Location data acquisition method and related apparatus

Abstract

Provided in the present application are a location data acquisition method and a related apparatus. The method comprises: acquiring measurement data and time data, wherein the measurement data comprises sensor data and wireless signal data, the sensor data is acquired by means of a sensor arranged on a terminal, and the time data represents an acquisition time of the measurement data; and on the basis of the sensor data, an acquisition time of the sensor data, outdoor positioning data and an acquisition time of the outdoor positioning data, acquiring location data of the terminal upon entering an indoor area. In the method, the location of a terminal upon entering an indoor area is determined on the basis of related information of outdoor positioning data and sensor data, thereby avoiding the problem of the terminal failing to transmit information to a network in a timely manner when the communication quality between the terminal and the network deteriorates after the terminal enters the indoor area, and thus enhancing the timeliness of terminal positioning. The outdoor positioning data and the sensor data are highly accurate, thereby improving the accuracy of location data of the terminal upon entering the indoor area. The sensor data has a higher stability, thereby meeting the positioning requirements of a user in a timely manner.

Description

Location data acquisition methods and related devices

[0001] This application claims priority to Chinese Patent Application No. 2024118017513, filed on December 9, 2024, entitled "Method and Apparatus for Acquiring Location Data", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of electronic information technology, and in particular to a method and related apparatus for acquiring location data. Background Technology

[0003] In the field of mobile communications, networks can locate terminals and obtain their location data in order to provide location-related services to the terminals.

[0004] However, because the environment in which the terminal operates is constantly changing, the positioning performance between the network and the terminal varies with these changes. For example, in some scenarios, the communication quality between the network and the terminal is poor, and the network cannot obtain the terminal's location in a timely manner, resulting in positioning errors and poor timeliness. Therefore, the accuracy of positioning between the network and the terminal needs to be improved. Summary of the Invention

[0005] This application provides a method and related apparatus for acquiring location data, aiming to acquire more accurate location data. The disclosed technical solution is as follows:

[0006] The first aspect of this application provides a method for acquiring location data, applied to an artificial intelligence (AI) model. The method includes: acquiring measurement data and time data, whereby the measurement data includes sensor data and wireless signal data. The sensor data is acquired through sensors installed on a terminal, and the time data represents the acquisition time of the measurement data. Based on the sensor data, the acquisition time of the sensor data, outdoor positioning data, and the acquisition time of the outdoor positioning data, the method acquires the terminal's indoor location data. In this method, the location of the terminal indoors is determined based on relevant information from the outdoor positioning data and the sensor data, avoiding the problem of delayed information transmission to the network when communication quality deteriorates after the terminal enters indoors, thus enhancing the timeliness of terminal positioning. Furthermore, the high accuracy of outdoor positioning data and sensor data further improves the accuracy of the indoor location data. Additionally, sensor data has higher stability (e.g., wireless signals are difficult to acquire in underground environments, but sensor data is easy to acquire), enabling timely fulfillment of the user's positioning needs.

[0007] In some implementations, obtaining the terminal's indoor location data includes: using pedestrian dead reckoning algorithms or vehicle dead reckoning algorithms. Pedestrian dead reckoning algorithms determine this based on the terminal's low-speed movement, while vehicle dead reckoning algorithms determine it based on high-speed movement. The determination of whether the terminal is moving at low or high speed is based on accelerometer data, or on navigation and map data. Selecting a dead reckoning algorithm based on the terminal's movement speed is beneficial for obtaining more accurate indoor location data.

[0008] In some implementations, the interval between the acquisition time of outdoor positioning data and the first time is satisfied with a first condition, where the first time is the time when the terminal enters the room. Outdoor positioning data, such as satellite positioning data, has high positioning accuracy. Furthermore, when using outdoor positioning data to derive indoor location data, the closer the acquisition time of the outdoor positioning data is to the time when the terminal enters the room from the outside, the higher the accuracy of the derived indoor location data. Therefore, filtering outdoor positioning data based on the acquisition time is beneficial to further improve the accuracy of indoor location data.

[0009] In some implementations, outdoor positioning data is acquired based on wireless signal data, including: outdoor positioning data is acquired based on geographic location coordinates corrected to first location data, where the geographic location coordinates are the coordinates of an exit or entrance, and the terminal moves from indoors to outdoors via an exit, or from outdoors to indoors via an entrance. Because the locations of building exits or entrances are fixed, correcting outdoor positioning data based on exit or entrance coordinates helps reduce the "drift" caused by outdoor obstructions in satellite positioning data or cellular communication data, improving the accuracy of outdoor positioning data, and further improving the accuracy of the terminal's indoor location data.

[0010] In some implementations, the distance between the location represented by the geographic coordinates and the location represented by the outdoor positioning data is less than or equal to a distance threshold. When a building has multiple exits or entrances, the location of the exit or entrance is filtered based on the outdoor positioning data, making the correction of the outdoor positioning data more accurate.

[0011] In some implementations, geographic location coordinates are determined based on the driving trajectory, which is obtained from the terminal's navigation data. Exit or entrance location data obtained from navigation data is more accurate, leading to more precise corrections to outdoor positioning data.

[0012] In some implementations, the first location data does not meet the accuracy requirements, while the outdoor positioning data (i.e., the first location data) obtained based on wireless signal data meets the accuracy requirements and does not need to be corrected. This is beneficial for saving computing resources while ensuring the accuracy of outdoor positioning data.

[0013] In some implementations, the determination of whether a terminal enters an indoor space is based on measurement data. This allows for the acquisition of the terminal's location data using appropriate data and algorithms based on the terminal's entry status from outdoors to indoors, thereby improving the accuracy of the terminal's location data.

[0014] In some implementations, the determination of a terminal entering indoors is based on at least one of the following: a decrease in light intensity greater than or equal to a first threshold and a degradation in satellite positioning signal quality greater than or equal to a second threshold. The decrease in light intensity is the reduction in second light intensity compared to first light intensity. The second light intensity is represented by visible light sensor data corresponding to a third time, and the first light intensity is represented by visible light sensor data corresponding to a second time. The degradation in satellite positioning signal quality is the degradation of a second quality parameter compared to a first quality parameter. The second quality parameter is represented by satellite positioning data corresponding to the third time, and the first quality parameter is represented by satellite positioning data corresponding to the second time, where the second time is earlier than the third time. In other words, if a sudden deterioration or even disappearance of GNSS quality is detected and / or a sudden dimming of light is detected by the light sensor, it is determined that the terminal has entered indoors from outdoors. This determination method fully utilizes the differences in wireless signals and sensor data between outdoors and indoors, and has high accuracy and feasibility.

[0015] In some implementations, the determination of whether a terminal enters an indoor environment is based on sensor data, including at least one of visible light sensor data, gyroscope data, acceleration data, and barometric pressure data. This determination method, along with the characteristics of wireless signals and sensor data from the terminal entering an indoor environment, especially underground, helps to accurately identify this scenario.

