Positioning method based on single Beidou satellite and related equipment

Abstract

One or more embodiments of the invention provide a positioning method based on a single Beidou satellite and related equipment. The method comprises the following steps: acquiring satellite positioning data of a single Beidou satellite, wherein the satellite positioning data comprises satellite time and satellite position coordinates; obtaining a coarse positioning result of the receiver through a coarse positioning calculation model according to the satellite positioning data and the receiver positioning data; and obtaining an accurate positioning result of the receiver through an accurate positioning calculation model according to the coarse positioning result and the movement track of the receiver. According to the invention, the positioning precision of a single Beidou satellite can be improved.

Description

Technical Field

[0001] One or more embodiments of the present application relate to the field of Beidou satellite positioning technology, and in particular, to a positioning method and related equipment based on a single Beidou satellite. Background Art

[0002] The Beidou Navigation Satellite System (BDS) enables satellite navigation receivers to determine position and velocity based on Beidou satellite signals. The Beidou Navigation Satellite System includes a constellation of Beidou satellites orbiting the Earth. At a specific time and location, at least four Beidou satellites are visible, and each Beidou satellite continuously broadcasts signals at a predetermined frequency, including Beidou satellite time and orbit information. The receiver synchronously receives signals from at least four Beidou satellites and calculates the receiver's geographic coordinates (longitude, latitude and altitude) based on this information.

[0003] However, in places such as parking lots, tunnels, urban canyons and near trees, the signal may be difficult to obtain or weakened due to obstruction of the BeiDou satellite line of sight. Therefore, improving the positioning accuracy of a single BeiDou satellite is an urgent problem to be solved. Summary of the invention

[0004] In view of this, an object of one or more embodiments of the present application is to propose a positioning method and related equipment based on a single Beidou satellite to solve the problems raised by the background technology.

[0005] Based on the above purpose, one or more embodiments of the present application provide a positioning method based on a single Beidou satellite. It includes:

[0006] Acquire satellite positioning data of a single Beidou satellite, wherein the satellite positioning data includes satellite time and satellite position coordinates;

[0007] Obtaining a coarse positioning result of the receiver through a coarse positioning calculation model according to the satellite positioning data and the receiver positioning data;

[0008] According to the coarse positioning result and the motion trajectory of the receiver, a precise positioning result of the receiver is obtained through a precise positioning calculation model.

[0009] Optionally, obtaining the precise positioning result of the receiver by using a precise positioning calculation model includes:

[0010] generating a candidate positioning set according to the coarse positioning result and the motion trajectory of the receiver;

[0011] Iterate and perform the following operations until the reward value converges:

[0012] According to the state of this round, generating the action of this round through the action generation network, the action represents the selection of the candidate positioning, and the candidate positioning belongs to the candidate positioning set;

[0013] Obtaining an action reward value according to the action;

[0014] Update the next round state according to the state transition probability.

[0015] Optionally, the action reward value is calculated by the following formula:

[0016] R π (t,b)=s(t,b)+β∑ t′ Q t,t′ (b)W π (t′);

[0017] Where s(t,b) represents the reward function for executing action b in state t, β represents the discount factor, β∈[0,1], Q t,t′ (b) represents the state transition probability, W π (t′) represents the total expected discounted return of the system in state t′.

[0018] Optionally, the reward function is defined as the Euclidean distance between the predicted location of the receiver under action b and the predicted location calculated according to the running trajectory of the receiver.

[0019] Optionally, the training step of the coarse positioning calculation model includes:

[0020] Acquire training satellite positioning data and training receiver positioning data;

[0021] Determining an actual positioning result corresponding to the training receiver positioning data;

[0022] Inputting the training satellite positioning data and the training receiver positioning data into a recursive neural network model to obtain a first training result;

[0023] The recursive neural network model is reversely trained according to the first training result and the actual positioning result to obtain the coarse positioning calculation model.

[0024] Optionally, the training step of the precise positioning calculation model includes:

[0025] Obtaining the motion trajectory of the training receiver;

[0026] Inputting the first training result and the training receiver motion trajectory into a deep reinforcement learning network model to obtain a second training result;

[0027] The deep reinforcement learning network model is reversely trained according to the second training result and the actual positioning result to obtain the precise positioning calculation model.

[0028] Optionally, it also includes:

[0029] The coarse positioning calculation model is reversely trained according to the precise positioning result and the coarse positioning result.

