Positioning method, system and device based on large model and mobile base station and medium

Abstract

The invention is applied to the technical field of wireless sensing, and discloses a positioning method, system and device based on a large model and a mobile base station, and a medium. The method is applied to a mobile base station. The mobile base station cruises in an area where the user terminal is located; the method comprises the following steps: acquiring uplink data information and downlink data information sent by a user terminal; fusing the uplink data information and the downlink data information to obtain fused data; and inputting the fused data into a preset positioning model to obtain a positioning coordinate corresponding to the user terminal. Therefore, compared with the prior art, one mobile base station actively acquires the uplink data information and the downlink data information sent by the user terminal, so that the user terminal can be positioned without depending on a plurality of fixed stations, and passive non-inductive positioning of the user terminal is realized while the deployment cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of wireless sensing technology, specifically to a positioning method, system, device, and medium based on a large model and a mobile base station. Background Technology

[0002] Currently, wireless sensing technology is gradually penetrating key scenarios in human life, demonstrating broad application prospects in areas such as smart homes, medical monitoring, and security emergency response. With the increasing demand for non-contact health monitoring, gesture interaction, and fall warning functions, achieving high-precision and robust sensing in complex indoor environments has become a key research focus. Among these, sensing technologies based on wireless signals such as 5G (5th Generation Mobile Communication Technology), Wi-Fi (Wireless Fidelity), and Bluetooth are gradually becoming a hot topic in academia and industry due to their advantages such as requiring no wearable devices and being privacy-friendly. Currently, indoor wireless sensing based on 5G cellular networks is attracting significant attention due to its excellent signal coverage, high stability, and low latency.

[0003] However, current wireless sensing technology based on 5G cellular network reference signals relies on fixed multiple base stations, which results in high infrastructure deployment costs. At the same time, traditional positioning methods rely on terminals actively reporting to base stations based on user operations, which requires user participation and cannot achieve passive, seamless positioning.

[0004] Therefore, in order to overcome the above-mentioned technical problems, the present invention provides a positioning method, system, device and medium based on a large model and a mobile base station. Summary of the Invention

[0005] The technical problem to be solved by this invention is how to reduce deployment costs while achieving passive and seamless positioning of user terminals. The purpose is to provide a positioning method, system, device and medium based on a large model and mobile base stations, so as to reduce deployment costs while achieving passive and seamless positioning of user terminals.

[0006] This invention is achieved through the following technical solution:

[0007] In a first aspect, a positioning method based on a large model and a mobile base station is applied to a mobile base station; the mobile base station patrols within the area where the user terminal is located; the method includes: acquiring uplink data information and downlink data information sent by the user terminal; fusing the uplink data information and downlink data information to obtain fused data; and inputting the fused data into a preset positioning model to obtain the positioning coordinates corresponding to the user terminal.

[0008] In some embodiments, acquiring uplink data information and downlink data information sent by a user terminal includes: sending uplink configuration information to the user terminal; the uplink configuration information specifies the transmission format of the uplink data information; sending uplink trigger information to the user terminal, triggering the user terminal to send the uplink data information according to the transmission format of the uplink data information based on the uplink link; sending downlink configuration information to the user terminal; the downlink configuration information specifies the transmission format of the downlink data information; sending a channel state information reference signal to the user terminal based on the downlink link, triggering the user terminal to generate and feed back a report signal corresponding to the channel state information reference signal based on the downlink configuration information; and determining the report signal as the downlink data information.

[0009] In some embodiments, fusing the uplink data information and downlink data information to obtain fused data includes: extracting a first signal strength and a first channel estimation information of the uplink from the uplink data information; extracting a second signal strength and a second channel estimation information of the downlink from the downlink data information; and fusing the first signal strength, the first channel estimation information, the second signal strength, and the second channel estimation information to obtain the fused data.

[0010] In some embodiments, the positioning model is obtained by: acquiring uplink data information to be trained, downlink data information to be trained, and the real coordinates corresponding to the user terminals sent by several user terminals; fusing the uplink data information to be trained and the downlink data information to be trained to obtain fused data to be trained; using the real coordinates as the label of the fused data to be trained to obtain several training samples; and inputting each training sample into a preset large model for training to obtain the positioning model.

