personnel finishes acquisition, the indoor personnel are handed to for processing, so that the efficiency is low, and the time delay is long; on the other hand, because the position points are directly marked on the indoor plane map manually, whether the position points are accurate depends on the professional level and experience of field workers, the marked position points are not accurate due to human factors, so that indoor positioning data obtained by processing is not accurate, and indoor positioning by subsequently using the indoor positioning data is also not accurate; on the other hand, the road is identified from the indoor plane graph manually, and the plane graph is rough in general drawing, so that the road is not easy to identify manually, and the problem of inaccurate or incomplete road identification is caused.

Disclosure of Invention

An object of an embodiment of the present application is to provide a method for collecting indoor positioning data, which improves indoor positioning data collection efficiency and accuracy.

According to an embodiment of the present application, there is provided a method of collecting indoor positioning data, the method including the steps of:

acquiring an indoor map of an area to be acquired and road network data corresponding to the indoor map;

generating an acquisition route comprising at least one path in the indoor map according to the road network data;

receiving positioning information generated by at least one signal source device acquired at an acquisition point along the acquisition route;

obtaining initial indoor positioning data according to the positioning information of each acquisition point and the position information of the acquisition points in the road network;

and compiling the initial indoor positioning data to obtain the indoor positioning data.

According to an embodiment of the present application, there is provided an indoor positioning method including:

receiving and storing indoor positioning data; the indoor positioning data is obtained according to the method for acquiring the indoor positioning data;

receiving a positioning request carrying positioning information;

determining the position information corresponding to the positioning information in the positioning request according to the positioning information in the positioning request, the positioning information in the stored indoor positioning data and the position information thereof;

and feeding back the determined position information.

According to an embodiment of the present application, there is provided an apparatus for collecting indoor positioning data, the apparatus including:

the acquisition unit is used for acquiring an indoor map of an area to be acquired and road network data corresponding to the indoor map;

the acquisition route generating unit is used for generating an acquisition route comprising at least one path in the indoor map according to the road network data;

the positioning information receiving unit is used for receiving positioning information generated by at least one signal source device collected at a collection point along the collection route;

the initial indoor positioning data generating unit is used for obtaining initial indoor positioning data according to the positioning information of each acquisition point and the position information of the acquisition points in the road network;

and the indoor positioning data generating unit is used for compiling the initial indoor positioning data to obtain the indoor positioning data.

According to an embodiment of the present application, there is provided an indoor positioning device including:

the indoor positioning data receiving unit is used for receiving and storing the indoor positioning data uploaded by the indoor positioning data acquisition device;

a positioning request receiving unit, configured to receive a positioning request carrying positioning information;

the position information determining unit is used for determining position information corresponding to the positioning information in the positioning request according to the positioning information in the positioning request, the positioning information in the stored indoor positioning data and the position information thereof;

and the positioning information feedback unit is used for feeding back the determined positioning information.

In one embodiment of the application, on one hand, an indoor map and road network data of an area to be collected are generated in advance, and the road network data comprises position information of all roads of the area to be collected, so that after positioning information is collected on the roads of the area to be collected through terminal equipment, indoor positioning data can be generated directly according to the positioning information and the position information of a collection point, and the problems of long time delay and low efficiency caused by centralized processing by an interior worker after wifi data is collected by an exterior worker in the prior art are avoided; on the other hand, the position information of the acquisition point already contains accurate longitude and latitude coordinate information in the road network data, so that the problem that the position point is possibly marked inaccurately because the position point of the acquisition point is marked on the plane graph manually in the acquisition process in the prior art is solved; on the other hand, the acquisition route is automatically generated according to the road network data corresponding to the indoor map of the area to be acquired, and the road to be included in the area to be acquired is already determined in the road network data, so that the generated acquisition route can clearly and definitely guide the road to be acquired to the user, and the problem that the indoor positioning data is not completely acquired due to difficulty in road identification or incomplete road identification caused by the fact that the user identifies the road according to the plan is avoided; on the last hand, the positioning information collected on the collection route can be generated by a plurality of signal source devices, and the positioning information can be collected as long as the positioning information can be generated, so that the collected positioning information is more comprehensive.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:

fig. 1 is a flowchart of a method for acquiring indoor positioning data according to the present application.

FIG. 2 illustrates various paths in an indoor map of an area to be collected according to one embodiment of the present application.

FIG. 2a illustrates an acquisition route generated according to one embodiment of the present application.

Fig. 3 shows one of the flowcharts of the specific implementation of step S140 according to one embodiment of the present application.

Fig. 3a shows a second flowchart of a specific implementation of step S140 according to an embodiment of the present application.

Fig. 3b shows a third flowchart of a specific implementation of step S140 according to an embodiment of the present application.

Fig. 4 shows a curve formed by sequentially connecting the signal strengths of the wireless access points acquired by the acquisition points according to the acquisition sequence according to an embodiment of the present application.

Fig. 5 shows a schematic diagram of rarefaction location information obtained by rarefaction according to an embodiment of the present application.

Fig. 6 illustrates a graph with a lack of strength values after the rarefied positioning information is sampled according to one embodiment of the present application.

FIG. 7 shows a schematic diagram of filling up the absence of intensity values in FIG. 6 by fitting according to one embodiment of the present application.

Fig. 8 is a second flowchart of a method for acquiring indoor positioning data according to the present application.

Fig. 9 is a third flowchart of a method for acquiring indoor positioning data according to the present application.

Fig. 10 is a flowchart of an indoor positioning method according to the present application.

Fig. 11 is one of block diagrams of an apparatus for acquiring indoor positioning data according to the present application.

Fig. 12 is one of the block diagrams of the structure of an initial indoor positioning data generating unit 140 according to the present application.

Fig. 12a is a second block diagram of an initial indoor positioning data generating unit 140 according to the present application.

Fig. 12b is a third block diagram of an initial indoor positioning data generating unit 140 according to the present application.

Fig. 13 is a second block diagram of an apparatus for acquiring indoor positioning data according to the present application.

Fig. 14 is a third block diagram of an apparatus for acquiring indoor positioning data according to the present application.

Fig. 15 is a block diagram of an indoor positioning device according to the present application.

