CN111885699B - Method and system for determining current position of user - Google Patents
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Abstract
The embodiment of the application discloses a method for determining the current position of a user. The method comprises the following steps: acquiring network information of a user; determining at least one pre-estimated position of the user according to the network information; determining the offset of each predicted position in the at least one predicted position; correcting the at least one pre-estimated position according to the offset of each pre-estimated position; and determining the position of the user according to the corrected at least one predicted position.
Description
Technical Field
The present application relates to the field of positioning, and in particular, to a method and system for determining a current location of a user.
Background
With the development and popularization of internet technology, people live more and more conveniently. For example, a user may use various applications (e.g., a web appointment application, a meal order application, a shared car application, etc.) to obtain or provide corresponding services. These services may want to acquire the user's location. When the GPS can not obtain the actual position of the user, the position of the user can be obtained by a network positioning technology, but the accuracy of network positioning is not high. Therefore, it is desirable to provide a method and system for determining the current location of a user through network positioning, which reduces the difference between the predicted location and the actual location, and realizes accurate positioning.
Disclosure of Invention
One embodiment of the present application provides a positioning method, including: acquiring network information of a user; determining at least one pre-estimated position of the user according to the network information; determining the offset of each predicted position in the at least one predicted position; correcting the at least one pre-estimated position according to the offset of each pre-estimated position; and determining the position of the user according to the corrected at least one predicted position.
One of the embodiments of the present application provides a positioning system, including: the acquisition module is used for acquiring network information of a user; the estimated position determining module is used for determining at least one estimated position of the user according to the network information; the offset determining module is used for determining the offset of each pre-estimated position in the at least one pre-estimated position; and the position correcting module is used for correcting the at least one predicted position according to the offset of each predicted position and determining the position of the user according to the corrected at least one predicted position.
One embodiment of the present application provides a positioning apparatus, which includes at least one storage medium and at least one processor; the at least one storage medium is configured to store computer instructions; the at least one processor is configured to execute the computer instructions to implement the positioning method as described above.
One of the embodiments of the present application provides a computer-readable storage medium, which stores computer instructions that, when executed by a processor, implement the positioning method as described above.
Drawings
The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals refer to like structures, wherein:
FIG. 1 is a diagram of an application scenario for a positioning system according to some embodiments of the present application;
FIG. 2 is a block diagram of a positioning system according to some embodiments of the present application;
FIG. 3 is an exemplary flow chart of a positioning method according to some embodiments of the present application;
FIG. 4 is a schematic diagram of a grid partitioning according to some embodiments of the present application; and
FIG. 5 is an exemplary flow chart of a method of training a first model according to some embodiments of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, and that for a person skilled in the art the application can also be applied to other similar contexts on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system," "device," "unit," and/or "module" as used herein is a method for distinguishing between different components, elements, parts, portions, or assemblies of different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" are intended to cover only the explicitly identified steps or elements as not constituting an exclusive list and that the method or apparatus may comprise further steps or elements.
Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Embodiments of the present application may be applied to a variety of location-related applications, including, for example and without limitation, web-based vehicle ordering applications, order-top applications, shared-vehicle applications, and the like, which may be used for order dispatch, path tracking, and the like. The method and the device can realize accurate network positioning under the condition that the GPS positioning signal is weak. It should be understood that the application scenarios of the system and method of the present application are merely examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios without inventive effort based on these drawings.
Fig. 1 is a diagram of an application scenario of a positioning system according to some embodiments of the present application.
The network 120 connects the various components of the system so that communication can occur between the various components. The network between the various parts in the system may be any one or more of a wired network or a wireless network. By way of example only, network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, the like, or any combination thereof. In some embodiments, network 120 may include at least one network access point. For example, network 120 may include wired or wireless network access points, such as base stations and/or Internet switching points 120-1, 120-2, … …, through which at least one component of positioning system 100 may connect to network 120 to exchange data and/or information.
