Positioning system, positioning method and device, storage medium and electronic equipment

Abstract

The present disclosure provides a positioning system, a positioning method, a positioning device, a computer readable storage medium and an electronic device, and relates to the technical field of positioning. The positioning system comprises: a positioning algorithm library containing a plurality of positioning algorithms, each positioning algorithm being configured with an identifier; a positioning data acquisition system for acquiring a plurality of types of positioning data; and a decision model for determining a target positioning algorithm identifier using the plurality of types of positioning data, the target positioning algorithm identifier being used to indicate a target positioning algorithm in the positioning algorithm library to perform a positioning task. The present disclosure can select a positioning algorithm that is currently adapted to perform a positioning task, thereby improving the accuracy of positioning.

Description

Technical Field

[0001] This disclosure relates to the field of positioning technology, and more specifically, to a positioning system, positioning method, positioning device, computer-readable storage medium, and electronic device. Background Technology

[0002] Positioning technology can be widely used in various intelligent business scenarios, such as augmented reality, metaverse, and autonomous driving. With the development of positioning technology, various positioning methods have been developed, such as GPS (Global Positioning System) positioning, Bluetooth-based positioning, and WiFi-based positioning.

[0003] However, not every positioning method can meet all positioning scenarios, and inaccurate positioning is often encountered.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This disclosure provides a positioning system, positioning method, positioning device, computer-readable storage medium, and electronic device, thereby overcoming, at least to some extent, the problem of inaccurate positioning.

[0006] According to a first aspect of this disclosure, a positioning system is provided, comprising: a positioning algorithm library containing multiple positioning algorithms, each positioning algorithm being configured with an identifier; a positioning data acquisition system for acquiring multiple types of positioning data; and a decision model for using the multiple types of positioning data to determine a target positioning algorithm identifier, the target positioning algorithm identifier being used to instruct a target positioning algorithm in the positioning algorithm library to perform a positioning task.

[0007] Optionally, the positioning algorithm library includes various positioning algorithms, including those determined based on a single positioning method and / or those determined by fusing two or more positioning methods.

[0008] Optionally, the positioning data acquisition system may collect various types of positioning data, including at least two of the following: inertial sensor positioning data, Bluetooth positioning data, WiFi positioning data, ultra-wideband sensor positioning data, 5G positioning data, and GPS positioning data.

[0009] Optionally, the decision model is configured to determine the target positioning algorithm identifier based on multiple types of positioning data and the weights of each type of positioning data; wherein the weights of each type of positioning data are determined based on current environmental parameters and / or historical data collected by the positioning data acquisition system.

[0010] Optionally, the decision model includes: an encoding unit for acquiring multiple types of positioning data and encoding the multiple types of positioning data to obtain a decision result vector; and a matching unit for determining the vector similarity between the decision result vector and multiple candidate vectors, and determining the positioning algorithm identifier corresponding to the candidate vector with the largest vector similarity as the target positioning algorithm identifier; wherein, the candidate vector and the positioning algorithm identifier correspond one-to-one.

[0011] According to a second aspect of this disclosure, a positioning method is provided, comprising: acquiring multiple types of positioning data; inputting the multiple types of positioning data into a decision model to obtain a target positioning algorithm identifier; determining a target positioning algorithm from a positioning algorithm library based on the target positioning algorithm identifier; and calling the target positioning algorithm to perform a positioning task.

[0012] Optionally, the positioning algorithm library includes various positioning algorithms, including those determined based on a single positioning method and / or those determined by fusing two or more positioning methods.

[0013] Optionally, the positioning data acquisition system may collect various types of positioning data, including at least two of the following: inertial sensor positioning data, Bluetooth positioning data, WiFi positioning data, ultra-wideband sensor positioning data, 5G positioning data, and GPS positioning data.

