Double-antenna BDS-R real-time water level monitoring method and integrated device

Abstract

The invention relates to a double-antenna BDS-R real-time water level monitoring method and an integrated device, and belongs to the technical field of GNSS remote sensing. The method comprises the following steps: data acquisition; data processing: firstly decoding the received data, and extracting a double-frequency pseudo range and a carrier phase observation value; a second step of performing quality control on the decoded data, including constraint of an elevation angle and an azimuth angle, cycle slip detection and the like, and eliminating non-water-surface reflection signals and abnormal data; a third step of establishing a double-difference observation equation based on the pseudo-range and carrier phase observation value, introducing a horizontal baseline constraint into the observation equation, and enhancing the resolving stability through virtual observation quantity; fourthly, Kalman filtering is used for achieving per-epoch baseline estimation, and a least square ambiguity decorrelation adjustment (LAMBDA) method is used for solving a baseline fixed solution; and finally calculating the water level of the current epoch by using the baseline vector. And transmitting data. According to the invention, the problems of poor real-time performance, complex deployment and high cost in the existing water level monitoring technology are solved.

Description

Technical Field

[0001] This invention belongs to the field of GNSS remote sensing technology, specifically relating to a dual-antenna BDS-R real-time water level monitoring method and integrated device. Background Technology

[0002] Water level measurement is of significant practical importance for sea level monitoring and water resource management. Traditional water level monitoring methods suffer from high costs and maintenance difficulties, especially in remote areas or extreme environments such as floods, where real-time and continuous monitoring is challenging. With the increasing application of Global Navigation Satellite Systems (GNSS), GNSS Reflection (GNSS-R) and GNSS Interferometric Reflection (GNSS-IR) measurements have provided new technological pathways for water level monitoring. GNSS-IR technology, based on signal-to-noise ratio (SNR), requires only a single receiver to automatically detect water level changes, but it is mostly suitable for post-processing and cannot meet the needs of real-time monitoring. In contrast, GNSS-R technology uses two antennas to receive direct and reflected GNSS signals respectively, combining geometric relationships with pseudorange and carrier phase observations, showing potential for real-time water level monitoring. However, its large-scale application remains limited by the need for low-cost, highly reliable, and easily deployed dedicated terminals.

[0003] Against this backdrop, the development of a hardware platform that integrates data acquisition, real-time processing, and result transmission is of great value in promoting the development of real-time water level monitoring technology. Summary of the Invention

[0004] The technical problem to be solved: To avoid the shortcomings of existing technologies, this invention provides a dual-antenna BDS-R real-time water level monitoring method and integrated device. The method of this invention solves the problems of poor real-time performance, complex deployment, and high cost in existing water level monitoring technologies by coordinating horizontal baseline constraints with Raspberry Pi and dual-mode communication.

[0005] The technical solution of this invention is: a dual-antenna BDS-R real-time water level monitoring method, comprising the following steps: Data acquisition: Direct signals from BeiDou navigation satellites are received via a right-hand circularly polarized (RHCP) antenna, and reflected signals from BeiDou navigation satellites reflected by the target water surface are received via a left-hand circularly polarized (LHCP) antenna. The phase centers of the RHCP and LHCP antennas are located on the same vertical line to optimize the signal reception geometry. The data processing procedure is as follows: The code phase information and carrier phase information of the direct and reflected signals are acquired in real time to construct the BeiDou pseudorange observation and carrier phase observation. Based on the BeiDou pseudorange and carrier phase observations, a dual-difference observation equation is established to eliminate satellite clock errors, receiver clock errors, and atmospheric delay errors. A horizontal baseline constraint is introduced into the dual-difference observation equation. By setting the virtual observation of the horizontal component of the baseline vector to zero, the stability and accuracy of the solution are enhanced, and the solution is adapted to the water surface undulation environment. Based on the dual-difference observation equation, extended Kalman filtering is used for epoch-by-epoch processing to estimate the baseline vector between the RHCP antenna and the LHCP antenna in real time; and based on the floating-point solution of the baseline vector in the current epoch, the LAMBDA algorithm is used to repair the ambiguity and obtain the fixed solution of the baseline vector. Based on the fixed solution of the baseline vector, the water surface height of the target water surface in the current epoch is calculated using geometric relationships; Data transmission: The calculated water level height is transmitted to the server or user terminal via 4G network or BeiDou short message dual-mode communication. A further technical solution of the present invention is as follows: In the data acquisition, the RHCP antenna is erected vertically towards the zenith, and the LHCP antenna is erected vertically towards the nadir. The RHCP antenna and the LHCP antenna adopt customized antenna modules to support multiple frequency signals of BeiDou B1I, B2I, B3I, B1C, B2a, and B2b. A further technical solution of the present invention is: after constructing the BeiDou pseudorange observation and carrier phase observation, it also includes data quality control, specifically, performing elevation angle constraints, azimuth angle constraints and cycle slip detection on the pseudorange observation and carrier phase observation to eliminate non-water surface reflection signals and abnormal data.

