CN111654818A - Bluetooth positioning method, mobile terminal and storage medium - Google Patents
Bluetooth positioning method, mobile terminal and storage medium Download PDFInfo
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- CN111654818A CN111654818A CN202010608363.9A CN202010608363A CN111654818A CN 111654818 A CN111654818 A CN 111654818A CN 202010608363 A CN202010608363 A CN 202010608363A CN 111654818 A CN111654818 A CN 111654818A
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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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06N3/02—Neural networks
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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
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
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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
- H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
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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 invention discloses a Bluetooth positioning method, which comprises the following steps: acquiring the signal intensity and the identification mark of a Bluetooth signal received by a mobile terminal; and determining the position information of the mobile terminal according to a pre-trained deep belief neural network model, the signal strength and the identification mark, wherein the deep belief neural network model is obtained based on the signal strength detected at a preset position, the identification mark and the coordinate position of the preset positioning point. The invention also discloses a mobile terminal and a computer readable storage medium, which achieve the effect of improving the positioning accuracy of the Bluetooth positioning scheme.
Description
Technical Field
The present invention relates to the field of positioning technologies, and in particular, to a bluetooth positioning method, a mobile terminal, and a computer-readable storage medium.
Background
In the navigation process, the mobile terminal needs to acquire positioning data. In general, a conventional mobile terminal obtains Positioning data of the mobile terminal from a GPS (Global Positioning System) mounted on the mobile terminal. Because the GPS is a satellite-based positioning method, when the mobile terminal is located in an indoor location, the signal is blocked by a building, which results in an inaccurate positioning. In addition, because GPS is a planar positioning method, it is impossible to identify positioning data in a vertical space. For example, the floor where the user is located cannot be determined, which also results in that the mobile terminal based on GPS positioning cannot implement indoor navigation and the like, and a function of acquiring accurate indoor positioning information is required.
To obtain indoor positioning information, an indoor positioning function may be generally implemented based on bluetooth, infrared and/or WiFi technology, etc. However, in the conventional scheme for implementing indoor positioning based on bluetooth, due to the high complexity of the indoor electromagnetic environment, the bluetooth information signal used for positioning may have absorption effect, signal reflection and/or signal diffraction, etc. This phenomenon may result in a reduction in the accuracy of the positioning result.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The present invention mainly aims to provide a bluetooth positioning method, a mobile terminal and a computer readable storage medium, aiming to achieve the purpose of improving the positioning accuracy of the bluetooth positioning scheme.
In order to achieve the above object, the present invention provides a bluetooth positioning method, which comprises the following steps:
acquiring the signal intensity and the identification mark of a Bluetooth signal received by a mobile terminal;
and determining the position information of the mobile terminal according to a pre-trained deep belief neural network model, the signal strength and the identification mark, wherein the deep belief neural network model is obtained based on the signal strength detected at a preset position, the identification mark and the coordinate position of the preset positioning point.
Optionally, the step of obtaining the signal strength and the identification of the bluetooth signal received by the mobile terminal includes:
acquiring the signal intensity of Bluetooth signals sent by at least two Bluetooth base stations received by a mobile terminal;
acquiring an identification mark of a Bluetooth base station corresponding to each Bluetooth signal;
the step of determining the position information of the mobile terminal according to the pre-trained deep belief neural network model, the signal strength and the identification mark comprises the following steps:
storing the signal intensity corresponding to each Bluetooth signal and the identification mark of the Bluetooth base station sending the Bluetooth signal in an associated manner, wherein the signal intensity is used as an input vector to obtain at least two input vectors;
and determining the position information of the mobile terminal through at least two input vectors and the deep belief neural network model.
Optionally, the step of determining the location information of the mobile terminal through at least two of the input vectors and the deep belief neural network model includes:
taking at least two input vectors as input parameters of the pre-trained deep belief neural network model;
the deep belief neural network determining an output parameter based on the input parameter;
and determining the position information according to the output parameters.
Optionally, the output parameter is longitude and latitude corresponding to the current position of the mobile terminal.
