WO2023185687A1 - 车辆位置的获取方法及电子设备 - Google Patents

车辆位置的获取方法及电子设备 Download PDF

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Publication number
WO2023185687A1
WO2023185687A1 PCT/CN2023/083798 CN2023083798W WO2023185687A1 WO 2023185687 A1 WO2023185687 A1 WO 2023185687A1 CN 2023083798 W CN2023083798 W CN 2023083798W WO 2023185687 A1 WO2023185687 A1 WO 2023185687A1
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WO
WIPO (PCT)
Prior art keywords
electronic device
vehicle
location information
geomagnetic
location
Prior art date
Application number
PCT/CN2023/083798
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English (en)
French (fr)
Inventor
张义芳
吴柏逸
尹维铭
黄正圣
Original Assignee
华为技术有限公司
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Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Publication of WO2023185687A1 publication Critical patent/WO2023185687A1/zh

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/04—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means
    • G01C21/08—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means involving use of the magnetic field of the earth
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • This application relates to the field of positioning technology, and in particular to methods and electronic devices for obtaining vehicle positions.
  • Embodiments of the present application provide methods and electronic devices for obtaining vehicle locations.
  • the electronic device inputs the extracted geomagnetic feature data recorded into the deep learning model, outputs multiple geomagnetic candidate matching positions from the deep learning model, and then combines the multiple geomagnetic candidate matching positions.
  • the confidence and weight are used to determine the final location and floor information of the vehicle parking.
  • the technical solution of the present application can reduce the time to determine the vehicle location, improve the accuracy of determining the vehicle location, and can also determine the vehicle location information across floors.
  • embodiments of the present application provide a method for obtaining a vehicle location, which method is applied to electronic devices, and is characterized in that the method includes:
  • the electronic device After the electronic device determines that the vehicle's geographical location is within the preset area, it obtains a geomagnetic fingerprint signal sequence; the geomagnetic fingerprint signal sequence includes N geomagnetic fingerprint signals, N is a positive integer; the electronic device passes pre-training based on the geomagnetic fingerprint signal sequence.
  • a good deep learning model determines the target location information, which indicates the floor on which the vehicle is located and its location on that floor; when the vehicle is located between two floors, the target location information indicates which two floors the vehicle is on. The location between floors and between two floors.
  • Implementing the method provided in the first aspect can realize the function of synchronously obtaining the location and floor of the vehicle, thereby more quickly obtaining the parking location of the vehicle indoors. Since the training data of the deep learning model includes the reachable locations of all vehicles in the indoor parking lot and the corresponding geomagnetic fingerprint signals, the trained deep learning model can obtain the location of the vehicle in addition to the vehicles parked on a single floor. The cross-floor location allows users to obtain the location of the vehicle more widely. In addition, when a vehicle enters a certain floor from between floors, it can also quickly obtain its location on that floor.
  • the electronic device determines that the vehicle's geographical location is within a preset area, which may specifically include: the electronic device recognizes that the vehicle's geographical location is within a specific area based on positioning technology; or, electronically The device receives an instruction sent by the server, which is sent to the electronic device after the server detects that the geographical location of the vehicle is within a specific area; or, the electronic device detects an instruction sent from a signal source within a preset area. signal to determine that the vehicle's geographical location is within a preset area. It is understood that the preset area may be where a specific indoor parking lot is located geographical location.
  • this positioning operation may be performed to obtain the parking location after parking is determined. In the same or other embodiments, this positioning operation can determine the current location of the vehicle in real time while the vehicle is traveling.
  • the electronic device determines the target location information through a pre-trained deep learning model according to the geomagnetic fingerprint signal sequence, including: when the electronic device detects that the vehicle is in a driving state, the electronic device determines the target location information according to the geomagnetic fingerprint signal. Sequence, the target position information is determined through the pre-trained deep learning model. It is described here that electronic devices can determine the current location of the vehicle in real time while the vehicle is traveling.
  • the electronic device determines the target location information through a pre-trained deep learning model according to the geomagnetic fingerprint signal sequence, including: when the electronic device detects that the vehicle is in a parking state, the electronic device determines the target location information according to the geomagnetic fingerprint signal. Sequence, the target position information is determined through the pre-trained deep learning model. It is described here that the electronic device can determine the current parking position of the vehicle after the vehicle is parked.
  • the electronic device determines the target location information through a pre-trained deep learning model according to the geomagnetic fingerprint signal sequence.
  • the previous step specifically includes: the electronic device detects that the electronic device or vehicle meets the first requirement. Preset conditions to determine that the vehicle is in a parking state. In this way, the electronic device can more accurately determine the specific time when the vehicle completes the parking action through preset conditions, and record the sensor data up to the specific time to determine the vehicle's location more accurately and effectively.
  • the first preset condition includes one or more of the following: the electronic device and the vehicle transition from the Bluetooth connection state to the Bluetooth disconnection state; or the electronic device receives a user's request to initiate the parking mode. instruction; or, the electronic device detects that the vehicle's driving speed is less than the first threshold and the driving direction changes. In this way, the electronic device can more accurately detect whether the vehicle has completed the parking action through the above judgment method, and can start to calculate and estimate the vehicle's position.
  • the electronic device determines the target location information through a pre-trained deep learning model according to the geomagnetic fingerprint signal sequence.
  • the process also includes: the electronic device obtains the weight of the deep learning model, and the deep learning The weight of the model is obtained by training with multiple geomagnetic fingerprint signal sequences and corresponding position information.
  • Each geomagnetic fingerprint signal sequence in the multiple geomagnetic fingerprint signal sequences corresponds to one position information.
  • the multiple geomagnetic fingerprint signal sequences include two geomagnetic fingerprint signal sequences.
  • the geomagnetic fingerprint signal sequence collected between two floors.
  • the location information includes the floor information and location between the two floors. In this way, the electronic device can directly obtain the vehicle's location information through the deep learning model.
  • the vehicle's location information can include single-plane floor information and cross-floor information, so that the electronic device can obtain vehicle information more accurately and quickly, and can also obtain Broader location information that is not limited to a single floor plan.
  • the electronic device determines the target location information through a pre-trained deep learning model according to the geomagnetic fingerprint signal sequence, specifically including: the electronic device determines the geomagnetic field through a pre-trained deep learning model.
  • the first driving trajectory corresponding to the fingerprint signal sequence the first driving trajectory includes M pieces of location information, M is less than or equal to N; based on the first driving trajectory, the electronic device filters out the geomagnetic fingerprint signal sequence to satisfy the second preset Conditional geomagnetic fingerprint signal; the electronic device uses the deep learning model to determine the second driving trajectory corresponding to the geomagnetic fingerprint signal that meets the second preset condition.
  • the second driving trajectory includes P pieces of location information, and P is less than or equal to M; electronic device The device determines target location information from the second driving trajectory.
  • the electronic device can more accurately determine the location of the vehicle by filtering out the geomagnetic fingerprint signals corresponding to the location information that satisfies the second preset condition, and then obtain the second driving trajectory through the deep learning model, thereby improving the efficiency of judgment.
  • the second preset condition specifically includes:
  • the floor information or location points of the location information corresponding to any two geomagnetic fingerprint signals are different; or, the distance between the location points of the location information corresponding to any two geomagnetic fingerprint signals is greater than the second threshold.
  • the electronic device can accurately determine the geomagnetic fingerprint signal that needs to be filtered, so that the filtered geomagnetic signal fingerprint can be matched to a more accurate vehicle location.
  • the first driving trajectory also includes the confidence of each of the M pieces of position information
  • the second driving trajectory includes P pieces of position information and each of the P pieces of position information.
  • the confidence level of the location information, P is less than or equal to M; the electronic device determines the target location information from the second driving trajectory, including: the electronic device determines one of the P pieces of location information with a confidence level higher than the third threshold or Multiple location information is used as target location information.
  • the electronic device can directly and accurately determine the location, floor and range of the vehicle through the second geomagnetic fingerprint signal, and use relevant algorithms to make the location information of the vehicle determined by the electronic device more accurate.
  • the first driving trajectory also includes the confidence of each of the M pieces of position information
  • the second driving trajectory includes P pieces of position information and each of the P pieces of position information.
  • the confidence of the location information, P is less than or equal to M
  • the electronic device determines the target location information from the second driving trajectory, including: the electronic device determines one or more of the P pieces of location information whose confidence is higher than the third threshold.
  • Location information the electronic device uses the mode of the floor information in one or more location information as the parking floor in the target location information; the electronic device determines the location information with the parking floor and a confidence level higher than the fourth threshold as the target location information. In this way, the electronic device can first determine the floor where the vehicle is parked, and then determine the specific location and range of the vehicle through confidence and other information.
  • the electronic device determines the target location information from the first location information, and then may further include: the electronic device outputting the target location information.
  • the electronic device can also specifically display the final parking location of the vehicle through the application, including the parking floor, location and range. The user can know the location of the vehicle more vividly and specifically, which is convenient for user operation.
  • the electronic device outputs the target location information, specifically including: the electronic device outputs the parking floor and location range of the vehicle, and the location range includes one or more locations in the target location information.
  • the location point corresponding to the information.
  • inventions of the present application provide an electronic device.
  • the electronic device includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program codes,
  • the computer program code includes computer instructions that are invoked by the one or more processors to cause the electronic device to execute.
  • FIG. 1 is a schematic structural diagram of an electronic device 100 provided by an embodiment of the present application.
  • FIG. 2 shows a system architecture 10 provided by an embodiment of the present application.
  • FIG. 3 is a schematic flowchart of a method for determining the indoor location of the electronic device 100 provided by an embodiment of the present application.
  • Figure 4 is a schematic flowchart of a geomagnetic matching floor determination algorithm provided by an embodiment of the present application.
  • FIG. 5 is a schematic flowchart of a method for automatically determining the position of the vehicle 101 provided by an embodiment of the present application.
  • Figure 6 is a schematic structural diagram of a software for automatically determining the position of the vehicle 101 provided by an embodiment of the present application.
  • FIG. 7 is a schematic flowchart of a method for obtaining the position of the vehicle 101 provided by an embodiment of the present application.
  • Figure 8A is a modeling system for obtaining a deep learning model of the position of the vehicle 101 provided by an embodiment of the present application.
  • FIG. 8B is a schematic diagram of a training method for obtaining the weight of a deep learning model of the position of the vehicle 101 provided by an embodiment of the present application.
  • FIG. 9A is a schematic diagram of a specific process of parking the vehicle 101 according to the embodiment of the present application.
  • FIG. 9B is a schematic diagram of a specific process of parking the vehicle 101 according to the embodiment of the present application.
  • Figure 10A is a schematic diagram of classification and screening of geomagnetic candidate matching positions provided by an embodiment of the present application.
  • Figure 10B is a flow chart of a method for obtaining the final predicted position of the vehicle 101 provided by an embodiment of the present application.
  • Figure 11 is a schematic diagram of a simulation of an output point at a specific location in an indoor parking lot provided by an embodiment of the present application.
  • FIG. 12 is a schematic block diagram of the software structure of an electronic device 100 provided by an embodiment of the present application.
  • Figure 13A is a schematic diagram of a user interface provided by an embodiment of the present application.
  • Figure 13B is a schematic diagram of a user interface provided by an embodiment of the present application.
  • Figure 13C is a schematic diagram of a user interface provided by an embodiment of the present application.
  • first and second are used for descriptive purposes only and shall not be understood as implying or implying relative importance or implicitly specifying the quantity of indicated technical features. Therefore, the features defined as “first” and “second” may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, “plurality” The meaning is two or more.
  • GUI graphical user interface
  • FIG. 1 shows a schematic structural diagram of an electronic device 100 .
  • the electronic device 100 may be a portable terminal device equipped with iOS, Android, Microsoft or other operating systems.
  • the electronic device 100 may be a mobile phone, a tablet computer, a desktop computer, a laptop computer, a handheld computer, a notebook computer, or a super mobile personal computer ( ultra-mobile personal computer (UMPC), netbook, and cellular phone, personal digital assistant (PDA), augmented reality (AR) devices, virtual reality (VR) devices, artificial intelligence (artificial intelligence) intelligence, AI) devices, wearable devices, vehicles, in-vehicle devices, smart home devices and/or smart city devices, but is not limited thereto, the electronic device 100 may also include a laptop computer ( laptop), desktop computers with touch-sensitive surfaces or touch panels and other non-portable terminal devices, etc.
  • the embodiment of the present application does not place any special restrictions on the specific type of the electronic device.
  • the electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2 , mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone interface 170D, sensor module 180, button 190, motor 191, indicator 192, camera 193, display screen 194, and Subscriber identification module (SIM) card interface 195 wait.
  • a processor 110 an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2 , mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone interface 170D, sensor module 180, button 190, motor 191, indicator 192, camera 193, display screen
  • the sensor module 180 may include a pressure sensor 180A, a gyro sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, and ambient light. Sensor 180L, bone conduction sensor 180M, etc.
  • the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the electronic device 100 .
  • the electronic device 100 may include more or fewer components than shown in the figures, or some components may be combined, some components may be separated, or some components may be arranged differently.
  • the components illustrated may be implemented in hardware, software, or a combination of software and hardware.
  • the processor 110 may include one or more processing units.
  • the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processing unit (GPU), and an image signal processor. (image signal processor, ISP), controller, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural network processor (neural-network processing unit, NPU), etc.
  • application processor application processor, AP
  • modem processor graphics processing unit
  • GPU graphics processing unit
  • image signal processor image signal processor
  • ISP image signal processor
  • controller video codec
  • digital signal processor digital signal processor
  • DSP digital signal processor
  • baseband processor baseband processor
  • neural network processor neural-network processing unit
  • the processor 110 may also be provided with a memory for storing instructions and data.
  • the memory in processor 110 is cache memory. This memory may hold instructions or data that have been recently used or recycled by processor 110 . If the processor 110 needs to use the instructions or data again, it can be called directly from the memory. Repeated access is avoided and the waiting time of the processor 110 is reduced, thus improving the efficiency of the system.
  • processor 110 may include one or more interfaces.
  • Interfaces may include integrated circuit (inter-integrated circuit, I2C) interface, integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, pulse code modulation (pulse code modulation, PCM) interface, universal asynchronous receiver and transmitter (universal asynchronous receiver/transmitter (UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO) interface, subscriber identity module (SIM) interface, and /or universal serial bus (USB) interface, etc.
  • I2C integrated circuit
  • I2S integrated circuit built-in audio
  • PCM pulse code modulation
  • UART universal asynchronous receiver and transmitter
  • MIPI mobile industry processor interface
  • GPIO general-purpose input/output
  • SIM subscriber identity module
  • USB universal serial bus
  • the I2C interface is a bidirectional synchronous serial bus, including a serial data line (SDA) and a serial clock line (derail clock line, SCL).
  • processor 110 may include multiple sets of I2C buses.
  • the processor 110 can separately couple the touch sensor 180K, charger, flash, camera 193, etc. through different I2C bus interfaces.
  • the processor 110 can be coupled to the touch sensor 180K through an I2C interface, so that the processor 110 and the touch sensor 180K communicate through the I2C bus interface to implement the touch function of the electronic device 100 .
  • the I2S interface can be used for audio communication.
  • processor 110 may include multiple sets of I2S buses.
  • the processor 110 can be coupled with the audio module 170 through the I2S bus to implement communication between the processor 110 and the audio module 170 .
  • the audio module 170 can transmit audio signals to the wireless communication module 160 through the I2S interface to implement the function of answering calls through a Bluetooth headset.
  • the PCM interface can also be used for audio communications to sample, quantize and encode analog signals.
  • the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface.
  • the audio module 170 can also transmit audio signals to the wireless communication module 160 through the PCM interface to implement the function of answering calls through a Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.
  • the UART interface is a universal serial data bus used for asynchronous communication.
  • the bus can be a bidirectional communication bus. It converts the data to be transmitted between serial communication and parallel communication.
  • a UART interface is generally used to connect the processor 110 and the wireless communication module 160 .
  • the processor 110 communicates with the Bluetooth module in the wireless communication module 160 through the UART interface to implement the Bluetooth function.
  • the audio module 170 can transmit audio signals to the wireless communication module 160 through the UART interface to implement the function of playing music through a Bluetooth headset.
  • the MIPI interface can be used to connect the processor 110 with peripheral devices such as the display screen 194 and the camera 193 .
  • MIPI interfaces include camera serial interface (CSI), display serial interface (DSI), etc.
  • the processor 110 and the camera 193 communicate through the CSI interface to implement the shooting function of the electronic device 100 .
  • the processor 110 and the display screen 194 communicate through the DSI interface to implement the display function of the electronic device 100 .
  • the GPIO interface can be configured through software.
  • the GPIO interface can be configured as a control signal or as a data signal.
  • the GPIO interface can be used to connect the processor 110 with the camera 193, display screen 194, wireless communication module 160, audio module 170, sensor module 180, etc.
  • the GPIO interface can also be configured as an I2C interface, I2S interface, UART interface, MIPI interface, etc.
  • the USB interface 130 is an interface that complies with the USB standard specification, and may be a Mini USB interface, a Micro USB interface, a USB Type C interface, etc.
  • the USB interface 130 can be used to connect a charger to charge the electronic device 100, and can also be used to transmit data between the electronic device 100 and peripheral devices. It can also be used to connect headphones to play audio through them. This interface can also be used to connect other electronic devices, such as AR devices, etc.
  • the interface connection relationships between the modules illustrated in the embodiment of the present invention are only schematic illustrations and do not constitute a structural limitation of the electronic device 100 .
  • the electronic device 100 may also adopt different interface connection methods in the above embodiments, or a combination of multiple interface connection methods.
  • the charging management module 140 is used to receive charging input from the charger.
  • the charger can be a wireless charger or a wired charger.
  • the charging management module 140 may receive charging input from the wired charger through the USB interface 130 .
  • the charging management module 140 may receive wireless charging input through the wireless charging coil of the electronic device 100 . While the charging management module 140 charges the battery 142, it can also provide power to the electronic device through the power management module 141.
  • the power management module 141 is used to connect the battery 142, the charging management module 140 and the processor 110.
  • the power management module 141 receives input from the battery 142 and/or the charging management module 140, and supplies power to the processor 110, the internal memory 121, the display screen 194, the camera 193, the wireless communication module 160, and the like.
  • the power management module 141 can also be used to monitor battery capacity, battery cycle times, battery health status (leakage, impedance) and other parameters.
  • the power management module 141 may also be provided in the processor 110 .
  • the power management module 141 and the charging management module 140 may also be provided in the same device.
  • the wireless communication function of the electronic device 100 can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor and the baseband processor.
  • Antenna 1 and Antenna 2 are used to transmit and receive electromagnetic wave signals.
  • Each antenna in electronic device 100 may be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example: Antenna 1 can be reused as a diversity antenna for a wireless LAN. In other embodiments, antennas may be used in conjunction with tuning switches.
  • the mobile communication module 150 can provide solutions for wireless communication including 2G/3G/4G/5G applied on the electronic device 100 .
  • the mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc.
  • the mobile communication module 150 can receive electromagnetic waves through the antenna 1, perform filtering, amplification and other processing on the received electromagnetic waves, and transmit them to the modem processor for demodulation.
  • the mobile communication module 150 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves through the antenna 1 for radiation.
  • at least part of the functional modules of the mobile communication module 150 may be disposed in the processor 110 .
  • at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be provided in the same device.
  • a modem processor may include a modulator and a demodulator.
  • the modulator is used to modulate the low-frequency baseband signal to be sent into a medium-high frequency signal.
  • the demodulator is used to demodulate 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 application processor outputs sound signals through audio devices (not limited to speaker 170A, receiver 170B, etc.), or displays images or videos through display screen 194.
  • the modem processor may be a stand-alone device.
  • the modem processor may be independent of the processor 110 and may be provided in the same device as the mobile communication module 150 or other functional modules.
  • the wireless communication module 160 can provide applications on the electronic device 100 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) network), Bluetooth (bluetooth, BT), and global navigation satellites.
  • WLAN wireless local area networks
  • System global navigation satellite system, GNSS
  • frequency modulation frequency modulation, FM
  • near field communication technology near field communication, NFC
  • infrared technology infrared, IR
  • the wireless communication module 160 may be one or more devices integrating at least one communication processing module.
  • the wireless communication module 160 receives electromagnetic waves via the antenna 2 , demodulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110 .
  • the wireless communication module 160 can also receive the signal to be sent from the processor 110, frequency modulate it, amplify it, and convert it into electromagnetic waves through the antenna 2 for radiation.
  • the antenna 1 of the electronic device 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the electronic device 100 can communicate with the network and other devices through wireless communication technology.
  • the wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), broadband 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), BT, GNSS, WLAN, NFC , FM, and/or IR technology, etc.
  • the GNSS may include global positioning system (GPS), global navigation satellite system (GLONASS), Beidou navigation satellite system (BDS), quasi-zenith satellite system (quasi) -zenith satellite system (QZSS) and/or satellite based augmentation systems (SBAS).
  • GPS global positioning system
  • GLONASS global navigation satellite system
  • BDS Beidou navigation satellite system
  • QZSS quasi-zenith satellite system
  • SBAS satellite based augmentation systems
  • the electronic device 100 implements display functions through a GPU, a display screen 194, an application processor, and the like.
  • the GPU is an image processing microprocessor and is connected to the display screen 194 and the application processor. GPUs are 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 alter display information.
  • the display screen 194 is used to display images, videos, etc.
  • Display 194 includes a display panel.
  • the display panel can use a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active matrix organic light emitting diode or an active matrix organic light emitting diode (active-matrix organic light emitting diode).
  • LCD liquid crystal display
  • OLED organic light-emitting diode
  • AMOLED organic light-emitting diode
  • FLED flexible light-emitting diode
  • Miniled MicroLed, Micro-oLed, quantum dot light emitting diode (QLED), etc.
  • the electronic device 100 may include 1 or N display screens 194, where N is a positive integer greater than 1.
  • the electronic device 100 can implement the shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like.
  • the ISP is used to process the data fed back by the camera 193. For example, when taking a photo, the shutter is opened, the light is transmitted to the camera sensor through the lens, the optical signal is converted into an electrical signal, and the camera sensor passes the electrical signal to the ISP for processing, and converts it into an image visible to the naked eye. ISP can also perform algorithm optimization on image noise and brightness. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, the ISP may be provided in the camera 193.
  • Camera 193 is used to capture still images or video.
  • the object passes through the lens to produce an optical image that is projected onto the photosensitive element.
  • the photosensitive element may be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor.
  • CMOS complementary metal-oxide-semiconductor
  • the photosensitive element converts light signals into electricity signal, and then pass the electrical signal to the ISP to convert it into a digital image signal.
  • ISP outputs digital image signals to DSP for processing.
  • DSP converts digital image signals into standard RGB, YUV and other format image signals.
  • the electronic device 100 may include 1 or N cameras 193, where N is a positive integer greater than 1.
  • Digital signal processors are used to process digital signals. In addition to digital image signals, they can also process other digital signals. For example, when the electronic device 100 selects a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy.
  • Video codecs are used to compress or decompress digital video.
  • Electronic device 100 may support one or more video codecs. In this way, the electronic device 100 can play or record videos in multiple encoding formats, such as moving picture experts group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.
  • MPEG moving picture experts group
  • MPEG2 MPEG2, MPEG3, MPEG4, etc.
  • NPU is a neural network (NN) computing processor.
  • NN neural network
  • Intelligent cognitive applications of the electronic device 100 can be implemented through the NPU, such as image recognition, face recognition, speech recognition, text understanding, etc.
  • the internal memory 121 may include one or more random access memories (RAM) and one or more non-volatile memories (NVM).