[0030] Based on the same inventive concept, one or more embodiments of the present application further provide a positioning device based on a single Beidou satellite, including:

[0031] A data acquisition module is configured to acquire satellite positioning data of a single Beidou satellite, wherein the satellite positioning data includes satellite time and satellite position coordinates;

[0032] A coarse positioning module is configured to obtain a coarse positioning result of the receiver through a coarse positioning calculation model according to the satellite positioning data and the receiver positioning data;

[0033] The precise positioning module is configured to obtain the precise positioning result of the receiver through a precise positioning calculation model according to the coarse positioning result and the motion trajectory of the receiver.

[0034] Based on the same inventive concept, one or more embodiments of the present application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a positioning method based on a single Beidou satellite as described in any one of the above is implemented.

[0035] Based on the same inventive concept, one or more embodiments of the present application also provide a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute any of the above-mentioned positioning methods based on a single Beidou satellite.

[0036] From the above, it can be seen that the positioning method based on a single Beidou satellite provided by one or more embodiments of the present application obtains the satellite positioning data of a single Beidou satellite, wherein the satellite positioning data includes the satellite time and the position coordinates of the satellite; based on the satellite positioning data and the receiver positioning data, a coarse positioning result of the receiver is obtained through a coarse positioning calculation model; based on the coarse positioning result and the motion trajectory of the receiver, a precise positioning result of the receiver is obtained through a precise positioning calculation model. On the one hand, one or more embodiments of the present application combine deep learning technology to improve the positioning accuracy of Beidou satellites; on the other hand, combined with the running trajectory of the receiver, the positioning accuracy is further improved. The positioning accuracy based on a single Beidou satellite can be effectively improved through this application.

[0037] The positioning device, electronic device and computer-readable storage medium based on a single Beidou satellite provided in the present application can all implement the steps of the above-mentioned positioning method based on a single Beidou satellite, and therefore also have the beneficial effects of the above-mentioned positioning method based on a single Beidou satellite. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate one or more embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only one or more embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A flowchart of a positioning method based on a single Beidou satellite in one or more embodiments of the present application;

[0040] Figure 2 A schematic diagram of the structure of a positioning device based on a single Beidou satellite in one or more embodiments of the present application;

[0041] Figure 3 This is a schematic diagram of the hardware structure of an electronic device of one or more embodiments of the present application. DETAILED DESCRIPTION

[0042] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0043] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present application should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in one or more embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0044] As described in the background technology section, improving the positioning accuracy of a single Beidou satellite is an issue that needs to be addressed urgently.

[0045] refer to Figure 1 The positioning method based on a single Beidou satellite of one or more embodiments of the present application comprises the following steps:

[0046] Step S101: Acquire satellite positioning data of a single Beidou satellite, where the satellite positioning data includes satellite time and satellite position coordinates.

[0047] In some embodiments of the present application, the receiver can be flexibly integrated into a variety of devices, such as smart phones, vehicles equipped with navigation systems, drones, various ships, surveying and mapping equipment, and aircraft, etc. The present application does not limit the types of devices applicable to the receiver, and those skilled in the art are allowed to innovate and improve according to actual needs.

[0048] The basic principle of the positioning method based on a single Beidou satellite can be explained as follows: first, the receiver needs to obtain the precise clock time and position information of the Beidou satellite; next, the pseudo-range between the Beidou satellite and the receiver is calculated by combining the clock time of the Beidou satellite, the position information of the Beidou satellite and the receiver's own clock time; finally, the position coordinates of the receiver are calculated based on the above pseudo-range and other relevant data, thereby completing the coordinate positioning of the receiver.

[0049] In the above process, obtaining the positioning data of Beidou satellites is the first step. These data should at least include the clock time and location information of the satellite. In some embodiments, in order to improve the accuracy and reliability of positioning, the above satellite positioning data may also include information such as the azimuth between the Beidou satellite and the receiver, signal strength, and the number of Beidou satellites. These additional data can provide more references for the positioning algorithm, thereby improving the final positioning accuracy and stability.

[0050] Step S102: obtaining a coarse positioning result of the receiver through a coarse positioning calculation model according to the Beidou satellite positioning data and the receiver positioning data.