[0011] In some embodiments, the step of inputting each of the training samples into a preset large model for training to obtain the localization model includes: inputting each of the training samples into a preset large model for training to obtain a candidate localization model; and using a preset low-rank adaptive algorithm to fine-tune the parameters of the candidate localization model to obtain the localization model.

[0012] In some embodiments, the mobile base station includes: a mobile intelligent vehicle and a base station server placed on the mobile intelligent vehicle; the mobile intelligent vehicle carries the base station server to patrol within the area where the user terminal is located.

[0013] Secondly, a positioning system based on a large model and a mobile base station, comprising a mobile base station and a user terminal; the mobile base station patrols within the area where the user terminal is located; the mobile base station positions the user terminal based on the aforementioned positioning method based on a large model and a mobile base station.

[0014] Thirdly, a positioning device based on a large model and a mobile base station is applied to a mobile base station; the mobile base station patrols within the area where the user terminal is located; the device includes: an acquisition module configured to acquire uplink data information and downlink data information sent by the user terminal; a fusion module configured to fuse the uplink data information and downlink data information to obtain fused data; and a positioning module configured to input the fused data into a preset positioning model to obtain the positioning coordinates corresponding to the user terminal.

[0015] Fourthly, a positioning device based on a large model and a mobile base station includes a processor and a memory storing program instructions, wherein the processor is configured to execute the above-described positioning method based on a large model and a mobile base station when running the program instructions.

[0016] Fifthly, a storage medium stores program instructions that, when executed, perform the aforementioned positioning method based on a large model and a mobile base station.

[0017] Compared with existing technologies, this invention acquires uplink and downlink data information sent by the user terminal, then fuses the uplink and downlink data information to obtain fused data, and then inputs the fused data into a preset positioning model to obtain the positioning coordinates of the user terminal. In this way, compared with existing technologies, positioning of the user terminal can be achieved by a single mobile base station actively collecting uplink and downlink data information sent by the user terminal, without relying on multiple fixed base stations. This reduces deployment costs while achieving passive and seamless positioning of the user terminal. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0019] Figure 1 This is a schematic diagram of a positioning system based on a large model and a mobile base station provided in an embodiment of this disclosure;

[0020] Figure 2 This is a schematic flowchart of a positioning method based on a large model and a mobile base station provided in an embodiment of this disclosure;

[0021] Figure 3 This is a schematic diagram illustrating the acquisition of uplink and downlink data information provided in an embodiment of this disclosure;

[0022] Figure 4 This is a flowchart illustrating a method for obtaining a positioning model provided in an embodiment of this disclosure;

[0023] Figure 5 This is a schematic diagram of a positioning device based on a large model and a mobile base station provided in an embodiment of this disclosure;

[0024] Figure 6 This is a schematic diagram of another positioning device based on a large model and a mobile base station provided in an embodiment of this disclosure. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0026] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0027] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can 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.

[0028] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily need to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0029] In this application, "multiple" refers to two or more. "And / or" describes 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. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0030] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating a positioning system based on a large model and a mobile base station, as shown in an exemplary embodiment of this application.

[0031] like Figure 1 As shown, the positioning system 1 based on a large model and a mobile base station includes: a mobile base station 2 and a user terminal 3; the mobile base station 2 patrols within the area where the user terminal 3 is located; the mobile base station locates the user terminal based on the positioning method based on a large model and a mobile base station described in this scheme.

[0032] In this embodiment, mobile base stations are used instead of the traditional multi-fixed base station deployment scheme, which significantly reduces infrastructure costs and deployment complexity. At the same time, it is easy to dynamically construct spatial perception reference points through the autonomous navigation of mobile base stations to achieve accurate positioning of user terminals.

[0033] It should be noted that the patrol route of mobile base station 2 within the area where user terminal 3 is located is preset. The factor for setting this patrol route is that the connection between user terminal 3 and mobile base station 2 is not interrupted.