[0014] According to a third aspect of this disclosure, a positioning device is provided, comprising: a data acquisition module for acquiring multiple types of positioning data; an algorithm identifier determination module for inputting the multiple types of positioning data into a decision model to obtain a target positioning algorithm identifier; and a positioning task execution module for determining a target positioning algorithm from a positioning algorithm library based on the target positioning algorithm identifier and calling the target positioning algorithm to execute a positioning task.

[0015] Optionally, the positioning algorithm library includes various positioning algorithms, including those determined based on a single positioning method and / or those determined by fusing two or more positioning methods.

[0016] Optionally, the positioning data acquisition system may collect various types of positioning data, including at least two of the following: inertial sensor positioning data, Bluetooth positioning data, WiFi positioning data, ultra-wideband sensor positioning data, 5G positioning data, and GPS positioning data.

[0017] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the above-described positioning method.

[0018] According to a fifth aspect of this disclosure, an electronic device is provided, including a processor; and a memory for storing one or more programs, which, when executed by the processor, cause the processor to perform the positioning method described above.

[0019] In some embodiments of the technical solutions provided in this disclosure, the positioning algorithm library is configured to include multiple positioning algorithms, each with an identifier. In this case, the target positioning algorithm identifier can be determined using various types of collected positioning data, so that the corresponding target positioning algorithm can be used to perform the positioning task. Based on the establishment of the positioning algorithm library and the adaptive selection of positioning algorithms, the positioning scheme of this disclosure can call a positioning algorithm adapted to the current environment to perform the positioning task, rather than using a fixed, single positioning method. This improves the accuracy of positioning and further enhances the intelligent control capabilities of the device. Furthermore, the configuration of the positioning algorithm library allows for the addition of new positioning algorithms, facilitating the expansion of the positioning algorithm library.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0022] Figure 1 A block diagram of a positioning system according to an embodiment of the present disclosure is shown schematically;

[0023] Figure 2 A schematic diagram of the positioning algorithm library according to an embodiment of the present disclosure is shown;

[0024] Figure 3 A schematic diagram of the model structure of the decision model according to an embodiment of this disclosure is shown;

[0025] Figure 4 A block diagram of a decision model according to another embodiment of this disclosure is shown;

[0026] Figure 5 A flowchart illustrating the positioning method according to an embodiment of the present disclosure is shown schematically;

[0027] Figure 6 A block diagram of a positioning device according to an embodiment of the present disclosure is shown schematically;

[0028] Figure 7A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown schematically. Detailed Implementation

[0029] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0030] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0031] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0032] Currently, positioning methods can be divided into outdoor positioning and indoor positioning. For outdoor positioning, most positioning problems can be solved using satellite navigation systems (SNS). For indoor positioning, methods include, but are not limited to, radio frequency (RF) based positioning systems, inertial navigation systems (IMU) based systems, and auxiliary positioning systems. RF based positioning systems include 5G positioning systems, WiFi positioning systems, Bluetooth positioning systems, and UWB (Ultra Wide Band) positioning systems; inertial navigation systems (INS) based systems include PDR (Pedestrian Dead Reckoning) systems and INS (Inertial Navigation System); auxiliary positioning systems include barometers and magnetometers.

[0033] However, these technologies have problems to varying degrees.

[0034] For example, radio frequency (RF) based positioning systems require the deployment of a high density of base stations in the scene beforehand to achieve good accuracy and coverage, resulting in high deployment costs. Time-of-flight (TOF) and time difference of arrival (TDOA) positioning algorithms, on the other hand, require the terminal device to receive signals from more than three base stations simultaneously to achieve positioning. Furthermore, these systems are significantly affected by the quality of signal transmission in the scene, leading to poor performance in some scenarios.

[0035] For example, inertial navigation systems based on inertial sensors, although they do not require additional infrastructure, require known initial position coordinates to achieve positioning, and have accumulated errors that increase with time and distance.

[0036] For example, assisted positioning systems can generally only provide the first part of the positioning information, such as the altitude information provided by a barometer, and cannot directly achieve accurate positioning.