  • RAM random access memories
  • NVM non-volatile memories
  • Random access memory can include static random-access memory (SRAM), dynamic random-access memory (DRAM), synchronous dynamic random-access memory (SDRAM), double data rate synchronous Dynamic random access memory (double data rate synchronous dynamic random access memory, DDR SDRAM, for example, the fifth generation DDR SDRAM is generally called DDR5SDRAM), etc.; non-volatile memory can include disk storage devices and flash memory (flash memory).
  • SRAM static random-access memory
  • DRAM dynamic random-access memory
  • SDRAM synchronous dynamic random-access memory
  • DDR SDRAM double data rate synchronous Dynamic random access memory
  • non-volatile memory can include disk storage devices and flash memory (flash memory).
  • Flash memory can be divided according to the operating principle to include NOR FLASH, NAND FLASH, 3D NAND FLASH, etc.
  • the storage unit potential level it can include single-level storage cells (single-level cell, SLC), multi-level storage cells (multi-level cell, MLC), third-level storage unit (triple-level cell, TLC), fourth-level storage unit (quad-level cell, QLC), etc., which can include universal flash storage (English: universal flash storage, UFS) according to storage specifications. , embedded multi media card (embedded multi media Card, eMMC), etc.
  • the random access memory can be directly read and written by the processor 110, can be used to store executable programs (such as machine instructions) of the operating system or other running programs, and can also be used to store user and application data, etc.
  • the non-volatile memory can also store executable programs and user and application program data, etc., and can be loaded into the random access memory in advance for direct reading and writing by the processor 110.
  • the external memory interface 120 can be used to connect an external non-volatile memory to expand the storage capacity of the electronic device 100 .
  • the external non-volatile memory communicates with the processor 110 through the external memory interface 120 to implement the data storage function. For example, save music, video and other files in external non-volatile memory.
  • the electronic device 100 can implement audio functions through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the headphone interface 170D, and the application processor. Such as music playback, recording, etc.
  • the audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signals. Audio module 170 may also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be provided in the processor 110 , or some functional modules of the audio module 170 may be provided in the processor 110 .
  • Speaker 170A also called “speaker” is used to convert audio electrical signals into sound signals.
  • the electronic device 100 can listen to music through the speaker 170A, or listen to hands-free calls.
  • Receiver 170B also called “earpiece” is used to convert audio electrical signals into sound signals.
  • the electronic device 100 receives a call
  • the voice can be heard by bringing the receiver 170B close to the human ear.
  • Microphone 170C also called “microphone” or “microphone” is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can speak close to the microphone 170C with the human mouth and input the sound signal to the microphone 170C.
  • the electronic device 100 may be provided with at least one microphone 170C. In other embodiments, the electronic device 100 may be provided with two microphones 170C, which in addition to collecting sound signals, may also implement a noise reduction function. In other embodiments, the electronic device 100 can also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, and implement directional recording functions, etc.
  • the headphone interface 170D is used to connect wired headphones.
  • the headphone interface 170D may be a USB interface 130, or may be a 3.5mm open mobile terminal platform (OMTP) standard interface, or a Cellular Telecommunications Industry Association of the USA (CTIA) standard interface.
  • OMTP open mobile terminal platform
  • CTIA Cellular Telecommunications Industry Association of the USA
  • the pressure sensor 180A is used to sense pressure signals and can convert the pressure signals into electrical signals.
  • pressure sensor 180A may be disposed on display screen 194 .
  • pressure sensors 180A there are many types of pressure sensors 180A, such as resistive pressure sensors, inductive pressure sensors, capacitive pressure sensors, etc.
  • a capacitive pressure sensor may include at least two parallel plates of conductive material.
  • the electronic device 100 determines the intensity of the pressure based on the change in capacitance.
  • the electronic device 100 detects the intensity of the touch operation according to the pressure sensor 180A.
  • the electronic device 100 may also calculate the touched position based on the detection signal of the pressure sensor 180A.
  • touch operations acting on the same touch location but with different touch operation intensities may correspond to different operation instructions. For example: when a touch operation with a touch operation intensity less than the first pressure threshold is applied to the short message application icon, an instruction to view the short message is executed. When a touch operation with a touch operation intensity greater than or equal to the first pressure threshold is applied to the short message application icon, an instruction to create a new short message is executed.
  • the gyro sensor 180B may be used to determine the motion posture of the electronic device 100 .
  • the angular velocity of electronic device 100 about three axes ie, X, Y, and Z axes
  • the gyro sensor 180B can be used for image stabilization. For example, when the shutter is pressed, the gyro sensor 180B detects the angle at which the electronic device 100 shakes, calculates the distance that the lens module needs to compensate based on the angle, and allows the lens to offset the shake of the electronic device 100 through reverse movement to achieve anti-shake.
  • the gyro sensor 180B can also be used for navigation and somatosensory game scenes.
  • the gyro sensor 180B can determine the driving status of the vehicle equipped with the electronic device 100 based on the angular velocity of the electronic device 100 in the X, Y and Z axis directions.
  • the driving status of the vehicle may include but is not limited to the vehicle. Driving up and down hills, driving the vehicle around corners, etc.
  • Air pressure sensor 180C is used to measure air pressure. In some embodiments, the electronic device 100 calculates the altitude through the air pressure value measured by the air pressure sensor 180C to assist positioning and navigation.
  • Magnetic sensor 180D includes a Hall sensor.
  • the electronic device 100 may utilize the magnetic sensor 180D to detect opening and closing of the flip holster.
  • the electronic device 100 may detect the opening and closing of the flip according to the magnetic sensor 180D. Then, based on the detected opening and closing status of the leather case or the opening and closing status of the flip cover, features such as automatic unlocking of the flip cover are set.
  • the acceleration sensor 180E can detect the acceleration of the electronic device 100 in various directions (generally the three axes of X, Y and Z). When the electronic device 100 is stationary, the magnitude and direction of gravity can be detected. It can also be used to identify the posture of the electronic device 100 and be used in horizontal and vertical screen switching, pedometer and other applications. In the embodiment of the present application, the acceleration sensor 180E can be used to measure the acceleration force.
  • the acceleration force refers to the force acting on the electronic device 100 when the electronic device 100 is accelerating, such as gravity, friction, etc., which is not limited. .
  • the acceleration sensor 180E can calculate the acceleration of the vehicle equipped with the electronic device 100 after entering a specific parking lot.
  • the magnitude of the acceleration can be a positive number, a negative number, or zero, and its direction can be consistent with the direction in which the vehicle is traveling.
  • the output type of the acceleration sensor 180E may be a digital output or a voltage output, and the output type depends on the interface between the electronic device 100 and the acceleration sensor 180E.
  • the microcontroller used in the electronic device 100 is a digital input
  • the acceleration sensor 180E in the electronic device 100 can choose to output digital, but it needs to occupy an additional clock unit to process the pulse width modulation (Pulse width modulation, PWM) signal. , this will increase the burden on the processor;
  • the microcontroller used by the electronic device is an analog input, the acceleration in the electronic device 100 can simulate the output voltage, and the processing speed is fast.
  • the voltage of the analog output and the acceleration can be proportional to each other. There is no limit to the ratio of voltage to acceleration. For example, a voltage of 2.5V may correspond to an acceleration of 0g, and a voltage of 3V may correspond to an acceleration of 1g.
  • Distance sensor 180F for measuring distance.
  • Electronic device 100 can measure distance via infrared or laser. In some embodiments, when shooting a scene, the electronic device 100 may utilize the distance sensor 180F to measure distance to achieve fast focusing.
  • 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 electronic device 100 emits infrared light outwardly through the light emitting diode.
  • Electronic device 100 uses photodiodes to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device 100 . When insufficient reflected light is detected, the electronic device 100 may determine that there is no object near the electronic device 100 .
  • the electronic device 100 can use the proximity light sensor 180G to detect when the user holds the electronic device 100 close to the ear for talking, so as to automatically turn off the screen to save power.
  • the proximity light sensor 180G can also be used in holster mode, and pocket mode automatically unlocks and locks the screen.
  • the ambient light sensor 180L is used to sense ambient light brightness.
  • the electronic device 100 can adaptively adjust the brightness of the display screen 194 according to the perceived ambient light brightness.
  • the ambient light sensor 180L can also be used to automatically adjust the white balance when taking pictures.
  • the ambient light sensor 180L can also cooperate with the proximity light sensor 180G to detect whether the electronic device 100 is in the pocket to prevent accidental touching.
  • Fingerprint sensor 180H is used to collect fingerprints.
  • the electronic device 100 can use the collected fingerprint characteristics to achieve fingerprint unlocking, access to application locks, fingerprint photography, fingerprint answering of incoming calls, etc.
  • Temperature sensor 180J is used to detect temperature.
  • the electronic device 100 utilizes the temperature detected by the temperature sensor 180J to execute the temperature processing strategy. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold, the electronic device 100 reduces the performance of a processor located near the temperature sensor 180J in order to reduce power consumption and implement thermal protection. In other embodiments, when the temperature is lower than another threshold, the electronic device 100 heats the battery 142 to prevent the low temperature from causing the electronic device 100 to shut down abnormally. In some other embodiments, when the temperature is lower than another threshold, the electronic device 100 performs boosting on the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperature.
  • Touch sensor 180K also known as "touch device”.
  • the touch sensor 180K can be disposed on the display screen 194.
  • 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 used to detect a touch operation on or near the touch sensor 180K.
  • the touch sensor can pass the detected touch operation to the application processor to determine the touch event type.
  • Visual output related to the touch operation may be provided through display screen 194 .
  • the touch sensor 180K may also be disposed on the surface of the electronic device 100 at a location different from that of the display screen 194 .
  • Bone conduction sensor 180M can acquire vibration signals.
  • the bone conduction sensor 180M can acquire the vibration signal of the vibrating bone mass of the human body's vocal part.
  • the bone conduction sensor 180M can also contact the human body's pulse and receive blood pressure beating signals.
  • the bone conduction sensor 180M can also be provided in an earphone and combined into a bone conduction earphone.
  • the audio module 170 can analyze the voice signal based on the vibration signal of the vocal vibrating bone obtained by the bone conduction sensor 180M to implement the voice function.
  • the application processor can analyze the heart rate information based on the blood pressure beating signal acquired by the bone conduction sensor 180M to implement the heart rate detection function.
  • the geomagnetic sensor 180Z is a type of measurement device that uses the different motion states of the electronic device 100 in the geomagnetic field to indicate information such as the posture and movement angle of the electronic device 100 by sensing the distribution changes of the geomagnetic field.
  • the electronic device 100 can communicate
  • the geomagnetic field and geomagnetic sensor 180Z implement the compass function of the electronic device 100 .
  • the geomagnetic sensor 180Z can be used to detect the presence of vehicles and model identification.
  • geomagnetic sensor 180Z may be used in indoor navigation applications.
  • the geomagnetic sensor 180Z can use the distribution of the geomagnetic field to obtain the position of the electronic device 100 .
  • the geomagnetic fingerprint signal of the location of the electronic device 100 acquired by the geomagnetic sensor 180Z can refer to the components of the magnetic field intensity in three directions under three-dimensional coordinates.
  • the geomagnetic fingerprint signal can be specifically expressed as (m x , m y , m z ), where m x corresponds to the component of the magnetic field intensity in the x direction, m y corresponds to the component of the magnetic field intensity in the y direction, and m z corresponds to the component of the magnetic field intensity in the z direction.
  • the buttons 190 include a power button, a volume button, etc.
  • Key 190 may be a mechanical key. It can also be a touch button.
  • the electronic device 100 may receive key inputs and generate key signal inputs related to user settings and function control of the electronic device 100 .
  • the motor 191 can generate vibration prompts.
  • the motor 191 can be used for vibration prompts for incoming calls and can also be used for touch vibration feedback.
  • touch operations for different applications can correspond to different vibration feedback effects.
  • the motor 191 can also respond to different vibration feedback effects for touch operations in different areas of the display screen 194 .
  • Different application scenarios such as time reminders, receiving information, alarm clocks, games, etc.
  • the touch vibration feedback effect can also be customized.
  • the indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, or may be used to indicate messages, missed calls, notifications, etc.
  • the SIM card interface 195 is used to connect a SIM card.
  • the SIM card can be connected to or separated from the electronic device 100 by inserting it into the SIM card interface 195 or pulling it out from the SIM card interface 195 .
  • the electronic device 100 can support 1 or N SIM card interfaces, where N is a positive integer greater than 1.
  • SIM card interface 195 can support Nano SIM card, Micro SIM card, SIM card, etc. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the plurality of cards may be the same or different.
  • the SIM card interface 195 is also compatible with different types of SIM cards.
  • the SIM card interface 195 is also compatible with external memory cards.
  • the electronic device 100 interacts with the network through the SIM card to implement functions such as calls and data communications.
  • the electronic device 100 uses an eSIM, that is, an embedded SIM card.
  • the eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100 .
  • the software system of the electronic device 100 may adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture.
  • This embodiment of the present invention takes the Android system with a layered architecture as an example to illustrate the software structure of the electronic device 100 .
  • FIG. 2 shows a system architecture 10 provided by this application.
  • the system architecture 10 may include an electronic device 100 and a vehicle 101 .
  • the electronic device 100 and the vehicle 101 may establish a communication connection.
  • the electronic device 100 and the vehicle 101 may establish a communication connection directly, or may communicate through the server 120 .
  • the electronic device 100 and the vehicle 101 can establish a wireless connection based on wireless communication technology.
  • the electronic device 100 can establish a 2.4G wireless connection or a Bluetooth connection with the vehicle 101.
  • the establishment of a wireless connection is The method is not limited.
  • the electronic device 100 and the vehicle 101 can also establish connections with the server 120 respectively.
  • the electronic device 100 and the vehicle 101 can log in to the same user account, and the electronic device 100 and the vehicle 101 can store relevant data in the server 120 or retrieve data from the server. 120 to obtain relevant data.
  • the embodiment of the present application does not limit the manner in which the electronic device 100 establishes a communication connection with the vehicle 101 .
  • relevant data information can be transmitted.
  • the vehicle 101 can send information about its own processing status (such as changing the driving mode) to the electronic device 100 or to the server 120 , and the electronic device 100 can improve the sensors on the electronic device 100 by obtaining the relevant processing information of the vehicle 101 Collected data information.
  • the electronic device 100 may be mounted on the vehicle 101 , that is, the electronic device 100 may move alone or move synchronously with the vehicle 101 .
  • the vehicle 101 can obtain the vehicle 101 position by processing relevant sensor data alone.
  • the vehicle 101 when the user is out, when the driving vehicle 101 arrives near the destination, the vehicle 101 needs to be parked.
  • users will choose to park the vehicle 101 in a parking lot, where the parking lot can be divided into an outdoor open-air parking lot and an indoor parking lot.
  • the space utilization rate of outdoor open-air parking lots is not high, and most of them are not safe. Moreover, weather factors such as long-term exposure to the sun, long-term rain, hail and snowfall, etc. will affect the vehicle 101, causing damage to the vehicle 101, which is not conducive to user parking. Vehicle 101.
  • the indoor parking lot can be divided into multiple floors to park vehicles 101, which has high space utilization and can record the information of vehicles 101, making parking safer and more convenient for users.
  • the ramp types of the cross-floor indoor parking lot may include but are not limited to: straight ramp type (such as outer straight ramp type, inner straight ramp type), split-level type (such as two-stage split-level, three-stage type). Split-level), spiral ramp type (such as single spiral ramp, double spiral ramp, jump-floor spiral ramp), inclined floor type.
  • straight ramp type such as outer straight ramp type, inner straight ramp type
  • split-level type such as two-stage split-level, three-stage type.
  • Split-level split-level
  • spiral ramp type such as single spiral ramp, double spiral ramp, jump-floor spiral ramp
  • inclined floor type such as single spiral ramp, double spiral ramp, jump-floor spiral ramp
  • the electronic device 100 can obtain the location of the vehicle 101 input by the user when the vehicle 101 is parked. However, this requires the user to manually input the location information of the vehicle 101, which is difficult to avoid the user forgetting to input and is inefficient.
  • the electronic device 100 can automatically obtain the location of the vehicle 101 based on positioning technology when the vehicle 101 is parked. Here are several ways to automatically obtain the vehicle 101 position.
  • Method 1 The electronic device 100 can be positioned outdoors through the Global Navigation Satellite System (GNSS) and combined with inertial navigation technology for assisted positioning; after entering the room, it can continue to be positioned through the inertial navigation technology when GNSS fails. Carry out extrapolation to obtain vehicle 101 position.
  • GNSS Global Navigation Satellite System
  • the Global Navigation Satellite System is a space-based radio navigation positioning system that provides users with all-weather three-dimensional coordinates, speed and time information at any location on the earth's surface or near-Earth space, including the United States' Global Positioning System. System, GPS), Russia's Global Navigation Satellite System (GLONASS), the European Union's Galileo satellite navigation system (GALILEO) and China's BeiDou Navigation Satellite System (BeiDou Navigation Satellite System, BDS).
  • GPS Global Positioning System
  • GLONASS Russia's Global Navigation Satellite System
  • GALILEO European Union's Galileo satellite navigation system
  • BeiDou Navigation Satellite System BeiDou Navigation Satellite System
  • the user may use the GNSS system to determine the location when driving the vehicle 101 outdoors or parking the vehicle 101 in an outdoor parking lot. Due to poor signals in most indoor areas, GNSS cannot accurately position vehicle 101 when it drives into the indoor parking lot.
  • Inertial navigation technology is an autonomous navigation system that does not rely on external information and does not radiate energy to the outside. Its working environment can include but is not limited to air, ground and underwater.
  • the basic working principle of inertial navigation technology is based on Newton's laws of mechanics. By measuring the acceleration of a carrier, such as the vehicle 101, in the inertial reference system, integrating it over time, and transforming it into the navigation coordinate system, we can obtain the Information such as speed, yaw angle and position in the navigation coordinate system.
  • inertial navigation technology can be used in combination with acceleration sensors and gyroscope sensors.
  • the electronic device 100 can obtain acceleration and other data of the electronic device 100 based on the acceleration sensor.
  • the angular velocity and other data of the electronic device 100 on the X, Y and Z axes can be obtained based on the gyroscope sensor, and then through inertial navigation technology.
  • the location information of vehicle 101 can be obtained.
  • the electronic device 100 can also use fingerprint signal matching technology such as WIFI, cellular network or geomagnetism to perform positioning and obtain the location of the vehicle 101 when it is in an indoor environment.
  • fingerprint signal matching technology such as WIFI, cellular network or geomagnetism
  • location fingerprint matching is a common positioning method. It is based on wireless communication and network technology, and has the characteristics of easy implementation, low cost, and access point (Accesspoint, Ap) Time synchronization has many characteristics such as low accuracy requirements, which can be achieved based on different wireless LAN sensors such as wifi and Bluetooth, and can be widely used in a variety of indoor positioning scenarios.
  • the electronic device 100 periodically acquires the geomagnetic fingerprint signal of the location of the electronic device 100 based on the geomagnetic sensor.
  • the geomagnetic fingerprint signal includes vector data in three-dimensional space, which refers to the components of the magnetic field intensity in three directions in three-dimensional coordinates.
  • the geomagnetic fingerprint The signal can be specifically expressed as (m x , my y , m z ), where m x corresponds to the component of the magnetic field intensity in the x direction, m y corresponds to the component of the magnetic field intensity in the y direction, and m z corresponds to the magnetic field in the z direction.
  • the weight of intensity is specifically expressed as (m x , my y , m z ), where m x corresponds to the component of the magnetic field intensity in the x direction, m y corresponds to the component of the magnetic field intensity in the y direction, and m z corresponds to the magnetic field in the z direction.
  • the weight of intensity is
  • the electronic device 100 obtains the geomagnetic fingerprint signal of the location of the electronic device 100 based on the geomagnetic sensor, matches the geomagnetic fingerprint signal with the location fingerprint information in the location fingerprint database using a certain algorithm, and selects the matching The result with the best similarity is used as the position estimate of vehicle 101.
  • the location fingerprint database may be pre-established before determining the location of the vehicle 101 .
  • FIG. 3 shows a schematic flowchart of a method for determining the indoor location of the electronic device 100 described in the second method above. Specifically, it may include the following steps:
  • the electronic device 100 collects the first geomagnetic fingerprint signal.
  • the electronic device 100 may obtain the first fingerprint signal by using a geomagnetic sensor on the electronic device 100 to obtain the geomagnetic fingerprint signal of the location of the electronic device 100 .
  • the electronic device 100 can be placed in a variety of postures.
  • the electronic device 100 can be placed horizontally forward, horizontally left, horizontally right, and so on.
  • Each placement posture can correspondingly convert the first geomagnetic data into a different second geomagnetic fingerprint signal.
  • the geomagnetic database includes multiple geomagnetic database tables DB[k] corresponding to different floors.
  • Each geomagnetic database table includes multiple geomagnetic fingerprint signals, and the geomagnetic fingerprint signals indicate the location of the electronic device 100 .
  • the electronic device 100 matches each second geomagnetic data segment with the plurality of geomagnetic database tables in parallel, and determines that the floor corresponding to the earliest successfully matched geomagnetic database table is the floor where the electronic device 100 is located;
  • the electronic device 100 matches the second geomagnetic fingerprint signal with multiple geomagnetic database tables, Confirm the floor where the electronic device 100 is located, and then match the specific location in the geomagnetic database table corresponding to the floor.
  • FIG. 4 shows a schematic flow chart of a geomagnetic matching floor determination algorithm.
  • the geomagnetic matching floor determination algorithm can include the following steps:
  • the floor corresponding to the DB[k] is the positioning target floor, and subsequent positioning matching is only performed in this DB[k]; if the geomagnetic fingerprint to be matched is If there is more than one DB[k] matching the signal successfully, that is, there are multiple DB[k] matching successfully, then the matching result with the highest confidence among all the DB[k] matching successfully is selected as the target matching result, and the target matches The floor corresponding to the result is the positioning target floor, and subsequent positioning matching is only performed on this target matching result.
  • the location of the electronic device 100 in the room can be determined, but it is necessary to first determine the floor where the electronic device 100 is located.
  • the method of confirming the floor where the electronic device 100 is located may include but is not limited to using a barometer, WIFI information, or directly sending the data first.
  • the geomagnetic fingerprint feature information of each floor is compared floor by floor. After the electronic device 100 completes the identification of the floor information, the fingerprint information of the floor is used to match and obtain the location. This takes a long time and the location acquisition speed is slow. In addition, when the floor determination is inaccurate, the electronic device 100 will perform position matching on the wrong floor, resulting in a decrease in accuracy.
  • Method 3 The electronic device 100 determines whether its current movement mode is a motor vehicle driving mode or a pedestrian walking mode, thereby determining whether a switch from the motor vehicle driving mode to the pedestrian walking mode has occurred. If so, the positioning information at the switching time is obtained as the parking mode. Location.
  • FIG. 5 shows a schematic flowchart of the method of automatically determining the position of the vehicle 101 described in this manner. Specifically, it includes the following steps:
  • the electronic device 100 collects detection data through sensors.
  • the sensor of the electronic device 100 can record raw data such as the acceleration, movement direction, magnetic field size and direction, and atmospheric level of the electronic device 100 at the current time and geographical location.
  • the electronic device 100 can obtain a set of time series key values by performing algorithmic processing such as filtering and integration on the original data. Then the electronic device 100 uses the characteristics of the time series key value changes in different scenarios to perform processing on the obtained time series key values. Recognize and detect whether the movement pattern of the electronic device 100 has changed.
  • the position of the electronic device 100 at this time is determined to be the parking time of the vehicle 101, and the position of the electronic device 100 at this time is the position of the vehicle 101.
  • the positioning information is obtained through any of the following methods: wifi, Bluetooth positioning technology, and GPS positioning technology.
