CN117014805A

CN117014805A - Positioning method and device

Info

Publication number: CN117014805A
Application number: CN202210461473.6A
Authority: CN
Inventors: 葛咏; 刘鑫; 韩华; 张蝶; 刘梦晓; 毛娜
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2022-04-28
Filing date: 2022-04-28
Publication date: 2023-11-07

Abstract

The application discloses a positioning method and a positioning device, which are used for matching a building where a terminal is located based on a Bayesian framework and accurately matching the building where the terminal is located. The method comprises the following steps: acquiring first probability, namely prior probability, of the first terminal being in each building in a plurality of buildings according to first environment information, wherein the first environment information comprises information generated by the acquired terminal in a first area, and the first area comprises the plurality of buildings; acquiring the likelihood that the first terminal is positioned in each building in a plurality of buildings according to second environment information, wherein the second environment information comprises information acquired by the first terminal, and the second environment information comprises information used for representing the building in which the first terminal is positioned; obtaining a second probability, namely a posterior probability, of each building in a plurality of buildings according to the first probability and the likelihood; and screening the first building from the plurality of buildings according to the second probability to serve as the building where the first terminal is located.

Description

Positioning method and device

Technical Field

The present application relates to the field of positioning, and in particular, to a positioning method and apparatus.

Background

Any location-based service among city services relies on accurate positioning capabilities. The building in which the indoor terminal is accurately identified can provide original information for upper-layer application, and the exploration of indoor positioning, automatic image identification, scene and activity perception and crowd space-time distribution rules all depend on high-precision terminal positioning information. Indoor positioning technology based on smart phones can help terminals to fulfill these location-based services requirements.

In some conventional positioning methods, a large amount of objective condition support is needed for obtaining high-precision positioning by using building positioning technology, and the execution efficiency of the positioning method is low. Improving positioning accuracy or by integrating additional hardware at the hardware level often means additional cost, higher power consumption and higher complexity. Therefore, how to improve the positioning accuracy of the building is a problem to be solved.

Disclosure of Invention

The application provides a positioning method and a positioning device, which are used for matching a building where a terminal is located based on a Bayesian framework and accurately matching the building where the terminal is located.

In a first aspect, the present application provides a positioning method, including: acquiring first environment information, wherein the first environment information comprises information generated by a terminal in a first acquired area, the first area comprises a plurality of buildings, and the first environment information is used for representing the information of the buildings where the plurality of terminals in the first area are located; acquiring second environment information, wherein the second environment information comprises information acquired by the first terminal, and the second environment information comprises information used for representing a building where the first terminal is located; acquiring a first probability of the first terminal being in each building in a plurality of buildings according to the first environment information, namely, a priori probability, wherein the first terminal is any terminal in a first area; acquiring the likelihood that the first terminal is positioned in each building in a plurality of buildings according to the second environment information; obtaining a second probability, namely a posterior probability, of each building in a plurality of buildings according to the first probability and the likelihood; and screening the first building from the plurality of buildings according to the second probability of the first terminal in each building, wherein the first building is the building in which the first terminal is located.

Therefore, in the embodiment of the application, the probability of the terminal in each building is calculated by using the building information, the probability of the terminal in each building is calculated by using the terminal sensor information, then the probability of the terminal in each building is obtained by combining the two probabilities, the building with the maximum probability is screened out as the building in which the terminal is positioned, which is equivalent to the building which is most matched with the terminal by using the Bayesian framework after the prior probability and the likelihood are calculated.

In one possible implementation manner, the acquiring the first probability that the first terminal is located in each building of the plurality of buildings according to the first environmental information includes: determining information generated by terminals in each building from the first environmental information; acquiring a first similarity between information generated by terminals in each building and information generated by the terminals in a first area; acquiring second similarity between information of wireless signal access points in each building and information of wireless access points in a first area; the first probability is calculated in combination with the first similarity and the second similarity.

Therefore, in the embodiment of the application, the prior probability that the terminal is in each building can be calculated based on the acquired environmental information, such as the information generated by the terminal in the area extracted from the crowdsourcing data, so that the posterior probability that the terminal is in each building can be calculated later, and the building in which the terminal is positioned can be positioned accurately.

In one possible embodiment, the method may further include: acquiring the number of users in a first area to obtain the number of the first users; acquiring the number of users in each building to obtain a second number of users; acquiring a third probability that the first terminal is positioned in each building according to the first user number and the second user number; and fusing the third probability and the first probability to obtain the updated first probability.

Therefore, in the embodiment of the application, the prior probability can be updated by combining the number of users in the area and the number of users in each building, so that the accuracy of the prior probability is improved. The accuracy of the prior probability is improved corresponding to the dimension from the number of users.

In one possible implementation manner, the determining information generated by the terminal in each building from the first environmental information includes: determining a buffer range according to the positioning precision of the terminal in the first area; and taking the information of the terminals in the buffer range of each building as the information generated by the terminals in each building. Therefore, in the embodiment of the application, when the terminal information is extracted, the building error zone can be defined based on the positioning accuracy of the terminal, for example, if the positioning accuracy of some terminals is overlarge, the situation that the terminals are positioned outside the building may occur, and after the building error zone is defined, some terminals with positioning errors can be defined in the building range, so that the building where the terminals are positioned can be more accurately judged.

In a possible implementation manner, the acquiring the likelihood that the first terminal is located in each building of the plurality of buildings according to the second environmental information may include: acquiring a third similarity between information acquired by a first terminal and information in a first area in first environment information; acquiring a fourth similarity between the information acquired by the first terminal and the information of each building in the first environment information; and calculating the likelihood that the first terminal is positioned in each building in the plurality of buildings according to the third similarity and the fourth similarity.

Therefore, in the embodiment of the application, the likelihood can be calculated based on the consistency of the information between the terminal and the area and the information between the terminal and the building, so that the accurate likelihood can be obtained.

In one possible implementation manner, the screening the first building from the plurality of buildings according to the second probability that the first terminal is in each building from the plurality of buildings may include: and screening the building with the highest second probability from the plurality of buildings as the first building.

Therefore, in the embodiment of the application, after the posterior probability that the terminal is in each building is calculated, the building with the highest posterior probability can be used as the building where the terminal is located. Based on Bayesian framework, posterior probability which can more accurately represent the probability of the terminal in each building is calculated according to prior probability and likelihood, so that the building which is more matched with the terminal is positioned.

In a second aspect, the present application provides a positioning device comprising:

the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring first environment information and second environment information, the first environment information comprises information generated by a terminal in a first acquired area, the first area comprises a plurality of buildings, the first environment information is used for representing the information of the buildings where the plurality of terminals in the first area are located, the second environment information comprises information acquired by the first terminal, and the second environment information comprises information used for representing the buildings where the first terminal is located;

the processing module is used for acquiring a first probability that the first terminal is positioned in each building in the plurality of buildings according to the first environment information, wherein the first terminal is any terminal in a first area;

the processing module is also used for acquiring the likelihood that the first terminal is positioned in each building in the plurality of buildings according to the second environment information;

the processing module is further used for obtaining a second probability of each building in the plurality of buildings of the first terminal according to the first probability and the likelihood;

the processing module is further used for screening the first building from the buildings according to the second probability of the first terminal in each building in the buildings, wherein the first building is the building where the first terminal is located.

In a possible implementation manner, the processing module is specifically configured to: determining information generated by terminals in each building from the first environmental information; acquiring a first similarity between information generated by terminals in each building and information generated by the terminals in a first area; acquiring second similarity between information of wireless signal access points in each building and information of wireless access points in a first area; the first probability is calculated in combination with the first similarity and the second similarity.

In a possible implementation manner, the obtaining module is further configured to obtain the number of users in the first area, so as to obtain the first number of users;

the acquisition module is also used for acquiring the number of users in each building to obtain a second number of users;

the processing module is further used for acquiring a third probability that the first terminal is positioned in each building according to the first user quantity and the second user quantity;

the processing module is further used for fusing the third probability and the first probability to obtain the updated first probability.

In one possible implementation manner, the acquiring module is specifically configured to: determining a buffer range according to the positioning precision of the terminal in the first area; and taking the information of the terminals in the buffer range of each building as the information generated by the terminals in each building.

In a possible implementation manner, the processing module is specifically configured to: acquiring a third similarity between information acquired by a first terminal and information in a first area in first environment information; acquiring a fourth similarity between the information acquired by the first terminal and the information of each building in the first environment information; and calculating the likelihood that the first terminal is positioned in each building in the plurality of buildings according to the third similarity and the fourth similarity.

In a possible implementation manner, the processing module is specifically configured to: and screening the building with the highest second probability from the plurality of buildings as the first building.

In a third aspect, an embodiment of the present application provides a positioning device, which has a function of implementing the positioning method of the first aspect. The functions can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the functions described above.

In a fourth aspect, an embodiment of the present application provides a positioning device, including: the processor and the memory are interconnected by a line, and the processor invokes the program code in the memory for performing the processing-related functions of the positioning method according to any one of the first aspect. Alternatively, the positioning device may be a chip.

In a fifth aspect, an embodiment of the present application provides a positioning device, which may also be referred to as a digital processing chip or a chip, the chip comprising a processing unit and a communication interface, the processing unit obtaining program instructions via the communication interface, the program instructions being executed by the processing unit, the processing unit being configured to perform a processing related function as in the first aspect or any of the alternative embodiments of the first aspect.

In a sixth aspect, embodiments of the present application provide a computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of the first aspect or any of the alternative embodiments of the first aspect.

In a seventh aspect, embodiments of the present application provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of the first aspect or any of the alternative embodiments of the first aspect.