[0037] In addition, some technologies offer solutions for the fusion of positioning technologies; however, these positioning strategies lack organic and deep integration, resulting in insufficient positioning accuracy. Therefore, this disclosure provides a new positioning solution.

[0038] The positioning scheme of this disclosure can be implemented by a terminal device. That is, the positioning system described below can be configured in the terminal device, and the terminal device can execute each step of the positioning method of this disclosure. This disclosure does not limit the type of terminal device, and includes devices such as smartphones, tablets, and smart wearable devices.

[0039] Figure 1A block diagram of a positioning system according to an embodiment of the present disclosure is shown schematically. (Reference) Figure 1 The positioning system 1 of this disclosure may include a positioning algorithm library 11, a positioning data acquisition system 12, and a decision model 13.

[0040] The positioning algorithm library 11 may include a variety of positioning algorithms, and each positioning algorithm is configured with an identifier (ID) for differentiation and subsequent processing.

[0041] It should be noted that one or more positioning algorithms in the positioning algorithm library 11 can be positioning algorithms determined based on a single positioning method. For example, the positioning algorithm can be configured as WiFi positioning method, Bluetooth positioning method, PDR positioning method, UWB positioning method, etc.

[0042] However, in other embodiments of this disclosure, one or more positioning algorithms in the positioning algorithm library 11 may be positioning algorithms determined by fusing two or more positioning methods. For example, the positioning algorithm may be configured as a positioning algorithm fused with 5G positioning and UWB positioning, or as a positioning algorithm fused with 5G positioning and PDR positioning, or as a positioning algorithm fused with UWB positioning, Bluetooth positioning, and TOF positioning. This disclosure does not limit the combination of positioning methods.

[0043] Figure 2 A schematic diagram of the positioning algorithm library 11 according to an embodiment of the present disclosure is shown. Figure 2 Each module shown corresponds to the positioning algorithm of this disclosure embodiment.

[0044] refer to Figure 2 The positioning algorithm library 11 may include, for example, a 5G-UWB fusion module, a 5G-PDR fusion module, a PDR module, a UWB-PDR fusion module, a deep learning PDR module, a PDR-barometer fusion module, and a UWB-TOF fusion module.

[0045] It should be noted that the positioning algorithm library 11 constructed according to the embodiments of this disclosure can be used to add positioning algorithms. After researchers develop new positioning algorithms, these new algorithms can be added to the positioning algorithm library 11 to integrate them into the positioning scheme of the embodiments of this disclosure. In addition to adding algorithms, the positioning algorithms in the positioning algorithm library 11 can also be modified or deleted, and this disclosure does not impose any restrictions on this.

[0046] The positioning data acquisition system 12 can be used to collect various types of positioning data. In exemplary embodiments of this disclosure, positioning data collected by different acquisition modules are generally considered to be of different types. The acquisition modules in the positioning data acquisition system 12 may include inertial data acquisition modules, Bluetooth data acquisition modules, WiFi data acquisition modules, UWB data acquisition modules, 5G positioning data acquisition modules, GPS modules, etc., and this disclosure does not impose any limitations on them.

[0047] Accordingly, the positioning data acquisition system 12 collects various types of positioning data, including at least two of the following: inertial sensor positioning data, Bluetooth positioning data, WiFi positioning data, ultra-wideband sensor positioning data, 5G positioning data, and GPS positioning data.

[0048] The decision model 13 can be used to determine the target positioning algorithm identifier using various types of positioning data collected by the positioning data acquisition system 12. The target positioning algorithm identifier is used to indicate the target positioning algorithm in the positioning algorithm library 11 so that the terminal device can use the target positioning algorithm to perform the positioning task.

[0049] According to some embodiments of this disclosure, the decision model 13 may be a multi-layer fully connected network for classification inference, with inputs of various types of positioning data and outputs an identifier of a positioning algorithm in the positioning algorithm library 11.