  • FIG. 6 exemplarily shows a schematic diagram of the software structure for the electronic device 100 to determine whether the vehicle 101 has switched from a motor vehicle driving mode to a pedestrian walking mode in step S503. Specifically, as shown in Figure 6, it includes the following modules:
  • the data collection module is used to collect detection data through the sensors of the electronic device 100 .
  • a motion mode judgment module configured to judge that the current motion mode of the electronic device 100 is maneuvering based on the collected detection data.
  • the positioning information acquisition module is used to determine whether a switch from the motor vehicle driving mode to the pedestrian walking mode has occurred based on the current movement mode. If so, obtain the positioning information at the switching time as the vehicle 101 position.
  • the positioning information is obtained through any of the following methods: WIFI, Bluetooth positioning technology, and GPS positioning technology.
  • the scene switching between the motor vehicle driving mode and the pedestrian walking mode can be automatically judged, and the specific position of the vehicle 101 at the switching moment can be obtained, allowing the user to record the position of the vehicle 101.
  • the location of the parking point of the vehicle 101 calculated in real time may be inaccurate, the reliability of the saved location information is poor, and the judgment error may be relatively large, and when the sports mode makes an incorrect judgment, for example, when the vehicle is driving At this time, the user changes the position of the electronic device 100, causing the electronic device 100 to mistakenly believe that the switch from the motor vehicle driving mode to the human walking mode has been completed, which will cause a judgment error in the motion mode, and the incorrectly judged position will be regarded as the position of the vehicle 101.
  • the above two methods of obtaining location information both first identify the floor where the electronic device 100 is located, and then perform location matching.
  • indoor parking lots will set up parking spaces at locations such as ramps or near floor entrances. This will make it impossible for the electronic device 100 to determine the correct location due to insufficient fingerprint information, and it will also be unable to identify people across floors. Location.
  • the embodiment of the present application provides a method for obtaining the location of the vehicle 101, which can reduce the time for determining the location of the vehicle 101 and improve the accuracy of determining the location of the vehicle 101.
  • the electronic device 100 can input the acquired geomagnetic feature data into a deep learning model, output multiple geomagnetic candidate matching positions from the deep learning model, and then combine the multiple geomagnetic feature data.
  • the final location, floor information and location range information of the vehicle 101 parked are determined based on the confidence of the candidate matching location.
  • the above-mentioned method of obtaining the position of the vehicle 101 can mainly include two stages: the offline collection and modeling stage and the positioning stage. It is worth noting that when the vehicle 101 is parked on the ramp of a specific indoor parking lot, the electronic device 100 can also obtain the location information of the vehicle 101 , that is, the electronic device 100 can obtain the cross-floor location information of the vehicle.
  • the electronic device 100 can be placed inside the vehicle 101 driven by the user and move synchronously with the vehicle 101.
  • the electronic device 100 can recognize the user's operation on the electronic device 100, for example, the user picks up the electronic device 100. operation, etc., and eliminate the influence of the operation on the electronic device 100's judgment of the driving state of the vehicle 101.
  • the electronic device 100 can be used to obtain the location information of the parked vehicle 101, which includes but is not limited to location, floor, confidence and other information, which can effectively improve the accuracy of the electronic device 100 in obtaining the location information of the parked vehicle 101, and It is not limited to determining the location of the floor where the vehicle 101 is parked before determining the location of the vehicle 101 on the electronic device 100 to improve the determination efficiency.
  • a method for obtaining the location of the vehicle 101 also includes: the electronic device 100 can save the sensor data for a period of time before the parking of the vehicle 101 is completed, and start to detect the location of the vehicle after the parking of the vehicle 101 is completed. 101 position for calculation and judgment.
  • This electronic device 100 can calculate the parking position of the vehicle 101 using sensor data within a period of time before the parking of the vehicle 101 is completed.
  • the electronic device 100 before the electronic device 100 inputs the measured geomagnetic feature data into the deep learning model, it is necessary to collect geomagnetic fingerprint signals and perform modeling processing on the corresponding location and floor information.
  • the electronic device 100 can directly obtain the location and floor information of the electronic device 100 at the same time through the deep learning model after the modeling is completed, thereby improving the judgment efficiency.
  • the electronic device 100 before outputting multiple geomagnetic candidate matching positions from the deep learning model, can also use data collected by the acceleration sensor and the gyroscope sensor to assist inertial navigation technology to extract the geomagnetic fingerprint signal to obtain geomagnetic feature data. .
  • the extracted geomagnetic feature data is input into the geomagnetic climate obtained from the deep learning model. Select the matching location to be more precise.
  • Figure 7 is a schematic flowchart of the method for obtaining the location of the vehicle 101 provided in the embodiment of the present application.
  • FIG. 7 schematically illustrates a method for the electronic device 100 to obtain the position of the vehicle 101 .
  • the database in the electronic device 100 may include sensor databases, cellular networks, WIFI databases, deep learning models and weights.
  • the electronic device 100 may also display a schematic diagram of an interface for obtaining the location of the vehicle 101 .
  • Sensors used in the electronic device 100 in this embodiment of the present application may include, but are not limited to, acceleration sensors, gyroscope sensors, and geomagnetic sensors.
  • the geomagnetic sensor can be used to measure the geomagnetic fingerprint signal of the vehicle 101 while it is driving.
  • the data collected by the acceleration sensor and the gyroscope sensor during the driving process of the vehicle 101 can not only help the vehicle 101 park and detect, but also assist the inertial navigation technology in geomagnetic detection. Fingerprint signals are extracted to obtain geomagnetic feature data.
  • the method for the electronic device 100 to obtain the location of the vehicle 101 may include:
  • the electronic device 100 After the electronic device 100 recognizes that the vehicle 101 has entered a specific indoor parking lot, the electronic device 100 starts recording sensor data.
  • the user drives the vehicle 101 outdoors and needs to park the vehicle 101 according to the user's own needs.
  • users can select a specific indoor parking lot to park the vehicle 101 .
  • the specific indoor parking lot may refer to an indoor parking lot where the collection vehicle has carried out all-round geomagnetic fingerprint collection.
  • sensor data may include, but is not limited to, data collected by an acceleration sensor, data collected by a gyroscope sensor, and data collected by a geomagnetic sensor.
  • the data collected by the acceleration sensor may include acceleration data of the electronic device 100 in the driving direction of the vehicle 101, and may be used to determine the driving speed, acceleration, etc. of the vehicle 101.
  • the data collected by the gyroscope sensor includes the angular velocity of the electronic device 100 around three axes (i.e., X, Y, and Z axes).
  • the electronic device 100 can determine the location of the electronic device 100 based on the angular velocity in the X, Y, and Z axes.
  • the driving conditions of the vehicle 101 where the driving conditions of the vehicle 101 may include but are not limited to the vehicle 101 traveling uphill and downhill, the vehicle 101 turning, and so on.
  • the data collected by the geomagnetic sensor is the geomagnetic fingerprint signal of the location of the electronic device 100 as vector data in the three-dimensional space, which is the geomagnetic fingerprint signal.
  • the geomagnetic fingerprint signal is different in each geographical location, and the geomagnetic fingerprint signal collected by the geomagnetic sensor corresponds to the location one-to-one.
  • the method for the electronic device 100 to recognize that the vehicle 101 has entered a specific indoor parking lot may specifically include one or more of the following: the electronic device 100 recognizes the electronic device 100 based on a global positioning system (Global Positioning System, GPS). The location of the electronic device 100 has entered a specific indoor parking lot; or, the electronic device 100 determines that the electronic device 100 has entered a specific indoor parking lot based on the received WiFi signal and determines that the WiFi signal is a WiFi signal of the specific indoor parking lot; or, the electronic device 100 The device 100 detects attenuation of the GPS signal or cellular signal; or the electronic device 100 detects a change in the slope on which the vehicle 101 travels in combination with the geomagnetic fingerprint signal, and so on. It can be understood that the present invention is not limited to using only a specific outdoor-indoor detection method.
  • GPS Global Positioning System
  • the electronic device 100 stores the license plate information of the vehicle 101.
  • the camera at the entrance of the specific indoor parking lot scans the license plate information of the vehicle 101, and the server uses the license plate information of the vehicle 101 based on the license plate information of the vehicle 101.
  • the information sends prompt information to the electronic device 100 to prompt the vehicle 101 to enter the specific indoor parking lot.
  • the electronic device 100 starts recording sensor data.
  • the electronic device 100 recognizing that the vehicle 101 enters a specific indoor parking lot can be understood as the electronic device 100 determining that the geographical location of the vehicle 101 is already within a preset area. That is to say, the present invention can also be applied to other venues, not limited to indoor parking lots, but can be other indoor or semi-open venues.
  • the electronic device 100 performs parking detection on the vehicle 101 and confirms that the vehicle 101 has completed the parking action.
  • the electronic device 100 After the user drives the vehicle 101 into the garage, the user selects an available parking space to park the vehicle 101 . After the user parks the vehicle 101, the electronic device 100 performs parking detection on the vehicle 101 to confirm that the vehicle 101 has been parked. It is worth noting that this embodiment is only an example.
  • the present invention can perform positioning after parking, and can also perform real-time positioning while the vehicle is traveling. Specifically, when the electronic device 100 detects that the vehicle 101 is parked, the following methods may be used but are not limited to:
  • the method by which the electronic device 100 detects completion of parking of the vehicle 101 may be referred to as the first preset condition.
  • the electronic device 100 and the vehicle 101 can be connected via Bluetooth.
  • the electronic device 100 and the vehicle 101 can be connected through Bluetooth to implement communication functions, music playback functions, etc.
  • the user drives the vehicle 101 into a specific indoor parking lot, selects a free parking space and parks the vehicle 101, the user turns off the engine.
  • the vehicle 101 is powered off, that is, the vehicle 101 disconnects the Bluetooth connection with the electronic device 100. Therefore, the electronic device 100 can determine that the parking action of the vehicle 101 is completed by detecting that the electronic device 100 and the vehicle 101 have disconnected the Bluetooth connection, thereby realizing the parking detection of the vehicle 101 .
  • the user can park the vehicle 101 by reversing into the parking lot or parking on the side.
  • the electronic device 100 can detect the reversing motion of the vehicle 101 in the above-described manner of parking the vehicle 101 . That is, the electronic device 100 can determine whether the reversing action is completed through the data collected by the acceleration sensor and the gyroscope sensor, thereby determining whether the vehicle 101 has completed the parking action, and realizes the parking detection of the vehicle 101.
  • the electronic device 100 when the electronic device 100 detects that the driving speed of the vehicle 101 is less than a threshold, the electronic device 100 determines that the vehicle 101 has completed the parking action.
  • the threshold may be called a first threshold.
  • the automatic parking mode means that during the automatic parking process of the vehicle 101, the parking controller completely controls the steering motor, electronic brake pump, and electronic throttle of the electric power steering (EPS), thereby controlling the steering wheel rotation and stably Parking speed.
  • the electronic device 100 can determine the completion of the automatic parking mode and thus the completion of the parking action of the vehicle 101, thereby realizing the parking detection of the vehicle 101.
  • the sensor in the electronic device 100 collects raw data such as the acceleration, movement direction, magnetic field size and direction, and atmospheric level of the electronic device 100, and performs filtering, integration and other related algorithms on the raw data. Process and obtain multiple time series key values.
  • the electronic device 100 determines the moment when the motion mode of the electronic device 100 changes based on the time series key value change characteristics in different scenarios, and uses the location of the electronic device 100 at that moment as the parking location of the vehicle 101 .
  • the electronic device 100 can communicate with the vehicle 101, for example, the electronic device 100 communicates with the vehicle.
  • the vehicle 101 can log in to the same user account, the vehicle 101 can send its own processing data (such as reversing, turning on the automatic parking mode) to the server 120, and the electronic device 100 can obtain the above processing data from the server 120 to control the electronic device 100 Data information not obtained by the upper sensor.
  • the above step S701 is optional, and is not limited to the electronic device 100 starting to record sensor data only after the electronic device 100 enters a specific indoor parking lot.
  • the electronic device 100 may start recording sensor data when the vehicle 101 enters the driving state, or may start recording sensor data during the driving state, which is not limited in this application.
  • the above-mentioned step S702 is optional and is not limited to the electronic device 100 confirming that the vehicle 101 has completed the parking action before performing subsequent steps, that is, locating the parking position of the vehicle 101; the electronic device 100 can be used when the vehicle 101 is traveling. Real-time positioning is performed during the process, that is to say, while the vehicle 101 is traveling, the electronic device 100 can encapsulate the sensor data and perform subsequent steps to position the real-time position of the vehicle 101 . This application does not limit this.
  • the electronic device 100 encapsulates the sensor data.
  • the electronic device 100 After the electronic device 100 confirms that the vehicle 101 has completed the parking action, the electronic device 100 encapsulates the stored sensor data.
  • the sensor data may be data collected by the sensor in the time interval N before the vehicle 101 completes parking. That is, the electronic device 100 extracts sensor data within the time interval N.
  • the electronic device 100 cannot completely record and save all the sensor data of the vehicle 101 from entering a specific indoor parking lot to completing parking.
  • the electronic device 100 may store the sensor data of the time interval N before the vehicle 101 is parked, and delete the remaining sensor data recorded before the time interval N. It can be understood that storing the sensor data for a period of time N before the parking of the vehicle 101 is completed can support the electronic device 100 to perform the exercise of the algorithm in the following steps.
  • the electronic device 100 inputs the geomagnetic fingerprint signal in the packaged sensor data into the deep learning model to obtain the initial geomagnetic matching position.
  • the geomagnetic fingerprint signal collected by the geomagnetic sensor can correspond to the location one-to-one. For example, all locations in a specific indoor parking lot, including but not limited to floor planes, floor passages, near floor entrances, and ramps between floors, have corresponding geomagnetic fingerprint signals.
  • the method provided by the embodiment of this application is to use the earth's magnetic field to obtain the position of the vehicle 101. Since the earth's magnetic field is emitted by the earth, the geomagnetic fingerprint signal can be received anywhere on the earth. Therefore, the solution provided by this application can be used without going through Specific indoor parking lots covered by WIFI can also obtain the vehicle 101 parking location.
  • the encapsulated sensor data mentioned above may include multiple geomagnetic fingerprint signals within the time interval N.
  • the time interval N may contain M geomagnetic fingerprint signals.
  • the electronic device 100 After the electronic device 100 extracts the geomagnetic fingerprint signal, it needs to obtain the location corresponding to the geomagnetic fingerprint signal through a deep learning model.
  • the following is an introduction to the deep learning model for obtaining the position of vehicle 101.
  • Figure 8A introduces the modeling system of the deep learning model for obtaining the position of the vehicle 101 provided by the embodiment of the present application.
  • FIG. 8A shows a modeling system for obtaining a deep learning model of the position of the vehicle 101 provided by the embodiment of the present application.
  • the modeling system may include an electronic device 100, a collection vehicle 801, and a building 802, where the collection vehicle 801 may be equipped with the electronic device 100.
  • Building 802 may be a double row spiral ramp as shown in Figure 8A
  • Indoor parking lots, the ramp types of indoor parking lots can include but are not limited to: straight ramp type (such as external straight ramp type, internal straight ramp type), split-level type (such as two-stage split-level type, three-stage type) Split-level), spiral ramp type (such as single spiral ramp, double spiral ramp, jump-floor spiral ramp), inclined floor type.
  • the electronic device 100 can collect the geomagnetic fingerprint signal in the building 802 through a magnetic sensor, and the collection vehicle 801 can obtain the precise location of the collection vehicle 801 by using high-precision instruments.
  • Location information in building 802. there is no limitation on the way in which the collection vehicle 801 obtains location information.
  • the collection range of the geomagnetic fingerprint signal collected by the electronic device 100 may include but is not limited to the flat floors 803 and all ramps 804 in the building 802 .
  • the geomagnetic fingerprint signals that the electronic device 100 can collect have a one-to-one location corresponding to them.
  • the electronic device 100 cannot directly obtain the location information that matches the geomagnetic fingerprint information.
  • the electronic device 100 needs to 100 completes the modeling process of the deep learning model by training and processing a large number of geomagnetic fingerprint signals and their corresponding positions, and obtaining the contact information (such as weights) between input and output.
  • Figure 8B introduces a schematic diagram of the training method for obtaining the weight of the deep learning model of the position of the vehicle 101 provided by the embodiment of the present application.
  • FIG. 8B shows a training method for a deep learning model for obtaining the position of the vehicle 101 provided by the embodiment of the present application.
  • the geomagnetic fingerprint signal sequence collected by the electronic device 100 can be used as the input source of the deep learning model, and the location and floor information obtained by the collection vehicle 801 through high-precision instruments can be used as label data of the deep learning model.
  • the weight between the input source and the label data is obtained, where there is a one-to-one correspondence between the input source and the label data. , that is, one input source corresponds to one label data.
  • the deep learning model used to obtain the position of the vehicle 101 in the embodiment of the present application can be constructed by training and learning the input source and label data.
  • the geomagnetic fingerprint signal sequences collected by the electronic device 100 have corresponding location and floor information. It can be understood that through the training process of collecting geomagnetic fingerprint signals by the electronic device 100 and obtaining the corresponding position by the collection vehicle 801, a deep learning model for any scene or location (such as a specific indoor parking lot) can be established.
  • the weight of the deep learning model corresponding to a specific indoor parking lot describes the relationship between the geomagnetic fingerprint signal sequence and its corresponding location and floor information.
  • the electronic device 100 can determine a location and floor information based on a geomagnetic fingerprint signal sequence. That is to say, after acquiring the geomagnetic fingerprint signal, the electronic device 100 can simultaneously acquire the location and floor. information instead of obtaining floor information first and then location information.
  • the electronic device 100 can obtain the weight of the deep learning model of a specific indoor parking lot in different ways. For example, the electronic device 100 can directly download the specific indoor parking lot selected by the user in the application to obtain the corresponding deep learning model weight, or the electronic device 100 can directly store the specific indoor parking lot selected by the user locally. The weights of the deep learning model are obtained directly. In the embodiment of the present application, there is no limitation on the way in which the electronic device 100 obtains the weight of the deep learning model of a specific indoor parking lot.
  • the electronic device 100 inputs the multiple geomagnetic fingerprint signals obtained from the sensor data packaged within the time interval N into the deep learning model, wherein the deep learning model completes the training process. It can refer to the collection and modeling process of all geomagnetic fingerprint signals of a specific indoor parking lot and their corresponding location floors.
  • the deep learning model processes multiple geomagnetic fingerprint signals, it can output multiple initial geomagnetic matching positions.
  • Each initial geomagnetic matching position can include information such as location, floor, and confidence. All initial geomagnetic matching positions are Relevant information and quantity will affect the confidence level of each geomagnetic initial matching position.
  • the continuous path of multiple geomagnetic initial matching positions obtained by the electronic device 100 by inputting the above-mentioned multiple geomagnetic fingerprint signals into the deep learning model may be called a first driving path.
  • the electronic device 100 uses the acceleration sensor data and the gyroscope sensor data in the packaged sensor data to assist the inertial navigation technology, and selects the geomagnetic fingerprint signal that satisfies the preset conditions from the geomagnetic fingerprint signal based on the initial geomagnetic matching position.
  • the acceleration and speed data of the electronic device 100 obtained by the acceleration sensor can determine whether the vehicle 101 is in a deceleration state or uphill or downhill.
  • the angular velocity on the X, Y and Z axes of the electronic device 100 obtained by the gyroscope sensor can determine whether the vehicle 101 Is it in a turning state?
  • the electronic device 100 can combine the above data to comprehensively determine whether the vehicle 101 has reversed or parked, or other actions that would generate repeated geomagnetic fingerprint signals.
  • the M geomagnetic fingerprint signals may contain repeated geomagnetic fingerprint signals in some possible cases.
  • the electronic device 100 screens the geomagnetic fingerprint signals based on the initial geomagnetic matching position to obtain the geomagnetic fingerprint signals that meet the preset conditions, which may be as follows:
  • the electronic device 100 filters out repeated position data from the initial geomagnetic matching positions, and then filters out the geomagnetic fingerprint signals corresponding to the repeated positions; and/or
  • the electronic device 100 filters out the location data that are very close to each other in the initial geomagnetic matching locations, and then filters out the geomagnetic fingerprint signals corresponding to the very close locations; and so on.
  • the electronic device 100 can perform extraction processing on the M geomagnetic fingerprint signals.
  • the extraction method can, for example, perform forward and backward filtering optimization processing on the M geomagnetic fingerprint signals.
  • the purpose is to eliminate repeated interference data. For example, repeated or static data generated due to reversing or temporary parking, etc.
  • the generation method of interference data is not limited here.
  • the preset conditions may include but are not limited to: the multiple geomagnetic initial matching positions corresponding to the multiple geomagnetic fingerprint signals do not contain repeated position points.
  • the geomagnetic fingerprint signals that meet the preset conditions can be extracted and obtained as geomagnetic characteristic data.
  • the electronic device 100 obtains M1 geomagnetic feature data by extracting M geomagnetic fingerprint signals, where M1 is less than or equal to M.
  • the M1 geomagnetic feature data may essentially refer to a set of correct geomagnetic fingerprint sequences.
  • the non-overlapping paths that the vehicle 101 traveled within the time interval N can be output after being processed by a deep learning model.
  • the specific form of geomagnetic characteristic data can also be expressed as (m x , my y , m z ). Among them, the embodiments of the present application do not limit the specific form of the geomagnetic characteristic data.
  • FIG. 9A introduces a schematic diagram of a specific process of parking the vehicle 101 provided in the embodiment of the present application.
  • the vehicle 101 is parked in an indoor parking lot 900 , which includes a total of 10 parking spaces from parking spaces 901 to 910 . Except for parking space 908 , the remaining parking spaces have already been parked by vehicles 101 .
  • the vehicle 101 completes entering the indoor parking lot 900 from the left in the time interval N and parking the vehicle 101 in the parking space 908 .
  • the driving path of the vehicle 101 includes the dotted line 920, the thickened solid line 921 and the bolded point 922 shown in Figure 9A.
  • the vehicle 101 has only traveled on the path of the dotted line 920 once, that is, the electronic device 100 has only obtained the dotted line once.
  • the geomagnetic fingerprint signal on the path 920, the geomagnetic fingerprint signal on the dotted line 920 recorded in the electronic device 100 is not repeated; the vehicle 101 has traveled twice on the path of the thick solid line 921, that is, the electronic device 100 has obtained it twice.
  • the geomagnetic fingerprint signal on the path of the thick solid line 921 and the geomagnetic fingerprint signal on the path of the thick solid line 921 recorded in the electronic device 100 are repeated; the vehicle 101 temporarily stopped at the bold point 922, that is, the electronic device 100 may have obtained the same geomagnetic fingerprint signal multiple times at the bold point 922, and the geomagnetic fingerprint signals recorded in the electronic device 100 on the path of the bold point 922 are repeated.
  • the electronic device 100 inputs multiple geomagnetic fingerprint signals obtained between the vehicle 101 entering from the left side of the indoor parking lot 900 and completing parking into the deep learning model, and obtains multiple initial geomagnetic matching positions.
  • the electronic device 100 determines the driving status of the vehicle 101 based on the inertial navigation system and combines the data obtained by the acceleration sensor and the gyroscope sensor, and selects a preset geomagnetic initial matching position from multiple geomagnetic initial matching positions.
  • the preset condition It may include but is not limited to: the floor information or position points of the position information corresponding to any two geomagnetic fingerprint signals are different; or the distance between any two initial geomagnetic matching positions is greater than the threshold; or the vehicle 101 is at the initial geomagnetic matching position.
  • the driving state is reverse driving and other conditions.
  • the threshold that limits the distance between two geomagnetic initial matching positions in the above preset conditions may be called a second threshold.
  • the above-mentioned preset condition for filtering from multiple geomagnetic fingerprint signals may be called a second preset condition.