Drawings

FIG. 1 is a schematic diagram of a system architecture for the application of the present application;

fig. 2 is a schematic diagram of a terminal structure according to the present application;

fig. 3 is a schematic view of an application scenario provided by the present application;

fig. 4 is a schematic diagram of another application scenario provided in an embodiment of the present application;

Fig. 5 is a flow chart of a positioning method according to an embodiment of the present application;

FIG. 6 is a flowchart of another positioning method according to an embodiment of the present application;

FIG. 7 is a flowchart of another positioning method according to an embodiment of the present application;

FIG. 8 is a flowchart of another positioning method according to an embodiment of the present application;

FIG. 9 is a flowchart of another positioning method according to an embodiment of the present application;

FIG. 10 is a schematic diagram of a building error band according to an embodiment of the present application;

FIG. 11 is a schematic view of another scenario provided in an embodiment of the present application;

FIG. 12 is a schematic view of another scenario provided by an embodiment of the present application;

FIG. 13 is a flowchart of another positioning method according to an embodiment of the present application;

FIG. 14 is a flowchart of another positioning method according to an embodiment of the present application;

fig. 15 is a schematic structural diagram of a positioning device according to an embodiment of the present application;

fig. 16 is a schematic structural diagram of another positioning device according to an embodiment of the present application.

Detailed Description

The following description of the technical solutions according to the embodiments of the present application will be given with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Location-based services are also one of the most widely used services for smartphones, however, typically the accuracy of positioning a phone is about 50 meters, which is not sufficient to match a terminal to a building, and to identify the specific location of the terminal in the building, such as which location or building body in the building. Building matching has wide application in reality, for example, by building matching, special service information can be provided for users in a specific building or monitored, for example, event organizers provide services for terminals in a specific building only, and whether specific people such as the old, the weak, the sick and the disabled, which need special care, are active in the specific building or not is monitored. In addition, the building matching technology can be utilized to realize the access control based on the position, such as hotels, companies and the like, only provide network access for terminals in a certain building, reduce information leakage and strengthen information security.

For example, the positioning may be performed by a sensor provided in the terminal. Multiple types of sensors can be supported in the terminal: such as dynamic sensors, position sensors, and environmental sensors. Dynamic sensors measure acceleration forces and rotational forces in three axes. Included in this category are accelerometers, gravity sensors, gyroscopes, and rotation vector sensors. Environmental sensors measure various environmental parameters such as ambient air temperature, air pressure, illuminance, and humidity. Included in this category are barometers, photometers, and thermometers. The position sensor measures the physical position of the device. Included in this category are screen orientation sensors and magnetometers. These sensors can provide highly accurate raw data, and are well suited for use in monitoring three-dimensional movement or positioning of a device, or monitoring changes in the environment surrounding the device. For example, the game may track readings of the device gravity sensor to infer complex user gestures and actions, such as tilting, shaking, rotating, or waving. Also, weather applications may use temperature and humidity sensors of the device to calculate and report dew points, while travel applications may use geomagnetic field sensors and accelerometers to report compass orientations. However, the positioning technology based on the built-in sensor of the mobile phone has accumulated errors, and the errors become larger and larger along with the increase of the travelling distance of the terminal.

As another example, a device signal source (e.g., wireless network, 5G signal, etc.) that is deployed in advance in the room or a new dedicated signal source (e.g., bluetooth low energy, etc.) may be used. The positioning technology mainly comprises a geometric measurement method based on an Access Point (AP) Point of a device signal source. The positioning accuracy of the TOA (Time of Arrival) based positioning algorithm can reach 2.4 meters in the case of 10 AP points, but the TOA based positioning system performance will be drastically reduced in the non-line of sight condition. The positioning technology based on RSSI (Received Signal Strength Indication) mainly adopts a triangular positioning technology, the distances between a target to be measured and a plurality of APs are calculated through a signal attenuation model, and prior conditions such as the position of the APs, the signal attenuation model and the like are required to be known in the positioning process. However, due to the complex indoor environment, multipath propagation and shadowing effects of wireless signals often occur, signal propagation is not easy to simulate, and indoor positioning accuracy is inaccurate depending on a signal source measurement principle.

Also for example, building location may be achieved by fingerprinting. The WiFi fingerprint matching positioning technology is divided into two stages of WiFi fingerprint library construction and real-time positioning. If the indoor positioning system based on WiFi fingerprint matching can be used, in the fingerprint acquisition stage, fingerprint information in four directions of southeast, northwest and north is acquired aiming at each reference point, and the fingerprint acquisition mode is complex; and (3) performing RSSI preprocessing in the real-time positioning stage, filtering abnormal values, and performing fingerprint matching to output final position information. The prior information such as a signal attenuation model and known AP point positions is not required to be constructed based on the WiFi fingerprint matching positioning technology, but the method has certain defects: the diversity of the smart phone, especially the fragmentation of the Android smart terminal, brings about the device isomerism, namely different smart terminals can obviously distinguish RSSIs collected at the same position due to different sensitivity degrees of built-in sensors of the smart terminals; the timeliness of signal propagation and the indoor people flow density degree at different moments are different, the influence on signal propagation is different, and the RSSI fluctuation acquired at the same position at different moments is obvious. The fingerprint positioning technology is simple and easy to realize, but a large amount of time is required to collect signal intensity data to construct a fingerprint database, and a large amount of fingerprint data are compared to realize online positioning.

Therefore, the application provides a positioning method which is used for matching the building where the terminal is located based on the Bayesian framework and accurately matching the building where the terminal is located.

First, a description will be given of a communication framework to which the method provided by the present application is applied.

Referring to fig. 1, a system architecture diagram is provided in the present application.

One or more terminals (terminal 1 to terminal N, N being a positive integer as shown in fig. 1) and a server may be included in the system architecture.

The connection between the terminal and the server may be established via a wired and/or wireless network. The wireless network may include, but is not limited to: fifth Generation mobile communication technology (5 th-Generation, 5G) systems, global system for mobile communications (global system for mobile communications, GSM), general packet radio service (general packet radio service, GPRS), code division multiple access (code division multiple access, CDMA), wideband code division multiple access (wideband code division multiple access, WCDMA), time division code division multiple access (time-division code division multiple access, TD-SCDMA), long term evolution (long term evolution, LTE), bluetooth (blue), global navigation satellite system (the global navigation satellite system, GNSS), wireless fidelity (wireless fidelity, wiFi), near field wireless communication (near field communication, NFC), FM (which may also be referred to as frequency modulation broadcast), zigbee, radio frequency identification technology (radio frequency identification, RFID) and/or Infrared (IR) technology, and the like. The GNSS may include a global satellite positioning system (global positioning system, GPS), a global navigation satellite system (global navigation satellite system, GLONASS), a beidou satellite navigation system (beidou navigation satellite system, BDS), a quasi zenith satellite system (quasi-zenith satellite system, QZSS) and/or a satellite based augmentation system (satellite based augmentation systems, SBAS), etc. The wired network includes, but is not limited to: optical transport network (optical transport network, OTN), synchronous digital hierarchy (synchronous digital hierarchy, SDH), passive optical network (passive optical network, PON), ethernet (Ethernet), or flexible Ethernet (FlexE), etc.

The server may be a single entity server, such as a distributed server or a centralized server, or may be a cloud server.

The terminal referred to in the present application may be a carrier device for a user account, etc. For example, the user may include an account logged in to the device, or may refer to a device that carries the account, etc. The terminal provided by the application can include, but is not limited to: smart mobile phones, televisions, tablet computers, hand rings, head mounted display devices (Head Mount Display, HMD), augmented reality (augmented reality, AR) devices, mixed Reality (MR) devices, cellular phones (cellular phones), smart phones (smart phones), personal digital assistants (personal digital assistant, PDA), tablet computers, vehicles, vehicle terminals, laptop computers (personal computer, PC), etc. Of course, in the following embodiments, there is no limitation on the specific form of the terminal.

The structure of the terminal may be as shown in fig. 2, for example.

The terminal 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (universal serial bus, USB) interface 130, a charge management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, keys 190, a motor 191, an indicator 192, a camera 193, a display 194, and a subscriber identity module (subscriber identification module, SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, a motion sensor 180N, and the like.

It should be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the terminal 100. In other embodiments of the application, terminal 100 may include more or less components than shown, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units, such as: the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), a controller, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and/or a neural network processor (neural-network processing unit, NPU), etc. Wherein the different processing units may be separate devices or may be integrated in one or more processors.

The controller can generate operation control signals according to the instruction operation codes and the time sequence signals to finish the control of instruction fetching and instruction execution.

A memory may also be provided in the processor 110 for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory may hold instructions or data that the processor 110 has just used or recycled. If the processor 110 needs to reuse the instruction or data, it can be called directly from the memory. Repeated accesses are avoided and the latency of the processor 110 is reduced, thereby improving the efficiency of the system.

In some embodiments, the processor 110 may include one or more interfaces. The interfaces may include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, a universal asynchronous receiver transmitter (universal asynchronous receiver/transmitter, UART) interface, a mobile industry processor interface (mobile industry processor interface, MIPI), a general-purpose input/output (GPIO) interface, a subscriber identity module (subscriber identity module, SIM) interface, and/or a universal serial bus (universal serial bus, USB) interface, among others.

The I2C interface is a bi-directional synchronous serial bus comprising a serial data line (SDA) and a serial clock line (derail clock line, SCL). In some embodiments, the processor 110 may contain multiple sets of I2C buses. The processor 110 may be coupled to the touch sensor 180K, charger, flash, camera 193, etc., respectively, through different I2C bus interfaces. For example: the processor 110 may be coupled to the touch sensor 180K through an I2C interface, so that the processor 110 and the touch sensor 180K communicate through an I2C bus interface to implement a touch function of the terminal 100.