[0050] Figure 3 A schematic diagram of the model structure for decision model 13 is shown. (Reference) Figure 3 Information is transmitted between neurons through fully connected networks. X1, X2, X3, ..., Xn represent different types of localization data, each configured as a one-dimensional vector. These vectors are then classified and inferred through a multi-layer fully connected network. The one-dimensional vector O is the output of decision model 13, serving as the identifier for one of the localization algorithms in the localization algorithm library 11.

[0051] It should be noted that the number of neural network layers in decision model 13 and the number of neurons in each layer can be adjusted according to the type of positioning data contained in the positioning system.

[0052] Furthermore, weights can be configured for different types of positioning data. These weights can be determined based on current environmental parameters and / or historical data collected by the positioning data acquisition system 12. The current environmental parameters can include parameters representing whether the location is indoors or outdoors. For example, in indoor positioning scenarios, the weights of WiFi and Bluetooth data can be configured to be greater than those of GPS positioning data. Additionally, in scenarios with poor WiFi signal, a lower weight can be configured for WiFi positioning data. The strength of the WiFi signal can be determined from historical data collected at that location.

[0053] In this case, the target positioning algorithm identifier can be determined based on multiple types of positioning data and the weights of each type of positioning data.

[0054] In addition, the weights can be configured as the values ​​of the model parameter items in the decision model 13, or they can be used as inputs to the decision module 13, along with the positioning data. This disclosure does not impose any restrictions on this.

[0055] Figure 4 A block diagram of a decision model 13 according to another embodiment of this disclosure is shown. (See reference...) Figure 4 The decision model 13 may include an encoding unit 401 and a matching unit 402.

[0056] Encoding unit 401 can be used to acquire various types of positioning data and encode these types of positioning data to obtain a decision result vector. For example... Figure 4 As shown, the encoding unit 401 may include an input layer, multiple cascaded fully connected layers, and an output layer. The input layer, multiple cascaded fully connected layers, and the output layer can be connected in a fully connected manner. Various types of positioning data can be encoded through the input layer, multiple cascaded fully connected layers, and the output layer to obtain a decision result vector. The number of fully connected layers can be adjusted according to actual needs, and this disclosure does not impose any limitations on it.

[0057] The matching unit 402 can be used to obtain the decision result vector, determine the vector similarity between the decision result vector and multiple candidate vectors (as shown in the figure, candidate vector 1, candidate vector 2, candidate vector 3, etc.), and determine the localization algorithm identifier corresponding to the candidate vector with the highest vector similarity as the target localization algorithm identifier. There is a one-to-one correspondence between the candidate vector and the localization algorithm identifier.

[0058] It is understandable that the positioning algorithm identifier can be an identifier that is unrelated to the features of the positioning data. In this case, the positioning algorithm identifier can be predetermined, and a mapping relationship between the positioning algorithm identifier and the features of the positioning data can be constructed so as to use the mapping relationship to determine the positioning algorithm identifier corresponding to each candidate vector.

[0059] In other words, the location algorithm identifier can be modified in response to user changes, and the new identifier still retains the same mapping relationship with the location data features as the old identifier. This allows for the fulfillment of the personalized identifier representation needs of different users.

[0060] The positioning method of this disclosure will be described below. The positioning method of this disclosure can be implemented by a terminal device. Specifically, the processor of the terminal device can execute each step of the positioning method.

[0061] Figure 5A flowchart illustrating a positioning method according to an embodiment of this disclosure is shown schematically. (Reference) Figure 5 The positioning method of this disclosure may include the following steps:

[0062] S52. Obtain various types of location data.

[0063] S54. Input various types of positioning data into the decision model to obtain the target positioning algorithm identifier.

[0064] S56. Determine the target positioning algorithm from the positioning algorithm library based on the target positioning algorithm identifier, and call the target positioning algorithm to perform the positioning task.

[0065] According to exemplary embodiments of this disclosure, the various positioning algorithms in the positioning algorithm library include positioning algorithms determined based on a single positioning method and / or positioning algorithms determined by fusing two or more positioning methods.