[0016] In some implementations, the location data of the terminal entering the indoor environment is obtained based on sensor data, the acquisition time of the sensor data, outdoor positioning data, and the acquisition time of the outdoor positioning data. This includes: obtaining the location data of the terminal entering the indoor environment based on at least one of short-range signal data acquired after the terminal enters the indoor environment, the reliability of sensor data, and the reliability of outdoor positioning data; and obtaining the location data of the terminal entering the indoor environment based on sensor data, the acquisition time of the sensor data, outdoor positioning data, and the acquisition time of the outdoor positioning data. Short-range signals serve as characteristic data of the indoor environment, and their reliability can be used to filter out sensor data and outdoor positioning data with higher reliability. These, along with the sensor data, outdoor positioning data, and their acquisition time, are all used as inputs to the AI ​​model, which helps to further improve the accuracy of the location data output by the AI ​​model.

[0017] A second aspect of this application provides an electronic device comprising: one or more processors and a memory; the memory being used to store program code; and the processor being used to run the program code, causing the electronic device to implement the method provided in the first aspect of this application.

[0018] A third aspect of this application provides a computer-readable storage medium having instructions stored thereon that, when executed on an electronic device, cause the electronic device to perform the method provided in the first aspect of this application.

[0019] A fourth aspect of this application provides a computer program product having stored thereon that, when executed on an electronic device, causes the electronic device to implement the method provided in the first aspect of this application.

[0020] A fifth aspect of this application provides a communication device, comprising: a communication module and a processing module. The communication module is used to acquire measurement data and time data. The measurement data includes sensor data and wireless signal data. The sensor data is acquired through a sensor installed on a terminal. The wireless signal data includes at least one of satellite positioning data and cellular communication data. The time data represents the acquisition time of the measurement data. The processing module is used to acquire indoor location data of the terminal based on the sensor data, the acquisition time of the sensor data, outdoor positioning data, and the acquisition time of the outdoor positioning data. The outdoor positioning data represents the outdoor location data of the terminal, and the outdoor positioning data is acquired based on the wireless signal data.

[0021] In some implementations, obtaining the location data of the terminal entering the indoor space includes: obtaining the location data of the terminal entering the indoor space based on a pedestrian dead reckoning algorithm or a vehicle dead reckoning algorithm. The pedestrian dead reckoning algorithm is determined based on the terminal being in a low-speed movement state, and the vehicle dead reckoning algorithm is determined based on the terminal being in a high-speed movement state. The determination of whether the terminal is in a low-speed or high-speed movement state is based on acceleration sensor data, or on navigation data and map data.

[0022] In some implementations, the interval between the acquisition time of the outdoor positioning data and the first time satisfies a first condition, where the first time is the time when the terminal enters the indoor environment.

[0023] In some implementations, the outdoor positioning data is obtained based on the wireless signal data, including: the outdoor positioning data is obtained based on geographic location coordinates corrected first location data, the first location data is obtained based on the wireless signal data, the geographic location coordinates are the coordinates of an exit or an entrance, the terminal moves from indoors to outdoors via the exit, and the terminal moves from outdoors to indoors via the entrance.

[0024] In some implementations, the distance between the location represented by the geographic coordinates and the location represented by the outdoor positioning data is less than or equal to a distance threshold.

[0025] In some implementations, the geographic location coordinates are determined based on the driving trajectory, which is obtained based on the navigation data of the terminal.

[0026] In some implementations, the first position data does not meet the accuracy requirements.

[0027] In some implementations, the terminal's entry into the indoor environment is determined based on at least one of the following: the decrease in light intensity is greater than or equal to a first threshold and the degradation of satellite positioning signal quality is greater than or equal to a second threshold. The decrease in light intensity is the decrease in second light intensity compared to first light intensity. The second light intensity is represented by visible light sensor data corresponding to a third time, and the first light intensity is represented by visible light sensor data corresponding to a second time. The degradation of satellite positioning signal quality is the degradation of a second quality parameter compared to a first quality parameter. The second quality parameter is represented by satellite positioning data corresponding to the third time, and the first quality parameter is represented by satellite positioning data corresponding to the second time. The second time is earlier than the third time.

[0028] In some implementations, the determination of whether the terminal enters the room is based on the sensor data, which includes at least one of visible light sensor data, gyroscope data, acceleration data, and air pressure data.

[0029] In some implementations, obtaining the location data of the terminal entering the indoor environment based on the sensor data, the acquisition time of the sensor data, outdoor positioning data, and the acquisition time of the outdoor positioning data includes: obtaining the location data of the terminal entering the indoor environment based on at least one of short-range signal data acquired after the terminal enters the indoor environment, the reliability of the sensor data, and the reliability of the outdoor positioning data, and obtaining the location data of the terminal entering the indoor environment based on the sensor data, the acquisition time of the sensor data, the outdoor positioning data, and the acquisition time of the outdoor positioning data.

[0030] The fifth aspect of this application provides a chip system including one or more processors for calling and executing instructions stored in a memory, causing the method for acquiring location data provided in the first aspect of this application to be executed. The chip system may be composed of a chip or may include chips and other discrete devices.

[0031] A sixth aspect of this application provides a communication system comprising: a terminal and a network device, wherein the terminal is configured to acquire measurement data and time data, the measurement data including sensor data and wireless signal data, the sensor data being acquired through sensors installed on the terminal, and the wireless signal data including at least one of satellite positioning data and cellular communication data, and the time data representing the acquisition time of the measurement data; the network device is configured to acquire indoor location data of the terminal based on the sensor data, the acquisition time of the sensor data, outdoor positioning data, and the acquisition time of the outdoor positioning data, wherein the outdoor positioning data represents the outdoor location data of the terminal, and the outdoor positioning data is acquired based on the wireless signal data. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 is an example diagram of an application scenario for obtaining location data of a terminal via a network, according to an embodiment of this application.

[0034] Figure 2 is an example architecture diagram of a communication system for implementing a location data acquisition method provided by an embodiment of this application;

[0035] Figure 3 is an example of a scenario where a user carries a terminal from outdoors into an indoor space;

[0036] Figure 4 is a flowchart of a method for acquiring location data provided in an embodiment of this application;

[0037] Figure 5 is an example architecture diagram for obtaining training data provided in an embodiment of this application;

[0038] Figure 6 is an example of the architecture of a communication system for implementing a location data acquisition method provided by an embodiment of this application;

[0039] Figure 7 is a structural example diagram of a terminal provided in an embodiment of this application;

[0040] Figure 8 is a structural example diagram of a communication device provided in an embodiment of this application;

[0041] Figure 9 is a structural example diagram of a communication device provided in an embodiment of this application. Detailed Implementation

[0042] The terms "first," "second," and "third," etc., used in this application specification, claims, and drawings are used to distinguish different objects, not to limit a specific order.

[0043] In the embodiments of this application, the words "in some implementations" or "for example" are used to indicate examples, illustrations or descriptions, and should not be construed as being more preferred or more advantageous than other embodiments or designs.

