Navigation satellite ground-based enhanced positioning method and system based on unmanned aerial vehicle nest

Abstract

The invention discloses a navigation satellite ground-based enhanced positioning method and system based on an unmanned aerial vehicle nest, and belongs to the technical field of satellite navigation positioning and electric power inspection, and the method comprises the steps: receiving GNSS satellite observation data; generating local RTK differential data according to accurate coordinates calibrated during installation of the unmanned aerial vehicle nest; gNSS satellite observation data and local RTK differential data are uploaded to a CORS service platform, a service mode is selected according to the position of a user terminal, the differential data are sent to the user terminal, the differential data are received through the user terminal, calculation is carried out by combining the differential data with the GNSS observation data of the user terminal, and a high-precision positioning result is obtained. According to the invention, GNSS satellite observation data sensed by a plurality of unmanned aerial vehicle nests and local RTK differential data calculated by the nests are gathered to the CORS service platform, so that a navigation satellite ground-based augmentation system with low cost and high benefit is constructed.

Description

Technical Field

[0001] This invention relates to a navigation satellite ground-based augmentation positioning method and system based on UAV nests, belonging to the field of satellite navigation and positioning and power line inspection technology. Background Technology

[0002] With the advancement of digital transformation in the power industry, the application of drones in power facility inspection is becoming increasingly widespread. To improve inspection efficiency and automation, power companies are deploying a large number of drone nests to enable automatic take-off and landing, charging, and data transmission for drones. These drone nests are typically equipped with high-precision GNSS (Global Navigation Satellite System) receiver modules, enabling them to acquire their own precise coordinates and provide "one-to-one" local real-time dynamic differential (RTK) data to the drones within the nest, achieving centimeter-level precision autonomous flight inspections. The demand for high-precision location services is also growing in other areas of the power industry (such as equipment positioning, personnel safety monitoring, and emergency repairs). Traditional high-precision positioning services rely on ground reference stations to build a CORS (Continuously Operating Reference Stations) network, which is costly, has a long deployment cycle, and is difficult to deploy in remote areas. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a navigation satellite ground-based augmentation positioning method and system based on UAV nests. This method aims to reuse power inspection UAV nests as satellite navigation and positioning reference stations. By aggregating GNSS satellite observation data sensed by multiple UAV nests and local RTK differential data calculated by the nests to the CORS service platform, a low-cost and high-efficiency navigation satellite ground-based augmentation system is constructed. While providing local differential services for UAVs, the nests can also provide network RTK services for other terminal devices throughout the province.

[0004] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0005] In a first aspect, the present invention provides a navigation satellite ground-based augmentation positioning method based on an unmanned aerial vehicle (UAV) nest, comprising:

[0006] GNSS satellite observation data is received by multiple drone nests deployed near power facilities;

[0007] Based on the precise coordinates calibrated during the installation of the drone nest, local RTK differential data is generated;

[0008] The GNSS satellite observation data and local RTK differential data are uploaded to the CORS service platform;

[0009] In the CORS service platform, based on received GNSS satellite observation data and local RTK differential data, the service mode is selected according to the user terminal's location, and differential data is sent to the user terminal. Specifically, this includes:

[0010] If the user terminal is located within a grid formed by multiple UAV nests, the CORS service platform uses virtual reference station technology to fuse GNSS satellite observation data from multiple UAV nests to generate virtual reference station differential data and sends it to the user terminal.

[0011] If the user terminal is not within the grid, the local RTK differential data of the nearest UAV nest will be forwarded to the user terminal through the CORS service platform;

[0012] The user terminal receives the differential data and combines it with its own GNSS observation data to perform calculations and obtain high-precision positioning results.

[0013] Furthermore, the method of employing virtual reference station technology to generate virtual reference station differential data by fusing GNSS satellite observation data from multiple UAV nests includes:

[0014] Select one of the multiple UAV nests as the primary reference station, and the rest as secondary reference stations;

[0015] Baseline calculation and error space modeling are performed based on GNSS satellite observation data from multiple UAV nests to generate virtual observation values ​​corresponding to the user terminal location;

[0016] The virtual observations are sent to the user terminal as differential data.

