Positioning fingerprint data generation method and apparatus, device, medium, and program product

Abstract

A positioning fingerprint data generation method and apparatus, a device, a medium, and a program product. The method comprises: acquiring device sensor data collected by two or more collection devices and used for positioning, and trajectory data of a moving carrier (S101); acquiring a positioning position of a target indoor area on the basis of the trajectory data of the moving carrier and pre-measured mounting parameters of the collection devices on the moving carrier (S102); and generating positioning fingerprint data of the target indoor area on the basis of the device sensor data and the positioning position of the target indoor area (S103). The method can improve the generation efficiency of positioning fingerprint data, and reduce costs.

Description

Methods, devices, equipment, media, and programs for generating location fingerprint data

[0001] This disclosure claims priority to Chinese Patent Application No. 202411579233.1, filed on November 7, 2024, entitled “Method, Apparatus, Device, Medium and Program Product for Generating Location Fingerprint Data”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the field of positioning technology, and in particular to a method, apparatus, device, medium, and program product for generating positioning fingerprint data. Background Technology

[0003] Indoor positioning is an important branch of positioning technology. Because satellite signals are blocked indoors, it is impossible to locate devices (such as smartphones, smartwatches, and vehicles) based on Global Navigation Satellite System (GNSS) signals, such as Global Positioning System (GPS) signals and BeiDou positioning system signals. Therefore, existing indoor positioning technologies usually adopt a scheme based on a pre-built positioning fingerprint database to locate devices. The positioning fingerprint database generally stores signal data from Wi-Fi, base stations, etc., and related location data.

[0004] Taking Wi-Fi and base stations as examples, the principle of indoor positioning technology can be simply understood as follows: when a device being positioned (such as a smartphone) scans for signals from Wi-Fi, base stations, etc., the device can send the scanned signal data to the indoor positioning service. The indoor positioning service then determines the device's location based on the Wi-Fi and base station signal data uploaded by the device and the data stored in the positioning fingerprint database. Therefore, building a positioning fingerprint database is crucial for achieving indoor positioning.

[0005] However, the existing technology for building a location fingerprint database requires personnel to collect data in indoor areas using handheld collection devices, which results in low collection efficiency and high costs. Summary of the Invention

[0006] This disclosure provides a method, apparatus, device, medium, and program product for generating location fingerprint data, which can improve the efficiency of location data collection, reduce collection costs, and thus improve the efficiency of obtaining location fingerprint data during the fingerprint data construction process.

[0007] In a first aspect, this disclosure provides a method for generating location fingerprint data, the method comprising:

[0008] The system acquires device sensor data and trajectory data of a moving vehicle collected by two or more acquisition devices that can be used for positioning. The device sensor data is collected when the moving vehicle carries the two or more acquisition devices and travels in the target indoor area. The trajectory data is generated in advance based on the vehicle sensor data collected by the moving vehicle carrying the two or more acquisition devices and traveling in the target indoor area.

[0009] Based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition device on the moving vehicle, the positioning location of the target indoor area is obtained;

[0010] Based on the device sensor data and the location of the target indoor area, a positioning fingerprint data of the target indoor area is generated.

[0011] Optionally, when the motion vehicle is traveling in an indoor area with the two or more acquisition devices, the working time standard of the device sensor integrated in the acquisition device and the working time standard of the vehicle sensor mounted on the motion vehicle are the same.

[0012] Optionally, one set of equipment sensor data corresponds to one acquisition time, and one set of vehicle sensor data corresponds to one acquisition time. If the operating times of the equipment sensor and the vehicle sensor are not synchronized, the method further includes:

[0013] Based on the acquisition time corresponding to the device sensor data and the acquisition time corresponding to the vehicle sensor, interpolation is performed at the acquisition time using linear interpolation to align the acquisition times of the device sensor data and the vehicle sensor data.

[0014] Based on the device sensor data and the acquisition time of the device sensor data, as well as the vehicle sensor data and the acquisition time of the vehicle sensor, the device sensor data or vehicle sensor data corresponding to the interpolated acquisition time is generated, so that each acquisition time after alignment corresponds to a set of device sensor data and a set of vehicle sensor data.

[0015] The trajectory data is generated in advance based on vehicle sensor data collected by the two or more acquisition devices carried by the vehicle while it is moving in the target indoor area, and the acquisition time is aligned with the sensor data of the devices.

[0016] Optionally, the target indoor area includes at least an indoor planar area, the positioning location includes latitude and longitude coordinates and elevation, and the method further includes:

[0017] Based on the elevation included in the positioning location, the positioning locations within the same indoor plane area are obtained;

[0018] The step of generating positioning fingerprint data for the target indoor area based on the device sensor data and the positioning location specifically includes:

[0019] Based on the device sensor data and the positioning location in the same indoor plane area, obtain the positioning fingerprint data of the corresponding indoor plane area.

[0020] Optionally, the method further includes:

[0021] Based on the location of the corresponding indoor plane area and the preset geographic grid size, the corresponding indoor plane area is divided into geographic grids;

[0022] Based on the device sensor data and the positioning location within the same indoor plane area, the positioning fingerprint data of the corresponding indoor plane area is obtained, specifically including:

[0023] Based on the device sensor data, the location within the same indoor plane area, and the geographic grid of the corresponding indoor plane area, the location fingerprint data of the corresponding indoor plane area is obtained.

[0024] Optionally, the target indoor area further includes: an indoor non-planar area, and the method further includes:

[0025] Based on the elevation included in the positioning location, the positioning location located in the same indoor non-planar area is obtained;

[0026] Based on the device sensor data and the positioning location in the same indoor non-planar area, obtain the positioning fingerprint data of the corresponding indoor non-planar area.

[0027] Optionally, the data acquisition device includes a first data acquisition device and a second data acquisition device, which are respectively installed on the left and right sides of the moving vehicle in the direction of travel. The installation parameters include a first installation parameter of the first data acquisition device and a second installation parameter of the second data acquisition device. The step of obtaining the positioning location of the target indoor area based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the data acquisition device on the moving vehicle specifically includes:

[0028] Based on the trajectory data of the moving vehicle, including a series of trajectory positions corresponding to the acquisition time and the first installation parameters, the positioning position of the first acquisition device corresponding to the acquisition time is obtained through lever compensation.

[0029] Based on the trajectory data of the moving vehicle, including a series of trajectory positions corresponding to the acquisition time and the second installation parameters, the positioning position of the second acquisition device corresponding to the acquisition time is obtained through lever compensation.

[0030] Based on the positioning positions of the first acquisition device and the second acquisition device corresponding to the acquisition time, the positioning positions of the target indoor area with respect to each acquisition time are obtained.

[0031] Secondly, this disclosure provides a device for generating location fingerprint data, the device comprising:

[0032] The acquisition module is used to acquire device sensor data and trajectory data of a moving vehicle collected by two or more acquisition devices that can be used for positioning. The device sensor data is collected when the moving vehicle carries the two or more acquisition devices and moves in the target indoor area. The trajectory data is generated in advance based on the vehicle sensor data collected by the moving vehicle carrying the two or more acquisition devices and moving in the target indoor area.