The same or similar reference numbers in the drawings identify the same or similar elements.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.

The computer equipment comprises user equipment and network equipment. Wherein the user equipment includes but is not limited to computers, smart phones, PDAs, etc.; the network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of computers or network servers, wherein Cloud Computing is one of distributed Computing, a super virtual computer consisting of a collection of loosely coupled computers. The computer equipment can be independently operated to realize the application, and can also be accessed into a network to realize the application through the interactive operation with other computer equipment in the network. The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, and the like.

It should be noted that the user equipment, the network device, the network, etc. are only examples, and other existing or future computer devices or networks may also be included in the scope of the present application, if applicable, and are included by reference.

The methods discussed below, some of which are illustrated by flow diagrams, may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine or computer readable medium such as a storage medium. The processor(s) may perform the necessary tasks.

Specific structural and functional details disclosed herein are merely representative and are provided for purposes of describing example embodiments of the present application. This application may, however, be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein.

It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element may be termed a second element, and, similarly, a second element may be termed a first element, without departing from the scope of example embodiments. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present. Other words used to describe the relationship between elements (e.g., "between" versus "directly between", "adjacent" versus "directly adjacent to", etc.) should be interpreted in a similar manner.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be noted that, in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed substantially concurrently, or the figures may sometimes be executed in the reverse order, depending upon the functionality/acts involved.

The technical solution of the present application is further described in detail below with reference to the accompanying drawings.

Fig. 1 is a flowchart of a method for acquiring indoor positioning data according to an embodiment of the present application, where the method may be implemented in a terminal device (e.g., a mobile phone, a PAD, a mobile computer, etc.). The method comprises the following steps:

step S110, acquiring an indoor map of an area to be acquired and road network data corresponding to the indoor map;

in the embodiment of the invention, map data is collected on each floor of a building in advance, wherein the map data can comprise rendering data (including POI data (such as shops, office areas and the like), public facility data (such as washrooms, stairwells and the like) and road data), and an indoor map and road network data of the floor are obtained. The indoor map of the floor can be further subdivided into a plurality of blocks according to regions (such as generating grids), and each block corresponds to one indoor map and road network data. In step S110, the area to be collected is an area where the user is to collect the indoor positioning data, and the area to be collected may be one floor or a part of the floor. And acquiring the indoor map and road network data of the area to be acquired from the pre-generated indoor map.

In the embodiment of the present application, the pre-generated road network data includes location information (e.g., longitude and latitude coordinates) of all roads included in the corresponding area.

Step S120, generating a collection route comprising at least one path in the indoor map according to the road network data;

step S130, receiving positioning information generated by at least one signal source device collected at a collection point along the collection route;

step S140, obtaining initial indoor positioning data according to the positioning information of each acquisition point and the position information of the acquisition points in the road network;

and S150, compiling the initial indoor positioning data to obtain the indoor positioning data.

Step S150, compiling the initial indoor positioning data, specifically, performing binary compilation on the initial indoor positioning data to obtain a binary file, so as to be suitable for network transmission and data reading.

In step S120, the specific implementation may adopt, but is not limited to, the following three ways:

the method comprises the steps that 1, according to a starting point and a finishing point selected by a user (the user can select points on an indoor map and can input the starting point and the finishing point in a search box of the indoor map), at least one to-be-selected collection route connected with the starting point and the finishing point is planned from the road network data; selecting an acquisition route from the to-be-selected acquisition routes according to the sum of the number of the paths and the path length of the to-be-selected acquisition routes;

mode 2, determining a starting point and a finishing point (for example, two doors with the farthest distance are used as the starting point and the finishing point) according to the entrance and exit information of the area to be collected, and planning at least one route to be collected, which is connected with the starting point and the finishing point, from the road network data; selecting an acquisition route from the to-be-selected acquisition routes according to the sum of the number of the paths and the path length of the to-be-selected acquisition routes;

and a mode 3 of planning at least a plurality of collection routes according to the paths in the road network data, wherein the paths contained in the collection routes are not overlapped, and the paths contained in the collection routes are all the paths contained in the road network data. The specific implementation can be as follows: selecting a plurality of pairs of start and end points on the road of the indoor map; for each pair of start and end points, planning a collection route connected with the start and end points from unplanned routes in the road network data, and marking the routes contained in the collection route as planned routes; until there is no unplanned path in the road network data.

In the foregoing mode 1 and mode 2, selecting an acquisition route from the to-be-selected acquisition routes according to a sum of the number of routes and the length of the routes included in the to-be-selected acquisition routes specifically includes:

step a1, aiming at each to-be-selected acquisition route, obtaining the recommendation degree of the to-be-selected acquisition route according to the sum of the number of the paths and the path length of the to-be-selected acquisition route.

Step a2, determining the collection route to be selected with the highest recommendation degree as the collection route.

In step a1, the specific implementation is as follows: determining a first score corresponding to the number of paths contained in the to-be-selected acquisition route according to the relation between the preset number of paths and the preset score; wherein the larger the number of paths, the higher the corresponding score; determining a second score corresponding to the total path length contained in the route to be collected according to the relation between the preset total path length and the preset score; wherein longer total lengths of the route correspond to higher scores. For example: presetting length ranges of the total lengths of a plurality of routes, wherein each length range corresponds to a score, comparing the total length of the paths contained in the to-be-selected acquisition route with the length ranges, and determining the score corresponding to the length range in which the path falls as the second score; and determining the recommendation degree of the to-be-selected collection route according to the first score and a preset first weight of the to-be-selected collection route (the first weight is set according to the number of routes, if the number of routes is larger, the corresponding first weight is larger), the second score and a preset second weight (the second weight is set according to the route length, if the length of the route is larger, the corresponding second weight is larger), wherein the recommendation degree of the to-be-selected collection route is determined (for example, the recommendation degree is the first score and the second score). In the embodiment of the present invention, the first weight and the second weight may be set in the following but not limited to two ways: the method 1, setting the first weight according to the number of paths, wherein the first weight is larger if the number of paths is larger; the second weight is set according to the length of the route, and if the length of the route is larger, the corresponding second weight is larger; when the recommendation degree of the to-be-selected acquisition route is subsequently calculated, acquiring a first weight corresponding to the number of paths contained in the to-be-selected acquisition route and a second weight corresponding to the total length of the to-be-selected acquisition route; mode 2, the first weight and the second weight are set to two constants.