[0066] According to an exemplary embodiment of this disclosure, the positioning data acquisition system acquires various types of positioning data, including at least two of inertial sensor positioning data, Bluetooth positioning data, WiFi positioning data, ultra-wideband sensor positioning data, 5G positioning data, and GPS positioning data.

[0067] According to an exemplary embodiment of this disclosure, a target positioning algorithm identifier can be determined based on the various types of positioning data and the weights of each type of positioning data; wherein the weights of each type of positioning data are determined based on current environmental parameters and / or historical data collected by the positioning data acquisition system.

[0068] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0069] Since the steps of the positioning method in this embodiment are the same as those in the above-described positioning system embodiment, they will not be repeated here.

[0070] Furthermore, this example embodiment also provides a positioning device.

[0071] Figure 6 A block diagram schematically illustrates a positioning device according to an exemplary embodiment of the present disclosure. (Reference) Figure 6 The positioning device 6 according to an exemplary embodiment of the present disclosure may include a data acquisition module 61, an algorithm identifier determination module 63, and a positioning task execution module 65.

[0072] Specifically, the data acquisition module 61 can be used to acquire various types of positioning data; the algorithm identifier determination module 63 can be used to input various types of positioning data into the decision model to obtain the target positioning algorithm identifier; and the positioning task execution module 65 can be used to determine the target positioning algorithm from the positioning algorithm library based on the target positioning algorithm identifier and call the target positioning algorithm to execute the positioning task.

[0073] According to exemplary embodiments of this disclosure, the various positioning algorithms in the positioning algorithm library include positioning algorithms determined based on a single positioning method and / or positioning algorithms determined by fusing two or more positioning methods.

[0074] According to an exemplary embodiment of this disclosure, the positioning data acquisition system acquires various types of positioning data, including at least two of inertial sensor positioning data, Bluetooth positioning data, WiFi positioning data, ultra-wideband sensor positioning data, 5G positioning data, and GPS positioning data.

[0075] Since the functional modules of the positioning device in this embodiment are the same as those in the above-described positioning system embodiment, they will not be described again here.

[0076] Figure 7 A schematic diagram is shown that is suitable for implementing exemplary embodiments of the present disclosure. The terminal device of the exemplary embodiments of the present disclosure can be configured as follows: Figure 7 In the form of. It should be noted that, Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0077] The electronic device disclosed herein includes at least a processor and a memory, the memory being used to store one or more programs, which, when executed by the processor, enable the processor to implement the positioning method of the exemplary embodiments of this disclosure.

[0078] Specifically, such as Figure 7As shown, the electronic device 70 may include: a processor 710, internal memory 721, external memory interface 722, Universal Serial Bus (USB) interface 730, charging management module 740, power management module 741, battery 742, antenna 1, antenna 2, mobile communication module 750, wireless communication module 760, audio module 770, sensor module 780, display screen 790, camera module 791, indicator 792, motor 793, buttons 794, and a Subscriber Identification Module (SIM) card interface 795, etc. The sensor module 780 may include depth sensors, pressure sensors, gyroscope sensors, barometric pressure sensors, magnetic sensors, accelerometers, distance sensors, proximity sensors, fingerprint sensors, temperature sensors, touch sensors, ambient light sensors, and bone conduction sensors, etc.

[0079] It is understood that the structures illustrated in the embodiments of this disclosure do not constitute a specific limitation on the electronic device 70. In other embodiments of this disclosure, the electronic device 70 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0080] The processor 710 may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). These different processing units may be independent devices or integrated into one or more processors. Additionally, the processor 710 may include memory for storing instructions and data.

[0081] The electronic device 70 can implement shooting functions through an ISP, camera module 791, video codec, GPU, display screen 790, and application processor. In some embodiments, the electronic device 70 may include one or N camera modules 791, where N is a positive integer greater than 1. If the electronic device 70 includes N cameras, one of the N cameras is the main camera.

[0082] Internal memory 721 can be used to store computer executable program code, which includes instructions. Internal memory 721 may include a program storage area and a data storage area. External memory interface 722 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of electronic device 70.