[0033] The processing module is used to obtain the positioning position of the target indoor area based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition device on the moving vehicle;

[0034] The generation module is used to generate positioning fingerprint data of the target indoor area based on the device sensor data and the positioning location of the target indoor area.

[0035] Thirdly, this disclosure provides an electronic device, including: a processor and a memory; the processor is communicatively connected to the memory;

[0036] The memory stores computer instructions;

[0037] The processor executes computer instructions stored in the memory to implement the method as described in any one of the first aspects.

[0038] Fourthly, this disclosure provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method for generating location fingerprint data as described in any one of the first aspects.

[0039] Fifthly, this disclosure provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the method as described in any one of the first aspects.

[0040] The method, apparatus, device, medium, and program product for generating positioning fingerprint data disclosed herein first acquires positioning sensor data and trajectory data of a moving vehicle collected by two or more acquisition devices. Then, based on the trajectory data of the moving vehicle and pre-measured installation parameters of the acquisition devices on the moving vehicle, the positioning location of the target indoor area is obtained. Finally, positioning fingerprint data of the target indoor area is generated based on the device sensor data and the positioning location of the target indoor area. Because the moving vehicle equipped with the acquisition devices disclosed herein can collect both vehicle sensor data and device sensor data simultaneously while moving within the target indoor area, the efficiency of sensor data acquisition in the target indoor area is significantly improved compared to the prior art method of manually collecting data using handheld acquisition devices. Furthermore, since the two or more acquisition devices are mounted at different positions on the moving vehicle, meaning that the installation parameters of different acquisition devices on the moving vehicle are different, the positioning location of the target indoor area obtained based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition devices on the moving vehicle covers a wider range of the target indoor area. This not only improves the efficiency of generating positioning fingerprint data for the target indoor area but also ensures the accuracy of the generated positioning fingerprint data. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this disclosure 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 this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 is a schematic diagram of a positioning data acquisition method;

[0043] Figure 2 is a schematic diagram of a scenario for a method for generating location fingerprint data according to an embodiment of this disclosure;

[0044] Figure 3 is a flowchart illustrating a method for generating location fingerprint data according to an embodiment of this disclosure;

[0045] Figure 4 is a flowchart illustrating another method for generating location fingerprint data provided in an embodiment of this disclosure;

[0046] Figure 5 is a flowchart illustrating another method for generating location fingerprint data provided in this embodiment of the present disclosure;

[0047] Figure 6 is a flowchart illustrating another method for generating location fingerprint data provided in an embodiment of this disclosure;

[0048] Figure 7 is a flowchart illustrating another method for generating location fingerprint data according to an embodiment of this disclosure;

[0049] Figure 8 is a flowchart illustrating another method for generating location fingerprint data provided in an embodiment of this disclosure;

[0050] Figure 9 is a schematic diagram of a device for generating location fingerprint data according to an embodiment of this disclosure;

[0051] Figure 10 is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure.

[0052] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this disclosure clearer, the technical solutions of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0054] In recent years, with the continuous development and enrichment of location-based services, the demand for positioning services has become increasingly prominent. Currently, outdoor positioning services are typically provided based on GNSS satellite signals. However, indoors, due to the obstruction of GNSS satellite signals, positioning services are generally provided through network fusion positioning. The realization of network fusion positioning relies on indoor positioning fingerprint data, and the efficiency and cost of generating indoor positioning fingerprint data are the main factors determining the scope of application of indoor positioning technology. Traditional indoor positioning fingerprint data collection usually requires professional personnel to carry specialized equipment to collect data in indoor areas first, and then generate indoor positioning fingerprint data based on the collected data. Because existing technologies require data collection at different locations for the same accessible indoor area to improve the usability of the generated indoor positioning fingerprint data, Figure 1 is a schematic diagram of a positioning data collection method. As shown in Figure 1, taking the collection of data from an indoor road as an example, it is often necessary for the collection personnel to walk along the left, middle, and right sides of the road and collect data separately. This method obviously suffers from low data collection efficiency, high cost, and personal safety issues for the collection personnel. The low efficiency and high cost of data collection also mean that generating indoor positioning fingerprint data based on the collected data will also suffer from low efficiency and high cost.

[0055] In view of this, the present disclosure provides a method for generating location fingerprint data. Figure 2 is a schematic diagram of a scenario for generating location fingerprint data according to an embodiment of the present disclosure. As shown in Figure 2, the method utilizes acquisition devices located at different positions on a moving vehicle (e.g., a car), such as acquisition devices 1-3 in Figure 2. When the moving vehicle travels in an indoor passable area (e.g., a parking lot driveway), sensor data is collected by sensors mounted on the moving vehicle. Simultaneously, sensors mounted on the acquisition devices collect location-related sensor data. Subsequently, based on the sensor data collected by the sensors mounted on the moving vehicle, trajectory data of the moving vehicle can be obtained. The trajectory data includes a series of trajectory positions formed by the moving vehicle as it travels in the passable area. Then, based on the trajectory data and the location-related sensor data, location fingerprint data is generated, which can significantly improve data acquisition efficiency and reduce costs.

[0056] For ease of distinction, this disclosure refers to sensors mounted on motion vehicles as vehicle sensors, and sensor data collected by sensors mounted on motion vehicles as vehicle sensor data; sensors mounted on data acquisition devices as device sensors, and sensor data collected by sensors mounted on data acquisition devices as device sensor data.

[0057] The aforementioned vehicle sensor data or device sensor data may include at least one of the following: wireless hotspot data, magnetic field data (e.g., geomagnetic data), visual data, etc. The aforementioned wireless hotspot data may include, for example, any one or more of Wi-Fi signals, Bluetooth signals, and communication signals. The communication signals mentioned here may be, for example, 5G signals or 4G signals, etc. The aforementioned data acquisition device may be any device capable of acquiring sensor data that can be used for positioning, such as consumer electronic devices like mobile phones and tablets, or hardware devices designed by the manufacturer implementing the solution provided in this disclosure, as long as the hardware device can acquire the corresponding sensor data.

[0058] Taking device sensor data as an example, if the device sensor data includes magnetic field data, the acquisition device can acquire magnetic field data, for example, through a magnetometer; if the device sensor data includes visual data, the acquisition device can acquire visual data, for example, through a camera device (e.g., a webcam); if the device sensor data includes wireless hotspot data, taking wireless hotspot data as Wi-Fi signal as an example, the acquisition device can acquire Wi-Fi data, for example, through its own built-in Wi-Fi module.

[0059] It should be understood that the above is merely an exemplary description of vehicle sensor data and equipment sensor data. The specific types of data included in vehicle sensor data and equipment sensor data depend on the types of vehicle sensors and equipment sensors, and this disclosure does not limit them.