[0034] In some embodiments, such as Figure 1 As shown, mobile base station 2 can cruise along the cruise route 4 indicated by the dashed line.

[0035] It should be noted that user terminal 3 may include user-used terminal devices such as mobile phones, tablets, laptops, smartwatches, and wearable devices.

[0036] It should be noted that there can be one or more user terminals 3. The mobile base station 2 can simultaneously locate one or more user terminals 3, and there is no limitation on this.

[0037] Furthermore, the mobile base station includes: a mobile intelligent vehicle and a base station server mounted on the mobile intelligent vehicle; the mobile intelligent vehicle carries the base station server to patrol the area where the user terminal is located. It should be noted that the base station server is the server equipment of the OAI (Open Air Interface) 5G-IAB (Integrated Access and Backhaul) base station node.

[0038] In this way, server equipment based on IAB base station nodes has access and backhaul capabilities, which can adapt to industrial environments without pre-wiring. At the same time, the strategy of a single mobile base station naturally avoids the NLOS (Non-Line of Sight) problem and improves the stability of positioning.

[0039] Please see Figure 2 , Figure 2 This is a schematic diagram illustrating a positioning method based on a large model and a mobile base station, as shown in an exemplary embodiment of this application.

[0040] This method is applied to mobile base stations that patrol the area where user terminals are located.

[0041] like Figure 2 As shown, this disclosure provides a positioning method based on a large model and a mobile base station, the method comprising:

[0042] Step S201: Obtain uplink and downlink data information sent by the user terminal.

[0043] Step S202: Merge uplink data information and downlink data information to obtain merged data.

[0044] Step S203: Input the fused data into the preset positioning model to obtain the positioning coordinates corresponding to the user terminal.

[0045] The positioning method based on a large model and mobile base station provided in this disclosure acquires uplink and downlink data information sent by the user terminal, then fuses the uplink and downlink data information to obtain fused data, and finally inputs the fused data into a preset positioning model to obtain the positioning coordinates corresponding to the user terminal. In this way, compared with the prior art, positioning of the user terminal can be achieved by actively collecting uplink and downlink data information sent by the user terminal through a single mobile base station, without relying on multiple fixed base stations. This reduces deployment costs while achieving passive and seamless positioning of the user terminal.

[0046] Furthermore, acquiring uplink and downlink data information sent by the user terminal includes: sending uplink configuration information to the user terminal; the uplink configuration information specifies the transmission format of the uplink data information; sending uplink trigger information to the user terminal, triggering the user terminal to send uplink data information based on the uplink link and the transmission format of the uplink data information; sending downlink configuration information to the user terminal; the downlink configuration information specifies the transmission format of the downlink data information; sending a channel state information reference signal to the user terminal based on the downlink link, triggering the user terminal to generate and feed back a report signal corresponding to the channel state information reference signal based on the downlink configuration information; and identifying the report signal as downlink data information. In this way, the mobile base station specifies and triggers the transmission format and timing of the uplink data information and downlink configuration information. Compared to existing technologies that require users to actively report to the base station based on their actions, this solution does not require user participation and achieves automatic acquisition of uplink data information and downlink configuration information.

[0047] In some embodiments, the uplink data information is SRS (Sounding Reference Signal) information.

[0048] The report signal corresponding to the channel state information reference signal includes the result parsed by the user terminal based on the downlink configuration information after receiving the channel state information reference signal.

[0049] In some embodiments, please refer to Figure 3 . Figure 3 This diagram illustrates the acquisition of uplink and downlink data information. In this diagram, UE represents user terminal 3; gNB represents mobile base station 2.

[0050] like Figure 3 As shown, the mobile base station obtains uplink data information through UL (Uplink) and downlink data information through DL (Downlink).

[0051] In the uplink, mobile base station 2 sends uplink configuration information to user terminal 3. Then, mobile base station 2 sends uplink trigger information to user terminal 3, triggering user terminal 3 to send uplink data information according to the uplink data information transmission format in the uplink configuration information.

[0052] In the downlink, mobile base station 2 sends downlink configuration information to user terminal 3. Then, mobile base station 2 sends downlink trigger information to user terminal 3, triggering user terminal 3 to send downlink data information according to the downlink data information transmission format in the downlink configuration information.