Optionally, the step of determining the position information according to the output parameter includes:
acquiring a contrast relation between longitude and latitude and a human position;
and determining the human position corresponding to the mobile terminal at present based on the longitude and latitude and the comparison relation, and taking the human position as the position information.
Optionally, the bluetooth positioning method further includes:
acquiring an offline sample library, wherein the offline sample library comprises the signal intensity of each Bluetooth base station detected at a plurality of preset positioning points in a positioning space, the identification of the Bluetooth base station and the longitude and latitude corresponding to the preset positioning points;
and training based on the offline sample library to obtain the deep belief neural network model.
Optionally, the identification is a MAC address of the bluetooth base station.
Optionally, after the step of determining the location information of the mobile terminal according to the pre-trained deep belief neural network model, the signal strength, and the identification flag, the method further includes:
the mobile terminal outputs the position information; and/or
And when a positioning data request initiated by a target application is received, using the position information as response data of the positioning data request, wherein the target application is an application loaded in the mobile terminal.
In addition, in order to achieve the above object, the present invention further provides a mobile terminal, which includes a memory, a processor, and a bluetooth positioning program stored in the memory and operable on the processor, wherein the bluetooth positioning program, when executed by the processor, implements the steps of the bluetooth positioning method as described above.
In addition, to achieve the above object, the present invention further provides a computer readable storage medium, which stores a bluetooth positioning program, and when the bluetooth positioning program is executed by a processor, the bluetooth positioning program implements the steps of the bluetooth positioning method as described above.
The embodiment of the invention provides a Bluetooth positioning method, a mobile terminal and a computer readable storage medium, which are characterized in that the signal intensity and the identification mark of a Bluetooth signal received by the mobile terminal are firstly obtained, and then the position information of the mobile terminal is determined according to a pre-trained deep belief neural network model, the signal intensity and the identification mark, wherein the deep belief neural network model is obtained based on the signal intensity detected at a preset position, the identification mark and the coordinate position of the preset positioning point. The Bluetooth positioning technology can be combined with the deep belief neural network model to predict the position of the mobile terminal, so that the phenomenon that the positioning precision is influenced by factors such as the absorption effect, signal reflection and/or signal diffraction of a Bluetooth signal in the traditional Bluetooth positioning process is avoided, and the effects of improving the positioning precision and robustness of Bluetooth positioning are achieved.
Drawings
Fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a Bluetooth positioning method according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a Bluetooth positioning method according to another embodiment of the present invention;
FIG. 4 is a flowchart illustrating a Bluetooth positioning method according to another embodiment of the present invention;
fig. 5 is a flowchart illustrating another embodiment of a bluetooth positioning method according to another embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Indoor positioning refers to determining location information of terminal devices located in a building. When the terminal is located in a building, positioning is carried out through satellite positioning methods such as GPS positioning and the like, so that the positioning result is inaccurate due to obstruction of the building to the positioning information number. Moreover, satellite positioning generally can only realize planar positioning, but cannot perform stereo positioning. For example, when the building is a floor building, the floor on which the mobile terminal is located cannot be determined by satellite positioning. Therefore, the mobile terminal needs to be located indoors through other methods.
In the conventional technology, indoor positioning of the mobile terminal may be implemented based on technologies such as bluetooth, infrared, or WiFi. However, in the conventional scheme for implementing indoor positioning based on bluetooth, due to the high complexity of the indoor electromagnetic environment, the bluetooth information signal used for positioning may have absorption effect, signal reflection and/or signal diffraction, etc. This phenomenon may result in a reduction in the accuracy of the positioning result.
In order to improve the positioning accuracy of the bluetooth positioning scheme, the present embodiment provides a bluetooth positioning method, and the main solution thereof includes the following steps:
acquiring the signal intensity and the identification mark of a Bluetooth signal received by a mobile terminal;
and determining the position information of the mobile terminal according to a pre-trained deep belief neural network model, the signal strength and the identification mark, wherein the deep belief neural network model is obtained based on the signal strength detected at a preset position, the identification mark and the coordinate position of the preset positioning point.