[0060] Furthermore, this disclosure does not limit the number of data collection devices. To improve collection efficiency, this disclosure preferably uses at least two data collection devices, which are preferably installed on the left and right sides of the moving vehicle to collect data from both sides of the indoor passable area (such as a road). As shown in Figure 2, the moving vehicle is a car equipped with three data collection devices, such as mobile phones. In one data collection process, the data collection devices can collect data from the left, right, and middle areas of the passable area, respectively. Compared with existing technologies that require data collection personnel to walk along the left, right, and middle of the passable area to collect data, this significantly improves the efficiency of data collection and ensures the personal safety of the data collection personnel.

[0061] The technical solutions of this disclosure will be described in detail below with reference to specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0062] Figure 3 is a flowchart illustrating a method for generating location fingerprint data according to an embodiment of this disclosure. As shown in Figure 3, the method may include the following steps:

[0063] S101. Acquire sensor data and trajectory data of moving vehicles from two or more acquisition devices that can be used for positioning.

[0064] Among them, the equipment sensor data is collected when the moving vehicle carries two or more acquisition devices and travels simultaneously in the same target indoor area, and the trajectory data is generated in advance based on the vehicle sensor data. The vehicle sensor data is collected when the moving vehicle carries two or more acquisition devices and travels simultaneously in the target indoor area.

[0065] The target indoor area can be, for example, an underground parking garage or an indoor parking structure, an area that allows the movement of vehicles. As mentioned earlier, the equipment sensor data refers to the data collected by the sensors mounted on the acquisition equipment, while the vehicle sensor data refers to the data collected by sensors integrated on or independent of the vehicle. Vehicle sensors can include, for example, any one or more of LiDAR, cameras, inertial sensors, wheel speedometers, etc. After collecting the corresponding vehicle sensor data, the trajectory data of the vehicle can be obtained (the trajectory data consists of a series of trajectory positions with timestamps).

[0066] For example, vehicle sensor data may include at least one of the following: Inertial Measurement Unit (IMU) data, wheel speed data, radar data, visual data, etc. Taking this as an example, if the vehicle sensor data includes only one of the above data types, the trajectory position data of the moving vehicle can be obtained based on only a single data point. For instance, if the vehicle sensor data includes only IMU data, the trajectory position of the moving vehicle can be obtained through mechanical arrangement based solely on the IMU data, and trajectory data can be generated based on the trajectory position at different times. If the vehicle sensor data includes two or more data types, the trajectory position data of the moving vehicle can be obtained based on two or more data types. For example, if the vehicle sensor data includes IMU data and wheel speed data, the trajectory position of the moving vehicle can be obtained using machine learning. For example, features can be extracted from the IMU data and wheel speed data, and the extracted features can be used as input to a pre-trained position prediction model to obtain the trajectory position of the moving vehicle. The aforementioned position prediction model can be, for example, trained by using features extracted from historically acquired IMU data and wheel speed data as input and the corresponding trajectory position as output. The machine learning models mentioned here can be, for example, Convolutional Neural Networks (CNN) models or Long Short-Term Memory (LSTM) network models. The above is a brief explanation of acquiring trajectory data based on vehicle sensor data; the specific implementation process can be found in existing technologies and will not be elaborated upon here.

[0067] Optionally, the data acquisition device can be independent of the moving vehicle, or it can be certain sensors mounted on the moving vehicle. For example, when the required positioning sensor data is magnetic field data, if the moving vehicle itself is equipped with a magnetometer, then the magnetometer is equivalent to the data acquisition device of this disclosure. If the moving vehicle itself is not equipped with a magnetometer, a data acquisition device equipped with a magnetometer can be installed on the moving vehicle to acquire magnetic field data. That is, the sensor mounted on the data acquisition device is generally different from the sensor on the moving vehicle. Alternatively, even if the moving vehicle itself is equipped with a magnetometer, the data acquisition device can also be equipped with a magnetometer. That is, the data acquisition device and the moving vehicle can be equipped with the same type of sensor. In specific implementation, the data used to generate positioning fingerprint data can be selected based on the data quality of the same type of sensor, which does not affect the implementation of this disclosure. For example, if the data acquisition accuracy of the magnetometer mounted on the data acquisition device is higher than the data acquisition accuracy of the magnetometer mounted on the moving vehicle itself, then acquiring magnetic field data through the magnetometer mounted on the data acquisition device can improve the data quality of the device sensor data and the data quality of the positioning fingerprint data generated based on the device sensor data.

[0068] Optionally, the device sensor data acquired in step S101 of this disclosure can be device sensor data sent to the server side in real time via the network when the acquisition device is working in the target indoor area, or it can be device sensor data sent to the server side via the network at a certain time period when the acquisition device is working in the target indoor area; or it can be device sensor data read in batches from the acquisition device after the acquisition device has completed the acquisition of device sensor data for the target indoor area. The way the acquisition device transmits data does not affect the implementation of this disclosure. When executing the solution provided by this disclosure, the device sensor data collected by the acquisition device can be obtained.

[0069] S102. Based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition equipment on the moving vehicle, obtain the positioning location of the target indoor area.

[0070] Among them, installation parameters (also known as lever parameters) are parameters used to characterize the relative positional relationship between the data acquisition device and the moving vehicle. For example, installation parameters may include the installation angle parameters, displacement vector parameters, rotation matrix, and relative position vector of the data acquisition device mounted on the moving vehicle.

[0071] In one embodiment, the installation parameters can be the relative position parameters of the acquisition device with respect to the GNSS positioning antenna module of the moving vehicle. Of course, the installation parameters can also be the relative position parameters of the acquisition device with respect to other sensors mounted on the moving vehicle. Considering that the GNSS positioning antenna module is usually located outside the moving vehicle, the parameters of the acquisition device relative to the GNSS positioning antenna module are relatively easy to measure. Therefore, it is preferable to measure the relative position parameters of the acquisition device with respect to the GNSS positioning antenna module of the moving vehicle.

[0072] Furthermore, the trajectory position in the trajectory data of the moving vehicle obtained in this disclosure can be equivalent to the trajectory position of the GNSS antenna of the moving vehicle. Therefore, based on the trajectory position in the trajectory data, the positioning position of the acquisition device corresponding to the trajectory position in the trajectory data can be obtained through pole arm compensation.

[0073] S103. Generate positioning fingerprint data of the target indoor area based on the device sensor data and the positioning location of the target indoor area.

[0074] In this step, based on the device sensor data and the location, the positioning fingerprint data of the target indoor area is obtained, so that the positioning fingerprint data of the target indoor area can be used to locate the device entering the target indoor area.

[0075] This disclosure does not limit the type of fingerprint data generated. For example, it can be raster fingerprint data (fingerprint data represented in the form of a two-dimensional raster with a row and column structure. Each raster cell contains information related to the fingerprint features of that raster cell), or it can be one-dimensional fingerprint data (arranging the key features of the fingerprint, such as the position and type of feature points, into a one-dimensional sequence in a certain order). The specific type can be determined according to actual needs.