  • the geomagnetic initial matching position acquired by the electronic device 100 includes all position points on the dotted line 920, the bold solid line 921, and the bold point 922, where the geomagnetic field on the bold point 922 and the bold solid line 921
  • the initial matching position does not meet the preset conditions, that is, the geomagnetic fingerprint signals obtained by the electronic device 100 on the bold point 922 and the bold solid line 921 are repeated, and the repeated geomagnetic fingerprint signals will affect the determination of the final predicted vehicle in S707 101 location.
  • the above multiple geomagnetic fingerprint signals are extracted, that is, all duplicate geomagnetic fingerprint signals are deleted, and the geomagnetic fingerprint signals without duplicates are retained, that is, the geomagnetic fingerprint signals obtained by the electronic device 100 on the dotted line 920, and no duplicate geomagnetic fingerprint signals can be It is called geomagnetic feature data, and the geomagnetic feature data can be arranged into a set of geomagnetic fingerprint sequences according to the time sequence obtained by the geomagnetic sensor.
  • the electronic device 100 inputs the extracted geomagnetic feature data into the deep learning model to obtain the geomagnetic candidate matching position.
  • the M1 geomagnetic feature data obtained after extraction can be used as an input source for deep learning.
  • the electronic device 100 can process the weights in the deep learning model to obtain M1 geomagnetic candidate matching positions, where each The geomagnetic candidate matching positions may include information such as location, floor, and confidence level, and the M1 geomagnetic candidate matching positions do not overlap each other, and can be arranged into a geomagnetic candidate matching position sequence in chronological order.
  • the continuous path of multiple geomagnetic candidate matching locations (ie, the sequence of geomagnetic candidate matching locations) obtained by the electronic device 100 by inputting the above multiple geomagnetic feature data into the deep learning model may be called a second driving path. .
  • FIG. 9B introduces a specific process of parking the vehicle 101 provided in the embodiment of the present application.
  • the vehicle 101 is parked in the indoor parking lot 900, which includes a total of 10 parking spaces from parking spaces 901 to 910. Except for parking space 908, the other parking spaces have already been parked by vehicles 101.
  • the dotted line 930 shown in FIG. 9B is the geomagnetic candidate matching position corresponding to the geomagnetic feature data after the geomagnetic fingerprint signal obtained by the electronic device 100 is extracted.
  • the geomagnetic feature data does not contain repeated data.
  • the geomagnetic candidate matching position is more accurate than the geomagnetic initial matching position, and repeated positions in the geomagnetic initial matching position are eliminated, eliminating some influencing factors for calculating the confidence and weight of the matching position in S707.
  • filtering the initial matching locations in steps S705 and S706, and inputting the filtered geomagnetic fingerprint signals into the deep learning model again is an optional step.
  • the electronic device 100 can directly use the initial matching location as a candidate matching location and directly perform location and floor decisions in the following steps.
  • the electronic device 100 sends the geomagnetic candidate matching location to the location and floor decision-making module to obtain the location, floor and confidence level where the vehicle 101 is parked.
  • the electronic device 100 filters and sorts the M1 geomagnetic candidate matching locations in the location and floor decision-making module, Then the weight of the high-priority candidate points after screening the M1 geomagnetic candidate matching positions is calculated to determine the final predicted position of vehicle 101.
  • Figure 10A introduces a schematic diagram of the classification and screening of geomagnetic candidate matching positions provided by the embodiment of the present application.
  • FIG. 10A shows an example diagram of classification and screening of geomagnetic candidate matching locations provided by embodiments of the present application.
  • the abscissa represents time, where the parking time T of the vehicle 101 is the instantaneous time when the electronic device 100 detects that the vehicle 101 has parked the vehicle 101. Since the sensor records the sensor data of the electronic device 100 during the N period of time before the vehicle 101 is parked, and the abscissa is shown in FIG. 10A starting from time 0, so the time T can be N, and the (T-W) time is when the vehicle 101 is parked.
  • the ordinate represents the confidence or error of the geomagnetic candidate matching position.
  • a confidence threshold 1001 is set.
  • the geomagnetic candidate matching position with a confidence higher than the confidence threshold 1001 can be classified as an unreliable prediction point.
  • Candidate matching positions whose confidence is lower than the confidence threshold 1001 can be classified as reliable prediction points.
  • reliable prediction points within the time interval W (the shaded portion as shown in FIG. 10A ) between time (T-W) and the time T when the vehicle 101 is parked can be filtered as high-priority candidate points.
  • the confidence level of the geomagnetic initial matching position corresponding to the same geomagnetic fingerprint signal and the confidence level of the geomagnetic candidate matching position corresponding to the same geomagnetic fingerprint signal may be different, and the confidence level of each position point is determined by this application.
  • the algorithm in the example is calculated by itself. In the embodiment of the present application, there is no limitation on the calculation method of the confidence level of the location point.
  • Figure 10B introduces the flow chart of the method for obtaining the final predicted position of the vehicle 101 provided by the embodiment of the present application.
  • FIG. 10B shows a flowchart of a method for obtaining the final predicted position of the vehicle 101 provided by the embodiment of the present application. As shown in Figure 10B, specific methods include:
  • the electronic device 100 obtains multiple geomagnetic candidate matching positions within the time interval N.
  • the electronic device 100 obtains the geomagnetic candidate matching positions obtained by processing the geomagnetic feature data using the deep learning model. And the multiple geomagnetic candidate matching positions are displayed as an example diagram of classification and screening as shown in FIG. 10A.
  • the electronic device 100 uses the method of classifying and screening geomagnetic candidate matching positions to obtain reliable points that are smaller than the confidence threshold.
  • the electronic device 100 selects a reliable prediction point that is smaller than the confidence threshold 1001.
  • the confidence threshold 1001 is set by the relevant algorithm according to its own logic. In the embodiment of the present application, the confidence threshold 1001 is not limited.
  • the electronic device 100 selects high-priority candidate points within a period of time W before the vehicle 101 completes the parking action.
  • the electronic device 100 selects all geomagnetic candidate matching positions within the shaded range and calls them high priority candidate points. Since there will be errors when the electronic device 100 processes the data taken by the sensor, a time interval W is set, and the final position of the vehicle 101 is expanded to the geomagnetic candidate matching position in the time interval W, instead of just the parking time T of the vehicle 101 The current position is determined as the final vehicle 101 position. At the same time, the electronic device 100 records the time difference between each high-priority candidate point and the parking time T of the vehicle 101. The larger the time difference, the closer it is to the parking time T of the vehicle 101.
  • the electronic device 100 performs weighting processing on the high priority candidate points, and selects the high priority candidate point with the highest weight as the final predicted parking position of the vehicle 101.
  • the electronic device 100 performs related processing operations on multiple high-priority candidate points to determine the final parked location of the vehicle 101 , where the parked location of the vehicle 101 includes location, floor, and confidence information.
  • Formula (1) calculates the weight of all high-priority candidate points. Among them, Weight of TOP Prediction n in formula (1) represents the weight of each high-priority candidate point;
  • Uncertainty n in formula (1) refers to the confidence.
  • T n in formula (1) refers to time.
  • T n the time of the predicted position of the high-priority candidate point is to the time when vehicle 101 completes parking. Therefore, when selecting a high-priority candidate point, the weight adjustment factor Scale2 is multiplied by the time T n , where the weight adjustment factor Scale2 is set according to the logic of the algorithm. In the embodiment of the present application, no changes are made to the size of the weight adjustment factor Scale2. limit;
  • Distance(n-1,n+1) in formula (1) refers to the distance between the previous and next predicted position points. The greater the distance between the previous high priority candidate point and the next high priority candidate point, the greater the distance between the previous high priority candidate point and the next high priority candidate point. The reliability of high-priority candidate points is lower. Therefore, based on the continuity principle of trajectory, the weighting factor ScaleX is multiplied by Distance(n-1,n+1) in formula (1) for weighting high-priority candidate points. The inverse of Distance(n-1,n+1) is greater than the threshold TH k , then the weight adjustment factor ScaleX is set to Scale4.
  • TH k can be set according to the logic of the algorithm, and the size of TH k is not limited.
  • Formula (2) selects the high priority candidate point with the highest weight from all high priority candidate points, and uses the high priority candidate point with the highest weight as the final predicted vehicle 101 position.
  • W represents the weight of all high candidate points calculated through formula (1)
  • MAX W (TOP Prediction n )) represents the selection of the largest weight among multiple weights
  • Estimated Location represents The specific location of the high-priority candidate point with the largest weight is used as the final vehicle 101 location.
  • Estimated Location uncertainty Uncertainty of MAX(W(TOP Prediction n )) (3)
  • Formula (3) is to obtain the confidence of the high-priority candidate point with the highest weight.
  • Estimated Location uncertainty represents the accuracy of the final predicted vehicle 101 position;
  • Uncertainty of MAX(W(TOP Prediction n )) represents the selection of the high-priority candidate point with the largest weight, and finds the weight according to Figure 10A
  • the electronic device 100 may select one or more high-priority candidate points whose confidence is greater than a threshold as the final prediction range for parking of the vehicle 101 .
  • the threshold that limits the confidence of high priority candidate points may be referred to as a third threshold.
  • the electronic device 100 obtains the floor with the highest frequency of occurrence among multiple high priority candidate points.
  • Estimated Floor MODE(Floor -m ,...,Floor m )in TOP Prediction(s) (4)
  • Formula (4) is to calculate the mode of the floor where the high priority candidate point is located.
  • Estimated Floor represents the floor where vehicle 101 is finally predicted to be parked;
  • MODE(X) represents the statistical calculation of the number of occurrences of X;
  • Floor -m represents the location of all high-priority candidate points. For all floors, the number of floors can appear repeatedly; in TOP Prediction(s) represents multiple high-priority candidate points to perform the operation of formula (4).
  • Estimated Floor uncertainty MAX(Probability(Floor -m ,...,Floor m ))in TOP Prediction(s) (5)
  • Formula (5) is a probability calculation for the floor where the vehicle 101 is finally parked, that is, the probability of the number of occurrences of the finally predicted floor where the vehicle 101 is parked accounts for the total number of occurrences of the floors of all high-priority candidate points.
  • Estimated Floor uncertainty represents the accuracy of the final prediction of the floor where vehicle 101 is parked;
  • (Probability(Floor -m ,...,Floor m ) represents the final floor where vehicle 101 is parked at all high-priority candidate points. The probability;
  • the electronic device 100 weights the high-priority candidate points located on the floor with the highest frequency of occurrence, and selects the high-priority candidate point with the highest weight as the final predicted parking location of the vehicle 101.
  • the way in which the electronic device 100 weights multiple high-priority candidate points located on the floor with the highest frequency of occurrence can refer to the above formula (1), which will not be described in detail here, where Weight of TOP Prediction n means that it is located on the floor with the highest frequency of occurrence.
  • Weight of multiple high-priority candidate points on the highest floor can refer to the above formula (1), which will not be described in detail here, where Weight of TOP Prediction n means that it is located on the floor with the highest frequency of occurrence.
  • the weight of multiple high-priority candidate points on the highest floor can refer to the above formula (1), which will not be described in detail here, where Weight of TOP Prediction n means that it is located on the floor with the highest frequency of occurrence.
  • the weight of multiple high-priority candidate points on the highest floor can refer to the above formula (1), which will not be described in detail here, where Weight of TOP Prediction n means that it is located on the floor with the highest frequency of occurrence.
  • the electronic device 100 can first determine the floor where the final vehicle 101 is parked based on multiple high-priority candidate points, and then select the high-priority candidate point with the largest weight from the high-priority candidate points located on the floor as the final parking location for the vehicle 101
  • the location, and the high priority candidate point whose confidence level is greater than the threshold is used as the prediction range of the final location where the vehicle 101 is parked.
  • the threshold that limits the confidence of the high-priority candidate point located on the floor where the final vehicle 101 is parked may be referred to as a fourth threshold.
  • the electronic device 100 can obtain the final predicted vehicle 101 position through high-priority candidate points by executing S1004a, by executing S1004b and S1005b, or by using two methods at the same time to obtain the final predicted vehicle 101 position. .
  • the electronic device 100 can select the intersection of the predicted ranges of the parked position of the vehicle 101 obtained by the two methods as the final result.
  • the union of the prediction ranges of the parking position of the vehicle 101 obtained in two ways can also be selected as the final prediction range of the parking position of the vehicle 101. In the embodiment of the present application, this is not limited.
  • the electronic device 100 executes S1004a and obtains that the floor where the vehicle 101 is located is the negative two floor.
  • the electronic device 100 performs S1004b and S1005b and obtains that the floor where the vehicle 101 is located is the negative third floor.
  • the user can choose to first go to the electronic device 100 in the application.
  • Vehicle 101 displayed on the program is parked at the indicated location on the second floor below for viewing. In vehicle 101 do not park in negative
  • the user can perform corresponding operations (such as click operations) on the electronic device 100 to cause the electronic device 100 to display in the application the indicated location where the vehicle 101 is parked on the third floor. In the embodiment of the present application, this is not limited.
  • Figure 11 is a schematic diagram of a simulation of an output point at a specific location in an indoor parking lot in an embodiment of the present application.
  • FIG. 11 exemplarily shows a simulation diagram of output points at all locations in a specific indoor parking lot.
  • the electronic device 100 simulates the relevant positions of a specific flat floor of a specific indoor parking lot.
  • the flat floor may include but is not limited to driveways, flat parking spaces, and ramp parking. bits and so on.
  • the location simulation diagram shown in Figure 11 contains three types of location information: collected location points, actual driving points, and predicted driving points.
  • the collection location points refer to multiple collection location points obtained after comprehensive collection of a specific indoor parking lot by the collection vehicle 801 equipped with the electronic device 100.
  • the collection location points can be represented by small light gray dots as shown in Figure 11 to indicate that its range can cover all locations that the vehicle 101 can travel to on a flat floor of a specific indoor parking lot;
  • the actual driving point refers to the actual driving position of the vehicle 101 driven by the user in a specific indoor parking lot within the time interval N.
  • the actual driving point can be represented by a large dark gray dot as shown in Figure 11.
  • Figure 11 Also shows the starting point and end point of the actual driving path of the vehicle 101 in the time interval N;
  • the predicted driving point refers to the geomagnetic candidate matching position obtained by inputting the geomagnetic feature data extracted from the geomagnetic fingerprint signal in the packaged sensor data into the deep learning model within the time interval N.
  • the predicted driving point Points can be represented by large light gray dots as shown in Figure 11, which also shows the starting point and end point of the predicted driving path of the vehicle 101 within the time interval N.
  • Figure 12 is a schematic block diagram of the software structure of the electronic device 100 in the embodiment of the present application.
  • the function of the electronic device 100 to obtain the location of the vehicle 101 can be implemented, where the location can include but is not limited to floor planes, floor passages, near floor entrances, and ramps between floors.
  • the software architecture of the vehicle 101 parking record acquisition method includes a vehicle 101 parking detection module, a sensor data collection module, a deep learning model, and a location and floor decision-making module.
  • the vehicle 101 parking detection module is used to detect whether the vehicle 101 has completed the parking action.
  • the electronic device 100 may determine that the parking of the vehicle 101 is completed in a manner including, but not limited to, Bluetooth disconnection, completion of the reversing action, completion of the automatic parking mode, etc.
  • the sensor data acquisition module is used to obtain sensor data and package the sensor data.
  • the sensor data may include, but is not limited to, data collected by acceleration sensors, gyroscope sensors, and geomagnetic sensors.
  • the deep learning module can be directly used to predict the geomagnetic fingerprint signal to obtain the initial geomagnetic matching position. It can also be used to predict the geomagnetic fingerprint sequence sorted by time to obtain multiple geomagnetic candidate matching positions, that is, the vehicle 101 position. prediction point.
  • the electronic device 100 uses inertial navigation technology to extract geomagnetic fingerprint signals to obtain geomagnetic feature data, and obtains a time-ordered geomagnetic fingerprint sequence from the extracted geomagnetic feature data.
  • the location and floor decision-making module is used to determine the geomagnetic candidate matching locations and select the record closest to where the vehicle 101 is actually parked.
  • the record includes but is not limited to the location, floor and confidence level where the vehicle 101 is parked. The method of judging and selecting multiple prediction points for the position of the vehicle 101 is described in detail above and will not be described in detail here.
  • the software structure of the electronic device 100 shown in Figure 12 is only an example.
  • the electronic device 100 may have more or fewer modules than shown in the figure, and two or more modules may be combined. Or can have different modules.
  • the various modules shown in the figures may be implemented in hardware, software, or a combination of hardware and software including one or more signal processing and/or application specific integrated circuits.
  • the following is a schematic diagram of the user interface of the method for obtaining the location of the vehicle 101 provided by the embodiment of the present application.
  • FIG. 13A illustrates an exemplary graphical user interface 131 for installing and running a parking application on the electronic device 100.
  • the user interface 131 displays: a "record parking” control 1301, a "display GNSS positioning” control 1302, location prompt information 1303, and a map display area 1304.
  • the user interface 131 may be the home page interface of the parking application.
  • the "Record Parking" control 1301 is used to monitor user operations, and the electronic device 100 can turn on the "Record Parking” function in response to the user operation, that is, the electronic device 100 can start to select and identify specific controls for the user to park the vehicle 101.
  • the garage when the vehicle 101 enters a specific indoor parking lot and completes parking, the location of the vehicle 101 is recorded.
  • the control 1302 of "Display GNSS positioning" is used to monitor user operations.
  • the electronic device 100 can display the GNSS positioning of the vehicle 101 in response to the user operation, and display location prompt information 1303 in the user interface 131.
  • the location prompt information 1303 is used to prompt the user of the vehicle 101. A specific location outdoors.
  • the map display area 1304 is used to provide reliable support for the display of the location prompt information 1303, and in the user interface 131, the map display area 1304 is an outdoor map centered on the location prompt information 1303.
  • the electronic device 100 can also enable the "Record Parking" function through other methods.
  • the electronic device 100 can also enable the "record parking” function by default, or the electronic device 100 can also enable the “record parking” function in response to a received voice command, or the electronic device 100 can use motion detection to (Motion detection technology) After the vehicle 101 is in the “driving" state, it automatically turns on the “record parking” function and so on.
  • motion detection Motion detection technology
  • motion detection may mean that the images collected by the camera at different frame rates will be calculated and compared by the electronic device 100 according to a certain algorithm.
  • the calculation and comparison results may exceed the threshold and may instruct the system to automatically perform corresponding processing, for example, determining that the vehicle 101 is in a "driving" state.
  • the principle of motion detection is not limited, and there may be other methods other than using cameras.
  • Geofence refers to when the electronic device 100 enters or leaves.
  • Geofence may refer to an area within a certain range near the garage of a specific building.
  • Figure 13B illustrates an exemplary graphical user interface 132 with a parking application installed and running on the electronic device 100.
  • the user interface 132 displays a "display GNSS positioning" control 1302, a "get location” control 1305, and a map display area 1306.
  • the electronic device 100 will change from displaying the user interface 131 to displaying the user interface 132.
  • the "get location” control 1305 is used to monitor user operations.
  • the electronic device 100 can obtain the vehicle 101 location in response to the user operation, that is, obtain the vehicle 101 location through the above-mentioned vehicle positioning algorithm.
  • the map display area 1306 is used to display a map of a specific indoor parking lot where the vehicle 101 is currently parked. Furthermore, the map displays a plan map of the floor where the vehicle 101 is parked.
  • the electronic device 100 can also obtain the location of the vehicle 101 through other methods.
  • the electronic device 100 can also turn on the "get location” function by default after displaying the user interface 132, or the electronic device 100 can also turn on the "get location” function in response to a received voice command, or the electronic device 100 After detecting that the vehicle 101 is in a "reversing" or “parking” state through motion, the "obtain location” function can be automatically turned on, and so on. For another example, when the electronic device 100 is disconnected from the Bluetooth of the vehicle 101, the electronic device 100 automatically turns on the "obtain location” function.
  • Figure 13C illustrates an exemplary graphical user interface 133 with a parking application installed and running on the electronic device 100.
  • the user interface 133 displays floor prompt information 1307 and location and range prompt information 1308 .
  • the floor prompt information 1307 is used to prompt the user on the floor where the current vehicle 101 is parked.
  • the current parking floor of vehicle 101 is -3 floors.
  • the location and range prompt information 1308 is used to prompt the user of the current location and range of the vehicle 101 . Further, the gray dots in the position and range prompt information 1308 shown in Figure 13C represent the predicted location point where the vehicle 101 is parked, and the black circular range in the position and range prompt information 1308 represents the location of the vehicle 101.
  • the prediction range includes the set range of high-priority candidate points with confidence greater than the threshold.
  • the electronic device 100 can also display the parking space number, the area to which the parking space belongs, and other other information in the user interface 133. Information indicating where the vehicle 101 is parked in a specific indoor parking lot.
  • the term “when” may be interpreted to mean “if" or “after” or “in response to determining" or “in response to detecting" depending on the context.
  • the phrase “when determining" or “if (stated condition or event) is detected” may be interpreted to mean “if it is determined" or “in response to determining" or “on detecting (stated condition or event)” or “in response to detecting (stated condition or event)”.
  • the computer program product includes one or more computer instructions.
  • the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable device.
  • the computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted over a wired connection from a website, computer, server, or data center (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.) means to transmit to another website, computer, server or data center.
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center integrated with one or more available media.
  • the available media may be magnetic media (eg, floppy disk, hard disk, tape), optical media (eg, DVD), or semiconductor media (eg, solid state drive), etc.

