Small reservoir water level inversion analysis method, system and equipment based on GNSS-R and medium

Abstract

The invention discloses a GNSS-R-based small reservoir water level inversion analysis method, system and device and a medium, and relates to the technical field of reservoir monitoring, and the method comprises the steps: synchronously receiving a direct signal and a water surface reflection signal from a satellite; performing Doppler frequency shift compensation on the received signal; processing multipath and clutter components in the direct signal, and suppressing the direct signal mixed in a reflection channel through an adaptive filtering algorithm to obtain a pure reflection signal; performing related processing on the processed reflected signal and the direct signal, and extracting code delay and phase information; and constructing a linear model between the frequency and the antenna height based on the three-frequency phase combination observation value, and performing inversion to obtain the water surface height. The reflected signal inversion method is introduced to replace a traditional manual water gauge or single-point ultrasonic distance measurement, the water surface height can be continuously monitored in real time in a non-contact mode under the condition that an underwater sensor is not needed, and the space-time continuity and the automation level of measurement are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of reservoir monitoring, and in particular to a small reservoir water level inversion analysis method, system, device and medium based on GNSS-R (Global Navigation Satellite System Reflectometry). BACKGROUND

[0002] Due to the very complex working conditions of the dam, the actual working state of the dam is difficult to accurately predict by calculation or model test, and there are some defects and diseases after long-term operation. Once the dam appears abnormal state, it must be discovered and handled in time, and the dam failure will not only lose all engineering benefits, but also the downstream people's life and property will suffer devastating losses.

[0003] Reservoir safety has always been a difficult and important point for flood control and flood fighting in China, and most of the current reservoirs lack the necessary reservoir area water and rainfall conditions and reservoir dam safety monitoring means. Traditional monitoring methods mainly include traditional deformation monitoring methods such as measurement point displacement method of total station, leveling method, and manual regular patrol inspection, video monitoring site image recognition, displacement instrument internal monitoring, etc. At the same time, local meteorological information is an important information to be obtained for reservoir and reservoir area displacement and settlement monitoring. For example, heavy rainfall will increase the soil humidity of the dam slope, thereby intensifying the landslide deformation, and extreme high temperature climate will cause soil dry shrinkage, thereby causing soil settlement. Traditional monitoring methods have many limitations, such as narrow monitoring range, low monitoring accuracy, long monitoring period, high monitoring cost, and too large environmental impact factors, etc., and it is urgent to develop new dam safety monitoring complete equipment and technology

[0004] By studying the small hydropower station dam and reservoir area comprehensive safety monitoring sensor terminal based on GNSS-R, the water level inversion method based on three-frequency phase combination observation values, and the dam safety monitoring digital twin system based on three-dimensional GIS one map, multi-element monitoring data can be uniformly obtained, the monitoring work is self-controllable, and the dam and the reservoir area can be effectively monitored in a large range to improve the operation reliability of the dam and the slope. SUMMARY

[0005] In view of the above problems, the present application is proposed.

[0006] Therefore, the problem to be solved by the present application is that the traditional monitoring method has many limitations, such as narrow monitoring range, low monitoring accuracy, long monitoring period, high monitoring cost, and too large environmental impact factors, etc.

[0007] To solve the above technical problems, the application provides the following technical scheme: a small reservoir water level inversion analysis method based on GNSS-R, which comprises the following steps: a reflection measurement technology water level monitoring device is used to synchronously receive direct signals and water surface reflection signals from a satellite; code Doppler frequency shift compensation is performed on the received signals to realize long-time coherent integration processing; the multipath and clutter components in the direct signals are processed, the direct signals mixed in the reflection channel are suppressed through an adaptive filtering algorithm, and pure reflection signals are obtained; the processed reflection signals and the direct signals are correlated, code delay and phase information are extracted; a linear model between frequency and antenna height is constructed based on three-frequency phase combination observation values, and water surface height is inverted; and the inverted water level result is integrated with other multi-source sensing data, and visualized display and analysis are performed through a three-dimensional geographic information system.

[0008] As a preferred scheme of the small reservoir water level inversion analysis method based on GNSS-R, the reflection measurement technology water level monitoring device comprises two channels, each channel is composed of an antenna and a receiver channel, and is used to receive direct signals and reflection signals; the two channels collect two signals through synchronous sampling, and are consistent in time, frequency and code phase.