The above-described procedure will be specifically described below by taking fig. 2 as an example of a road network of an indoor map of a certain floor.

Such as FIG. 2, including paths AB, BE, DE, AD, BC, CF, EF, FH, EG, GH, HK, GI, IK. Assume that the user selects node a in fig. 2 as the starting point and K as the ending point. Planning the following 15 to-be-selected acquisition routes connecting A and K from the road network data:

route 1: AB-BC-CF-FH-HK

Route 2: AB-BC-CF-FH-HG-GI-IK

Route 3: AB-BC-CF-FE-EG-GH-HK

Route 4: AB-BC-CF-FE-EG-GI-IK

Route 5: AB-BE-EF-FH-HK

Route 6: AB-BE-EF-FH-HG-GI-IK

Route 7: AB-BE-EG-GH-HK

Route 8: AB-BE-EG-GI-IK

Route 9: AD-DE-EF-FH-HK

Route 10: AD-DE-EF-FH-HG-GI-IK

Route 11: AD-DE-EG-GH-HK

Route 12: AD-DE-EG-GI-IK

Route 13: AD-DE-EB-BC-CF-FH-HK

Route 14: AD-DE-EB-BC-CF-FE-EG-GH-HK

Route 15: AD-DE-EB-BC-CF-FE-EG-GI-IK

The greater the number of paths traversed, the greater the first score. The longer the total length of the route, the larger the second score. For example, the first score is the number of paths traversed and the second score is the total length of the route. Assuming in FIG. 2 that AB, BE, DE, AD, BC, CF, EF, FH, EG, GH, HK, GI, IK are all 1 meter, the first and second scores for routes 1-15 are as follows:

assuming that the first weight is 0.4 and the second weight is 0.6, according to the following equation:

the recommendation degree of each route is the first score × the weight of the first score + the second score × the weight of the second score (formula 1)

And finally, calculating the recommendation degree of each route as follows:

route of road	Degree of recommendation
		Route
1	5×0.4+0.6×5＝5
		Route 2	7×0.4+0.6×7＝7
Route 3	7×0.4+0.6×7＝7
		Route 4	7×0.4+0.6×7＝7
Route 5	5×0.4+0.6×5＝5
		Route 6	7×0.4+0.6×7＝5
Route 7	5×0.4+0.6×5＝5
		Route 8	5×0.4+0.6×5＝5
Route 9	5×0.4+0.6×5＝5
		Route 10	7×0.4+0.6×7＝7
Route 11	5×0.4+0.6×5＝5
		Route 12	5×0.4+0.6×5＝5
Route 13	7×0.4+0.6×7＝7
		Route 14	9×0.4+0.6×9＝9
Route 15	9×0.4+0.6×9＝9

As can be seen from the above table, the recommendations of the routes 14 and 15 are the highest, and therefore, one of the routes 14 and 15, for example, the route 14, can be determined as the collection route, as shown in fig. 2 a.

In the above example, the starting point is a and the end point is K. If the user selects another starting or ending point, the resulting collection route may be different. However, the acquisition route is selected based on the sum of the number of the paths and the length of the paths included in the to-be-selected acquisition route, so that the acquisition route can cover a longer path, and the acquired indoor positioning data is more comprehensive.

In step S130, the collection point may be a position point marked on the collection route in advance after the collection person obtains the collection route, and the collection person collects positioning information at the collection point; or a timer is arranged in the terminal equipment, timing is started when the acquisition personnel acquires along the acquisition route, positioning information is acquired once every preset time interval (such as 10ms), and the position point of the acquired positioning information is determined as an acquisition point; when the road network data is generated, an acquisition point is set for each path in advance and stored (for example, an acquisition point is set every 0.3 meter on the path), and after the acquisition route is generated, the acquisition point preset on the path included in the acquisition route can be directly known.

In step S130, the signal source device may include, but is not limited to, at least one of the following devices: AP (corresponding positioning information may include wifi, bluetooth, etc.), base station, sensor (such as barometer (corresponding positioning information is barometric information), gravimeter (corresponding positioning information is gravity information), magnetometer (corresponding positioning information is magnetic field information), gyroscope (corresponding positioning information is speed information and direction information), and optical sensor, etc.).

In step S140, the initial indoor positioning data is obtained according to the positioning information of each acquisition point and the position information of the acquisition point in the road network, which can be specifically realized by the method flow shown in fig. 3, where the method includes:

step S141, dividing the positioning information corresponding to the acquisition points, which is the positioning information of the same signal source device, into a group to obtain at least one group of positioning information;

s142, sequencing each group of positioning information according to the sequence of the acquisition points corresponding to the group of positioning information;

s143, performing rarefaction on each group of sorted positioning information respectively to obtain rarefaction positioning information;

s144, dividing the same collection points in the rarefaction positioning information into a group to obtain rarefaction positioning information corresponding to the collection points;

and S145, correlating the rarefaction positioning information corresponding to the acquisition point with the position information of the acquisition point in the road network to form initial indoor positioning data.

The step S143 may be implemented as follows: aiming at each group of sequenced positioning information, executing the following steps:

b1, determining the intensity change gradient of the positioning information according to the intensity value of each group of positioning information;

step b2, aiming at each gradient of the intensity change gradient, determining a sampling frequency according to the gradient of the gradient, and performing rarefaction on the positioning information contained in the gradient according to the sampling frequency to obtain rarefaction positioning information; wherein the lower the sampling frequency the more gradual the gradient, the higher the sampling frequency the more steep the gradient.

The gradient of each gradient in the present application refers to an absolute value of a slope of a straight line formed by the positioning information intensities at both ends in the gradient.

Fig. 4-5 are two schematic diagrams specifically describing the implementation process of step S143, taking the signal source device as the AP and the corresponding positioning information of the AP as the wifi signal as an example.