The I2S interface may be used for audio communication. In some embodiments, the processor 110 may contain multiple sets of I2S buses. The processor 110 may be coupled to the audio module 170 via an I2S bus to enable communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 may transmit an audio signal to the wireless communication module 160 through the I2S interface, to implement a function of answering a call through the bluetooth headset.

PCM interfaces may also be used for audio communication to sample, quantize and encode analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 may also transmit audio signals to the wireless communication module 160 through the PCM interface to implement a function of answering a call through the bluetooth headset. Both the I2S interface and the PCM interface may be used for audio communication.

The UART interface is a universal serial data bus for asynchronous communications. The bus may be a bi-directional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, a UART interface is typically used to connect the processor 110 with the wireless communication module 160. For example: the processor 110 communicates with a bluetooth module in the wireless communication module 160 through a UART interface to implement a bluetooth function. In some embodiments, the audio module 170 may transmit an audio signal to the wireless communication module 160 through a UART interface, to implement a function of playing music through a bluetooth headset.

The MIPI interface may be used to connect the processor 110 to peripheral devices such as a display 194, a camera 193, and the like. The MIPI interfaces include camera serial interfaces (camera serial interface, CSI), display serial interfaces (display serial interface, DSI), and the like. In some embodiments, processor 110 and camera 193 communicate through a CSI interface to implement the photographing function of terminal 100. The processor 110 and the display 194 communicate through a DSI interface to implement the display function of the terminal 100.

The GPIO interface may be configured by software. The GPIO interface may be configured as a control signal or as a data signal. In some embodiments, a GPIO interface may be used to connect the processor 110 with the camera 193, the display 194, the wireless communication module 160, the audio module 170, the sensor module 180, and the like. The GPIO interface may also be configured as an I2C interface, an I2S interface, a UART interface, an MIPI interface, etc.

The USB interface 130 is an interface conforming to the USB standard specification, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, or the like. The USB interface 130 may be used to connect a charger to charge the terminal 100, or may be used to transfer data between the terminal 100 and a peripheral device. And can also be used for connecting with a headset, and playing audio through the headset. The interface may also be used to connect other electronic devices, such as AR devices, etc. It should be understood that the USB interface 130 may be replaced by other interfaces, such as Type-c or Lighting, which may implement charging or data transmission, and the USB interface 130 is merely illustrated herein as an example.

It should be understood that the interfacing relationship between the modules illustrated in the embodiment of the present application is only illustrative, and does not limit the structure of the terminal 100. In other embodiments of the present application, the terminal 100 may also use different interfacing manners in the above embodiments, or a combination of multiple interfacing manners.

The charge management module 140 is configured to receive a charge input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charge management module 140 may receive a charging input of a wired charger through the USB interface 130. In some wireless charging embodiments, the charge management module 140 may receive wireless charging input through a wireless charging coil of the terminal 100. The charging management module 140 may also supply power to the electronic device through the power management module 141 while charging the battery 142.

The power management module 141 is used for connecting the battery 142, and the charge management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charge management module 140 to power the processor 110, the internal memory 121, the display 194, the camera 193, the wireless communication module 160, and the like. The power management module 141 may also be configured to monitor battery capacity, battery cycle number, battery health (leakage, impedance) and other parameters. In other embodiments, the power management module 141 may also be provided in the processor 110. In other embodiments, the power management module 141 and the charge management module 140 may be disposed in the same device.

The wireless communication function of the terminal 100 may be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, a modem processor, a baseband processor, and the like.

The antennas 1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in terminal 100 may be configured to cover a single or multiple communication bands. Different antennas may also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed into a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 150 may provide a solution including 2G/3G/4G/5G wireless communication applied to the terminal 100. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (low noise amplifier, LNA), etc. The mobile communication module 150 may receive electromagnetic waves from the antenna 1, perform processes such as filtering, amplifying, and the like on the received electromagnetic waves, and transmit the processed electromagnetic waves to the modem processor for demodulation. The mobile communication module 150 can amplify the signal modulated by the modem processor, and convert the signal into electromagnetic waves through the antenna 1 to radiate. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be disposed in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be provided in the same device as at least some of the modules of the processor 110.

The modem processor may include a modulator and a demodulator. The modulator is used for modulating the low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used for demodulating the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low frequency baseband signal to the baseband processor for processing. The low frequency baseband signal is processed by the baseband processor and then transferred to the application processor. The application processor outputs sound signals through an audio device (not limited to the speaker 170A, the receiver 170B, etc.), or displays images or video through the display screen 194. In some embodiments, the modem processor may be a stand-alone device. In other embodiments, the modem processor may be provided in the same device as the mobile communication module 150 or other functional module, independent of the processor 110.

The wireless communication module 160 may provide solutions for wireless communication including wireless local area network (wireless local area networks, WLAN) (e.g., wireless fidelity (wireless fidelity, wiFi) network), bluetooth (BT), global navigation satellite system (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field wireless communication technology (near field communication, NFC), ultra Wideband (UWB), infrared technology (IR), etc., applied on the terminal 100. The wireless communication module 160 may be one or more devices that integrate at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, modulates the electromagnetic wave signals, filters the electromagnetic wave signals, and transmits the processed signals to the processor 110. The wireless communication module 160 may also receive a signal to be transmitted from the processor 110, frequency modulate it, amplify it, and convert it to electromagnetic waves for radiation via the antenna 2.

In some embodiments, antenna 1 and mobile communication module 150 of terminal 100 are coupled, and antenna 2 and wireless communication module 160 are coupled, such that terminal 100 may communicate with a network and other devices via wireless communication techniques. The wireless communication techniques may include, but are not limited to: fifth Generation mobile communication technology (5 th-Generation, 5G) systems, global system for mobile communications (global system for mobile communications, GSM), general packet radio service (general packet radio service, GPRS), code division multiple access (code division multiple access, CDMA), wideband code division multiple access (wideband code division multiple access, WCDMA), time division code division multiple access (time-division code division multiple access, TD-SCDMA), long term evolution (long term evolution, LTE), bluetooth (blue), global navigation satellite system (the global navigation satellite system, GNSS), wireless fidelity (wireless fidelity, wiFi), near field wireless communication (near field communication, NFC), FM (which may also be referred to as frequency modulation broadcast), zigbee, radio frequency identification technology (radio frequency identification, RFID) and/or Infrared (IR) technology, and the like. The GNSS may include a global satellite positioning system (global positioning system, GPS), a global navigation satellite system (global navigation satellite system, GLONASS), a beidou satellite navigation system (beidou navigation satellite system, BDS), a quasi zenith satellite system (quasi-zenith satellite system, QZSS) and/or a satellite based augmentation system (satellite based augmentation systems, SBAS), etc.

In some embodiments, the terminal 100 may also include a wired communication module (not shown in fig. 1), or the mobile communication module 150 or the wireless communication module 160 may be replaced with a wired communication module (not shown in fig. 1) herein, which may enable the electronic device to communicate with other devices through a wired network. The wired network may include, but is not limited to, one or more of the following: optical transport network (optical transport network, OTN), synchronous digital hierarchy (synchronous digital hierarchy, SDH), passive optical network (passive optical network, PON), ethernet (Ethernet), or flexible Ethernet (FlexE), etc.

Terminal 100 implements display functions via a GPU, display 194, and application processor, etc. The GPU is a microprocessor that displays an interface, and is connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 110 may include one or more GPUs that execute program instructions to generate or change display information.

The display screen 194 is used to display images, videos, and the like. The display 194 includes a display panel. The display panel may employ a liquid crystal display (liquid crystal display, LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED) or an active-matrix organic light-emitting diode (matrix organic light emitting diode), a flexible light-emitting diode (flex), a mini, a Micro led, a Micro-OLED, a quantum dot light-emitting diode (quantum dot light emitting diodes, QLED), or the like. In some embodiments, the terminal 100 may include 1 or N display screens 194, N being a positive integer greater than 1.

The terminal 100 may implement photographing functions through an ISP, a camera 193, a video codec, a GPU, a display 194, an application processor, and the like.

The ISP is used to process data fed back by the camera 193. For example, when photographing, the shutter is opened, light is transmitted to the camera photosensitive element through the lens, the optical signal is converted into an electric signal, and the camera photosensitive element transmits the electric signal to the ISP for processing and is converted into an image visible to naked eyes. ISP can also optimize the noise, brightness and skin color of the image. The ISP can also optimize parameters such as exposure, color temperature and the like of a shooting scene. In some embodiments, the ISP may be provided in the camera 193.

The camera 193 is used to capture still images or video. The object generates an optical image through the lens and projects the optical image onto the photosensitive element. The photosensitive element may be a charge coupled device (charge coupled device, CCD) or a Complementary Metal Oxide Semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, which is then transferred to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a format of a standard RGB camera, YUV, or the like. In some embodiments, terminal 100 may include 1 or N cameras 193, N being a positive integer greater than 1.

The digital signal processor is used for processing digital signals, and can process other digital signals besides digital image signals. For example, when the terminal 100 selects a frequency bin, the digital signal processor is used to fourier transform the frequency bin energy, etc.

Video codecs are used to compress or decompress digital video. The terminal 100 may support one or more video codecs. In this way, the terminal 100 may play or record video in a variety of encoding formats, such as: dynamic picture experts group (moving picture experts group, MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

The NPU is a neural-network (NN) computing processor, and can rapidly process input information by referencing a biological neural network structure, for example, referencing a transmission mode between human brain neurons, and can also continuously perform self-learning. Applications such as intelligent cognition of the terminal 100 can be implemented by the NPU, for example: image recognition, face recognition, speech recognition, text understanding, etc.

