CN111464938B - Positioning method, positioning device, electronic equipment and computer readable storage medium - Google Patents
Positioning method, positioning device, electronic equipment and computer readable storage medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
Abstract
The embodiment of the invention discloses a positioning method, a positioning device, electronic equipment and a computer readable storage medium, wherein a corresponding central point is determined through a received positioning request, a plurality of input grids are determined according to the central point, map images of coverage areas corresponding to the input grids are obtained, characteristic information corresponding to the input grids is obtained according to the map images, a plurality of characteristic graphs are determined according to the characteristic information corresponding to the input grids, and the characteristic graphs are input to a pre-trained convolutional neural network model to obtain positioning information.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a positioning method, an apparatus, an electronic device, and a computer-readable storage medium.
Background
With the development of the mobile internet, a large number of Location Based Services (LBS) have emerged. The location accuracy of a mobile user or terminal is critical to the efficiency of these services. In the related art, GPS/GNSS positioning or positioning by a machine learning model is generally employed. However, in some situations, such as indoors, or bad weather, the GPS/GNSS based positioning may not be accurate.
To deal with the above situation, a network Location provider (nlp) service is generally used. The NLP service provides location estimation by combining wireless signal information in an environment with database information that remains online. In the existing machine learning model positioning method, the whole geographic space is divided into a large number of very small geographic grids, and the grid closest to the real position is obtained through recall, sorting, smoothing and other processing and is used as the position of a user or a terminal. However, this method cannot describe the local correlation of the mesh in space, and the addition of a smoothing process may lead to a situation where the optimization objective is not consistent with the final objective, and thus may lead to positioning inaccuracies.
Disclosure of Invention
In view of the above, the present invention provides a positioning method, an apparatus, an electronic device and a computer-readable storage medium, so as to reduce the loss of feature information and improve the accuracy of positioning.
In a first aspect, an embodiment of the present invention provides a positioning method, where the method includes:
receiving a positioning request;
determining a central point corresponding to the positioning request, wherein the central point is used for representing a relative position point corresponding to the positioning request;
determining a plurality of input grids according to the central point, wherein the input grids are pre-divided geographical areas;
acquiring map images of coverage areas corresponding to the plurality of input grids;
acquiring characteristic information corresponding to a plurality of input grids according to the map image, wherein the characteristic information comprises color characteristic information of the map image;
determining a plurality of feature maps according to feature information corresponding to each input grid, wherein the value of each pixel of the feature maps corresponds to the feature information of each input grid;
and inputting the plurality of characteristic graphs into a pre-trained convolutional neural network model for processing to obtain positioning information.
Optionally, the obtaining, according to the map image, feature information corresponding to a plurality of the input grids includes:
carrying out RGB color layering on the map image to obtain corresponding sub-images;
dividing each of the sub-images into a plurality of grid images, the grid images having corresponding input grids;
performing pooling processing on pixels in at least one grid image corresponding to the input grid to determine at least one color feature value of the input grid.
Optionally, the location request includes fingerprint information corresponding to a current location, where the fingerprint information includes a base station identifier and a base station signal strength corresponding to at least one piece of base station information scanned at the current location;
determining a central point corresponding to the positioning request comprises:
determining at least one cluster point corresponding to the base station according to the base station identifier, wherein the cluster point of the base station comprises at least one piece of position information;
and determining the central point according to the clustering points of all base stations in the fingerprint information.
Optionally, determining at least one cluster point corresponding to the base station according to the base station identifier includes:
acquiring at least one historical positioning record corresponding to the base station identifier, wherein the historical positioning record comprises the base station identifier and corresponding position information;
determining at least one cluster point of the base station according to the at least one historical positioning record.
Optionally, determining at least one cluster point corresponding to the base station according to the base station identifier includes:
and inquiring a base station cluster point database according to the base station identification, and determining at least one cluster point corresponding to the base station, wherein the base station cluster point database is determined according to a plurality of historical positioning records in a preset time period.
Optionally, determining the central point according to the clustering point of each base station in the fingerprint information includes:
and determining the position with the shortest total distance to each cluster point as the central point.
Optionally, the location request includes fingerprint information corresponding to the current location, where the fingerprint information includes an AP identifier and an AP signal strength of at least one wireless access point AP;
determining a central point corresponding to the positioning request comprises:
calculating the similarity between the fingerprint information of the positioning request and the fingerprint information of a plurality of predetermined candidate grids;
sorting each candidate grid according to the similarity;
obtaining at least one similar grid according to the similarity sorting result;
and determining the central point according to the position information of the at least one similar grid.
Optionally, the determining the central point according to the position of the at least one similar grid includes:
calculating a median or average of the position coordinates of the at least one similar grid;
and determining the median or the average value as the position coordinates of the central point.
Optionally, the feature information further includes a matching probability between the signal strength of the base station received by each input mesh and the signal strength of the base station in the positioning request, and/or a matching probability between the signal strength of the wireless access point AP received by each input mesh and the signal strength of the AP in the positioning request.
Optionally, inputting the plurality of feature maps into a pre-trained convolutional neural network model to obtain the positioning information includes:
inputting the plurality of feature maps into the convolutional neural network model for feature processing, and outputting a position offset, wherein the position offset is an offset of the positioning information relative to the central point;
and acquiring the positioning information according to the position deviation and the central point position.