[0017] Furthermore, baseline calculations are performed based on GNSS satellite observation data from multiple UAV nests, including:

[0018] Let the double-difference ambiguities resolved on two carrier frequencies between two UAV nesting stations be respectively and Calculate the double-difference tropospheric error for each satellite pair on the baseline formed by the two UAV nesting stations. and ionospheric delay error The formula is as follows:

[0019] (1);

[0020] In the formula, For inter-station to inter-satellite double difference operators, Represents the speed of light. and These represent the frequencies on the two carrier waves. and These represent the wavelengths on the two carrier waves. and These are carrier observations at two different frequencies. This indicates the distance between the station and the satellite with double differences.

[0021] Furthermore, the error space modeling adopts a multi-reference-station linear interpolation model. For ionospheric errors or tropospheric errors, the model is as follows:

[0022] (2);

[0023] Among them, subscript This indicates the auxiliary reference station participating in the modeling; Indicates the main reference station; and ( () represents the difference in planar position between the t-1 auxiliary reference stations and the t-th primary reference station; parameter and These are the model coefficients to be determined. When the number of reference stations is greater than 3, the coefficients are obtained by solving formula (2). and ;

[0024] For user terminals The double-difference ionospheric delay value is obtained according to formula (3). The formula is as follows:

[0025] (3);

[0026] in, and This represents the difference in planar position between user terminal g and the t-th master reference station.

[0027] Furthermore, in the multi-station service mode, virtual observations of the user terminal's location are generated using the following formula:

[0028] (4);

[0029] In the formula, and These are the pseudorange and virtual carrier observations of the user terminal, respectively. and These represent the user terminal and the main reference station, respectively. and These are the pseudorange and carrier observations of the main reference station, respectively. This is the difference in distance between the user terminal and the main reference station corresponding to the same satellite; Indicates wavelength; subscript Indicates frequency, The tropospheric error modeling values ​​for CORS service platform network elements. Ionospheric error modeling values ​​for CORS service platform network elements.

[0030] Furthermore, the CORS service platform sends virtual observations as differential data to the user terminal through a predefined communication protocol, which includes any one or more of TCP, Ntrip, and HTTP protocols.

[0031] Secondly, the present invention provides a navigation satellite ground-based augmentation positioning system based on UAV nests, used to implement the navigation satellite ground-based augmentation positioning method based on UAV nests as described in any of the preceding claims, comprising:

[0032] Multiple drone nests are deployed near power facilities, each nest comprising:

[0033] GNSS receiver module, used to receive GNSS satellite observation data;

[0034] The local RTK differential data calculation module is used to generate local RTK differential data based on the precise coordinates of the nest.

[0035] The first communication module is used to upload the GNSS satellite observation data and local RTK differential data;

[0036] CORS service platform, including:

[0037] The second communication module is used to receive GNSS satellite observation data and local RTK differential data from the multiple UAV nests;

[0038] The service processing module is used to select the service mode based on the received GNSS satellite observation data and local RTK differential data, and send the differential data to the user terminal according to the user terminal location.

[0039] User terminals include:

[0040] The third communication module is used to interact with the CORS service platform and receive differential data;

[0041] The positioning solution module is used to combine differential data with its own GNSS observation data to perform calculations and obtain high-precision positioning results.

[0042] Furthermore, the service processing module supports two service modes: data forwarding mode and virtual reference station mode;

[0043] The virtual reference station mode is used when the user terminal is located within a grid formed by multiple UAV nests. The CORS service platform uses virtual reference station technology to fuse GNSS satellite observation data from multiple UAV nests to generate virtual reference station differential data and send it to the user terminal.

[0044] The data forwarding mode is used to forward local RTK differential data from the nearest UAV nest to the user terminal through the CORS service platform when the user terminal is not within the grid.

