Water level management method and system for unattended water level station based on Beidou positioning

Abstract

The application discloses a Beidou positioning-based unattended water level station water level management method and system, relates to the water level management field, and the method comprises the following steps: collecting water level monitoring data of a target water area through an unattended water level station for any unattended water level station in the target water area; receiving a Beidou direct signal and a Beidou reflected signal, and solving reference elevation data of the location of the unattended water level station; completing data offset correction of the water level monitoring data to obtain standard water level data; completing water level inversion calculation according to the Beidou reflected signal, and completing equipment state checking of the unattended water level station to obtain an equipment checking result; and generating a water level station management strategy of all unattended water level stations in the target water area according to the equipment checking result, wherein the water level station management strategy comprises a resource management strategy and a data management strategy. The application can effectively optimize the overall operation energy efficiency of the unattended water level station under the condition of limited resources.

Description

Technical Field

[0001] This application relates to the field of water level management, and in particular to a method and system for managing water levels at unmanned water level stations based on BeiDou positioning. Background Technology

[0002] In the field of hydrological monitoring, accurate acquisition and long-term reliable management of water level data are of great significance for flood control and disaster reduction, water resource allocation, water conservancy project safety, and ecological protection. Traditional water level monitoring relies heavily on manual observation or manned monitoring stations with fixed infrastructure, which suffers from high deployment costs, maintenance difficulties, and limited coverage. In recent years, with the development of the Internet of Things and unmanned technology, unmanned water level stations have been gradually applied to water level monitoring in remote or harsh environments. They collect water level data through automatic sensors and transmit it remotely via communication networks, significantly improving the level of automation in monitoring. However, due to the lack of human intervention, it is difficult to detect abnormalities in the sensors within unmanned water level stations in a timely manner. This results in abnormal sensors still consuming resources for data acquisition and transmission, potentially leading to a large amount of invalid data transmission and resource waste. Especially under resource constraints, this can also reduce the overall operational efficiency of all unmanned water level stations in the target water area. Summary of the Invention

[0003] This application provides a method and system for managing water levels at unmanned water level stations based on BeiDou positioning, which is used to optimize the overall operational efficiency of unmanned water level stations under resource constraints.

[0004] To achieve the above objectives, the embodiments of this application adopt the following technical solutions: Firstly, a method for managing water levels at unmanned water level stations based on BeiDou positioning is provided, the method comprising: For any unmanned water level station within the target water area, water level monitoring data of the target water area is collected through the unmanned water level station. It receives direct BeiDou signals and reflected BeiDou signals, and calculates the benchmark elevation data of the location of the unmanned water level station based on the direct BeiDou signals. Based on the benchmark elevation data, the data offset of the water level monitoring data is corrected to obtain the standard water level data; The water level inversion calculation is completed based on the BeiDou reflected signal, and the equipment status verification of the unmanned water level station is completed by combining the water level inversion calculation results and water level monitoring data to obtain the equipment verification results. Based on the equipment calibration results, a water level station management strategy is generated for all unmanned water level stations within the target water area. The water level station management strategy includes a resource management strategy and a data management strategy.

[0005] Optionally, calculating the benchmark elevation data of the location of the unmanned water level station based on the BeiDou direct signal includes the following steps: Interpolation was used to repair the jumps in the BeiDou direct signal, resulting in a continuous direct signal. The carrier phase and pseudorange observations of the continuous direct signal are extracted, and the ionospheric delay of the carrier phase and pseudorange observations is eliminated by using the ionization cancellation algorithm to obtain the reference carrier phase and reference pseudorange observations. By combining reference carrier phase and reference pseudorange observations and using satellite positioning algorithms, the positioning solution of continuous direct signals is completed, and the reference elevation data of the location of the unmanned water level station is calculated based on the positioning solution stage.

[0006] Optionally, the data offset correction of the water level monitoring data based on the benchmark elevation data to obtain standard water level data includes the following steps: The data trend analysis of the benchmark elevation data is extracted using a sliding window, and the elevation correction is calculated based on the data trend analysis results. Align the benchmark elevation data with the water level monitoring data using timestamps; Based on the elevation correction, the water level monitoring data that has been aligned with the completed timestamps are corrected for monitoring drift to obtain standard water level data.

[0007] Optionally, the water level inversion calculation is performed based on the BeiDou reflected signal, and the equipment status verification of the unmanned water level station is completed by combining the water level inversion calculation results with water level monitoring data. The equipment verification results include the following steps: The carrier phase data of the direct BeiDou signal and the reflected BeiDou signal are retrieved by the satellite receiver of the unmanned water level station, and the carrier phase difference between the direct BeiDou signal and the reflected BeiDou signal is calculated based on the carrier phase data. The signal path spacing between the direct BeiDou signal and the reflected BeiDou signal is calculated based on the carrier phase difference. The water level reflection data of the target water area was calculated by combining the benchmark elevation data and the signal path spacing; Calculate the water level difference between standard water level data and water level reflection data; Determine the time of water level anomalies at unmanned water level stations based on water level difference data; If the abnormal water level lasts for a longer period than a preset time threshold, the unmanned water level station is determined to be in an abnormal equipment state. If the abnormal water level time is less than or equal to the time threshold, the unmanned water level station is determined to be in normal condition. If the unmanned water level station is in an abnormal state, the abnormality type of the unmanned water level station shall be determined according to the standard water level data, and the abnormality type shall be output as the equipment verification result. If the unmanned water level station is in normal operating condition, then the normal operating condition will be output as the equipment verification result.

[0008] Optionally, determining the type of equipment malfunction at an unmanned water level station based on standard water level data includes the following steps: Time series analysis of standard water level data is performed to obtain the time series variation status of standard water level data; If the time-series change of the standard water level data is in a time-stable state, then it is determined that there is an equipment failure anomaly at the unmanned water level station. If the time-series change status of the standard water level data is in a time-series drift state, it is determined that there is an equipment zero-point anomaly at the unmanned water level station. If the time-series change state of the standard water level data is a time-series pulse state, it is determined that there is equipment interference anomaly at the unmanned water level station.

[0009] Optionally, generating a water level management strategy for all unmanned water level stations within the target water area based on the equipment calibration results includes the following steps: Collect communication transmission data from communication equipment within the unmanned water level station; The health status of the unmanned water level station's water level monitoring was determined by combining communication transmission data and equipment verification results. Based on the health status of water level monitoring, complete the dynamic allocation of power and bandwidth for all unmanned water level stations in the target water area, and integrate the results of dynamic allocation of power and bandwidth into a resource management strategy for all unmanned water level stations. Based on the water level monitoring health status, complete the data transmission configuration for all unmanned water level stations, and integrate the data transmission configuration results into a data management strategy for all unmanned water level stations.