The Bluetooth positioning technology can be combined with the deep belief neural network model to predict the position of the mobile terminal, so that the phenomenon that the positioning precision is influenced by factors such as the absorption effect, signal reflection and/or signal diffraction of a Bluetooth signal in the traditional Bluetooth positioning process is avoided, and the effects of improving the positioning precision and robustness of Bluetooth positioning are achieved.
As shown in fig. 1, fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a mobile terminal such as a mobile phone, a tablet computer, an intelligent bracelet or a game machine.
As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the like, and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a bluetooth positioning program.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the processor 1001 may be configured to invoke a bluetooth positioning program stored in the memory 1005 and perform the following operations:
acquiring the signal intensity and the identification mark of a Bluetooth signal received by a mobile terminal;
and determining the position information of the mobile terminal according to a pre-trained deep belief neural network model, the signal strength and the identification mark, wherein the deep belief neural network model is obtained based on the signal strength detected at a preset position, the identification mark and the coordinate position of the preset positioning point.
Further, the processor 1001 may call the bluetooth positioning program stored in the memory 1005, and further perform the following operations:
acquiring the signal intensity of Bluetooth signals sent by at least two Bluetooth base stations received by a mobile terminal;
acquiring an identification mark of a Bluetooth base station corresponding to each Bluetooth signal;
the step of determining the position information of the mobile terminal according to the pre-trained deep belief neural network model, the signal strength and the identification mark comprises the following steps:
storing the signal intensity corresponding to each Bluetooth signal and the identification mark of the Bluetooth base station sending the Bluetooth signal in an associated manner, wherein the signal intensity is used as an input vector to obtain at least two input vectors;
and determining the position information of the mobile terminal through at least two input vectors and the deep belief neural network model.
Further, the processor 1001 may call the bluetooth positioning program stored in the memory 1005, and further perform the following operations:
taking at least two input vectors as input parameters of the pre-trained deep belief neural network model;
the deep belief neural network determining an output parameter based on the input parameter;
and determining the position information according to the output parameters.
Further, the processor 1001 may call the bluetooth positioning program stored in the memory 1005, and further perform the following operations:
acquiring a contrast relation between longitude and latitude and a human position;
and determining the human position corresponding to the mobile terminal at present based on the longitude and latitude and the comparison relation, and taking the human position as the position information.
Further, the processor 1001 may call the bluetooth positioning program stored in the memory 1005, and further perform the following operations:
acquiring an offline sample library, wherein the offline sample library comprises the signal intensity of each Bluetooth base station detected at a plurality of preset positioning points in a positioning space, the identification of the Bluetooth base station and the longitude and latitude corresponding to the preset positioning points;
and training based on the offline sample library to obtain the deep belief neural network model.
Further, the processor 1001 may call the bluetooth positioning program stored in the memory 1005, and further perform the following operations:
the mobile terminal outputs the position information; and/or
And when a positioning data request initiated by a target application is received, using the position information as response data of the positioning data request, wherein the target application is an application loaded in the mobile terminal.
Referring to fig. 2, in an embodiment of the bluetooth positioning method of the present invention, the bluetooth positioning method includes the following steps:
step S10, acquiring the signal intensity and the identification mark of the Bluetooth signal received by the mobile terminal;
step S20, determining the position information of the mobile terminal according to a pre-trained deep belief neural network model, the signal strength and the identification mark, wherein the deep belief neural network model is obtained by training based on the signal strength detected at a preset position, the identification mark and the coordinate position of the preset positioning point.
Indoor positioning refers to determining location information of terminal devices located in a building. When the terminal is located in a building, positioning is carried out through satellite positioning methods such as GPS positioning and the like, so that the positioning result is inaccurate due to obstruction of the building to the positioning information number. Moreover, satellite positioning generally can only realize planar positioning, but cannot perform stereo positioning. For example, when the building is a floor building, the floor on which the mobile terminal is located cannot be determined by satellite positioning. Therefore, the mobile terminal needs to be located indoors through other methods.