[0045] Furthermore, the first communication module supports uploading data via power fiber optic networks or 4G / 5G cellular networks.

[0046] Furthermore, the user terminal is a high-precision positioning terminal with RTK function, including any one or more of the following: Beidou safety helmets worn by power line inspectors, high-precision coordinate acquisition instruments, and remote-controlled inspection drones.

[0047] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0048] This invention provides a navigation satellite ground-based augmentation positioning method and system based on UAV nests. By reusing power line inspection UAV nests as satellite navigation and positioning reference stations, it aggregates GNSS satellite observation data sensed by multiple UAV nests and local RTK differential data calculated by the nests to the CORS service platform, thereby constructing a low-cost, high-efficiency navigation satellite ground-based augmentation system. While providing local differential services for UAVs, the nests can also provide network RTK services to other terminal devices throughout the province. This invention makes full use of the existing UAV nests and power fiber optic network resources in the power industry, saving the huge costs of building and maintaining dedicated navigation satellite ground reference station equipment, and realizing the reuse of resources. This invention provides two service modes: direct forwarding and virtual reference station, which are highly flexible and can select the optimal service strategy according to factors such as the location of the terminal and data quality requirements, balancing server load and service quality. Attached Figure Description

[0049] Figure 1 This is a flowchart of a navigation satellite ground-based augmentation positioning method based on an unmanned aerial vehicle (UAV) nest, provided by an embodiment of the present invention;

[0050] Figure 2 This is a schematic diagram of the overall architecture of a navigation satellite ground-based augmentation positioning system based on an unmanned aerial vehicle (UAV) nest, provided by an embodiment of the present invention. Detailed Implementation

[0051] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0052] Example 1, such as Figure 1 As shown in the figure, this embodiment introduces a navigation satellite ground-based augmentation positioning method based on UAV nests, including:

[0053] GNSS satellite observation data is received by multiple drone nests deployed near power facilities;

[0054] Based on the precise coordinates calibrated during the installation of the drone nest, local RTK differential data is generated;

[0055] The GNSS satellite observation data and local RTK differential data are uploaded to the CORS service platform;

[0056] In the CORS service platform, based on received GNSS satellite observation data and local RTK differential data, the service mode is selected according to the user terminal's location, and differential data is sent to the user terminal. Specifically, this includes:

[0057] If the user terminal is located within a grid formed by multiple UAV nests, the CORS service platform uses virtual reference station technology to fuse GNSS satellite observation data from multiple UAV nests to generate virtual reference station differential data and sends it to the user terminal.

[0058] If the user terminal is not within the grid, the local RTK differential data of the nearest UAV nest will be forwarded to the user terminal through the CORS service platform;

[0059] The user terminal receives the differential data and combines it with its own GNSS observation data to perform calculations and obtain high-precision positioning results.

[0060] The navigation satellite ground-based augmentation positioning method based on UAV nests provided in this embodiment involves the following steps in its application process:

[0061] S1: Each UAV nest continuously receives GNSS satellite observation data through its GNSS receiving module;

[0062] S2: When each drone nest is installed with its own precise coordinates, local RTK differential data is generated through the local differential generation module for the inspection operation of drones in its nest.

[0063] S3: The precise coordinates of the GNSS signal receiving antennas of each UAV nest are manually calibrated during installation. The UAV nest receives satellite navigation and positioning signals and calculates local RTK differential data based on its own coordinates. This data is transmitted to the UAV via the nest's local area network. In differential positioning mode, the UAV's positioning accuracy can reach centimeter level. The local RTK differential data calculated by the nest and the navigation GNSS satellite observation data are uploaded to the power company's CORS service platform via fiber optic network or mobile cellular network.

[0064] S4: The power company’s CORS service platform’s base station management module incorporates all drone nests into the base station network for unified management.