[0044] The communication systems applicable to the embodiments of this application can be second-generation (2G) communication systems, third-generation (3G) communication systems, long-term evolution (LTE) systems, fifth-generation (5G) communication systems, LTE and 5G hybrid architectures, 5G new radio (5G NR) systems, and new communication systems that will emerge in the future development of communication.

[0045] A communication system includes terminals and network equipment. Network equipment includes access network equipment and core network equipment.

[0046] Terminals can take various forms, such as mobile phones, tablets, computers with wireless transceiver capabilities, virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminals in industrial control, vehicle-mounted terminal devices, wireless terminals in self-driving vehicles, wireless terminals in remote medical care, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, wearable terminal devices, and so on. Terminals are sometimes also referred to as terminal equipment, user equipment (UE), access terminal equipment, vehicle-mounted terminals, industrial control terminals, UE units, UE stations, mobile stations, mobile stations, remote stations, remote terminal equipment, mobile devices, UE terminal equipment, terminal equipment, wireless communication equipment, UE agents, or UE devices, etc. Terminals can also be fixed terminals or mobile terminals.

[0047] Access network equipment includes base stations. A base station is any device located on the network side with wireless transceiver capabilities, including but not limited to: evolved Node Bs (NodeBs, eNBs, or e-NodeBs) in LTE, base stations (gNodeBs or gNBs) or transmission receiving points (TRPs) in new radio (NR), base stations evolved later in 3GPP, access nodes, wireless relay nodes, and wireless backhaul nodes in Wi-Fi systems. Base stations can be macro base stations, micro base stations, pico base stations, small cells, relay stations, or balloon stations, etc. A base station can contain one or more co-located or non-co-located TRPs. A base station can also be a radio controller, centralized unit (CU), and / or distributed unit (DU) in a cloud radio access network (CRAN) scenario. Base stations can communicate with terminals directly or via relay stations.

[0048] Core network equipment and base stations can be independent physical devices, or the functions of core network equipment and the logical functions of base stations can be integrated into the same physical device, or a single physical device can integrate some of the functions of core network equipment and some of the functions of base stations. For example, as shown in Figure 1, the core network equipment runs LMF.

[0049] In the following text, core network equipment and access network equipment will be collectively referred to as network equipment.

[0050] Figure 1 illustrates a process for acquiring terminal location data. As shown in Figure 1, the UE acquires wireless local area network (WLAN) signals, positioning reference signal (PRS) sent by the base station (gNB), and Bluetooth signals. Based on the acquired signals, it sends wireless signal data to the location management function (LMF). The wireless signal data can be understood as data obtained by parsing or measuring at least one of the WLAN signal, PRS, and Bluetooth signal. The LMF module is configured with a location estimation model (or positioning model), which outputs a location estimation result based on the wireless signal data.

[0051] However, because the environment in which the terminal operates is constantly changing, the positioning performance between network devices and terminals varies with these changes. For example, in some scenarios, the communication quality between network devices and terminals is poor, and the terminal may not be able to obtain PRS, Bluetooth signals, etc., in a timely manner, or may fail to provide timely feedback of wireless signal data. This results in the network device being unable to obtain the terminal's location in a timely manner, causing positioning errors and poor timeliness. Therefore, the accuracy of positioning between network devices and terminals needs to be improved.

[0052] In view of this, this application provides a method and related apparatus for acquiring location data, so as to achieve more accurate acquisition of location data.

[0053] Figure 2 is an example architecture diagram of a communication system used to implement a location data acquisition method.

[0054] Referring to Figure 2, the terminal includes: a global navigation satellite system (GNSS) module, a cellular module, a wireless local area network (WLAN) module, and a Bluetooth module.

[0055] A GNSS module is used to implement GNSS positioning functionality. Specifically, the GNSS module receives signals transmitted by satellites and acquires the GNSS data carried within those signals. GNSS data includes the terminal's position data acquired via GNSS and evaluation parameters for that position data. Evaluation parameters represent the accuracy of the terminal's position data acquired via satellite positioning; examples of evaluation parameters include at least one of the horizontal dilution of precision (HDOP) and carrier-to-noise ratio. GNSS data may also include the number of satellites searched. In environments with good satellite signal coverage, such as open outdoor areas, GNSS positioning provides relatively accurate terminal position data. However, in more enclosed or heavily obstructed environments, such as indoors, the GNSS module may not be able to receive GNSS signals of sufficient quality for positioning. GNSS is one example of satellite positioning and can be replaced by other satellite positioning technologies or used in conjunction with them.

[0056] The cellular module is used to implement cellular communication functions. Cellular communication data related to positioning includes, but is not limited to: PRS (Positioning Response System), Angle-of-Arrival (AOA), and Timing Advance (TA). Taking PRS as an example, the terminal receives the PRS sent by access network equipment such as a base station. PRS is used to assist positioning services in mobile communication. PRS that meets positioning requirements can be received both indoors and outdoors. The terminal can measure the PRS to obtain cellular communication data.

[0057] The WLAN module is used to implement wireless local area network functions such as wireless fidelity (Wi-Fi). Location-related wireless local area network functions include, but are not limited to: after receiving a Wi-Fi signal sent by an access point (AP), obtaining Wi-Fi data from the Wi-Fi signal. The Wi-Fi data includes AP information, Wi-Fi signal strength, basic service set identifier (BSSID), service set identified (SSID), and received signal strength indication (RSSI), etc.

[0058] The Bluetooth module is used to implement Bluetooth functionality. Location-related Bluetooth functions include, but are not limited to, detecting Bluetooth signals emitted by other electronic devices and acquiring Bluetooth data carried within those signals. Bluetooth data includes information about the sending device and the strength of the Bluetooth signal.

[0059] Both WLAN and Bluetooth are short-range communication technologies, so it is possible that short-range communication data sufficient for positioning may not be available outdoors.

[0060] The terminal also includes sensors, including but not limited to: gyroscopes, accelerometers, visible light sensors, and barometers, which are related to the technical solutions provided in the embodiments of this application.

[0061] Sensors are used to sense corresponding environmental data, such as gyroscopes to sense the terminal's pose data, such as horizontal angles; accelerometers to sense the terminal's acceleration data; visible light sensors to sense the visible light intensity data of the environment in which the terminal is located; and barometers to sense the air pressure value of the environment in which the terminal is located.

[0062] In the embodiments of this application, wireless signal data and sensor data are collectively referred to as measurement data.

[0063] The LMF incorporates an artificial intelligence (AI) model for location tracking. It also includes a scene determination module that uses at least one of the following: wireless signal data and sensor data, to determine the terminal's location—whether it's indoors, moving from outdoors to indoors, or outdoors. The AI ​​model's location tracking function utilizes different methods and data to acquire the terminal's location data for different scenes.

[0064] The modules in the terminal shown in Figure 2 are merely examples and not limitations. For example, the terminal may include more or fewer types of sensors than those shown in Figure 2. Also, LMF is an example of a network element, and other network elements in the network may also interact with the UE.

