A Dual-Process RTK Solving Method and Apparatus Based on an Embedded Platform

Abstract

This disclosure provides a dual-process RTK solution method and apparatus based on an embedded platform. The method comprises two independent solution processes: a first process and a second process. The first process performs RTK solution on N epochs of GNSS observation data to obtain fixed RTK position information, RTK velocity information, and fixed ambiguities. The second process performs solution on GNSS observation data for each subsequent epoch, achieving full-frequency solution. The first process executes 1 / M of the entire solution every 1 / M second, significantly reducing the computational load per unit time. The second process executes a complete solution every 1 / M second, extrapolating the motion state based on the fixed RTK position information and RTK velocity information obtained from the first process to obtain a priori position estimate for the current moment. Based on this, the second process performs single-point positioning and uses the fixed ambiguities from the first process for rapid fixed solution calculation, thus maintaining the accuracy and stability of high-frequency output without repeating the complete RTK process. In summary, the proposed solution improves the computation efficiency of the RTK algorithm on embedded platforms, reduces hardware costs, and meets the requirements for high-frequency computation across the entire system and frequency range.

Description

Technical Field

[0001] This disclosure relates to the field of RTK positioning, and in particular to a dual-process RTK solution method and apparatus based on an embedded platform. Background Technology

[0002] RTK (Real-Time Kinematic) calculations involve extensive use of computationally intensive algorithms such as multidimensional matrix operations and LAMBDA search. As a result, RTK calculations consume significant embedded resources, especially in full-system, full-frequency calculations. Current solutions cannot reduce computational load while maintaining positioning accuracy, thus failing to reduce hardware costs. Summary of the Invention

[0003] This disclosure provides at least one dual-process RTK solution method and apparatus based on an embedded platform, which improves the solution efficiency of RTK algorithm on embedded platforms, reduces hardware costs, and meets the requirements of high-frequency solution across the entire system and frequency range.

[0004] According to one aspect of this disclosure, a dual-process RTK solution method based on an embedded platform is provided, comprising:

[0005] The first solution process is used to perform RTK solution on GNSS observation data of N epochs to obtain RTK fixed solution position information, RTK velocity information, and fixed ambiguity; the RTK fixed solution position information, RTK velocity information, and fixed ambiguity are sent to the second solution process at a predetermined frequency.

[0006] The second solution process uses the RTK fixed solution position information and RTK velocity information to extrapolate the motion state of GNSS observation data to obtain the prior position estimate at the current time; single-point positioning is performed based on the prior position estimate; and fixed solution is solved based on the ambiguity fixed in the first solution process to obtain the target fixed solution positioning result.

[0007] The first and second solution processes are independent of each other, and the second solution process performs solution on the GNSS observation data of each epoch after the N epochs of GNSS observation data; the second solution process executes a complete solution process every 1 / M seconds, and the first solution process executes 1 / M of the entire first solution process every 1 / M seconds; the frequency at which the second solution process injects GNSS observation data is M times the frequency at which the first solution process injects GNSS observation data; where M and N are positive integers.

[0008] In one possible implementation, the embedded platform-based dual-process RTK solution method further includes:

[0009] After obtaining the RTK fixed solution position information, RTK velocity information, and fixed ambiguity using the first solution process, the obtained RTK fixed solution position information, RTK velocity information, and fixed ambiguity are stored in an intermediate file.

[0010] In one possible implementation, the single-point localization based on the prior location estimation includes:

[0011] The least squares method is used to perform single-point localization based on the prior location estimate.

[0012] In one possible implementation, the step of solving for a fixed solution based on the ambiguity fixed in the first solution process to obtain the target fixed solution localization result includes:

[0013] After single-point positioning, the observation data cycle slip detection, common-view star extraction, and non-combined double-difference observation equations are performed sequentially. Then, based on the ambiguity fixed in the first solution process, a fixed solution is obtained to solve for the target fixed solution positioning result.

[0014] In one possible implementation, the first solution process includes the following steps:

[0015] Single-point positioning is performed based on GNSS observation data to obtain single-point positioning results;

[0016] Construct an ultra-wide aisle double-difference observation equation or a wide aisle double-difference observation equation, and fix the ambiguity based on the ultra-wide aisle double-difference observation equation or the wide aisle double-difference observation equation; if the fixation is successful, determine the ultra-wide aisle fixed solution positioning result or the wide aisle fixed solution positioning result based on the fixed first ambiguity; if the fixation fails, determine the ultra-wide aisle floating-point solution positioning result or the wide aisle floating-point solution positioning result.