In some embodiments, the user terminal 130 may be one or more users, and may include users who directly use the location services, as well as other associated users. For example, in a net appointment application, the user may be a passenger and/or a driver.
The server 110 and the user terminal 130 may communicate with each other. For example, the user terminal 130 may send a positioning request to the server 110 and receive position information fed back by the server 110. Storage device 140 and network 120 serve the processes described above.
It should be noted that the above description of the positioning system 100 is for illustration and explanation only, and does not limit the scope of applicability of the present application. Various modifications and changes may be made to positioning system 100 by those skilled in the art in light of the present teachings. However, such modifications and variations are intended to be within the scope of the present application.
FIG. 2 is a block diagram of a positioning system according to some embodiments of the present application.
As shown in fig. 2, the system may include an acquisition module 210, a predicted position determination module 220, an offset determination module 230, a position correction module 240, and a training module 250.
The obtaining module 210 may be configured to obtain network information of a user. In some embodiments, the obtaining module 210 may also obtain mesh information for the mesh. In some embodiments, the obtaining module 210 may further obtain a first model for determining the estimated position offset distance, a second model for determining the estimated position offset direction, and/or a third model for determining the estimated position offset (the offset includes the offset distance and the offset direction). In some embodiments, the obtaining module 210 may also obtain historical data from the storage device 140 or otherwise for training the first model, the second model, and/or the third model.
The estimated location determination module 220 may determine at least one estimated location of the user based on the network information. In some embodiments, the estimated position determining module 220 may match the grid information of the multiple grids with the network information of the user, obtain a matching result of each grid, and determine at least one estimated position according to at least one grid of which the matching result satisfies a preset condition.
The offset determination module 230 may determine an offset distance and an offset direction for each of the estimated positions. In some embodiments, for each of the predicted locations, the offset determination module 230 may process the network information of the user and the predicted location with a first model to obtain an offset distance of the predicted location. In some embodiments, for each of the estimated locations, the offset determination module 230 may process the estimated location of the user's network information with a second model to obtain an offset direction of the estimated location. In some embodiments, for each predicted location, the offset determination module 230 may process the predicted location of the network information of the user with a third model to obtain an offset of the predicted location, where the offset includes an offset distance and an offset direction.
The position correcting module 240 may correct each of the estimated positions according to the offset of the estimated position, and determine the position of the user according to the corrected estimated position.
The training module 250 may be configured to train a model to obtain the first model, the second model, and/or the third model. The training module 250 may train the initial model with a training set comprising a plurality of sample pairs, resulting in the first model, the second model, and/or the third model.
It should be understood that the system and its modules shown in FIG. 2 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, for example such code provided on a carrier medium such as a diskette, CD-or DVD-ROM, programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
It should be noted that the above description of the positioning system and its modules is merely for convenience of description and should not limit the present application to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, in some embodiments, the obtaining module 210, the estimated position determining module 220, the offset determining module 230, the position correcting module 240, and the training module 250 disclosed in fig. 2 may be different modules in a system, or may be a module that implements the functions of two or more of the above modules. For example, the offset determination module 230 and the position correction module 240 may be combined into one module having both the functions of determining an offset and correcting a position. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present application.
Fig. 3 is an exemplary flow chart of a positioning method according to some embodiments of the present application. The process 300 may be performed by the server 110.
When the user needs to use various services, for example, a car booking service, a meal ordering service, and the like, applications related to the various services installed in the user terminal 130 may be opened. The location of the user may be needed when using the relevant application.
The obtaining module 210 may obtain the network information of the user through the user terminal 130 used by the user. The devices used by the user may include mobile devices, tablet computers, laptop computers, and the like.
The network information may include, but is not limited to, Mac (Media Access Control) addresses of WiFi devices around the user terminal 130, base station information around the user terminal 130, signal strength received by the user terminal 130, and the like, or any combination thereof.