[0065] Figure 3 is a scenario example. In Figure 3, it is assumed that a user is carrying a terminal and traveling along a certain path, part of which is outdoors and part is indoors. The circles in Figure 3 represent positions on the path, namely positions 1 to 7.

[0066] In the scenario shown in Figure 3, Figure 4 is a flowchart of a method for acquiring location data provided by an embodiment of this application, which includes the following steps:

[0067] S101, The terminal acquires measurement data and time data.

[0068] The measurement data includes wireless signal data and sensor data.

[0069] Wireless signal data refers to data related to wireless communication signals. The terminal obtains wireless signal data by parsing the received wireless signals, or by measuring the received wireless signals, or by parsing and measuring the wireless signals.

[0070] Wireless signal data includes, but is not limited to: GNSS data, cellular communication data, Wi-Fi data, and Bluetooth data. It is understood that the terminal acquires wireless signal data through the wireless communication module illustrated in Figure 2.

[0071] Based on the scenario shown in Figure 3, it can be understood that when the terminal is outdoors, it can usually acquire the required GNSS and cellular communication data, but may not be able to acquire the required short-range communication data such as Bluetooth and Wi-Fi data. When the terminal is indoors, it can usually acquire the required cellular and short-range communication data, but may have difficulty acquiring the required GNSS data.

[0072] Sensor data includes, but is not limited to, at least one of the following: pose data, acceleration data, visible light intensity data, and air pressure values. The terminal acquires sensor data through the sensor illustrated in Figure 2.

[0073] In one method of terminal data acquisition, the terminal acquires data at a preset period. The period for acquiring wireless signal data and the period for acquiring sensor data can be the same or different. In another method of terminal data acquisition, the terminal acquires data in response to preset trigger conditions, and the conditions for acquiring wireless signal data and the conditions for acquiring sensor data can be the same or different. It is understood that the acquisition period or trigger conditions for each type of wireless signal data or each type of sensor data can be the same or different.

[0074] Time data represents the time when the measurement data was acquired. The acquisition time of the measurement data is the time when the terminal obtains the measurement data. Time data can be a single moment or a time interval.

[0075] It is understandable that different types of data may have different acquisition times. For example, GNSS signals have certain transmission time slots, so the acquisition time of GNSS data is related to the time domain resources for transmitting GNSS signals. The terminal acquires sensor data collected by the sensor with an inherent period. Therefore, the acquisition time of GNSS data may be different from the acquisition time of some sensor data, for example, there may be a certain time delay.

[0076] S102, the terminal sends measurement data and time data to the network device, and the network device receives the measurement data and time data accordingly.

[0077] In some implementations, the terminal filters the data before sending the filtered data to the network device. Data filtering can be understood as removing data that does not meet requirements, which include, but are not limited to, at least one of the following: location requirements, model training requirements, and data quality requirements. For example, if the Wi-Fi signal strength in a Wi-Fi data set does not meet a threshold, then that Wi-Fi data set is deleted.

[0078] In some implementations, to reduce the complexity of subsequent data processing, the terminal aligns data with similar acquisition times (i.e., the interval between acquisition times does not exceed a pre-configured threshold) to the same time. For example, 18:10:20 and 18:10:22 are aligned to 18:10:22, that is, the acquisition time of data acquired at 18:10:20 is modified to 18:10:22.

[0079] The aforementioned data filtering and time data processing can also be performed by network devices instead of the terminal.

[0080] Understandably, the terminal may be unable to acquire some data, such as Wi-Fi data outdoors. In this case, the terminal can send the acquired data along with the corresponding time data. Alternatively, the terminal can send data based on a pre-configured content template. All data included in the content template must be sent. For data that is not acquired, the terminal will indicate that it has not been acquired using a preset value. The content template includes the types of data the terminal needs the base station to send, such as visible light sensor data, gyroscope data, GNSS data, and wireless cellular data. The types of data included in the content template can be adjusted as needed.

[0081] In some implementations, the network device described in the embodiments of this application can be a core network device, and the interaction between the terminal and the network device is the interaction between the terminal and a network element in the core network. An example of a network element is the LMF in Figure 2.

[0082] S103. Based on measurement data and time data, the network device determines whether the terminal is outdoors, moving from outdoors to indoors, or indoors at the first moment.

[0083] It is understandable that the judgment result of the network device may or may not match the real-time location of the terminal. Therefore, the judgment result obtained in this step represents the result obtained by the network device based on the received data and the data acquisition time, and does not necessarily represent the real-time location status of the terminal.

[0084] The opposite of being outdoors is being indoors. Being indoors includes both entering from outdoors and being indoors. Entering from outdoors can be simply referred to as "entering indoors." The first position after entering indoors from outdoors is the state of entering indoors from outdoors. Taking Figure 3 as an example, the user carries the terminal from position 2 and moves sequentially to position 3, position 4, ..., according to time. Position 3 is the first position after entering indoors, that is, entering indoors from outdoors. Positions 3 to 7 are the positions indoors.

[0085] It is understood that the embodiments of this application are not limited to the first location where the terminal enters the room from the outside as "entering the room from the outside". For example, the location corresponding to "entering the room from the outside" can be any location inside the room. Or, the location corresponding to "entering the room from the outside" can be a location during the process of moving from the outside to the inside. Or, in other words, the location corresponding to "entering the room from the outside" is not distinguished by the geographical boundary between indoors and outdoors, and can depend on the specific application scenario and requirements.

[0086] In some implementations, the GNSS data with the first acquisition time includes the carrier-to-noise ratio and the number of satellites searched. Based on the carrier-to-noise ratio being greater than or equal to a carrier-to-noise ratio threshold and the number of satellites searched being greater than or equal to a number threshold, it is determined that the data indicates that the terminal is outdoors at the first acquisition time.

[0087] In some other implementations, if the carrier-to-noise ratio of the GNSS data with the second acquisition time is less than the carrier-to-noise ratio threshold and the number of satellites searched is less than the number threshold, it is determined that the data indicates that the terminal is indoors during the second acquisition time.

[0088] One way to determine whether a terminal has entered an indoor space from outdoors is to make a judgment based on data acquired at different times (such as adjacent acquisition times) and the comparison results between the data.

[0089] For example, data with a third acquisition time includes acceleration data, GNSS data, and visible light sensor data. Based on the acceleration data, it is determined that the terminal is in a low-speed movement state, such as when the user is walking while carrying the terminal. Furthermore, based on the GNSS data and visible light sensor data with a third acquisition time, it is determined that the terminal is outdoors. In other words, based on the data with a third acquisition time, it is determined that the terminal is in a low-speed movement state outdoors.