[0010] Optionally, determining the health status of an unmanned water level station by combining communication transmission data and equipment verification results includes the following steps: The current wave characteristic parameters of the communication transmission data are extracted, and the communication reliability of the unmanned water level station is calculated by combining the current wave characteristic parameters with the pre-acquired current wave reference parameters. The monitoring accuracy of unmanned water level stations is determined based on water level difference data; A time-series stability analysis was performed on the water level difference data, and the monitoring stability of the unmanned water level station was determined based on the stability analysis results. The monitoring anomaly level of the unmanned water level station is determined based on the equipment calibration results; The health status of unmanned water level stations is calculated by combining communication reliability, monitoring accuracy, monitoring stability, and monitoring anomaly.

[0011] Optionally, the dynamic allocation of power and bandwidth for all water level monitoring equipment within the unmanned water level station based on the water level monitoring health status includes the following steps: Obtain all power supply and communication equipment parameters within the unmanned water level station; Determine the total available electrical energy of all unmanned water level stations in the target water area within a preset time period based on the power supply equipment parameters; Energy priorities are allocated to all unmanned water level stations based on their water level monitoring health status. Dynamic allocation of power for all unmanned water level stations is completed by combining energy priorities and the total amount of available power. The total available bandwidth of all unmanned water level stations within a preset time period is calculated based on the communication equipment parameters. Dynamic bandwidth allocation for all unmanned water level stations is completed based on the total available bandwidth and energy priority.

[0012] Secondly, this application provides a machine-readable storage medium storing instructions that cause a machine to execute the unmanned water level management method for a water level station based on BeiDou positioning as described in the first aspect.

[0013] Thirdly, this application provides an unmanned water level management system for water level stations based on BeiDou positioning, including: The memory is configured to store instructions; and The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the unmanned water level management method for water level stations based on BeiDou positioning as described in the first aspect.

[0014] The above technical solution acquires water level monitoring data from unmanned water level stations, providing a foundational data input for all subsequent analyses. Then, the received BeiDou direct signal is used to accurately calculate the benchmark elevation data of the unmanned water level station, overcoming the limitations of traditional water level monitoring that relies on ground leveling. This corrects for data drift caused by geological subsidence, equipment displacement, and other factors, thereby improving the accuracy of water level monitoring and preventing large errors in water level monitoring data from affecting subsequent water management decisions. Receiving BeiDou reflected signals is primarily used for water level monitoring equipment status verification and water level monitoring data quality control. Utilizing the interference phenomenon between the BeiDou satellite signal reflected from the water surface and the direct signal, water level reflection data is retrieved. Since BeiDou reflection signal verification is a non-contact self-checking method, it is unaffected by anomalies in the water level monitoring equipment itself. Therefore, based on the water level reflection data, anomalies in the water level monitoring equipment can be accurately identified, avoiding invalid data acquisition and transmission (such as continuously sending erroneous data) caused by sensor malfunctions. The generation of water level station management strategies further optimizes the energy allocation and data transmission settings of water level monitoring equipment, concentrating limited energy on normally operating water level monitoring equipment and reducing unnecessary communication bandwidth and power consumption. In summary, this application effectively reduces communication bandwidth and power consumption while further improving the overall operational energy efficiency of unattended water level stations.

[0015] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0016] Figure 1 A schematic diagram illustrating the process of water level management at an unmanned water level station based on BeiDou positioning, provided as an embodiment of this application; Figure 2 This is a flowchart illustrating a method for calculating benchmark elevation data provided in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0019] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0020] Figure 1 The illustration schematically shows a flowchart of a method for managing water levels at an unmanned water level station based on BeiDou positioning, according to an embodiment of this application. Figure 1 As shown in the figure, this application provides a method for water level management of unmanned water level stations based on BeiDou positioning. The method may include the following steps: S101. For any unmanned water level station in the target water area, collect water level monitoring data of the target water area through the unmanned water level station. S102. Receive BeiDou direct signal and BeiDou reflected signal, and calculate the benchmark elevation data of the location of the unmanned water level station based on the BeiDou direct signal; S103. Based on the benchmark elevation data, complete the data offset correction of the water level monitoring data to obtain the standard water level data; S104. Based on the BeiDou reflected signal, complete the water level inversion calculation, and combine the water level inversion calculation results with the water level monitoring data to complete the equipment status verification of the unmanned water level station and obtain the equipment verification results. S105. Generate a water level management strategy for all unmanned water level stations in the target water area based on the equipment calibration results. The water level management strategy includes a resource management strategy and a data management strategy.

[0021] In this embodiment, the water level monitoring data is collected by water level sensors installed at the unmanned water level station. Both the direct BeiDou signal and the reflected BeiDou signal originate from the B1C frequency signal emitted by the BeiDou-3 satellite and are then received by a satellite receiver. Since the direct BeiDou signal may experience signal jumps during transmission due to obstruction, multipath effects, etc., signal jumps refer to sudden glitches or interruptions in the direct BeiDou signal. Therefore, interpolation methods are needed to repair these jumps and obtain a continuous direct signal. Commonly used interpolation methods for jump repair include linear interpolation, polynomial interpolation, and spline interpolation. Next, the reference carrier phase and reference pseudorange observations of the continuous direct signal are extracted. Combining these observations with a satellite positioning algorithm, the positioning of the continuous direct signal is calculated, and the reference elevation data for the location of the unmanned water level station is calculated based on the positioning calculation. After calculating the reference elevation data, the data offset of the water level monitoring data is corrected using the reference elevation data to obtain standard water level data.