[0053] Furthermore, uplink and downlink data are fused to obtain fused data, including: acquiring the first signal strength and first channel estimation information of the uplink data; acquiring the second signal strength and second channel estimation information of the downlink data; and fusing the first signal strength, first channel estimation information, second signal strength, and second channel estimation information to obtain fused data. Thus, by acquiring the first signal strength and first channel estimation information of the uplink data and the second signal strength and second channel estimation information of the downlink data, and then fusing them, fused data is obtained. This achieves the fusion of uplink and downlink data, thereby constructing high-dimensional, multi-view perception feature data and improving the ability to represent dynamic environmental changes.

[0054] Furthermore, acquiring the first signal strength and first channel estimation information of the uplink data information includes: preprocessing the uplink data information to obtain reference uplink information; acquiring the signal strength of the reference uplink information to obtain the first signal strength; and performing channel estimation on the reference uplink information based on the least squares method to obtain the first channel estimation information.

[0055] The preprocessing of uplink data includes one or more of the following: smoothing, outlier removal, and noise filtering. No restrictions are imposed here.

[0056] Furthermore, obtaining the second signal strength and second channel estimation information of the downlink data information includes: preprocessing the downlink data information to obtain reference downlink information; obtaining the signal strength of the reference downlink information to obtain the second signal strength; and performing channel estimation on the reference downlink information based on the least squares method to obtain the second channel estimation information.

[0057] The preprocessing of downlink data includes one or more of the following: smoothing, outlier removal, and noise filtering. No restrictions are imposed here.

[0058] It should be noted that the fused data is obtained by fusing the first signal strength, the first channel estimation information, the second signal strength, and the second channel estimation information. This fused data is obtained by concatenating the first signal strength, the first channel estimation information, the second signal strength, and the second channel estimation information. The fused data is an array of data that conforms to the input of the large model data.

[0059] For further details, please refer to Figure 4 , Figure 4 This is a schematic diagram for obtaining the localization model. (For example...) Figure 4 As shown, the localization model is obtained in the following way:

[0060] Step S401: Obtain the uplink data information to be trained, the downlink data information to be trained, and the real coordinates corresponding to the user terminals sent by several user terminals.

[0061] Step S402: Fuse the uplink data information to be trained and the downlink data information to be trained to obtain the fused data to be trained.

[0062] Step S403: Use the real coordinates as labels for the fused data to be trained to obtain several training samples.

[0063] Step S404: Input each sample to be trained into a preset large model for training to obtain a localization model.

[0064] In this way, training uplink data, training downlink data, and the corresponding real coordinates of the user terminals are acquired from several user terminals. The training uplink and downlink data are then fused to obtain training fused data, with the real coordinates used as labels to generate several training samples. These training samples are then input into a pre-defined large model for training, resulting in a positioning model. This allows the pre-defined large model to learn the relationship between the training fused data and the real coordinates within the area where the user terminal is located. Therefore, in practical applications, the obtained positioning model can accurately predict the location of the user terminal based on the input fused data, achieving accurate positioning of the user terminal.

[0065] It should be noted that the method for obtaining the uplink data information to be trained is the same as the method for obtaining the uplink data information; the method for obtaining the downlink data information to be trained is the same as the method for obtaining the downlink data information; the method for fusing the uplink data information to be trained and the downlink data information to be trained to obtain the fused data to be trained is the same as the method for fusing the uplink data information and the downlink data information to obtain the fused data, and will not be repeated here.

[0066] Furthermore, each training sample is input into a pre-defined large model for training to obtain a localization model. This includes: inputting each training sample into a pre-defined large model for training to obtain a candidate localization model; and using a pre-defined low-rank adaptive algorithm to fine-tune the parameters of the candidate localization model to obtain the final localization model. In this way, by fine-tuning the parameters of the candidate localization model using a pre-defined low-rank adaptive algorithm, the localization model's ability to resist interference from non-line-of-sight signals is strengthened, further improving localization stability.