[0009] As a preferred scheme of the small reservoir water level inversion analysis method based on GNSS-R, the code Doppler frequency shift compensation on the received signals comprises the following steps: real-time sampling point adjustment is performed on the expected signals, when the sampling point misalignment with the reference signals reaches a certain threshold, data points are actively deleted or inserted, so that the code rate of the expected signals and the reference signals is aligned.

[0010] As a preferred scheme of the small reservoir water level inversion analysis method based on GNSS-R, the suppression of the direct signals mixed in the reflection channel through the adaptive filtering algorithm comprises the following steps: the signals collected by the direct channel are input as reference, the reflection channel signals are input as expected output, a transverse structure finite impulse response filter framework is constructed, the filter continuously adjusts the coefficients through iteration, so that the direct component in the error signal in the output result is minimized, and the reflection signals are extracted; and the error signal is the residual error of the expected signals and the reference signals.

[0011] As a preferred scheme of the small reservoir water level inversion analysis method based on GNSS-R, the correlation processing of the processed reflected signal and the direct signal includes sliding correlation matching of the obtained pure reflected signal and the processed direct signal after the multipath and clutter components; the code delay, i.e., the time difference of the reflected signal from the direct signal, is obtained by analyzing the correlation waveform, and the carrier phase difference, i.e., the change of the reflected signal propagation path, is obtained; the code delay and the phase difference together constitute the basis for measuring the path length difference, reflecting the extension of the satellite signal propagation path after reflection on the water surface.

[0012] As a preferred scheme of the small reservoir water level inversion analysis method based on GNSS-R, the inversion of the water surface height includes designing a linear combination according to the difference in the propagation characteristics of signals of different frequencies in the ionosphere, so that the ionospheric delay terms in the combined observation are offset to each other, while the sensitive components of the reflection path difference are reserved; the obtained phase difference and code delay are introduced into the geometric propagation model constructed based on the mirror method to establish the spatial geometric relationship among the satellite elevation angle, the antenna installation height and the water surface reflection point; through multi-epoch continuous observation data, the model is fitted and solved by using an estimation optimization algorithm to invert the vertical distance from the antenna to the water surface; the relationship between the antenna and the water surface height is converted into a standard water level elevation by combining the initial elevation of the equipment installation or historical measurement data, and continuous water level time series data are formed.

[0013] As a preferred scheme of the small reservoir water level inversion analysis method based on GNSS-R, the other multi-source sensing data include meteorological, geological, hydrological and dam stress data; the visualization display and analysis by the three-dimensional geographic information system include superimposing various monitoring data in the form of icons, heat maps and curves on the three-dimensional scene by layer superimposition, and intuitively displaying the running state at different positions and different times.

[0014] To solve the above technical problems, the application provides the following technical scheme: a small reservoir water level inversion analysis system based on GNSS-R, comprising a signal acquisition module, a signal processing module, an inversion module and a visual analysis module; the signal acquisition module uses a reflection measurement technology water level monitoring device to synchronously receive direct signals and water surface reflection signals from satellites; the signal processing module performs code Doppler frequency shift compensation on the received signals to realize long-time coherent integration processing; the signal processing module processes multipath and clutter components in the direct signals, suppresses mixed direct signals in the reflection channel through an adaptive filtering algorithm, obtains pure reflection signals, and performs correlation processing on the processed reflection signals and the direct signals to extract code delay and phase information; the inversion module constructs a linear model between frequency and antenna height based on three-frequency phase combination observation values, and inversely obtains water surface height; and the visual analysis module integrates the inversion water level results with other multi-source sensing data, and performs visual display and analysis through a three-dimensional geographic information system.

[0015] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the small reservoir water level inversion analysis method based on GNSS-R as described above when executing the computer program.

[0016] A computer readable storage medium stores a computer program, and the computer program implements the steps of the small reservoir water level inversion analysis method based on GNSS-R as described above when executed by a processor.

[0017] The application has the beneficial effects that: the application introduces a GNSS-R reflection signal inversion method to replace traditional manual water gauges or single-point ultrasonic ranging, can continuously, non-contact and real-time monitor water surface height without underwater sensors, significantly improves the spatiotemporal continuity and automation level of measurement, and is suitable for remote or complex terrain small reservoir environments.