[0022] Next, carrier phase data of the BeiDou direct signal and the BeiDou reflected signal are simultaneously retrieved from two channels in the satellite receiver at the same time. The carrier phase difference is input into the path spacing calculation formula to obtain the signal path spacing between the BeiDou direct signal and the BeiDou reflected signal. Then, the reference elevation data and the signal path spacing are substituted into the core formula for reflection water level inversion to calculate the water level reflection data of the target water area. The water level reflection data refers to the water level data calculated through inversion. Next, the water level difference between the standard water level data and the water level reflection data at the same time is calculated. The water level difference values ​​at all times are integrated to obtain the water level difference data. Then, the water level difference data is checked for anomalies in chronological order. If the duration of the water level difference value being greater than a preset difference threshold is greater than a preset time threshold, where the difference threshold can be set to 100cm and the time threshold can be set to 20 minutes, then the water level monitoring equipment of the unmanned water level station is determined to be in an abnormal state. If there is no abnormal water level time with a difference greater than the preset difference threshold, or if the abnormal water level time is less than or equal to the preset time threshold, the water level monitoring equipment of the unmanned water level station is determined to be in normal condition. If the water level monitoring equipment is in an abnormal condition, the type of equipment abnormality needs to be determined based on standard water level data. Equipment abnormality types include equipment failure, zero-point abnormality, and interference abnormality, and the type of equipment abnormality is output as the equipment verification result. If the water level monitoring equipment is in normal condition, the normal condition is directly output as the equipment verification result.

[0023] After calculating the equipment calibration results for all unmanned water level stations, the water level monitoring health of each station is determined based on these results. Energy priorities are then assigned to each station according to its water level monitoring health; higher health results result in higher energy priority. Similarly, the total available bandwidth for all stations is calculated. When available bandwidth is insufficient, priority is given to ensuring bandwidth for stations with higher health. Next, data transmission configuration is completed for all stations based on their water level monitoring health. Higher health results correspond to higher data transmission frequencies, while lower health results in lower frequencies. For example, healthy stations collect data every 10 minutes and transmit it immediately, sub-healthy stations collect data every 30 minutes and transmit it every hour, and faulty stations only need to report their fault status to minimize resource consumption.

[0024] In one embodiment, reference is made to Figure 2 The steps for calculating the benchmark elevation data of the location of the unmanned water level station based on the direct BeiDou signal are as follows: S201. Use interpolation to repair the jumps in the BeiDou direct signal and obtain a continuous direct signal. S202. Extract the carrier phase and pseudorange observations of the continuous direct signal, and use the ionospheric elimination algorithm to complete the elimination of the ionospheric delay of the carrier phase and pseudorange observations, so as to obtain the reference carrier phase and reference pseudorange observations; S203. Combine the reference carrier phase and reference pseudorange observations and use the satellite positioning algorithm to complete the positioning calculation of the continuous direct signal, and calculate the reference elevation data of the location of the unmanned water level station according to the positioning calculation stage.

[0025] In this embodiment, since the Beidou direct signal may have signal jumps during the transmission process due to reasons such as being blocked by buildings and trees, or the multipath effect when the signal reaches the satellite receiver after being reflected by other objects. Signal jump means that the Beidou direct signal suddenly appears with glitches or interruptions. Signal jumps will destroy the continuity of the signal, resulting in the distortion of the subsequently extracted carrier phase and pseudorange observations, and further affecting the positioning accuracy. Therefore, interpolation methods need to be used to repair the jumps. Commonly used interpolation methods for jump repair include linear interpolation, polynomial interpolation, spline interpolation, etc. Taking the linear interpolation method as an example, assume that the last normal observation value before the jump is Y1, the time is T1, the first normal observation value after the jump is Y2, and the time is T2. Then the interpolation Y during the jump period is Y = Y1+(T - T1)×(Y2 - Y1) / (T2 - T1), where the time during the jump period is T, and T1 < T < T2. After completing the jump repair, the signal can be further smoothed through low-pass filtering (such as moving average filtering) to reduce the small fluctuations introduced by interpolation and obtain a continuous direct signal.

[0026] Then extract the carrier phase and pseudorange observations of the continuous direct signal. The carrier phase refers to the change amount of the phase of the Beidou direct signal during the propagation process. The coherent accumulation technology can be used to accumulate the phases of the continuous direct signal within a coherent integration time (such as 1 millisecond), and then perform a correlation operation on the accumulated direct signal and the locally generated reference carrier signal with the same frequency as the satellite carrier frequency, so as to obtain the carrier phase of the continuous direct signal. The pseudorange observation value is the virtual distance from the satellite to the satellite receiver calculated by the satellite receiver by measuring the time delay of the satellite signal from transmission to reception and combining the accurate time when the satellite is launched. The ionosphere is the ionized region in the upper atmosphere of the earth. When the satellite signal passes through the ionosphere, refraction will occur, resulting in an overestimated pseudorange measurement value and a systematic deviation in the carrier phase, that is, the ionospheric delay error. The ionospheric elimination algorithm can be used to eliminate the ionospheric delay error of the carrier phase and pseudorange observations. Commonly used ionospheric elimination algorithms include dual-frequency observation technology, differential processing technology, etc. Taking the dual-frequency observation technology as an example, the dual-frequency observation technology refers to using the difference in the influence of signals with different frequencies in the continuous direct signal on the ionospheric delay to achieve the joint correction of the pseudorange observation value and the carrier phase. Specifically, assume that the pseudorange observation value of the L1 frequency band is The pseudorange observation value of the L2 band is The ionospheric delay error of pseudorange observations can be calculated using the following formula: in, and These are the signal frequencies of the L1 and L2 frequency bands, respectively.

[0027] Next, the pseudorange observations are corrected using the ionospheric delay error to obtain the baseline pseudorange observations. The specific correction formula is as follows: Similarly, ionospheric delay correction of the carrier phase can be achieved through dual-frequency combination. Let the carrier phase of the L1 band be... The carrier phase of the L2 band is The formula for calculating the phase of the reference carrier without ionospheric delay is as follows: Next, combining the reference carrier phase and reference pseudorange observations with satellite positioning algorithms, the positioning solution for continuous direct signals is completed, and the reference elevation data of the unmanned water level station location is calculated based on the positioning solution stage. Specifically, the positioning solution observation equation is first constructed, the core of which is to establish a mathematical relationship between the reference pseudorange observations and the three-dimensional coordinates of the satellite receiver (parameters to be solved). Observation equations for the reference pseudorange observations and the reference carrier phase are constructed separately and integrated into a joint solution model as the positioning solution observation equation. After constructing the positioning solution observation equation model, initial positioning solution is performed. Initial values ​​can be obtained by using pseudorange single-point positioning. Then, the positioning solution observation equation is solved using the least squares algorithm. The core is to minimize the weighted sum of squares of the observation residual vector, where the observation values ​​include the reference carrier phase and the reference pseudorange observations. When the pseudorange residual is less than or equal to a preset pseudorange residual threshold (e.g., 0.5 meters) and the phase residual is less than or equal to a preset phase threshold (e.g., 0.1 cycles), the initial values ​​of the satellite receiver's three-dimensional coordinates are output. Next, in the precise positioning calculation stage, the initial three-dimensional coordinates of the satellite receiver are substituted into the positioning calculation observation equations, and the parameter corrections are solved again using the least squares algorithm. The parameter corrections include coordinate corrections and clock error corrections. When the parameter corrections are less than preset thresholds, such as coordinate correction < 0.1 mm and clock error correction < 0.1 ns, the precise three-dimensional coordinates of the satellite receiver are obtained. Then, a quasi-geoid model (such as the CGCS2000 quasi-geoid model for the Chinese region) is used to obtain the quasi-geoid difference of the satellite receiver. Finally, the vertical axis coordinates of the precise three-dimensional coordinates are subtracted from the quasi-geoid difference of the satellite receiver to obtain the elevation data. Since the positioning calculation of continuous direct signals outputs continuous elevation data, with a sampling frequency typically between 1 Hz and 10 Hz, time-series smoothing processing is required to eliminate the influence of random noise and obtain baseline elevation data. This time-series smoothing can be achieved using sliding window mean filtering or Kalman filtering. By using the above method, the baseline elevation data of the observation point where the unmanned water level station is located can be calculated accurately over a long period of time. This can identify whether the observation point where the unmanned water level station is located, i.e. the monitoring point of the water level monitoring equipment, has experienced subsidence, thus avoiding the distortion of water level monitoring data due to subsidence.