The strengths of the WIFI signals sequentially collected by the collection points along the collection route are connected into a curve, as shown in fig. 4. That is, along the route 14, AD-DE-EB-BC-CF-FE-EG-GH-HK, 1 meter per route segment, and once WIFI signal is collected on each route segment, for example, at an interval of 0.125 meters, the strengths of the collected WIFI signals are connected to form the curve of fig. 4. The curve shows the following variation characteristics: from the nodes A-F, the intensity change of the WIFI signal gradually rises; in the node F-H, the intensity change of the WIFI signal is smooth; at the node F-K, the intensity change of the WIFI signal is sharply reduced. The intensity of the WIFI signal changes smoothly, the characteristic change of the WIFI signal is small, and therefore a small amount of positioning information can be diluted in the gradient with a small gradient, so that the data volume is reduced while the loss of the information volume is small; the intensity of the WIFI signal changes more sharply, and the characteristic change of the WIFI signal is larger, so that more positioning information can be extracted on the gradient with a larger gradient, and the loss of information amount is ensured to be smaller while the data amount is reduced.

Thus, three gradient changes are identified in FIG. 4: the node A-F section has a gradient change, the node F-H section has a gradient change, and the node H-K section has a gradient change, wherein the gradient of the node H-K section > the gradient of the node A-F section > the gradient of the node F-H section. The gradient of the H-K section is the largest, and the sampling frequency from the H-K section is 8/m; the gradient of the A-F section is followed by a sampling frequency of 4/m from the A-F section; the gradient of the node F-H section is the smallest, the sampling frequency from the F-H section is 1/m, and the rarefaction is shown in fig. 5.

According to the embodiment of the application, the strength change gradient of the positioning information is determined firstly, more samples are taken at the part with large change gradient, and less samples are taken at the part with small change gradient, because the part with large change gradient can represent the characteristics of the wireless access point information. Through the processing, on one hand, the data volume is reduced through sampling, so that a terminal device end with processing capacity not as strong as that of the server can bear the task of collecting indoor positioning data without the need of the server for processing, the waiting time is shortened, and the efficiency is improved.

Preferably, in order to improve the accuracy of the rarefaction positioning information, a step S143a may be further included between the foregoing steps S143 and S144, as shown in fig. 3a, further including:

step S143a, after each group of positioning information is rarefied, rarefied positioning information to be corrected, with the strength value missing or the strength value jumping, is determined; fitting the strength values of the before and after adjacent sparse positioning information to be corrected to obtain the strength value of the sparse positioning information to be corrected, for example: and determining the average value of the intensity values of the front and rear adjacent sparse positioning information as the intensity value of the sparse positioning information to be corrected.

In fig. 6, in the sections a to F, the intensity information of the rarefaction positioning information b is missing, but the adjacent rarefaction positioning information a and c of the rarefaction positioning information b both include intensity information, and the intensity information of the rarefaction positioning information b is obtained by fitting according to the intensity information of the rarefaction positioning information a and c. In the H-K section, the intensity information of the rarefaction positioning information m is lost, but the adjacent rarefaction positioning information l and n of the rarefaction positioning information m both comprise intensity information, and the intensity information of the rarefaction positioning information m is obtained by fitting according to the intensity information of the rarefaction positioning information l and n. As shown in fig. 7.

The jump of the strength value of the positioning information means that the difference between the strength value of a certain rarefaction positioning information and the strength values of two adjacent rarefaction positioning information is large, for example, the difference exceeds a predetermined threshold. For example, the intensity values of the consecutive 3 pieces of rarefied location information are 40, 90, and 50, respectively, and the predetermined threshold is 30. The difference between 40 and 90 and the difference between 90 and 50 exceed the threshold, and the intensity value 90 is determined to jump, and the jump intensity value can be fitted, for example, to 45, according to the intensity values 40 and 50 of the pre-and post-rarefaction location information.

Wherein the step S143a may be executed immediately after the step S143 performs the rarefaction on each pair of the positioning information; in step S143, all the groups of positioning information may be thinned and then executed, which is not strictly limited in this application.

Preferably, in order to further ensure the accuracy of the positioning information after thinning out, invalid data elimination is performed in advance on each set of positioning information after sorting, so step S142a may be further included before step S143, as shown in fig. 3 b:

step S142a, for each group of positioning information, determining invalid positioning information according to the attribute information of the group of positioning information, and removing the invalid positioning information. Determining the invalid positioning information may specifically be implemented as follows: judging whether the positioning information meets any one of the following conditions, and if so, determining that the positioning information is invalid positioning information: the method comprises the steps that under a first condition, the coverage range of positioning information is smaller than a preset coverage range threshold value; a second condition that the duration of the positioning information is less than a preset duration threshold; and under a third condition, the intensity of the positioning information value exceeds a standard intensity range corresponding to the type of the positioning information.

Taking the positioning information as the WIFI signal as an example, the terminal device collects the WIFI signal at a collection point on a collection route, so that not only can the intensity value of the WIFI signal be collected, but also the coverage area of the WIFI signal and the duration of the WIFI signal can be collected. If the coverage range of the WIFI signal is too small or the duration of the WIFI signal is too short, the detected WIFI signal is an unstable signal. Therefore, the coverage range of the WIFI signal is compared with a preset coverage range threshold value, and if the coverage range is smaller than the coverage range threshold value, the WIFI signal is regarded as invalid data to be removed; and comparing the duration of the WIFI signal with a preset duration threshold, and if the duration is less than the duration threshold, determining the WIFI signal as invalid data to be rejected. The normal WIFI signal strength should fall within a range, for example, the range of [ a, b ]. When the intensity of the acquired WIFI signal is smaller than a, the WIFI signal is not considered to be a normal WIFI signal, and the WIFI signal is probably generated due to acquisition errors or other abnormal conditions and is rejected as non-standard data.