[0065] S5: When a user terminal needs high-precision positioning services, it initiates a service request to the power company's CORS service platform and reports its approximate location coordinates.

[0066] S6: The service processing module of the provincial CORS service platform selects the service mode based on the location of the requesting terminal.

[0067] 1) If the user terminal is located within a triangular area enclosed by three drone nests, the platform uses the observation data from these three drone nests to calculate differential data for a virtual reference station (VRS) using a network RTK algorithm, and then broadcasts this VRS differential data to the user terminal; specifically including:

[0068] First, baseline resolution is performed. Assume the double-difference ambiguities resolved at the two carrier frequencies between the two satellite stations are as follows: and Then the double-difference tropospheric error of each satellite pair on the baseline formed by these two stations. and ionospheric delay error The following formula can be used to solve for:

[0069] (1);

[0070] In the formula, For inter-station to inter-satellite double difference operators, Represents the speed of light. and These represent the frequencies on the two carrier waves. and These represent the wavelengths on the two carrier waves. and These are carrier observations at two different frequencies. This indicates the distance between the station and the satellite with double differences.

[0071] For spatial modeling of errors, ionospheric and tropospheric errors employ a multi-reference-station linear interpolation model:

[0072] (2);

[0073] Among them, subscript This indicates the auxiliary reference station participating in the modeling; Indicates the main reference station; and ( () represents the difference in planar position between the t-1 auxiliary reference stations and the t-th primary reference station; parameter and These are the model coefficients to be determined. When the number of reference stations is greater than 3, the coefficients are obtained by solving formula (2). and .

[0074] For user terminals The double-difference ionospheric delay value can be obtained by the following formula. The formula is as follows:

[0075] (3);

[0076] in, and This represents the difference in planar position between user terminal g and the t-th master reference station.

[0077] User location is generated according to the following formula. Virtual observations, the formula is as follows:

[0078] (4);

[0079] In the formula, and These are the pseudorange and virtual carrier observations of the user terminal, respectively. and These represent the user terminal and the main reference station, respectively. and These are the pseudorange and carrier observations of the main reference station, respectively. This is the difference in distance between the user terminal and the main reference station corresponding to the same satellite; Indicates wavelength; subscript Indicates frequency, The tropospheric error modeling values ​​for CORS service platform network elements. Ionospheric error modeling values ​​for CORS service platform network elements;

[0080] After generating the observation, the network RTK platform sends it to the end user through an internal communication protocol or a standard communication protocol, which includes, but is not limited to, existing TCP, Ntrip, and HTTP protocols.

[0081] 2) If the user terminal is not within the grid composed of all the drone nests, the platform calculates and selects the nearest drone nest based on the approximate coordinates of the terminal, and forwards the local RTK differential data calculated by that nest to the user terminal.

[0082] S7: After receiving the differential data, the user terminal performs joint calculations with the GNSS satellite observation data it receives to obtain a positioning result with centimeter-level accuracy.

[0083] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0084] 1. Significantly reduced costs: By making full use of the existing drone nests and power fiber optic network resources in the power industry, the huge costs of building and maintaining dedicated navigation satellite ground reference station equipment are eliminated, and the resources are reused.

[0085] 2. Highly efficient coverage: Power drone nests are usually widely distributed near power facilities, and using them as base stations can quickly form a high-precision positioning service network covering power grid facilities.

[0086] 3. Dual service modes: It provides two service modes: direct forwarding and virtual reference station. It is highly flexible and can select the optimal service strategy based on factors such as the location of the terminal and data quality requirements to balance server load and service quality.

[0087] Example 2, as Figure 2 As shown, this embodiment provides a BeiDou ground-based augmentation positioning system based on UAV nests, including:

[0088] 1. Multiple drone nests: Deployed near power facilities (such as substations and transmission towers) throughout the province. Each drone nest subsystem includes:

[0089] 1) GNSS receiver module: used to receive GNSS satellite observation data (including carrier phase, pseudorange, etc.).