[0076] In this embodiment, firstly, device sensor data and trajectory data of the moving vehicle, collected by two or more acquisition devices, are acquired for positioning purposes. Then, based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition devices on the moving vehicle, the positioning position of the target indoor area is obtained. Finally, positioning fingerprint data of the target indoor area is generated based on the device sensor data and the positioning position of the target indoor area. Since the moving vehicle equipped with the acquisition devices in this disclosure can collect both vehicle sensor data and device sensor data simultaneously while moving through the target indoor area, compared to the prior art method of manually collecting data using handheld acquisition devices, the efficiency of sensor data acquisition in the target indoor area is significantly improved. Furthermore, since the two or more acquisition devices are mounted at different positions on the moving vehicle, meaning that the installation parameters of different acquisition devices on the moving vehicle are different, the positioning position of the target indoor area obtained based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition devices on the moving vehicle covers a wider range of the target indoor area. This not only improves the efficiency of generating positioning fingerprint data of the target indoor area but also ensures the accuracy of the generated positioning fingerprint data.

[0077] In one possible implementation, before performing the method described in Figure 3, the device sensors and vehicle sensors need to be calibrated to improve the accuracy of the generated positioning fingerprint data. For example, the device sensors and / or vehicle sensors can be calibrated in advance using initial positioning data collected outdoors. Initial positioning data can be, for example, any one or more of GNSS positioning data, or Real-Time Kinematic (RTK) positioning data. The specific calibration method depends on the type of device sensors and / or vehicle sensors. For example, if calibrating an IMU, the initial positioning data can be obtained by a moving vehicle carrying the data acquisition equipment performing a figure-eight rotation or traveling around a rectangle outdoors, and then the IMU can be calibrated using the initial positioning data collected outdoors. For wireless hotspot modules, calibration can be performed, for example, through mean filtering. Calibration methods for different types of sensors can be implemented with reference to existing technologies, and will not be elaborated further here.

[0078] Furthermore, when a moving vehicle carries two or more data acquisition devices while traveling in an indoor area, the operating time standard of the device sensors integrated into the data acquisition devices and the operating time standard of the vehicle sensors mounted on the moving vehicle must be the same. The operating times of the device sensors and vehicle sensors disclosed herein will be described in detail below.

[0079] Data acquisition devices and sensors on both devices and vehicles can be synchronized via clocks to ensure that different devices and / or sensors use the same operating time standard. This disclosure does not limit the specific type of operating time standard; for example, the operating time standard can be uniformly Coordinated Universal Time (UTC). For instance, data acquisition devices and / or moving vehicles can obtain UTC time by accessing a Network Time Protocol (NTP) server, thus synchronizing their operating time with UTC time. Alternatively, data acquisition devices and / or moving vehicles can each be equipped with a GNSS receiver. Taking a GPS receiver as an example, after the moving vehicle receives GPS satellite time synchronization in an outdoor area, the time is converted to UTC time, also achieving synchronization. This implementation method ensures that the operating time of device sensors and vehicle sensors uses the same operating time standard, improving the efficiency and availability of location fingerprint data generation.

[0080] Specifically, in this disclosure, one set of equipment sensor data corresponds to one acquisition time, and one set of vehicle sensor data corresponds to one acquisition time. Although, as mentioned above, the operating time standards of the equipment sensors and vehicle sensors are the same, there is still a problem of asynchronous operating times between the equipment sensors and vehicle sensors (e.g., different sensors have different data acquisition periods / frequencies or different data acquisition start times). This leads to misalignment of acquisition times between the equipment sensor data and vehicle sensor data. For example, the acquisition times of the equipment sensor data are 0 seconds, 1 second, 2 seconds, 3 seconds, and 4 seconds, while the acquisition times of the vehicle sensor data are 0 seconds, 2 seconds, 4 seconds, 6 seconds, and 8 seconds. The equipment sensor data has data acquired at 1 second and 3 seconds, but the vehicle sensor data lacks data acquired at 1 second and 3 seconds. Since the trajectory data is generated based on the vehicle sensor data, and the vehicle sensor data lacks data acquired at 1 second and 3 seconds, the generated trajectory data will lack the trajectory positions corresponding to the acquisition times of 1 second and 3 seconds, thus making it impossible to match the trajectory data corresponding to the same acquisition time with the equipment sensor data.

[0081] Therefore, this disclosure provides another method for generating positioning fingerprint data, which can align the acquisition times of device sensor data and vehicle sensor data to ensure the matching rate of device sensor data and trajectory data, thereby improving the accuracy of positioning fingerprint data. Figure 4 is a flowchart illustrating another method for generating positioning fingerprint data provided by this disclosure. The parts of this method that are the same as those shown in Figure 3 will not be repeated. The parts that differ from Figure 3 are shown in Figure 4. The steps shown in Figure 4 are executed before step S101, which involves acquiring the device sensor data collected by the acquisition device and the vehicle sensor data collected by the vehicle sensor, and specifically include:

[0082] S201. Based on the acquisition time corresponding to the equipment sensor data and the acquisition time corresponding to the vehicle sensor data, the acquisition time is interpolated by linear interpolation to align the acquisition time of the equipment sensor data and the acquisition time of the vehicle sensor data.

[0083] One possible implementation is to use the acquisition time of either the device sensor data or the vehicle sensor data as a reference, and then perform linear interpolation on the acquisition time of the other data to obtain the acquisition times of the other data that are not aligned with the reference. Alternatively, the device sensor data and the vehicle sensor data can be used as references to align their acquisition times.

[0084] For example, assuming the acquisition times of the device sensor data include 0 seconds, 1 second, 2 seconds, 3 seconds, 4 seconds, and 5 seconds, and the acquisition times of the vehicle sensor data include 0 seconds, 2 seconds, 4 seconds, 6 seconds, and 8 seconds, assuming the acquisition time of the device sensor data is used as a reference, interpolation calculations can be performed on the acquisition time of the vehicle sensor data to obtain interpolated acquisition times of 1 second, 3 seconds, and 5 seconds, thus aligning the acquisition times of the device sensor data and the vehicle sensor data. Alternatively, if the acquisition times of the device sensor data and the vehicle sensor data are used as references, interpolation calculations can be performed on the acquisition time of the vehicle sensor data to obtain interpolated acquisition times of 1 second, 3 seconds, and 5 seconds, and interpolation calculations can be performed on the acquisition time of the device sensor data to obtain interpolated acquisition times of 6 seconds and 8 seconds. It should be understood that this example is for more intuitive illustration of this disclosure and should not be considered as a limitation of this disclosure.

[0085] Another possible implementation involves using a pre-set acquisition time as a reference to perform linear interpolation on the acquisition times of the equipment sensor data and the vehicle sensor data to align their acquisition times. The pre-set acquisition time can be determined according to actual needs, and this disclosure does not impose any restrictions on it. For example, if the acquisition times of the equipment sensor data include 0 seconds, 2 seconds, 4 seconds, and 6 seconds, and the acquisition times of the vehicle sensor data include 0 seconds, 3 seconds, 5 seconds, and 7 seconds, and the pre-set acquisition times are 0 seconds, 1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds, 6 seconds, and 7 seconds, then the equipment sensor data can be interpolated using the pre-set acquisition time as a reference to obtain acquisition times of 1 second, 3 seconds, 5 seconds, and 7 seconds; similarly, the vehicle sensor data can be interpolated using the pre-set acquisition time as a reference to obtain acquisition times of 2 seconds, 4 seconds, and 6 seconds. It should be understood that this example is for more intuitive illustration of this disclosure and should not be considered a limitation thereof.