The external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to realize the memory capability of the extension terminal 100. The external memory card communicates with the processor 110 through an external memory interface 120 to implement data storage functions. For example, files such as music, video, etc. are stored in an external memory card.

The internal memory 121 may be used to store computer executable program code including instructions. The internal memory 121 may include a storage program area and a storage data area. The storage program area may store an application program (such as a sound playing function, an image playing function, etc.) required for at least one function of the operating system, etc. The storage data area may store data (e.g., audio data, phonebook, etc.) created during use of the terminal 100, and the like. In addition, the internal memory 121 may include a high-speed random access memory, and may further include a nonvolatile memory such as at least one magnetic disk storage device, a flash memory device, a universal flash memory (universal flash storage, UFS), and the like. The processor 110 performs various functional applications of the terminal 100 and data processing by executing instructions stored in the internal memory 121 and/or instructions stored in a memory provided in the processor.

The terminal 100 may implement audio functions through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, an application processor, and the like. Such as music playing, recording, etc.

The audio module 170 is used to convert digital audio information into an analog audio signal output and also to convert an analog audio input into a digital audio signal. The audio module 170 may also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be disposed in the processor 110, or a portion of the functional modules of the audio module 170 may be disposed in the processor 110.

The speaker 170A, also referred to as a "horn," is used to convert audio electrical signals into sound signals. The terminal 100 can listen to music or to handsfree calls through the speaker 170A.

A receiver 170B, also referred to as a "earpiece", is used to convert the audio electrical signal into a sound signal. When the terminal 100 receives a telephone call or voice message, it is possible to receive voice by approaching the receiver 170B to the human ear.

Microphone 170C, also referred to as a "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or transmitting voice information, the user can sound near the microphone 170C through the mouth, inputting a sound signal to the microphone 170C. The terminal 100 may be provided with at least one microphone 170C. In other embodiments, the terminal 100 may be provided with two microphones 170C, and may implement a noise reduction function in addition to collecting sound signals. In other embodiments, the terminal 100 may be further provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify the source of sound, implement directional recording functions, etc.

The earphone interface 170D is used to connect a wired earphone. The headset interface 170D may be a USB interface 130 or a 3.5mm open mobile electronic device platform (open mobile terminal platform, OMTP) standard interface, a american cellular telecommunications industry association (cellular telecommunications industry association of the USA, CTIA) standard interface.

The pressure sensor 180A is used to sense a pressure signal, and may convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be disposed on the display screen 194. The pressure sensor 180A is of various types, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like. The capacitive pressure sensor may be a capacitive pressure sensor comprising at least two parallel plates with conductive material. The capacitance between the electrodes changes when a force is applied to the pressure sensor 180A. The terminal 100 determines the strength of the pressure according to the change of the capacitance. When a touch operation is applied to the display 194, the terminal 100 detects the intensity of the touch operation according to the pressure sensor 180A. The terminal 100 may also calculate the location of the touch based on the detection signal of the pressure sensor 180A. In some embodiments, touch operations that act on the same touch location, but at different touch operation strengths, may correspond to different operation instructions. For example: and executing an instruction for checking the short message when the touch operation with the touch operation intensity smaller than the first pressure threshold acts on the short message application icon. And executing an instruction for newly creating the short message when the touch operation with the touch operation intensity being greater than or equal to the first pressure threshold acts on the short message application icon.

The gyro sensor 180B may be used to determine a motion gesture of the terminal 100. In some embodiments, the angular velocity of terminal 100 about three axes (i.e., x, y, and z axes) may be determined by gyro sensor 180B. The gyro sensor 180B may be used for photographing anti-shake. Illustratively, when the shutter is pressed, the gyro sensor 180B detects the angle of the shake of the terminal 100, calculates the distance to be compensated by the lens module according to the angle, and allows the lens to counteract the shake of the terminal 100 by the reverse motion, thereby realizing anti-shake. The gyro sensor 180B may also be used for navigating, somatosensory game scenes.

The air pressure sensor 180C is used to measure air pressure. In some embodiments, the terminal 100 calculates altitude from barometric pressure values measured by the barometric pressure sensor 180C, aiding in positioning and navigation.

The magnetic sensor 180D includes a hall sensor. The terminal 100 may detect the opening and closing of the flip cover using the magnetic sensor 180D. In some embodiments, when the terminal 100 is a folder, the terminal 100 may detect opening and closing of the folder according to the magnetic sensor 180D. And then according to the detected opening and closing state of the leather sheath or the opening and closing state of the flip, the characteristics of automatic unlocking of the flip and the like are set.

The acceleration sensor 180E may detect the magnitude of acceleration of the terminal 100 in various directions (typically three axes). The magnitude and direction of gravity may be detected when the terminal 100 is stationary. The electronic equipment gesture recognition method can also be used for recognizing the gesture of the electronic equipment, and is applied to horizontal and vertical screen switching, pedometers and other applications.

A distance sensor 180F for measuring a distance. The terminal 100 may measure the distance by infrared or laser. In some embodiments, the terminal 100 may range using the distance sensor 180F to achieve quick focusing.

The proximity light sensor 180G may include, for example, a Light Emitting Diode (LED) and a light detector, such as a photodiode. The light emitting diode may be an infrared light emitting diode. The terminal 100 emits infrared light outward through the light emitting diode. The terminal 100 detects infrared reflected light from nearby objects using a photodiode. When sufficient reflected light is detected, it may be determined that there is an object near the terminal 100. When insufficient reflected light is detected, the terminal 100 may determine that there is no object in the vicinity of the terminal 100. The terminal 100 can detect that the user holds the terminal 100 close to the ear by using the proximity light sensor 180G, so as to automatically extinguish the screen for the purpose of saving power. The proximity light sensor 180G may also be used in holster mode, pocket mode to automatically unlock and lock the screen.

The ambient light sensor 180L is used to sense ambient light level. The terminal 100 may adaptively adjust the brightness of the display 194 according to the perceived ambient light level. The ambient light sensor 180L may also be used to automatically adjust white balance when taking a photograph. The ambient light sensor 180L may also cooperate with the proximity light sensor 180G to detect whether the terminal 100 is in a pocket to prevent false touches.

The fingerprint sensor 180H is used to collect a fingerprint. The terminal 100 can utilize the collected fingerprint characteristics to realize fingerprint unlocking, access an application lock, fingerprint photographing, fingerprint incoming call answering and the like.

The temperature sensor 180J is for detecting temperature. In some embodiments, terminal 100 performs a temperature processing strategy using the temperature detected by temperature sensor 180J. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold, the terminal 100 performs a reduction in performance of a processor located near the temperature sensor 180J in order to reduce power consumption for implementing thermal protection. In other embodiments, when the temperature is below another threshold, the terminal 100 heats the battery 142 to avoid the terminal 100 from being abnormally shut down due to low temperatures. In other embodiments, when the temperature is below a further threshold, terminal 100 performs boosting of the output voltage of battery 142 to avoid abnormal shutdown caused by low temperatures.

The touch sensor 180K, also referred to as a "touch device". The touch sensor 180K may be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 form a touch screen, which is also called a "touch screen". The touch sensor 180K is for detecting a touch operation acting thereon or thereabout. The touch sensor may communicate the detected touch operation to the application processor to determine the touch event type. Visual output related to touch operations may be provided through the display 194. In other embodiments, the touch sensor 180K may be disposed on the surface of the terminal 100 at a different location than the display 194.

The bone conduction sensor 180M may acquire a vibration signal. In some embodiments, bone conduction sensor 180M may acquire a vibration signal of a human vocal tract vibrating bone pieces. The bone conduction sensor 180M may also contact the pulse of the human body to receive the blood pressure pulsation signal. In some embodiments, bone conduction sensor 180M may also be provided in a headset, in combination with an osteoinductive headset. The audio module 170 may analyze the voice signal based on the vibration signal of the sound portion vibration bone block obtained by the bone conduction sensor 180M, so as to implement a voice function. The application processor may analyze the heart rate information based on the blood pressure beat signal acquired by the bone conduction sensor 180M, so as to implement a heart rate detection function.

The motion sensor 180N may be used to detect a moving object within a range shot by the camera, and collect a motion profile or a motion track of the moving object. For example, the motion sensor 180N may be an infrared sensor, a laser sensor, a dynamic vision sensor (dynamic vision sensor, DVS), etc., which may include, in particular, a DAVIS (Dynamic and Active-pixel Vision Sensor), ATIS (Asynchronous Time-based Image Sensor), or CeleX sensor, etc. DVS uses the biological vision characteristics to simulate a neuron per pixel, and responds independently to the relative change in illumination intensity (hereinafter referred to as "light intensity"). When the relative change in light intensity exceeds a threshold, the pixel outputs an event signal including the position of the pixel, a time stamp, and characteristic information of the light intensity.

The keys 190 include a power-on key, a volume key, etc. The keys 190 may be mechanical keys. Or may be a touch key. The terminal 100 may receive key inputs, generating key signal inputs related to user settings and function controls of the terminal 100.

The motor 191 may generate a vibration cue. The motor 191 may be used for incoming call vibration alerting as well as for touch vibration feedback. For example, touch operations acting on different applications (e.g., photographing, audio playing, etc.) may correspond to different vibration feedback effects. The motor 191 may also correspond to different vibration feedback effects by touching different areas of the display screen 194. Different application scenarios (such as time reminding, receiving information, alarm clock, game, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect may also support customization.

The indicator 192 may be an indicator light, may be used to indicate a state of charge, a change in charge, a message indicating a missed call, a notification, etc.