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Abstract

本申请提供了一种车辆位置的获取方法及电子设备，该方法包括：电子设备在执行定位操作过程中，通过将所记录到的地磁特征数据输入至深度学习的模型中，从深度学习模型中输出多个地磁候选匹配位置，再结合多个地磁候选匹配位置的置信度以及权重来判断获取车辆停放的最终位置与楼层信息。本申请技术方案可以减少判断车辆位置的时间，提高判断车辆位置的准确性，还可以获取到跨楼层的车辆位置信息。

Description

车辆位置的获取方法及电子设备
本申请要求于2022年03月26日提交中国专利局、申请号为202210304573.8、申请名称为“车辆位置的获取方法及电子设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及定位技术领域，尤其涉及车辆位置的获取方法及电子设备。
背景技术
随着定位技术的发展，目前获取室内车辆位置的方法大多是先在室外通过全球导航卫星系统(Global Navigation Satellite System，GNSS)卫星系统定位，并结合利用传感器的惯性导航辅助定位。但是获取到的车辆位置时常有较大的误差且计算车辆位置所用的时间较长，同时也无法获取在楼层之间车辆的位置。如何精确且快速的获取室内车辆位置(包括跨楼层的位置)，是本领域值得研究的方向。
发明内容
本申请实施例提供了车辆位置的获取方法及电子设备。电子设备在执行定位操作过程中，将所记录到经过萃取处理后的地磁特征数据输入至深度学习的模型中，从深度学习模型中输出多个地磁候选匹配位置，再结合多个地磁候选匹配位置的置信度以及权重来判断获取车辆停放的最终位置与楼层信息。本申请技术方案可以减少判断车辆位置的时间，提高判断车辆位置的准确性，还可以判断跨楼层的车辆位置信息。
第一方面，本申请实施例提供了一种车辆位置的获取方法，该方法应用于电子设备，其特征在于，该方法包括：
电子设备确定车辆的地理位置在预设区域之内后，取得地磁指纹信号序列；该地磁指纹信号序列包括N个地磁指纹信号，N为正整数；电子设备根据该地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，该目标位置信息指示了该车辆所在的楼层以及于该楼层的位置；当该车辆位于两楼层之间时，该目标位置信息指示了该车辆所在于哪两楼层之间以及于该两楼层之间的位置。
实施第一方面提供的方法，可以实现同步获取到车辆所在位置与楼层的功能，从而更加快速的获取到车辆在室内的停放位置。由于深度学习模型的训练数据具包含有室内停车场内所有车辆能抵达的位置和与之对应的地磁指纹信号，因此透过训练后的深度学习模型可以获取到车辆除停放在单一平面楼层之外的跨楼层位置，使得用户能够更加广泛的获取到车辆的位置。此外，当车辆由楼层之间进入某一楼层时，也可以快速地获取在该楼层的所在位置。
结合第一方面，在一些实施例中，电子设备确定车辆的地理位置在预设区域之内，可以具体包括：电子设备基于定位技术识别到该车辆的地理位置在特定区域之内；或者，电子设备接收到服务器发送的指令，该指令为服务器检测到该车辆的地理位置在特定区域之内后，向该电子设备发出的；或者，电子设备检测到来自于预设区域内的信号源发送的信号，确定该车辆的地理位置在预设区域之内。可以理解的是，预设区域可以是特定室内停车场所在的 地理位置。这样能够比较准确的识别到车辆进入到特定室内停车场，电子设备可以开始记录传感器数据，为获取车辆位置提供数据支持。在一些实施例中，此定位操作可以在确定停车后进行以获得停车位置。在相同或其他实施例中，此定位操作可以在车辆行进过程中，实时判断车辆当下的位置。
结合第一方面，在一些实施例中，电子设备根据地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，包括：当电子设备检测到车辆处于行驶状态，电子设备根据地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息。这里描述了电子设备可以在车辆行进过程中，实时判断车辆当下的位置。
结合第一方面，在一些实施例中，电子设备根据地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，包括：当电子设备检测到车辆处于停车状态，电子设备根据地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息。这里描述了电子设备可以在车辆停车后，判断车辆当下的停放位置。
结合第一方面，在一些实施例中，电子设备根据该地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，之前具体还包括：电子设备检测到该电子设备或车辆满足第一预设条件，确定该车辆处于停车状态。这样，电子设备可以通过预设条件比较准确的判断出车辆完成停放动作的具体时间，并记录截止到具体时间的传感器数据，更加精准有效的判断车辆位置。
结合第一方面，在一些实施例中，第一预设条件包括以下一项或多项：电子设备和车辆从蓝牙连接状态转变为蓝牙断开状态；或者，电子设备接收到启动停车模式的用户指令；或者，电子设备检测到该车辆的行驶速度小于第一阈值且行驶方向发生变化。这样，电子设备可以通过上述判断方法较为精确的检测车辆是否已经完成了停车动作，并可以开始进行对车辆位置的计算与估计。
结合第一方面，在一些实施例中，电子设备根据地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，之前还包括：该电子设备获取该深度学习模型的权重，该深度学习模型的权重是以多个地磁指纹信号序列和对应的位置信息训练得到的，该多个地磁指纹信号序列中每个地磁指纹信号序列对应一个位置信息，该多个地磁指纹信号序列中包括从两个楼层之间采集到的地磁指纹信号序列，该位置信息中包括两个楼层之间的楼层信息和位置。这样，电子设备通过深度学习模型可以直接得到车辆的位置信息，其中，车辆的位置信息可以包括单一平面楼层信息和跨楼层信息，使得电子设备能够更加准确快速的获取车辆信息，同时还能够获取到不限于单一平面楼层的更为广泛的位置信息。
结合第一方面，在一些实施例中，电子设备根据地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，具体包括：该电子设备通过预先训练好的深度学习模型，确定该地磁指纹信号序列对应的第一行驶轨迹，该第一行驶轨迹包括M个位置信息，M小于或等于N；电子设备基于该第一行驶轨迹，在该地磁指纹信号序列中筛选得到满足第二预设条件的地磁指纹信号；电子设备通过该深度学习模型，确定满足第二预设条件的地磁指纹信号对应的第二行驶轨迹，该第二行驶轨迹包括P个位置信息，P小于或等于M；电子设备从所述第二行驶轨迹中确定目标位置信息。
这样，电子设备通过将满足第二预设条件的位置信息对应的地磁指纹信号筛选出来，经过深度学习模型再次获取到第二行驶轨迹，可以更加准确的判断出车辆的所在位置，提高判断效率。
结合第一方面，在一些实施例中，该第二预设条件，具体包括：
任意两个地磁指纹信号对应的位置信息的楼层信息或位置点不同；或者，任意两个地磁指纹信号对应的位置信息的位置点之间的距离大于第二阈值。
这样，电子设备能够准确的判断出需要进行过滤的地磁指纹信号，能够使得过滤后的地磁信号指纹能够匹配到更加精准的车辆位置。
结合第一方面，在一些实施例中，第一行驶轨迹还包括M个位置信息中每个位置信息的置信度，第二行驶轨迹中包括P个位置信息和所述P个位置信息中每个位置信息的置信度，P小于或等于M；电子设备从该第二行驶轨迹中确定所述目标位置信息，包括：电子设备将所述P个位置信息中置信度高于第三阈值的一个或多个位置信息作为目标位置信息。这样，电子设备可以直接通过第二地磁指纹信号同时准确的判断出车辆的所在位置、楼层与范围，利用相关算法使得电子设备判断出来的车辆的位置信息更为精准。
结合第一方面，在一些实施例中，第一行驶轨迹还包括M个位置信息中每个位置信息的置信度，第二行驶轨迹中包括P个位置信息和所述P个位置信息中每个位置信息的置信度，P小于或等于M；电子设备从该第二行驶轨迹中确定所述目标位置信息，包括：电子设备确定P个位置信息中置信度高于第三阈值的一个或多个位置信息；电子设备将一个或多个位置信息中楼层信息的众数作为该目标位置信息中的停车楼层；电子设备确定具有该停车楼层并且置信度高于第四阈值的位置信息作为该目标位置信息。这样，电子设备可以通过优先判断出车辆所停放的楼层，再通过置信度等信息判断出车辆所在的具体位置与范围。
结合第一方面，在一些实施例中，电子设备从第一位置信息中确定目标位置信息，之后还可以包括：该电子设备输出该目标位置信息。这样，电子设备还可以将车辆最终停放位置通过应用程序具体的显示出来，包括停放楼层，位置以及范围，用户可以比较形象具体的获知车辆的位置，方便用户操作使用。
结合第一方面，在一些实施例中，电子设备输出所述目标位置信息，具体包括：电子设备输出车辆的停车楼层以及位置范围，位置范围中包括所述目标位置信息中的一个或多个位置信息对应的位置点。这样，用户可以通过地图直观的观察到车辆的位置信息，使得用户能够更加迅速的获取到车辆信息。
第二方面，本申请实施例提供了一种电子设备，该电子设备包括：一个或多个处理器和存储器；该存储器与该一个或多个处理器耦合，该存储器用于存储计算机程序代码，该计算机程序代码包括计算机指令，该一个或多个处理器调用该计算机指令以使得该电子设备执行。
附图说明
图1为本申请实施例提供的一种电子设备100的结构示意图。
图2为本申请实施例提供的一种系统架构10。
图3为本申请实施例提供的一种电子设备100室内位置确定方法的流程示意图。
图4为本申请实施例提供的一种地磁匹配楼层判定算法的流程示意图。
图5为本申请实施例提供的一种自动判别车辆101位置的方法的流程示意图。
图6为本申请实施例提供的一种自动判别车辆101位置的软件结构示意图。
图7为本申请实施例提供的一种获取车辆101位置的方法流程示意图。
图8A为本申请实施例提供的一种获取车辆101位置的深度学习模型的建模系统。
图8B为本申请实施例提供的一种获取车辆101位置的深度学习模型权重的训练方法的示意图。
图9A为本申请实施例提供的一种车辆101停放的具体过程的示意图。
图9B为本申请实施例提供的一种车辆101停放的具体过程的示意图。
图10A为本申请实施例提供的一种地磁候选匹配位置的分类与筛选的示意图。
图10B为本申请实施例提供的一种获取最终预测车辆101位置的方法流程图。
图11为本申请实施例提供的一种特定室内停车场中位置的输出点的仿真示意图。
图12为本申请实施例提供的一种电子设备100的软件结构示意框图。
图13A为本申请实施例提供的一种用户界面示意图。
图13B为本申请实施例提供的一种用户界面示意图。
图13C为本申请实施例提供的一种用户界面示意图。
具体实施方式
本申请以下实施例中所使用的术语只是为了描述特定实施例的目的，而并非旨在作为对本申请的限制。如在本申请的说明书和所附权利要求书中所使用的那样，单数表达形式“一个”、“一种”、“该”、“上述”、“该”和“这一”旨在也包括复数表达形式，除非其上下文中明确地有相反指示。还应当理解，本申请中使用的术语“和/或”是指并包含一个或多个所列出项目的任何或所有可能组合。
以下，术语“第一”、“第二”仅用于描述目的，而不能理解为暗示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括一个或者更多个该特征，在本申请实施例的描述中，除非另有说明，“多个”的含义是两个或两个以上。
本申请以下实施例中的术语“用户界面(user interface，UI)”，是应用程序或操作系统与用户之间进行交互和信息交换的介质接口，它实现信息的内部形式与用户可以接受形式之间的转换。用户界面是通过java、可扩展标记语言(extensible markup language，XML)等特定计算机语言编写的源代码，界面源代码在电子设备上经过解析，渲染，最终呈现为用户可以识别的内容。用户界面常用的表现形式是图形用户界面(graphic user interface，GUI)，是指采用图形方式显示的与计算机操作相关的用户界面。它可以是在电子设备的显示屏中显示的文本、图标、按钮、菜单、选项卡、文本框、对话框、状态栏、导航栏、Widget等可视的界面元素。
图1示出了电子设备100的结构示意图。
电子设备100可以是搭载iOS、Android、Microsoft或者其它操作系统的便携式终端设备，电子设备100可以是手机、平板电脑、桌面型计算机、膝上型计算机、手持计算机、笔记本电脑、超级移动个人计算机(ultra-mobile personal computer，UMPC)、上网本，以及蜂窝电话、个人数字助理(personal digital assistant，PDA)、增强现实(augmented reality，AR)设备、虚拟现实(virtual reality，VR)设备、人工智能(artificial intelligence,AI)设备、可穿戴式设备、车辆、车载设备、智能家居设备和/或智慧城市设备，不限于此，电子设备100还可以包括具有触敏表面或触控面板的膝上型计算机(laptop)、具有触敏表面或触控面板的台式计算机等非便携式终端设备等等。本申请实施例对该电子设备的具体类型不作特殊限制。
电子设备100可以包括处理器110，外部存储器接口120，内部存储器121，通用串行总线(universal serial bus，USB)接口130，充电管理模块140，电源管理模块141，电池142，天线1，天线2，移动通信模块150，无线通信模块160，音频模块170，扬声器170A，受话器170B，麦克风170C，耳机接口170D，传感器模块180，按键190，马达191，指示器192，摄像头193，显示屏194，以及用户标识模块(subscriber identification module，SIM)卡接口195 等。其中传感器模块180可以包括压力传感器180A，陀螺仪传感器180B，气压传感器180C，磁传感器180D，加速度传感器180E，距离传感器180F，接近光传感器180G，指纹传感器180H，温度传感器180J，触摸传感器180K，环境光传感器180L，骨传导传感器180M等。
可以理解的是，本发明实施例示意的结构并不构成对电子设备100的具体限定。在本申请另一些实施例中，电子设备100可以包括比图示更多或更少的部件，或者组合某些部件，或者拆分某些部件，或者不同的部件布置。图示的部件可以以硬件，软件或软件和硬件的组合实现。
处理器110可以包括一个或多个处理单元，例如：处理器110可以包括应用处理器(application processor，AP)，调制解调处理器，图形处理器(graphics processing unit，GPU)，图像信号处理器(image signal processor，ISP)，控制器，视频编解码器，数字信号处理器(digital signal processor，DSP)，基带处理器，和/或神经网络处理器(neural-network processing unit，NPU)等。其中，不同的处理单元可以是独立的器件，也可以集成在一个或多个处理器中。
处理器110中还可以设置存储器，用于存储指令和数据。在一些实施例中，处理器110中的存储器为高速缓冲存储器。该存储器可以保存处理器110刚用过或循环使用的指令或数据。如果处理器110需要再次使用该指令或数据，可从所述存储器中直接调用。避免了重复存取，减少了处理器110的等待时间，因而提高了系统的效率。
在一些实施例中，处理器110可以包括一个或多个接口。接口可以包括集成电路(inter-integrated circuit，I2C)接口，集成电路内置音频(inter-integrated circuit sound，I2S)接口，脉冲编码调制(pulse code modulation，PCM)接口，通用异步收发传输器(universal asynchronous receiver/transmitter，UART)接口，移动产业处理器接口(mobile industry processor interface，MIPI)，通用输入输出(general-purpose input/output，GPIO)接口，用户标识模块(subscriber identity module，SIM)接口，和/或通用串行总线(universal serial bus，USB)接口等。
I2C接口是一种双向同步串行总线，包括一根串行数据线(serial data line，SDA)和一根串行时钟线(derail clock line，SCL)。在一些实施例中，处理器110可以包含多组I2C总线。处理器110可以通过不同的I2C总线接口分别耦合触摸传感器180K，充电器，闪光灯，摄像头193等。例如：处理器110可以通过I2C接口耦合触摸传感器180K，使处理器110与触摸传感器180K通过I2C总线接口通信，实现电子设备100的触摸功能。
I2S接口可以用于音频通信。在一些实施例中，处理器110可以包含多组I2S总线。处理器110可以通过I2S总线与音频模块170耦合，实现处理器110与音频模块170之间的通信。在一些实施例中，音频模块170可以通过I2S接口向无线通信模块160传递音频信号，实现通过蓝牙耳机接听电话的功能。
PCM接口也可以用于音频通信，将模拟信号抽样，量化和编码。在一些实施例中，音频模块170与无线通信模块160可以通过PCM总线接口耦合。在一些实施例中，音频模块170也可以通过PCM接口向无线通信模块160传递音频信号，实现通过蓝牙耳机接听电话的功能。所述I2S接口和所述PCM接口都可以用于音频通信。
UART接口是一种通用串行数据总线，用于异步通信。该总线可以为双向通信总线。它将要传输的数据在串行通信与并行通信之间转换。在一些实施例中，UART接口通常被用于连接处理器110与无线通信模块160。例如：处理器110通过UART接口与无线通信模块160中的蓝牙模块通信，实现蓝牙功能。在一些实施例中，音频模块170可以通过UART接口向无线通信模块160传递音频信号，实现通过蓝牙耳机播放音乐的功能。
MIPI接口可以被用于连接处理器110与显示屏194，摄像头193等外围器件。MIPI接口包括摄像头串行接口(camera serial interface，CSI)，显示屏串行接口(display serial interface，DSI)等。在一些实施例中，处理器110和摄像头193通过CSI接口通信，实现电子设备100的拍摄功能。处理器110和显示屏194通过DSI接口通信，实现电子设备100的显示功能。
GPIO接口可以通过软件配置。GPIO接口可以被配置为控制信号，也可被配置为数据信号。在一些实施例中，GPIO接口可以用于连接处理器110与摄像头193，显示屏194，无线通信模块160，音频模块170，传感器模块180等。GPIO接口还可以被配置为I2C接口，I2S接口，UART接口，MIPI接口等。
USB接口130是符合USB标准规范的接口，具体可以是Mini USB接口，Micro USB接口，USB Type C接口等。USB接口130可以用于连接充电器为电子设备100充电，也可以用于电子设备100与外围设备之间传输数据。也可以用于连接耳机，通过耳机播放音频。该接口还可以用于连接其他电子设备，例如AR设备等。
可以理解的是，本发明实施例示意的各模块间的接口连接关系，只是示意性说明，并不构成对电子设备100的结构限定。在本申请另一些实施例中，电子设备100也可以采用上述实施例中不同的接口连接方式，或多种接口连接方式的组合。
充电管理模块140用于从充电器接收充电输入。其中，充电器可以是无线充电器，也可以是有线充电器。在一些有线充电的实施例中，充电管理模块140可以通过USB接口130接收有线充电器的充电输入。在一些无线充电的实施例中，充电管理模块140可以通过电子设备100的无线充电线圈接收无线充电输入。充电管理模块140为电池142充电的同时，还可以通过电源管理模块141为电子设备供电。
电源管理模块141用于连接电池142，充电管理模块140与处理器110。电源管理模块141接收电池142和/或充电管理模块140的输入，为处理器110，内部存储器121，显示屏194，摄像头193，和无线通信模块160等供电。电源管理模块141还可以用于监测电池容量，电池循环次数，电池健康状态(漏电，阻抗)等参数。在其他一些实施例中，电源管理模块141也可以设置于处理器110中。在另一些实施例中，电源管理模块141和充电管理模块140也可以设置于同一个器件中。
电子设备100的无线通信功能可以通过天线1，天线2，移动通信模块150，无线通信模块160，调制解调处理器以及基带处理器等实现。
天线1和天线2用于发射和接收电磁波信号。电子设备100中的每个天线可用于覆盖单个或多个通信频带。不同的天线还可以复用，以提高天线的利用率。例如：可以将天线1复用为无线局域网的分集天线。在另外一些实施例中，天线可以和调谐开关结合使用。
移动通信模块150可以提供应用在电子设备100上的包括2G/3G/4G/5G等无线通信的解决方案。移动通信模块150可以包括至少一个滤波器，开关，功率放大器，低噪声放大器(low noise amplifier，LNA)等。移动通信模块150可以由天线1接收电磁波，并对接收的电磁波进行滤波，放大等处理，传送至调制解调处理器进行解调。移动通信模块150还可以对经调制解调处理器调制后的信号放大，经天线1转为电磁波辐射出去。在一些实施例中，移动通信模块150的至少部分功能模块可以被设置于处理器110中。在一些实施例中，移动通信模块150的至少部分功能模块可以与处理器110的至少部分模块被设置在同一个器件中。
调制解调处理器可以包括调制器和解调器。其中，调制器用于将待发送的低频基带信号调制成中高频信号。解调器用于将接收的电磁波信号解调为低频基带信号。随后解调器将解调得到的低频基带信号传送至基带处理器处理。低频基带信号经基带处理器处理后，被传递 给应用处理器。应用处理器通过音频设备(不限于扬声器170A，受话器170B等)输出声音信号，或通过显示屏194显示图像或视频。在一些实施例中，调制解调处理器可以是独立的器件。在另一些实施例中，调制解调处理器可以独立于处理器110，与移动通信模块150或其他功能模块设置在同一个器件中。
无线通信模块160可以提供应用在电子设备100上的包括无线局域网(wireless local area networks，WLAN)(如无线保真(wireless fidelity，Wi-Fi)网络)，蓝牙(bluetooth，BT)，全球导航卫星系统(global navigation satellite system，GNSS)，调频(frequency modulation，FM)，近距离无线通信技术(near field communication，NFC)，红外技术(infrared，IR)等无线通信的解决方案。无线通信模块160可以是集成至少一个通信处理模块的一个或多个器件。无线通信模块160经由天线2接收电磁波，将电磁波信号解调以及滤波处理，将处理后的信号发送到处理器110。无线通信模块160还可以从处理器110接收待发送的信号，对其进行调频，放大，经天线2转为电磁波辐射出去。
在一些实施例中，电子设备100的天线1和移动通信模块150耦合，天线2和无线通信模块160耦合，使得电子设备100可以通过无线通信技术与网络以及其他设备通信。所述无线通信技术可以包括全球移动通讯系统(global system for mobile communications，GSM)，通用分组无线服务(general packet radio service，GPRS)，码分多址接入(code division multiple access，CDMA)，宽带码分多址(wideband code division multiple access，WCDMA)，时分码分多址(time-division code division multiple access，TD-SCDMA)，长期演进(long term evolution，LTE)，BT，GNSS，WLAN，NFC，FM，和/或IR技术等。所述GNSS可以包括全球卫星定位系统(global positioning system，GPS)，全球导航卫星系统(global navigation satellite system，GLONASS)，北斗卫星导航系统(beidou navigation satellite system，BDS)，准天顶卫星系统(quasi-zenith satellite system，QZSS)和/或星基增强系统(satellite based augmentation systems，SBAS)。
电子设备100通过GPU，显示屏194，以及应用处理器等实现显示功能。GPU为图像处理的微处理器，连接显示屏194和应用处理器。GPU用于执行数学和几何计算，用于图形渲染。处理器110可包括一个或多个GPU，其执行程序指令以生成或改变显示信息。