[0018] The application adopts code Doppler frequency shift compensation technology, effectively overcomes the integral loss problem caused by code rate drift in the long-time coherent integration process, combines an adaptive filtering algorithm, effectively suppresses strong direct interference signals in the reflection channel, extracts reflection signals with high signal-to-noise ratio, and overall improves the stability, accuracy and robustness of the inversion link.

[0019] By constructing a linear combination of three-frequency GNSS signals, geometric path error and ionospheric delay error are eliminated, high linear correlation between water surface height and observation signals is realized, the suppression ability of the inversion model to system errors is enhanced, the water level inversion precision reaches the centimeter level, and the demand of reservoir safety management for high-precision monitoring is met. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0021] Figure 1 A flow chart of a small reservoir water level inversion analysis method based on GNSS-R in Embodiment 1.

[0022] Figure 2 A direct signal reconstruction diagram of a small reservoir water level inversion analysis method based on GNSS-R in Embodiment 1. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned objects, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0024] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the concept of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0025] Embodiment 1, with reference to Figure 1 and Figure 2 , the first embodiment of the present application provides a small reservoir water level inversion analysis method based on GNSS-R, which comprises, as shown in Figure 1 .

[0026] Step 1, using the reflection measurement technology water level monitoring equipment to synchronously receive the direct signal and water surface reflection signal from the satellite.

[0027] This step adopts a double-channel receiving system designed based on the GNSS-R reflection principle, which includes two groups of antennas and receiver channels for receiving direct signal and reflection signal respectively. The system selects right-handed polarized antenna to receive GNSS satellite downlink signal, in which the direct antenna faces the zenith direction for high-quality reception of direct signal, and the reflection antenna faces the water surface at a lower elevation angle for receiving the signal reflected by the water surface. The two-channel receiving system collects two signals through synchronous sampling, which keeps consistent in time, frequency and code phase, laying data foundation for subsequent correlation operation and interference processing.

[0028] The GNSS-R device is deployed on the small reservoir dam body or the edge of the reservoir area, and the antenna is installed at a position with an open view and no obvious obstructions to reduce multipath and obstruction interference. In the collected original signal, the direct signal power is strong, the signal-to-noise ratio is high, and the complete pseudo-code structure and navigation text are contained; the reflected signal strength is weak, and is significantly affected by the water surface state, and the phase, amplitude and delay information thereof can be used to derive the additional propagation path difference from the water surface to the receiving point, and then the water level height is inversely calculated.

[0029] Step 2, code Doppler frequency shift compensation is performed on the received signal to realize long-time coherent integration processing.

[0030] Due to the relative motion between the GNSS satellite and the ground receiving equipment, the received signal will produce code Doppler frequency shift at the chip level, which shows a slight change in the pseudo-code period. If compensation is not performed, the code alignment of the expected signal and the reference signal will be offset during long-time coherent integration, which will cause the correlation integration result to be distorted rapidly, and the gain of interference processing is limited.

[0031] To solve this problem, a code Doppler frequency shift compensation mechanism is introduced in the baseband processing in this step. The specific method is: the expected signal is adjusted in real time, and when the sampling point offset of the expected signal and the reference signal is detected to reach a certain threshold (for example, 1 sampling point), the data points are actively deleted or inserted, so that the code rate alignment of the expected signal and the reference signal is realized. The compensation period is determined by the actual code Doppler frequency, for example, when the code Doppler is 3.2 Hz under the condition of 20 MHz sampling rate, the data needs to be adjusted once about every 15.6 ms.

[0032] The compensation mechanism does not depend on the accurate solution of the carrier Doppler, but performs exploratory compensation by traversing the code rate offset values in the set interval, and selects the parameter with the best linear growth effect of the correlation value over time as the final compensation amount. In this way, the coherent integration time length and gain in interference GNSS-R processing are effectively improved, and a more stable basis is provided for the detection of weak reflected signals.

[0033] Step 3, the multipath and clutter components in the direct signal are processed, and the direct signal mixed in the reflected channel is suppressed by an adaptive filtering algorithm to obtain a pure reflected signal.