The SIM card interface 195 is used to connect a SIM card. The SIM card may be contacted and separated from the terminal 100 by being inserted into the SIM card interface 195 or by being withdrawn from the SIM card interface 195. The terminal 100 may support 1 or N SIM card interfaces, N being a positive integer greater than 1. The SIM card interface 195 may support Nano SIM cards, micro SIM cards, and the like. The same SIM card interface 195 may be used to insert multiple cards simultaneously. The types of the plurality of cards may be the same or different. The SIM card interface 195 may also be compatible with different types of SIM cards. The SIM card interface 195 may also be compatible with external memory cards. The terminal 100 interacts with the network through the SIM card to realize functions such as call and data communication. In some embodiments, the terminal 100 employs esims, i.e.: an embedded SIM card. The eSIM card may be embedded in the terminal 100 and cannot be separated from the terminal 100.

It should be noted that, in some practical application scenarios, the electronic device may include more or fewer components than those shown in fig. 1, and may be specifically adjusted according to the practical application scenario, which is not limited by the present application.

The following describes some application scenarios of the method provided by the present application.

Taking a terminal as an example of a mobile phone, in some scenarios, a user may use the mobile phone to navigate. In navigating, it may be inaccurate because it is impossible to distinguish whether the user is indoors or outdoors. For example, as shown in fig. 3, when the user performs navigation, it may be that the starting point of the navigation is different from the actual location of the user because it is impossible to distinguish which building of a certain area the user is located in, and the final navigation path cannot cover the location where the user is located, which results in a reduced user experience.

For another example, taking the terminal as a hand, in some scenarios, the user may use take-away software to take-away. As shown in fig. 4, there may be a situation that it is impossible to distinguish which building the user is in a certain area, and generally only fuzzy positioning is given, accurate positioning cannot be performed, and the positioning position does not coincide with the actual position of the user, so that the user may need to manually input an address when placing a call, and user experience is reduced.

Therefore, the positioning method provided by the application can be used for efficiently and accurately matching the building where the terminal is located, and the positioning accuracy of the terminal is improved. The method provided by the application can be deployed on a server, a terminal or a part of the server, the other part of the server, the terminal and the like, for example, a trained model is issued to the terminal and the like, and the method can be specifically adjusted according to actual application scenes. In the following, by taking the method provided by the application as an example to be deployed on a cloud server for example, all or part of the steps mentioned can be performed by the terminal.

Referring to fig. 5, a flow chart of a positioning method provided by the present application is as follows.

501. And acquiring the first environment information.

The first environmental information may include information generated by a plurality of terminals in the collected first area, and may be used to represent information of a building where the plurality of terminals are located. The first environmental information may be directly acquired in the first area, or may be acquired from crowd-sourced data of the first area. The first area may be an area divided from a map, or may be a range covered by a cell under an access point, or may be a range divided by a user, or the like, and specifically may be adjusted according to an actual application scenario, which is not limited by the present application.

The first environmental information may be extracted from crowdsourcing data, including information generated by a plurality of terminals in the first area or information collected by a plurality of terminal sensors, such as information of wireless signals connected by the terminals, such as frequency band, bandwidth, access point name, access point location, and the like.

For another example, a monitoring device may be disposed in the first area, and when the terminal accesses an access point in the first area, access information of the terminal, such as information of an access frequency band, a bandwidth, an access point name, an access point position, and the like, may be collected.

It should be noted that, the present application is exemplified by extracting the first environmental information, that is, the information of each terminal in the area, from the crowd-sourced data, and is not limited thereto.

502. And acquiring second environment information.

The second environmental information may include data indicating the geographic location of the terminal (or referred to as the first terminal, i.e. one of the terminals in the first area), which may be reported by the terminal, or may be obtained by positioning the terminal in other manners, such as information collected by a sensor or other modules in the terminal.

Specifically, the terminal may actively report the data collected by the terminal to the cloud server, and the cloud server may also obtain positioning data of the terminal through other devices, for example, read data generated by the terminal through a base station or an Access Point (AP) connected to the terminal.

The second environmental information may include information collected by the terminal, such as information of a wireless signal accessed by the terminal, such as information of a frequency band, a bandwidth, a signal quality, a receiving power, an access point name, an access point position, and the like.

The first environmental information is different from the second environmental information in that the first environmental information may be information extracted from crowdsourcing data or manually acquired, and the second environmental information is information acquired by the terminal itself.

It should be noted that, the execution sequence of the step 501 and the step 502 is not limited in the present application, the step 501 may be executed first, or the step 502 may be executed first, and specifically, the present application may be adjusted according to the actual application scenario.

503. A first probability that the terminal is in each of the plurality of buildings is calculated based on the first environmental information.

After the first environmental information is obtained, a probability that the terminal is in each building in the first area may be calculated based on the first environmental information, which is referred to as a first probability for convenience of distinction.

Specifically, the information in the first environment information may be divided into information of terminals in each building, and the prior probability that each terminal is in each building, that is, the first probability, may be calculated based on the similarity between the information of each building and the information of the first region.

Alternatively, the similarity between the information generated by the terminal in each building and the information generated by the terminal in the first area may be obtained, so as to be convenient for distinguishing the first similarity, then the similarity between the information of the wireless signal access point in each building and the information of the wireless access point in the first area is obtained, so as to be convenient for distinguishing the second similarity, and then the probability that the terminal is in each building in the first area, namely the first probability, is calculated by combining the first similarity and the second similarity. The information of the wireless access point may include WiFi information, bluetooth information, or other short-range communication information, such as a name, a code, or an identifier of the wireless access point. Therefore, in the embodiment of the application, the prior probability of the terminal in each building can be calculated by combining the similarity between the information generated by the terminal in the building and the information generated by the terminal in the area and the similarity between the information of the wireless signal access point in the building and the information of the wireless signal access point in the area.

The manner of calculating the similarity may include various manners, such as cosine similarity, pearson correlation coefficient, euclidean distance, manhattan distance, and the like, for calculating the similarity between features.

Optionally, the probability that the terminal is in each building may also be calculated in combination with the number of users in the area and the number of users in the building. Specifically, the number of users in the first area, called the first number of users, and the number of users in each building, called the second number of users, may be obtained, and then the probability that the terminal is in each building is calculated according to the first number of users and the second number of users, so as to be called the third probability for convenience of distinguishing. And updating the first probability by using the third probability to obtain the updated first probability.

Specifically, the first environmental information may directly include the number of terminals in the area and the number of terminals in the building, or may include information generated by the terminals in the area, such as information of APs to which the terminals access, the number of terminals to which each AP accesses, and the like, so that the number of terminals in the area and the number of terminals in the building may be obtained based on the first environmental information. After determining the number of terminals, the number of terminals can be used as the number of users, or the number of users can be calculated based on the number of terminals.

Therefore, in the embodiment of the application, the prior probability that the terminal is in each building can be calculated by combining the number of the users, and the accuracy of the obtained prior probability is improved.

Further, alternatively, when determining the information of the terminal corresponding to each building from the first environment information, the buffer range may be determined based on the positioning accuracy of the terminal, or referred to as a buffer zone, and the terminal positioned in the buffer range of the building is used as the terminal in the building, and the information of the terminal is extracted from the first environment information, so as to obtain the information of the terminal in each building.

It will be appreciated that the prior probability that the terminal is within each building may be estimated based on information generated by the terminal within the region collected from the crowd-sourced data, so that the posterior probability of the terminal within each building may be accurately calculated based on a bayesian model later.

504. And calculating the likelihood that the terminal is in each building in the plurality of buildings according to the second environment information.

After the second context information is obtained, the likelihood that the terminal is within each building may be calculated based on the second context information.

Specifically, the similarity between the information collected by the terminal and the information in the first area included in the first environmental information may be calculated, which is referred to as a third similarity for convenience of distinction, and the similarity between the information collected by the terminal and each building in the first environmental information may be calculated, which is referred to as a fourth similarity for convenience of distinction. And then, the third similarity and the fourth similarity are collected, and the likelihood that the terminal is in each building is calculated.

505. And obtaining a second probability of the first terminal in each building in the plurality of buildings according to the first probability and the likelihood.

After the first probability and likelihood are obtained, the first probability and likelihood can be fused to obtain the probability that the terminal is in each building, which is called a second probability for convenience in distinguishing.

506. And screening the first building from the plurality of buildings according to the second probability of the first terminal in each building from the plurality of buildings.

After the second probability of the terminal being in each building is calculated, the first building may be screened from the plurality of buildings in the first area based on the second probability of the terminal being in each building.

Specifically, the building with the highest probability value may be selected from the multiple buildings as the building where the terminal is located, which may be referred to as the first building for convenience of distinction.

Therefore, in the embodiment of the application, the prior probability of the terminal in each building can be calculated based on the information of the terminal in the acquired area, then the likelihood of the terminal in each building is calculated based on the information acquired by the terminal, then the posterior probability of the terminal in each building is calculated by using a Bayesian model, and the buildings matched with the terminal are screened out based on the posterior probability, so that the accurate building positioning of the terminal is realized.

The foregoing describes the flow of the method provided by the present application, and the flow of the positioning method provided by the present application is described in more detail below in conjunction with a specific application scenario.

The positioning method provided by the application can be fully deployed on the cloud server, or can be partially deployed on the cloud server, and the other part is deployed on the terminal.

For example, when the method is deployed on a cloud server, the flow of the method provided by the application may be as shown in fig. 6.

The user can initiate a building matching request in the area to the server by using the terminal, and the building matching request is used for requesting to match the building in which the terminal is located, and reporting the environment information acquired by the terminal to the cloud server. If the cloud server includes a distributed server, the terminal may initiate a building matching request to an edge server closest to the terminal. The environment information may include a rough positioning result of the terminal itself or information acquired by a terminal sensor, which may represent an environment in which the terminal is located.