In the conventional technology, indoor positioning of the mobile terminal may be implemented based on technologies such as bluetooth, infrared, or WiFi. However, in the conventional scheme for implementing indoor positioning based on bluetooth, due to the high complexity of the indoor electromagnetic environment, the bluetooth information signal used for positioning may have absorption effect, signal reflection and/or signal diffraction, etc. This phenomenon may result in a reduction in the accuracy of the positioning result.
In order to improve the positioning accuracy of the Bluetooth positioning scheme, the Bluetooth positioning method is provided.
In this embodiment, the mobile terminal is provided with a bluetooth signal receiving device, and a plurality of bluetooth base stations are arranged in advance in the positioning space. The mobile terminal comprises a Bluetooth base station, a Bluetooth signal receiving device and a Bluetooth signal transmitting device, wherein the Bluetooth base station is set to continuously transmit Bluetooth information for indoor positioning, and the Bluetooth signal receiving device of the mobile terminal is set to receive Bluetooth signals transmitted by the Bluetooth base station. The bluetooth base station can send out bluetooth signals in a mode of broadcasting the bluetooth signals.
It should be noted that the positioning space refers to a space corresponding to the coverage of the bluetooth base station disposed in a specific space. I.e. the area in which the bluetooth base station is arranged, so that the mobile terminal can receive the positioning signal sent by the bluetooth base station to perform positioning based on the positioning signal. The positioning space may be provided as an indoor space or an outdoor space. For example, in a specific application scenario, the positioning space may be an indoor space of a shopping mall, or an outdoor space such as an overpass with a multi-layer structure, so that the mobile terminal may perform stereo positioning in the positioning space.
After the mobile terminal enters the positioning space, when the Bluetooth signal receiving device of the mobile terminal is in an open state, the mobile terminal can receive Bluetooth signals sent by each Bluetooth base station. The mobile terminal is further provided with a Signal Strength calculation module, so that after the mobile terminal receives the bluetooth Signal, the RSSI (Received Signal Strength Indicator) of the currently Received bluetooth Signal can be calculated through the Signal Strength calculation module, wherein the RSSI of the bluetooth Signal is the Signal Strength.
Because a plurality of bluetooth base stations used for realizing bluetooth positioning are arranged in the positioning space, the mobile terminal can receive bluetooth signals sent by at least two bluetooth base stations after entering the positioning space, and when the relative position relationship between the mobile terminal and the bluetooth base stations is different, the RSSI of the received bluetooth signals sent by different bluetooth base stations is also different and the same. Therefore, the mobile terminal can acquire the RSSI corresponding to the received bluetooth signals sent by the plurality of bluetooth base stations.
In addition, when receiving the bluetooth signal, the mobile terminal may further obtain an identification corresponding to the bluetooth base station that sent the bluetooth information. The identification mark is used for enabling the mobile terminal to distinguish a plurality of Bluetooth base stations, so that the specific content of the identification mark can be customized by a designer. For example, the MAC Address (Media Access Control Address) of each bluetooth base station may be set as the corresponding identification identifier of each bluetooth base station. Alternatively, other unique identification marks of the bluetooth base station may be predefined as the identification marks, which is not specifically limited in this embodiment.
Further, after the RSSI and the identification are determined, the RSSI corresponding to the bluetooth signal sent by the same bluetooth base station received by the mobile terminal and the identification corresponding to the bluetooth base station may be stored in association as an input vector. Such that a plurality of input vectors can be determined when a mobile terminal receives a plurality of bluetooth signals transmitted by a plurality of different bluetooth base stations.
After the input vectors are determined, the plurality of input vectors can be used as input parameters of a pre-trained deep belief neural network model, so that the deep belief neural network model determines output parameters according to the input parameters. So that the mobile terminal can move the current position information of the mobile terminal according to the output parameters.