In a second aspect, an embodiment of the present invention provides a positioning apparatus, where the apparatus includes:
a positioning request receiving unit configured to receive a positioning request;
a central point determining unit configured to determine a central point corresponding to the positioning request, where the central point is used to characterize a relative position point corresponding to the positioning request;
an input grid determining unit configured to determine a plurality of input grids according to the central point, the input grids being pre-divided geographical areas;
a map image acquisition unit configured to acquire map images of coverage areas corresponding to the plurality of input grids;
a feature information acquisition unit configured to acquire feature information corresponding to a plurality of the input grids from the map image, the feature information including color feature information of the map image;
a feature map determination unit configured to determine a plurality of feature maps from feature information corresponding to each of the input grids, a value of each pixel of the feature maps corresponding to the feature information of each of the input grids;
and the positioning information acquisition unit is configured to input the plurality of characteristic graphs into a pre-trained convolutional neural network model for processing, and acquire positioning information.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory is used to store one or more computer program instructions, where the one or more computer program instructions are executed by the processor to implement the method described above.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement a method as described above.
The method comprises the steps of determining a corresponding central point through a received positioning request, determining a plurality of input grids according to the central point, obtaining map images of coverage areas corresponding to the input grids, obtaining characteristic information corresponding to the input grids according to the map images, determining a plurality of characteristic graphs according to the characteristic information corresponding to the input grids, and inputting the characteristic graphs into a pre-trained convolutional neural network model to obtain positioning information.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of a positioning method of an embodiment of the present invention;
FIG. 2 is a flow chart of a method of determining a center point according to an embodiment of the present invention;
FIGS. 3 and 4 are schematic diagrams of another method of determining a center point according to an embodiment of the present invention;
FIGS. 5 and 6 are schematic diagrams of input grid determination processes according to embodiments of the present invention;
FIG. 7 is a schematic diagram of a process for obtaining a map image according to an embodiment of the invention;
FIG. 8 is a flow chart of a method of obtaining characteristic information in accordance with an embodiment of the present invention;
FIG. 9 is a process diagram of determining a feature map according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of a process for obtaining a plurality of feature maps according to an embodiment of the present invention;
FIG. 11 is a schematic illustration of a localization model of an embodiment of the present invention;
FIG. 12 is a schematic view of a positioning device of an embodiment of the present invention;
fig. 13 is a schematic diagram of an electronic device of an embodiment of the invention.
Detailed Description
The present invention will be described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details. Well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present invention.
Further, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout the description, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
Fig. 1 is a flowchart of a positioning method according to an embodiment of the present invention. As shown in fig. 1, the positioning method according to the embodiment of the present invention includes the following steps:
step S110, receiving a positioning request. In an optional implementation manner, the location request includes fingerprint information corresponding to a current location, where the fingerprint information includes a base station identifier and a base station signal strength corresponding to at least one piece of base station information scanned at the current location, and/or an AP identifier and an AP signal strength of at least one wireless access point AP, and the like. Alternatively, the identifier of the wireless access point AP may be a name or a MAC address of the wireless network.
Step S120, determining a central point corresponding to the positioning request. And the central point is used for representing a relative position point corresponding to the positioning request.
Fig. 2 is a flowchart of a method for determining a center point according to an embodiment of the present invention. If the positioning request includes the identification and signal strength of the AP scanned at the current location. In an alternative implementation, as shown in fig. 2, step S120 may include the following steps:
step S121 calculates the similarity between the fingerprint information of the positioning request and the fingerprint information of each of the predetermined candidate grids. Wherein, the grid is a pre-divided geographical area. In this embodiment, a large area (e.g., a city area) is pre-divided into a plurality of grids, optionally, each grid is N × N, and N is greater than or equal to 1 meter. Optionally, the candidate grids are part or all grids in the downtown area.
In an alternative implementation manner, the similarity and the difference between the AP identifier and the signal strength in the fingerprint information of the positioning request and the AP identifier and the signal strength in the fingerprint information of each candidate grid may be directly compared to determine the similarity. In another alternative implementation manner, the fingerprint information in the positioning request and the fingerprint information of each candidate grid may be subjected to discrete processing and/or normalization processing to form a corresponding first vector and a second vector, and a cosine similarity (or euclidean distance, etc.) between the first vector and the second vector is calculated to determine the similarity. It is easily understood that the higher the similarity is, the higher the probability that the current position of the user terminal is in the corresponding candidate grid is.
In an alternative implementation, the fingerprint information for each candidate mesh is obtained from a fingerprint database. Optionally, when each user terminal requests positioning in a grid, fingerprint information corresponding to the grid is determined and uploaded to the fingerprint database, or a terminal is adopted to scan a plurality of grids in advance to obtain a corresponding relationship between the position information of each grid and the fingerprint information scanned in the grid, and the corresponding relationship between the position information of each grid and the fingerprint information scanned in the grid is uploaded to the fingerprint database. Optionally, the fingerprint information of each grid is updated in real time, for example, the fingerprint information of the grid can be changed due to the fact that the name of the AP changes, the new AP is added, or the original AP is invalid, and therefore, the accuracy of positioning can be improved by updating the fingerprint information of each grid in real time.
And step S122, sorting the candidate grids according to the similarity. Optionally, the candidate grids may be ranked from large to small according to the corresponding similarity, or the candidate grids may be ranked from small to large according to the corresponding similarity, which is not limited in this embodiment.
And S123, acquiring at least one similar grid according to the similarity sorting result. Optionally, the first predetermined candidate grids with the highest similarity are obtained as similar grids.