After receiving the information reported by the terminal, the cloud server combines the information in the area acquired from the crowdsourcing data to determine the building in which the terminal is located, and feeds back the result to the terminal.

When the method provided by the application is deployed at a terminal, as shown in fig. 7.

The difference from the foregoing fig. 6 is that the terminal may not need to report the collected environmental information to the cloud server, and may only send a building matching request to the cloud server, so as to inform the cloud server that the terminal needs to perform building matching. After receiving the request, the cloud server can send information in the area, such as information of the terminal acquired in the area, to the terminal, so that the terminal can determine the building where the terminal is located by combining the information acquired by the terminal and the received information.

For example, the terminal itself may collect information that may be used to indicate the building in which the terminal is located, e.g., the terminal side is responsible for collecting sensing information such as GNSS, wiFi, bluetooth, etc. And requesting the environment scene of the roughly positioned terminal position point to the cloud side or the edge side, sensing information including GNSS, wiFi, bluetooth and the like in surrounding buildings, surrounding building people stream characteristics and the like, so that the terminal can determine the building in which the terminal is positioned based on the received information.

For example, the method provided by the application can be deployed in the terminal and the cloud server at the same time. If the terminal is in a single terminal reasoning mode different from terminal deployment, batch judgment can be carried out on buildings where batch terminals are located in cloud deployment, and finally a building label is recorded to the terminals.

The building matching tag is favorable for further optimizing the positioning of the terminal, in general, accurate positioning is slightly different from indoor and outdoor technical means, positioning accuracy is greatly different, for example, outdoor accuracy is generally high in reliability, and therefore screening and distinguishing are important steps. According to the label, an important basis can be provided for subsequent location-based services, end network collaboration and terminal-based network planning optimization.

The positioning method provided by the application is described in more detail below in connection with the aforementioned deployment mode. The method of the application can be executed by the terminal or the cloud server according to

First, the flow of another positioning method provided by the present application may be as shown in fig. 8.

801. The prior probability of the terminal in each building is built.

Wherein the prior probabilities of the terminals in the respective buildings can be calculated using information extracted from the crowdsourcing data.

Specifically, two object scales of a building and an area can be established, information of terminals in different buildings is extracted as priori knowledge of the building according to daily distribution spatial characteristics of people, and the relationship between the building and the area is quantified based on the priori knowledge of the building, so that the priori probability of the terminals in the area in each building is judged.

802. And calculating the standard likelihood of the terminal and each building.

The method comprises the steps of establishing three object scales of a pending terminal, a building and an area, calculating information consistency of the pending terminal and the area and information consistency of the pending terminal and the building, respectively obtaining probability of the pending terminal in the area and probability of the pending terminal in the building, and further obtaining standard likelihood of the pending terminal and each building.

803. And calculating the posterior probability that the terminal is in each building.

According to the Bayesian formula, after the information reported by the to-be-determined terminal is known, the posterior probability of the to-be-determined terminal in each building is equal to the prior probability multiplied by the standard likelihood.

804. And screening out the building with the highest posterior probability as the building where the terminal is located.

And judging the building corresponding to the maximum value of the posterior probability as the building where the terminal is located based on the generated posterior probability table of the terminal appearing in each building.

According to the application, a building error zone is defined based on positioning errors and known building shapes, sensor information and population space-time distribution in each building error zone (hereinafter referred to as building) are extracted, and the prior probability that any terminal appears in each building at a certain moment is calculated; acquiring the probability of the undetermined terminal in the area and the probability of the undetermined terminal in the building, and further acquiring the standard likelihood of the undetermined terminal and each building; and (3) reasoning posterior probability of the occurrence of the undetermined terminal in each building by combining the consistency of the information reported by the undetermined terminal and the building, and providing a reference for accurate estimation of the indoor terminal positioning. The method can be concretely divided into: building prior probability construction of the terminals in each building, standard likelihood calculation of the terminals and the building, and terminal building matching with maximized posterior probability.

The following is an exemplary description of the flow of the positioning method provided by the present application in more detail.

Referring to fig. 9, a flow chart of a positioning method according to the present application is shown.

For easy understanding, the method provided by the application is divided into three parts, such as calculating prior probability, calculating likelihood, calculating posterior probability and matching buildings, and the like, and the following description is provided.

1. Calculating a priori probabilities

The probability that any terminal appears in a certain building under the daily condition is obtained, and is called the prior probability that the terminal appears in the building. Building and regional two object scales are established, terminal reporting information in different buildings is extracted as priori knowledge of the buildings according to daily distribution spatial characteristics of people, and based on the priori knowledge, relationship between the buildings and the region is quantified, so that the priori probability of the terminal in each building in the region is judged.

First, a building error band 902 may be established based on a building shape 901.

In general, there is an error in the positioning of the terminal, and it is understood that the actual position of the terminal may be within a certain range of its positioning position. Therefore, the building buffer area can be generated based on the shape of the building according to the positioning accuracy of the terminal, for example, with a positioning error range of 2 times, and the building buffer area becomes a building error zone, so that the range of capturing the terminal in the building is enlarged, namely, the terminals in the building error zone are assumed to belong to the terminal in the building, which is equivalent to demarcating the coverage range of each building.

For example, as shown in fig. 10, the building error bands corresponding to the buildings with different shapes are shown as broken lines. It will be appreciated that a building surface of a building is considered to be within a certain range of building error bands, which can be determined based on the positioning error of the terminal, e.g. if the positioning error is 5m, a building surface of a building is considered to be within 10m of building error bands.

Data for each building is then extracted from the crowdsourcing data 903 based on the established building error bands.

It may be appreciated that after the coverage of each building is defined, data corresponding to each building range is extracted from the crowdsourcing data, so as to facilitate subsequent computation of the prior probability.

Specifically, a population distribution 904 for each building at different times may be plotted based on crowd-sourced data, followed by calculating a population ratio 905 for the population in the building in the area.

Specifically, after the coverage range of each building is defined, information of the terminals in the coverage range of the building can be collected from the crowdsourcing data, including information of satellites, wiFi, bluetooth, wireless signals and the like of each building. And, the number of users in each building can be estimated according to the number distribution of the terminals in each building, for example, each user may hold one or more terminals, each terminal may correspond to one user number, or each terminal may correspond to 0.5 user number, etc. For example, the user profiles within each building may be as shown in FIG. 11, which shows the user profiles for building A, building B, building C, and building D, respectively.

Assuming time k, the number of people in building x is P _k The total number of people in the area is Sigma P _k The building with more people reflects that the terminal has higher probability of appearing in the building, the probability that any terminal appears in a certain building at the moment k can be calculated according to the crowd proportion is as follows:

building wireless signal sensor information 906 may also be extracted from the crowd-sourced data and information consistency 907 of the building to the area may be calculated based on the extracted wireless sensor information for each building.

Specifically, the wireless signal sensor information may include information collected by a wireless signal sensor in an area, such as information of a frequency band, a bandwidth, signal quality, signal strength, and the like of a terminal accessing a wireless network. The consistency of sensor information between the building (denoted building x) and the area, or similarity, is calculated based on the wireless signal sensor information within the area, denoted cor (building x & area). Specifically, the similarity or consistency calculation mentioned below in the present application may be calculated by adopting a cosine similarity, a pearson similarity coefficient, and the like, which are not described in detail below.

In addition, the AP information in the building and the AP information in the area can be matched, for example, the similarity of the WiFi name, the bluetooth name and the like is calculated and recorded as WiFi (building x & area).

Information consistency between building and area:wherein (1)>For the standardized correlation of sensor characteristics between a building and an area, the consistency of reported information between a terminal in the area and a terminal in building x is represented, and cor (area) identifies the autocorrelation of the characteristics in the area; wifi (building x)&Region) indicates the probability that the WiFi searched by the terminal in the region belongs to WiFi in building x; both reflect the probability that a user present in the area belongs to a terminal in building x.

The prior probabilities of the terminals in each building are then calculated based on the population ratio of the building in the area and the consistency of the information of the building and the area, i.e., step 908 is performed.

Specifically, the prior probability that the terminal is in each building can be obtained by fusing the above building crowd ratio and the information consistency of the building and the area. The specific fusion mode can comprise weighted fusion, average value and the like, and can be determined according to the actual application scene. For example, taking an average as an example, the prior probability that the terminal is at each building can be expressed as:

therefore, in the embodiment of the application, the prior probability that each terminal is in each building can be calculated based on the rough positioning data extracted from the crowdsourcing data, so that the prior probability in the Bayesian model is constructed and obtained.

2. Calculating likelihood

And after the terminal reports the information acquired by the sensor, judging the relationship between the terminal and each building. Specifically establishing three object scales of the undetermined terminal, the building and the area, calculating the information consistency of the undetermined terminal and the area and the information consistency of the undetermined terminal and the building, respectively acquiring the probability of the undetermined terminal in the area and the probability of the undetermined terminal in the building, and further acquiring the standard likelihood of the undetermined terminal and each building.

Taking the method provided by the application as an example of deployment on a cloud server, the terminal can report data 909 to obtain the wireless information 910 of the terminal, i.e. report information acquired by the terminal, such as information of terminal satellite access information (such as frequency band, identifier, etc.), wiFi access frequency band, bluetooth frequency band, signal strength or signal quality, etc., to the server.

Specifically, the information consistency 912 between the terminal and the area and the information consistency between the terminal and each building may be calculated based on the wireless signal sensor information 911 of each building extracted in the foregoing step 906 and the wireless signal sensor information 912 reported by the terminal.