[0051] Taking into account the possible error between pseudorange and actual distance, as well as the limitations of positioning accuracy faced by a single Beidou satellite, this application proposes to integrate deep learning technology into the receiver's position calculation process to improve calculation accuracy. By using advanced machine learning models, more efficient and accurate positioning services can be achieved based on traditional positioning technology.

[0052] In some embodiments of the present application, a coarse positioning calculation model can be trained based on a recursive neural network model, which is good at processing time series data and can capture hidden patterns in time series changes. In the implementation process, the construction of the coarse positioning calculation model may include the following steps:

[0053] Training data preparation: First, obtain satellite positioning data and receiver positioning data for training. These data should fully represent different scenarios and conditions to improve the generalization ability of the model.

[0054] Determination of actual positioning results: For the collected training receiver positioning data, it is necessary to determine the corresponding actual positioning results as the basis for the training model. The actual positioning results can be determined using the receiver positioning method of the prior art.

[0055] Model training: The prepared training satellite positioning data and training receiver positioning data are input into the recursive neural network model to obtain the first training result. Then, based on the difference between the first training result and the actual positioning result, back propagation and adjustment are performed to optimize the recursive neural network model and finally form a coarse positioning calculation model.

[0056] In order to further improve the performance of the model, the training of the coarse positioning calculation model can be divided into two parts. The first part is pre-training following the above steps; the second part is migration training, which uses the precise positioning results obtained in actual applications as real positioning data to retrain the coarse positioning calculation model. This process aims to ensure that the model is effective and adaptable in both the time domain and the space domain.

[0057] These measures can not only improve the accuracy of coarse positioning calculations, but also provide a more reliable data foundation for downstream high-precision positioning algorithms, thereby improving the overall positioning service quality. Such deep learning applications have opened up new directions for future positioning technology and provided stronger support for practical applications.

[0058] Step S103: According to the rough positioning result and the motion trajectory of the receiver, a precise positioning result of the receiver is obtained through a precise positioning calculation model.

[0059] Although step S102 has significantly improved the positioning accuracy, in order to further enhance the positioning accuracy, the technical solution of the present application also takes into account the running trajectory of the receiver. The running trajectory may specifically include dynamic characteristics such as the moving speed, direction and acceleration of the receiver, which are crucial for understanding the motion pattern of the receiver and its position change in space.

[0060] To achieve this goal, the precise positioning calculation model can be trained based on a deep reinforcement learning network. This method can adaptively learn in a dynamic environment and optimize the decision-making process. The specific training process can be divided into the following steps:

[0061] Data collection: First, the motion trajectory data of the receiver for training is collected. In some embodiments, the above data may include not only position, speed and direction, but also time series to capture the time-varying characteristics of the motion.

[0062] Model training: The first training result obtained in step S102 is input into the deep reinforcement learning network model together with the collected receiver motion trajectory to generate a second training result. At this time, the model will use the motion trajectory information to learn the behavior strategy related to the position state. In this step, based on the difference between the second training result and the above-mentioned actual positioning result, back propagation and adjustment are performed to finally form a precise positioning calculation model. This process will enable the model to better adapt to the motion state of the receiver and improve the positioning accuracy.

[0063] The precise positioning result of the receiver can be obtained through the trained precise positioning calculation model. The specific steps are as follows: First, combine the coarse positioning result with the motion trajectory of the receiver to generate a candidate positioning set. These candidate positionings will represent the possible positions of the receiver in the future state; next, iteratively perform the following operations until the reward value converges; according to the current state, select the action of this round through the action generation network, which represents the specific positioning point selected from the candidate positioning set; according to the executed action, obtain the action reward value, which will feedback the selection quality of the model and guide subsequent training and adjustment; according to the state transition probability, update the next round state, so that in each iteration, the model will gradually improve its decision-making strategy to achieve higher positioning accuracy and reliability.

[0064] Through the above steps, the deep reinforcement learning model can combine the motion state of the receiver, optimize the positioning strategy, and ultimately provide more accurate positioning results. This method not only improves the model's adaptability, but also enhances its robustness in complex dynamic environments, allowing the positioning system to maintain a high level of performance in a variety of application scenarios.

[0065] In some embodiments, the above action reward value is calculated by the following formula: π (t,b)=s(t,b)+β∑ t′ Q t,t′ (b)W π (t′); where s(t,b) represents the reward function for executing action b in state t, β represents the discount factor, β∈[0,1], Q t,t′ (b) represents the state transition probability, W π (t′) represents the total expected discounted return of the system in state t′.