[0090] 2) Local RTK differential data calculation module: Based on the precise coordinates of the UAV nest GNSS signal receiving antenna and real-time satellite observation data, it generates local RTK differential data required for the UAV's autonomous inspection and positioning.

[0091] 3) First communication module: used to upload the GNSS satellite observation data and local RTK differential data to the power company's CORS service platform via the power fiber optic network and / or 4G / 5G mobile cellular network.

[0092] 2. CORS Service Platform: Deployed in the power company's data center, including:

[0093] 1) Second communication module and processing cluster: used to receive and process the uploaded data from all UAV nests.

[0094] 2) Base station management module: Used to manage each UAV nest and treat it as a base station, managing its status, coordinates and data quality.

[0095] 3) Service Processing Module: Used to provide two modes of network RTK service based on terminal requests:

[0096] Mode 1 (Data Forwarding Mode): Based on the location coordinates of the service terminal, the nearest drone nest is selected as the reference base station, and the local RTK differential data generated by the nest is directly forwarded to the service terminal.

[0097] Mode 2 (Virtual Reference Station - VRS Mode): The CORS service platform uses GNSS satellite observation data uploaded by multiple UAV nests to generate differential data of a virtual reference station through network calculation, and then broadcasts the virtual differential data to the service terminal.

[0098] 4) User Management Module: Used to manage user terminal accounts, permissions, and service requests.

[0099] 3. User Terminal: This is a high-precision positioning terminal with RTK functionality, such as a Beidou safety helmet worn by power line inspectors, a high-precision coordinate acquisition instrument, or a remote-controlled inspection drone. The user terminal accesses the power company's CORS service platform via a wireless network (4G / 5G) to receive differential data and achieve precise positioning.

[0100] The user terminal includes: a third communication module, used to interact with the CORS service platform and receive differential data;

[0101] The positioning solution module is used to combine differential data with its own GNSS observation data to perform calculations and obtain high-precision positioning results.

[0102] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0103] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0104] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0106] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims.

Claims

1. A navigation satellite ground-based augmentation positioning method based on UAV nests, characterized in that, include: GNSS satellite observation data is received by multiple drone nests deployed near power facilities; Based on the precise coordinates calibrated during the installation of the drone nest, local RTK differential data is generated; The GNSS satellite observation data and local RTK differential data are uploaded to the CORS service platform; In the CORS service platform, based on received GNSS satellite observation data and local RTK differential data, the service mode is selected according to the user terminal's location, and differential data is sent to the user terminal. Specifically, this includes: If the user terminal is located within a grid formed by multiple UAV nests, the CORS service platform uses virtual reference station technology to fuse GNSS satellite observation data from multiple UAV nests to generate virtual reference station differential data and sends it to the user terminal. If the user terminal is not within the grid, the local RTK differential data of the nearest UAV nest will be forwarded to the user terminal through the CORS service platform; The user terminal receives the differential data and combines it with its own GNSS observation data to perform calculations and obtain high-precision positioning results.

2. The navigation satellite ground-based augmentation positioning method based on UAV nests according to claim 1, characterized in that, The method employs virtual reference station technology, fusing GNSS satellite observation data from multiple UAV nests to generate virtual reference station differential data, including: Select one of the multiple UAV nests as the primary reference station, and the rest as secondary reference stations; Baseline calculation and error space modeling are performed based on GNSS satellite observation data from multiple UAV nests to generate virtual observation values ​​corresponding to the user terminal location; The virtual observations are sent to the user terminal as differential data.

3. The navigation satellite ground-based augmentation positioning method based on UAV nests according to claim 2, characterized in that, Baseline calculations were performed based on GNSS satellite observation data from multiple UAV nests, including: Let the double-difference ambiguities resolved on two carrier frequencies between two UAV nesting stations be respectively and Calculate the double-difference tropospheric error for each satellite pair on the baseline formed by the two UAV nesting stations. and ionospheric delay error The formula is as follows: （1）； In the formula, For inter-station to inter-satellite double difference operators, Represents the speed of light. and These represent the frequencies on the two carrier waves. and These represent the wavelengths on the two carrier waves. and These are carrier observations at two different frequencies. This indicates the distance between the station and the satellite with double differences.