Specifically, as an implementation manner, the deep confidence neural network may be selected in advance through an offline sample library. The offline sample library comprises the signal intensity and the base station identification mark corresponding to a plurality of preset points in a positioning space. An example of the generation of the above-described off-line sample library is given below to explain the present invention.
Illustratively, a corresponding bluetooth base station device is installed indoors for broadcasting bluetooth signals, and other bluetooth terminal devices (such as mobile terminals) can receive the bluetooth signals broadcasted by the bluetooth base station for positioning. Then, the positioning space is divided into uniform grids according to a certain distance, and coordinate values corresponding to the grid nodes are recorded, wherein longitude and latitude corresponding to the grid nodes can be used as the coordinate values corresponding to the grid nodes. Further, the RSSI value of the bluetooth signal sent by the corresponding bluetooth base station is collected at each mesh node, and the coordinate values and the corresponding RSSI values are stored in an associated manner, so as to generate an offline RSSI library (i.e., an offline sample library). The RSSI value can be obtained by the following formula:
RSSI＝A-10n·log d
wherein, RSSI is signal strength, and a is signal strength of a bluetooth signal received by the bluetooth receiving device when a unit distance is between the bluetooth signal transmitting end and the receiving end (in this example, RSSI value of a bluetooth signal transmitted by a bluetooth base station received by a mobile terminal when a distance between the bluetooth base station and the mobile terminal for receiving the bluetooth signal transmitted by the bluetooth base station is 1 meter); d is the distance between the signal transmitting end and the receiving end (in this example, the distance between the bluetooth base station and the mobile terminal for receiving the bluetooth signal); n is an environmental impact factor. It will be appreciated that a and n can be found experimentally and by the least squares principle.
Further, in the present example, in the above-described offline sample library, the coordinate values and the RSSI values may be recorded as follows:
Beaj＝[MAC1:RSSI1,MAC2:RSSI2,···，MACj:RSSIj]
Li＝[Xi,Yi,Beaj]
wherein, Xi、YiRespectively the longitude and latitude values of the ith grid node; MACjAs the address of the jth Bluetooth base station, RSSIjThe signal strength of the corresponding Bluetooth signal of the jth Bluetooth base station.
Further, when training the deep belief neural network based on the offline sample library, Bea may be usedjAs input eigenvalues of the neural network model, LiAnd as an output value of the deep confidence neural network model, training an offline sample library. To obtain a trained deep belief neural network.
It is understood that, in the present example, when the deep confidence neural network is trained, a preset amount of data in the offline sample library may be used as training data, and the rest may be used as test data to train and measure the deep confidence neural network model. For example, by surgically harvesting the footEnough BeajAnd LiAfter the off-line sample library is established, the off-line sample library is divided into a training set and a testing set according to the proportion of 70% and 30% respectively and is used for training and verifying the deep confidence network. Wherein: the deep belief network is essentially a stack of RBMs (Restricted Boltzmann machines) that can be used as a regression model to match the inputs and outputs of the network by finding optimal weights. The training process can be mainly divided into the following two stages:
the first stage is a process of unsupervised training of the RBMs from bottom to top, and the process of training the RBMs can be regarded as a process of feature extraction of training samples by pre-training stacked RBMs layer by layer to obtain a proper weight of each RBM.
In the second stage, supervised learning from top to bottom is adopted, the whole network is optimized by using a gradient descent or other optimization algorithms, the process can be regarded as a downward reconstruction process, and the downward reconstruction refers to the process of reconstructing information to obtain a proper weight value based on the abstract representation and the generated weight generated in the upward process. Before the process starts, the number of the neuron nodes of the network and the number of the network layers are reasonably set, a proper initial value is given to the network, and then the network is trained. And when the accuracy of the training set is higher than a certain threshold value, verifying the network by using the sample data of the test set, and outputting the predicted accuracy. And when the accuracy of the test set meets the requirement, storing the trained deep belief neural network model.
And finally, in the positioning process, taking a vector formed by the currently acquired RSSI and the identification mark as an input parameter of the deep belief neural network model, and then acquiring an output parameter according to the input parameter. And the output parameter is the longitude and latitude corresponding to the current position of the mobile terminal. And then the longitude and latitude are used as the position information corresponding to the current position of the mobile terminal.