Step S124, determining the central point according to the position information of at least one similar grid. In an alternative implementation manner, a median or an average of the position coordinates of each similar grid is calculated, and the median or the average is determined as the position coordinates of the central point.
Optionally, longitude and latitude are used to represent the position coordinates of each similar grid and the central point, and the position coordinate of the center of each similar grid is used to represent the position coordinate of the similar grid, taking an average value as an example, the position coordinate of the central point satisfies the following formula:
wherein center is_lonAs the longitude of the center point, center_latIs the latitude of the center point, K is the number of similar grids, gk_lonLongitude of center of k-th similar grid, gk_latThe latitude of the center of the kth similar grid.
In this embodiment, the coordinates of the center of the similar grid are used to represent the position coordinates of the similar grid, and it should be understood that the coordinates of other points in the grid can also be used as the position coordinates of the grid, which is not limited in this embodiment. It should be understood that other methods for determining the center point according to the positions of the similar grids may be applied to the embodiment, for example, weights are given to the grids based on the corresponding similarities, a weighted average of the position coordinates of the similar grids is calculated to obtain the position coordinate of the center point, and the like, which is not limited in the embodiment.
Fig. 3 and 4 are schematic diagrams of another method for determining a center point according to an embodiment of the present invention. And if the positioning request comprises the base station identification and the base station signal strength corresponding to the at least one piece of base station information scanned at the current position. In another alternative implementation manner, the central point corresponding to the positioning request may be determined according to the currently scanned information of the at least one base station. Optionally, at least one cluster point corresponding to the base station is determined according to the base station identifier, and a central point corresponding to the positioning request is determined according to the cluster point of each base station in the fingerprint information of the positioning request. Wherein the cluster point of the base station includes at least one location information.
In an optional implementation manner, at least one historical positioning record corresponding to the base station identifier is obtained, and at least one cluster point of the base station is determined according to the at least one historical positioning record. The historical positioning record comprises base station identification and corresponding position information.
As shown in fig. 3, assuming that the fingerprint information in the current positioning request includes the signal of the base station 31, a historical positioning record corresponding to the base station 31 or a historical positioning record within a predetermined time period (for example, within 1 month) is obtained, where the historical positioning record is a record of positioning performed by the base station 31 in the past. In the embodiment, the example of acquiring 3 historical positioning records is described, and in the 1 st historical positioning record, the terminal device 32 determines the position information as the position a according to the signal strength of the scanned base station 31, and/or the signal strength of other base stations, and/or the GPS signal. In the 2 nd historic positioning record, the terminal device 33 determines the position information as the position b through the scanned signal strength of the base station 31, and/or the signal strength of other base stations, and/or the GPS signal and the like. In the 3 rd historical positioning record, the terminal device 34 determines the location information as the location c according to the signal strength of the scanned base station 31, and/or the signal strength of other base stations, and/or GPS signals and the like. It is assumed that the distance between the position a and the position b is 15m, the distance between the position a and the position c is 100m, and the distance between the position b and the position c is 112 m. In the present embodiment, the cluster point range is a predetermined value, and assuming that the cluster point range is 20m, the position a and the position B form one cluster point a, and the position c forms another cluster point, that is, the base station 31 has two cluster points a and B.
In another optional implementation manner, a predetermined base station cluster point database is queried according to the base station identifier, and at least one cluster point corresponding to the base station is determined. The base station cluster point database is predetermined according to a plurality of historical positioning records. In this embodiment, at least one cluster point of each base station in an area (e.g., an urban area) may be determined in advance according to a large number of historical positioning records corresponding to each base station in the area within a predetermined time period (e.g., 1 month), and stored in the base station cluster point database. Optionally, the base station cluster point database is updated periodically, for example, once a week. Therefore, after the base station identifiers in the current positioning information are determined, one or more cluster points corresponding to the base stations can be determined according to the base station identifiers.
In an alternative implementation, the position where the average or total distance from each cluster point is the shortest is determined as the center point. As shown in fig. 4, the terminal device 44 sends a positioning request at the location x, wherein the fingerprint information in the positioning request includes base station signals of the base station 41, the base station 43 and the base station 43. That is, the terminal device 44 can receive the base station signals transmitted by the base stations 41, 43 and 43 at the position x. If it is found from step S120 that the base station 41 has cluster point C, the base station 42 has cluster point D, and the base station 43 has cluster points E and F, the position where the total distance (or average distance) from the cluster points C, D, E and F is shortest is determined as the center point p. That is, a position p is determined such that the total distance ((L) from the position p to the cluster points C, D, E and FpC+LpD+LpE+LpF) Shortest). Alternatively, the centers of cluster points C, D, E and F are taken as the coordinates of cluster points C, D, E and F.
Step S130, determining a plurality of input grids according to the central point. In an alternative implementation, the center point is used as a center, and the center point is expanded to M grids as input grids, wherein M is larger than 1. Optionally, when M is an odd number, as shown in fig. 5, the grid where the central point p is located is used as the central grid Cg, and the grid is expanded to M × M grids to be used as the input grid Pin, where M is taken as an example in fig. 5. Optionally, when M is an even number, the center point is taken as a relative center, so that the number of input grids on one side of the boundary where the center point is close to is greater than that on the other side, as shown in fig. 6, assuming that M is 6 and the center point p 'is close to the lower left boundary of the grids, the number of grids extending in the opposite direction of the x axis and the opposite direction of the y axis is 3, and the number of grids extending in the positive direction of the x axis and the positive direction of the y axis is 2, so as to form 6 × 6 input grids Pin'. It should be understood that other expansion methods can be applied to the present embodiment, for example, random expansion is performed such that the center point is located on one side of the grid with more rows or columns than on the other side of the grid, so as to form M × M grids.