[0028] In one embodiment, the process of correcting the data offset of water level monitoring data based on benchmark elevation data to obtain standard water level data includes the following steps: The data trend analysis of the benchmark elevation data is extracted using a sliding window, and the elevation correction is calculated based on the data trend analysis results. Align the benchmark elevation data with the water level monitoring data using timestamps; Based on the elevation correction, the water level monitoring data that has been aligned with the completed timestamps are corrected for monitoring drift to obtain standard water level data.

[0029] In this embodiment, based on the sampling frequency of the benchmark elevation data (usually 1Hz~10Hz) and the time scale of elevation changes, since subsidence and topographic changes are mostly slow processes with cycles generally measured in weeks, the core parameters of the sliding window are configured. For example, 10~20 consecutive sampling points can be selected as a window. Then, a linear weighting strategy with high weight for recent data and low weight for distant data is used to assign weights to the sampling points in each window. The weight of the i-th sampling point in the window (i ranges from 1 to the window size, where 1 represents distant data) can be set to i divided by the total number of sampling points in the window to ensure that the latest data has a greater impact on trend calculation and to match the real-time nature of elevation changes. Next, the window sliding mode is set, which can be a point-by-point sliding mode. That is, every time a new benchmark elevation data is acquired, the window moves forward by one sampling point, discarding the earliest data and retaining the latest data in the window to achieve continuous trend tracking.

[0030] Simultaneously, a Kalman filter algorithm can be introduced to fuse the weighted baseline elevation data within the window, further suppressing random noise such as measurement errors and instantaneous environmental interference, outputting a smooth trend curve. For the baseline elevation data within each sliding window, weighting is first performed, and then the Kalman filter predicts the elevation trend value of the current window based on the optimal trend value and trend change rate of the previous window. Next, the weighted elevation value within the window is used as the observed value and compared with the predicted value (elevation trend value) using the Kalman filter algorithm. The residual is calculated, which is the difference between the observed and predicted values. Then, the predicted value is corrected by adjusting the weights of process noise and observation noise, obtaining the optimal elevation trend value and the updated trend change rate of the current window, completing one filtering iteration. Through continuous sliding window filtering iterations, a time-optimal elevation trend curve is obtained, and then the elevation correction amount is calculated based on this time-optimal elevation trend curve.

[0031] For each sampling time of the reference elevation data, the difference between the reference elevation data and the corresponding elevation value in the curve is calculated; this difference is the elevation correction amount. Next, the reference elevation data and the water level monitoring data are timestamped. Then, based on the elevation correction, the timestamped water level monitoring data is corrected for monitoring drift to obtain the standard water level data. Specifically, if the time-optimal elevation trend curve shows a continuous downward (settlement) or upward trend, it indicates that the installation reference surface of the water level monitoring equipment at the unmanned water level station, such as the water level sensor, is changing. The corresponding water level monitoring data will therefore exhibit a systematic deviation. For example, if settlement causes the water level sensor installation position to become lower, the original water level data will be too high. In this case, the correction method is: Standard water level data = Water level monitoring data + Elevation correction amount. If the time-optimal elevation trend curve exhibits irregular fluctuations, such as no obvious trend and only fluctuating around 0, it indicates that the baseline elevation data contains random noise. The corresponding water level monitoring data may be affected by instantaneous environmental interference, such as vibration or electromagnetic interference. The correction method is the same: standard water level data = water level monitoring data + elevation correction amount, using the correction amount to offset the deviation caused by random interference. If the time-optimal elevation trend curve shows no significant change, but the elevation correction amount shows a continuous positive or negative deviation, such as all correction amounts being +0.5cm, it indicates that the water level monitoring equipment itself has a zero-point offset. The correction method is: standard water level data = water level monitoring data + average of all elevation correction amounts, using this to offset the zero-point offset in one go.

[0032] The above methods can correct monitoring errors caused by changes in the geographical environment, such as subsidence, random disturbances such as vibration and electromagnetic interference, as well as the inherent limitations of the water level monitoring equipment.

[0033] In one embodiment, the water level inversion calculation is performed based on the BeiDou reflected signal, and the equipment status verification of the unmanned water level station is completed by combining the water level inversion calculation result and water level monitoring data. The equipment verification result includes the following steps: The carrier phase data of the direct BeiDou signal and the reflected BeiDou signal are retrieved by the satellite receiver of the unmanned water level station, and the carrier phase difference between the direct BeiDou signal and the reflected BeiDou signal is calculated based on the carrier phase data. The signal path spacing between the direct BeiDou signal and the reflected BeiDou signal is calculated based on the carrier phase difference. The water level reflection data of the target water area was calculated by combining the benchmark elevation data and the signal path spacing; Calculate the water level difference between standard water level data and water level reflection data; Determine the time of water level anomalies at unmanned water level stations based on water level difference data; If the abnormal water level lasts for a longer period than a preset time threshold, the unmanned water level station is determined to be in an abnormal equipment state. If the abnormal water level time is less than or equal to the time threshold, the unmanned water level station is determined to be in normal condition. If the unmanned water level station is in an abnormal state, the abnormality type of the unmanned water level station shall be determined according to the standard water level data, and the abnormality type shall be output as the equipment verification result. If the unmanned water level station is in normal operating condition, then the normal operating condition will be output as the equipment verification result.