[0017] In one possible implementation, the first solution process further includes the following steps:

[0018] Construct an observation equation for a fixed solution in an ultra-wide alley or a fixed solution in a wide alley, and perform ambiguity fixing based on the aforementioned equation. If the fixing is successful, determine the RTK fixed solution location information based on the fixed second ambiguity. If the fixing fails, determine the RTK floating-point solution location result.

[0019] In one possible implementation, the second solution process uses the second ambiguity fixed by the first solution process to solve for a fixed solution and obtain the target fixed solution location result.

[0020] In one possible implementation, it also includes:

[0021] Based on at least one of the following: the ultra-wide aisle floating-point solution positioning result, the single-point positioning result obtained in the first solution process, the ultra-wide aisle fixed solution positioning result, the wide aisle fixed solution positioning result, the wide aisle floating-point solution positioning result, the target fixed solution positioning result, the single-point positioning result obtained in the second solution process, the RTK floating-point solution positioning result, and the RTK fixed solution position information, determine the final RTK velocity result and the final RTK positioning result.

[0022] In one possible implementation, the ambiguity fixing based on the ultra-wide alley fixed solution observation equation or the wide alley fixed solution observation equation includes:

[0023] A non-combined double-difference observation equation is constructed, and Kalman filtering and ambiguity fixing are performed based on the non-combined double-difference observation equation, the ultra-wide aisle fixed solution observation equation, or the wide aisle fixed solution observation equation.

[0024] According to another aspect of this disclosure, a dual-process RTK solving apparatus based on an embedded platform is provided, comprising:

[0025] The first solution module is used to perform RTK solution on GNSS observation data of N epochs using the first solution process to obtain RTK fixed solution position information, RTK velocity information, and fixed ambiguity; and to send the RTK fixed solution position information, RTK velocity information, and fixed ambiguity to the second solution process according to a predetermined frequency.

[0026] The second solution module is used to implement the second solution process by extrapolating the motion state of GNSS observation data using the RTK fixed solution position information and RTK velocity information to obtain the prior position estimate at the current time; perform single-point positioning based on the prior position estimate; and solve the fixed solution based on the ambiguity fixed in the first solution process to obtain the target fixed solution positioning result.

[0027] The first and second solution processes are independent of each other, and the second solution process performs solution on the GNSS observation data of each epoch after the N epochs of GNSS observation data; the second solution process executes a complete solution process every 1 / M seconds, and the first solution process executes 1 / M of the entire first solution process every 1 / M seconds; the frequency at which the second solution process injects GNSS observation data is M times the frequency at which the first solution process injects GNSS observation data; where M and N are positive integers.

[0028] This disclosure discloses a dual-process RTK solution method and apparatus based on an embedded platform. First, a first solution process performs RTK solution on N epochs of GNSS observation data to obtain fixed RTK solution position information, RTK velocity information, and fixed ambiguities. Then, according to a predetermined frequency, the fixed RTK solution position information, RTK velocity information, and fixed ambiguities are sent to a second solution process. Upon receiving the above information, the second solution process starts. Specifically, the second solution process uses the fixed RTK solution position information and RTK velocity information to extrapolate the motion state of the GNSS observation data to obtain a priori position estimate for the current moment. Single-point positioning is performed based on the prior position estimate. Fixed solution is solved based on the fixed ambiguities from the first solution process to obtain the target fixed solution positioning result. In this disclosure, the first and second solution processes are independent of each other, and the second solution process performs solution on GNSS observation data for each epoch after the N epochs of GNSS observation data, achieving full-frequency solution. The first solution process executes 1 / M of the entire solution process every 1 / M seconds, thus significantly reducing the computational load per unit time. The second solution process executes a complete solution process every 1 / M seconds, and extrapolates the motion state based on the RTK fixed solution position information and RTK velocity information obtained from the first solution process to obtain the prior position estimate at the current moment. Based on this, the second solution process performs single-point localization and uses the ambiguity fixed by the first solution process to perform rapid fixed solution calculation, thereby maintaining the accuracy and stability of high-frequency output without repeating the complete RTK process. In summary, the scheme disclosed in this invention improves the solution efficiency of the RTK algorithm on embedded platforms, reduces hardware costs, and meets the requirements of high-frequency solution across the entire system and frequency range.