To facilitate an understanding of the arrangement shown in fig. 3 by those skilled in the art, a specific example will be described. In this example, it is assumed that four signal source devices, A, B, C, D respectively, are arranged on the acquisition route, and the corresponding positioning information is a, b, c, and d; the collecting points are sequentially t1, t2, t3, t4, t5, t6, t7 and t8, and the positions of the collecting points in the road network are p1, p2, p3, p4, p5, p6, p7 and p 8; positioning information respectively acquired at acquisition points t1, t2, t3, t4, t5, t6, t7 and t8 is respectively as follows: { a1, b1} { a2, b2, c2} { a3, b3, c3} { b4, c4, d4} { b5, c5, d5} { c6, d6} { c7, d7} { d8 }. According to the steps shown in fig. 3: firstly, grouping positioning information acquired by 8 acquisition points according to signal source equipment to obtain four groups of positioning information, namely { a1, a2, a3}, { b1, b2, b3, b4, b5}, { c2, c3, c4, c5, c6, c7}, { d4, d5, d6, d7 and d8 }; secondly, respectively thinning the four groups of positioning information to obtain thinned positioning information which is { a1, a3}, { b1, b3, b5}, { c2, c4, c6}, { d4, d6, d8 }; then, the same acquisition points in the four groups of positioning information are grouped into one group, and 7 groups of sparse positioning information are { a1, b1}, { c2}, { a3, b3}, { c4, d4}, { b5}, { c6, d6}, and { d8 }; and finally, correlating the position information of the acquisition point with the positioning information after rarefaction to obtain initial indoor positioning data, such as: p1- { a1, b1}, p2- { c2}, p3- { a3, b3}, p4- { c4, d4}, p5- { b5}, p6- { c6, d6}, and p8- { d8} are used as initial indoor positioning data.

Preferably, in order to ensure the accuracy of the obtained indoor positioning data, in the aforementioned flow shown in fig. 3, before the initial indoor positioning data is compiled in step S150, the terminal device may further perform real-time positioning by using the generated initial indoor positioning data, and determine the accuracy of the initial indoor positioning data according to a comparison between the positioning result and a corresponding position on the road network. Specifically, the following steps are included before the aforementioned step S150, as shown in fig. 8:

step S140a, receiving positioning information collected at the verification point along the collection route;

step S140b, obtaining predicted position information of the verification point according to the positioning information of the verification point and the initial indoor positioning data;

the verification point may be a position point selected by the user in the acquisition route, or may be a verification point where the user holds the terminal device to start a timer when the user travels along the acquisition route, performs positioning once every set time interval, and uses the positioning point as the verification point, or may be a verification point where indoor positioning data is verified and generated in advance for each path and stored when the road network data is generated.

Step S140c, determining the positioning accuracy according to the predicted position information of the verification point and the position information of the verification point in the network;

the step S140c may be implemented as follows: matching the predicted position information of the verification point with the position information of the verification point in the road network, and determining that the verification point is successfully positioned if the matching is successful; and determining the ratio of the number of verification points with successful positioning to the total number of verification points as positioning accuracy. For example: the verification points are N, wherein N1 verification points are successfully positioned, and N2 verification points are failed to be positioned (wherein N1+ N2 is N), so that the positioning precision is N1/N. For example, there are 10 verification points in total, and if there are 9 verification points that match successfully, the positioning accuracy is 90%.

Step S140d, determining whether the positioning accuracy is greater than or equal to a preset accuracy threshold, if so, executing step S150; if not, the rarefaction strategy is adjusted to execute again S143.

For example, if the preset accuracy threshold is 80%, the positioning accuracy 90% is greater than the accuracy threshold, and step S150 may be executed. If the positioning accuracy is 70%, the positioning accuracy does not reach the accuracy threshold, and the sampling frequency of the H-K gradient and the sampling frequency of the a-F gradient may be increased in fig. 5, for example, so that the adjusted positioning accuracy can satisfy the accuracy threshold.

In step S140b, the predicted location information of the verification point is obtained according to the location information of the verification point and the initial indoor location data, and the specific implementation may be as follows:

step 1, respectively calculating the similarity between the positioning information of the verification point and each indoor positioning data in the initial indoor positioning data;

and step 2, determining the position information corresponding to the indoor positioning data with the highest similarity as the predicted position information of the verification point.

In the foregoing step 1, for each indoor positioning data in each initial indoor positioning data (each indoor positioning data includes at least one positioning information and position information), the specific implementation may be as follows:

step 1a, determining whether the indoor positioning data contains positioning information of the same type as the positioning information of the verification point; if not, determining that the similarity between the indoor positioning data and the positioning information of the verification point is 0; if yes, executing step 1 b;

step 1b, respectively calculating the similarity between each piece of positioning information in the verification point and the same type of positioning information in the indoor positioning data according to the same type of positioning information determined in the step 1 a; for example: the intensity value of a certain positioning information a1 of the verification point is k1, the intensity value of a positioning information a2 of the indoor positioning data with the same type as the positioning information a1 is k2, and the similarity between the positioning information a1 and the positioning information a2 is k1

And step 1c, comprehensively obtaining the similarity between the indoor positioning data and the positioning information of the verification point according to the similarities obtained in the step 1b and the quantity of the positioning information of the same type obtained in the step 1 a. The similarity may be obtained in, but is not limited to, the following manner:

mode 1, calculating the average value of each similarity obtained in step 1b (the average value can be an arithmetic average value, a geometric average value or a weighted average value); and multiplying the average value by a preset first weight to obtain a first product, multiplying the number of the positioning information with the same type as the positioning information of the verification point in the indoor positioning data by a second weight to obtain a second product, and determining the sum of the first product and the second product as the similarity of the indoor positioning data and the positioning information of the verification point.

Mode 2, judging the number n1 of positioning information of the same type as the verification point positioning information in the indoor positioning data determined in the step 1a, and determining the ratio of the number n1 to the total number n2 of the positioning information contained in the verification point; and judging whether the ratio is greater than or equal to a preset threshold value, if so, calculating an average value (the average value can be an arithmetic average value, a geometric average value or a weighted average value) of the similarity obtained in the step 1b, taking the average value as the similarity of the indoor positioning data and the positioning information of the verification point, and if not, determining that the similarity of the indoor positioning data and the positioning information of the verification point is 0.