[0034] Specifically, the elimination of the direct signal in the reflected channel is based on the signal in the direct channel as the reference signal, and therefore the quality of the direct signal determines the quality of the target reflected signal obtained finally. Usually, there are multipath and clutter in the direct signal. If the multipath and clutter components in the direct signal are not processed, it may lead to incomplete elimination of the direct component in the reflected channel or damage to the structure of the reflected signal.

[0035] To solve this problem, the interference in the direct channel can be suppressed to obtain a relatively pure signal, which includes the following:

[0036] Purification of direct signal: Multipath interference is the main reason that the direct signal cannot be directly used as a reference signal. Therefore, purification is mainly aimed at multipath signals. Multipath interference refers to the existence of not only a direct signal but also one or more reflected interference signals of the signal in the reference channel.

[0037] Multipath suppression can be approached from suppression and elimination. The suppression method reduces the energy of the multipath signal or reduces the source of the multipath signal by other methods. The elimination technique completely eliminates the multipath signal from the direct channel signal by eliminating the source of the multipath signal or separating the direct component and the multipath component in the signal, leaving only the direct signal. The specific implementation can be performed from the following three aspects.

[0038] Selection of satellite signal source: Different code division multiple access technologies and modulation methods will result in different anti-multipath performance of signals. The size of the signal carrier frequency directly affects the propagation and reflection, scattering characteristics of signals in space. Although there are corresponding spectrum resource management agencies in the international community and various countries to manage and allocate spectrum resources to improve the utilization rate of spectrum resources and reduce the mutual interference between electromagnetic waves, under the condition of selection, it is very important to select an appropriate carrier frequency. For example, a choice is made between different satellite navigation systems or between different frequency points in the same system.

[0039] Design and placement of receiving antenna: Multipath will cause errors in navigation and positioning, so in order to improve the positioning accuracy, the multipath signal will be processed when designing the receiver. The receiver receives signals through the antenna, and then processes them through the radio frequency module and the baseband module. If the multipath signal is prevented from entering from the antenna end, the multipath signal will not exist in the subsequent signal processing. Conversely, if the receiving antenna cannot suppress the entry of the multipath signal, the subsequent signal processing will become complex and difficult. Generally, we prefer to process the multipath signal from the antenna end, which can reduce the workload. The multipath suppression of the antenna can be performed from the design of the antenna and the selection of the antenna. When designing the receiving antenna, the multipath signal is generally suppressed from two aspects: one is to use the difference in polarization between the direct and reflected multipath signals, and to use a right-handed antenna as the receiving antenna of the direct channel. In theory, when the polarization of the antenna and the reflected signal is completely orthogonal, the multipath signal can be prevented from entering the direct channel. The second is to develop an antenna with directional gain to attenuate the power of other azimuth multipath signals. In addition, the reasonable placement of the antenna is also a common method to eliminate multipath. Since the multipath signal is reflected by an object, avoiding the reflecting object can eliminate the multipath signal from the source. Generally, the antenna is erected on a high point with an open view to avoid high buildings, mountains, and lake surfaces that are easy to reflect signals. If conditions permit, the receiver can be placed in the air.

[0040] Baseband signal processing: It is difficult for a general user to get a high performance receiving antenna and an ideal receiving environment. Therefore, it is the choice of most users to suppress the multipath signal in the baseband part. Especially for the user of a software receiver, a flexible software algorithm is more conducive to the improvement and optimization of the multipath suppression algorithm. The multipath processing means in the baseband part also have suppression and elimination. The elimination is to separate the multipath signal by estimating the three parameters of the amplitude, chip time delay and carrier Doppler shift of the multipath signal, so as to purify the signal. The suppression does not need to extract the parameters of the multipath signal, but uses multiple correlators to collect the samples on both sides of the correlation peak, and directly obtains the correlation result after the multipath is suppressed through a code ring discriminator. The purpose of the multipath suppression in the direct signal is to obtain a relatively pure direct signal, so the suppression method is no longer applicable. The commonly used techniques in elimination include adaptive filtering and cepstrum technology.