[0006] A further technical solution of the present invention is: the double-difference observation equation is expressed as:

[0007] in, These are double-difference observations between satellites and between stations; Represents pseudorange observations; Indicates the carrier wavelength; These are carrier phase observations; Indicates the distance between the receiver and the satellite; Indicates carrier phase ambiguity; To observe noise.

[0008] A further technical solution of the present invention is that the horizontal baseline constraint is achieved through the following virtual observations:

[0009] In the formula, , These are the observations from two directions, both set to 0; , Baseline vectors b Components in two directions; , These are the observation residuals in two directions, respectively.

[0010] A further technical solution of the present invention is: the time update process of the extended Kalman filter is expressed as:

[0011]

[0012]

[0013] in, Represents the state matrix, Represents the variance matrix. Represents the vector of parameters to be estimated. Indicates an epochal time. Indicates before measurement update, This indicates that after the measurement is updated, Represents the measurement model vector. This represents the measurement error weight matrix. This represents the error vector.

[0014] A further technical solution of the present invention is: the water level height h The calculation formula is:

[0015] in, b Indicates the length of the baseline vector. d This indicates the fixed distance between the phase centers of the RHCP antenna and the LHCP antenna. A further technical solution of the present invention is: in the data transmission, 4G network is used first for data transmission, and when 4G network is unavailable, it automatically switches to Beidou short message for data transmission to ensure communication reliability. A dual-antenna BDS-R real-time water level monitoring device for implementing the method, comprising: Data acquisition unit: includes RHCP antenna and LHCP antenna, used to receive direct and reflected signals; Data processing unit: Based on Raspberry Pi processor, used to perform data decoding, quality control, double-difference observation equation establishment, extended Kalman filtering, horizontal baseline constraint application, LAMBDA algorithm and water level calculation; Data transmission unit: includes a 4G module and a Beidou short message module, used for dual-mode communication; Power module: used to supply power to the device; The data acquisition unit connects to the Raspberry Pi processor via a TTL-to-USB module to achieve stable data communication.

[0016] A further technical solution of the present invention is that the data processing unit is also configured to perform real-time quality control on the decoded data, including elevation angle constraint, azimuth angle constraint and cycle slip detection, and the device is integrated in a protective chassis and has an external interface for connecting the antenna and communication module.

[0017] Beneficial effects The beneficial effects of this invention are as follows: Through collaborative innovation of software and hardware, this invention provides a dual-antenna BDS-R real-time water level monitoring method and integrated device, which achieves the following significant advantages compared to existing technologies: First, the high integration of the dual-antenna BDS-R real-time water level integrated monitoring device effectively reduces the difficulty of system deployment.

[0018] This invention integrates three major functional modules—data acquisition, real-time processing, and result transmission—into a single, complete "edge-cloud" solution. On the hardware side, a Raspberry Pi is used as the core processor, paired with a mature BeiDou module and a custom antenna, replacing the traditional high-cost, multi-device measurement system. This highly integrated design simplifies the device deployment process, enabling rapid installation in outdoor environments such as bridges and waterways, significantly improving the feasibility of large-scale, wide-area deployment.

[0019] Second, it achieves high-precision and high-fixed-rate real-time water level calculation, significantly improving the timeliness of monitoring.

[0020] This invention utilizes self-developed code written in C / C++ compiled on a Raspberry Pi to achieve real-time processing from data decoding to water level calculation. In data processing, a horizontal baseline constraint is introduced into the double-difference observation equation, and ambiguity is quickly fixed using Kalman filtering and the LAMBDA algorithm. These measures ensure the accuracy of water level results and change the traditional post-processing mode of GNSS-IR technology, providing technical support for flood warning and real-time water resource allocation.

[0021] Third, it has strong environmental adaptability, ensuring communication reliability in areas without network coverage.

