An Adaptive Networking-Based Cooperative Solution Method for Deformation Monitoring of Beidou Front-End

Abstract

The present invention discloses a front-end Beidou deformation monitoring collaborative solution method based on adaptive networking. By considering the computing power of terminal devices, as well as the influence of factors such as receiver, antenna type, and observation occlusion environment, intelligent numbering and grouping are performed on all terminal devices, so as to achieve more efficient data management and processing. By adaptively selecting the forward and backward baseline vectors for solution, this method can optimize the baseline solution process and improve the accuracy and timeliness of monitoring when data of neighboring devices is available; in addition, by selecting a reference station as a constraint point and using the baseline vector observation values to solve the precise relative position relationship of each terminal in the network, this method solves the limitations of existing traditional satellite navigation systems in deformation monitoring. Through adaptive networking and collaborative solution, the accuracy, timeliness, and environmental adaptability of monitoring are improved to meet broader application requirements.

Description

Technical Field

[0001] The present invention belongs to the field of global satellite navigation systems, and particularly relates to a front-end Beidou deformation monitoring collaborative solution method based on adaptive networking. Background Art

[0002] In traditional deformation monitoring systems, the method of manual total station measurement is usually adopted. Not only is the labor intensity high, but there are also situations such as long observation time, high sampling density for real-time dynamic measurement, and large amount of calculation in its periodic repeated deformation monitoring. These traditional methods can meet the deformation monitoring requirements to a certain extent, but there are also some limitations. At the same time, existing Beidou terminals have computing capabilities, but due to the limitations of the computing capabilities of terminal devices, the number of baselines that can be processed in real-time solution is limited. In addition, when the monitoring system processes different types of receivers, antennas, and complex observation environments, it often lacks flexibility and adaptability. These factors together lead to insufficient accuracy and timeliness of monitoring results, low processing efficiency, especially in complex observation conditions such as canyons and mountains, where signal occlusion and multipath effects will have a significant impact on monitoring results.

[0003] Therefore, it is necessary to design a front-end Beidou deformation monitoring collaborative solution method based on adaptive networking to solve the above problems. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a front-end Beidou deformation monitoring collaborative solution method based on adaptive networking. By considering the computing capabilities of terminal devices, as well as the influence of factors such as receiver, antenna types, and observation occlusion environment, intelligent numbering and grouping of all terminal devices are carried out, so as to achieve more efficient data management and processing. By adaptively selecting the forward and backward baseline vectors for solution, this method can optimize the baseline solution process when data of neighboring devices is available, improving the accuracy and timeliness of monitoring; in addition, by selecting a reference station as a constraint point and using the baseline vector observation values to solve the precise relative position relationship of each terminal in the network, this method can achieve an optimal solution in different observation environments, enhancing the adaptability and reliability of the monitoring system, solving the limitations of existing traditional satellite navigation systems in deformation monitoring, and improving the accuracy, timeliness, and environmental adaptability of monitoring through adaptive networking and collaborative solution to meet a wider range of application requirements.

[0005] To achieve the above technical effects, the technical solution adopted by the present invention is:

[0006] A front-end Beidou deformation monitoring collaborative solution method based on adaptive networking, comprising:

[0007] Determine the maximum number of baselines for real-time solution of the terminal based on the computing capabilities of the terminal device;

[0008] Calculate the correlation of surrounding devices based on the receiver, antenna type, and observation occlusion environment type attribute factors, and number all terminal devices;

[0009] The terminal device receives the Beidou observation data of other devices, and adaptively selects and resolves a forward baseline vector and a backward baseline vector when the data of adjacent devices is available;

[0010] Each terminal device receives and broadcasts the baseline vector results, selects the reference station as the constraint point, and uses the baseline vector observation values to resolve the precise relative position relationship in the network.

[0011] Preferably, in step S1, determining the maximum number of baselines for real-time resolution of the terminal based on the computing power of the terminal device includes:

[0012] Determine according to the computing power, CPU model, and memory value of the terminal device n The maximum number of baselines m for real-time resolution of the terminal.

[0013] Preferably, in step S2, calculating the correlation of surrounding devices based on the receiver, antenna type, and observation occlusion environment type attribute factors, and numbering all terminal devices includes:

[0014] a. Preset the reference station device number as Rb ( i ), according to the receiver, antenna type, and observation occlusion environment type attributes And those of other devices , use the following formula to calculate the correlation of surrounding devices, select the device with the maximum correlation value and number it as Rb ( i+ 1):

[0015] ;

[0016] In the formula, Represents the correlation;

[0017] b. Calculate the correlation of the remaining devices for the surrounding devices numbered Rb ( i+ 1) in step a, select the device with the maximum correlation value and number it as Rb ( i+ 2);

[0018] c. Loop through steps a to b above until all n Device numbers are determined.