[0034] In this embodiment, carrier phase data of the BeiDou direct signal and carrier phase data of the BeiDou reflected signal are retrieved simultaneously from two channels in the satellite receiver at the same time. This ensures that the two sets of data are perfectly aligned in the time dimension, avoiding phase difference calculation errors caused by time misalignment. Next, all carrier phase data of the BeiDou reflected signal corresponding to satellite elevation angles less than a preset angle threshold (e.g., 10°) are removed. This is because when the satellite elevation angle is too low, the propagation path of the BeiDou reflected signal is too long, making it susceptible to atmospheric interference and shoreline multipath effects, resulting in weak signal strength and large errors. Including such data in the calculation would severely reduce the inversion accuracy. Finally, the carrier phase difference between the BeiDou direct signal and the BeiDou reflected signal is calculated, and the absolute value is taken to ensure that the carrier phase difference is always non-negative. Next, instantaneous anomaly detection is performed on the calculated carrier phase difference. If the deviation between the carrier phase difference at a certain moment and the average phase difference at adjacent moments exceeds a preset deviation threshold, for example, three times the average phase difference, where the adjacent moments can be the five moments surrounding a certain moment, then it is determined that there is an abnormal phase difference at that moment caused by instantaneous signal interference, and the data is directly discarded and not included in subsequent calculations.

[0035] The carrier phase difference retained in the above steps is input into the path spacing calculation formula to obtain the signal path spacing between the BeiDou direct signal and the BeiDou reflected signal. The path spacing calculation formula is as follows: in, The carrier wavelength is the B1C frequency. The BeiDou B1C frequency refers to the civilian navigation frequency specially planned for the BeiDou-3 Global Navigation Satellite System (BDS-3). It is the core carrier for the BeiDou system to provide high-precision positioning, navigation, and timing (PNT) services to civilian users worldwide. Both the direct BeiDou signal and the BeiDou reflected signal originate from the B1C frequency signal emitted by the BeiDou-3 satellite. This represents the carrier phase difference.

[0036] Next, by substituting the baseline elevation data and signal path spacing into the core formula for water level reflection inversion, the water level reflection data of the target water area can be calculated. The water level reflection data refers to the water level data calculated through inversion. The core formula for water level reflection inversion is as follows: in, For water level reflection data, For benchmark elevation data, For signal path spacing, The satellite elevation angle refers to the angle between the GNSS satellite and the horizontal plane where the satellite receiver is located. It can convert the oblique path difference of signal propagation into the vertical water level difference, and then obtain the water level reflection data through calculation using the reference elevation data.

[0037] Next, the water level difference between the standard water level data and the water level reflection data at the same moment is calculated, and the water level differences from all moments are integrated to obtain the water level difference data. Then, anomaly checks are performed on the water level difference data in chronological order. If the duration for which the water level difference is greater than a preset difference threshold is greater than a preset time threshold, the water level monitoring equipment of the unmanned water level station is determined to be in an abnormal state. If there is no abnormal water level difference exceeding the preset difference threshold, or if the abnormal water level difference is less than or equal to the preset time threshold, the water level monitoring equipment of the unmanned water level station is determined to be in a normal state. The difference threshold can be set to 100cm, and the time threshold can be set to 20 minutes.

[0038] If the water level monitoring equipment is in an abnormal state, the type of equipment abnormality at the unmanned water level station needs to be determined based on standard water level data. These abnormality types include equipment failure, zero-point abnormality, and interference abnormality. The type of abnormality should be output as the equipment verification result. If the water level monitoring equipment is in a normal state, the normal state should be directly output as the equipment verification result.

[0039] In one embodiment, determining the equipment anomaly type of an unmanned water level station based on standard water level data includes the following steps: Time series analysis of standard water level data is performed to obtain the time series variation status of standard water level data; If the time-series change of the standard water level data is in a time-stable state, then it is determined that there is an equipment failure anomaly at the unmanned water level station. If the time-series change status of the standard water level data is in a time-series drift state, it is determined that there is an equipment zero-point anomaly at the unmanned water level station. If the time-series change state of the standard water level data is a time-series pulse state, it is determined that there is equipment interference anomaly at the unmanned water level station.

[0040] In this embodiment, time-series analysis is performed on the standard water level data to plot the time-water level curve. When the time-water level curve is almost a horizontal straight line, the water level fluctuation is extremely small (≤0.5cm), there is no obvious upward or downward trend, and there are no instantaneous changes, showing a stable time-series state, it is determined that the water level monitoring equipment of the unmanned water level station has an equipment failure abnormality, such as sensor blockage or transmission line breakage. This is because when the pressure sensor probe is blocked by silt, the water pressure cannot be transmitted to the sensing element, and the output data is fixed at a certain value. Similarly, when the pressure sensor bus is broken, the sensor data cannot be transmitted to the processor, and the processor continues to read the cached old data, causing its output result to remain unchanged for a long time.

[0041] When the time-water level curve shows a continuous, unidirectional shift trend, that is, either a continuous slow rise or a continuous slow fall, the shift process is stable without fluctuations, and the shift amount accumulates evenly over time, such as a shift of about 0.5cm per hour, and a cumulative shift of about 2cm per hour, showing a time-series drift state, it indicates that the water level monitoring equipment of the unmanned water level station has a zero-point abnormality, that is, the pressure sensor of the water level monitoring equipment has zero-point drift, resulting in a systematic deviation in the measured value.

[0042] When the time-water level curve has no fixed trend, exhibits irregular instantaneous abrupt changes, fluctuates up and down, with large fluctuation amplitudes, and the abrupt changes have no obvious pattern and cannot be eliminated by smoothing, presenting a time-series pulse state, it is determined that the water level monitoring equipment of the unmanned water level station has abnormal equipment interference. For example, when using pressure sensors for water level monitoring, the pressure caused by water plants or wind and waves may affect the normal operation of the pressure sensors. Among them, fluctuation amplitudes exceeding 2cm can be defined as large fluctuation amplitudes.

[0043] The above methods can preliminarily determine whether there are equipment abnormalities in the unmanned water level station and what the corresponding causes are, which can provide data guidance for the subsequent maintenance work of the water station maintenance personnel.