[0022] This invention designs a dual-mode backup communication mechanism using 4G and BeiDou short message service. In areas with public network signals, the 4G module is prioritized for data backhaul, ensuring communication efficiency and low cost. In remote areas without terrestrial network coverage, the BeiDou short message module can be used for data transmission. This flexible communication design ensures that monitoring data can be stably and reliably transmitted back to the server or user terminal in any environment, solving the problem of "loss of connection" in extreme disasters or remote scenarios using traditional communication methods, and greatly expanding the applicable geographical range of the system. Attached Figure Description Figure 1 This is a block diagram of the dual-antenna BDS-R real-time integrated water level monitoring device.

[0023] Figure 2 This is a schematic diagram of the geometric principle of dual-antenna GNSS-R altimeter measurement.

[0024] Figure 3 This is a flowchart of real-time water level inversion data processing.

[0025] Figure 4 It is a prototype of the dual-antenna BDS-R real-time integrated water level monitoring device.

[0026] Figure 5 This is a water level result diagram.

[0027] Figure 6 This is a comparison chart of the measured water level and the radar level gauge readings. Detailed Implementation

[0028] The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the invention, and should not be construed as limiting the invention.

[0029] Currently, there are two main technical paths for water level monitoring based on GNSS reflection signals: GNSS interferometric reflection (GNSS-IR) and GNSS reflection (GNSS-R).

[0030] Firstly, GNSS-IR technology based on signal-to-noise ratio (SNR) analysis (such as that disclosed in CN120742354A) requires only a single antenna and receiver. It retrieves water level height by analyzing the SNR sequence formed by the interference of direct and reflected signals, using spectral analysis. While this technology has a simple hardware structure, its core algorithm relies on post-processing of long-term data series, making it unable to achieve real-time water level monitoring and unsuitable for applications with extremely high timeliness requirements, such as flood warnings.

[0031] Secondly, GNSS-R technology based on carrier phase double-difference measurement uses two antennas to receive direct and reflected signals respectively, and utilizes high-precision carrier phase observations, possessing the potential for real-time, high-precision altitude measurement. However, existing GNSS-R technology solutions still have significant drawbacks: Real-time performance and accuracy are difficult to balance: While some methods (such as CN118393541A) employ double-difference and extended Kalman filtering, and introduce virtual observations for quality control, they primarily focus on post-processing to verify reliable results. In dynamically changing water environments, the stability and fixation success rate of real-time solutions still need improvement. Rapid and accurate fixation of integer ambiguities is a key challenge in achieving real-time centimeter-level accuracy.

[0032] Low system integration and high deployment cost: Existing solutions mostly rely on high-precision geodetic receivers or distributed hardware combinations (such as the reference station and complex attitude calculation mentioned in CN115165029B), resulting in high system costs and complex deployment processes, making it difficult to promote and apply in scenarios such as rivers and reservoirs that require large-scale deployment.

[0033] Insufficient environmental adaptability: Especially in remote areas, without public network coverage, or under extreme disaster conditions, existing devices often lack reliable communication backup methods (CN115165029B mentions communication but does not emphasize redundancy design), and the long-term stable operation capability of the hardware platform in complex field environments is put to the test.