[0086] S202. Based on the equipment sensor data and the acquisition time of the equipment sensor data, as well as the vehicle sensor data and the acquisition time of the vehicle sensor, generate the equipment sensor data or vehicle sensor data corresponding to the interpolated acquisition time, so that each acquisition time after alignment corresponds to a set of equipment sensor data and a set of vehicle sensor data.

[0087] Continuing the previous example, if the data acquisition time for vehicle sensor data interpolation is the time corresponding to 1 second, then at least based on the vehicle sensor data at acquisition time 0 and acquisition time 2, the interpolated vehicle sensor data corresponding to acquisition time 1 second can be generated. That is, acquisition time 1 second corresponds to both one set of equipment sensor data and one set of vehicle sensor data. The specific process for generating the interpolated sensor data at the acquisition time can refer to existing technologies, and this disclosure does not impose any limitations.

[0088] The above is a method embodiment of the present disclosure including the step of aligning the acquisition time. In this embodiment, the trajectory data is generated in advance based on the vehicle sensor data collected by two or more acquisition devices when the vehicle is traveling in the target indoor area and the acquisition time is aligned with the device sensor data. This ensures the matching rate between the trajectory position in the trajectory data and the device sensor data.

[0089] Continuing the previous example, for instance, the data acquisition times after interpolation of vehicle sensor data include 0 seconds, 1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds, 6 seconds, and 8 seconds. The data acquisition times after interpolation of equipment sensor data also include 0 seconds, 1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds, 6 seconds, and 8 seconds. Based on the vehicle sensor data corresponding to the data acquisition times of 0 seconds, 1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds, 6 seconds, and 8 seconds after interpolation of vehicle sensor data, trajectory positions corresponding to the acquisition times of 0 seconds, 1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds, 6 seconds, and 8 seconds can be generated. Each acquisition time corresponds to one trajectory position. That is, the trajectory data includes the trajectory positions corresponding to the acquisition times of 0 seconds, 1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds, 6 seconds, and 8 seconds, and these trajectory positions all have equipment sensor data corresponding to the same time.

[0090] The above is a method embodiment provided by this disclosure. This embodiment realizes that the device sensor and the vehicle sensor adopt the same working time standard. When the acquisition time of the device sensor and the vehicle sensor are not synchronized, linear interpolation is used to align the acquisition time of the device sensor data and the acquisition time of the vehicle sensor data. Trajectory data is generated based on the vehicle sensor data after the acquisition time is aligned, which ensures the matching rate of trajectory positions in the device sensor data and trajectory data and improves the usability of the positioning fingerprint data generated based on the device sensor data and trajectory data.

[0091] Furthermore, the trajectory positions in the trajectory data of this disclosure have corresponding acquisition times. This ensures that the positioning position of the target indoor area obtained by this disclosure based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition device on the moving vehicle also has a corresponding acquisition time. For example, if the positioning position is generated based on the trajectory position corresponding to acquisition time 0 seconds in the trajectory data, then the acquisition time of the positioning position is also 0 seconds. Therefore, this disclosure can generate the positioning fingerprint data of the target indoor area based on the device sensor data and the positioning position of the target indoor area in the following manner:

[0092] First, for the target indoor area, establish a mapping relationship between the device sensor data and the positioning location of the target indoor area at the same time of data acquisition, and use the positioning location and device sensor data as the positioning fingerprint data of the target indoor area;

[0093] Second, based on the location of the target indoor area, the target indoor area is divided into grids (also known as geographic grids), and the grid size can be preset. Sensor data from devices belonging to the same grid are aggregated to generate the grid's location fingerprint data. During aggregation, different aggregation methods can be used depending on the type of device sensor. For example, magnetic field data belonging to the same grid but collected by different acquisition devices do not need to be fused; only a mapping relationship with the grid needs to be established. However, Bluetooth data belonging to the same grid but collected by different acquisition devices can be fused. The specific aggregation method needs to be determined based on actual needs and sensor type; this disclosure does not impose any restrictions on it.

[0094] The target indoor area disclosed herein may include at least an indoor planar area. However, if the target indoor area is an indoor parking lot or parking garage, in most cases, the indoor parking lot or parking garage has more than one floor. That is, the target indoor area includes both indoor planar areas and indoor non-planar areas. An indoor planar area may be, for example, the area of ​​the same floor in a multi-level underground parking garage. An indoor non-planar area may be, for example, a passageway connecting two adjacent floors. In this passageway area, the movement direction of the moving vehicle is usually regular. For example, for a certain passageway, the moving vehicle usually has two movement directions: upward and downward, to reach the adjacent floor through the passageway. In a planar area, the movement direction of the moving vehicle is usually more complex. Therefore, in order to improve the positioning accuracy of different target indoor areas, this disclosure determines whether different positioning locations belong to an indoor planar area or an indoor non-planar area based on the elevation information of the positioning location, and determines whether different positioning locations belong to the same indoor planar area. For indoor planar areas and indoor non-planar areas, different methods are used to obtain positioning fingerprint data to obtain more accurate positioning fingerprint data. Specifically, embodiments of this disclosure provide another method for generating positioning fingerprint data. Figure 5 is a flowchart illustrating another method for generating location fingerprint data according to an embodiment of this disclosure. As shown in Figure 5, this method uses the same technical means as existing embodiments, which will not be repeated here. The difference between this embodiment and existing embodiments lies in that it provides a preferred embodiment for cases where the target indoor area includes an indoor planar area. The difference between this embodiment and existing embodiments is that the location of the target indoor area includes latitude and longitude coordinates and elevation, and the method further includes:

[0095] S301. Based on the elevation included in the positioning location, obtain the positioning location within the same indoor plane area.

[0096] In this disclosure, the positioning location of the target indoor area is obtained based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition device on the moving vehicle. The elevation of the trajectory position in the trajectory data can be obtained based on the elevation sensor data mounted on the moving vehicle. Generally, the elevation of the positioning location is the same as the elevation of the corresponding trajectory position in the trajectory data. That is, when obtaining the positioning location of the target indoor area based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition device on the moving vehicle, the latitude and longitude coordinates of the positioning location of the target indoor area can be determined based on the latitude and longitude coordinates of the trajectory position in the trajectory data and the pre-measured installation parameters of the acquisition device on the moving vehicle, and the elevation of the trajectory position can be directly used as the elevation of the positioning location. For example, if the positioning location is generated based on the trajectory position corresponding to the acquisition time 0 seconds in the trajectory data, then the latitude and longitude coordinates of the positioning location are generated based on the latitude and longitude coordinates of the trajectory position corresponding to the acquisition time 0 seconds and the installation parameters, and the elevation of the trajectory position corresponding to the acquisition time 0 seconds is used as the elevation of the positioning location.