4. The navigation satellite ground-based augmentation positioning method based on UAV nests according to claim 3, characterized in that, The error space modeling adopts a multi-reference-station linear interpolation model. For ionospheric errors or tropospheric errors, the model is as follows: （2）； Among them, subscript This indicates the auxiliary reference station participating in the modeling; Indicates the main reference station; and ( () represents the difference in planar position between the t-1 auxiliary reference stations and the t-th primary reference station; parameter and These are the model coefficients to be determined. When the number of reference stations is greater than 3, the coefficients are obtained by solving formula (2). and ; For user terminals The double-difference ionospheric delay value is obtained according to formula (3). The formula is as follows: （3）； in, and This represents the difference in planar position between user terminal g and the t-th master reference station.

5. The navigation satellite ground-based augmentation positioning method based on UAV nests according to claim 4, characterized in that, In the multi-station service mode, the virtual observation value of the user terminal location is generated using the following formula: （4）； In the formula, and These are the pseudorange and virtual carrier observations of the user terminal, respectively. and These represent the user terminal and the main reference station, respectively. and These are the pseudorange and carrier observations of the main reference station, respectively. This is the difference in distance between the user terminal and the main reference station corresponding to the same satellite; Indicates wavelength; subscript Indicates frequency, The tropospheric error modeling values ​​for CORS service platform network elements. Ionospheric error modeling values ​​for CORS service platform network elements.

6. The navigation satellite ground-based augmentation positioning method based on UAV nests according to claim 5, characterized in that, The CORS service platform sends virtual observations as differential data to the user terminal through a predefined communication protocol, which includes any one or more of TCP, Ntrip, and HTTP protocols.

7. A navigation satellite ground-based augmentation positioning system based on UAV nests, used to implement the navigation satellite ground-based augmentation positioning method based on UAV nests as described in any one of claims 1-6, characterized in that, include: Multiple drone nests are deployed near power facilities, each nest comprising: GNSS receiver module, used to receive GNSS satellite observation data; The local RTK differential data calculation module is used to generate local RTK differential data based on the precise coordinates of the nest. The first communication module is used to upload the GNSS satellite observation data and local RTK differential data; CORS service platform, including: The second communication module is used to receive GNSS satellite observation data and local RTK differential data from the multiple UAV nests; The service processing module is used to select the service mode based on the received GNSS satellite observation data and local RTK differential data, and send the differential data to the user terminal according to the user terminal location. User terminals include: The third communication module is used to interact with the CORS service platform and receive differential data; The positioning solution module is used to combine differential data with its own GNSS observation data to perform calculations and obtain high-precision positioning results.

8. The navigation satellite ground-based augmentation positioning system based on UAV nests according to claim 7, characterized in that, The service processing module supports two service modes: data forwarding mode and virtual reference station mode. The virtual reference station mode is used when the user terminal is located within a grid formed by multiple UAV nests. The CORS service platform uses virtual reference station technology to fuse GNSS satellite observation data from multiple UAV nests to generate virtual reference station differential data and send it to the user terminal. The data forwarding mode is used to forward local RTK differential data from the nearest UAV nest to the user terminal through the CORS service platform when the user terminal is not within the grid.

9. The navigation satellite ground-based augmentation positioning system based on UAV nests according to claim 7, characterized in that, The first communication module supports uploading data via power fiber optic networks or 4G / 5G cellular networks.

10. The navigation satellite ground-based augmentation positioning system based on UAV nests according to claim 7, characterized in that, The user terminal is a high-precision positioning terminal with RTK function, including any one or more of the following: Beidou safety helmets worn by power line inspectors, high-precision coordinate acquisition instruments, and remote-controlled inspection drones.

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