[0034] To address the above problems, this invention proposes a dual-antenna BDS-R real-time water level monitoring method, comprising the following steps: Data acquisition: Direct signals from BeiDou navigation satellites are received via a right-hand circularly polarized (RHCP) antenna, and reflected signals from BeiDou navigation satellites reflected by the target water surface are received via a left-hand circularly polarized (LHCP) antenna. The phase centers of the RHCP and LHCP antennas are located on the same vertical line to optimize the signal reception geometry. The data processing procedure is as follows: The code phase information and carrier phase information of the direct and reflected signals are acquired in real time to construct the BeiDou pseudorange observation and carrier phase observation. Based on the BeiDou pseudorange and carrier phase observations, a dual-difference observation equation is established to eliminate satellite clock errors, receiver clock errors, and atmospheric delay errors. A horizontal baseline constraint is introduced into the dual-difference observation equation. By setting the virtual observation of the horizontal component of the baseline vector to zero, the stability and accuracy of the solution are enhanced, and the solution is adapted to the water surface undulation environment. Based on the dual-difference observation equation, extended Kalman filtering is used for epoch-by-epoch processing to estimate the baseline vector between the RHCP antenna and the LHCP antenna in real time; and based on the floating-point solution of the baseline vector in the current epoch, the LAMBDA algorithm is used to repair the ambiguity and obtain the fixed solution of the baseline vector. Based on the fixed solution of the baseline vector, the water surface height of the target water surface in the current epoch is calculated using geometric relationships; Data transmission: The calculated water level height is transmitted to the server or user terminal via 4G network or BeiDou short message dual-mode communication. Preferably, after constructing the BeiDou pseudorange observation and carrier phase observation, data quality control is also included. Specifically, the pseudorange observation and carrier phase observation are subjected to elevation angle constraints, azimuth angle constraints, and cycle slip detection to eliminate non-water surface reflection signals and abnormal data. The present invention also proposes a dual-antenna BDS-R real-time water level monitoring device for implementing the method, comprising: Data acquisition unit: includes RHCP antenna and LHCP antenna, used to receive direct and reflected signals; Data processing unit: Based on Raspberry Pi processor, used to perform data decoding, quality control, double-difference observation equation establishment, extended Kalman filtering, horizontal baseline constraint application, LAMBDA algorithm and water level calculation; Data transmission unit: includes a 4G module and a Beidou short message module, used for dual-mode communication; Power module: used to supply power to the device; The data acquisition unit connects to the Raspberry Pi processor via a TTL-to-USB module to achieve stable data communication.

[0035] Preferably, the data processing unit is further configured to perform real-time quality control on the decoded data, including elevation angle constraints, azimuth angle constraints, and cycle slip detection, and the device is integrated into a protective chassis and has an external interface for connecting the antenna and communication module.

[0036] The above technical solution will be further explained below with reference to the accompanying drawings: In one embodiment, refer to Figure 1 As shown, a dual-antenna BDS-R real-time integrated monitoring device integrates a data acquisition unit, a data processing unit, and a data transmission unit. A GNSS board acquires raw observation data from both antennas and sends it to a Raspberry Pi. The Raspberry Pi performs real-time calculations, and after the calculations are completed, the monitoring results are transmitted back using 4G / BeiDou short messages.

[0037] In one embodiment, a dual-antenna BDS-R real-time water level monitoring method mainly includes two core technologies: (1) Dual-antenna BDS-R real-time integrated water level monitoring device The dual-antenna BDS-R real-time water level integrated monitoring device consists of three parts: a GNSS data acquisition unit, a Raspberry Pi processing unit, and a 4G / BeiDou short message transmission unit. The hardware structure is as follows: Figure 1 As shown.

[0038] The GNSS data acquisition section employs customized RHCP and LHCP antenna modules, used to receive direct and reflected signals, respectively. The UMD982 single-BeiDou high-precision positioning and orientation module from Hexin Xingtong is selected, capable of simultaneously tracking multiple frequency signals including B1I, B2I, B3I, B1C, B2a, and B2b. Furthermore, the UMD982 has multiple UART communication interfaces to meet the requirements of simultaneous dual-antenna data acquisition. Since the UART interface output is TTL level, a TTL-to-USB module is used to convert the signal level for stable communication with the Raspberry Pi.

[0039] The core processing unit uses a Raspberry Pi Zero 2W as its computing and control platform. Equipped with a quad-core ARM Cortex-A53 processor and 512MB of LPDDR2 memory, this device provides sufficient computing power to handle real-time GNSS data streams. Its rich GPIO interfaces and standard USB interface greatly facilitate device integration and functional expansion. To enable remote, real-time data transmission, a Zero_4G Cat1-Hub expansion board, perfectly matched in size, is provided for the Raspberry Pi. This expansion board connects directly to the Raspberry Pi via pins, featuring a compact and reliable structure that simultaneously provides power and data communication. The expansion board supports 4G IoT cards, ensuring communication speed and coverage while also offering advantages in low power consumption and low cost, making it ideal for building stable, long-term data transmission links in environments without Wi-Fi coverage, such as in the wild. Furthermore, the device integrates a BeiDou short message module, enabling real-time transmission of computation results even in network-free environments.