[0097] Specifically, the elevations of the target indoor areas can be compared, and the locations with the same elevation or a difference less than a set difference threshold can be identified as locations belonging to the same indoor plane area.

[0098] Step S103 is performed according to step S302, specifically:

[0099] S302. Based on the device sensor data and the positioning location in the same indoor plane area, obtain the positioning fingerprint data of the corresponding indoor plane area.

[0100] The embodiment shown in Figure 5 determines the location of the same indoor plane area based on the elevation of the target indoor area. Then, based on the location of the same indoor plane area and the device sensor data corresponding to the location, the positioning fingerprint data of the indoor plane area is obtained. That is, this embodiment can generate special positioning fingerprint data for the indoor plane area when generating positioning fingerprint data, ensuring the availability of positioning fingerprint data.

[0101] As mentioned above, this disclosure provides two embodiments for generating positioning fingerprint data for a target indoor area. The first embodiment establishes a mapping relationship between the device sensor data at the same acquisition time and the positioning location of the target indoor area, which serves as the positioning fingerprint data for the target indoor area. For indoor planar areas, the established mapping relationship is specifically as follows:

[0102] For the positioning locations located in the same indoor plane area, and the corresponding device sensor data of these positioning locations, a mapping relationship between these positioning locations and device sensor data is established. The mapping relationship between positioning locations and device sensor data, the positioning locations, and the device sensor data corresponding to the positioning locations are used as positioning fingerprint data of the indoor plane area.

[0103] The second embodiment involves dividing the target indoor area into a grid (also known as a geographic grid) based on its location. The grid size can be preset. Device sensor data belonging to the same grid are aggregated to generate location fingerprint data for that grid. To this end, this disclosure provides another method for generating location fingerprint data. Figure 6 is a flowchart illustrating this method. As shown in Figure 6, the difference between this method and the method shown in Figure 5 is that this method further includes the following steps:

[0104] S401. Based on the location of the corresponding indoor plane area and the preset geographic grid size, divide the corresponding indoor plane area into geographic grids.

[0105] The preset geographic grid size mentioned above refers to the size of the geographic grid, such as a rectangular geographic grid with a length and width of 0.5 meters. The geographic grid size can be set according to actual needs, and this disclosure does not impose any restrictions on it.

[0106] In this case, one possible implementation is to divide the indoor planar area into a geographic grid based on the location within the same indoor planar area and a preset geographic grid size. This can be implemented as follows:

[0107] The location is used as the center of the geographic grid. Based on the preset geographic grid size and the center of the geographic grid, the location range of the corresponding geographic grid is determined.

[0108] S402. Based on the device sensor data, the location within the same indoor plane area, and the geographic grid of the corresponding indoor plane area, obtain the location fingerprint data of the corresponding indoor plane area.

[0109] In this embodiment, based on the location positions within the same indoor planar area, the device sensor data corresponding to these locations are divided according to a geographic grid. That is, the device sensor data corresponding to these locations are assigned to the geographic grid in which the location position falls. Then, based on the location positions stored in the geographic grid and the device sensor data corresponding to those locations, the location fingerprint data for the indoor planar area is obtained.

[0110] Methods for determining which geographic grid a location falls into can include ray casting, bisection, etc. For specific implementation methods, please refer to existing technologies, which will not be elaborated here.

[0111] The embodiment shown in Figure 6 divides the indoor planar area into a geographic grid, and then generates location fingerprint data for the geographic grid, which makes the positioning process based on the location fingerprint data more efficient and accurate.

[0112] Furthermore, as mentioned above, in addition to indoor planar areas, some target indoor areas also include indoor non-planar areas. Therefore, this disclosure provides another method for generating location fingerprint data, as shown in Figure 7. The difference between this method and the method shown in Figure 6 is that it may further include the following steps:

[0113] S501. Based on the elevation included in the positioning location of the target indoor area, obtain the positioning location of the non-planar area within the same indoor area.

[0114] In practical implementation, the positioning location of a non-planar area within the same indoor space can be obtained in the following way:

[0115] The first method identifies locations where the elevation difference exceeds a set threshold but the interval between data collection times meets set conditions as locations belonging to the same indoor non-planar area.

[0116] The second method involves first determining the set of locations belonging to the same indoor planar area (e.g., the set of locations corresponding to two different floors in an indoor parking garage) and the locations not belonging to any indoor planar area based on the elevation of the locations in the set. Then, based on the elevation of the locations in the set, the elevation value interval between two adjacent indoor planar areas is determined as the elevation value interval connecting these two indoor planar areas to form the indoor non-planar area. Finally, based on the elevations included in the locations not located in any indoor planar area, the elevation value interval into which these non-planar locations fall is determined. Locations whose elevation values ​​fall within the same elevation interval are considered to be located within the same indoor non-planar area.

[0117] For example, assuming the target indoor area is a three-level underground parking garage, the corresponding location sets for the first, second, and third underground levels are determined based on elevation. The average elevation values ​​determined from these location sets are -3 meters, -6 meters, and -9 meters, respectively. For locations not located on the indoor plane, those with elevations within the range of -3 meters to -6 meters belong to the same indoor non-plane area (i.e., the first passageway area between the first and second underground levels); those with elevations within the range of -6 meters to -9 meters belong to the same indoor non-plane area (i.e., the second passageway area between the second and third underground levels).

[0118] S502. Based on the device sensor data and the positioning location in the same indoor non-planar area, obtain the positioning fingerprint data of the corresponding indoor non-planar area.

[0119] In practical implementation, based on device sensor data and the location within the same indoor non-planar area, the corresponding indoor non-planar area's location fingerprint data is obtained. This can be achieved using the two methods described above for obtaining location fingerprint data for indoor planar areas: First, the mapping relationship between the location and device sensor data, the location itself, and the corresponding device sensor data are used to generate the location fingerprint data for the indoor non-planar area. Second, the indoor non-planar area is divided into a geographic grid, and location fingerprint data is generated for each geographic grid. Detailed descriptions of these two methods can be found in the relevant sections above and will not be repeated here.

[0120] In this embodiment, the positioning locations within the same indoor non-planar area are first obtained based on the elevation of the positioning location within the target indoor area. Then, based on the positioning locations within the same indoor non-planar area and the corresponding device sensor data, positioning fingerprint data corresponding to that indoor non-planar area is obtained. This implementation method allows for the creation of positioning fingerprint data adapted to indoor non-planar areas, based on their characteristics, resulting in more accurate positioning fingerprint data. Subsequently, when this positioning fingerprint data is applied to a positioning scenario, it can be used to locate devices entering indoor non-planar areas, improving the accuracy of positioning within those areas.

[0121] The above are several method embodiments of this disclosure. As mentioned above, the data acquisition devices of this disclosure preferably include at least two. The following example illustrates how, in step S102, the target indoor area's location is obtained based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition devices on the moving vehicle. Figure 8 is a flowchart illustrating another method for generating location fingerprint data according to an embodiment of this disclosure. As shown in Figure 8, this method may specifically include the following steps:

[0122] S601. Based on the trajectory data of the moving vehicle, including a series of trajectory positions and first installation parameters corresponding to the acquisition time, the positioning position of the first acquisition device corresponding to the acquisition time is obtained through lever compensation.