[0090] The data acquired at the fourth acquisition time includes GNSS data and visible light sensor data. The fourth acquisition time follows the third acquisition time, and the preceding data in the order of acquisition time is the data acquired at the third acquisition time. If the visible light sensor data acquired at the fourth acquisition time, compared to the visible light sensor data acquired at the third acquisition time, indicates a decrease in light intensity greater than or equal to a first threshold, meaning a significant deterioration in GNSS signal quality, and if the GNSS data acquired at the fourth acquisition time, compared to the GNSS data acquired at the third acquisition time, indicates a degradation in GNSS signal quality greater than or equal to a second threshold, meaning a significant decrease in light intensity, then it is determined that the terminal has moved from outdoors to indoors.

[0091] When a terminal is moving at low speed outdoors, the changes in data collected sequentially are relatively gradual. Therefore, determining whether a terminal has entered indoors based on sequentially acquired data has high accuracy. However, whether the terminal is moving at low speed is not a prerequisite for determining whether the terminal has entered indoors from outdoors. In other words, it is not necessary to determine whether the terminal is moving at low speed before determining whether the terminal has entered indoors from outdoors.

[0092] For example, data acquired at the third and fourth times both include acceleration data, pose data, and air pressure data. Based on acceleration data acquired at multiple different times, it can be determined that the terminal is in a high-speed moving state (such as driving). Furthermore, based on pose and air pressure data acquired at multiple different times, it can be determined that the terminal is in a downward-moving state. It can also be determined by combining at least one of visible light sensor data and GNSS data, etc., that the terminal is not outdoors, thus determining that the terminal is in an indoor state, such as an underground parking lot. Similarly, determining that the terminal is in a high-speed moving state is an optional step.

[0093] Regardless of the judgment method used or the judgment result obtained, the first time is determined based on the acquisition time of the data used as the basis for judgment: for example, the first time is the acquisition time of the data used as the basis for judgment, or the first time is obtained based on the acquisition time of the data used as the basis for judgment.

[0094] Referring to Figure 3, for example, if the LMF determines that the terminal is outdoors based on the data obtained by the terminal near location 2, then the time when the data was obtained near location 2 (assuming that the acquisition time of the data obtained near location 2 is aligned, referred to as the time corresponding to location 2) is taken as the first time. For another example, based on the data obtained by the terminal near location 2 and near location 3, the LMF determines that the terminal enters indoors from outdoors at the first time between the time corresponding to location 2 and the time corresponding to location 3.

[0095] Referring to Figure 3, the position for entering the room from the outside refers to the first position after entering the room from the outside, as shown in position 3 in Figure 3.

[0096] If it is determined that the terminal is outdoors, execute S104.

[0097] In this embodiment, when it is determined that the terminal is moving from outdoors to indoors, the position coordinates of the terminal after moving from outdoors to indoors are derived based on sensor data and its acquisition time, as well as the last GNSS coordinates acquired outdoors and its acquisition time. That is, S105-S106 are executed.

[0098] If it is determined that the terminal is indoors, the position coordinates of the terminal indoors are derived by using a correction method for short-range positioning results, as shown in S107.

[0099] S104. The network device obtains the outdoor location coordinates of the terminal at the first moment based on the GNSS data corresponding to the first moment.

[0100] As mentioned earlier, GNSS data is satellite positioning data, including the terminal's position coordinates obtained through satellite positioning. Therefore, one example is to use the position coordinates from the GNSS data at the first available time as the terminal's position coordinates.

[0101] The GNSS coordinates corresponding to the first time may be GNSS coordinates acquired at the first time, or they may be GNSS coordinates acquired at a time close to the first time (i.e., the interval between them and the first time is within a preset range).

[0102] S105. The network device determines the starting coordinates based on the GNSS coordinates acquired before the first moment.

[0103] In this embodiment, the GNSS coordinates obtained by the terminal outdoors are used as the starting coordinates for deriving the indoor location coordinates.

[0104] As shown in Figure 3, both location 1 and location 2 are outdoors and can be used as starting coordinates. However, it is understood that the closer to indoors, the more accurate the derivation result may be. Therefore, in this embodiment, location 2 is used as the starting position for derivation. Location 2 is the position represented by the last GNSS data acquired before the terminal enters the indoor environment.

[0105] One specific method for finding the starting coordinates is to find the coordinates of the last outdoor location of the terminal before it moves from indoors to outdoors. That is, in the GNSS coordinates representing the terminal's outdoor location, find the GNSS coordinates whose acquisition time and the interval between the first time satisfy a first condition. The first condition is that the interval is less than or equal to a preset interval threshold, or within a preset range. The first condition can be configured as needed. Referring to Figure 3, assuming location 3 corresponds to the first time, and the interval between the data acquisition time at location 2 and the first time is less than or equal to the interval threshold, the GNSS coordinates acquired at location 2 are used as the starting coordinates. It is understandable that the accuracy of the GNSS coordinates at location 2 may be insufficient. In this case, other outdoor location coordinates, such as the GNSS coordinates at location 1, can be selected. The interval threshold can be pre-configured.

[0106] It is understandable that GNSS coordinates may not be accurate enough. For example, location 2 in Figure 3 is near a bridge or other structure, which reduces the accuracy of GNSS positioning and causes GNSS coordinate "drift." Therefore, to improve the accuracy of GNSS coordinates and thus the accuracy of subsequent indoor location derivation results, some implementations correct the starting coordinates if the accuracy of the GNSS coordinates used to derive the indoor location does not meet the accuracy requirements. The accuracy requirement is that the GNSS evaluation data does not meet a preset accuracy threshold. It is understandable that the step of correcting the GNSS coordinates not meeting the accuracy requirements is optional; the accuracy can be corrected directly without a precision check.

[0107] One method for correcting GNSS coordinates is to obtain the coordinates of the entrance and correct the GNSS coordinates based on these coordinates. As shown in Figure 3, users need to pass through the entrance to carry their terminals from outdoors to indoors. Since the entrance coordinates are relatively fixed, the GNSS coordinates can be corrected. LMF can obtain the entrance coordinates by accessing maps or other methods.

[0108] It's understandable that a building the terminal might enter may have multiple entrances. In this case, one approach is to use GNSS coordinates as the starting coordinates, select one entrance coordinate from the multiple entrance coordinates (e.g., choose the entrance coordinate closest to the starting GNSS coordinates), and then adjust the starting GNSS coordinates based on the selected entrance coordinates. Another approach is to acquire navigation data from the terminal, obtain the terminal's driving trajectory based on the navigation data, and determine the coordinates of the entrance the terminal entered into the building based on the driving trajectory. Typically, navigation data includes map data and driving trajectory data.

[0109] S106. The network device obtains the location coordinates of the terminal entering the indoor space at the first moment based on the starting coordinates, the acquisition time of the starting coordinates, sensor data, and the acquisition time of the sensor data.

[0110] In some implementations, if the terminal is determined to be in a low-speed movement state, the network device uses sensor data such as the initial coordinates, the corresponding acquisition time, pose data from the gyroscope, and acceleration data from the accelerometer, along with the corresponding acquisition time, and calls the pedestrian dead reckoning (PDR) algorithm to obtain the position coordinates at the first moment. Alternatively, if the terminal is determined to be in a high-speed movement state, the network device uses the vehicle dead reckoning (VDR) algorithm to obtain the position coordinates at the first moment. It can be understood that the position coordinates at the first moment represent the terminal's position when it moves from outdoors to indoors.