In step S140, map images of coverage areas corresponding to the plurality of input grids are acquired.
Fig. 7 is a schematic diagram of a process for acquiring a map image according to an embodiment of the present invention. In an alternative implementation, a map image of a coverage area of the input grid is obtained according to the longitude and latitude of the boundary of the grid. As shown in fig. 7, the longitude and latitude of a part of the peripheral input grid boundary in the matrix formed by the input grid are obtained, taking the input grids 7a and 7b as an example, the longitude of the input grid 7a is x1 and the latitude is y1, and the longitude of the input grid 7b is x2 and the latitude is y 2. Thus, it is easy to know that the coverage areas corresponding to the plurality of input grids have longitude ranges of x1-x2 and latitude ranges of y1-y 2. A map image 72 obtained in the wide-range map 71 based on the longitude range of the coverage area corresponding to the plurality of input grids.
Step S150, acquiring characteristic information corresponding to a plurality of input grids according to the map image. Wherein the feature information includes color feature information of the map image. In current map images or map data, different colors are typically used to distinguish different geographical environments, e.g. water surface areas are typically blue, green areas are typically green, public buildings are typically pink, etc. Therefore, the map image can participate in positioning, the positioning to the surrounding river and other positions is avoided, and the positioning accuracy can be improved.
Fig. 8 is a flowchart of a method for obtaining feature information according to an embodiment of the present invention. As shown in fig. 8, in an alternative implementation, step S150 may include the following steps:
step S151, RGB color layering is performed on the map image, and a corresponding sub-image is acquired. The R value, the G value and the B value of each pixel in the map image are respectively acquired, and the sub-image of only the R channel, the sub-image of the G channel and the sub-image of the B channel are acquired.
In step S152, each sub-image is divided into a plurality of mesh images. Wherein the grid image has a corresponding input grid. Optionally, each sub-image is divided according to the size of the input grid, so as to obtain a plurality of grid images with the size substantially consistent with that of the input grid. Thus, each input grid has a corresponding three grid images.
Step S153, performing pooling processing on the pixels in at least one grid image corresponding to the input grid to determine at least one color feature value of the input grid. Since the RGB values of each pixel in the same grid image may be unbalanced, for example, a part of the grid image is green, a part of the grid image is blue, and the like, the pixels in each grid image need to be pooled to obtain the color feature value corresponding to the grid image. Alternatively, the pooling treatment may be maximum pooling (max pooling) or average pooling (average pooling). It should be understood that other processing, such as summing, averaging, etc., may also be performed on the pixels in the grid image, and this embodiment is not limited thereto.
In an alternative implementation, in the feature map formed by the input grids, that is, each input grid corresponds to one pixel, the feature maps corresponding to the three channels of R, G and B can be obtained by using the color feature value of the grid image corresponding to each input grid as the pixel value of each pixel of the feature map. In other alternative implementations, a vector formed by three color feature values corresponding to each input grid may also be used as a pixel value of each pixel of the feature map, so that a feature map of one channel may be obtained.
Step S160, determining a plurality of feature maps according to the feature information corresponding to each input grid. Wherein the value of each pixel of the feature map corresponds to the feature information of each input mesh.
FIG. 9 is a process diagram for determining a feature map according to an embodiment of the present invention. As shown in fig. 9, the acquired map image 91 is RGB color-layered, and corresponding sub-images 9R, 9G, and 9B are acquired. Wherein the sub-images 9R, 9G, and 9B are acquired by acquiring monochrome channel images of the map image. Thereafter, the sub-images 9R, 9G, and 9B are divided into a plurality of grid images, forming grid image matrices 9R ', 9G ', and 9B '. In the present embodiment, the sub-images 9R, 9G, and 9B are divided according to the size of the input mesh, and since the map image 91 is the coverage area of the input mesh, each mesh image has the same size as the input mesh and has a corresponding input mesh. Furthermore, since the RGB values of each pixel may be unbalanced in the same grid image, for example, a part of the grid image is green, a part of the grid image is blue, and the like, it is necessary to perform pooling processing on the pixels in each grid image to obtain the color feature value corresponding to the grid image. Finally, the color feature value of each mesh image in the mesh image matrix 9R ' is used as a pixel value to form a feature map 92, wherein each pixel in the feature map 92 corresponds to one input mesh, the color feature value of each mesh image in the mesh image matrix 9G ' is used as a pixel value to form a feature map 93, and the color feature value of each mesh image in the mesh image matrix 9B ' is used as a pixel value to form a feature map 94. Therefore, the map image can participate in positioning, the positioning to the surrounding river and other positions is avoided, and the positioning accuracy can be improved.
In another optional implementation manner, the feature information further includes a matching probability between the signal strength of the base station received by each input mesh and the signal strength of the base station in the positioning request, and/or a matching probability between the signal strength of the wireless access point AP received by each input mesh and the signal strength of the AP in the positioning request. Therefore, at least one type of characteristic information can be related to the fingerprint information in the positioning request, so that even if the central points obtained by calculation are the same, the characteristic information related to the fingerprint information is different due to the difference of the fingerprint information in the positioning request, and finally the obtained positioning information is different. Optionally, the feature value of the feature information may be a numerical value, or may also be a vector or other expression form, and this embodiment does not limit this.