In the technical scheme disclosed in this embodiment, the signal strength and the identification identifier of the bluetooth signal received by the mobile terminal are obtained first, and then the position information of the mobile terminal is determined according to a pre-trained deep belief neural network model, the signal strength and the identification identifier, wherein the deep belief neural network model is obtained based on the signal strength detected at a preset location, the identification identifier and the coordinate position training of the preset location point. The Bluetooth positioning technology can be combined with the deep belief neural network model to predict the position of the mobile terminal, so that the phenomenon that the positioning precision is influenced by factors such as the absorption effect, signal reflection and/or signal diffraction of a Bluetooth signal in the traditional Bluetooth positioning process is avoided, and the effects of improving the positioning precision and robustness of Bluetooth positioning are achieved.
Referring to fig. 3, based on the foregoing embodiment, in another embodiment, the step S20 includes:
step S21, taking at least two input vectors as input parameters of the pre-trained deep belief neural network model;
step S22, the deep belief neural network determines an output parameter based on the input parameter;
and step S23, determining the position information according to the output parameters.
In this embodiment, at least two input vectors may be used as input parameters of the deep belief neural network model trained in advance, so that the deep belief neural network determines output parameters based on the input parameters. The output parameter is longitude and latitude corresponding to the current position of the mobile terminal.
After the longitude and latitude corresponding to the current position of the mobile terminal are obtained, the longitude and latitude can be directly used as the position information of the mobile terminal.
Optionally, after acquiring the longitude and latitude corresponding to the current position of the mobile terminal, the comparison relationship between the longitude and latitude and the human position may also be acquired. The human position is named by people according to the object corresponding to the position space. For example, a store named "excellent clothing library" with a location corresponding to longitude and latitude as a mall. After the longitude and latitude are obtained, or the comparison relationship between the longitude and latitude and the human position, the human position corresponding to the longitude and latitude can be determined to be the optimal clothes library according to the comparison relationship.
Further, after the human position corresponding to the longitude and latitude is determined, the human position can be used as the position information of the mobile terminal.
In the technical scheme disclosed in this embodiment, at least two input vectors are first used as input parameters of the deep belief neural network model trained in advance, then the deep belief neural network determines output parameters based on the input parameters, and determines the location information according to the output parameters, so that the effect of improving the diversity of the location information is achieved because the location information can be determined according to the output parameters.
Referring to fig. 4, based on any one of the above embodiments, in a further embodiment, after the step S30, the method further includes:
step S30, the mobile terminal outputs the position information;
in the present embodiment; after the mobile terminal determines the position information, the position information can be output in a display interface. The display interface may be an operation interface of a map application, so that the position information may be displayed in the operation interface of the map application.
Optionally, referring to fig. 5, as another implementation manner of this embodiment, after step S30, the method further includes:
step S40, when receiving a positioning data request initiated by a target application, using the location information as response data of the positioning data request, where the target application is an application loaded in the mobile terminal
In this embodiment, an application loaded in the mobile terminal may serve as a target application to initiate a positioning data request. For example, when the mobile terminal is loaded with the WeChat, the order-ordering operation can be performed by a tea-liking applet in the WeChat. Therefore, the current position of the mobile terminal needs to be acquired to determine the store closest to the current position of the mobile middle terminal. Thus, the WeChat may initiate a request for location data as a target application. And when a positioning data request initiated by WeChat is received at present, the position information is used as response data of the positioning data request.
Optionally, when the mobile terminal responds to the positioning data request of the target application, the location information acquisition permission of the target application may be determined first, and when it is determined that the target application has the location information acquisition permission, the mobile terminal responds to the positioning data request, otherwise, the mobile terminal does not respond to the positioning data request.
It can be understood that the mobile terminal may automatically set the location information acquisition permission of any application through the system, or may also output a setting interface, so as to set the location information acquisition permission of any application through the setting interface or a user.