[0111] Based on the different states of the terminal: low-speed movement (such as a user walking while carrying the terminal) or high-speed movement (such as driving), determining (or selecting) the corresponding algorithm is beneficial for obtaining more accurate indoor location coordinates.

[0112] In addition to the methods mentioned above, such as using acceleration data, other methods can be used to determine whether a terminal is moving at low or high speed. For example, navigation data can be used to obtain the movement trajectory, which can then be combined with road information and the movement trajectory represented by map data to obtain the location data of the entrance from indoors to indoors. Based on the pre-configured entrance type represented by the map data, it can be determined whether it is a pedestrian or a driver's vehicle. For example, if the entrance type is a garage entrance, it is determined to be a driver's vehicle; otherwise, it is determined to be a pedestrian. Alternatively, gyroscope-collected pose data can be combined with acceleration data and barometer-collected barometric pressure data to determine if the terminal is moving downwards at a high speed, i.e., a high-speed movement.

[0113] It is understandable that, based on the first moment described in S103, the data used to determine whether the terminal is in a low-speed or high-speed movement state is data acquired before the first moment.

[0114] Furthermore, the step of determining whether the terminal is in a low-speed or high-speed movement state, as mentioned above, can be executed in S103, or it can be executed before S106 if the terminal is determined to be moving from outdoors to indoors.

[0115] In some implementations, in addition to the starting coordinates, the time of acquisition of the starting coordinates, sensor data, and the time of acquisition of sensor data as input data for the AI ​​model, at least one of the following will also be used as the location coordinates of the terminal entering the indoor space at the first moment, in order to further improve the accuracy of the obtained terminal location data.

[0116] S107. The network device obtains the location coordinates of the terminal indoors at the first moment by correcting the short-range signal positioning result.

[0117] As shown in Figure 3, position 3 represents the state of entering the room from the outside, while positions 4-7 represent the state of being indoors. In this step, we will take obtaining the coordinates of position 4 as an example.

[0118] Short-range signal positioning is used to obtain indoor location coordinates. This method includes, but is not limited to, at least one based on Wi-Fi and Bluetooth data. The location coordinates of the terminal indoors are obtained through triangulation or fingerprint matching algorithms. For example, a triangulation algorithm obtains the coordinates of a location based on Wi-Fi data generated at a location from signals from at least three access points, along with the coordinates of those three access points. Alternatively, a fingerprint database of various indoor locations can be pre-established. This database includes fingerprints from multiple locations, and each fingerprint includes Bluetooth data features and coordinates. When performing positioning based on the fingerprint database, the Bluetooth features of the location to be located are matched with the Bluetooth data features in the fingerprint database to find a matching fingerprint. The coordinates of the location to be located are then obtained based on the coordinates included in the matching fingerprint.

[0119] Referring to the scenario shown in Figure 3, if the terminal is already at position 4 or later during its movement, it means the terminal has passed position 3. Therefore, to improve the accuracy of short-range signal positioning results, an offset is used to correct the short-range signal positioning results. That is, based on sensor data (which may also include wireless signal data) and its acquisition time, the position offset from position 3 to position 4 is calculated. Then, using the position offset and the short-range signal positioning result, a new short-range signal positioning result is obtained. This method can improve the accuracy of short-range signal positioning results.

[0120] In some implementations, S104-S108 are implemented by the network device calling the AI ​​model.

[0121] The location data acquisition method provided in this embodiment distinguishes between when the terminal is outdoors, when it enters the room from outdoors, and when it is indoors. Different data is used based on different situations, and different algorithms are called based on different movement speeds to obtain the terminal's location data, which helps to improve the accuracy of the obtained location data.

[0122] For example, when moving from outdoors to indoors, the GNSS coordinates outside the building before entering are used as the starting coordinates for deriving the indoor position coordinates. Combined with sensor data and corresponding time data acquired by the terminal, as well as an algorithm matched with the terminal's movement speed, the network device obtains the terminal's position coordinates after entering the building. Compared with the method of obtaining indoor position coordinates based on short-range signals, on the one hand, outdoor GNSS coordinates are more accurate, thus laying a good foundation for obtaining accurate indoor position coordinates; on the other hand, sensor data is more stable than short-range signals, improving the reliability of obtaining indoor position coordinates.

[0123] Especially in scenarios where the quality of short-range signals, cellular communication signals, and satellite positioning signals is poor, such as underground parking lots, in this embodiment, the network device obtains indoor location coordinates based on outdoor coordinates and corresponding time, sensor data, and acquisition time, which is beneficial for obtaining more accurate location data in such scenarios.

[0124] The process of interaction between the terminal and network devices has been explained in detail above. It is understandable that the data processing methods described above can also be applied to the training of AI models, which will be discussed in more detail below.

[0125] To enable the AI ​​model to output more accurate location data, it needs to be trained using labeled data. The training process for the AI ​​model will be explained in detail below.

[0126] The current challenge in training AI models lies in obtaining sufficient and accurate location tags. For example, in some scenarios, the quality of wireless signals is insufficient to support location tag acquisition, such as in underground parking lots where the signal quality of WLAN, gNB, and Bluetooth is generally poor, making it impossible to obtain location tags. Another example is indoor scenarios where current positioning methods based on short-range signals like WLAN or Bluetooth typically rely on triangulation or fingerprinting. Triangulation usually requires known access point location data, while fingerprinting requires massive fingerprint databases, making it difficult to obtain enough location tags.

[0127] The embodiments of this application also provide a method for training an AI model, as shown in Figure 5:

[0128] The training dataset includes multiple training data points, each containing location labels and feature data. The training data is input into the AI ​​model. The AI ​​model obtains location data based on the feature data in the input training data, and adjusts its parameters based on the location data and the location labels in the training data, until training is complete.

[0129] The acquisition method of training data in the training dataset is shown in Figure 5: The terminal acquires measurement data and time data through the wireless communication module and sensors, as detailed in S101. The terminal sends the measurement data and time data to a network device such as the LMF, which in turn receives the measurement data and time data. Based on the measurement data and time data, the scene judgment module in the network device such as the LMF determines whether the terminal is outdoors, moving from outdoors to indoors, or indoors at the first moment. The specific judgment process is detailed in S103. The scene judgment module outputs a scene identifier to the training data acquisition module, indicating whether the terminal is outdoors, moving from outdoors to indoors, or indoors.

[0130] The training data acquisition module obtains location labels based on scene identifiers and constructs training data including these location labels. Specifically, the scene identifier indicates that the terminal is outdoors, and the GNSS coordinates corresponding to the first moment are used as the location labels to construct the training data. The scene identifier also indicates that the terminal moved from outdoors to indoors. Based on sensor data and its acquisition time, as well as the last GNSS coordinates acquired outdoors and their acquisition time, the module derives the terminal's location coordinates after moving from outdoors to indoors. In some implementations, the LMF calls an AIPDR model or an AI VDR model, inputting the sensor data and its acquisition time, as well as the last GNSS coordinates acquired outdoors and their acquisition time, into the AIPDR model or AI VDR model to obtain the location labels. More detailed explanations can be found in S105-S106.