[0066] In some embodiments, the above reward function is defined as the Euclidean distance between the predicted location of the receiver under action b and the predicted location calculated according to the running trajectory of the receiver.

[0067] Based on the above content, this application aims to significantly improve the accuracy of single Beidou satellite positioning. The realization of this goal depends on the comprehensive application of multiple innovative technologies. First, by introducing deep learning and deep reinforcement learning technology, the analysis and processing capabilities of Beidou satellite positioning data are enhanced. By considering dynamic information such as the motion trajectory, speed and direction of the receiver, the model can more comprehensively understand the spatial changes of the receiver, thereby reducing positioning deviations caused by pseudorange errors and environmental changes.

[0068] In addition, this application emphasizes a data-driven approach. Through a large number of training samples, the model of this application can learn and adapt to a variety of complex situations, thereby improving its ability to respond to new environments and conditions. For example, with the help of recursive neural networks and reinforcement learning frameworks, the model not only learns patterns in past data, but can also adjust itself to adapt to changes in real-time data. This adaptability allows a single Beidou satellite positioning to maintain stable accuracy even when the receiver moves rapidly or changes position.

[0069] Furthermore, the innovative method of the present application also includes the generation and optimization selection process of the candidate positioning set, and through the iterative feedback mechanism, the selected positioning result is more in line with the actual situation. This flexible selection strategy can cope with various complex environments and diverse usage scenarios, ensuring that the receiver can obtain the best positioning results under different operating conditions.

[0070] In general, by integrating and applying these advanced technologies and methods, this application not only improves the accuracy of single Beidou satellite positioning, improves the reliability and robustness of the system, but also lays a solid foundation for the future development of positioning technology. This has extremely important practical significance and application value for various industries that require precise positioning, such as unmanned driving, aerospace, surveying and mapping, and navigation.

[0071] It can be understood that the method can be executed by any device, equipment, platform, or device cluster with computing and processing capabilities.

[0072] It should be noted that the method of one or more embodiments of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of one or more embodiments of the present application, and the multiple devices will interact with each other to complete the described method.

[0073] It should be noted that the above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0074] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a positioning device based on a single Beidou satellite. Figure 2 As shown, the above device comprises:

[0075] A data acquisition module is configured to acquire satellite positioning data of a single Beidou satellite, wherein the satellite positioning data includes a satellite clock time and a satellite position coordinate;

[0076] A coarse positioning module is configured to obtain a coarse positioning result of the receiver through a coarse positioning calculation model according to the satellite positioning data and the receiver positioning data;

[0077] The precise positioning module is configured to obtain the precise positioning result of the above receiver through a precise positioning calculation model according to the above coarse positioning result and the motion trajectory of the above receiver.

[0078] Optionally, obtaining the precise positioning result of the receiver by the precise positioning calculation model includes:

[0079] Generate a candidate positioning set according to the rough positioning result and the motion trajectory of the receiver;

[0080] Iterate and perform the following operations until the reward value converges:

[0081] According to the state of this round, generating the action of this round through the action generation network, the above action represents the selection of the candidate positioning, and the above candidate positioning belongs to the candidate positioning set;

[0082] Obtaining an action reward value according to the action;

[0083] Update the next round state according to the state transition probability.

[0084] Optionally, the above action reward value is calculated by the following formula:

[0085] R π (t,b)=s(t,b)+β∑ t′ Q t,t′ (b)W π (t′);

[0086] Where s(t,b) represents the reward function for executing action b in state t, β represents the discount factor, β∈[0,1], Q t,t′ (b) represents the state transition probability, W π (t′) represents the total expected discounted return of the system in state t′.

[0087] Optionally, the above reward function is defined as the Euclidean distance between the predicted location of the receiver under action b and the predicted location calculated according to the running trajectory of the receiver.

[0088] Optionally, the training step of the above coarse positioning calculation model includes:

[0089] Acquire training satellite positioning data and training receiver positioning data;

[0090] Determine the actual positioning result corresponding to the above training receiver positioning data;

[0091] Inputting the training satellite positioning data and the training receiver positioning data into the recursive neural network model to obtain a first training result;

[0092] The recursive neural network model is reversely trained according to the first training result and the actual positioning result to obtain the rough positioning calculation model.