For example: assuming that the verification point P comprises positioning information { a, b }; initial indoor positioning data includes four indoor positioning arrays, is respectively: p1- { a1}, p2- { a2, b2}, p3- { a3, b3, c3} p4- { c4, d4 }. According to the foregoing step 1a, a1 in p1- { a1} is the same type as the positioning information a of the verification point, a1 and b2 in p2- { a2 and b2} are the same type as the positioning information a and b of the verification point, a3 and b3 in p3- { a3, b3 and c3} are the same type as the positioning information a and b of the verification point, and p4- { c4 and d4} have no positioning information of the same type as the positioning information a of the verification point, so that the similarity between p4- { c4 and d4} and the positioning information a of the verification point is 0; according to the step 1b, calculating the similarity of a1 and a as p 11; and calculating the similarity of a2 and a as p21, the similarity of b2 and b as p22, the similarity of a3 and a as p31 and the similarity of b3 and b as p 32. According to the step 1c, the similarity of p1- { a1} and { a, b } is x1, and the similarity of p2- { a2, b2} and { a, b } is x2, and the similarity of p3- { a3, b3, c3} and { a, b } is x 3; for example: x1 ═ k1 × p11+ k2 × 1; x2 ═ k1 (p21+ p22)/2+ k2 × 2; x3 ═ k1 (p31+ p32)/2+ k2 × 2.

Preferably, the embodiment of the present invention may further include step S160 in the foregoing method flows shown in fig. 1 and fig. 8, as shown in fig. 9.

And step S160, uploading the indoor positioning data to a server.

As shown in fig. 10, an embodiment of the present application further provides an indoor positioning method at a server, where the positioning method may be executed in a terminal device or at the server. The indoor positioning method comprises the following steps:

step S210, receiving and storing indoor positioning data, wherein the indoor positioning data are acquired according to the method and are not described again;

step S220, receiving a positioning request carrying positioning information;

step S230, determining location information corresponding to the location information in the location request according to the location information in the location request, the location information in the stored indoor location data, and the location information thereof;

and step S240, feeding back the determined position information.

If the method is applied to the server, step S210 receives the indoor positioning data from the terminal device; step S220, receiving a positioning request sent by a user terminal; step S240 feeds back the determined location information to the user terminal.

In one embodiment, step S230 may be implemented as follows: and calculating the similarity between the positioning information in the positioning request and the positioning information in the indoor positioning data respectively, and taking the position information corresponding to the positioning information with the maximum similarity as the position information of the user terminal. How to calculate the similarity can be referred to the foregoing, and details are not repeated herein.

As shown in fig. 11, an embodiment of the present application provides an apparatus 100 for collecting indoor positioning data, the apparatus comprising:

the acquiring unit 110 is configured to acquire an indoor map of an area to be acquired and road network data corresponding to the indoor map;

an acquisition route generating unit 120, configured to generate an acquisition route including at least one path in the indoor map according to the road network data;

the collection route generating unit 120 is specifically configured to: determining a starting and ending point according to a starting and ending point selected by a user or according to entrance and exit information of an area to be collected, and planning at least one to-be-collected route connected with the starting and ending point from the road network data; selecting an acquisition route from the to-be-selected acquisition routes according to the sum of the number of the paths and the path length of the to-be-selected acquisition routes; or planning at least a plurality of collection routes according to the routes in the road network data, wherein the routes included in the collection routes are not overlapped, and the routes included in the collection routes are all the routes included in the road network data.

The acquisition route generating unit 120 selects an acquisition route from the acquisition routes to be selected according to the sum of the number of routes and the length of the routes included in the acquisition routes to be selected, and is specifically configured to:

for each to-be-selected acquisition route, obtaining the recommendation degree of the to-be-selected acquisition route according to the sum of the number of the paths and the path length of the to-be-selected acquisition route; and determining the to-be-selected acquisition route with the highest recommendation degree as the acquisition route.

The acquisition route generating unit 120 obtains the recommendation degree of the to-be-selected acquisition route according to the sum of the number of paths and the path length included in the to-be-selected acquisition route, and specifically includes:

determining a first score corresponding to the number of paths contained in the to-be-selected acquisition route according to the relation between the preset number of paths and the preset score; wherein the larger the number of paths, the higher the corresponding score;

determining a second score corresponding to the total path length contained in the route to be collected according to the relation between the preset total path length and the preset score; wherein longer total lengths of the route correspond to higher scores;

and determining the recommendation degree of the to-be-selected acquisition route according to the first score of the to-be-selected acquisition route, the preset first weight, the preset second score and the preset second weight.

A positioning information receiving unit 130, configured to receive positioning information generated by at least one signal source device collected at a collection point along the collection route;

the initial indoor positioning data generating unit 140 is configured to obtain initial indoor positioning data according to the positioning information of each acquisition point and the position information of the acquisition point in the road network;

and an indoor positioning data generating unit 150, configured to compile the initial indoor positioning data to obtain indoor positioning data.

Optionally, the structure of the initial indoor positioning data generating unit 140 is shown in fig. 12, and specifically includes:

a grouping subunit 1401, configured to group, into a group, the positioning information corresponding to the acquisition point, which is the positioning information of the same signal source device, to obtain at least one group of positioning information;

a sorting subunit 1402, configured to, for each group of positioning information, sort the group of positioning information according to the order in which the corresponding acquisition points are acquired;

a rarefying subunit 1403, configured to rarefy each set of sorted positioning information, respectively, to obtain rarefying positioning information;

a rarefaction positioning information generating subunit 1404, configured to divide the collection points in the rarefaction positioning information into a group, so as to obtain rarefaction positioning information corresponding to the collection points;

the initial indoor positioning data generating subunit 1405 is configured to associate rarefaction positioning information corresponding to the acquisition point with position information of the acquisition point in the road network, so as to form initial indoor positioning data.

Optionally, the thinning subunit 1403 is specifically configured to:

aiming at each group of sequenced positioning information, executing the following steps:

determining the intensity change gradient of the positioning information according to the intensity value of each group of positioning information;

aiming at each gradient of the intensity change gradient, determining sampling frequency according to the gradient of the gradient, and performing rarefaction on the positioning information contained in the gradient according to the sampling frequency to obtain rarefaction positioning information; wherein the lower the sampling frequency the more gradual the gradient, the higher the sampling frequency the more steep the gradient.