The Mac address is written inside hardware of the network device when produced by a network device manufacturer, and serves as a unique network identifier of the network device. The obtaining module 210 may obtain the Mac address from the user terminal 130. In some embodiments, when the user terminal 130 searches for one or more WiFi hotspots in the surrounding, the user terminal 130 may record and store the Mac addresses associated with the WiFi hotspots.
The base station information may include, but is not limited to, a Location Area Identification (LAI) and a base station number (CID). The LAI includes a Mobile Country Code (MCC), a Mobile Network Code (MNC), and a Location Area Code (LAC). The base station information may be acquired by the user terminal 130. The obtaining module 210 may obtain the base station information from the user terminal 130 through the network 120. In some embodiments, when the user terminal 130 searches for one or more surrounding base stations, the user terminal 130 may record and store base station information for the base stations.
The signal strength may include WiFi signal strength and/or base station signal strength. The signal strength may be obtained by the user terminal 130. The obtaining module 210 may obtain the base station information from the user terminal 130 through the network 120. The user terminal 130 may obtain a signal with a certain strength when it scans WiFi and a base station. The signal strength of the WiFi and the base station can be saved and recorded in the terminal 130, and the obtaining module 210 can obtain the signal strength from the user terminal 130. In some embodiments, the signal strength depends on the distance between the user terminal 130 and the Wifi device and/or the base station, the greater the distance, the lower the signal strength. Therefore, the distance between the user terminal 130 and the Wifi device and/or the base station may be determined according to the signal strength for determining the location of the user terminal 130 (i.e., the location of the user).
It should be noted that the above description of network information is by way of example only and should not be construed as limiting this application. Content known to those skilled in the art may be included in the network information.
After obtaining the network information, the estimated location determination module 220 may determine at least one estimated location of the user according to the network information.
In some embodiments, the area may be divided into several grids, each defining a certain geographic area. The grid may be hexagonal, quadrilateral, or other shape. The meshes may or may not overlap each other. Fig. 4 is a schematic diagram of meshing according to some embodiments of the present application. As shown in fig. 4, a plurality of squares are partially divided by dotted lines, each square representing a grid, and each grid is covered by at least one Wifi signal and/or base station signal. For example, mesh a, mesh B, and mesh C are covered by signals of Wifi1, Wifi2, base station 1, and base station 2. Each mesh has certain mesh information. For example, the mesh information may include network information and/or location information of the mesh. The network information of the mesh reflects network characteristics of the mesh, such as one or any combination of Mac addresses of Wifi devices around the mesh, base station information around the mesh, signal strength received at the mesh, and the like. The position information of the grid reflects the geographical position of the grid. For example, the location information of the grid may be expressed as latitude and longitude.
In some embodiments, the estimated position determining module 220 may match the grid information of the grid with the network information of the user to obtain a matching result of each grid, and determine the estimated position according to the grid meeting a preset condition of the matching result. For example, the estimated position determining module 220 may match the network information of the grids with the network information of the user, determine at least one grid with a matching degree satisfying a preset condition, and determine the position of each matched grid as an estimated position. As shown in fig. 4, when the user terminal 130 scans WiFi1, WiFi2, base station 1, and base station 2, the user's network information may include a first Mac address corresponding to WiFi1, a second Mac address corresponding to WiFi2, first base station information, second base station information, and corresponding signal strengths. And if the network information of the user is completely consistent with the network information in the grid information of a certain grid, the matching degree is 100%. If the network information of a certain mesh includes the same Mac address and base station as those in the network information of the user, but the signal strength is different, the matching degree is related to the signal strength. The higher the signal strength similarity, the higher the degree of matching. The combination calculation may be performed for the results of the plurality of matching degrees of one mesh in various ways.