[0040] (2) Dual-antenna BDS-R real-time water level monitoring method GNSS-R dual-antenna water level inversion requires the use of two GNSS positioning antennas, the geometric principle diagram is as follows: Figure 2 As shown. The upper antenna uses RHCP mode to receive direct signals, and the lower antenna uses LHCP mode to receive reflected signals. The phase centers of the two antennas are located on the same vertical line. Water surface reflection can be approximated as specular reflection. According to the principle of specular reflection, the LHCP antenna can project a virtual antenna. The mirrored LHCP antenna and the RHCP antenna form a short baseline vector. The baseline length will vary with the height from the phase center of the LHCP antenna to the reflector surface. The baseline changes with the change of the antenna. Since the upper and lower antennas are located on the same vertical line, the horizontal component of the baseline can be approximated as 0, and the vertical component... .therefore, and The geometric relationship can be expressed as: (1) In the formula, The distance between the phase centers of the two antennas. For short baselines, differential observations can eliminate satellite clock errors, receiver clock errors, and mitigate the effects of ionospheric delay errors, tropospheric delay errors, and satellite orbital errors. The double-difference observation equation can be expressed as: (2) in, These are double-difference observations between satellites and between stations; Represents pseudorange observations; Indicates the carrier wavelength; These are carrier phase observations; Indicates the distance between the receiver and the satellite; Indicates carrier phase ambiguity; To account for observation noise, since the dual antennas are placed vertically, the horizontal component of the baseline is theoretically zero. Therefore, constraints are imposed on the east and north components in the station-centric coordinate system, two virtual observations are added, and these are substituted into the double-difference observation equation: (3) In the formula, , These are the observations from two directions, both set to 0; , Baseline vectors Components in two directions; , These are the observation residuals in two directions, respectively.

[0041] After the double-difference observation equations are constructed, the position parameters in the state equations are solved epoch-by-epoch using Kalman filtering to obtain the floating-point solution of the baseline vector. Then, the ambiguity is fixed using the LAMBDA method to obtain the baseline length. Finally, its vertical component is substituted into formula (4) to calculate the water level height. .

[0042] The water level inversion program uses self-developed code in C / C++ and is compiled and implemented on the Raspberry Pi platform. It mainly includes the following five steps: ① The program decodes the raw data collected by the dual antennas in real time, extracting the dual-frequency carrier phase and pseudorange observations; ② Quality control is performed on the decoding results, including satellite elevation and azimuth constraints, cycle slip detection, non-water surface reflection, and abnormal data detection and removal; ③ Horizontal baseline constraints are introduced during the solution process, and the solution stability is enhanced through virtual observations; ④ Epoch-by-epoch baseline estimation is achieved using extended Kalman filtering, and the fixed solution of the baseline is obtained using the LAMBDA method; ⑤ The water level is calculated in real time using formula (4), and the solution results are uploaded to the server via a 4G module or BeiDou short message to achieve real-time water level monitoring. Its data processing flowchart is as follows: Figure 3 As shown.

[0043] Embodiments of the present invention include a data acquisition unit, a data processing unit, and a result transmission unit, which are integrated into a dual-antenna BDS-R real-time integrated water level monitoring device. A prototype device is shown below. Figure 4 As shown.

[0044] The data processing unit processes the data in five steps to obtain the final water level, as shown below. Figure 5 As shown. ① The received data is decoded to extract dual-frequency pseudorange and carrier phase observations; ② Quality control is performed on the decoded data, including constraints on elevation and azimuth angles, cycle slip detection, etc.; ③ A double-difference observation equation is established based on the pseudorange and carrier phase observations, and a horizontal baseline constraint is introduced into the observation equation; ④ Kalman filtering is used to achieve epoch-by-epoch baseline estimation, and the least squares ambiguity reduction adjustment (LAMBDA) method is used to obtain the fixed baseline solution; ⑤ The water level of the current epoch is calculated using the baseline vector. Figure 5 and Figure 6 It can be seen that the measured results of the present invention are significantly consistent with the actual water level measurement results, indicating that the device of the present invention has good continuity and high measurement accuracy in real-time water level measurement.

[0045] Finally, the water level data is uploaded to the server or user terminal in real time via 4G or Beidou short message module.