Optionally, the initial indoor positioning data generating unit 140 further includes a correcting subunit 1406, as shown in fig. 12 a:

a correcting subunit 1406, configured to determine rarefying-to-be-corrected positioning information with missing intensity value or jumping intensity value after the rarefying subunit 1403 rarefies each group of positioning information; and fitting the intensity values of the before and after adjacent sparse positioning information to be corrected to obtain the intensity value of the sparse positioning information to be corrected.

Preferably, the initial indoor positioning data generating unit 140 described in fig. 12 or fig. 12a further includes a filtering subunit 1407, and as shown in fig. 12b, the initial indoor positioning data generating unit 140 further includes the filtering subunit 1407 in fig. 12 a:

a filtering subunit 1407, configured to determine, before the rarefying subunit 1403 rarefies each set of positioning information, invalid positioning information according to the attribute information of the set of positioning information, and remove the invalid positioning information.

The filtering subunit 1407 determines invalid positioning information according to the attribute information of the group of positioning information, and is specifically configured to:

judging whether the positioning information meets any one of the following conditions, and if so, determining that the positioning information is invalid positioning information:

the method comprises the steps that under a first condition, the coverage range of positioning information is smaller than a preset coverage range threshold value;

a second condition that the duration of the positioning information is less than a preset duration threshold;

and under a third condition, the intensity of the positioning information value exceeds a standard intensity range corresponding to the type of the positioning information.

Preferably, the apparatus shown in fig. 12, 12a and 12b may further include a verification unit 160, and as shown in fig. 13, the apparatus shown in fig. 12 further includes a verification unit 160:

a verification unit 160, configured to receive positioning information collected at a verification point along the collection route before the indoor positioning data generation unit 150 compiles the initial indoor positioning data; obtaining predicted position information of the verification point according to the positioning information of the verification point and the initial indoor positioning data; determining the positioning accuracy according to the predicted position information of the verification point and the position information of the verification point in the road network; judging whether the positioning accuracy is greater than or equal to a preset accuracy threshold value, and if so, triggering the indoor positioning data generation unit 150; if not, the rarefying subunit 1403 is triggered to adjust the rarefying strategy to perform rarefying on each set of sequenced positioning information again.

Preferably, the verification unit 160 obtains predicted location information of the verification point according to the location information of the verification point and the initial indoor positioning data, and is specifically configured to:

calculating the similarity between the positioning information of the verification point and the positioning information in the initial indoor positioning data respectively, and taking the position information corresponding to the positioning information with the maximum similarity as the predicted position information of the verification point;

the verification unit 160 determines the positioning accuracy according to the predicted location information of the verification point and the location information of the verification point in the network, and is specifically configured to: matching the predicted position information of the verification point with the position information of the verification point in the road network, and determining that the verification point is successfully positioned if the matching is successful; and determining the ratio of the number of verification points with successful positioning to the total number of verification points as positioning accuracy.

Preferably, the apparatus shown in fig. 11 to 13 further includes an uploading unit 170, and as shown in fig. 14, the apparatus shown in fig. 11 further includes an uploading unit 170:

an uploading unit 170, configured to upload the indoor positioning data to a server.

As shown in fig. 15, an embodiment of the present application also provides an indoor positioning device 200, including:

an indoor positioning data receiving unit 210, configured to receive and store indoor positioning data uploaded by the foregoing device;

a positioning request receiving unit 220, configured to receive a positioning request carrying positioning information;

a location information determining unit 230, configured to determine, according to the location information in the location request, the location information in the stored indoor location data, and location information thereof, location information corresponding to the location information in the location request;

and a positioning information feedback unit 240, configured to feed back the determined positioning information.

It should be noted that some of the present invention can be applied as a computer program product, for example, computer program instructions, which when executed by a computer, can invoke or provide the method and/or technical solution according to the present invention through the operation of the computer. Program instructions which invoke the methods of the present invention may be stored on a fixed or removable recording medium and/or transmitted via a data stream on a broadcast or other signal-bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. An embodiment according to the invention herein comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform a method and/or solution according to embodiments of the invention as described above.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Claims

1. A method of collecting indoor positioning data, the method comprising the steps of:

compiling the initial indoor positioning data to obtain indoor positioning data;

the method comprises the following steps of obtaining initial indoor positioning data according to positioning information of each acquisition point and position information of the acquisition point in a road network, and specifically comprises the following steps:

dividing the positioning information corresponding to the acquisition points, which is the positioning information of the same signal source device, into a group to obtain at least one group of positioning information;

for each group of positioning information, sequencing the group of positioning information according to the sequence of the acquisition points corresponding to the group of positioning information;

respectively performing rarefaction on each group of sequenced positioning information to obtain rarefaction positioning information;

dividing the same collection points in the rarefaction positioning information into a group to obtain rarefaction positioning information corresponding to the collection points;

and correlating the rarefaction positioning information corresponding to the acquisition point with the position information of the acquisition point in the road network to form initial indoor positioning data.

2. The method according to claim 1, wherein the rarefying is performed on each set of sorted positioning information to obtain rarefying positioning information, and specifically comprises:

3. The method according to claim 1 or 2, wherein after performing the thinning for each set of positioning information, further comprising:

determining rarefaction positioning information to be corrected, wherein the intensity value is absent or jumped;

and fitting the intensity values of the before and after adjacent sparse positioning information to be corrected to obtain the intensity value of the sparse positioning information to be corrected.

4. The method of claim 1 or 2, wherein before performing the thinning for each set of positioning information, further comprising:

and determining invalid positioning information according to the attribute information of the group of positioning information, and removing the invalid positioning information.

5. The method of claim 4, wherein determining invalid positioning information according to the attribute information of the set of positioning information comprises:

6. The method according to claim 1 or 2, wherein before compiling the initial indoor positioning data, further comprising:

receiving positioning information acquired at a verification point along the acquisition route;

obtaining predicted position information of the verification point according to the positioning information of the verification point and the initial indoor positioning data;

determining the positioning accuracy according to the predicted position information of the verification point and the position information of the verification point in the road network;

judging whether the positioning precision is greater than or equal to a preset precision threshold value, if so, executing the step of compiling the initial indoor positioning data; and if not, adjusting the rarefaction strategy to re-execute the step of rarefaction on each group of the sorted positioning information respectively to obtain rarefaction positioning information.