For example only, the mesh information of mesh a in fig. 4 indicates that the mesh is covered by WiFi1, WiFi2, base station 1, and base station 2. The network information of the user indicates that the user terminal 130 scans WiFi1, WiFi2, base station 1, and base station 2, and compared with the network information of grid a, the signal strength of WiFi1 received by the user terminal 130 is the same, the signal strength of WiFi2 is different (e.g., -60dbm in grid a, and-70 dbm in user terminal 130), the signal strength of base station 1 is different (e.g., -65dbm in grid, and-55 dbm in user terminal 130), and the signal strength of base station 2 is also different (e.g., -40dbm in grid, and-50 dbm in user terminal 130), so the matching degree of the grid information of grid a and WiFi1 in the network information of the user is 100%, the matching degree of WiFi2 is 85.7%, the matching degree of base station 1 is 84.6%, and the matching degree of base station 2 is 80%. For the network, the matching degree with the user's mesh information may be an average of the above-mentioned matching degrees of WiFi and base station, for example, 87.57%.
In some embodiments, the grids may be sorted according to the matching degree of the grid information of the grid and the network information of the user, and the position of the grid of several bits before sorting is determined as the estimated position. In some embodiments, a location of a grid whose matching degree of grid information with the user's network information is greater than a set threshold may be determined as the estimated location.
In step 330, the offset of each estimated position is determined. In particular, step 330 may be performed by the offset determination module 230.
In some embodiments, for each of the predicted locations, the offset determination module 230 may process the network information of the user and the predicted location with a first model to obtain an offset distance of the predicted location. In some embodiments, for each estimated location, the offset determination module 230 may process the estimated location of the user's network information with a second model to obtain an offset direction of the estimated location. In some embodiments, for each predicted location, the offset determination module 230 may process the predicted location of the network information of the user with a third model to obtain an offset of the predicted location, where the offset includes an offset distance and an offset direction. The first model, the second model, and/or the third model may be supervised learning models such as neural networks, support vector machines, naive bayes, factorizers/field-aware decomposers (FM/FFM), KMNs, AdaBoost, Apriori algorithms, and the like. The first model, the second model, and/or the third model may be trained from a training set comprising a plurality of sample pairs. For more details on the model training, reference may be made to fig. 5 and the description thereof, which are not repeated herein.
The position correcting module 240 may correct the corresponding estimated position according to the determined offset of each estimated position, so as to obtain at least one corrected estimated position. Further, the location correction module 240 may determine the current location of the user based on the corrected at least one predicted location. For example only, the current location of the user may be obtained by averaging the corrected at least one estimated location. The categories of averaging include, but are not limited to, arithmetic mean, geometric mean, squared mean, harmonic mean. It should be noted that, the determining of the current position of the user according to the corrected at least one estimated position is only an example, and a person skilled in the art may perform various processes on the corrected at least one estimated position to obtain the current position of the user, which is not limited herein.
It should be noted that the above description related to the flow 300 is only for illustration and explanation, and does not limit the applicable scope of the present application. Various modifications and changes to flow 300 will be apparent to those skilled in the art in light of this disclosure. However, such modifications and variations are intended to be within the scope of the present application.
FIG. 5 is an exemplary flow chart of a method of training a first model according to some embodiments of the present application.
Since the first model and the second model only have different inputs and outputs, but are trained in a similar manner, the first model is taken as an example for illustration.
At step 510, an initial first model is obtained. In particular, this step may be performed by the acquisition module 210.
In some embodiments, the initial first model may have default settings (e.g., default parameters) determined by the system 100 or the initial first model may be adjustable under different circumstances.
In some embodiments, the historical network information of the user is obtained in the same or similar manner as described in step 310. In some embodiments, the historical predicted location of the user is determined in the same or similar manner as described in step 320. In some embodiments, the historical true location is determined by Global Positioning System (GPS) technology. It will be appreciated that in some cases (e.g., both GPS and network signals), the user's location may be determined via the network, as well as the user's true location via GPS technology, so that historical network information, historical estimated locations, and historical true locations of the user may be obtained. The historical network information, the historical estimated location, and the historical true location of the user may be stored in a storage device (e.g., storage device 140). The offset distance between the historical predicted position of the user and the historical true position may be a straight-line distance between the two.