显示屏194用于显示图像，视频等。显示屏194包括显示面板。显示面板可以采用液晶显示屏(liquid crystal display，LCD)，有机发光二极管(organic light-emitting diode，OLED)，有源矩阵有机发光二极体或主动矩阵有机发光二极体(active-matrix organic light emitting diode的，AMOLED)，柔性发光二极管(flex light-emitting diode，FLED)，Miniled，MicroLed，Micro-oLed，量子点发光二极管(quantum dot light emitting diodes，QLED)等。在一些实施例中，电子设备100可以包括1个或N个显示屏194，N为大于1的正整数。
电子设备100可以通过ISP，摄像头193，视频编解码器，GPU，显示屏194以及应用处理器等实现拍摄功能。
ISP用于处理摄像头193反馈的数据。例如，拍照时，打开快门，光线通过镜头被传递到摄像头感光元件上，光信号转换为电信号，摄像头感光元件将所述电信号传递给ISP处理，转化为肉眼可见的图像。ISP还可以对图像的噪点，亮度进行算法优化。ISP还可以对拍摄场景的曝光，色温等参数优化。在一些实施例中，ISP可以设置在摄像头193中。
摄像头193用于捕获静态图像或视频。物体通过镜头生成光学图像投射到感光元件。感光元件可以是电荷耦合器件(charge coupled device，CCD)或互补金属氧化物半导体(complementary metal-oxide-semiconductor，CMOS)光电晶体管。感光元件把光信号转换成电 信号，之后将电信号传递给ISP转换成数字图像信号。ISP将数字图像信号输出到DSP加工处理。DSP将数字图像信号转换成标准的RGB，YUV等格式的图像信号。在一些实施例中，电子设备100可以包括1个或N个摄像头193，N为大于1的正整数。
数字信号处理器用于处理数字信号，除了可以处理数字图像信号，还可以处理其他数字信号。例如，当电子设备100在频点选择时，数字信号处理器用于对频点能量进行傅里叶变换等。
视频编解码器用于对数字视频压缩或解压缩。电子设备100可以支持一种或多种视频编解码器。这样，电子设备100可以播放或录制多种编码格式的视频，例如：动态图像专家组(moving picture experts group，MPEG)1，MPEG2，MPEG3，MPEG4等。
NPU为神经网络(neural-network，NN)计算处理器，通过借鉴生物神经网络结构，例如借鉴人脑神经元之间传递模式，对输入信息快速处理，还可以不断的自学习。通过NPU可以实现电子设备100的智能认知等应用，例如：图像识别，人脸识别，语音识别，文本理解等。
内部存储器121可以包括一个或多个随机存取存储器(random access memory，RAM)和一个或多个非易失性存储器(non-volatile memory，NVM)。
随机存取存储器可以包括静态随机存储器(static random-access memory，SRAM)、动态随机存储器(dynamic random access memory，DRAM)、同步动态随机存储器(synchronous dynamic random access memory,SDRAM)、双倍资料率同步动态随机存取存储器(double data rate synchronous dynamic random access memory,DDR SDRAM，例如第五代DDR SDRAM一般称为DDR5SDRAM)等；非易失性存储器可以包括磁盘存储器件、快闪存储器(flash memory)。
快闪存储器按照运作原理划分可以包括NOR FLASH、NAND FLASH、3D NAND FLASH等，按照存储单元电位阶数划分可以包括单阶存储单元(single-level cell,SLC)、多阶存储单元(multi-level cell,MLC)、三阶储存单元(triple-level cell,TLC)、四阶储存单元(quad-level cell,QLC)等，按照存储规范划分可以包括通用闪存存储(英文：universal flash storage，UFS)、嵌入式多媒体存储卡(embedded multi media Card，eMMC)等。
随机存取存储器可以由处理器110直接进行读写，可以用于存储操作系统或其他正在运行中的程序的可执行程序(例如机器指令)，还可以用于存储用户及应用程序的数据等。
非易失性存储器也可以存储可执行程序和存储用户及应用程序的数据等，可以提前加载到随机存取存储器中，用于处理器110直接进行读写。
外部存储器接口120可以用于连接外部的非易失性存储器，实现扩展电子设备100的存储能力。外部的非易失性存储器通过外部存储器接口120与处理器110通信，实现数据存储功能。例如将音乐，视频等文件保存在外部的非易失性存储器中。
电子设备100可以通过音频模块170，扬声器170A，受话器170B，麦克风170C，耳机接口170D，以及应用处理器等实现音频功能。例如音乐播放，录音等。
音频模块170用于将数字音频信息转换成模拟音频信号输出，也用于将模拟音频输入转换为数字音频信号。音频模块170还可以用于对音频信号编码和解码。在一些实施例中，音频模块170可以设置于处理器110中，或将音频模块170的部分功能模块设置于处理器110中。
扬声器170A，也称“喇叭”，用于将音频电信号转换为声音信号。电子设备100可以通过扬声器170A收听音乐，或收听免提通话。
受话器170B，也称“听筒”，用于将音频电信号转换成声音信号。当电子设备100接听电 话或语音信息时，可以通过将受话器170B靠近人耳接听语音。
麦克风170C，也称“话筒”，“传声器”，用于将声音信号转换为电信号。当拨打电话或发送语音信息时，用户可以通过人嘴靠近麦克风170C发声，将声音信号输入到麦克风170C。电子设备100可以设置至少一个麦克风170C。在另一些实施例中，电子设备100可以设置两个麦克风170C，除了采集声音信号，还可以实现降噪功能。在另一些实施例中，电子设备100还可以设置三个，四个或更多麦克风170C，实现采集声音信号，降噪，还可以识别声音来源，实现定向录音功能等。
耳机接口170D用于连接有线耳机。耳机接口170D可以是USB接口130，也可以是3.5mm的开放移动电子设备平台(open mobile terminal platform，OMTP)标准接口，美国蜂窝电信工业协会(cellular telecommunications industry association of the USA，CTIA)标准接口。
压力传感器180A用于感受压力信号，可以将压力信号转换成电信号。在一些实施例中，压力传感器180A可以设置于显示屏194。压力传感器180A的种类很多，如电阻式压力传感器，电感式压力传感器，电容式压力传感器等。电容式压力传感器可以是包括至少两个具有导电材料的平行板。当有力作用于压力传感器180A，电极之间的电容改变。电子设备100根据电容的变化确定压力的强度。当有触摸操作作用于显示屏194，电子设备100根据压力传感器180A检测所述触摸操作强度。电子设备100也可以根据压力传感器180A的检测信号计算触摸的位置。在一些实施例中，作用于相同触摸位置，但不同触摸操作强度的触摸操作，可以对应不同的操作指令。例如：当有触摸操作强度小于第一压力阈值的触摸操作作用于短消息应用图标时，执行查看短消息的指令。当有触摸操作强度大于或等于第一压力阈值的触摸操作作用于短消息应用图标时，执行新建短消息的指令。
陀螺仪传感器180B可以用于确定电子设备100的运动姿态。在一些实施例中，可以通过陀螺仪传感器180B确定电子设备100围绕三个轴(即X、Y和Z轴)的角速度。陀螺仪传感器180B可以用于拍摄防抖。示例性的，当按下快门，陀螺仪传感器180B检测电子设备100抖动的角度，根据角度计算出镜头模组需要补偿的距离，让镜头通过反向运动抵消电子设备100的抖动，实现防抖。陀螺仪传感器180B还可以用于导航，体感游戏场景。
本申请实施例中，陀螺仪传感器180B可以依据电子设备100在X、Y和Z轴方向上的角速度来判断搭载有电子设备100的车辆的行驶情况，其中车辆的行驶情况可以包括但不限于车辆上下坡行驶、车辆转弯行驶等等。
气压传感器180C用于测量气压。在一些实施例中，电子设备100通过气压传感器180C测得的气压值计算海拔高度，辅助定位和导航。
磁传感器180D包括霍尔传感器。电子设备100可以利用磁传感器180D检测翻盖皮套的开合。在一些实施例中，当电子设备100是翻盖机时，电子设备100可以根据磁传感器180D检测翻盖的开合。进而根据检测到的皮套的开合状态或翻盖的开合状态，设置翻盖自动解锁等特性。
加速度传感器180E可检测电子设备100在各个方向上(一般为X、Y和Z三轴)加速度的大小。当电子设备100静止时可检测出重力的大小及方向。还可以用于识别电子设备100的姿态，应用于横竖屏切换，计步器等应用。本申请实施例中，加速度传感器180E可以用于测量加速力，加速力是指当电子设备100在加速过程中作用在电子设备100上的力，例如重力，摩擦力等等，对此不做限定。在一些实施例中，加速度传感器180E可以计算搭载有电子设备100的车辆进入特定停车场之后的加速度，该加速度大小可以是正数、负数或零，其方向可以与车辆行驶的方向一致。
加速度传感器180E的输出型式可以为数字型输出和电压式输出，该输出型式取决于电子设备100与加速度传感器180E之间的接口。其中，若电子设备100中使用的微控制器为数字输入，则电子设备100中的加速度传感器180E可以选择输出数字，但是需要额外占用一个时钟单位来处理脉冲宽度调制(Pulse width modulation，PWM)信号，这样会加重处理器的负担；若电子设备使用的微控制器为模拟输入，则电子设备100中的加速度可以模拟输出电压，且处理速度快，一般模拟输出的电压与加速度可以成比例对应，对电压与加速度的比例大小不做限定。例如，2.5V的电压可以对应于0g的加速度，3V的电压可以对应于1g的加速度。
距离传感器180F，用于测量距离。电子设备100可以通过红外或激光测量距离。在一些实施例中，拍摄场景，电子设备100可以利用距离传感器180F测距以实现快速对焦。
接近光传感器180G可以包括例如发光二极管(LED)和光检测器，例如光电二极管。发光二极管可以是红外发光二极管。电子设备100通过发光二极管向外发射红外光。电子设备100使用光电二极管检测来自附近物体的红外反射光。当检测到充分的反射光时，可以确定电子设备100附近有物体。当检测到不充分的反射光时，电子设备100可以确定电子设备100附近没有物体。电子设备100可以利用接近光传感器180G检测用户手持电子设备100贴近耳朵通话，以便自动熄灭屏幕达到省电的目的。接近光传感器180G也可用于皮套模式，口袋模式自动解锁与锁屏。
环境光传感器180L用于感知环境光亮度。电子设备100可以根据感知的环境光亮度自适应调节显示屏194亮度。环境光传感器180L也可用于拍照时自动调节白平衡。环境光传感器180L还可以与接近光传感器180G配合，检测电子设备100是否在口袋里，以防误触。
指纹传感器180H用于采集指纹。电子设备100可以利用采集的指纹特性实现指纹解锁，访问应用锁，指纹拍照，指纹接听来电等。
温度传感器180J用于检测温度。在一些实施例中，电子设备100利用温度传感器180J检测的温度，执行温度处理策略。例如，当温度传感器180J上报的温度超过阈值，电子设备100执行降低位于温度传感器180J附近的处理器的性能，以便降低功耗实施热保护。在另一些实施例中，当温度低于另一阈值时，电子设备100对电池142加热，以避免低温导致电子设备100异常关机。在其他一些实施例中，当温度低于又一阈值时，电子设备100对电池142的输出电压执行升压，以避免低温导致的异常关机。
触摸传感器180K，也称“触控器件”。触摸传感器180K可以设置于显示屏194，由触摸传感器180K与显示屏194组成触摸屏，也称“触控屏”。触摸传感器180K用于检测作用于其上或附近的触摸操作。触摸传感器可以将检测到的触摸操作传递给应用处理器，以确定触摸事件类型。可以通过显示屏194提供与触摸操作相关的视觉输出。在另一些实施例中，触摸传感器180K也可以设置于电子设备100的表面，与显示屏194所处的位置不同。
骨传导传感器180M可以获取振动信号。在一些实施例中，骨传导传感器180M可以获取人体声部振动骨块的振动信号。骨传导传感器180M也可以接触人体脉搏，接收血压跳动信号。在一些实施例中，骨传导传感器180M也可以设置于耳机中，结合成骨传导耳机。音频模块170可以基于所述骨传导传感器180M获取的声部振动骨块的振动信号，解析出语音信号，实现语音功能。应用处理器可以基于所述骨传导传感器180M获取的血压跳动信号解析心率信息，实现心率检测功能。
地磁传感器180Z是一类利用电子设备100在地磁场中的运动状态不同，通过感应地磁场的分布变化而指示电子设备100的姿态和运动角度等信息的测量装置。电子设备100可以通 过地磁场和地磁传感器180Z实现电子设备100的指南针功能。地磁传感器180Z可用于检测车辆的存在和车型识别。在一些实施例中，地磁传感器180Z可以用于室内导航的应用中。本申请实施例中，地磁传感器180Z可以利用地磁场的分布来获取电子设备100的位置。例如，地磁传感器180Z获取的电子设备100所在位置的地磁指纹信号作为三维空间上的矢量数据，可以是指在三维坐标下三个方向上的磁场强度的分量，地磁指纹信号具体可以表示为(mx，my，mz)，其中，mx对应x方向上的磁场强度的分量，my对应y方向上的磁场强度的分量，mz对应z方向上的磁场强度的分量。
按键190包括开机键，音量键等。按键190可以是机械按键。也可以是触摸式按键。电子设备100可以接收按键输入，产生与电子设备100的用户设置以及功能控制有关的键信号输入。
马达191可以产生振动提示。马达191可以用于来电振动提示，也可以用于触摸振动反馈。例如，作用于不同应用(例如拍照，音频播放等)的触摸操作，可以对应不同的振动反馈效果。作用于显示屏194不同区域的触摸操作，马达191也可对应不同的振动反馈效果。不同的应用场景(例如：时间提醒，接收信息，闹钟，游戏等)也可以对应不同的振动反馈效果。触摸振动反馈效果还可以支持自定义。
指示器192可以是指示灯，可以用于指示充电状态，电量变化，也可以用于指示消息，未接来电，通知等。
SIM卡接口195用于连接SIM卡。SIM卡可以通过插入SIM卡接口195，或从SIM卡接口195拔出，实现和电子设备100的接触和分离。电子设备100可以支持1个或N个SIM卡接口，N为大于1的正整数。SIM卡接口195可以支持Nano SIM卡，Micro SIM卡，SIM卡等。同一个SIM卡接口195可以同时插入多张卡。所述多张卡的类型可以相同，也可以不同。SIM卡接口195也可以兼容不同类型的SIM卡。SIM卡接口195也可以兼容外部存储卡。电子设备100通过SIM卡和网络交互，实现通话以及数据通信等功能。在一些实施例中，电子设备100采用eSIM，即：嵌入式SIM卡。eSIM卡可以嵌在电子设备100中，不能和电子设备100分离。
电子设备100的软件系统可以采用分层架构，事件驱动架构，微核架构，微服务架构，或云架构。本发明实施例以分层架构的Android系统为例，示例性说明电子设备100的软件结构。
下面介绍本申请实施例提供的一种系统架构。
示例性地，图2示出了本申请提供的一种系统架构10。如图2所示，系统架构10可以包括电子设备100、以及车辆101。
电子设备100与车辆101可以建立通信连接。其中，电子设备100与车辆101可以直接建立通信连接，也可以通过服务器120进行通信连接。
具体地，电子设备100和车辆101可以基于无线通信技术建立无线连接，例如，电子设备100可以与车辆101建立2.4G无线连接或者蓝牙连接等等；在本申请实施例中，对建立无线连接的方式不做限定。
电子设备100和车辆101还可以分别与服务器120建立连接，例如，电子设备100可以与车辆101登录同一用户账号，电子设备100可以与车辆101可以将相关数据存储在服务器120内，也可以从服务器120中获取相关数据。
可以理解的是，本申请实施例对电子设备100与车辆101建立通信连接的方式不做限定。
电子设备100与车辆101完成通信连接之后可以进行相关数据信息的传输。具体地，车辆101可以将自身的处理状态(例如改变行驶模式)的信息发送至电子设备100或发送至服务器120，电子设备100可以通过获取到车辆101的相关处理信息来完善电子设备100上传感器采集的数据信息。
在一些实施例中，电子设备100可以是搭载在车辆101上的，即电子设备100可以单独移动，也可以跟随车辆101进行同步移动。例如，若车辆101上安装有相关处理器110和多个传感器，车辆101可以通过独自处理相关传感器数据来获取车辆101位置。
基于上述系统架构10，在一些应用场景中，用户在外出的情况下，当行驶车辆101到达目的地附近之后，需要对车辆101进行停放处理。一般用户会选择将车辆101停放在停车场内，其中，停车场可以分为室外露天停车场和室内停车场。
室外露天停车场空间利用率不高，大多没有安全保障，且由于天气因素例如长时间暴晒、长时间下雨、冰雹降雪等会对车辆101产生影响，导致车辆101受损，这样不利于用户停放车辆101。
室内停车场可以分为多楼层来停放车辆101，空间利用率高，且能够记录车辆101的信息，使用户停车更安全、更便捷。其中，跨楼层的室内停车场的坡道类型可以包括但不限于：直坡道式(例如外直坡道式、内直坡道式)、错层式(例如二段式错层、三段式错层)、螺旋坡道式(例如单螺旋坡道、双行螺旋坡道、跳层螺旋坡道)、斜楼板式。在一些实施例中，若室内停车场中的坡道上设有停车位，则车辆101可以停放在室内停车场中的坡道停车位上。
基于室内停车场的诸多优点，目前大多用户选择在室内停放车辆101。但是，用户在车辆101停放完成之后，由于室内停车场楼层复杂，车辆101众多，间隔一段时间回来取车时常常会忘记自己的车辆101停放的具体位置。在一些实施例中，电子设备100可以在车辆101停放完成时获取到用户输入的车辆101位置，但是这需要用户手动输入车辆101的位置信息，难以避免用户忘记输入的情况，效率不高。
在一些实施例中，电子设备100可以在车辆101停放完成时基于定位技术自动获取车辆101位置。下面介绍几种自动获取车辆101位置的方式。
方式一，电子设备100在室外可以通过全球导航卫星系统(Global Navigation Satellite System，GNSS)进行定位，并且结合惯性导航技术进行辅助定位；进入室内后，在GNSS失效的情况下可以通过惯性导航技术持续进行推算得到车辆101位置。其中，
全球导航卫星系统(GNSS)作为一个在地球表面或近地空间的任何地点为用户提供全天候的三维坐标和速度以及时间信息的空基无线电导航定位系统，其中包括有美国的全球定位系统(Global Positioning System，GPS)、俄罗斯的格洛纳斯卫星导航系统(Global Navigation Satellite System，GLONASS)、欧盟的伽利略卫星导航系统(Galileo satellite navigation system，GALILEO)和中国的北斗卫星导航系统(BeiDou Navigation Satellite System，BDS)。在一些实施例中，用户在室外驾驶车辆101或在室外停车场停放车辆101时可以使用到GNSS系统进行位置确定。由于大部分室内的信号较差，当车辆101驶入室内停车场时，GNSS无法对其进行精确定位。
惯性导航技术是一种不依赖于外部信息、也不向外部辐射能量的自主式导航系统。其工作环境可以包括但不限于空中、地面以及水下。惯性导航技术的基本工作原理是以牛顿力学定律为基础，通过测量载体，例如车辆101在惯性参考系的加速度，将它对时间进行积分，且把它变换到导航坐标系中，就能够得到在导航坐标系中的速度、偏航角和位置等信息。在 一些实施例中，惯性导航技术可以与加速度传感器、陀螺仪传感器结合使用。
本申请实施例中，电子设备100基于加速度传感器可以获取到电子设备100的加速度等数据，基于陀螺仪传感器获取到的电子设备100在X、Y以及Z轴上角速度等数据，再通过惯性导航技术可以得到车辆101的位置信息。
但是使用方式一中的技术来获取车辆101位置的精确度较低甚至无法有效获取。
方式二，电子设备100还可以在处于室内环境的情况下利用WIFI、蜂窝网络或地磁等指纹信号的匹配技术进行定位获取车辆101位置。
在目前多种室内定位技术中，位置指纹匹配(Fingerprint)为一种常见的定位方法，是以无线通信与网络技术为基础，且具有易于实现、成本低、对接入点(Accesspoint，Ap)时间同步精度要求低等诸多特点，可基于wifi、蓝牙(Bluetooth)等不同无线局域网传感器来实现，可以在多种室内定位场景下被广泛使用。
电子设备100基于地磁传感器周期性的获取电子设备100所在位置的地磁指纹信号，地磁指纹信号包括三维空间上的矢量数据，指的是在三维坐标下三个方向上的磁场强度的分量，地磁指纹信号具体可以表示为(mx，my，mz)，其中，mx对应x方向上的磁场强度的分量，my对应y方向上的磁场强度的分量，mz对应z方向上的磁场强度的分量。
车辆101停车的过程中，电子设备100基于地磁传感器获取到电子设备100所在位置的地磁指纹信号，将该地磁指纹信号与位置指纹数据库中的位置指纹信息以一定的算法进行匹配，并选取出匹配相似度最优的结果作为车辆101的位置估计。其中，位置指纹数据库可以是在对车辆101位置进行判断之前预先建立的。
但是使用方式二中的技术来获取车辆101位置需要先将数据直接对各楼层指纹特征信息逐一楼层比对，且等待楼层辨识出来后，才用该楼层的指纹信息进行匹配得到位置，花费时间相当冗长。
图3示出了上述方式二中描述的一种电子设备100室内位置确定方法的流程示意图。具体的，可以包括以下步骤：
S301、电子设备100采集第一地磁指纹信号。
电子设备100获取第一指纹信号可以是利用电子设备100上的地磁传感器取得电子设备100所在位置的地磁指纹信号。
S302、根据电子设备100的摆放姿态将所述第一地磁指纹信号转换为第二地磁指纹信号。
其中，电子设备100的摆放姿态可以有多种，例如，电子设备100可以正向水平放置、可以左横向水平放置、可以右横向水平放置等等。每一种摆放姿态可以将第一地磁数据对应转换为不同的第二地磁指纹信号。
S303、在预先建立的地磁数据库中查询与所述第二地磁指纹信号匹配的第三地磁指纹信号。
地磁数据库包括对应不同楼层的多个地磁数据库表DB[k]。每一个地磁数据库表中包括多个地磁指纹信号，地磁指纹信号指示了电子设备100所在的位置。
电子设备100将每个第二地磁数据段分别与所述多个地磁数据库表进行并行匹配，确定最早匹配成功的地磁数据库表对应的楼层为电子设备100所在楼层；
在所述最早匹配成功的地磁数据库表中查询与第二地磁指纹信号匹配的第三指纹信号。
S304、将所述第三地磁指纹信号的坐标值作为所述电子设备100的位置。
在上述步骤S303中，电子设备100将第二地磁指纹信号与多个地磁数据库表进行匹配， 确认电子设备100所在楼层，然后再在所在楼层对应的地磁数据库表中匹配具体的位置。
图4示出了一种地磁匹配楼层判定算法的流程示意图。如图4所示地磁匹配楼层判定算法可以包括以下步骤：
S401、将待匹配地磁指纹信号带入到每一个DB[k]中，并行执行地磁定位匹配算法。
S402、判断待匹配地磁指纹信号在DB[k]中是否匹配成功。
若待匹配的地磁指纹信号在DB[k]中匹配成功，则执行S403；若地磁指纹信号在DB[k]中匹配不成功，则执行S402，继续进行匹配。
S403、判断待匹配地磁指纹信号是否只在一个DB[k]中匹配成功。
若待匹配的地磁指纹信号只存在一个DB[k]匹配成功，则该DB[k]对应的楼层为定位目标楼层，后续定位匹配只在该DB[k]中进行；若待匹配的地磁指纹信号不止存在一个DB[k]匹配成功，即有多个匹配成功的DB[k]，则在所有匹配成功的DB[k]中选择置信度最高的匹配结果为目标匹配结果，且该目标匹配结果对应的楼层为定位目标楼层，后续定位匹配只在该目标匹配结果中进行。
这样可以实现在确定电子设备100在室内的位置，但是需要先确定电子设备100所在楼层，其中，确认电子设备100所在的楼层的方法可以包括但不限于使用气压计、WIFI信息或先将数据直接对各楼层地磁指纹特征信息逐一楼层比对，在电子设备100对楼层信息辨识完成出来后，才用该楼层的指纹信息进行匹配得到位置，花费时间相当冗长，获取位置速率较慢。除此之外，当楼层判断不准确时，电子设备100会在错误的楼层进行位置匹配，导致准确率下降。
方式三，电子设备100判断自身的当前运动模式为机动车行驶模式还是人行走模式，从而判断是否发生了机动车行驶模式向人行走模式的切换，如果是，则获取切换时刻的定位信息作为停车位置。图5示出了该方式中描述的自动判别车辆101位置的方法的流程示意图。具体的，包括以下步骤：
S501、电子设备100的通过传感器采集检测数据。
其中电子设备100的传感器可以记录到当前时刻和地理位置下的电子设备100的加速度大小、运动方向、磁场大小及方向以及大气水平高度等原始数据。
S502、根据采集的检测数据判定电子设备100的当前运动模型为机动车行驶模式还是人行走模式。