For example, the probability that the terminal appears in the area may be calculated, and the probability that the record y appears in the area is calculated according to the consistency of the sensor information in the area and the report (recorded as record y) of the terminal, which is expressed as:

The cor (record y & area) represents consistency of information reported by the terminal (record y) and sensor information in the area, the cor (area) represents autocorrelation of information in the area, and WiFi (record y & area) represents similarity between identification (such as bluetooth name and WiFi name) of a wireless signal reported by the terminal and identification of the wireless signal in the area.

The probability that the terminal appears in each building can also be calculated, for example, the probability that the information reported by the terminal to be determined appears in the building is calculated according to the consistency of the sensor information reported by the terminal and the sensor information of each building:

the cor (record y & building x) represents the consistency of information reported by the terminal (record y) and sensor information in the building x, the cor (building x) represents the autocorrelation of information in the building x, and WiFi (record y & building x) represents the similarity between the identification (such as Bluetooth name, wiFi name and the like) of the wireless signal reported by the terminal and the identification of the wireless signal in the building x.

Then according to the scallop leavesThe standard likelihood is calculated by defining a si formula, expressed as:

therefore, in the embodiment of the application, the probability that the terminal is in the area or the building can be calculated based on the data reported by the terminal and the data extracted from the crowdsourcing data, so as to obtain the standard likelihood of the terminal in each building.

3. Calculating posterior probability and matching building

After the prior probability of the terminal in each building and the standard likelihood of the terminal in each building are obtained through calculation, the posterior probability 915 of the terminal in each building can be updated based on the prior probability and the likelihood, and the building 916 with the highest posterior probability is screened out of a plurality of buildings and is used as the building in which the terminal is located.

Updating the posterior probability of the occurrence of the undetermined terminal in each building. According to a Bayesian formula, after knowing the information reported by the to-be-determined terminal, the posterior probability of the to-be-determined terminal in each building is equal to the prior probability multiplied by the standard likelihood:

after the posterior probability of the terminal in the plurality of buildings is obtained, the building with the highest posterior probability can be selected from the plurality of buildings and used as the building where the terminal is located.

For example, as shown in fig. 12, the posterior probabilities of the terminals at the buildings a, B, C and D are calculated to be 0.4, 0.2, 0.8 and 0.5, respectively, that is, the posterior probability of the terminal at the building C is the largest, so the building C is taken as the building where the terminal is located.

Therefore, in the embodiment of the application, the prior probability that each terminal is in each building can be calculated based on the rough positioning data extracted from the crowdsourcing data, so that the prior probability in the Bayesian model is constructed and obtained. The probability that the terminal is in an area or a building can be calculated based on the data reported by the terminal and the data extracted from the crowdsourcing data, and the standard likelihood of the terminal in each building is obtained. Therefore, the posterior probability of the terminal in each building can be calculated through a Bayesian algorithm. Thus, the building where the terminal is located can be accurately determined.

In order to facilitate understanding, the following describes the flow of the positioning method provided by the application in detail in connection with a more specific application scenario.

Scene one

Referring to fig. 13, a flow chart of another positioning method according to the present application is shown.

Building matching is carried out on the actual measured indoor terminal positions of three similar buildings based on real priori sensor information of the buildings. The three building indoor terminals collect 587 dotting data, and the number of buildings A, B and C is 253, 140 and 194 respectively, wherein each piece of data contains sensor information such as satellite, wiFi, bluetooth and wireless signals.

1. Prior probability calculation

And selecting part of terminal data as a training set, and extracting sensor information of each building as priori knowledge.

First, each measured data corresponds to a sensor characteristic as shown in Table 1

TABLE 1

Based on known building tags (e.g., manual labeling), pearson correlation coefficients for all data sensor features in each building and all data sensor features in the area are calculated as the consistency of sensor information between the building and the area. The pearson correlation coefficient of the sensor characteristics of the building and the area is calculated as index one based on the respective building sensor characteristics and the area sensor characteristics as shown in fig. 13.

Illustratively, the wireless signal is exemplified by a WiFi/bluetooth signal, and may be replaced by other long-distance communication signals or short-distance communication signals, such as a satellite signal, a 5G communication signal, or an NFC communication signal.

Specifically, feature correlation between the WiFi/Bluetooth aggregation sets of each building and the WiFi/Bluetooth aggregation sets in the area can be calculated, and WiFi/Bluetooth name similarity of the building and the area can be obtained. If a WiFi/bluetooth name set (denoted as set a) and a WiFi/bluetooth name set (denoted as set b) of a region of each building are extracted, the intersection number ratio of the two sets, i.e., length (set (a n b))/length (set b), is calculated as the consistency of the building and the region, and is used as an index two.

For each building, taking the product of two indexes, namely an index I and an index II, as the prior probability of any terminal in each building.

2. Likelihood calculation

And screening out a part from the measured data as a data set to be estimated, wherein the measured data can comprise information reported by one or more terminals, namely information acquired by a terminal sensor. I.e., the likelihood that each terminal is located within each building, respectively, may be calculated.

And then calculating the consistency of the sensor information of the undetermined terminal and the area and the consistency of the sensor information and the information of each building, wherein the calculation mode can refer to the calculation mode of similarity in the prior probability, and the description is omitted.

The probability of the undetermined terminal in the area and the probability of the undetermined terminal in the building are respectively obtained, and then the standard likelihood of the undetermined terminal and each building is obtained.

The consistency of a terminal with a building divided by its consistency with an area is taken as the standard likelihood of the terminal at each building.

3. Building positioning

Based on Bayesian formula, the prior probability of the terminal in each building is multiplied by the standard likelihood to obtain the posterior probability of the undetermined terminal in each building, and the building with the highest probability is taken as the building in which the terminal is located.

And comparing the matching result with the real building label of the measured data, obtaining the matching precision, and assigning a value (the content is the name or number of the building) to the terminal.

For example, a total of 103 data sets to be estimated, wherein the true and predicted numbers for each building are shown in table 2, it can be seen that the overall correct match rate is 0.75= (28+39+10)/103.

TABLE 2

Therefore, in the embodiment of the application, the object is divided into three scales of the area, the building and the terminal, and the consistency of the sensor information among the terminal data under different scales is compared, so that the building with the most possible information reported by the terminal to be estimated is obtained. In this embodiment, no additional software or hardware needs to be arranged, and only a small amount of data of the known building tag is required to achieve 75% matching accuracy.

Scene two

Referring to fig. 14, a flow chart of another positioning method provided by the present application is shown.

The scene can be used for carrying out matching of different buildings on terminals (containing 587 terminal records) in three similar buildings based on crowdsourcing data, and the specific flow comprises the following steps:

1. prior probability calculation

First, for building information stored in a database, a 20-meter buffer of a building is established as a building error zone according to the shape of the building.

For a single building, reporting information for locating terminals within the building error zone is extracted from crowd-sourced data, each piece of crowd-sourced data possibly containing sensor information such as satellite, wiFi, bluetooth, wireless signals and the like as prior information of the terminals within the building.

Each crowdsourcing data in the extracted building corresponds to one sensor characteristic, as shown in the table 1, pearson correlation coefficients of all data sensor characteristics in each building and all data sensor characteristics in the area are calculated (all sensor characteristics can be combined in various types, for example, satellite characteristics are only used, satellite and WiFi characteristics are simultaneously used, satellite, wiFi and wireless signal characteristics are simultaneously considered, and the like) to be used as a sensor information consistency index I between the building and the area; extracting a WiFi/Bluetooth name set (denoted as set a) and a WiFi/Bluetooth name set (denoted as set b) of the area of each building, and calculating the intersection number ratio of the two sets, namely length (set (a and b))/length (set b), as an information consistency index II of the building and the area; for each building, taking the product of two indexes as the prior probability of any terminal in each building.

2. Likelihood calculation

And screening out part from the measured data as a data set to be estimated, wherein the data set to be estimated comprises information reported by one or more terminals, namely information acquired by a terminal sensor. I.e., the likelihood that each terminal is located within each building, respectively, may be calculated.

3. Building positioning

Therefore, the application can acquire the sensor information of each building through crowdsourcing data, provide priori knowledge for the distribution of the terminals in each building, and select the building in which the terminal to be estimated is most likely to appear by taking the information consistency of the terminal to be determined and the building as a matching principle. According to the embodiment, data acquisition is not needed manually, and reliable judgment basis can be provided for terminal building matching only through extraction of a small amount of crowdsourcing data of daily users.

The foregoing describes the flow of the method provided by the present application, and the following describes the structure of the apparatus for performing the foregoing method.

Referring to fig. 15, a schematic structural diagram of a positioning device according to the present application may include:

an acquiring module 1501, configured to acquire first environmental information, where the first environmental information includes information generated by a terminal in a first acquired area, and the first area includes a plurality of buildings;

the obtaining module 1501 is further configured to obtain second environmental information, where the second environmental information includes information collected by the first terminal, and the second environmental information includes information used for indicating a building where the first terminal is located;

a processing module 1502, configured to obtain, according to first environmental information, a first probability that a first terminal is in each building of a plurality of buildings, where the first terminal is any terminal in a first area;

the processing module 1502 is further configured to obtain likelihood that the first terminal is located in each building of the plurality of buildings according to the second environmental information;

the processing module 1502 is further configured to obtain, according to the first probability and the likelihood, a second probability of the first terminal in each building of the plurality of buildings;

the processing module 1502 is further configured to screen the first building from the plurality of buildings according to a second probability of the first terminal being in each building from the plurality of buildings, where the first terminal is located.

In one possible implementation, the processing module 1502 is specifically configured to: determining information generated by terminals in each building from the first environmental information; acquiring a first similarity between information generated by terminals in each building and information generated by the terminals in a first area; acquiring second similarity between information of wireless signal access points in each building and information of wireless access points in a first area; the first probability is calculated in combination with the first similarity and the second similarity.