[0044] In one embodiment, generating a water level management strategy for all unmanned water level stations within the target water area based on equipment calibration results includes the following steps: Collect communication transmission data from communication equipment within the unmanned water level station; The health status of the unmanned water level station's water level monitoring was determined by combining communication transmission data and equipment verification results. Based on the health status of water level monitoring, complete the dynamic allocation of power and bandwidth for all unmanned water level stations in the target water area, and integrate the results of dynamic allocation of power and bandwidth into a resource management strategy for all unmanned water level stations. Based on the water level monitoring health status, complete the data transmission configuration for all unmanned water level stations, and integrate the data transmission configuration results into a data management strategy for all unmanned water level stations.

[0045] In this embodiment, communication transmission data refers to the current data during the BeiDou short message transmission process, which can be collected by a current sensor (such as a Hall current sensor). The communication reliability, monitoring accuracy, monitoring stability, and monitoring anomaly rate of the unmanned water level station are calculated by combining the communication transmission data and equipment verification results. The negative values ​​of these four metrics are then weighted, summed, and normalized to represent the water level monitoring health of the unmanned water level station. The weights can be determined using expert scoring or entropy weighting. Next, energy priorities are assigned to all unmanned water level stations based on their water level monitoring health status. The higher the water level monitoring health status, the higher the energy priority assigned to the unmanned water level station. For example, if the water level monitoring health status is ≥0.8, the unmanned water level station is marked as a healthy water level station and assigned a first-level priority to ensure its power supply. If the water level monitoring health status is 0.4 ≤ water level monitoring health status <0.8, the unmanned water level station is marked as a sub-healthy water level station and assigned a second-level priority to provide power to the healthy water level station while ensuring its sufficient power supply. If the water level monitoring health status is <0.4, the unmanned water level station is marked as a faulty water level station and assigned a third-level priority to minimize power supply or cut off power supply to reduce unnecessary energy loss. When the total power loss is insufficient to maintain the full-load operation of all healthy and sub-healthy water level stations, power should be allocated to healthy water level stations first, and the remaining power should be allocated to sub-healthy water level stations. If there is still remaining power, the minimum maintenance power consumption can be allocated to the faulty water level station with the third priority. If there is no remaining power, its power supply should be cut off to prevent it from dragging down the bus voltage.

[0046] Similarly, the maximum number of communications for each unmanned water level station is obtained by multiplying the upper limit of the communication frequency by the preset time period. The effective bandwidth for a single communication is obtained by multiplying the maximum number of bytes per communication, the encoding compression ratio, and the communication success rate. The available bandwidth for each unmanned water level station is obtained by multiplying the effective bandwidth for a single communication by the maximum number of communications. The total available bandwidth is obtained by summing the available bandwidth of all unmanned water level stations. Similarly, when available bandwidth is insufficient, priority is given to ensuring the bandwidth needs of healthy water level stations (Level 1 priority). If there is remaining bandwidth, bandwidth is allocated to Level 2 priority stations. Regular data transmission of faulty water level stations (Level 3 priority) is suspended, and only emergency bandwidth for fault alarms is reserved (since emergency bandwidth occupies the emergency time slot of BeiDou short messages, it is not included in the regular quota).

[0047] Next, the data transmission configuration for all unmanned water level stations is completed based on the water level monitoring health status. That is, the higher the water level monitoring health status, the higher the corresponding data transmission frequency. For unmanned water level stations with low water level monitoring health status, the corresponding data transmission frequency is also lower. For example, a healthy water level station collects data once every 10 minutes and transmits it immediately, a sub-healthy water level station collects data once every 30 minutes and transmits it once every hour, and for a faulty water level station, only the fault status needs to be reported to minimize resource consumption.

[0048] In one embodiment, determining the health status of an unmanned water level station by combining communication transmission data and equipment verification results includes the following steps: The current wave characteristic parameters of the communication transmission data are extracted, and the communication reliability of the unmanned water level station is calculated by combining the current wave characteristic parameters with the pre-acquired current wave reference parameters. The monitoring accuracy of unmanned water level stations is determined based on water level difference data; A time-series stability analysis was performed on the water level difference data, and the monitoring stability of the unmanned water level station was determined based on the stability analysis results. The monitoring anomaly level of the unmanned water level station is determined based on the equipment calibration results; The health status of unmanned water level stations is calculated by combining communication reliability, monitoring accuracy, monitoring stability, and monitoring anomaly.

[0049] In this embodiment, the communication transmission data refers to the current data during the BeiDou short message transmission process. This data can be collected using a current sensor (such as a Hall current sensor). The current wave characteristic parameters include peak current, average current, and current waveform duration. The current waveform duration refers to the continuous time from when the current information maintains a weak base current to when it rapidly rises and exceeds the base current, and then falls back to the base current after signal transmission is completed. The feature similarity between the current wave characteristic parameters and the pre-acquired current wave reference parameters is calculated. The two parameters can be vectorized and calculated using the cosine similarity formula. The obtained feature similarity is used as the communication reliability. The current wave reference parameters include the average peak current, average current, and average current waveform duration during multiple short message transmissions of the communication equipment under fault-free, environmentally stable, and parameter-normal conditions. These parameters can be collected using the same current sensor after the unmanned water level station's equipment has undergone periodic inspections and the inspection results indicate normal operation. This allows for the assessment of whether the unmanned water level station's communication equipment has malfunctioned during long-term operation. Next, the monitoring accuracy of the unmanned water level station is determined based on the water level difference data. The average of all water level difference data within a preset time window can be taken, and the reciprocal of the average can be normalized to obtain the monitoring accuracy. The preset time window can be determined based on the sampling frequency of the water level monitoring data, for example, it can be set to one hour.

[0050] Next, the variation range of water level difference between adjacent moments over a recent period is calculated. The mean of all water level difference variations is calculated, and this mean is normalized. The normalized result (1 - this mean) is then calculated to obtain the data stability. Next, the monitoring anomaly degree of the unmanned water level station is determined based on the equipment calibration results. If the equipment calibration results show that the unmanned water level station is in normal condition, its monitoring anomaly degree is set to 0. If the equipment calibration results show that the unmanned water level station has equipment failure, zero-point anomaly, or interference anomaly, a monitoring anomaly degree greater than 0 needs to be assigned to it. For example, equipment failure is the most serious, so the monitoring anomaly degree is the highest and can be set to 0.8. Long-term accumulation of zero-point anomalies will also affect the water level monitoring results, so it can be set to 0.4. Equipment interference anomalies are occasional anomalies and have a small impact on the water level monitoring results, so it can be set to 0.2. The negative values ​​of communication reliability, monitoring accuracy, monitoring stability, and monitoring anomaly are weighted, summed, and then normalized to represent the water level monitoring health of the unmanned water level station. This weight can be determined using expert scoring or entropy weighting.