[0029] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0030] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0031] Figure 1 This is one of the flowcharts of the dual-process RTK solution method based on an embedded platform in this embodiment;

[0032] Figure 2 This is the second flowchart of the dual-process RTK solution method based on an embedded platform in this embodiment;

[0033] Figure 3 This is the third flowchart of the dual-process RTK solution method based on an embedded platform in this embodiment;

[0034] Figure 4 This is a schematic diagram of the dual-process RTK solver based on an embedded platform in this embodiment. Detailed Implementation

[0035] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0036] This disclosure addresses the shortcomings of current RTK solution schemes in achieving high accuracy and low computational load under full-frequency solution requirements by providing a dual-process RTK solution method and apparatus based on an embedded platform. This disclosure divides the entire solution process into a first solution process and a second solution process, which are independent of each other. The first solution process performs RTK solution on GNSS observation data for N epochs to obtain fixed RTK solution position information, RTK velocity information, and fixed ambiguities. The second solution process performs solution on GNSS observation data for each subsequent epoch after the N epochs, achieving full-frequency solution. The first solution process executes 1 / M of the entire solution process every 1 / M seconds, thus significantly reducing the computational load per unit time. The second solution process executes a complete solution process every 1 / M seconds. The second solution process extrapolates the motion state based on the RTK fixed solution position information and RTK velocity information obtained from the first solution process to obtain the prior position estimate at the current moment. On this basis, the second solution process performs single-point positioning and uses the ambiguity fixed by the first solution process to perform fast fixed solution calculation, thereby maintaining the accuracy and stability of high-frequency output without repeating the complete RTK process.

[0037] The technical solution of this disclosure will be described below through specific embodiments.

[0038] like Figure 1 The diagram shown is a flowchart of the dual-process RTK solution method based on an embedded platform according to this embodiment. The execution subject of this embodiment is a computing device or component with data processing capabilities. Specifically, the method of this embodiment may include the following steps:

[0039] S110. RTK calculation is performed on the GNSS observation data of N epochs using the first calculation process to obtain RTK fixed solution position information, RTK velocity information, and fixed ambiguity; the RTK fixed solution position information, RTK velocity information, and fixed ambiguity are sent to the second calculation process according to a predetermined frequency.

[0040] This embodiment divides the entire solution process into a first solution process and a second solution process.

[0041] After obtaining the RTK fixed solution position information, RTK velocity information, and fixed ambiguity using the first solution process, the obtained RTK fixed solution position information, RTK velocity information, and fixed ambiguity can be stored in an intermediate file.

[0042] S120. The second solution process uses the RTK fixed solution position information and RTK velocity information to extrapolate the motion state of GNSS observation data to obtain the prior position estimate at the current time; performs single-point positioning based on the prior position estimate; and solves the fixed solution based on the ambiguity fixed in the first solution process to obtain the target fixed solution positioning result.

[0043] The program initially runs only the first solution process. The data from each epoch is processed completely until a fixed solution is calculated, such as the RTK fixed solution location information mentioned above. Then, the first and second solution processes start running simultaneously. These two processes are executed by two separate processes and do not interfere with each other during operation.

[0044] The above-mentioned single-point localization based on the prior location estimation includes: using the least squares method to perform single-point localization based on the prior location estimation.

[0045] The above-mentioned fixed solution solution based on the fixed ambiguity of the first solution process to obtain the target fixed solution positioning result may specifically include: after single-point positioning, performing cycle slip detection of observation data, extracting common-view stars, forming non-combined double-difference observation equations in sequence, and then solving the fixed solution based on the fixed ambiguity of the first solution process to obtain the target fixed solution positioning result.

[0046] The first solution process and the second solution process are independent of each other, and the second solution process solves the GNSS observation data for each epoch after the N epochs of GNSS observation data; the second solution process executes a complete solution process every 1 / M seconds, and the first solution process executes 1 / M of the entire first solution process every 1 / M seconds; the frequency of the second solution process injecting GNSS observation data is M times the frequency of the first solution process injecting GNSS observation data; where M and N are positive integers.