[0041] In another optional embodiment, the navigation data in the direct signal can also be acquired and the signal can be reconstructed to obtain a signal without noise and multipath, specifically including:

[0042] The C / A code (Coarse / Acquisition Code) signal is used for the detection of an aerial target, and the C / A code of the GPS L1 frequency point signal is publicly available. Therefore, only the starting position of the C / A code of a satellite in the direct signal, the carrier Doppler frequency, and the navigation message carried therein need to be known to construct a pure direct signal according to the corresponding modulation rule. The C / A code phase and the carrier Doppler frequency can be obtained through acquisition processing. Acquisition is a two-dimensional rough search of the satellite signal, and the obtained direct signal parameters have poor accuracy and the possibility of false capture, so the satellite signal must be tracked to achieve fine tracking of the pseudo code and the carrier through a carrier loop and a code loop. The tracking principle is not described in detail herein. When the carrier synchronization and the pseudo code synchronization are achieved in the tracking process, the output result of the integrator in the tracking loop is the navigation message. The navigation message obtained at this time needs to be bit-synchronized and checked to obtain the final correct navigation message. The reconstruction principle of the direct signal is shown in Figure 2 .

[0043] Due to the difficulty in completely shielding the direct signal by the antenna directivity and polarization mode in the actual environment, a certain intensity of direct signal is inevitably mixed into the GNSS-R reflection channel. Since the power of the direct signal is much stronger than that of the reflection signal, the autocorrelation sidelobe of the direct signal can easily cover the correlation peak of the reflection signal, which seriously interferes with the accurate estimation of the reflection path difference. Therefore, it is necessary to effectively suppress the direct signal mixed into the reflection channel in the baseband processing stage.

[0044] The step uses an adaptive filter to process the interference cancellation of the reflected signal. Among them, the signal collected by the direct channel is taken as the reference input, and the reflected channel signal is taken as the expected output, and the framework of the finite impulse response (FIR) filter of the transverse structure is constructed. The filter continuously adjusts the coefficients through iteration, so that the direct component in the error signal (i.e. the residual of the expected signal and the reference signal) in the output result is minimized, thereby extracting the reflected signal.

[0045] Among them, the adaptive filter can use the LMS (least mean square) algorithm to process the interference cancellation of the reflected signal, and the specific steps include:

[0046] Signal modeling: Let the reference input signal S(n) be the direct signal received from the direct channel. The expected signal S r (n) = S1(n) + S2(n), where S1(n) is the direct component mixed into the reflected channel, and S2(n) is the actual reflected signal needed.

[0047] The filter output is y(n), which is used to estimate S1(n); the error signal e(n) = S r (n) - y(n), which represents the difference between the filter output and the expected signal, i.e. the target reflected signal.

[0048] Initialize the filter: the filter order M (for example, M = 32); the initial filter coefficient H(0) = [0, 0, …, 0] T ; learning rate (convergence factor) μ, used to control the amplitude of coefficient update (for example, μ = 0.6).

[0049] Construct the LMS cost function: the minimization criterion is represented as,

[0050] E(n) = e 2 (n) = (S r (n) - H T (n) · S(n)) 2

[0051] The goal is to minimize the error signal square E(n).

[0052] Update the filter coefficients using the gradient descent method: based on the least mean square error criterion (LMS simplified version) for iteration, the formula is,

[0053] H(n + 1) = H(n) + 2μe(n)S(n)

[0054] Iterate until convergence: repeatedly perform the calculation of the filter output y(n) = H T (n) · S(n); update the error e(n) = S r(n) = y(n) - H(n) x(n); update coefficient H(n+1) iteratively until error signal e(n) converges to a stable value, indicating that the filter has successfully modeled the direct component and achieved its cancellation from the reflected channel.

[0055] Error signal e(n) output by the LMS filter is the pure reflected signal after removing the direct interference.

[0056] In another alternative embodiment, the adaptive filter can also use RLS (Recursive Least Square) algorithm to perform interference cancellation on the reflected signal, the specific steps including:

[0057] Define signal structure: the desired signal is the signal collected in the reflected channel, containing "reflected signal + mixed direct signal", and the reference signal is the direct signal obtained from the direct channel.

[0058] The filter aims to make the reference signal pass through the adaptive weight combination as close as possible to the "direct component" in the desired signal, so as to cancel the interference by subtraction and extract the pure reflected signal.

[0059] Initialize parameters: set the order of the filter, i.e. the number of reference signal samples participating in weighting at the same time, and set the initial filter coefficient to zero or a small value.