[0131] The scene identifier indicates that the terminal is indoors. The location tag is obtained by correcting the short-range signal positioning result. See S107.

[0132] After obtaining the location labels, network devices, such as the training data acquisition module, construct training data:

[0133] The training data includes location labels and corresponding feature data, and may also include the confidence level of the location labels. Since the derivation of location labels from outdoor to indoor states is based on the trajectory derivation algorithm and sensor data acquisition, the confidence level of the location labels in this step is determined based on the accuracy of the sensor data and the confidence level of the trajectory derivation algorithm. The accuracy of the sensor data can be obtained from the sensors, and the confidence level of the trajectory derivation algorithm is output by the trajectory derivation algorithm. For example, the AI-based PDR algorithm outputs the confidence level of the location labels along with the location labels.

[0134] The feature data in this step includes, but is not limited to, at least one of the following: sensor data, short-range signal data, cellular communication data, and GNSS data. In some implementations, after filtering the available data, data that meets the quality requirements is used as feature data.

[0135] The more and higher the quality of the feature data included in the training data, the better it is for improving the accuracy of the AI ​​model. In some implementations, training data can also be filtered based on the credibility of location labels, using location labels with high credibility and their corresponding feature data as training data to further improve the accuracy of AI model predictions.

[0136] In this embodiment, based on the different environments and speeds of the terminal, such as outdoors, entering indoors from outdoors, or indoors, the location tags of the terminal are obtained based on different data and dead reckoning algorithms, thereby constructing training data with location tags. Therefore, the positioning accuracy of the trained model can be improved.

[0137] It is understood that the above embodiments are illustrated using the scenario shown in Figure 3 as an example. In addition to this type of scenario, the method provided by the embodiments of this application is also applicable to other scenarios, such as a path opposite to the direction of travel shown by the arrow in Figure 3, where the terminal first moves indoors and then moves from indoors to outdoors. In this scenario, similar to the way shown in Figure 4 or Figure 5 when moving from outdoors to indoors, the first outdoor GNSS coordinate after moving from indoors to outdoors can be used as the reference coordinate. Based on sensor data and the corresponding time, the coordinates from indoors to outdoors and the position coordinates inside the room can be derived.

[0138] It is understood that the "entering indoors" mentioned in the embodiments of this application covers both the state of the first position from the outside to the inside and the state of the last indoor position before going from the inside to the outside. As shown by the arrow in Figure 3, on the path with the opposite direction of travel, position 3 is the last indoor position before going from the inside to the outside, so the state of position 3 is "entering indoors".

[0139] In the above embodiments, the data used as the starting coordinates for the derivation algorithm is GNSS coordinates. It is understood that an alternative approach is to determine the starting coordinates based on at least one of outdoor satellite positioning coordinates and cellular communication data.

[0140] In the above embodiments, taking the acquisition of training data and the determination and localization of scene using LMF as an example, it can be understood that the functions of LMF can also be implemented by the terminal.

[0141] The use of LMF as the execution entity in the above text is merely an example and not a limitation. The method described above can also be completed by different network elements working together. For example, as shown in Figure 6, the operation, administration, and maintenance (OAM) network element determines whether the terminal is outdoors, has entered from outdoors, or is indoors, and sends the determination result to LMF. After receiving the determination result, LMF executes the steps of obtaining the terminal's location data in the above embodiment.

[0142] It should be understood that the training process shown in Figure 5 is based on the AI ​​model being built into the LMF as an example, but this application is not limited to this. For example, the AI ​​model can also be built into the terminal or other network elements. Regarding the training methods of the AI ​​model, such as the data source of the AI ​​model, the specific implementation may be different in different architectures, but all should be within the scope of protection of this application.

[0143] Figure 7 is a structural example diagram of a terminal provided by an embodiment of this application. Figure 7 includes: processor 10, memory 11 and wireless communication module 12.

[0144] The wireless communication module 12 includes short-range communication modules, such as the WLAN module, Bluetooth module, and GNSS module shown in Figure 2. The functions of each module are as described above.

[0145] The processor 10 includes a modem and an application processor. The modem can be used to implement cellular network-based communication functions, as shown in Figure 2 of the cellular module.

[0146] The acquisition of measurement data in the foregoing embodiments can be achieved by the Modem, or by the Modem and the application processor together.

[0147] The memory 11 is used to store program code, and the processor 10 is used to run the program code, so that the terminal implements the steps executed by the terminal in the above embodiments, or the steps executed by the LMF, or the steps executed by the terminal and the LMF.

[0148] Figure 8 is a schematic block diagram of a communication device provided in an embodiment of this application. The communication device 700 may include a processing module 710 and a communication module 720. The communication module 720 can implement corresponding communication functions, which can be internal communication functions of the communication device 700 or communication functions between the communication device 700 and other devices. Optionally, the communication module 720 may also be referred to as a communication interface or a transceiver module. The processing module 710 can implement corresponding processing functions.

[0149] Optionally, the communication device 700 further includes a storage module, which can be used to store instructions and / or data; the processing module 710 can read the instructions and / or data in the storage module so that the communication device 700 can implement the aforementioned method embodiments.

[0150] In one possible design, the communication device 700 may correspond to the network device in the above method embodiments, or to a component (such as a circuit, chip, or chip system) configured in the network device. The communication device 700 can be used to perform the steps or processes performed by the network device in any of the above method embodiments.

[0151] For example, the communication module 720 is used to acquire measurement data and time data. The measurement data includes sensor data and wireless signal data. The sensor data is acquired through a sensor installed on the terminal. The wireless signal data includes at least one of satellite positioning data and cellular communication data. The time data represents the acquisition time of the measurement data.

[0152] The processing module 710 is used to obtain the location data of the terminal entering the indoor space based on the sensor data, the acquisition time of the sensor data, the outdoor positioning data, and the acquisition time of the outdoor positioning data. The outdoor positioning data represents the location data of the terminal outdoors, and the outdoor positioning data is obtained based on the wireless signal data.

[0153] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0154] In one possible design, the communication device 700 may correspond to the terminal in the above method embodiments, or a component (such as a circuit, chip, or chip system) configured in the terminal. The communication device 700 can be used to execute the steps or processes performed by the terminal in any of the above method embodiments.

[0155] For example, the communication module 720 is used to acquire measurement data and time data, and to send the measurement data and time data to a network device. The measurement data includes sensor data and wireless signal data. The sensor data is acquired by a sensor installed on the terminal. The wireless signal data includes at least one of satellite positioning data and cellular communication data.

[0156] It is understandable that the steps or processes performed by the aforementioned network devices can also be performed by terminal devices.