[0047] like Figure 2As shown, both the first solution process (Process A) and the second solution process (Process B) are executed upon receiving GNSS observation data. Process B executes the entire process, while Process A only runs 1 / n of the entire process. Process A is run beforehand using typical data, and its execution time is statistically analyzed and then divided into n C sub-processes. Only one C sub-process is executed at a time. Taking 10Hz as an example, C1 and the entire Process B are executed at 0 seconds, C2 and the entire Process B are executed at 0.1 seconds, and so on, until C10 and the entire Process B are executed at 0.9 seconds. That is, within 1 second, the entire Process B is executed 10 times and the entire Process A is executed once. The observation values ​​injected for Process A (i.e., the aforementioned GNSS observation data) are at 1Hz, and the observation values ​​injected for Process B are at 10Hz. C1-C10 represent the average allocation of Process A. The positioning results of Process A (i.e., the aforementioned RTK fixed solution position information, RTK velocity information, and fixed ambiguity) are updated for Process B every second.

[0048] In some embodiments, the first solution process may include the following steps:

[0049] Step 1: Perform single-point positioning based on GNSS observation data to obtain the single-point positioning result.

[0050] like Figure 3 As shown, GNSS observation data includes raw data received by the base station and raw data received by the terminal. This data includes GNSS pseudorange, GNSS carrier phase, GNSS Doppler information, GNSS signal-to-noise ratio, and GNSS ephemeris. Then, the GNSS pseudorange, GNSS carrier phase, GNSS Doppler information, GNSS signal-to-noise ratio, and GNSS ephemeris are subjected to quality checks. After passing the quality checks, single-point positioning is performed based on this data.

[0051] Step 2: Construct the ultra-wide aisle double-difference observation equation or the wide aisle double-difference observation equation, and fix the ambiguity based on the ultra-wide aisle double-difference observation equation or the wide aisle double-difference observation equation; if the fixation is successful, determine the ultra-wide aisle fixed solution positioning result or the wide aisle fixed solution positioning result based on the fixed first ambiguity; if the fixation fails, determine the ultra-wide aisle floating-point solution positioning result or the wide aisle floating-point solution positioning result.

[0052] like Figure 3As shown, before constructing the ultra-wide lane double-difference observation equation or the wide lane double-difference observation equation, cycle slip detection and common-view satellite extraction of observation data are required. During the ambiguity fixing process based on the ultra-wide lane double-difference observation equation or the wide lane double-difference observation equation, Kalman filtering of the ultra-wide lane floating-point solution can be performed. Before outputting the ultra-wide lane fixed solution positioning result or the wide lane fixed solution positioning result, a ambiguity fixing solution verification step is required; only after passing the verification can the ultra-wide lane fixed solution positioning result or the wide lane fixed solution positioning result be output.

[0053] Step 3: Construct the observation equation of the ultra-wide lane fixed solution or the wide lane fixed solution, and perform ambiguity fixing based on the ultra-wide lane fixed solution observation equation or the wide lane fixed solution observation equation; if the fixing is successful, determine the RTK fixed solution location information (i.e., the fixed solution positioning result) based on the fixed second ambiguity; if the fixing fails, determine the RTK floating point solution positioning result.

[0054] In step three, during the ambiguity fixing process based on the ultra-wide lane fixed solution observation equation or the wide lane fixed solution observation equation, Kalman filtering floating-point solution operations can be performed; and the Kalman filtering floating-point solution operations are based on the constructed non-combined double-difference observation equation. Before outputting the RTK fixed solution location information, an ambiguity fixed solution verification step is required; only after passing the verification can the RTK fixed solution location information be output.

[0055] The first solution process described above includes modules for complete observation model construction, differential processing, floating-point ambiguity estimation and search, and fixed solution calculation. This process is divided according to time averages; taking an NHz output frequency as an example, only 1 / N of the overall process is executed every 1 / N seconds, thus significantly reducing the computational load per unit time.