[0060] Initialize "covariance matrix" to represent the estimation of the input characteristics of the reference signal (which can be set to a unit matrix), and set the forgetting factor (usually less than 1), which is used to weigh the importance of "recent data" and "historical data".

[0061] Iterate point by point in time sequence: every time a new reference signal sample (i.e. direct signal sample) is received, it is combined with the previous M samples to form an input vector.

[0062] The input vector is weighted and superimposed with the current filter coefficient to generate an estimated output, and the desired signal is subtracted from the output to obtain the current error signal (i.e. the estimated reflected signal).

[0063] Use the error signal, the current reference signal sample and the covariance matrix to update the filter coefficient together, so that the output can more accurately fit the interference part in the next iteration.

[0064] Core update mechanism: the core of RLS is the iterative update of filter coefficients, and the basic update formula is: new coefficient = old coefficient + gain x current error.

[0065] Where, the new coefficient is the updated filter coefficient vector at the current time; the old coefficient is the filter coefficient vector used at the last time; the current error is the residual after the expected signal at the current time minus the filter output; the gain is a vector, indicating the contribution weight of each reference signal sample to the update at the current time.

[0066] The calculation of the gain depends on the covariance matrix and the current reference input vector, which ensures the optimal update of the coefficient in the sense of minimum mean square error.

[0067] After the above processing, the output error signal of the filter is the reflected signal after removing the direct component from the reflected channel. This signal has higher signal-to-noise ratio and stronger independence, and is suitable for subsequent code delay measurement and phase difference analysis.

[0068] Step 4, correlate the processed reflected signal with the direct signal to extract the code delay and phase information.

[0069] Specifically, after completing the code Doppler compensation and direct signal suppression, the relatively pure reflected signal and the reference direct signal are obtained. At this time, the correlation operation technology is used to pair analyze the two signals to extract key parameters for subsequent geometric modeling and water level inversion. The specific method is as follows:

[0070] The obtained pure reflected signal is matched with the direct signal processed by the multipath and clutter components through sliding correlation.

[0071] The code delay (time difference between the reflected signal and the direct signal) is obtained by analyzing the correlation waveform.

[0072] The carrier phase difference (reflecting the slight change of the signal propagation path) is also obtained.

[0073] Both of them constitute the basis for measuring the path length difference, reflecting the extension of the satellite signal propagation path after reflection on the water surface.

[0074] In order to improve the accuracy and stability of the correlation result: long-time coherent integration is used to improve the detection gain of the reflected signal.

[0075] Set appropriate correlation window length and step size to ensure accurate peak alignment.

[0076] For phase information, the integer cycle ambiguity effect needs to be eliminated, which can be supplemented by continuous time sliding window tracking method for phase unwrapping.

[0077] Through this step, the system accurately extracts the propagation difference of GNSS signals on the direct and reflected paths, realizes the mapping of electromagnetic signal changes into quantifiable geometric observations, and provides data support for high-precision water surface height inversion.

[0078] Step 5, Construct a linear model between frequency and antenna height based on three-frequency phase combination observations, and invert the water surface height.

[0079] Specifically, to improve the accuracy and error resistance of water level inversion, this step uses three-frequency carrier phase observations (such as L1, L2, L5) in the GNSS system for combination processing, constructs a linear model that cancels out the ionospheric effect, and converts the reflection path length difference directly into antenna height change, i.e., water surface height. The specific method includes:

[0080] Frequency combination construction: According to the differences in the propagation characteristics of signals of different frequencies in the ionosphere, a linear combination is designed so that the ionospheric delay terms in the combined observations cancel each other out while retaining the sensitive components of the reflection path difference.

[0081] Path difference and geometric model: The obtained phase difference and code delay are introduced into the geometric propagation model constructed based on the mirror method to establish the spatial geometric relationship between the satellite elevation angle, antenna installation height, and water reflection point.

[0082] Linear regression solution: Through multi-epoch continuous observation data, the least squares or Kalman filtering method is used to fit and solve the model, and the vertical distance from the antenna to the water surface is inverted.

[0083] Water level calibration and correction: Combined with the initial elevation of equipment installation or historical measurement data, the relationship between the antenna and the water surface height is converted into the standard water level elevation to form continuous water level time series data.