7. The method according to claim 6, wherein obtaining the predicted location information of the verification point according to the location information of the verification point and the initial indoor positioning data specifically comprises:

determining the positioning accuracy according to the predicted position information of the verification point and the position information of the verification point in the road network, specifically comprising: matching the predicted position information of the verification point with the position information of the verification point in the road network, and determining that the verification point is successfully positioned if the matching is successful; and determining the ratio of the number of verification points with successful positioning to the total number of verification points as positioning accuracy.

8. The method of claim 1, wherein generating a collection route from said road network data comprising at least one path comprises:

determining a starting and ending point according to a starting and ending point selected by a user or according to entrance and exit information of an area to be collected, and planning at least one to-be-collected route connected with the starting and ending point from the road network data; selecting an acquisition route from the to-be-selected acquisition routes according to the sum of the number of the paths and the path length of the to-be-selected acquisition routes;

or planning at least a plurality of collection routes according to the routes in the road network data, wherein the routes included in the collection routes are not overlapped, and the routes included in the collection routes are all the routes included in the road network data.

9. The method according to claim 8, wherein selecting the collection route from the collection routes to be selected according to a sum of a number of paths and a path length included in the collection routes to be selected specifically comprises:

for each to-be-selected acquisition route, obtaining the recommendation degree of the to-be-selected acquisition route according to the sum of the number of the paths and the path length of the to-be-selected acquisition route;

and determining the to-be-selected acquisition route with the highest recommendation degree as the acquisition route.

10. The method according to claim 9, wherein obtaining the recommendation degree of the to-be-selected collection route according to a sum of the number of paths and the path length included in the to-be-selected collection route specifically includes:

11. The method of claim 1, further comprising:

and uploading the indoor positioning data to a server.

12. An indoor positioning method, characterized in that the indoor positioning method comprises:

receiving and storing indoor positioning data; wherein the indoor positioning data is obtained by the method for acquiring indoor positioning data according to any one of claims 1-11;

receiving a positioning request carrying positioning information;

determining position information corresponding to the positioning information in the positioning request according to the positioning information in the positioning request, the positioning information in the stored indoor positioning data and the position information thereof;

and feeding back the determined position information.

13. An apparatus for acquiring indoor positioning data, the apparatus comprising:

the indoor positioning data generating unit is used for compiling the initial indoor positioning data to obtain indoor positioning data;

wherein, initial indoor positioning data generation unit specifically includes:

the grouping subunit is used for grouping the positioning information of the same signal source device in the positioning information corresponding to the acquisition point to obtain at least one group of positioning information;

the sorting subunit is used for sorting each group of positioning information according to the sequence of the acquisition points corresponding to the group of positioning information;

the rarefying subunit is used for rarefying each group of sequenced positioning information respectively to obtain rarefying positioning information;

the rarefaction positioning information generation subunit is used for dividing the same acquisition points in the rarefaction positioning information into a group to obtain rarefaction positioning information corresponding to the acquisition points;

and the initial indoor positioning data generating subunit is used for correlating the rarefaction positioning information corresponding to the acquisition point with the position information of the acquisition point in the road network to form initial indoor positioning data.

14. The apparatus of claim 13, wherein the thinning subunit is specifically configured to:

15. The apparatus according to claim 13 or 14, wherein the initial indoor positioning data generating unit further comprises:

the correction subunit is used for determining the rarefaction positioning information to be corrected, with the intensity value missing or the intensity value jumping, after the rarefaction subunit rarefaction the positioning information of each group; and fitting the intensity values of the before and after adjacent sparse positioning information to be corrected to obtain the intensity value of the sparse positioning information to be corrected.

16. The apparatus according to claim 13 or 14, wherein the initial indoor positioning data generating unit further comprises:

and the filtering subunit is used for determining invalid positioning information according to the attribute information of the group of positioning information and rejecting the invalid positioning information before the rarefying subunit rarefies each group of positioning information.

17. The apparatus according to claim 16, wherein the filtering subunit determines invalid positioning information according to the attribute information of the group of positioning information, and is specifically configured to:

18. The apparatus of claim 13 or 14, further comprising:

a verification unit for receiving positioning information collected at a verification point along the collection route before the indoor positioning data generation unit compiles the initial indoor positioning data; obtaining predicted position information of the verification point according to the positioning information of the verification point and the initial indoor positioning data; determining the positioning accuracy according to the predicted position information of the verification point and the position information of the verification point in the road network; judging whether the positioning precision is greater than or equal to a preset precision threshold value, and if so, triggering the indoor positioning data generation unit; and if not, triggering the rarefying subunit to adjust the rarefying strategy and carrying out rarefying on each group of the sequenced positioning information again and respectively.

19. The apparatus according to claim 18, wherein the verification unit obtains predicted location information of the verification point according to the location information of the verification point and the initial indoor positioning data, and is specifically configured to:

the verification unit determines the positioning accuracy according to the predicted position information of the verification point and the position information of the verification point in the road network, and is specifically configured to: matching the predicted position information of the verification point with the position information of the verification point in the road network, and determining that the verification point is successfully positioned if the matching is successful; and determining the ratio of the number of verification points with successful positioning to the total number of verification points as positioning accuracy.

20. The apparatus according to claim 13, wherein the acquisition route generating unit is specifically configured to:

21. The apparatus according to claim 20, wherein the acquisition route generating unit selects an acquisition route from the acquisition routes to be selected according to a sum of a number of paths and a path length included in the acquisition routes to be selected, and is specifically configured to:

22. The apparatus according to claim 21, wherein the acquisition route generating unit obtains the recommendation degree of the to-be-selected acquisition route according to a sum of the number of paths and the length of the path included in the to-be-selected acquisition route, and specifically includes:

23. The apparatus of claim 13, further comprising:

and the uploading unit is used for uploading the indoor positioning data to a server.

24. An indoor positioning device, characterized in that, indoor positioning device includes:

an indoor positioning data receiving unit, which is used for receiving and storing indoor positioning data uploaded by the device of any one of claims 13-22;

25. A computer-readable storage medium, on which a computer program is stored which, when executed, implements the method of any of claims 1-12.