And 530, performing model training by using the first training set to obtain the first model. In particular, this step may be performed by the training module 250.
The training module 250 takes the historical network information and the historical predicted position of the user as the input of the model, takes the offset distance between the historical predicted position and the historical real position as the correct standard (Ground route) to train the first initial model, and stops the training when certain conditions are met (for example, the value of the loss function is smaller than the set value, and the number of the sample pairs used for training reaches the set value), so as to obtain the first model.
In some embodiments, the sample pairs may be partitioned into a training set and a test set. The training set is used for training the model, and the testing set is used for testing whether the prediction accuracy of the model trained by the training set reaches the standard or not. For example, the sample pairs may be apportioned into a training set and a test set, e.g., 50%, 60%, 70%, 80%, 90%, etc. In some embodiments, a validation set may be further partitioned from the sample pair, and the trained model may be validated.
In some embodiments, training module 250 may store the trained first model in a storage device (e.g., storage device 140) in the form of structured data.
The training process for the second model is as follows: acquiring a second training set, wherein the second training set comprises a plurality of sample pairs, and each sample pair comprises historical network information and a historical estimated position of a user, which are input as a model, and an offset direction of the historical estimated position and a historical real position, which are used as a model correct standard; and performing model training by using the second training set to obtain the second model. The training process of the third model is as follows: acquiring a third training set, wherein the third training set comprises a plurality of sample pairs, each sample pair comprises historical network information and a historical estimated position of a user, which are input as a model, and an offset between the historical estimated position and a historical real position, which is used as a model correct standard, and the offset comprises an offset distance and an offset direction; and performing model training by using the third training set to obtain the third model. The training process of the second model and the third model is similar to the training process of the first model, and is not repeated herein.
The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: (1) network positioning is calibrated through the model, and accuracy based on the network positioning is improved; (2) the difference value of the distance and the direction between the predicted position and the actual position is obtained through model training, so that the information loss in the process from two dimensions to one dimension is compensated, and accurate positioning is realized in two dimensions. It is to be noted that different embodiments may produce different advantages, and in different embodiments, the advantages that may be produced may be any one or combination of the above, or any other advantages that may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, unless explicitly recited in the claims, the order of processing elements and sequences, use of numbers and letters, or use of other designations in this application is not intended to limit the order of the processes and methods in this application. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
Claims (14)
1. A method of positioning, comprising:
acquiring network information of a user;
determining at least one pre-estimated position of the user according to the network information;
for each of the estimated positions, the position of the vehicle,
inputting the network information and the estimated position into a first model to obtain the offset distance of the estimated position;
inputting the network information and the estimated position into a second model to obtain the offset direction of the estimated position;
determining the offset of the estimated position and the real position according to the offset distance and the offset direction;
correcting the at least one estimated position according to the offset of each estimated position and the real position; and
determining the location of the user based on the corrected at least one predicted location.
2. The method of claim 1, wherein the determining at least one pre-estimated location of the user based on the network information comprises:
obtaining grid information for a plurality of grids, wherein each grid defines a particular geographic area;
matching the network information with the grid information of the grids to obtain a matching result; and
and determining the at least one estimated position according to at least one grid with the matching result meeting a preset condition, wherein each grid with the matching result meeting the preset condition corresponds to one estimated position.
3. The method of claim 1, wherein the first model is obtained by the following training process:
acquiring a first training set, wherein the first training set comprises a plurality of sample pairs, and each sample pair comprises historical network information and a historical estimated position of a user, which are input as a model, and an offset distance between the historical estimated position and a historical real position, which is used as a model correct standard;
and carrying out model training by using the first training set to obtain the first model.
4. The method of claim 3, wherein the historical true location is obtained by global positioning system technology.
5. The method of claim 1, wherein the second model is obtained by the following training process:
acquiring a second training set, wherein the second training set comprises a plurality of sample pairs, and each sample pair comprises historical network information and a historical estimated position of a user, which are input as a model, and an offset direction of the historical estimated position and a historical real position, which are used as a model correct standard;
and performing model training by using the second training set to obtain the second model.