In a possible implementation manner, the obtaining module 1501 is further configured to obtain the number of users in the first area, to obtain the first number of users;

the obtaining module 1501 is further configured to obtain the number of users in each building, to obtain a second number of users;

the processing module 1502 is further configured to obtain a third probability that the first terminal is in each building according to the first number of users and the second number of users;

the processing module 1502 is further configured to fuse the third probability and the first probability to obtain an updated first probability.

In one possible implementation, the obtaining module 1501 is specifically configured to: determining a buffer range according to the positioning precision of the terminal in the first area; and taking the information of the terminals in the buffer range of each building as the information generated by the terminals in each building.

In one possible implementation, the processing module 1502 is specifically configured to: acquiring a third similarity between information acquired by a first terminal and information in a first area in first environment information; acquiring a fourth similarity between the information acquired by the first terminal and the information of each building in the first environment information; and calculating the likelihood that the first terminal is positioned in each building in the plurality of buildings according to the third similarity and the fourth similarity.

In one possible implementation, the processing module 1502 is specifically configured to: and screening the building with the highest second probability from the plurality of buildings as the first building.

Referring to fig. 16, a schematic structure of another positioning device provided by the present application is as follows.

The positioning device may include a processor 1601, a memory 1602, and a transceiver 1603. The processor 1601 and the memory 1602 are interconnected by wires. Wherein program instructions and data are stored in memory 1602.

The memory 1602 stores program instructions and data corresponding to the steps in fig. 5 to 14.

The processor 1601 is configured to perform method steps performed by the first device or positioning apparatus as described in any of the previous embodiments shown in fig. 5-14.

A transceiver 1603 for performing the steps of receiving or transmitting data performed by the first device or positioning apparatus as described in any of the embodiments of fig. 5-14.

Embodiments of the present application also provide a computer-readable storage medium having a program stored therein, which when run on a computer causes the computer to perform the steps of the method described in the embodiments shown in fig. 5-14.

Alternatively, the positioning device shown in fig. 16 described above is a chip.

The embodiment of the application also provides a positioning device, which can also be called as a digital processing chip or a chip, wherein the chip comprises a processing unit and a communication interface, the processing unit obtains program instructions through the communication interface, the program instructions are executed by the processing unit, and the processing unit is used for executing the method steps executed by the positioning device shown in any embodiment of the foregoing fig. 5-14.

The embodiment of the application also provides a digital processing chip. The digital processing chip has integrated therein circuitry and one or more interfaces for implementing the functions of the processor 1601, or processor 1601, described above. When the memory is integrated into the digital processing chip, the digital processing chip may perform the method steps of any one or more of the preceding embodiments. When the digital processing chip is not integrated with the memory, the digital processing chip can be connected with the external memory through the communication interface. The digital processing chip realizes the actions executed by the positioning device in the above embodiment according to the program codes stored in the external memory.

Embodiments of the present application also provide a computer program product which, when run on a computer, causes the computer to perform the steps performed by the positioning device in the method described in the embodiments of figures 5-14 described above.

The positioning device provided by the embodiment of the application can be a chip, and the chip comprises: a processing unit, which may be, for example, a processor, and a communication unit, which may be, for example, an input/output interface, pins or circuitry, etc. The processing unit may execute the computer-executable instructions stored in the storage unit, so that the chip in the server performs the device search method described in the embodiment shown in fig. 5 to 14. Optionally, the storage unit is a storage unit in the chip, such as a register, a cache, etc., and the storage unit may also be a storage unit in the wireless access device side located outside the chip, such as a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a random access memory (random access memory, RAM), etc.

In particular, the aforementioned processing unit or processor may be a central processing unit (central processing unit, CPU), a Network Processor (NPU), a graphics processor (graphics processing unit, GPU), a digital signal processor (digital signal processor, DSP), an application specific integrated circuit (application specific integrated circuit, ASIC) or field programmable gate array (field programmable gate array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The general purpose processor may be a microprocessor or may be any conventional processor or the like.

The processor referred to in any of the foregoing may be a general purpose central processing unit, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the programs of the methods of fig. 5-14 described above.

It should be further noted that the above-described apparatus embodiments are merely illustrative, and that the units described as separate units may or may not be physically separate, and that units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the application, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines.

From the above description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented by means of software plus necessary general purpose hardware, or of course by means of special purpose hardware including application specific integrated circuits, special purpose CPUs, special purpose memories, special purpose components, etc. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions can be varied, such as analog circuits, digital circuits, or dedicated circuits. However, a software program implementation is a preferred embodiment for many more of the cases of the present application. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a readable storage medium, such as a floppy disk, a U-disk, a removable hard disk, a Read Only Memory (ROM), a random access memory (random access memory, RAM), a magnetic disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method according to the embodiments of the present application.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.

The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.

The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Finally, it should be noted that: the foregoing is merely illustrative embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present application, and the application should be covered. Therefore, the protection scope of the application is subject to the protection scope of the claims.

Claims

1. A positioning method, comprising:

acquiring first environment information and second environment information, wherein the first environment information comprises information generated by a plurality of terminals in a first acquired area, the first area comprises a plurality of buildings, the first environment information is used for representing the information of the buildings where the plurality of terminals are located, the second environment information comprises information acquired by the first terminal, and the second environment information comprises information used for representing the buildings where the first terminal is located;

acquiring a first probability that the first terminal is located in each building in the plurality of buildings according to the first environment information, wherein the first terminal is one terminal in the first area;

acquiring the likelihood that the first terminal is positioned in each building in the plurality of buildings according to the second environment information;

obtaining a second probability of each building in the plurality of buildings of the first terminal according to the first probability and the likelihood;

and screening a first building from the buildings according to the second probability of the first terminal in each building, wherein the first building is the building in which the first terminal is located.

2. The method of claim 1, wherein the obtaining a first probability that the first terminal is in each of the plurality of buildings based on the first environmental information comprises:

determining information generated by terminals in each building from the first environmental information;

acquiring a first similarity between information generated by terminals in each building and information generated by the terminals in the first area;

acquiring second similarity between the information of the wireless signal access points in each building and the information of the wireless access points in the first area;

the first probability is calculated in combination with the first similarity and the second similarity.

3. The method according to claim 2, wherein the method further comprises:

acquiring the number of users in the first area to obtain a first number of users;

acquiring the number of users in each building to obtain a second number of users;

acquiring a third probability that the first terminal is positioned in each building according to the first user quantity and the second user quantity;

and fusing the third probability and the first probability to obtain the updated first probability.

4. A method according to claim 2 or 3, wherein said determining information generated by terminals in each building from said first environmental information comprises:

determining a buffer range according to the positioning precision of the terminal in the first area;

and taking the information of the terminals in the buffer range of each building as the information generated by the terminals in each building.

5. The method of any of claims 1-4, wherein the obtaining the likelihood that the first terminal is in each of the plurality of buildings based on the second environmental information comprises:

acquiring a third similarity between the information acquired by the first terminal and the information in the first area in the first environment information;

acquiring a fourth similarity between the information acquired by the first terminal and the information of each building in the first environment information;

and calculating the likelihood that the first terminal is positioned in each building in the plurality of buildings according to the third similarity and the fourth similarity.

6. The method of any of claims 1-5, wherein the screening the first building from the plurality of buildings based on the second probability that the first terminal is in each of the plurality of buildings comprises:

And selecting the building with the highest second probability from the buildings as the first building.

7. A positioning device, comprising:

the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring first environment information and second environment information, the first environment information comprises information generated by a plurality of terminals in a first acquired area, the first area comprises a plurality of buildings, the first environment information is used for representing the information of the buildings where the plurality of terminals are located, the second environment information comprises information acquired by the first terminal, and the second environment information comprises information used for representing the buildings where the first terminal is located;

the processing module is used for acquiring a first probability that the first terminal is located in each building in the plurality of buildings according to the first environment information, wherein the first terminal is one terminal in the first area;

the processing module is further configured to obtain likelihood that the first terminal is located in each building of the plurality of buildings according to the second environmental information;

the processing module is further configured to obtain a second probability of the first terminal in each building of the plurality of buildings according to the first probability and the likelihood;

The processing module is further configured to screen a first building from the multiple buildings according to a second probability that the first terminal is located in each building of the multiple buildings, where the first building is the building where the first terminal is located.

8. The apparatus of claim 7, wherein the processing module is specifically configured to:

9. The apparatus of claim 8, wherein the device comprises a plurality of sensors,

the acquisition module is further used for acquiring the number of users in the first area to obtain a first number of users;

the acquisition module is further used for acquiring the number of users in each building to obtain a second number of users;

the processing module is further configured to obtain a third probability that the first terminal is located in each building according to the first number of users and the second number of users;

The processing module is further configured to fuse the third probability and the first probability to obtain the updated first probability.

10. The apparatus according to claim 8 or 9, wherein the acquisition module is specifically configured to:

11. The apparatus according to any one of claims 8-10, characterized in that the processing module is specifically configured to:

12. The apparatus according to any one of claims 7-11, wherein the processing module is specifically configured to screen out the buildings with the largest second probability from the plurality of buildings as the first building.

13. A positioning device comprising a processor coupled to a memory, the memory storing a program that when executed by the processor, performs the method of any of claims 1-6.

14. A computer readable storage medium comprising a program which, when executed by a processing unit, performs the method of any of claims 1 to 6.

15. An apparatus comprising a processing unit and a communication interface, the processing unit obtaining program instructions via the communication interface, the program instructions, when executed by the processing unit, implementing the method of any of claims 1 to 6.