[0046] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A dual-antenna BDS-R real-time water level monitoring method, characterized in that, Includes the following steps: Data acquisition: Direct signals from BeiDou navigation satellites are received via a right-hand circularly polarized (RHCP) antenna, and reflected signals from BeiDou navigation satellites reflected by the target water surface are received via a left-hand circularly polarized (LHCP) antenna. The phase centers of the RHCP and LHCP antennas are located on the same vertical line to optimize the signal reception geometry. The data processing procedure is as follows: The code phase information and carrier phase information of the direct and reflected signals are acquired in real time to construct the BeiDou pseudorange observation and carrier phase observation. Based on the BeiDou pseudorange and carrier phase observations, a dual-difference observation equation is established to eliminate satellite clock errors, receiver clock errors, and atmospheric delay errors. A horizontal baseline constraint is introduced into the dual-difference observation equation. By setting the virtual observation of the horizontal component of the baseline vector to zero, the stability and accuracy of the solution are enhanced, and the solution is adapted to the water surface undulation environment. Based on the dual-difference observation equation, extended Kalman filtering is used for epoch-by-epoch processing to estimate the baseline vector between the RHCP antenna and the LHCP antenna in real time; and based on the floating-point solution of the baseline vector in the current epoch, the LAMBDA algorithm is used to repair the ambiguity and obtain the fixed solution of the baseline vector. Based on the fixed solution of the baseline vector, the water surface height of the target water surface in the current epoch is calculated using geometric relationships; Data transmission: The calculated water level height is transmitted to the server or user terminal via 4G network or BeiDou short message dual-mode communication.

2. The dual-antenna BDS-R real-time water level monitoring method according to claim 1, characterized in that: During the data acquisition, the RHCP antenna is erected vertically towards the zenith, and the LHCP antenna is erected vertically towards the nadir. Both the RHCP and LHCP antennas use customized antenna modules that support multiple frequency signals from BeiDou B1I, B2I, B3I, B1C, B2a, and B2b.

3. The dual-antenna BDS-R real-time water level monitoring method according to claim 1, characterized in that: After constructing the BeiDou pseudorange and carrier phase observations, data quality control is also included. Specifically, the pseudorange and carrier phase observations are subjected to elevation angle constraints, azimuth angle constraints, and cycle slip detection to eliminate non-water surface reflection signals and abnormal data.

4. The dual-antenna BDS-R real-time water level monitoring method according to claim 1, characterized in that: The double-difference observation equation is expressed as: in, These are double-difference observations between satellites and between stations; Represents pseudorange observations; Indicates the carrier wavelength; These are carrier phase observations; Indicates the distance between the receiver and the satellite; Indicates carrier phase ambiguity; To observe noise.

5. The dual-antenna BDS-R real-time water level monitoring method according to claim 1, characterized in that: The horizontal baseline constraint is achieved through the following virtual observations: In the formula, , These are the observations from two directions, both set to 0; , Baseline vectors b Components in two directions; , These are the observation residuals in two directions, respectively.

6. The dual-antenna BDS-R real-time water level monitoring method according to claim 1, characterized in that: The time update process of the extended Kalman filter is represented as follows: in, Represents the state matrix, Represents the variance matrix. Represents the vector of parameters to be estimated. Indicates an epochal time. Indicates before measurement update, This indicates that after the measurement is updated, Represents the measurement model vector. This represents the measurement error weight matrix. This represents the error vector.

7. The dual-antenna BDS-R real-time water level monitoring method according to claim 6, characterized in that: The water level height h The calculation formula is: in, b Indicates the length of the baseline vector. d This indicates the fixed distance between the phase centers of the RHCP antenna and the LHCP antenna.

8. The dual-antenna BDS-R real-time water level monitoring method according to claim 1, characterized in that: During data transmission, 4G network is used first. When 4G network is unavailable, it automatically switches to BeiDou short message service for data transmission to ensure communication reliability.

9. A dual-antenna BDS-R real-time water level monitoring device for implementing the method, used to execute the dual-antenna BDS-R real-time water level monitoring method according to any one of claims 1-8, characterized in that, include: Data acquisition unit: includes RHCP antenna and LHCP antenna, used to receive direct and reflected signals; Data processing unit: Based on Raspberry Pi processor, used to perform data decoding, quality control, double-difference observation equation establishment, extended Kalman filtering, horizontal baseline constraint application, LAMBDA algorithm and water level calculation; Data transmission unit: includes a 4G module and a Beidou short message module, used for dual-mode communication; Power module: used to supply power to the device; The data acquisition unit connects to the Raspberry Pi processor via a TTL-to-USB module to achieve stable data communication.

10. The apparatus according to claim 9, characterized in that: The data processing unit is also configured to perform real-time quality control on the decoded data, including elevation angle constraints, azimuth angle constraints, and cycle slip detection. The device is integrated into a protective enclosure and has an external interface for connecting the antenna and communication module.

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