[0123] S602. Based on the trajectory data of the moving vehicle, including a series of trajectory positions and second installation parameters corresponding to the acquisition time, the positioning position of the second acquisition device corresponding to the acquisition time is obtained through lever compensation.

[0124] Since the trajectory position and installation parameters are known, the specific implementation method of obtaining the positioning position through lever compensation can refer to existing technologies, and will not be elaborated here.

[0125] S603. Based on the positioning position of the first acquisition device corresponding to the acquisition time and the positioning position of the second acquisition device corresponding to the acquisition time, the positioning position of the target indoor area corresponding to each acquisition time is obtained.

[0126] The location of the target indoor area at each acquisition time includes the location of the first acquisition device at the acquisition time and the location of the second acquisition device at the acquisition time. When generating location fingerprint data subsequently, a mapping relationship is constructed between the location of the first acquisition device at the acquisition time and the sensor data collected by the first acquisition device at that time; similarly, a mapping relationship is constructed between the location of the second acquisition device at the acquisition time and the sensor data collected by the second acquisition device at that time. This generates location fingerprint data for multiple acquisition devices corresponding to the trajectory positions in the trajectory data.

[0127] In this embodiment, firstly, based on the trajectory data of the moving vehicle, including a series of trajectory positions corresponding to the acquisition time and first installation parameters, the positioning position of the first acquisition device corresponding to the acquisition time is obtained through lever compensation. Similarly, the positioning position of the second acquisition device corresponding to the acquisition time is obtained based on the second installation parameters using the same method. Then, based on the positioning positions of the first and second acquisition devices corresponding to the acquisition time, the positioning position of the target indoor area relative to each acquisition time is obtained. Since the trajectory position of the moving vehicle represents its position (e.g., the trajectory position is located on the centerline of the moving vehicle's direction of travel), the positioning position obtained based on the trajectory position and the first installation parameters of the first acquisition device can cover one side of the moving vehicle's direction of travel, while the positioning position obtained based on the trajectory position and the second installation parameters of the second acquisition device can cover the other side of the moving vehicle's direction of travel. The method disclosed herein allows for simultaneous acquisition of the vehicle's position along its centerline, on its left side, and on its right side, without the need for manual data collection. This is achieved by having the vehicle equipped with both the first and second data acquisition devices travel once within the target indoor area. This improves the efficiency of acquiring the target indoor area's position and consequently enhances the efficiency of generating the positioning fingerprint data. Furthermore, by applying the vehicle's trajectory to the creation of roads within the target indoor area, the generated positioning fingerprint data and the roads can be automatically matched, further improving the usability of the generated positioning fingerprint data.

[0128] Figure 9 is a schematic diagram of a positioning fingerprint data generation device provided in an embodiment of this disclosure. As shown in Figure 9, the positioning fingerprint data generation device includes: an acquisition module 11, a processing module 12, and a generation module 13.

[0129] The acquisition module 11 is used to acquire device sensor data and trajectory data of the moving vehicle collected by two or more acquisition devices that can be used for positioning. The device sensor data is collected when the moving vehicle carries two or more acquisition devices and moves in the target indoor area. The trajectory data is generated in advance based on the vehicle sensor data collected by the moving vehicle carrying two or more acquisition devices and moving in the target indoor area.

[0130] The processing module 12 is used to obtain the positioning location of the target indoor area based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition equipment on the moving vehicle.

[0131] The generation module 13 is used to generate positioning fingerprint data of the target indoor area based on the device sensor data and the positioning location of the target indoor area.

[0132] Optionally, when a sports vehicle is equipped with two or more data acquisition devices and is traveling in an indoor area, the operating time standard of the device sensors integrated in the data acquisition devices is the same as the operating time standard of the vehicle sensors mounted on the sports vehicle.

[0133] Optionally, one set of device sensor data corresponds to one acquisition time, and one set of vehicle sensor data corresponds to one acquisition time. If the operating times of the device sensor and the vehicle sensor are not synchronized, the processing module 12 is further used to perform linear interpolation on the acquisition times based on the acquisition times corresponding to the device sensor data and the vehicle sensor data, so as to align the acquisition times of the device sensor data and the vehicle sensor data. Based on the device sensor data and the acquisition times of the device sensor data and the vehicle sensor data and the acquisition times of the vehicle sensor data, the interpolated acquisition times corresponding to the device sensor data or vehicle sensor data are generated, so that each aligned acquisition time corresponds to one set of device sensor data and one set of vehicle sensor data. Among them, the trajectory data is pre-generated based on the vehicle sensor data collected by two or more acquisition devices on the moving vehicle while traveling in the target indoor area, and the acquisition time is aligned with the device sensor data.

[0134] Optionally, if the target indoor area includes at least an indoor planar area, and the positioning location includes latitude and longitude coordinates and elevation, then the processing module 12 is further configured to obtain the positioning location within the same indoor planar area according to the elevation included in the positioning location. The generation module 13 is specifically configured to obtain the positioning fingerprint data of the corresponding indoor planar area based on the device sensor data and the positioning location within the same indoor planar area.

[0135] Optionally, the processing module 12 is further configured to divide the corresponding indoor plane area into a geographic grid based on the positioning location within the same indoor plane area and a preset geographic grid size. The generation module 13 is specifically configured to obtain the positioning fingerprint data of the corresponding indoor plane area based on the device sensor data, the positioning location within the same indoor plane area, and the geographic grid of the corresponding indoor plane area.

[0136] Optionally, if the target indoor area also includes an indoor non-planar area, then the processing module 12 is further configured to obtain the positioning position located in the same indoor non-planar area according to the elevation included in the positioning position. The generation module 13 is specifically configured to obtain the positioning fingerprint data of the corresponding indoor non-planar area based on the device sensor data and the positioning position located in the same indoor non-planar area.

[0137] Optionally, if the data acquisition devices include a first data acquisition device and a second data acquisition device, and the first and second data acquisition devices are respectively installed on the left and right sides of the moving vehicle's direction of travel, and the installation parameters include a first installation parameter for the first data acquisition device and a second installation parameter for the second data acquisition device, then the processing module 12 is specifically used to obtain the positioning position of the first data acquisition device corresponding to the acquisition time based on a series of trajectory positions corresponding to the acquisition time and the first installation parameters included in the trajectory data of the moving vehicle, through lever compensation. Based on a series of trajectory positions corresponding to the acquisition time and the second installation parameters included in the trajectory data of the moving vehicle, the positioning position of the second data acquisition device corresponding to the acquisition time is obtained through lever compensation. Based on the positioning positions of the first and second data acquisition devices corresponding to the acquisition time, the positioning position of the target indoor area relative to each acquisition time is obtained.

[0138] The data processing apparatus provided in this embodiment can execute the method for generating location fingerprint data in the above method embodiments. Its implementation principle and technical effects are similar, and will not be repeated here. It should be noted that the division of modules shown in Figure 9 is merely illustrative, and this disclosure does not limit the division of modules or their naming.