[0056] Meanwhile, the second solution process (i.e., process B) serves as an auxiliary solution process and is triggered to run within each 1 / N second cycle. Its core idea is to extrapolate the motion state based on the fixed solution position and velocity information (RTK fixed solution position information, RTK velocity information) obtained in the previous solution process (process A) to obtain the prior position estimate at the current moment. On this basis, process B uses the least squares method for single-point positioning and utilizes the ambiguity fixed in process A (i.e., the aforementioned second ambiguity) to perform fast fixed solution calculation, thereby maintaining the accuracy and stability of high-frequency output without repeating the complete RTK process.

[0057] In some embodiments, such as Figure 3 The solution method may further include:

[0058] Based on at least one of the following: the ultra-wide aisle floating-point solution positioning result, the single-point positioning result obtained in the first solution process, the ultra-wide aisle fixed solution positioning result, the wide aisle fixed solution positioning result, the wide aisle floating-point solution positioning result, the target fixed solution positioning result, the single-point positioning result obtained in the second solution process, the RTK floating-point solution positioning result, and the RTK fixed solution position information, determine the final RTK velocity result and the final RTK positioning result.

[0059] In addition, such as Figure 2 As shown, the target fixed solution location result obtained from the second solution process can also be used as the final location result.

[0060] In some embodiments, after each run of process A, the running information (RTK fixed solution location information, RTK speed information, and fixed ambiguity) is stored as an intermediate file and handed over to process B for use. If process A solves a non-fixed solution, process B will initialize and repeat the initial process until a fixed solution is obtained.

[0061] This design approach, which alternates between A / B processes, not only effectively reduces the real-time computational burden on embedded devices in high-frequency RTK calculations, but also improves the overall system response speed and resource utilization, providing a feasible technical path for low-cost, high-performance GNSS high-precision positioning applications.

[0062] Based on the same inventive concept, this disclosure provides a dual-process RTK solving device based on an embedded platform. The steps performed by the components of this device are the same as or similar to the methods described above, therefore, similar details will not be repeated. Figure 4 As shown, the dual-process RTK solver based on an embedded platform in this embodiment includes:

[0063] The first solution module 410 is used to perform RTK solution on GNSS observation data of N epochs using the first solution process to obtain RTK fixed solution position information, RTK velocity information, and fixed ambiguity; and to send the RTK fixed solution position information, RTK velocity information, and fixed ambiguity to the second solution process according to a predetermined frequency.

[0064] The second solution module 420 is used to implement the second solution process by extrapolating the motion state of GNSS observation data using the RTK fixed solution position information and RTK velocity information to obtain the prior position estimate at the current moment; performing single-point positioning based on the prior position estimate; and solving for the fixed solution based on the ambiguity fixed in the first solution process to obtain the target fixed solution positioning result. The first and second solution processes are independent of each other, and the second solution process solves the GNSS observation data for each epoch after the N epochs of GNSS observation data; the second solution process executes a complete solution process every 1 / M seconds, and the first solution process executes 1 / M of the entire first solution process every 1 / M seconds; the frequency of injecting GNSS observation data into the second solution process is M times the frequency of injecting GNSS observation data into the first solution process; where M and N are positive integers.

[0065] The various embodiments of the techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0066] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0067] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on 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 of the foregoing.

[0068] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0069] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0070] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0071] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0072] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A dual-process RTK solution method based on an embedded platform, characterized in that, include: The first solution process is used to perform RTK solution on GNSS observation data of N epochs to obtain RTK fixed solution position information, RTK velocity information, and fixed ambiguity. According to a predetermined frequency, the RTK fixed solution position information, RTK speed information, and fixed ambiguity are sent to the second solution process; The second solution process uses the RTK fixed solution position information and RTK velocity information to extrapolate the motion state of GNSS observation data to obtain the prior position estimate at the current time; single-point positioning is performed based on the prior position estimate; and fixed solution is solved based on the ambiguity fixed in the first solution process to obtain the target fixed solution positioning result. The first and second solution processes are independent of each other, and the second solution process performs solution on the GNSS observation data of each epoch after the N epochs of GNSS observation data; the second solution process performs a complete solution process every 1 / M seconds, and the first solution process performs 1 / M of the entire first solution process every 1 / M seconds; the frequency at which the second solution process injects GNSS observation data is M times the frequency at which the first solution process injects GNSS observation data; where M and N are positive integers.