[0051] Through the above steps, the water level monitoring function of unmanned water level stations can be evaluated, and unmanned water level stations with equipment malfunctions can be identified.

[0052] In one embodiment, the dynamic allocation of power and bandwidth for all water level monitoring equipment within an unmanned water level station based on the water level monitoring health status includes the following steps: Obtain all power supply and communication equipment parameters within the unmanned water level station; Determine the total available electrical energy of all unmanned water level stations in the target water area within a preset time period based on the power supply equipment parameters; Energy priorities are allocated to all unmanned water level stations based on their water level monitoring health status. Dynamic allocation of power for all unmanned water level stations is completed by combining energy priorities and the total amount of available power. The total available bandwidth of all unmanned water level stations within a preset time period is calculated based on the communication equipment parameters. Dynamic bandwidth allocation for all unmanned water level stations is completed based on the total available bandwidth and energy priority.

[0053] In this embodiment, the power supply equipment parameters include key parameters of energy storage devices such as lithium battery packs and supercapacitors, including rated capacity (e.g., 12V / 100Ah), representing total energy storage and actual remaining capacity, which are read in real time by the battery management system (BMS); charge / discharge efficiency (e.g., 90%), meaning that 90% of the electrical energy can be converted into stored energy during charging and 90% of the stored energy can be output during discharging; charge / discharge cutoff voltage (e.g., charging upper limit 14.4V, discharging lower limit 10.8V, exceeding which will damage the battery); self-discharge rate (e.g., 2% per month), representing energy loss under no-load conditions; and energy consumption parameters of the UAV water level station, such as operating current. Communication equipment parameters mainly include the maximum number of bytes per communication (e.g., 140 bytes / communication), the standard bandwidth limit of BeiDou short messages, the upper limit of communication frequency (e.g., a maximum of 10 transmissions per hour), constraints from satellite channel resources, coding compression ratio, and communication success rate. Next, the initial energy of the lithium battery is calculated: Initial energy = Rated voltage × Actual remaining capacity × Discharge efficiency. Then, the total replenishment energy within a preset time period is calculated. Taking solar energy as an example, the total solar replenishment energy = Rated power of the solar panel × Preset sunshine duration × Energy conversion efficiency × Charging efficiency. Next, the total energy loss within the preset time period is calculated. Total energy loss includes equipment self-discharge loss, power conversion loss, and line transmission loss. Equipment self-discharge loss = Initial energy storage × Self-discharge rate × Preset time period; Power conversion loss = (Total operating current of all equipment × Operating voltage × Operating time) × Power module loss rate; Line transmission loss = Wire resistance × Operating current² × Operating time. Operating time refers to the unmanned water level station, which can be obtained from the unmanned water level station's operation log. The total available energy equals the sum of the initial energy storage and the total replenishment energy, minus the total energy loss.

[0054] In the event of continuous sandstorms or rain, the unmanned water level stations in the target water area may experience insufficient energy from solar or wind power. In such cases, in order to ensure that most of the normal unmanned water level stations in the target water area can still carry out water level monitoring, it is necessary to allocate power according to the water level monitoring health status of each unmanned water level station, so as to maximize the normal operation of most unmanned water level stations until the power supply is restored to normal. Next, energy priorities are assigned to all unmanned water level stations based on their water level monitoring health status. The higher the water level monitoring health status, the higher the energy priority assigned to the unmanned water level station. For example, if the water level monitoring health status is ≥0.8, the unmanned water level station is marked as a healthy water level station and assigned a first-level priority to ensure its power supply. If the water level monitoring health status is 0.4 ≤ water level monitoring health status <0.8, the unmanned water level station is marked as a sub-healthy water level station and assigned a second-level priority to provide power to the healthy water level station while ensuring its sufficient power supply. If the water level monitoring health status is <0.4, the unmanned water level station is marked as a faulty water level station and assigned a third-level priority to minimize power supply or cut off power supply to reduce unnecessary energy loss. When the total power loss is insufficient to maintain the full-load operation of all healthy and sub-healthy water level stations, power should be allocated to healthy water level stations first, and the remaining power should be allocated to sub-healthy water level stations. If there is still remaining power, the minimum maintenance power consumption can be allocated to the faulty water level station with the third priority. If there is no remaining power, its power supply should be cut off to prevent it from dragging down the bus voltage.

[0055] In addition, in emergency scenarios such as continuous heavy rain and floods, to prevent data transmission failure due to a large number of unmanned water level stations simultaneously sending alarm messages, bandwidth resources are limited. Therefore, bandwidth allocation needs to be based on the water level monitoring health status of each unmanned water level station, prioritizing stations with higher water level monitoring health status for normal communication. The maximum number of communications for each unmanned water level station is calculated by multiplying the upper limit of the communication frequency by the preset time period. The effective bandwidth for a single communication is calculated by multiplying the maximum number of bytes per communication, the encoding compression ratio, and the communication success rate. The available bandwidth for each unmanned water level station is calculated by multiplying the effective bandwidth for a single communication by the maximum number of communications. The total available bandwidth is calculated by summing the available bandwidth of all unmanned water level stations. Similarly, when available bandwidth is insufficient, priority is given to ensuring the bandwidth needs of first-priority healthy water level stations. If there is remaining bandwidth, bandwidth is allocated to second-priority stations, and regular data transmission for third-priority faulty water level stations is suspended. Only emergency bandwidth for fault alarms is reserved (because emergency bandwidth occupies the emergency time slot of BeiDou short messages and is not included in the regular quota).

[0056] This application also provides a machine-readable storage medium storing instructions for causing a machine to execute the unattended water level management method for a water level station based on BeiDou positioning according to any one of the preceding claims.

[0057] This application also provides an unmanned water level management system for water level stations based on BeiDou positioning, including: The memory is configured to store instructions; and The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the unmanned water level management method for a water level station based on BeiDou positioning according to any one of the preceding statements.

[0058] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0059] The memory can be an internal storage unit of a computer device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) provided on the computer device. Furthermore, the memory can be a combination of internal storage units and external storage devices of a computer device. The memory is used to store computer programs and other programs and data required by the computer device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0060] This application also provides a machine-readable storage medium storing instructions that cause a machine to execute the above-described method for managing water levels at unattended water level stations based on BeiDou positioning.

[0061] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0062] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as 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, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] 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.

[0064] 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.