[0139] Figure 10 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. As shown in Figure 10, the electronic device 1000 may include at least one processor 1001 and a memory 1002.

[0140] The memory 1002 is used to store programs. Specifically, the program may include program code, which includes computer operation instructions.

[0141] The memory 1002 may include high-speed RAM (Random Access Memory) and may also include non-volatile memory, such as at least one disk storage device.

[0142] The processor 1001 is used to execute computer execution instructions stored in the memory 1002 to implement the method for generating location fingerprint data in the foregoing method embodiments. The processor 1001 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this disclosure.

[0143] The electronic device 1000 may also include a communication interface 1003, through which it can communicate and interact with external devices, such as terminals (e.g., computers, tablets). In specific implementations, if the communication interface 1003, memory 1002, and processor 1001 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0144] Optionally, in a specific implementation, if the communication interface 1003, memory 1002 and processor 1001 are integrated on a single chip, then the communication interface 1003, memory 1002 and processor 1001 can communicate through an internal interface.

[0145] This disclosure also provides a computer-readable storage medium, which may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Specifically, the computer-readable storage medium stores program instructions, which are used for the method of generating location fingerprint data in the above embodiments.

[0146] This disclosure also provides a computer program product including executable instructions stored in a readable storage medium. At least one processor of the navigation system can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the navigation system to implement the positioning fingerprint data generation method provided in the various embodiments described above.

[0147] The term "multiple" in this document refers to two or more. The term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the preceding and following related objects; in formulas, the character " / " indicates a "division" relationship between the preceding and following related objects. Additionally, it should be understood that in the descriptions of this disclosure, terms such as "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or order.

[0148] It is understood that the various numerical designations used in the embodiments of this disclosure are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this disclosure.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A method for generating location fingerprint data, wherein, The method includes: The system acquires device sensor data and trajectory data of a moving vehicle collected by two or more acquisition devices that can be used for positioning. The device sensor data is collected when the moving vehicle carries the two or more acquisition devices and travels in the target indoor area. The trajectory data is generated in advance based on the vehicle sensor data collected by the moving vehicle carrying the two or more acquisition devices and traveling in the target indoor area. Based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition device on the moving vehicle, the positioning location of the target indoor area is obtained; Based on the device sensor data and the location of the target indoor area, a positioning fingerprint data of the target indoor area is generated.

2. The method according to claim 1, wherein, When the moving vehicle is equipped with two or more data acquisition devices and is traveling in an indoor area, the operating time standard of the device sensors integrated in the data acquisition devices and the operating time standard of the vehicle sensors mounted on the moving vehicle are the same.

3. The method according to claim 2, wherein, One set of equipment sensor data corresponds to one acquisition time, and one set of vehicle sensor data corresponds to one acquisition time. If the operating times of the equipment sensor and the vehicle sensor are not synchronized, the method further includes: Based on the acquisition time corresponding to the device sensor data and the acquisition time corresponding to the vehicle sensor, interpolation is performed at the acquisition time using linear interpolation to align the acquisition times of the device sensor data and the vehicle sensor data. Based on the device sensor data and the acquisition time of the device sensor data, as well as the vehicle sensor data and the acquisition time of the vehicle sensor, the device sensor data or vehicle sensor data corresponding to the interpolated acquisition time is generated, so that each acquisition time after alignment corresponds to a set of device sensor data and a set of vehicle sensor data. The trajectory data is generated in advance based on vehicle sensor data collected by the two or more acquisition devices carried by the vehicle while it is moving in the target indoor area, and the acquisition time is aligned with the sensor data of the devices.

4. The method according to claim 3, wherein, The target indoor area includes at least an indoor planar area, the positioning location includes latitude and longitude coordinates and elevation, and the method further includes: Based on the elevation included in the positioning location, the positioning locations within the same indoor plane area are obtained; The step of generating positioning fingerprint data for the target indoor area based on the device sensor data and the positioning location specifically includes: Based on the device sensor data and the positioning location in the same indoor plane area, obtain the positioning fingerprint data of the corresponding indoor plane area.

5. The method according to claim 4, wherein, The method further includes: Based on the location of the corresponding indoor plane area and the preset geographic grid size, the corresponding indoor plane area is divided into geographic grids; Based on the device sensor data and the positioning location within the same indoor plane area, the positioning fingerprint data of the corresponding indoor plane area is obtained, specifically including: Based on the device sensor data, the location within the same indoor plane area, and the geographic grid of the corresponding indoor plane area, the location fingerprint data of the corresponding indoor plane area is obtained.

6. The method according to claim 4, wherein, The target indoor area further includes: an indoor non-planar area, and the method further includes: Based on the elevation included in the positioning location, the positioning location located in the same indoor non-planar area is obtained; Based on the device sensor data and the positioning location in the same indoor non-planar area, the positioning fingerprint data of the corresponding indoor non-planar area is obtained.

7. The method according to any one of claims 3-6, wherein, The data acquisition device includes a first data acquisition device and a second data acquisition device, which are respectively installed on the left and right sides of the moving vehicle's direction of travel. The installation parameters include a first installation parameter for the first data acquisition device and a second installation parameter for the second data acquisition device. The step of obtaining the positioning location of the target indoor area based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the data acquisition device on the moving vehicle specifically includes: Based on the trajectory data of the moving vehicle, including a series of trajectory positions corresponding to the acquisition time and the first installation parameters, the positioning position of the first acquisition device corresponding to the acquisition time is obtained through lever compensation. Based on the trajectory data of the moving vehicle, including a series of trajectory positions corresponding to the acquisition time and the second installation parameters, the positioning position of the second acquisition device corresponding to the acquisition time is obtained through lever compensation. Based on the positioning positions of the first acquisition device and the second acquisition device corresponding to the acquisition time, the positioning positions of the target indoor area with respect to each acquisition time are obtained.

8. A device for generating location fingerprint data, wherein, The device includes: The acquisition module is used to acquire device sensor data and trajectory data of a moving vehicle collected by two or more acquisition devices that can be used for positioning. The device sensor data is collected when the moving vehicle carries the two or more acquisition devices and moves in the target indoor area. The trajectory data is generated in advance based on the vehicle sensor data collected by the moving vehicle carrying the two or more acquisition devices and moving in the target indoor area. The processing module is used to obtain the positioning position of the target indoor area based on the trajectory data of the moving vehicle and the pre-measured installation parameters of the acquisition device on the moving vehicle; The generation module is used to generate positioning fingerprint data of the target indoor area based on the device sensor data and the positioning location of the target indoor area.

9. An electronic device, wherein, include: Processor and memory; The processor is communicatively connected to the memory; The memory stores computer instructions; The processor executes computer instructions stored in the memory to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, wherein, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for generating location fingerprint data as described in any one of claims 1-7.

11. A computer program product, wherein, It includes a computer program or instructions, which, when executed by a processor, implement the method for generating location fingerprint data as described in any one of claims 1-7.

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