6. The method of claim 1, wherein determining the location of the user based on the corrected at least one predicted location comprises:
and averaging the corrected at least one estimated position to obtain the position of the user.
7. A positioning system, comprising:
the acquisition module is used for acquiring network information of a user;
the estimated position determining module is used for determining at least one estimated position of the user according to the network information;
an offset determination module to, for each of the predicted positions: inputting the network information and the estimated position into a first model to obtain the offset distance of the estimated position, inputting the network information and the estimated position into a second model to obtain the offset direction of the estimated position, and determining the offset of the estimated position and the real position according to the offset distance and the offset direction;
and the position correcting module is used for correcting the at least one predicted position according to the offset between each predicted position and the real position and determining the position of the user according to the corrected at least one predicted position.
8. The location system of claim 7, wherein the predicted location determination module is further configured to:
obtaining grid information for a plurality of grids, wherein each grid defines a particular geographic area;
matching the network information with the grid information of the grids to obtain a matching result; and
and determining the at least one estimated position according to at least one grid with the matching result meeting a preset condition, wherein each grid with the matching result meeting the preset condition corresponds to one estimated position.
9. The positioning system of claim 7, wherein the first model is obtained by the following training process:
acquiring a first training set, wherein the first training set comprises a plurality of sample pairs, and each sample pair comprises historical network information and a historical estimated position of a user, which are input as a model, and an offset distance between the historical estimated position and a historical real position, which is used as a model correct standard;
and performing model training by using the first training set to obtain the first model.
10. The location system of claim 9, wherein the historical true location is obtained by global positioning system technology.
11. The positioning system of claim 7, wherein the second model is obtained by a training process comprising:
acquiring a second training set, wherein the second training set comprises a plurality of sample pairs, and each sample pair comprises historical network information and a historical estimated position of a user, which are input as a model, and an offset direction of the historical estimated position and a historical real position, which are used as a model correct standard;
and performing model training by using the second training set to obtain the second model.
12. The positioning system of claim 7, wherein the position correction module is further configured to:
and averaging the corrected at least one estimated position to obtain the position of the user.
13. A positioning apparatus, the apparatus comprising at least one storage medium and at least one processor;
the at least one storage medium is configured to store computer instructions;
the at least one processor is configured to execute the computer instructions to implement the positioning method of any one of claims 1 to 6.
14. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the positioning method according to any one of claims 1 to 6.
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| US7933612B2 (en) * | 2006-02-28 | 2011-04-26 | Microsoft Corporation | Determining physical location based upon received signals |
| CN102573049B (en) * | 2010-12-14 | 2014-10-22 | 北京智慧图科技发展有限责任公司 | Method and system for indoor positioning |
| CN104144493B (en) * | 2013-05-10 | 2018-05-15 | 中国电信股份有限公司 | Localization method, alignment system and base station location platform |
| CN106507476A (en) * | 2016-11-22 | 2017-03-15 | 上海斐讯数据通信技术有限公司 | A kind of WiFi localization methods and server and location model construction method |
| CN107182124A (en) * | 2017-05-19 | 2017-09-19 | 北京旋极伏羲大数据技术有限公司 | A kind of high-precision locating method and device based on grid |
| CN107872875A (en) * | 2017-12-21 | 2018-04-03 | 维沃移动通信有限公司 | The method and device of network positions |
| CN109257700B (en) * | 2018-11-19 | 2020-11-06 | 广东小天才科技有限公司 | Positioning method, server and system based on positioning deviation rectification |
| CN111148028B (en) * | 2019-08-08 | 2021-08-24 | 广东小天才科技有限公司 | Positioning deviation rectifying method and server |
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| CN108303672A (en) * | 2017-12-26 | 2018-07-20 | 武汉创驰蓝天信息科技有限公司 | WLAN indoor positionings error correcting method based on location fingerprint and system |
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