In the technical scheme disclosed in the embodiment, the position information can be output and/or used as response data of the low-temperature data request, so that the effect of enriching the use of the position information is achieved.
In addition, an embodiment of the present invention further provides a mobile terminal, where the mobile terminal includes a memory, a processor, and a bluetooth positioning program stored in the memory and capable of running on the processor, and the bluetooth positioning program is executed by the processor to implement the steps of the bluetooth positioning method according to the above embodiments.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a bluetooth positioning program is stored on the computer-readable storage medium, and the bluetooth positioning program, when executed by a processor, implements the steps of the bluetooth positioning method according to the above embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a mobile terminal (e.g., a mobile phone, a tablet computer, a smart band, a game console, etc.) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A Bluetooth positioning method, characterized in that the Bluetooth positioning method comprises the following steps:
acquiring the signal intensity and the identification mark of a Bluetooth signal received by a mobile terminal;
and determining the position information of the mobile terminal according to a pre-trained deep belief neural network model, the signal strength and the identification mark, wherein the deep belief neural network model is obtained based on the signal strength detected at a preset position, the identification mark and the coordinate position of the preset positioning point.
2. The bluetooth positioning method according to claim 1, wherein the step of obtaining the signal strength and the identification of the bluetooth signal received by the mobile terminal comprises:
acquiring the signal intensity of Bluetooth signals sent by at least two Bluetooth base stations received by a mobile terminal;
acquiring an identification mark of a Bluetooth base station corresponding to each Bluetooth signal;
the step of determining the position information of the mobile terminal according to the pre-trained deep belief neural network model, the signal strength and the identification mark comprises the following steps:
storing the signal intensity corresponding to each Bluetooth signal and the identification mark of the Bluetooth base station sending the Bluetooth signal in an associated manner, wherein the signal intensity is used as an input vector to obtain at least two input vectors;
and determining the position information of the mobile terminal through at least two input vectors and the deep belief neural network model.
3. The bluetooth positioning method according to claim 2, wherein the step of determining the position information of the mobile terminal through at least two of the input vectors and the deep belief neural network model comprises:
taking at least two input vectors as input parameters of the pre-trained deep belief neural network model;
the deep belief neural network determining an output parameter based on the input parameter;
and determining the position information according to the output parameters.
4. The bluetooth positioning method according to claim 3, wherein the output parameter is a longitude and latitude corresponding to a current location of the mobile terminal.
5. The bluetooth positioning method according to claim 4, wherein the step of determining the location information according to the output parameter comprises:
acquiring a contrast relation between longitude and latitude and a human position;
and determining the human position corresponding to the mobile terminal at present based on the longitude and latitude and the comparison relation, and taking the human position as the position information.
6. The bluetooth positioning method of claim 1, wherein the bluetooth positioning method further comprises:
acquiring an offline sample library, wherein the offline sample library comprises the signal intensity of each Bluetooth base station detected at a plurality of preset positioning points in a positioning space, the identification of the Bluetooth base station and the longitude and latitude corresponding to the preset positioning points;
and training based on the offline sample library to obtain the deep belief neural network model.
7. The bluetooth positioning method according to claim 1, wherein the identification is a MAC address of the bluetooth base station.
8. The bluetooth positioning method according to claim 1, wherein after the step of determining the position information of the mobile terminal according to the pre-trained deep belief neural network model, the signal strength and the identification, further comprising:
the mobile terminal outputs the position information; and/or
And when a positioning data request initiated by a target application is received, using the position information as response data of the positioning data request, wherein the target application is an application loaded in the mobile terminal.
9. A mobile terminal, characterized in that the mobile terminal comprises: memory, a processor and a bluetooth positioning program stored on the memory and executable on the processor, the bluetooth positioning program when executed by the processor implementing the steps of the bluetooth positioning method according to any of claims 1 to 8.
10. A computer-readable storage medium, having a bluetooth positioning program stored thereon, which when executed by a processor implements the steps of the bluetooth positioning method of any one of claims 1 to 8.
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