In an alternative implementation, the signal strength (base station signal strength or AP signal strength) is divided into S levels, for example 7 levels 0-6, and the probability of matching the signal strength received by each input grid with the signal strength in the positioning request satisfies the following formula:
wherein f isp,iFor the match probability, S is the highest level of signal strength, hi,sReceiving the number of times the signal strength is s for the ith input grid, t is the signal strength in the positioning request, hi,tFor the number of times t the signal strength is received for the ith input grid, hi,t-1For the number of times the i-th input grid receives a signal strength of t-1, hi,t-1For the number of times the ith input trellis received signal strength is t +1, w1, w2, and w3 are the corresponding weights.
In an alternative implementation, the characteristic information of the input mesh includes at least one of a communication relationship of each input mesh with the main base station, a sum of signal strength matching probabilities of a plurality of base stations adjacent to the main base station, a distance of each input mesh from a center of the front base station, a communication relationship of each input mesh with the front base station, and a sum of heat information of the front base station. The main base station is a base station to which the terminal corresponding to the positioning request is currently connected, and the previous base station is a base station to which the terminal is connected before being connected with the main base station. Wherein the communication relationship of the input mesh and the main base station is used for representing whether the input mesh is in the coverage range of the main base station. Therefore, the positioning accuracy can be further improved by adding more base station information.
In an alternative implementation, the characteristic information further includes at least one of heat information of each input grid, geographical information, minimum signal strength scanned in each grid, and maximum signal strength scanned in each grid. Wherein the heat information is used to characterize the reachable state of the corresponding input grid and the number of arrivals for a predetermined time period. Optionally, the reachable state is used to indicate whether the grid is in a place where the vehicle can reach, for example, if there is an intersection in the grid, the grid is reachable, and if the grid is a green area, for example, a lawn, the grid is in a non-reachable state. The predetermined period of time may be one day, one week, one month, etc., and this embodiment is not limited thereto. The geographical information of the input grid may characterize the building density and/or greening density, etc. in the input grid. It should be understood that other characteristic information, such as people stream density, etc., may also be included, and the embodiment is not limited thereto. Optionally, the number of the feature information corresponds to the number of the feature maps.
Step S170, inputting a plurality of characteristic graphs into a pre-trained convolutional neural network model for processing, and acquiring positioning information. In an optional implementation manner, the plurality of feature maps are input to the convolutional neural network model for feature processing, a position offset is output, and positioning information is acquired according to the position offset and the position of the central point. Wherein the position offset is an offset of the positioning information relative to a position of the center point.
The convolutional neural network CNN is a feedforward neural network, and is composed of several convolutional layers and pooling layers. The CNN model has the characteristics of local area connection, weight sharing, down sampling and the like, the number of weights needing to be trained is reduced through the weight sharing, the computational complexity of the network is reduced, meanwhile, the network has certain invariance to input local transformation, such as translation invariance, scaling invariance and the like, and the generalization capability of the network is improved. Meanwhile, the CNN model has features of cross-feature extraction and fusion of peripheral information, so that the embodiment adopts the CNN model, does not need to do a large amount of complex feature work, can conveniently extend feature information, reduces loss of feature information of an input model, and has flexibility for adding features.
In this embodiment, the CNN model calculates a position offset of the current position corresponding to the positioning request, optionally, the position offset includes a longitude offset and a latitude offset relative to the center point position, and based on the position offset and the position information of the center point, the position information of the current position may be obtained through calculation. Therefore, the positioning problem can be converted into a regression problem, and the positioning accuracy is improved.
The CNN model can complete classification tasks and can also complete regression tasks. If the positioning problem of this embodiment is converted into a classification problem, it can only be determined by the CNN model that each grid is or is not the grid where the true value point is located. In this case, if the M × M meshes obtained do not cover the true value points, a situation in which no localization is possible occurs. Therefore, in the embodiment, the positioning problem is converted into the regression problem by using the CNN model, so that the position offset can be always output, and the positioning accuracy is improved.
In an alternative implementation, the convolutional neural network model of the present embodiment is trained according to a predetermined loss function. The loss function may directly affect the performance of the convolutional neural network model, and optionally, the loss function adopted in this embodiment is:
and the delta lon _ pred and the delta lat _ pred are the deviations of the predicted position of the convolutional neural network model and the central point in longitude and latitude, and the delta lon _ label and the delta lat _ label are the deviations of the satellite positioning position of the sample and the central point in longitude and latitude.
Therefore, the convolutional neural network model is trained based on the loss function until the loss function is minimized, namely the error distance is minimized, and the trained convolutional neural network model is obtained.
In an optional implementation manner, by acquiring training data, where the training data includes a plurality of sample data, and the sample data includes a satellite positioning position of the sample and fingerprint information corresponding to a grid where the satellite positioning position of the sample is located, steps S120 to S160 are performed to process the training data, and based on the loss function, a loss is calculated according to the positioning information predicted in step S160 and the satellite positioning position of the corresponding sample, and a parameter of the convolutional neural network model is adjusted according to the loss until the loss function is minimized.
In this embodiment, the fingerprint information of the grid where the satellite positioning position of the sample is located is acquired, the central point corresponding to each sample data is determined according to each fingerprint information, the corresponding map image is determined based on the multiple input grids determined by each central point, the map color feature map is determined according to the map image, and/or the multiple feature maps are determined according to the fingerprint information of the sample data and the fingerprint information of each input grid, the multiple feature maps corresponding to each sample data are used as input, and the convolutional neural network model is trained based on the loss function, so that the trained convolutional neural network model can predict the position information more accurately according to the input feature maps.