电子设备100通过对原始数据进行滤波、积分等算法处理，可以获取一组时间序列关键值，然后电子设备100利用不同场景下的时间序列关键值形态变化的特性，对获取的时间序列关键值进行识别检测，判断电子设备100的运动模式是否发生了变化。
S503、根据当前运动模型判断是否发生了机动车行驶模式向人行走模式的切换，如果是，则获取切换时刻的定位信息作为车辆101位置。
电子设备100判断出在某一时刻发生了机动车行驶模式向人行走模式的切换，则确定该时刻电子设备100的位置为车辆101停放时刻，该时刻电子设备100所在的位置为车辆101位置。其中，定位信息通过以下任一方式获取：wifi，蓝牙定位技术，GPS定位技术。
图6示例性示出了步骤S503中电子设备100判别车辆101是否发生了机动车行驶模式向人行走模式切换的软件结构示意图。具体的，如图6所示，包括以下模块：
数据采集模块，用于通过电子设备100的传感器采集检测数据。
运动模式判断模块，用于根据采集的检测数据判断电子设备100的当前运动模式为机动 车行驶模式还是人行走模式。
定位信息获取模块，用于根据当前运动模式判断是否发生了机动车行驶模式向人行走模式的切换，如果是，则获取切换时刻的定位信息作为车辆101位置。优选地，在定位信息获取模块中，定位信息通过以下任一方式获取：WIFI，蓝牙定位技术，GPS定位技术。
通过上述方法，能够自动判断机动车行驶模式和人行走模式的场景切换，并能获取切换时刻的车辆101具体位置，使用户能够记录车辆101位置。但是由于实时计算的车辆101停放点的位置可能不准确，所保存的位置信息可靠度较差，会出现判断误差比较大的情况，并且当运动模式出现了错误的判断，例如在车在行驶状态时，用户改变了电子设备100的位置，使得电子设备100误以为已经完成了机动车行驶模式到人行走模式的切换，会使得运动模式产生判断失误，会将判断错误的位置当成车辆101位置。
上述两种获取位置信息的方法都是通过先识别出电子设备100所在的楼层，再进行位置匹配。但由于目前车位紧缺，室内停车场会在例如坡道或者楼层入口处附近等等位置设置车位，这样会使得电子设备100由于指纹信息不充分而无法判断出正确位置，也无法识别出跨楼层的位置。
本申请实施例提供了一种获取车辆101位置的方法，可以减少判断车辆101位置的时间，提高判断车辆101位置的准确性。在该方法中，电子设备100在车辆101完成停放后，可以将所获取到的地磁特征数据输入至深度学习的模型中，从深度学习模型中输出多个地磁候选匹配位置，再结合多个地磁候选匹配位置的置信度来判断获取车辆101停放的最终位置与楼层信息以及位置范围信息。上述获取车辆101位置方法主要可以包括有两个阶段：离线采集与建模阶段以及定位阶段。值得说明的是，当车辆101停放在特定室内停车场的坡道时，电子设备100也可以获取到该车辆101的位置信息，即电子设备100可以获取到车辆的跨楼层的位置信息。
在一种可能的实施例中，电子设备100可以放置在用户驾驶的车辆101内部，与车辆101同步移动，电子设备100可以识别出用户对电子设备100的操作，例如用户拿起电子设备100的操作等等，并在排除该操作对电子设备100判断车辆101行驶状态的影响。
实施上述方法，可以通过电子设备100来获取车辆101停放的位置信息其中包括但不限于位置、楼层以及置信度等信息，能够有效地提高电子设备100获取车辆101停放的位置信息的精准度，并且不仅限于在对电子设备100判断车辆101位置之前，先判断车辆101停放的楼层位置，提高判断效率。
在一些实施例中，本申请实施例提供的一种获取车辆101位置的方法还包括：电子设备100可以保存车辆101停放完成前一段时间内的传感器数据，并在车辆101停放完成后开始对车辆101位置进行计算判断。这种电子设备100可以利用车辆101停放完成前一段时间内的传感器数据来计算车辆101的停放位置。
在一些实施例中，在电子设备100将所测量到的地磁特征数据输入至深度学习的模型之前，需要对地磁指纹信号进行采集和与其对应的位置楼层信息进行建模处理。电子设备100可以通过建模完成后的深度学习模型直接同时获取电子设备100的位置与楼层信息，提高判断效率。
在一些实施例中，从深度学习模型中输出多个地磁候选匹配位置之前，电子设备100还可以将加速度传感器和陀螺仪传感器采集的数据辅助惯性导航技术来对地磁指纹信号进行萃取得到地磁特征数据。这样将萃取之后的地磁特征数据输入至深度学习模型中获取的地磁候 选匹配位置更为精确。
图7为本申请实施例中提供的获取车辆101位置方法的流程示意图。
参考图7，图7示例性的示出了电子设备100获取车辆101位置的方法。电子设备100中的数据库可以包含传感器数据库、蜂窝网络、WIFI数据库、深度学习模型和权重。电子设备100还可以显示有获取车辆101位置的界面示意图。
本申请实施例中电子设备100中使用到的传感器可以包括但不限于加速度传感器、陀螺仪传感器和地磁传感器。其中地磁传感器可以用于测量车辆101在行驶过程中的地磁指纹信号，加速度传感器和陀螺仪传感器在车辆101行驶过程中采集到的数据既可以帮助车辆101停放检测，还可以辅助惯性导航技术对地磁指纹信号进行萃取来得到地磁特征数据。
具体地，电子设备100获取车辆101位置的方法具体可以包括：
S701、电子设备100识别到车辆101进入特定室内停车场后，电子设备100开始记录传感器数据。
用户在室外驾驶车辆101，根据用户自身的需求，需要对车辆101进行停放操作。在一些实施中，基于室内停车场的优点，用户可以选择特定室内停车场进行车辆101的停放。通过室外GNSS定位等方式，用户驾驶车辆101进入特定室内停车场，该特定室内停车场可以是指采集车已经全方位进行过地磁指纹采集的室内停车场。
当电子设备100识别用户驾驶车辆101进入特定室内停车场时，电子设备100可以开始记录并存储传感器数据。在一些实施例中，传感器数据可以包括但不限于包括：加速度传感器采集的数据、陀螺仪传感器采集的数据和地磁传感器采集的数据。
加速度传感器采集的数据可以包括电子设备100在车辆101行驶方向上的加速度数据，可以用于确定车辆101的行驶速度、加速度等等。
陀螺仪传感器采集的数据包括电子设备100围绕三个轴(即X,Y和Z轴)的角速度，电子设备100可以依据在X,Y和Z轴方向上的角速度来判断搭载有电子设备100的车辆101的行驶情况，其中车辆101的行驶情况可以包括但不限于车辆101上下坡行驶、车辆101转弯行驶等等。
地磁传感器采集的数据为电子设备100所在位置的地磁指纹信号作为三维空间上的矢量数据，即为地磁指纹信号。每个地理位置上的地磁指纹信号是不同的，地磁传感器采集到的地磁指纹信号与位置一一对应。
在一些实施例中，电子设备100识别出车辆101进入特定室内停车场的方法具体可以包括以下的一项或多项：电子设备100基于全球定位系统(Global Positioning System，GPS)识别到电子设备100的位置进入了特定室内停车场；或者，电子设备100基于接收到的WiFi信号并确定该WiFi信号为特定室内停车场的WiFi信号，从而确定出电子设备100进入了特定室内停车场；或者，电子设备100检测到GPS信号或蜂窝信号有衰减；或者，电子设备100结合地磁指纹信号检测到车辆101行驶的坡道的变化等等。可以理解的是，本发明不限定只使用特定的室外进入室内的侦测方法。
在一种可能的实施例中，电子设备100内存有车辆101的车牌信息，在车辆101进入特定室内停车场时，特定室内停车场入口的摄像头扫描车辆101的车牌信息，服务器基于车辆101的车牌信息向电子设备100发送提示信息，提示车辆101进入该特定室内停车场。这时，电子设备100开始记录传感器数据。
在本申请实施例中，对电子设备100识别出车辆101进入特定室内停车场的方法不做限 定。
在一些实施例中，电子设备100识别到车辆101进入特定室内停车场可以被理解为电子设备100确定车辆101的地理位置已经位于预设区域内。亦即，本发明亦可套用在其他的场域，不限于室内停车场，而可为其他室内或半开放式场域。
S702、电子设备100对车辆101进行停车检测，确认车辆101已经完成停车动作。
当用户驾驶车辆101进入车库之后，用户选择空闲车位对车辆101进行停放。电子设备100在用户停放好车辆101后，对车辆101进行停车检测，确认车辆101已经停放完毕。值得注意的是，此实施例仅作范例说明。本发明可以在停车后执行定位，亦可在车辆行进过程中执行实时定位。具体地，电子设备100检测到车辆101停放完成可以利用以下几种方法但不限定：
在一些实施例中，电子设备100检测到车辆101停放完成的方法可以被称为第一预设条件。
(1)蓝牙断开连接
当车辆101发动引擎启动后，电子设备100与车辆101可以进行蓝牙连接。例如，电子设备100与车辆101可以通过蓝牙连接实现通讯功能、音乐播放功能等。当用户驾驶车辆101进入特定室内停车场，选择空闲车位并停放车辆101后，用户熄灭引擎，此时车辆101断电即车辆101断开与电子设备100的蓝牙连接。因此电子设备100可以通过检测到电子设备100与车辆101断开蓝牙连接从而判断车辆101停放动作完成，实现对车辆101停放检测。
(2)倒车动作完成
在车辆101进入特定室内停车场后，一般情况下，用户可以使用倒车入库或侧方位停车等方式来停放车辆101，在本申请实施例中，对用户完成车辆101停放动作的方式不作限制。
电子设备100可以在上述停放车辆101的方式中检测到车辆101的倒车动作。即电子设备100可以通过加速度传感器和陀螺仪传感器采集到的数据来判断倒车动作是否完成，从而判断车辆101是否已经完成停放动作，实现对车辆101停放检测。
在一些实施例中，电子设备100检测到车辆101的行驶速度小于阈值时，电子设备100判断车辆101已经完成停放动作，该阈值可以被称为第一阈值。
(3)自动泊车模式完成
在用户驾驶车辆101进入特定室内停车场后，用户可以选择自动泊车模式来停放车辆101。自动泊车模式是指车辆101在自动泊车过程中，完全由泊车控制器控制电动助力转向(Electric Power Steering，EPS)的转向电机、电子刹车泵、电子油门，从而控制方向盘转动，并且稳定泊车车速。电子设备100可以通过判断自动泊车模式完成从而判断车辆101停放动作完成，实现对车辆101停放检测。
(4)行驶模式和人行走模式
在用户驾驶车辆101时，电子设备100中的传感器采集到电子设备100的加速度大小、运动方向、磁场大小及方向以及大气水平高度等原始数据，并对该原始数据进行滤波、积分等相关算法的处理，获取多个时间序列关键值。电子设备100根据在不同场景下，时间序列关键值变化特征来判断出电子设备100运动模式发生改变的时刻，并以该时刻电子设备100所在的位置作为车辆101的停放位置。
在本申请实施例中，对电子设备100检测车辆101停放动作完成的方式不做限定。
在一些实施例中，电子设备100可以与车辆101进行通信连接，例如电子设备100与车 辆101可以登录同一用户账号，车辆101可以将自身的处理数据(例如倒车、开启自动泊车模式)发送至服务器120，电子设备100可以从服务器120获取上述处理数据，以此来掌握电子设备100上传感器未获取的数据信息。
在一些实施例中，上述步骤S701为可选的，不限于电子设备100进入到特定室内停车场后，电子设备100才开始记录传感器数据。可选的，电子设备100可以在车辆101进入行驶状态时即开始记录传感器数据，也可以在行驶状态中开始记录传感器数据，本申请对此不作限制。
在一些实施例中，上述步骤S702为可选的，不限于电子设备100确认车辆101已经完成停车动作后再执行后续步骤，即对车辆101的停放位置进行定位；电子设备100可以在车辆101行进过程中执行实时定位，也即是说，在车辆101行驶过程中，电子设备100可以对传感器数据进行封装，执行后续步骤，对车辆101的实时位置进行定位。本申请对此不作限制。
S703、电子设备100对传感器数据进行封装。
当电子设备100确认车辆101已经完成停放动作后，电子设备100对存储的传感器数据进行封装处理。例如，该传感器数据可以为车辆101停放完成前时间区间N内传感器所采集的数据。即电子设备100提取时间区间N内的传感器数据。
在一些实施例中，由于电子设备100内的存储器的存储空间有限，电子设备100无法完全记录并保存车辆101从进入特定室内停车场到停放完成之间所有的传感器数据。
可选的，电子设备100可能会存储车辆101停放完成前的时间区间N的传感器数据，并对其余在该段时间区间N之前记录到的传感器数据进行删除处理。可以理解的是，存储车辆101停放完成前一段时间N内的传感器数据能够支撑电子设备100执行下述步骤中算法的演练。
S704、电子设备100将封装的传感器数据中的地磁指纹信号输入至深度学习模型中，获得地磁初始匹配位置。
地磁传感器采集到的地磁指纹信号可以与位置一一对应。例如特定室内停车场中的所有位置包括但不限于楼层平面、楼层通道、楼层入口附近以及楼层之间的坡道等都存在与其相对应的地磁指纹信号。
本申请实施例提供的方法是利用地球磁场来获取车辆101位置，由于地球磁场是由地球发出的，地球上任何地方都能接收到地磁指纹信号，因此，采用本申请所提供的方案在未经过WIFI覆盖的特定室内停车场也可以获取车辆101停车位置。
上文中所提到的封装的传感器数据可以包含时间区间N内的多个地磁指纹信号。例如，时间区间N中可以包含有M个地磁指纹信号。
电子设备100提取到地磁指纹信号之后，需要通过深度学习模型来获取地磁指纹信号所对应的位置。
下面是对获取车辆101位置的深度学习模型进行介绍。
图8A介绍了本申请实施例提供的获取车辆101位置的深度学习模型的建模系统。
示例性地，图8A示出了本申请实施例提供的一种获取车辆101位置的深度学习模型的建模系统。如图8A所示，建模系统可以包括电子设备100、采集车801以及建筑物802，其中，采集车801可以搭载有电子设备100。建筑物802可以是如图8A中所示的双行螺旋坡道 室内停车场，室内停车场的坡道类型可以包括但不限于：直坡道式(例如外直坡道式、内直坡道式)、错层式(例如二段式错层、三段式错层)、螺旋坡道式(例如单螺旋坡道、双行螺旋坡道、跳层螺旋坡道)、斜楼板式。
在获取车辆101位置的深度学习模型的建模系统中，电子设备100可以通过磁力传感器来采集建筑物802中的地磁指纹信号，采集车801可以通过使用高精度仪器来获取精准的采集车801在建筑物802中所在位置信息。在本申请实施例中，对采集车801获取位置信息的方式不做限定。电子设备100采集地磁指纹信号的采集范围可以包括但不限于建筑物802内的平面楼层803以及所有的坡道804。
可以理解的是，在深度学习模型完成训练之前，电子设备100可以采集到的地磁指纹信号存在有位置与其一一对应，但电子设备100无法直接获取与地磁指纹信息匹配的位置信息，需要电子设备100通过对大量的地磁指纹信号和与其对应的位置进行训练处理，获取输入与输出之间的联系信息(例如权重)，来完成深度学习模型的建模处理。
图8B介绍了本申请实施例提供的获取车辆101位置的深度学习模型权重的训练方法的示意图。
示例性地，图8B示出了本申请实施例提供的一种获取车辆101位置的深度学习模型的训练方法。如图8B所示，电子设备100采集的地磁指纹信号序列可以作为深度学习模型的输入源，采集车801通过高精度仪器获取的位置和楼层信息可以作为深度学习模型的标签数据。通过对多个输入源和多个标签数据的训练，即通过相关深度学习的算法的计算，来获取输入源和标签数据之间的权重，其中，输入源和标签数据之间是一一对应的，即一个输入源对应一个标签数据。通过对输入源和标签数据进行训练学习可以构建出本申请实施例使用获取车辆101位置的深度学习模型。
在一些实施例中，电子设备100采集到的地磁指纹信号序列都存在与之对应的位置和楼层信息。可以理解的是，通过电子设备100对地磁指纹信号的采集和采集车801获取相对应位置的训练过程，可以建立任意场景或地点(例如特定室内停车场)的深度学习模型。
下面以特定室内停车场这个地点做示例性的介绍，其他的场景或地点同理。其中，特定室内停车场对应的深度学习模型的权重描述了地磁指纹信号序列和与其对应的位置和楼层信息之间的关系。具体的，当电子设备100获取到该深度学习模型之后，基于一个地磁指纹信号序列可以确定一个位置和楼层信息，也就是说，电子设备100在获取地磁指纹信号之后，可以同时获取到位置和楼层信息，而不是先获取楼层信息再获取位置信息。
电子设备100可以通过不同的方式来获取到特定室内停车场的深度学习模型的权重。例如，电子设备100可以直接通过在应用程序中下载用户所选择的特定室内停车场，从而获取相对应的深度学习模型权重，或者电子设备100直接从本地存储有用户所选择的特定室内停车场的深度学习模型权重直接获取。在本申请实施例中，对电子设备100获取特定室内停车场的深度学习模型权重的方式不做限定。
电子设备100从在时间区间N内封装的传感器数据中获取到的多个地磁指纹信号，将多个地磁指纹信号输入至深度学习模型中，其中，该深度学习模型完成了训练过程，该训练过程可以是指对特定室内停车场的所有地磁指纹信号和与其对应的位置楼层进行的采集和建模过程。深度学习模型对多个地磁指纹信号进行处理后，可以输出多个地磁初始匹配位置，其中，每一个地磁初始匹配位置都可以包括有位置、楼层以及置信度等信息，而所有地磁初始匹配位置的相关信息及数量会对每一个地磁初始匹配位置的置信度大小有所影响。
在一些实施例中，电子设备100通过输入上述多个地磁指纹信号至深度学习模型而获取到的多个地磁初始匹配位置的连续路径可以被称为第一行驶路径。
S705、电子设备100将封装的传感器数据中的加速度传感器数据与陀螺仪传感器数据用来辅助惯性导航技术，基于地磁初始匹配位置从地磁指纹信号中筛选得到满足预设条件的地磁指纹信号。
加速度传感器获取到的电子设备100的加速度和速度等数据可以判断车辆101是否处于减速状态或上下坡状态，陀螺仪传感器获取到的电子设备100的X、Y和Z轴上的角速度可以判断车辆101是否处于转弯状态。电子设备100结合上述数据可以综合判断车辆101是否有倒车或停车等会产生重复地磁指纹信号的动作。
其中，由于上文中M个地磁指纹信号是按时间顺序来获取的，因而M个地磁指纹信号中在一些可能的情况下会包含有重复地磁指纹信号。电子设备100基于地磁初始匹配位置从地磁指纹信号中筛选得到满足预设条件的地磁指纹信号，具体可以为：
电子设备100在地磁初始匹配位置中筛选出重复的位置数据，再将该重复的位置对应的地磁指纹信号过滤掉；和/或
电子设备100在地磁初始匹配位置中筛选出距离很近的位置数据，再将该距离很近的位置对应的地磁指纹信号过滤掉；等等。
在一些实施例中，电子设备100可以对M个地磁指纹信号进行萃取处理，萃取的方式例如可以是对M个地磁指纹信号进行前后向的滤波优化处理，其目的是为了剔除重复的干扰数据，例如由于倒车或临时停车而产生的重复或静止的数据等等，在此对干扰数据的产生方式不做限定。其中，预设条件可以包括但不限于：多个地磁指纹信号对应的多个地磁初始匹配位置中不包含有重复位置点。满足预设条件的地磁指纹信号进过萃取后得到的可以是地磁特征数据。
电子设备100通过对M个地磁指纹信号进行萃取后获取到M1个地磁特征数据，其中M1小于或者等于M。
M1个地磁特征数据在本质上可以是指一组正确的地磁指纹序列，例如，可以通过深度学习模型处理后输出车辆101在时间区间N内行驶过的无重合的路径。地磁特征数据的具体形式也可以表示为(mx，my，mz)。其中，本申请实施例对地磁特征数据的具体形式不作限制。
示例性地，图9A介绍本申请实施例中提供的一种车辆101停放的具体过程的示意图。如图9A所示，车辆101在室内停车场900内进行车辆101停放，其中包含有停车位901～停车位910共10个车位，除停车位908外其余停车位都已有车辆101停放。
车辆101在时间区间N中完成从左侧进入室内停车场900，并在停车位908进行车辆101的停放的动作。车辆101的行驶路径包括图9A中所示的虚线920、加粗实线921以及加粗点922，其中，车辆101在虚线920的路径上只行驶过一次，即电子设备100只取得过一次虚线920路径上的地磁指纹信号，电子设备100中记录的虚线920路径上的地磁指纹信号没有重复的；车辆101在加粗实线921的路径上行驶过两次，即电子设备100取得过两次加粗实线921的路径上的地磁指纹信号，电子设备100中记录的加粗实线921路径上的地磁指纹信号有重复的；车辆101在加粗点922上进行了临时停车，即电子设备100在加粗点922处可能对同一个地磁指纹信号取得了多次，电子设备100中记录的加粗点922路径上的地磁指纹信号有重复的。
例如，电子设备100将车辆101从室内停车场900的左侧驶入到停放完成之间取得的多个的地磁指纹信号输入至深度学习模型中，获取到多个地磁初始匹配位置。电子设备100基于惯性导航系统，再结合加速度传感器和陀螺仪传感器取得的数据，判断车辆101的行驶状态，从多个地磁初始匹配位置中筛选得到预设条件的地磁初始匹配位置，该预设条件可以包括但不限于：任意两个地磁指纹信号对应的位置信息的楼层信息或位置点不同；或者任意两点地磁初始匹配位置之间的距离都大于阈值；或者，车辆101在该地磁初始匹配位置的行驶状态为倒车行驶等条件。
在一些实施例中，上述预设条件中限制两点地磁初始匹配位置之间的距离的阈值可以被称为第二阈值。
在一些实施例中，上述从多个地磁指纹信号中进行筛选的预设条件可以被称为第二预设条件。
示例性的，电子设备100获取到的地磁初始匹配位置包括在虚线920、加粗实线921以及加粗点922上的所有位置点，其中在加粗点922和加粗实线921上的地磁初始匹配位置不满足预设条件，即在加粗点922和加粗实线921上的电子设备100取得的地磁指纹信号是有重复的，且该重复地磁指纹信号会影响S707中判断最终预测车辆101位置。
因此对上述多个地磁指纹信号进行萃取，即删除重复的所有地磁指纹信号，保留没有重复的地磁指纹信号，即电子设备100在虚线920上取得的地磁指纹信号，且没有重复的地磁指纹信号可以称为地磁特征数据，并按地磁传感器取得的时间顺序可以将地磁特征数据排列成一组地磁指纹序列。
S706、电子设备100将萃取后得到的地磁特征数据再输入至深度学习模型中，获得地磁候选匹配位置。
在本申请实施例中，萃取后得到的M1个地磁特征数据可以作为深度学习的输入源，电子设备100可以在深度学习模型中对权重进行处理来得到M1个地磁候选匹配位置，其中，每一个地磁候选匹配位置都可以包括有位置、楼层以及置信度等信息，且该M1个地磁候选匹配位置互不重复，且按时间顺序可以排列成一个地磁候选匹配位置序列。
在一些实施例中，电子设备100通过输入上述多个地磁特征数据至深度学习模型而获取到的多个地磁候选匹配位置的连续路径(即地磁候选匹配位置序列)可以被称为第二行驶路径。
示例性地，图9B介绍本申请实施例中提供的一种车辆101停放的具体过程。
如图9B所示，车辆101在室内停车场900内进行车辆101停放，其中包含有停车位901～停车位910共10个车位，除停车位908外其余停车位都已有车辆101停放。
图9B中所示的虚线930是电子设备100取得的地磁指纹信号经过萃取之后的地磁特征数据所对应的地磁候选匹配位置，该地磁特征数据中不包含有重复数据。
这样，地磁候选匹配位置相比起地磁初始匹配位置更加精确，剔除了地磁初始匹配位置中重复的位置，为在S707中计算匹配位置的置信度和权重消除了部分影响因素。
在一些实施例中，对于步骤S705和步骤S706中对于初始匹配位置进行筛选，并将筛选后的地磁指纹信号再次输入至深度学习模型，这为可选的步骤。电子设备100可以直接将初始匹配位置作为候选匹配位置，直接执行下列步骤中的位置及楼层决策。
S707、电子设备100将地磁候选匹配位置发送至位置及楼层决策模块，获得车辆101停放的位置、楼层以及置信度。
电子设备100在位置及楼层决策模块中，对M1个地磁候选匹配位置进行筛选和分类， 再对M1个地磁候选匹配位置进行筛选后的高优先候选点进行权重的计算，从而判断出最终预测的车辆101位置。
下面对位置及楼层决策模块确定最终预测位置的方法流程进行详细介绍。
图10A介绍了本申请实施例提供的地磁候选匹配位置的分类与筛选的示意图。
示例性地，图10A示出了本申请实施例提供的地磁候选匹配位置的分类与筛选的示例图。如图10A所示，在图10A中，多个地磁候选匹配位置散布在平面坐标中，横坐标表示时间，其中车辆101停放时刻T为电子设备100检测到车辆101停放车辆101完成的瞬时时间，由于传感器记录了电子设备100在车辆101停放前N段时间内的传感器数据，且横坐标由图10A所示是从时刻0开始，因此该时刻T可以为N，(T-W)时刻为车辆101停放完成前W段时间的瞬时时间。其中，W小于或者等于N。本申请实施例中，对W的大小不做限制，即根据算法要求设备W的大小。
纵坐标表示地磁候选匹配位置的置信度或者误差，其中，本申请实施例设置有置信度门限1001，地磁候选匹配位置的置信度高于置信度门限1001的可以被划分为不可靠预测点，地磁候选匹配位置的置信度低于置信度门限1001的可以被划分为可靠预测点。并且，在(T-W)时刻到车辆101停放时刻T之间的时间区间W(如图10A中所示的阴影部分)内的可靠预测点可以被筛选为高优先候选点。
可以理解的是，同一地磁指纹信号对应的地磁初始匹配位置的置信度和同一地磁指纹信号对应的地磁候选匹配位置的置信度大小可能会不一样，且每一个位置点的置信度的有本申请实施例中的算法自行计算得到。在本申请实施例中，对位置点的置信度大小的计算方式不做限定。