[0083] This disclosure also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device.

[0084] Computer-readable storage media can be, for example—but not limited to—electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0085] A computer-readable storage medium can be sent, propagated, or transmitted for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0086] A computer-readable storage medium carries one or more programs that, when executed by an electronic device, cause the electronic device to perform the methods described in the embodiments of this disclosure.

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0088] The units described in the embodiments of this disclosure can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the unit itself.

[0089] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0090] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0091] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0092] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0093] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A positioning system, characterized in that, include: A positioning algorithm library, containing a variety of positioning algorithms, each of which is configured with an identifier; A positioning data acquisition system used to collect various types of positioning data; A decision model is used to determine a target positioning algorithm identifier using the various types of positioning data, the target positioning algorithm identifier being used to instruct a target positioning algorithm in the positioning algorithm library to perform a positioning task. The decision-making model includes: An encoding unit is used to acquire the various types of positioning data and to encode the various types of positioning data to obtain a decision result vector; The matching unit is used to determine the vector similarity between the decision result vector and multiple candidate vectors, and to determine the localization algorithm identifier corresponding to the candidate vector with the largest vector similarity as the target localization algorithm identifier; The candidate vectors correspond one-to-one with the localization algorithm identifiers.

2. The positioning system according to claim 1, characterized in that, The various positioning algorithms in the positioning algorithm library include positioning algorithms determined based on a single positioning method and / or positioning algorithms determined by fusing two or more positioning methods.

3. The positioning system according to claim 1, characterized in that, The positioning data acquisition system collects various types of positioning data, including at least two of the following: inertial sensor positioning data, Bluetooth positioning data, WiFi positioning data, ultra-wideband sensor positioning data, 5G positioning data, and GPS positioning data.

4. The positioning system according to claim 1, characterized in that, The decision model is configured to determine the target positioning algorithm identifier based on the multiple types of positioning data and the weights of each type of positioning data. The weights of each type of positioning data are determined based on current environmental parameters and / or historical data collected by the positioning data acquisition system.

5. A positioning method, characterized in that, include: Acquire various types of location data; The various types of positioning data are input into the decision model to obtain the target positioning algorithm identifier; The target positioning algorithm is determined from the positioning algorithm library based on the target positioning algorithm identifier, and the target positioning algorithm is invoked to perform the positioning task; The decision-making model includes: An encoding unit is used to acquire the various types of positioning data and to encode the various types of positioning data to obtain a decision result vector; The matching unit is used to determine the vector similarity between the decision result vector and multiple candidate vectors, and to determine the localization algorithm identifier corresponding to the candidate vector with the largest vector similarity as the target localization algorithm identifier; The candidate vectors correspond one-to-one with the localization algorithm identifiers.

6. The positioning method according to claim 5, characterized in that, The positioning algorithms in the positioning algorithm library include positioning algorithms determined based on a single positioning method and / or positioning algorithms determined by fusing two or more positioning methods.

7. A positioning device, characterized in that, include: The data acquisition module is used to acquire various types of location data; The algorithm identifier determination module is used to input the various types of positioning data into the decision model to obtain the target positioning algorithm identifier; The positioning task execution module is used to determine the target positioning algorithm from the positioning algorithm library according to the target positioning algorithm identifier, and call the target positioning algorithm to execute the positioning task; The decision-making model includes: An encoding unit is used to acquire the various types of positioning data and to encode the various types of positioning data to obtain a decision result vector; The matching unit is used to determine the vector similarity between the decision result vector and multiple candidate vectors, and to determine the localization algorithm identifier corresponding to the candidate vector with the largest vector similarity as the target localization algorithm identifier; The candidate vectors correspond one-to-one with the localization algorithm identifiers.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the positioning method as described in claim 5 or 6.

9. An electronic device, characterized in that, include: processor; A memory for storing one or more programs that, when executed by the processor, cause the processor to implement the positioning method as described in claim 5 or 6.

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