[0067] It should be noted that the parameters of the candidate positioning model are fine-tuned using a preset low-rank adaptive algorithm, that is, a low-rank matrix is ​​introduced using the low-rank adaptive algorithm to update a small part of the parameters of the model.

[0068] Combination Figure 5 As shown, this embodiment of the disclosure provides a positioning device 50 based on a large model and a mobile base station; the mobile base station patrols within the area where the user terminal is located; the device includes: an acquisition module 51, a fusion module 52, and a positioning module 53.

[0069] Among them, the acquisition module 51 is configured to acquire uplink data information and downlink data information sent by the user terminal;

[0070] The fusion module 52 is configured to fuse uplink data information and downlink data information to obtain fused data;

[0071] The positioning module 53 is configured to input the fused data into a preset positioning model to obtain the positioning coordinates corresponding to the user terminal.

[0072] The positioning device based on a large model and mobile base station provided in this disclosure acquires uplink and downlink data information sent by the user terminal, then fuses the uplink and downlink data information to obtain fused data, and finally inputs the fused data into a preset positioning model to obtain the positioning coordinates corresponding to the user terminal. In this way, compared with the prior art, positioning of the user terminal can be achieved by actively collecting uplink and downlink data information sent by the user terminal through a single mobile base station, without relying on multiple fixed base stations. This reduces deployment costs while achieving passive and seamless positioning of the user terminal.

[0073] Furthermore, the acquisition module is configured to acquire uplink and downlink data information sent by the user terminal in the following ways: sending uplink configuration information to the user terminal; the uplink configuration information specifies the transmission format of the uplink data information; sending uplink trigger information to the user terminal, triggering the user terminal to send uplink data information according to the transmission format of the uplink data information; sending downlink configuration information to the user terminal; the downlink configuration information specifies the transmission format of the downlink data information; sending downlink trigger information to the user terminal, triggering the user terminal to send downlink data information according to the transmission format of the downlink data information.

[0074] Furthermore, the fusion module is configured to fuse uplink data information and downlink data information to obtain fused data in the following manner: acquiring the first signal strength and first channel estimation information of the uplink data information; acquiring the second signal strength and second channel estimation information of the downlink data information; and fusing the first signal strength, the first channel estimation information, the second signal strength and the second channel estimation information to obtain fused data.

[0075] Furthermore, the positioning device based on a large model and mobile base stations also includes a model acquisition module. The model acquisition module is configured to acquire the positioning model in the following manner: acquire uplink data information to be trained, downlink data information to be trained, and the real coordinates corresponding to the user terminals sent by several user terminals; fuse the uplink data information to be trained and the downlink data information to be trained to obtain fused data to be trained; use the real coordinates as labels for the fused data to be trained to obtain several training samples; input each training sample into a preset large model for training to obtain the positioning model.

[0076] Furthermore, the model acquisition module is configured to input each training sample into a preset large model for training to obtain a localization model, including: inputting each training sample into a preset large model for training to obtain a candidate localization model; and using a preset low-rank adaptive algorithm to fine-tune the parameters of the candidate localization model to obtain a localization model.

[0077] Furthermore, the mobile base station includes: a mobile intelligent vehicle and a base station server placed on the mobile intelligent vehicle; the mobile intelligent vehicle carries the base station server to patrol the area where the user terminal is located.

[0078] It should be noted that the positioning device based on large model and mobile base station provided in the above embodiments and the positioning method based on large model and mobile base station provided in the above embodiments belong to the same concept. The specific way in which each module and unit performs operations has been described in detail in the method embodiments, and will not be repeated here. In practical applications, the positioning device based on large model and mobile base station provided in the above embodiments can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not a limitation here.

[0079] Combination Figure 6 As shown, this disclosure provides another positioning device based on a large model and mobile base station, including a processor 61 and a memory 62. Optionally, the device may further include a communication interface 63 and a bus 64. The processor 61, communication interface 63, and memory 62 can communicate with each other via the bus 64. The communication interface 63 can be used for information transmission. The processor 61 can call logical instructions in the memory 62 to execute the positioning method based on a large model and mobile base station described in the above embodiments.