图10B介绍了本申请实施例提供的获取最终预测车辆101位置的方法流程图。
示例性地，图10B示出了本申请实施例提供的获取最终预测车辆101位置的方法流程图。如图10B所示，具体方法包括：
S1001、电子设备100获取到时间区间N内多个地磁候选匹配位置。
电子设备100获取到由深度学习模型处理地磁特征数据后得到的地磁候选匹配位置。且将该多个地磁候选匹配位置显示成如图10A所示的分类和筛选的示例图。
S1002、电子设备100运用对地磁候选匹配位置的分类与筛选的方法，获取小于置信度门限的可靠点。
如图10A所示，电子设备100选择出小于置信度门限1001的可靠预测点，该置信度门限1001由相关算法根据自身逻辑设置，在本申请实施例中，对置信度门限1001不做限定。
S1003、电子设备100筛选出在车辆101完成停车动作前的一段时间区间W内的高优先候选点。
如图10A所示，电子设备100选择出在阴影范围内的所有地磁候选匹配位置，并称之为高优先候选点。由于电子设备100对传感器采取的数据进行处理时会存在误差，因此设置有时间区间W，最终的车辆101位置扩大在时间区间W中的地磁候选匹配位置中，而不是仅仅将车辆101停放时刻T所在的位置判定成最终的车辆101位置。同时，电子设备100记录了每一个高优先候选点与车辆101停放时刻T之间的时间差，时间差越大表示越接近车辆101停放时刻T。
S1004a、电子设备100对高优先候选点进行加权处理，并将权重最高的高优先候选点选做最终预测的车辆101停放位置。
电子设备100对多个高优先候选点进行相关的处理运算，可以判断出最终的车辆101停放的位置，其中，车辆101停放的位置包括有位置、楼层以及置信度信息。
下面是对多个高优先候选点进行加权处理的公式：
公式(1)是对所有高优先候选点进行权重计算。其中，公式(1)中的Weight of TOP Predictionn表示各个高优先候选点的权重；
公式(1)中的Uncertaintyn指的是置信度，高优先候选点的置信度值越大，表示该高优先候选点所预测的位置与实际位置误差越大，因此在对高优先候选点进行加权处理的公式(1)中将调权因子Scale1乘以置信度Uncertaintyn的倒数，其中调权因子Scale1是根据算法的逻辑设置的，在本申请实施例中，对调权因子Scale1的大小不做限制；
公式(1)中的Tn指的是时间，高优先候选点的时间Tn越大，表示该高优先候选点所预测的位置的时间越靠近车辆101停放完成的时间，因此在对高优先候选点进行加权的方法公式(1)中将调权因子Scale2乘以时间Tn，其中调权因子Scale2是根据算法的逻辑设置的，在本申请实施例中，对调权因子Scale2的大小不做限制；
公式(1)中的Distance(n-1,n+1)指的是前后预测位置点之间的距离，前一个高优先候选点与后一个高优先候选点之间的距离越大，表示该高优先候选点的可靠性越低，因此依据轨迹的连续性原理，在对高优先候选点进行加权处理的公式(1)中将调权因子ScaleX乘以Distance(n-1,n+1)的倒数；其中，若前后预测位置点之间的距离Distance(n-1,n+1)小于或等于门限THk，则将调权因子ScaleX设置成Scale3；若前后预测位置点之间的距离Distance(n-1,n+1)大于门限THk，则将调权因子ScaleX设置成Scale4。在本申请实施例中，THk可以根据算法的逻辑设置，对THk的大小不做限定。
下面是确定最终预测的车辆101位置的公式：
Estimated Location＝MAX(W(TOP Predictionn)) (2)
公式(2)是对从所有高优先候选点中选出权重最高的高优先候选点，并将该权重最大的高优先候选点作为最终预测的车辆101位置。其中，W(TOP Predictionn)表示的是所有高候选点通过公式(1)计算得到的权重；MAX(W(TOP Predictionn))表示在多个权重中选出权重最大的一个；Estimated Location表示权重最大的高优先候选点所在的具体位置作为最终的车辆101位置。
下面是预测位置准确率的公式：
Estimated Locationuncertainty＝Uncertainty of MAX(W(TOP Predictionn))  (3)
公式(3)是获取权重最高的高优先候选点的置信度。其中，Estimated Locationuncertainty表示了最终预测的车辆101位置的准确率；Uncertainty of MAX(W(TOP Predictionn))表示的是选择出了权重最大的高优先候选点，并根据图10A找出该权重最大的高优先候选点的置信度。电子设备100可以选出高优先候选点的置信度大于阈值的一个或多个高优先候选点作为最终车辆101停放的预测范围。在一些实施例中，限制高优先候选点的置信度的阈值可以被称为第三阈值。
S1004b、电子设备100获取多个高优先候选点中出现频度最高的楼层。
下面是获取多个高优先候选点中出现频度最高的楼层的公式：
Estimated Floor＝MODE(Floor-m,…,Floorm)in TOP Prediction(s)  (4)
公式(4)是对高优先候选点所在的楼层进行众数计算。其中Estimated Floor表示的是最终预测的车辆101停放的位置的楼层；MODE(X)表示的是对X出现的次数进行统计计算；Floor-m,…,Floorm表示的是所有高优先候选点所在的所有楼层，其楼层层数可以重复出现；in TOP Prediction(s)表示的在是多个高优先候选点进行公式(4)的运算。
下面是预测楼层准确率的公式：
Estimated Flooruncertainty＝MAX(Probability(Floor-m,…,Floorm))in TOP Prediction(s)  
(5)
公式(5)是对最终预测的车辆101停放的位置的楼层进行概率计算，即计算最终预测的车辆101停放的楼层出现的次数占所有高优先候选点的楼层出现次数的总数的概率大小。其中，Estimated Flooruncertainty表示的是最终预测车辆101停放的楼层的准确率；(Probability(Floor-m,…,Floorm)表示的是最终车辆101停放在所有高优先候选点的所在的每一个楼层的概率；
例如，高优先候选点有10个，其中有8个高优先候选点在负二楼(即m大小为-2)，有2个高优先候选点在负三楼(即m大小为-3)，则最终车辆101停放在负二楼的概率(Probability(Floor-2)＝80％，最终车辆101停放在负三楼的概率(Probability(Floor-3)＝20％。
MAX(X)表示的是在X中选出最大的数值。例如，在上述例子中，(Probability(Floor-2)＝80％，(Probability(Floor-3)＝20％，则MAX(Probability(Floor-2,Floor-3))＝80％，即预测车辆101所在楼层的准确的概率为80％。
S1005b、电子设备100对位于出现频度最高的楼层的高优先候选点进行加权处理，并将权重最高的高优先候选点选做最终预测的车辆101停放位置。
其中，电子设备100对位于出现频度最高的楼层的多个高优先候选点进行加权处理的方式可以参考上述公式(1)，在此不做赘述，其中Weight of TOP Predictionn表示位于出现频度最高的楼层的多个高优先候选点的权重。
可选的，电子设备100可以先基于多个高优先候选点确定最终车辆101停放的楼层，再从位于该楼层的高优先候选点中选出权重最大的高优先候选点作为最终车辆101停放的位置，且将位于该楼层的高优先候选点的置信度大于阈值的高优先候选点作为最终车辆101停放的位置的预测范围。在一些实施例中，限制位于最终车辆101停放的楼层的高优先候选点的置信度的阈值可以被称为第四阈值。
值得说明的是，电子设备100通过高优先候选点来获取最终预测车辆101位置可以通过执行S1004a来获取，可以通过执行S1004b和S1005b来获取，还可以同时使用两种方式来获取最终预测车辆101位置。
当两种方式获取到的车辆101所停放得楼层一致且车辆101停放的位置的预测范围有重叠部分时，电子设备100可以选择两种方式得到的车辆101停放的位置的预测范围的交集作为最终车辆101停放的位置的预测范围，也可以选择两种方式得到的车辆101停放的位置的预测范围的并集作为最终车辆101停放的位置的预测范围。在本申请实施例中，对此不做限定。
当两种方式获取到的车辆101所停放得楼层不一致时，用户可以根据实际情况来选择。例如，电子设备100执行S1004a获取到的车辆101所在的楼层为负二层，电子设备100执行S1004b和S1005b获取到的车辆101所在的楼层为负三层，用户可以选择先去电子设备100在应用程序上显示的车辆101所停放在负二层的指示位置进行查看。在车辆101不停放在负 二层的情况下，用户可以对电子设备100进行相应操作(例如点击操作)，使电子设备100在应用程序中显示出车辆101所停放在负三层的指示位置。在本申请实施例中，对此不做限定。
图11为本申请实施例中特定室内停车场中位置的输出点的仿真示意图。
参考图11，如图11示例性示出了特定室内停车场中所有位置的输出点的仿真图。如图11所示，示例性地，电子设备100对特定室内停车场的具体的一层平面楼层进行了相关位置的仿真模拟，该平面楼层可以包含但不限于车道、平面停车位以及坡道停车位等等。图11所示的位置的仿真图中包含了采集位置点、实际行驶点以及预测行驶点三种位置信息。
其中采集位置点指的是搭载有电子设备100的采集车801对特定室内停车场进行全面采集后获取的多个采集位置点，该采集位置点可以用图11中所示的浅灰色小圆点来表示，其范围可以覆盖特定室内停车场的一层平面楼层的车辆101可以行驶到达的所有位置；
实际行驶点指的是在时间区间N内，用户驾驶的车辆101在特定室内停车场的实际的行驶位置，该实际行驶点可以用图11中所示的深灰色大圆点来表示，图11中还示出了在时间区间N内车辆101实际行驶路径的起点和终点；
预测行驶点指的是在时间区间N内，电子设备100将从封装的传感器数据中的地磁指纹信号中萃取后得到的地磁特征数据输入至深度学习模型后得到的地磁候选匹配位置，该预测行驶点可以用图11中所示的浅灰色大圆点表示，图11中还示出了在时间区间N内所预测的车辆101行驶路径的起点和终点。
图12是本申请实施例中电子设备100的软件结构示意框图。
实施上述方法，可以实现电子设备100获取车辆101位置的功能，其中该位置可以包括但不限于楼层平面、楼层通道、楼层入口附近以及楼层之间的坡道。
如图12所示，本申请实施例提供的车辆101停放记录获取方法的软件架构包括车辆101停放检测模块、传感器数据采集模块、深度学习模型以及位置与楼层决策模块。
车辆101停放检测模块用于检测车辆101是否已经完成停放动作。电子设备100判断车辆101停放完成的方式可以包括但不限于：蓝牙断开连接、倒车动作完成以及自动泊车模式完成等。
传感器数据采集模块用于取得传感器数据，并对传感器数据进行封装处理。其中，传感器数据可以包括但不限于加速度传感器、陀螺仪传感器和地磁传感器采集的数据。
深度学习模块可以直接用于对地磁指纹信号进行预测处理来获得地磁初始匹配位置，还可以用于对按时间排序的地磁指纹序列进行预测处理，从而得到地磁候选匹配位置即车辆101位置的多个预测点。其中，电子设备100利用惯性导航技术对地磁指纹信号进行萃取后得到地磁特征数据，并从萃取后的地磁特征数据中得到按时间排序的地磁指纹序列。
位置与楼层决策模块用于对地磁候选匹配位置进行判断，并从中选择出最接近车辆101实际停放的记录。其中，该记录包括但不限于车辆101停放的位置、楼层以及置信度。对车辆101位置的多个预测点进行判断选择的方法在上文中有详细介绍，在此不做赘述。
应该理解的是，图12所示的电子设备100的软件结构仅为示例，电子设备100可以具有比图中所示的更多的或者更少的模块，可以组合两个或多个的模块，或者可以具有不同的模块。图中所示出的各种模块可以在包括一个或多个信号处理和/或专用集成电路在内的硬件、软件、或硬件和软件的组合中实现。
下面介绍本申请实施例提供的获取车辆101位置的方法的用户界面示意图。
图13A示例性示出了电子设备100上安装并运行停车应用的示例性图形用户界面131。
如图13A所示，用户界面131中显示有：“记录泊车”的控件1301、“显示GNSS定位”的控件1302、位置提示信息1303、地图显示区域1304。在一些实施例中，用户界面131可以是停车应用的首页界面。
其中，“记录泊车”的控件1301用于监听用户操作，电子设备100可以响应于该用户操作开启“记录泊车”功能，即电子设备100可以开始甄选识别用于用户停放车辆101的特定的车库，在车辆101进入特定室内停车场，完成停放后，记录车辆101位置。
“显示GNSS定位”的控件1302用于监听用户操作，电子设备100可以响应于该用户操作显示车辆101GNSS定位，并且在用户界面131中显示位置提示信息1303，位置提示信息1303用于提示用户车辆101在室外的具体位置。
地图显示区域1304用于为位置提示信息1303的显示提供可靠支持，且在用户界面131中该地图显示区域1304为室外的以位置提示信息1303为中心的地图。
不限于图13A所示的作用于“记录泊车”的控件1301的用户操作，在本申请的其他一些实施例中，电子设备100还可以通过其他方式来开启“记录泊车”功能。
例如，电子设备100还可以默认开启“记录泊车”的功能，或者，电子设备100还可以响应于接收到的语音指令开启“记录泊车”的功能，或者，电子设备100可以通过运动侦测(Motion detection technology)到车辆101为“行车驾驶”状态后，自动开启“记录泊车”的功能等等。
其中，运动侦测可以是指，由摄像头按照不同帧率采集得到的图像会被电子设备100按照一定算法进行计算和比较，当画面中场景变化时，例如电子设备100摄像头画面中车外的场景发生快速变化等等，计算和比较得出的结果可能超过阈值并可以指示系统能自动作出相应的处理，例如判断车辆101为“行车驾驶”状态。在本申请实施例中，对运动侦测的原理不做限定，可能还存在其他除使用摄像头之外的方式。
又例如，在车辆101行驶时，当电子设备100检测到车辆101靠近地理围栏(Geofence)时，电子设备100自动开启“记录泊车”的功能，其中，Geofence是指当电子设备100进入或离开某个特定地理区域，或在该区域内活动时，电子设备100可以自动接收通知或警告的特定地理区域。在一些实施例中，Geofence可以是指特定建筑物车库附近一定范围内的区域。
图13B示例性示出了电子设备100上安装并运行停车应用的示例性图形用户界面132。
如图13B所示，用户界面132中显示有“显示GNSS定位”的控件1302、“获取位置”的控件1305、地图显示区域1306。在一些实施例中，当车辆101完成停放动作后或者其他一些能够说明车辆101已经停放完成的操作，电子设备100会从显示用户界面131变成显示用户界面132。
其中，“显示GNSS定位”的控件1302的详细介绍可以参考图13A中的描述，在图13B中，由于车辆101已经进入室内，而在室内中无法使用GNSS定位技术，故未能显示位置提示信息1303。
“获取位置”的控件1305用于监听用户操作，电子设备100可以响应于该用户操作来获取车辆101位置，即通过上文中所提到的车载定位算法来获取车辆101位置。
地图显示区域1306用于显示车辆101当前停放的特定室内停车场的地图，进一步地，该地图显示的为车辆101所停放楼层的平面地图。
不限于图13B所示的作用于“获取位置”的控件1305的用户操作，在本申请的其他一些实 施例中，电子设备100还可以通过其他方式来获取车辆101位置。
例如，电子设备100还可以在显示有用户界面132后默认开启“获取位置”的功能，或者，电子设备100还可以响应于接收到的语音指令开启“获取位置”的功能，或者，电子设备100可以通过运动侦测到车辆101为“倒车”、“停车”状态后，自动开启“获取位置”的功能等等。又例如，当电子设备100与车辆101的蓝牙断开连接后，电子设备100自动开启“获取位置”的功能。
图13C示例性示出了电子设备100上安装并运行停车应用的示例性图形用户界面133。
如图13C所示，用户界面133中显示有楼层提示信息1307、位置与范围提示信息1308。
其中，楼层提示信息1307用于提示用户当前车辆101停放楼层。例如，如图13C所示，当前车辆101停放楼层为-3层。
位置与范围提示信息1308用于提示用户当前车辆101位置以及范围。进一步地，图13C中所示的位置与范围提示信息1308中的灰色圆点代表的是车辆101停放的预测位置点，位置与范围提示信息1308中的黑色圆形范围代表的是车辆101位置的预测范围，其中包括高优先候选点中置信度大于阈值的高优先候选点集合范围。
不限于图13C所示的楼层提示信息1307、位置与范围提示信息1308，在本申请的其他一些实施例中，电子设备100还可以在用户界面133中显示有车位号、车位所属区域等其他可以指示车辆101在特定室内停车场中停放位置的信息。
上述实施例中所用，根据上下文，术语“当…时”可以被解释为意思是“如果…”或“在…后”或“响应于确定…”或“响应于检测到…”。类似地，根据上下文，短语“在确定…时”或“如果检测到(所陈述的条件或事件)”可以被解释为意思是“如果确定…”或“响应于确定…”或“在检测到(所陈述的条件或事件)时”或“响应于检测到(所陈述的条件或事件)”。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。该计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行该计算机程序指令时，全部或部分地产生按照本申请实施例该的流程或功能。该计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。该计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，该计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线)或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。该计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。该可用介质可以是磁性介质，(例如，软盘、硬盘、磁带)、光介质(例如DVD)、或者半导体介质(例如固态硬盘)等。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，该流程可以由计算机程序来指令相关的硬件完成，该程序可存储于计算机可读取存储介质中，该程序在执行时，可包括如上述各方法实施例的流程。而前述的存储介质包括：ROM或随机存储记忆体RAM、磁碟或者光盘等各种可存储程序代码的介质。

Claims (14)

  1. 一种车辆位置的获取方法，所述方法应用于电子设备，其特征在于，包括：
    电子设备确定车辆的地理位置在预设区域之内后，取得地磁指纹信号序列；所述地磁指纹信号序列包括N个地磁指纹信号，N为正整数；
    所述电子设备根据所述地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，所述目标位置信息同时指示了所述车辆所在的楼层以及于所述楼层的位置；当所述车辆位于两楼层之间时，所述目标位置信息同时指示了所述车辆所在于两楼层之间以及于所述两楼层之间的位置。
  2. 根据权利要求1所述的方法，其特征在于，所述电子设备确定车辆的地理位置在预设区域之内，包括：
    所述电子设备基于定位技术识别到所述车辆的地理位置在特定区域之内；
    或者，所述电子设备接收到服务器发送的指令，所述指令为所述服务器检测到所述车辆的地理位置在特定区域之内后，向所述电子设备发出的；
    或者，所述电子设备检测到来自于所述预设区域内的信号源发送的信号，确定所述车辆的地理位置在预设区域之内。
  3. 权利要求1所述的方法，其特征在于，所述电子设备根据所述地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，包括：
    当所述电子设备检测到所述车辆处于行驶状态，所述电子设备根据所述地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息。
  4. 权利要求1所述的方法，其特征在于，所述电子设备根据所述地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，包括：
    当所述电子设备检测到所述车辆处于停车状态，所述电子设备根据所述地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息。
  5. 根据权利要求4所述的方法，其特征在于，所述电子设备根据所述地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，之前还包括：
    所述电子设备检测到所述电子设备或所述车辆满足第一预设条件，确定所述车辆处于停车状态。
  6. 根据权利要求5所述的方法，其特征在于，所述第一预设条件包括以下一项或多项：
    所述电子设备和所述车辆从蓝牙连接状态转变为蓝牙断开状态；
    或者，所述电子设备接收到启动停车模式的用户指令；
    或者，所述电子设备检测到所述车辆的行驶速度小于第一阈值且行驶方向发生变化。
  7. 根据权利要求1所述的方法，其特征在于，所述电子设备根据所述地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，之前还包括：
    所述电子设备获取所述深度学习模型的权重，所述深度学习模型的权重是以多个地磁指纹信号序列和对应的位置信息训练得到的，所述多个地磁指纹信号序列中每个地磁指纹信号序列对应一个位置信息，所述多个地磁指纹信号序列中包括从各楼层以及两个楼层之间采集到的地磁指纹信号序列，所述位置信息中包括各楼层与两个楼层之间的楼层信息和位置。
  8. 根据权利要求1所述的方法，其特征在于，所述电子设备根据所述地磁指纹信号序列，通过预先训练好的深度学习模型确定目标位置信息，包括：
    所述电子设备通过预先训练好的深度学习模型，确定所述地磁指纹信号序列对应的第一行驶轨迹，所述第一行驶轨迹包括M个位置信息，M小于或等于N；
    所述电子设备基于所述第一行驶轨迹，在所述地磁指纹信号序列中筛选得到满足第二预设条件的地磁指纹信号；
    所述电子设备通过所述深度学习模型，确定所述满足第二预设条件的地磁指纹信号对应的第二行驶轨迹，所述第二行驶轨迹包括P个位置信息，P小于或等于M；
    所述电子设备从所述第二行驶轨迹中确定所述目标位置信息。
  9. 根据权利要求8所述的方法，其特征在于，所述第二预设条件，包括：
    任意两个地磁指纹信号对应的位置信息的楼层信息或位置点不同；
    或者，任意两个地磁指纹信号对应的位置信息的位置点之间的距离大于第二阈值。
  10. 根据权利要求8所述的方法，其特征在于，所述第一行驶轨迹还包括所述M个位置信息中每个位置信息的置信度，所述第二行驶轨迹中包括P个位置信息和所述P个位置信息中每个位置信息的置信度；
    所述电子设备从所述第二行驶轨迹中确定所述目标位置信息，包括：
    所述电子设备将所述P个位置信息中置信度高于第三阈值的一个或多个位置信息作为目标位置信息。
  11. 根据权利要求10所述的方法，其特征在于，所述第一行驶轨迹还包括所述M个位置信息中每个位置信息的置信度，所述第二行驶轨迹中包括P个位置信息和所述P个位置信息中每个位置信息的置信度，P小于或等于M；
    所述电子设备从所述第二行驶轨迹中确定所述目标位置信息，包括：
    所述电子设备确定所述P个位置信息中置信度高于第三阈值的一个或多个位置信息；
    所述电子设备将所述一个或多个位置信息中楼层信息的众数作为所述目标位置信息中的停车楼层；
    所述电子设备确定具有所述停车楼层并且置信度高于第四阈值的位置信息作为所述目标位置信息。
  12. 根据权利要求1-11任一项所述的方法，其特征在于，所述电子设备从所述第一位置信息中确定目标位置信息，之后还包括：所述电子设备输出所述目标位置信息。
  13. 根据权利要求12所述的方法，其特征在于，所述电子设备输出所述目标位置信息，包括：
    所述电子设备输出所述车辆的停车楼层以及位置范围，所述位置范围中包括所述目标位置信息中的一个或多个位置信息对应的位置点。
  14. 一种电子设备，其特征在于，所述电子设备包括存储器、一个或多个处理器，所述存储器用于存储计算机程序，所述处理器用于调用计算机程序，使得所述电子设备执行如权利要求1至13任一项所述的方法。
PCT/CN2023/083798 2022-03-26 2023-03-24 车辆位置的获取方法及电子设备 WO2023185687A1 (zh)

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