[0019] Preferably, in step S3, the terminal device receives the Beidou observation data of other devices. When the data of neighboring devices is available, adaptively selecting and resolving a forward baseline vector and a backward baseline vector includes:

[0020] Taking m = 2 as an example, the device number Rb ( i ) Receive the Beidou observation data of other devices. When Rb ( i-1 ) and Rb ( i+1 ) When the device data is available, resolve a forward and a backward baseline vector for baseline solution; when Rb ( i+1 ) When the device data is unavailable, select Rb ( i+2 ) The device data for solution.

[0021] Furthermore, the device in the text all represents the terminal device.

[0022] The front-end Beidou deformation monitoring collaborative solution device based on adaptive networking, the device includes:

[0023] The terminal device computing power measurement module is used to measure the configuration and computing power of the terminal device;

[0024] The correlation calculation module is used to collect the property factors such as the receiver, antenna type, and observation occlusion environment type, and calculate the correlation magnitude of the surrounding devices;

[0025] The terminal device numbering module is used to select the device with the largest correlation among multiple devices after calculating the correlation and number it according to the preset rules;

[0026] The data availability determination module is used to determine whether the Beidou observation data received by the terminal device is available;

[0027] The terminal device baseline solution module is used to perform baseline solution;

[0028] The terminal networking position determination module is used to receive and broadcast the baseline vector result, select the reference station as the constraint point, and use the baseline vector observation value to resolve the precise relative position relationship in the network.

[0029] The beneficial effects of the present invention are as follows:

[0030] 1. The present invention proposes a front-end Beidou deformation monitoring collaborative solution scheme based on adaptive networking, which can quickly and efficiently resolve the relative position relationship between all monitoring points under limited data processing capabilities and communication bandwidth.

[0031] 2. The relative independent solution is calculated among the monitoring points of the present invention. Even if the data communication of some monitoring points is interrupted, it will not affect the solution among other monitoring points, thus improving the robustness of the overall network adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is the flowchart of the method of the present invention;

[0033] Figure 2 is the flowchart of the adaptive baseline solution of the embodiment of the present invention;

[0034] Figure 3 is the structural schematic diagram of the computer device provided in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] Embodiment 1:

[0036] As Figure 1 shown, a front-end Beidou deformation monitoring collaborative solution method based on adaptive networking includes:

[0037] Determine the maximum number of baselines for real-time solution by the terminal device based on the computing power of the terminal device;

[0038] Calculate the correlation of surrounding devices according to the receiver, antenna type, and observation occlusion environment type attribute factors, and number all terminal devices;

[0039] The terminal device receives the Beidou observation data of other devices. When the data of adjacent devices is available, adaptively select to solve a forward baseline vector and a backward baseline vector;

[0040] Each terminal device receives and broadcasts the baseline vector results, selects the reference station as the constraint point, and uses the baseline vector observation values to solve the precise relative position relationship in the network.

[0041] Preferably, in step S1, determining the maximum number of baselines for real-time solution by the terminal device based on the computing power of the terminal device includes:

[0042] Determine n m, the maximum number of baselines for real-time solution by the terminal according to the computing power of the terminal device, CPU model, and memory value.

[0043] Preferably, in step S2, calculating the correlation of surrounding devices according to the receiver, antenna type, and observation occlusion environment type attribute factors, and numbering all terminal devices includes:

[0044] a. Preset the device number of the reference station as Rb ( i ), according to the receiver, antenna type, and observation occlusion environment type attribute and the , calculate the correlation of peripheral devices using the following formula, select the device with the largest correlation value and number it as Rb ( i+ 1):

[0045] ;

[0046] In the formula, represents the correlation;

[0047] b. Calculate the correlation of the remaining devices for the peripheral devices numbered Rb ( i+ 1) using the formula in step a, and select the device with the largest correlation value and number it as Rb ( i+ 2);

[0048] c. Loop through steps a to b above until all n device numbers are determined.