2. The dual-process RTK solution method based on an embedded platform according to claim 1, characterized in that, Also includes: After obtaining the RTK fixed solution position information, RTK velocity information, and fixed ambiguity using the first solution process, the obtained RTK fixed solution position information, RTK velocity information, and fixed ambiguity are stored in an intermediate file.

3. The dual-process RTK solution method based on an embedded platform according to claim 1, characterized in that, The single-point localization based on the prior location estimation includes: The least squares method is used to perform single-point localization based on the prior location estimate.

4. The dual-process RTK solution method based on an embedded platform according to claim 1, characterized in that, The step of solving for a fixed solution based on the ambiguity fixed in the first solution process to obtain the target fixed solution localization result includes: After single-point positioning, the observation data cycle slip detection, common-view star extraction, and non-combined double-difference observation equations are performed sequentially. Then, based on the ambiguity fixed in the first solution process, a fixed solution is obtained to solve for the target fixed solution positioning result.

5. The dual-process RTK solution method based on an embedded platform according to claim 1, characterized in that, The first solution process includes the following steps: Single-point positioning is performed based on GNSS observation data to obtain single-point positioning results; Construct an ultra-wide aisle double-difference observation equation or a wide aisle double-difference observation equation, and fix the ambiguity based on the ultra-wide aisle double-difference observation equation or the wide aisle double-difference observation equation; if the fixation is successful, determine the ultra-wide aisle fixed solution positioning result or the wide aisle fixed solution positioning result based on the fixed first ambiguity; if the fixation fails, determine the ultra-wide aisle floating-point solution positioning result or the wide aisle floating-point solution positioning result.

6. The dual-process RTK solution method based on an embedded platform according to claim 5, characterized in that, The first solution process also includes the following steps: Construct an observation equation for a fixed solution in an ultra-wide alley or a fixed solution in a wide alley, and perform ambiguity fixing based on the aforementioned equation. If the fixing is successful, determine the RTK fixed solution location information based on the fixed second ambiguity. If the fixing fails, determine the RTK floating-point solution location result.

7. The dual-process RTK solution method based on an embedded platform according to claim 6, characterized in that, The second solution process uses the second ambiguity fixed by the first solution process to solve for the fixed solution and obtain the target fixed solution location result.

8. The dual-process RTK solution method based on an embedded platform according to claim 6, characterized in that, Also includes: Based on at least one of the following: the ultra-wide aisle floating-point solution positioning result, the single-point positioning result obtained in the first solution process, the ultra-wide aisle fixed solution positioning result, the wide aisle fixed solution positioning result, the wide aisle floating-point solution positioning result, the target fixed solution positioning result, the single-point positioning result obtained in the second solution process, the RTK floating-point solution positioning result, and the RTK fixed solution position information, determine the final RTK velocity result and the final RTK positioning result.

9. The dual-process RTK solution method based on an embedded platform according to claim 6, characterized in that, The ambiguity fixing based on the ultra-wide lane fixed solution observation equation or the wide lane fixed solution observation equation includes: A non-combined double-difference observation equation is constructed, and Kalman filtering and ambiguity fixing are performed based on the non-combined double-difference observation equation, the ultra-wide aisle fixed solution observation equation, or the wide aisle fixed solution observation equation.

10. A dual-process RTK solving device based on an embedded platform, characterized in that, include: The first solution module is used to perform RTK solution on GNSS observation data of N epochs using the first solution process to obtain RTK fixed solution position information, RTK velocity information, and fixed ambiguity. According to a predetermined frequency, the RTK fixed solution position information, RTK speed information, and fixed ambiguity are sent to the second solution process; The second solution module is used to implement the second solution process by extrapolating the motion state of GNSS observation data using the RTK fixed solution position information and RTK velocity information to obtain the prior position estimate at the current time; perform single-point positioning based on the prior position estimate; and solve the fixed solution based on the ambiguity fixed in the first solution process to obtain the target fixed solution positioning result. The first and second solution processes are independent of each other, and the second solution process performs solution on the GNSS observation data of each epoch after the N epochs of GNSS observation data; the second solution process performs a complete solution process every 1 / M seconds, and the first solution process performs 1 / M of the entire first solution process every 1 / M seconds; the frequency at which the second solution process injects GNSS observation data is M times the frequency at which the first solution process injects GNSS observation data; where M and N are positive integers.

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