[0065] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0066] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0067] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0068] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0069] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for managing water levels at unmanned water level stations based on BeiDou positioning, characterized in that, The method includes the following steps: For any unmanned water level station within the target water area, water level monitoring data of the target water area is collected through the unmanned water level station. It receives direct BeiDou signals and reflected BeiDou signals, and calculates the benchmark elevation data of the location of the unmanned water level station based on the direct BeiDou signals. Based on the benchmark elevation data, the data offset of the water level monitoring data is corrected to obtain the standard water level data; The water level inversion calculation is completed based on the BeiDou reflected signal, and the equipment status verification of the unmanned water level station is completed by combining the water level inversion calculation results and water level monitoring data to obtain the equipment verification results. Based on the equipment calibration results, a water level station management strategy is generated for all unmanned water level stations within the target water area. The water level station management strategy includes a resource management strategy and a data management strategy.

2. The method according to claim 1, characterized in that, The process of calculating the benchmark elevation data of the location of the unmanned water level station based on the direct BeiDou signal includes the following steps: Interpolation was used to repair the jumps in the BeiDou direct signal, resulting in a continuous direct signal. The carrier phase and pseudorange observations of the continuous direct signal are extracted, and the ionospheric delay of the carrier phase and pseudorange observations is eliminated by using the ionization cancellation algorithm to obtain the reference carrier phase and reference pseudorange observations. By combining reference carrier phase and reference pseudorange observations and using satellite positioning algorithms, the positioning solution of continuous direct signals is completed, and the reference elevation data of the location of the unmanned water level station is calculated based on the positioning solution stage.

3. The method according to claim 1, characterized in that, The process of correcting the data offset of water level monitoring data based on benchmark elevation data to obtain standard water level data includes the following steps: The data trend analysis of the benchmark elevation data is extracted using a sliding window, and the elevation correction is calculated based on the data trend analysis results. Align the benchmark elevation data with the water level monitoring data using timestamps; Based on the elevation correction, the water level monitoring data that has been aligned with the completed timestamps are corrected for monitoring drift to obtain standard water level data.

4. The method according to claim 1, characterized in that, The process of performing water level inversion calculations based on BeiDou reflected signals, and combining the water level inversion calculation results with water level monitoring data to verify the equipment status of the unmanned water level station, and obtaining the equipment verification results includes the following steps: The carrier phase data of the direct BeiDou signal and the reflected BeiDou signal are retrieved by the satellite receiver of the unmanned water level station, and the carrier phase difference between the direct BeiDou signal and the reflected BeiDou signal is calculated based on the carrier phase data. The signal path spacing between the direct BeiDou signal and the reflected BeiDou signal is calculated based on the carrier phase difference. The water level reflection data of the target water area was calculated by combining the benchmark elevation data and the signal path spacing; Calculate the water level difference between standard water level data and water level reflection data; Determine the time of water level anomalies at unmanned water level stations based on water level difference data; If the abnormal water level lasts for a longer period than a preset time threshold, the unmanned water level station is determined to be in an abnormal equipment state. If the abnormal water level time is less than or equal to the time threshold, the unmanned water level station is determined to be in normal condition. If the unmanned water level station is in an abnormal state, the abnormality type of the unmanned water level station shall be determined according to the standard water level data, and the abnormality type shall be output as the equipment verification result. If the unmanned water level station is in normal operating condition, then the normal operating condition will be output as the equipment verification result.

5. The method according to claim 1, characterized in that, Determining the equipment anomaly type of the unmanned water level station based on standard water level data includes the following steps: Time series analysis of standard water level data is performed to obtain the time series variation status of standard water level data; If the time-series change of the standard water level data is in a time-stable state, then it is determined that there is an equipment failure anomaly at the unmanned water level station. If the time-series change status of the standard water level data is in a time-series drift state, it is determined that there is an equipment zero-point anomaly at the unmanned water level station. If the time-series change state of the standard water level data is a time-series pulse state, it is determined that there is equipment interference anomaly at the unmanned water level station.

6. The method according to claim 5, characterized in that, The process of generating a water level station management strategy for all unmanned water level stations within the target water area based on equipment calibration results includes the following steps: Collect communication transmission data from communication equipment within the unmanned water level station; The health status of the unmanned water level station's water level monitoring was determined by combining communication transmission data and equipment verification results. Based on the health status of water level monitoring, complete the dynamic allocation of power and bandwidth for all unmanned water level stations in the target water area, and integrate the results of dynamic allocation of power and bandwidth into a resource management strategy for all unmanned water level stations. Based on the water level monitoring health status, complete the data transmission configuration for all unmanned water level stations, and integrate the data transmission configuration results into a data management strategy for all unmanned water level stations.

7. The method according to claim 6, characterized in that, Determining the health status of unmanned water level monitoring stations by combining communication transmission data and equipment verification results includes the following steps: The current wave characteristic parameters of the communication transmission data are extracted, and the communication reliability of the unmanned water level station is calculated by combining the current wave characteristic parameters with the pre-acquired current wave reference parameters. The monitoring accuracy of unmanned water level stations is determined based on water level difference data; A time-series stability analysis was performed on the water level difference data, and the monitoring stability of the unmanned water level station was determined based on the stability analysis results. The monitoring anomaly level of the unmanned water level station is determined based on the equipment calibration results; The health status of unmanned water level stations is calculated by combining communication reliability, monitoring accuracy, monitoring stability, and monitoring anomaly.

8. The method according to claim 1, characterized in that, The process of dynamically allocating power and bandwidth for all water level monitoring equipment in unmanned water level stations based on water level monitoring health status includes the following steps: Obtain all power supply and communication equipment parameters within the unmanned water level station; Determine the total available electrical energy of all unmanned water level stations in the target water area within a preset time period based on the power supply equipment parameters; Energy priorities are allocated to all unmanned water level stations based on their water level monitoring health status. Dynamic allocation of power for all unmanned water level stations is completed by combining energy priorities and the total amount of available power. The total available bandwidth of all unmanned water level stations within a preset time period is calculated based on the communication equipment parameters. Dynamic bandwidth allocation for all unmanned water level stations is completed based on the total available bandwidth and energy priority.

9. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to execute the unmanned water level management method for a water level station based on BeiDou positioning according to any one of claims 1 to 8.

10. A water level management system for an unmanned water level station based on BeiDou positioning, characterized in that, include: The memory is configured to store instructions; as well as The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the unmanned water level management method for a water level station based on BeiDou positioning according to any one of claims 1 to 8.

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