[0049] As Figure 2 shown, in step S3, the terminal device receives the Beidou observation data of other devices. When the data of adjacent devices is available, adaptively select and solve a forward baseline vector and a backward baseline vector, including:

[0050] Taking m = 2 as an example, the device numbered Rb ( i ) receives the Beidou observation data of other devices. When Rb ( i-1 ) and Rb ( i+1 ) device data is available, perform baseline solution for a forward and a backward baseline vector; when Rb ( i+1 ) device data is unavailable, select Rb ( i+2 ) device data for solution.

[0051] Furthermore, the devices in the text all represent terminal devices.

[0052] Embodiment 2:

[0053] This embodiment provides a front-end Beidou deformation monitoring collaborative solution device based on adaptive networking. The device includes a terminal device computing power measurement module for measuring the configuration and computing power of the terminal device;

[0054] a correlation calculation module for collecting attribute factors such as receiver, antenna type, and observation occlusion environment type, and calculating the correlation of peripheral devices;

[0055] A terminal device numbering module, which is used to select the device with the largest correlation among multiple devices after calculating the correlation and number it according to a preset rule;

[0056] A data availability determination module, which is used to determine whether the Beidou observation data received by the terminal device is available;

[0057] A terminal device baseline solution module, which is used to perform baseline solution;

[0058] A terminal networking position determination module, which is used to receive and broadcast the baseline vector result, select a reference station as a constraint point, and use the baseline vector observation value to calculate the accurate relative position relationship in the network.

[0059] Embodiment 3:

[0060] The embodiment of the present invention further provides a computer device, which has the front-end Beidou deformation monitoring collaborative solution device based on adaptive networking described in the second embodiment above.

[0061] As Figure 3 shown, it is a schematic structural diagram of a computer device provided by an optional embodiment of the present invention. As Figure 3 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device such as a display device coupled to the interface. In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations. For example, as a server array, a set of blade servers, or a multi-processor system; Figure 3 Taking one processor 10 as an example.

[0062] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip; the above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0063] Among them, the memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0064] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device and the like. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely disposed relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0065] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memories.

[0066] The computer device further includes a communication interface 30 for communicating the computer device with other devices or a communication network.

Claims

1. A front-end Beidou deformation monitoring collaborative solution method based on adaptive networking, characterized in that: include: Based on the computing power of the terminal equipment, determine the maximum number of baselines that the terminal can solve in real time; According to the attributes of the receiver, antenna type and observation obstruction environment type, the correlation of surrounding devices is calculated and all terminal devices are numbered, including: a. The preset base station device number is R ( i ), according to the receiver, antenna type and observation occlusion environment type attributes and other equipment , use the following formula to calculate the correlation of the surrounding devices, select the device with the largest correlation value and number it R ( i+ 1): ； In the formula, Indicates relevance; b. Number the numbers using the formula in step a R ( i+ 1) Calculate the remaining device correlations of the peripheral devices and select the device with the largest correlation value as R ( i+ 2); c. Repeat the above steps a to b until all n Device number; The terminal device receives Beidou observation data from other devices. When the data of neighboring devices is available, it adaptively selects and solves a forward baseline vector and a backward baseline vector, including: The device with device number Rb(i+x) in the xth round of calculation receives BeiDou observation data from other devices. When the device data of Rb(i+x-1) and Rb(i+x+1) are available, a forward and a backward baseline vector are calculated. When the device data of Rb(i+x+1) is not available, the device data of Rb(i+x+2) is selected for calculation. Each terminal device receives and broadcasts the baseline vector results, selects the reference station as the constraint point, and uses the baseline vector observation value to solve the precise relative position relationship in the network.

2. According to the method of claim 1, the method is characterized in that: In step S1, based on the computing capability of the terminal device, determining the maximum number of baselines solved by the terminal in real time includes: Determine based on the computing power, CPU model and memory value of the terminal device n The maximum number of baselines m that can be solved in real time by a terminal.

3. The front-end Beidou deformation monitoring collaborative solution device based on adaptive networking as described in any one of claims 1 to 2 is characterized in that: The device includes: The terminal device computing power measurement module is used to measure the configuration and computing power of the terminal device; The correlation calculation module is used to collect the attribute factors of the receiver, antenna type and observation shielding environment type, and calculate the correlation size of the surrounding equipment; The terminal device numbering module is used to select the device with the greatest correlation from the multiple devices after the correlation is calculated and number it according to the preset rules; A data availability determination module is used to determine whether the Beidou observation data received by the terminal device is available; A terminal equipment baseline solution module, used for performing baseline solution; The terminal networking position determination module is used to receive and broadcast baseline vector results, select reference stations as constraint points, and use baseline vector observations to solve the precise relative position relationship in the network.

Images