[0157] Figure 8 is another schematic block diagram of a communication device 800 provided in an embodiment of this application. The communication device 800 may be a chip, chip system, or processor, etc., used by a terminal or network device to implement the above-described methods. The communication device 800 can be used to implement the methods described in the above-described method embodiments; for details, please refer to the descriptions in the above-described method embodiments.

[0158] As shown in Figure 8, the communication device 800 may include one or more processors 810, which may also be referred to as processing units or processing modules, and can implement certain control functions. The processor 810 may be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, while the central processing unit can be used to control the communication device 800 (e.g., a base station, baseband chip, user, user chip), execute software programs, and process data from the software programs.

[0159] In an alternative design, the processor 810 may also store instructions and / or data that can be executed by the processor 810 to cause the communication device 800 to perform the methods described in the above method embodiments.

[0160] In another alternative design, the communication device 800 may include a communication interface 820 for implementing receiving and transmitting functions. For example, the communication interface 820 may be a transceiver circuit, interface, interface circuit, or transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing receiving and transmitting functions may be separate or integrated. The aforementioned transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or it may be used for transmitting or relaying signals.

[0161] Optionally, the communication device 800 may include one or more memories 830, which may store instructions that can be executed on the processor 810, causing the communication device 800 to perform the methods described in the above method embodiments. Optionally, the memories 830 may also store data. Optionally, the processor 810 may also store instructions and / or data. The processor 810 and the memories 830 may be provided separately or integrated together.

[0162] It should be understood that, in one possible design, the steps in the method embodiments provided in this application can be implemented by integrated logic circuits in the processor's hardware or by instructions in software form. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0163] In one implementation, the communication device 800 may correspond to the network device in the above method embodiments and may be used to execute the various steps and / or processes executed by the network device in the above method embodiments. The processor 810 may be used to execute instructions stored in the memory 830, and when the processor 810 executes the instructions stored in the memory, the processor 810 is used to execute the various steps and / or processes of the above method embodiments corresponding to the network device.

[0164] It should be understood that the aforementioned processing device can be one or more chips. For example, the processing device can be a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0165] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0166] An embodiment of the application also provides a computer-readable storage medium having instructions stored thereon that, when executed on an electronic device, cause the electronic device to perform the location data acquisition method described in the above embodiments.

[0167] An embodiment of the application also provides a computer program product that stores an executable program that, when run on an electronic device, causes the electronic device to implement the location data acquisition method described in the above embodiments.

[0168] This application also provides a chip system including one or more processors for calling and executing instructions stored in memory, causing the methods described in the above embodiments to be executed. The chip system may be composed of a chip or may include chips and other discrete devices. The chip system may include input circuitry or interfaces for transmitting information or data, and output circuitry or interfaces for receiving information or data.

[0169] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0170] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0171] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0172] In summary, the above description is merely a preferred embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for acquiring location data, characterized in that, The method, applied to artificial intelligence (AI) models, includes: Acquire measurement data and time data. The measurement data includes sensor data and wireless signal data. The sensor data is acquired through sensors installed on the terminal. The wireless signal data includes at least one of satellite positioning data and cellular communication data. The time data represents the acquisition time of the measurement data. Based on the sensor data, the acquisition time of the sensor data, the outdoor positioning data, and the acquisition time of the outdoor positioning data, the location data of the terminal entering the indoor space is obtained. The outdoor positioning data represents the location data of the terminal outdoors, and the outdoor positioning data is obtained based on the wireless signal data.

2. The method according to claim 1, characterized in that, The process of obtaining the location data of the terminal entering the indoor space includes: The location data of the terminal entering the indoor environment is obtained based on the pedestrian dead reckoning algorithm or the vehicle dead reckoning algorithm. The pedestrian dead reckoning algorithm is determined based on the terminal being in a low-speed movement state, and the vehicle dead reckoning algorithm is determined based on the terminal being in a high-speed movement state. The determination of whether the terminal is in a low-speed or high-speed movement state is based on acceleration sensor data, or on navigation data and map data.

3. The method according to claim 1 or 2, characterized in that, The interval between the acquisition time of the outdoor positioning data and the first time satisfies the first condition, where the first time is the time when the terminal enters the indoor space.

4. The method according to claim 1 or 2, characterized in that, The outdoor positioning data is obtained based on the wireless signal data, including: the outdoor positioning data is obtained based on the geographic location coordinates corrected first location data, the first location data is obtained based on the wireless signal data, the geographic location coordinates are the coordinates of an exit or an entrance, the terminal moves from indoors to outdoors via the exit, and the terminal moves from outdoors to indoors via the entrance.

5. The method according to claim 4, characterized in that, The distance between the location represented by the geographic coordinates and the location represented by the outdoor positioning data is less than or equal to a distance threshold.

6. The method according to claim 4, characterized in that, The geographical coordinates are determined based on the driving trajectory, which is obtained based on the navigation data of the terminal.

7. The method according to claim 4, characterized in that, The first position data does not meet the accuracy requirements.

8. The method according to claim 1 or 2, characterized in that, The terminal's entry into the indoor environment is determined based on at least one of the following: the decrease in light intensity is greater than or equal to a first threshold and the degradation of satellite positioning signal quality is greater than or equal to a second threshold. The decrease in light intensity is the decrease in second light intensity compared to first light intensity. The second light intensity is represented by visible light sensor data corresponding to a third time, and the first light intensity is represented by visible light sensor data corresponding to a second time. The degradation of satellite positioning signal quality is the degradation of a second quality parameter compared to a first quality parameter. The second quality parameter is represented by satellite positioning data corresponding to the third time, and the first quality parameter is represented by satellite positioning data corresponding to the second time. The second time is earlier than the third time.

9. The method according to claim 1 or 2, characterized in that, The determination of whether the terminal enters the room is based on the sensor data, which includes at least one of visible light sensor data, gyroscope data, acceleration data, and air pressure data.

10. The method according to claim 1 or 2, characterized in that, The step of obtaining the location data of the terminal entering the indoor environment based on the sensor data, the acquisition time of the sensor data, outdoor positioning data, and the acquisition time of the outdoor positioning data includes: Based on at least one of the short-range signal data acquired after the terminal enters the indoor environment, the reliability of the sensor data, and the reliability of the outdoor positioning data, as well as the sensor data, the acquisition time of the sensor data, the outdoor positioning data, and the acquisition time of the outdoor positioning data, the location data of the terminal entering the indoor environment is obtained.

11. An electronic device, characterized in that, The electronic device includes: one or more processors and a memory; the memory is used to store program code; the processor is used to run the program code, causing the electronic device to implement the method as described in any one of claims 1 to 10.

12. A computer-readable storage medium, characterized in that, It stores instructions that, when executed on an electronic device, cause the electronic device to perform the method as described in any one of claims 1 to 10.

13. A computer program product, characterized in that, It stores an execution method that, when the computer program product is run on the electronic device, causes the electronic device to perform the method as described in any one of claims 1 to 10.

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