In an optional implementation manner, the positioning method according to the embodiment of the present invention further includes:
and obtaining the confidence coefficient of the positioning information according to the confidence coefficient network model. Optionally, the non-empty feature pixel proportion in the feature map is calculated, the non-empty feature pixel proportion is input into the confidence coefficient network model for feature processing, the error distance is output, and then the confidence coefficient of the positioning information is obtained according to the mapping formula. Optionally, the confidence network model of this embodiment is a gradient boosting decision tree network model GBDT.
In an optional implementation manner, the mapping formula (2) of the GBDT model of this embodiment is:
and the confidence is used for representing the confidence of the positioning information obtained based on the output of the convolutional neural network model. Optionally, in this embodiment, a plurality of feature maps are obtained based on training data, the GBDT model is trained according to the non-empty feature pixel ratios in the feature maps, the error distance between the predicted position and the satellite positioning position of the sample is regressed, and the confidence is obtained according to the error distance and the mapping formula. Therefore, the embodiment can estimate the confidence of the obtained positioning information while predicting the position, so as to be used as the basis of other services.
The method comprises the steps of determining a corresponding central point through a received positioning request, determining a plurality of input grids according to the central point, obtaining map images of coverage areas corresponding to the input grids, obtaining characteristic information corresponding to the input grids according to the map images, determining a plurality of characteristic graphs according to the characteristic information corresponding to the input grids, and inputting the characteristic graphs into a pre-trained convolutional neural network model to obtain positioning information.
Fig. 10 is a schematic diagram of a process for acquiring a plurality of feature maps according to an embodiment of the present invention. As shown in fig. 10, it is assumed that a user uses a terminal device to send a location request at a location Q, where the location request includes fingerprint information, and the fingerprint information includes at least a base station identifier and a base station signal strength corresponding to a base station signal scanned by the terminal device, an AP identifier (e.g., name, MAC address, etc.), an AP signal, and the like. Then, a central point p1 corresponding to the positioning request is determined, the grid where the central point p1 is located is used as a central grid, the central point p1 is expanded outwards to obtain M × M input grids Pin1, the map image 101 of the area covered by the M × M input grids Pin1 is determined, the RGB layering is performed on the map image according to the color features used for distinguishing different geographic environments in the map image, so as to obtain a feature map about the map color, and a plurality of feature maps are determined according to other feature information of each input grid, for example, the matching probability fp, i of each input grid and the fingerprint information in the positioning request, the maximum signal intensity RSSImax in each input grid, the heat information heat of each grid, and the like. Assuming that each input mesh has c pieces of feature information, c pieces of feature maps with a size of M × M can be obtained.
In this embodiment, each feature information of the input grid includes color feature information of a map image, so that the map image participates in positioning, positioning to a surrounding river or the like is avoided, and positioning accuracy is improved. Meanwhile, certain characteristics in the positioning request are introduced into the convolutional neural network for processing, so that the loss of characteristic information is further reduced, and the positioning accuracy is improved.
FIG. 11 is a schematic diagram of a localization model according to an embodiment of the present invention. As shown in fig. 11, in the present embodiment, the positioning model includes a pre-trained CNN model and a GBDT model. The GBDT model is used for obtaining a confidence level of the current position information obtained according to the CNN model.
In this embodiment, the c feature maps with size M × M obtained are input into the CNN model trained in advance, and the longitude shift Δ lon and the latitude shift Δ lat with respect to the center point are output, and the positioning information of the current position L is obtained by calculation based on the position coordinates of the center point p1 and the longitude shift Δ lon and the latitude shift Δ lat output by the CNN model.
And (3) calculating the non-empty feature pixel proportion corresponding to each feature map, inputting the non-empty feature pixel proportion of each feature map as a feature (feature 111) into the GBDT model for feature processing to obtain an error distance, and obtaining the corresponding confidence according to the mapping formula. Optionally, the non-null feature pixel proportion may be a ratio of the number of pixels in the feature map whose pixel value is not 0 to the total number of pixels.
In this embodiment, a central point is determined according to fingerprint information in a positioning request, an input grid is determined according to the central point, a feature map corresponding to the positioning request is determined according to feature information of the input grid, a plurality of feature maps are input into a pre-trained CNN model, a position offset relative to a position coordinate of the central point is output, current positioning information is determined according to the position offset, and a non-empty feature pixel proportion of each feature map is input into a confidence level network model as a feature to obtain a confidence level of predicted positioning information. Therefore, the embodiment can reduce the loss of the characteristic information, improve the positioning accuracy, and simultaneously perform confidence evaluation on the obtained positioning information to be used as the basis of other services.
FIG. 12 is a schematic view of a positioning device according to an embodiment of the present invention. As shown in fig. 12, the positioning apparatus 12 of the embodiment of the present invention includes a positioning request receiving unit 121, a center point determining unit 122, an input mesh determining unit 123, a map image acquiring unit 124, a feature information acquiring unit 125, a feature map determining unit 126, and a positioning information acquiring unit 127.