[0093] Optionally, the training step of the above precise positioning calculation model includes:

[0094] Obtaining the motion trajectory of the training receiver;

[0095] Inputting the first training result and the training receiver motion trajectory into a deep reinforcement learning network model to obtain a second training result;

[0096] The deep reinforcement learning network model is reversely trained according to the second training result and the actual positioning result to obtain the coarse positioning calculation model.

[0097] Optionally, it also includes:

[0098] The above coarse positioning calculation model is reversely trained based on the above precise positioning results and coarse positioning results.

[0099] For the convenience of description, the above devices are described in terms of functions and modules. Of course, when implementing one or more embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0100] The apparatus of the above-mentioned embodiment is used to implement the corresponding method in the above-mentioned embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0101] Figure 3 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0102] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0103] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solution provided in the embodiment of the present application is implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0104] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0105] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0106] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0107] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiment of the present application, and does not necessarily include all the components shown in the figure.

[0108] The electronic device of the above embodiment is used to implement the corresponding method in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0109] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0110] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0111] In addition, to simplify the description and discussion, and in order not to make one or more embodiments of the present application difficult to understand, the known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making one or more embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which one or more embodiments of the present application will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present disclosure, it is obvious to those skilled in the art that one or more embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0112] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0113] One or more embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application should be included in the scope of protection of this disclosure.

Claims

1. A positioning method based on a single Beidou satellite, characterized in that: include: Acquire satellite positioning data of a single Beidou satellite, wherein the satellite positioning data includes satellite time and satellite position coordinates; Obtaining a coarse positioning result of the receiver through a coarse positioning calculation model according to the satellite positioning data and the receiver positioning data; According to the coarse positioning result and the motion trajectory of the receiver, a precise positioning result of the receiver is obtained through a precise positioning calculation model.

2. The method according to claim 1, characterized in that: The obtaining of the precise positioning result of the receiver by using the precise positioning calculation model includes: generating a candidate positioning set according to the coarse positioning result and the motion trajectory of the receiver; Iterate and perform the following operations until the reward value converges: According to the state of this round, generating the action of this round through the action generation network, the action represents the selection of the candidate positioning, and the candidate positioning belongs to the candidate positioning set; Obtaining an action reward value according to the action; Update the next round state according to the state transition probability.

3. The method according to claim 2, characterized in that The action reward value is calculated by the following formula: R π (t,b)=s(t,b)+β∑ t′ Q t,t′ (b)W π (t′); Where s(t,b) represents the reward function for executing action b in state t, β represents the discount factor, β∈[0,1], Q t,t′ (b) represents the state transition probability, W π (t′) represents the total expected discounted return of the system in state t′.

4. The method according to claim 3, characterized in that The reward function is defined as the Euclidean distance between the predicted location of the receiver under action b and the predicted location calculated based on the running trajectory of the receiver.

5. The method according to claim 4, characterized in that The training steps of the coarse positioning calculation model include: Acquire training satellite positioning data and training receiver positioning data; Determining an actual positioning result corresponding to the training receiver positioning data; Inputting the training satellite positioning data and the training receiver positioning data into a recursive neural network model to obtain a first training result; The recursive neural network model is reversely trained according to the first training result and the actual positioning result to obtain the coarse positioning calculation model.

6. The method according to claim 5, characterized in that The training steps of the precise positioning calculation model include: Obtaining the motion trajectory of the training receiver; Inputting the first training result and the training receiver motion trajectory into a deep reinforcement learning network model to obtain a second training result; The deep reinforcement learning network model is reversely trained according to the second training result and the actual positioning result to obtain the precise positioning calculation model.

7. The method according to claim 6, characterized in that Also includes: The coarse positioning calculation model is reversely trained according to the precise positioning result and the coarse positioning result.

8. A positioning device based on a single Beidou satellite, characterized in that: include: A data acquisition module is configured to acquire satellite positioning data of a single Beidou satellite, wherein the satellite positioning data includes satellite time and satellite position coordinates; A coarse positioning module is configured to obtain a coarse positioning result of the receiver through a coarse positioning calculation model according to the satellite positioning data and the receiver positioning data; The precise positioning module is configured to obtain the precise positioning result of the receiver through a precise positioning calculation model according to the coarse positioning result and the motion trajectory of the receiver.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute any one of claims 1 to 7.

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