[0080] Furthermore, the logical instructions in the aforementioned memory 62 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0081] The memory 62, as a storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 61 executes functional applications and data processing by running the program instructions / modules stored in the memory 62, thereby implementing the positioning method based on a large model and a mobile base station in the above embodiments.

[0082] The memory 62 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 62 may include high-speed random access memory and may also include non-volatile memory.

[0083] This disclosure provides a storage medium storing computer-executable instructions configured to execute the aforementioned positioning method based on a large model and a mobile base station.

[0084] The aforementioned storage media can be either transient computer-readable storage media or non-transitory computer-readable storage media. Non-transitory storage media include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, and can also be transient storage media.

[0085] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0086] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[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 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. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A positioning method based on a large model and mobile base stations, characterized in that, Applications in mobile base stations; The mobile base station patrols the area where the user terminal is located; the method includes: Acquire uplink and downlink data information sent by the user terminal; By fusing the uplink and downlink data information, fused data is obtained; The fused data is input into a preset positioning model to obtain the positioning coordinates corresponding to the user terminal.

2. The method according to claim 1, characterized in that, The acquisition of uplink and downlink data information sent by the user terminal includes: Uplink configuration information is sent to the user terminal; the uplink configuration information specifies the transmission format of the uplink data information. Send uplink trigger information to the user terminal to trigger the user terminal to send the uplink data information according to the uplink data information sending format based on the uplink link; Downlink configuration information is sent to the user terminal; the downlink configuration information specifies the transmission format of the downlink data information; Based on the downlink, a channel state information reference signal is sent to the user terminal, triggering the user terminal to generate and feed back a report signal corresponding to the channel state information reference signal based on the downlink configuration information; The reported signal is identified as the downlink data information.

3. The method according to claim 1, characterized in that, The process of fusing the uplink and downlink data information to obtain fused data includes: Obtain the first signal strength and first channel estimation information of the uplink data information; Obtain the second signal strength and second channel estimation information of the downlink data information; The fused data is obtained by fusing the first signal strength, the first channel estimation information, the second signal strength, and the second channel estimation information.

4. The method according to claim 1, characterized in that, The localization model is obtained in the following way: Acquire the uplink data information to be trained, the downlink data information to be trained, and the real coordinates of the user terminals sent by several user terminals; The uplink data and downlink data to be trained are fused to obtain the fused data to be trained; Using the real coordinates as labels for the fused data to be trained, several training samples are obtained; Each of the training samples is input into a preset large model for training to obtain the localization model.

5. The method according to claim 4, characterized in that, The step of inputting each of the training samples into a preset large model for training to obtain the localization model includes: Each of the training samples is input into a pre-set large model for training to obtain alternative localization models; The candidate positioning model is obtained by fine-tuning the parameters using a preset low-rank adaptive algorithm.

6. The method according to any one of claims 1 to 5, characterized in that, The mobile base station includes: a mobile intelligent vehicle and a base station server placed on the mobile intelligent vehicle; the mobile intelligent vehicle carries the base station server to patrol within the area where the user terminal is located.

7. A positioning system based on a large model and a mobile base station, characterized in that, include: Mobile base stations and user terminals; The mobile base station patrols the area where the user terminal is located; The mobile base station locates the user terminal based on the positioning method based on a large model and a mobile base station as described in any one of claims 1 to 6.

8. A positioning device based on a large model and a mobile base station, characterized in that, Applications in mobile base stations; The mobile base station patrols the area where the user terminal is located; the device includes: The acquisition module is configured to acquire uplink and downlink data information sent by the user terminal; The fusion module is configured to fuse the uplink data information and downlink data information to obtain fused data; The positioning module is configured to input the fused data into a preset positioning model to obtain the positioning coordinates corresponding to the user terminal.

9. A positioning device based on a large model and a mobile base station, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to execute the positioning method based on a large model and a mobile base station as described in any one of claims 1 to 6 when running the program instructions.

10. A storage medium storing program instructions, characterized in that, When the program instructions are executed, they perform the positioning method based on a large model and a mobile base station as described in any one of claims 1 to 6.

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