The positioning request receiving unit 121 is configured to receive a positioning request. The center point determining unit 122 is configured to determine a center point corresponding to the positioning request, where the center point is used to characterize a relative position point corresponding to the positioning request. The input mesh determination unit 123 is configured to determine a plurality of input meshes, which are pre-divided geographical areas, from the center point. The map image acquisition unit 124 is configured to acquire map images of coverage areas corresponding to the plurality of input grids. The feature information obtaining unit 125 is configured to obtain feature information corresponding to a plurality of the input meshes from the map image, the feature information including color feature information of the map image. The feature map determining unit 126 is configured to determine a plurality of feature maps according to feature information corresponding to each of the input grids, and a value of each pixel of the feature maps corresponds to the feature information of each of the input grids. The localization information obtaining unit 127 is configured to input the plurality of feature maps to a previously trained convolutional neural network model to obtain localization information.
The method comprises the steps of determining a corresponding central point through a received positioning request, determining a plurality of input grids according to the central point, obtaining map images of coverage areas corresponding to the input grids, obtaining characteristic information corresponding to the input grids according to the map images, determining a plurality of characteristic graphs according to the characteristic information corresponding to the input grids, and inputting the characteristic graphs into a pre-trained convolutional neural network model to obtain positioning information.
Fig. 13 is a schematic diagram of an electronic device of an embodiment of the invention. As shown in fig. 13, the electronic device shown in fig. 13 is a general-purpose data processing apparatus including a general-purpose computer hardware structure including at least a processor 131 and a memory 132. The processor 131 and the memory 132 are connected by a bus 133. The memory 132 is adapted to store instructions or programs executable by the processor 131. The processor 131 may be a stand-alone microprocessor or a collection of one or more microprocessors. Thus, processor 131 implements the processing of data and the control of other devices by executing instructions stored by memory 132 to perform the method flows of embodiments of the present invention as described above. The bus 133 connects the above components together, and also connects the above components to a display controller 134 and a display device and an input/output (I/O) device 135. Input/output (I/O) devices 135 may be a mouse, keyboard, modem, network interface, touch input device, motion sensing input device, printer, and other devices known in the art. Typically, the input/output devices 135 are coupled to the system through input/output (I/O) controllers 136.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus (device) or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may employ a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations of methods, apparatus (devices) and computer program products according to embodiments of the application. It will be understood that each flow in the flow diagrams can be implemented by computer program instructions.
These computer program instructions may be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
Another embodiment of the invention is directed to a non-transitory storage medium storing a computer-readable program for causing a computer to perform some or all of the above-described method embodiments.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (13)
1. A method of positioning, the method comprising:
receiving a positioning request;
determining a central point corresponding to the positioning request, wherein the central point is used for representing a relative position point corresponding to the positioning request;
determining a plurality of input grids according to the central point, wherein the input grids are pre-divided geographical areas;
acquiring map images of coverage areas corresponding to the plurality of input grids;
acquiring characteristic information corresponding to a plurality of input grids according to the map image, wherein the characteristic information comprises color characteristic information of the map image;
determining a plurality of feature maps according to feature information corresponding to each input grid, wherein the value of each pixel of the feature maps corresponds to the feature information of each input grid;
and inputting the plurality of characteristic graphs into a pre-trained convolutional neural network model to execute a regression task, and acquiring positioning information.
2. The positioning method according to claim 1, wherein obtaining feature information corresponding to a plurality of the input grids from the map image comprises:
carrying out RGB color layering on the map image to obtain corresponding sub-images;
dividing each of the sub-images into a plurality of grid images, the grid images having corresponding input grids;
performing pooling processing on pixels in at least one grid image corresponding to the input grid to determine at least one color feature value of the input grid.
3. The positioning method according to claim 1, wherein the positioning request includes fingerprint information corresponding to a current location, the fingerprint information includes a base station identifier and a base station signal strength corresponding to at least one base station information scanned at the current location;
determining a central point corresponding to the positioning request comprises:
determining at least one cluster point corresponding to the base station according to the base station identifier, wherein the cluster point of the base station comprises at least one piece of position information, and the cluster point is used for representing a range formed by clustering the position information corresponding to the base station;
and determining the central point according to the clustering points of all base stations in the fingerprint information.
4. The method of claim 3, wherein determining at least one cluster point corresponding to a base station according to the base station identifier comprises:
acquiring at least one historical positioning record corresponding to the base station identifier, wherein the historical positioning record comprises the base station identifier and corresponding position information;
determining at least one cluster point of the base station according to the at least one historical positioning record.
5. The method of claim 3, wherein determining at least one cluster point corresponding to a base station according to the base station identifier comprises:
and inquiring a base station cluster point database according to the base station identification, and determining at least one cluster point corresponding to the base station, wherein the base station cluster point database is determined according to a plurality of historical positioning records in a preset time period.
6. The method according to any one of claims 3-5, wherein determining the center point according to the cluster point of each base station in the fingerprint information comprises:
and determining the position with the shortest total distance to each cluster point as the central point.
7. The positioning method according to claim 1, wherein the positioning request includes fingerprint information corresponding to the current location, and the fingerprint information includes an AP identifier and an AP signal strength of at least one wireless access point AP;
determining a central point corresponding to the positioning request comprises:
calculating the similarity between the fingerprint information of the positioning request and the fingerprint information of a plurality of predetermined candidate grids;
sorting each candidate grid according to the similarity;
obtaining at least one similar grid according to the similarity sorting result;
and determining the central point according to the position information of the at least one similar grid.
8. The method of claim 7, wherein determining the center point according to the location of the at least one similar grid comprises:
calculating a median or average of the position coordinates of the at least one similar grid;
and determining the median or the average value as the position coordinates of the central point.
9. The method according to claim 1, wherein the characteristic information further comprises a matching probability of the signal strength of the base station received by each of the input grids with the signal strength of the base station in the positioning request, and/or a matching probability of the signal strength of the wireless access point AP received by each of the input grids with the signal strength of the AP in the positioning request.
10. The method of claim 1, wherein inputting the plurality of feature maps into a pre-trained convolutional neural network model to obtain positioning information comprises:
inputting the plurality of feature maps into the convolutional neural network model for feature processing, and outputting a position offset, wherein the position offset is an offset of the positioning information relative to the central point;
and acquiring the positioning information according to the position deviation and the central point position.
11. A positioning device, the device comprising:
a positioning request receiving unit configured to receive a positioning request;
a central point determining unit configured to determine a central point corresponding to the positioning request, where the central point is used to characterize a relative position point corresponding to the positioning request;
an input grid determining unit configured to determine a plurality of input grids according to the central point, the input grids being pre-divided geographical areas;
a map image acquisition unit configured to acquire map images of coverage areas corresponding to the plurality of input grids;
a feature information acquisition unit configured to acquire feature information corresponding to a plurality of the input grids from the map image, the feature information including color feature information of the map image;
a feature map determination unit configured to determine a plurality of feature maps from feature information corresponding to each of the input grids, a value of each pixel of the feature maps corresponding to the feature information of each of the input grids;
and the positioning information acquisition unit is configured to input the plurality of feature maps into a pre-trained convolutional neural network model to execute a regression task, and acquire positioning information.
12. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method of any of claims 1-10.
13. A computer-readable storage medium on which computer program instructions are stored, which computer program instructions, when executed by a processor, are to implement a method according to any one of claims 1-10.
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|---|---|---|---|---|
| CN111954154B (en) * | 2020-08-19 | 2022-08-12 | 北京京东振世信息技术有限公司 | Positioning method and device, computer readable storage medium and electronic device |
| CN112165684B (en) * | 2020-09-28 | 2021-09-14 | 上海大学 | High-precision indoor positioning method based on joint vision and wireless signal characteristics |
| CN112235724B (en) * | 2020-10-12 | 2022-05-17 | 腾讯科技（深圳）有限公司 | Indoor positioning method and device, electronic equipment and computer readable storage medium |
| CN112284394A (en) * | 2020-10-23 | 2021-01-29 | 北京三快在线科技有限公司 | Map construction and visual positioning method and device |
| CN112511985A (en) * | 2020-12-21 | 2021-03-16 | 迪爱斯信息技术股份有限公司 | Alarm position positioning method, system, computer equipment and storage medium |
| CN113490272B (en) * | 2021-09-08 | 2021-12-28 | 中铁工程服务有限公司 | UWB positioning-based safe hoisting early warning method, system and medium |
| CN113810919B (en) * | 2021-09-17 | 2023-06-23 | 杭州云深科技有限公司 | Thermodynamic diagram generation method, electronic equipment and medium for base station coverage area |
| CN114152189B (en) * | 2021-11-09 | 2022-10-04 | 武汉大学 | Four-quadrant detector light spot positioning method based on feedforward neural network |
| CN117078985B (en) * | 2023-10-17 | 2024-01-30 | 之江实验室 | Scene matching method and device, storage medium and electronic equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110166991A (en) * | 2019-01-08 | 2019-08-23 | 腾讯大地通途（北京）科技有限公司 | For the method for Positioning Electronic Devices, unit and storage medium |
| CN110234085A (en) * | 2019-05-23 | 2019-09-13 | 深圳大学 | Based on the indoor location fingerprint to anti-migration network drawing generating method and system |
| CN110858954A (en) * | 2018-08-22 | 2020-03-03 | 中国移动通信集团河北有限公司 | Data processing method, device, equipment and medium |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103068035B (en) * | 2011-10-21 | 2016-03-02 | 中国移动通信集团公司 | A kind of wireless network localization method, Apparatus and system |
| CN105044662B (en) * | 2015-05-27 | 2019-03-01 | 南京邮电大学 | A kind of fingerprint cluster multi-point joint indoor orientation method based on WIFI signal intensity |
| CN107831765B (en) * | 2017-10-23 | 2021-07-13 | 广州视源电子科技股份有限公司 | Positioning method, device, equipment and storage medium |
| CN110658539B (en) * | 2018-06-29 | 2022-03-18 | 比亚迪股份有限公司 | Vehicle positioning method, device, vehicle and computer readable storage medium |
| US10810466B2 (en) * | 2018-08-23 | 2020-10-20 | Fuji Xerox Co., Ltd. | Method for location inference from map images |
| CN109697428B (en) * | 2018-12-27 | 2020-07-07 | 江西理工大学 | Unmanned aerial vehicle identification and positioning system based on RGB _ D and depth convolution network |
| CN110702120A (en) * | 2019-11-06 | 2020-01-17 | 小狗电器互联网科技（北京）股份有限公司 | Map boundary processing method, system, robot and storage medium |
-
2020
- 2020-03-30 CN CN202010239568.4A patent/CN111464938B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110858954A (en) * | 2018-08-22 | 2020-03-03 | 中国移动通信集团河北有限公司 | Data processing method, device, equipment and medium |
| CN110166991A (en) * | 2019-01-08 | 2019-08-23 | 腾讯大地通途（北京）科技有限公司 | For the method for Positioning Electronic Devices, unit and storage medium |
| CN110234085A (en) * | 2019-05-23 | 2019-09-13 | 深圳大学 | Based on the indoor location fingerprint to anti-migration network drawing generating method and system |
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