[0084] This three-frequency combination model not only effectively suppresses system errors such as ionospheric errors and satellite orbit errors, but also significantly improves the stability and spatial resolution of phase inversion results, achieving centimeter-level precision water level monitoring.

[0085] Step 6, Integrate the inverted water level results with other multi-source sensing data and visualize and analyze them through a three-dimensional geographic information system.

[0086] Specifically, after completing water level inversion, to improve the intelligent level of reservoir and dam operation management, this step integrates the water level data obtained by GNSS-R inversion with other environmental monitoring data (such as weather, geology, hydrology, dam stress, etc.) for multi-source fusion, and integrates them into a digital twin platform based on three-dimensional GIS, realizing comprehensive visualization, dynamic display, and intelligent analysis of monitoring information. The specific process includes:

[0087] Three-dimensional model construction: Using close-range photogrammetry and three-dimensional reconstruction technology, high-precision orthophotos and three-dimensional real scene models of the reservoir area and the dam are generated.

[0088] Attribute data integration: Format standardization and spatio-temporal synchronization of GNSS-R water level inversion results with temperature, rainfall, wind speed, dam displacement, and other sensor data, unified access to platform database.

[0089] Digital twin platform construction: The platform introduces a Web-based three-dimensional geographic engine (such as UE4 / U3D), supports GPU parallel computing and high-performance rendering, and realizes real-time simulation of water level dynamic changes and dam structure response.

[0090] Monitoring information "GIS one map" visualization: Through layer superposition, various monitoring data are superimposed on the three-dimensional scene in the form of icons, heat maps, curves, etc., to intuitively display the running status at different positions and different times.

[0091] Early warning and trend analysis: The system supports setting safety thresholds and alarm rules, can analyze water level change rate based on historical data trend, predict over-limit risk, and realize early warning.

[0092] This step not only improves the interpretability and intelligence level of reservoir water level monitoring, but also provides a panoramic and real-time decision-making aid tool for management units.

[0093] Embodiment 2, which is different from the first embodiment, is a small reservoir water level inversion analysis system based on GNSS-R, which includes a signal acquisition module, a signal processing module, an inversion module, and a visualization analysis module. The signal acquisition module uses a reflection measurement technology water level monitoring device to synchronously receive direct signals and water surface reflection signals from satellites; the signal processing module compensates the code Doppler frequency shift of the received signals to realize long-time coherent integration processing; processes the multipath and clutter components in the direct signal, suppresses the direct signal mixed in the reflection channel through an adaptive filtering algorithm, obtains a pure reflection signal, and performs correlation processing on the processed reflection signal and the direct signal to extract code delay and phase information; the inversion module constructs a linear model between frequency and antenna height based on three-frequency phase combination observation values, and inversely obtains water surface height; the visualization analysis module integrates the inversion water level results with other multi-source sensing data, and performs visualization display and analysis through a three-dimensional geographic information system.

[0094] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the technical solutions that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0095] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a list of executable instructions for implementing logic functions, which can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus or device, such as a computer-based system, a system including a processor or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or in conjunction with these instructions execution systems, apparatus or devices. For the purpose of this specification, the "computer-readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by or in connection with an instruction execution system, apparatus or device, or in conjunction with these instruction execution systems, apparatus or devices.

[0096] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by editing, interpreting or otherwise processing, if necessary, in other suitable ways, to be electronically obtained and then stored in the computer memory.

[0097] It should be understood that portions of the present application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, implementation can be with a combination of any of the following technologies, which are all well known in the art: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0098] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A small reservoir water level inversion analysis method based on GNSS-R, characterized by: The application relates to a water level monitoring device based on reflection measurement technology. The device synchronously receives direct signals and water surface reflection signals from a satellite through reflection measurement technology; Code Doppler frequency shift compensation is performed on the received signals to realize long-time coherent integration processing; The multipath and clutter components in the direct signals are processed, the direct signals mixed in the reflection channel are suppressed through an adaptive filtering algorithm, and pure reflection signals are obtained; The processed reflection signals and the direct signals are correlated to extract code delay and phase information; A linear model between frequency and antenna height is constructed based on three-frequency phase combination observation values, and water surface height is inverted; The inverted water level results are integrated with other multi-source sensing data, and visual display and analysis are realized through a three-dimensional geographic information system.

2. The small reservoir water level retrieval analysis method based on GNSS-R according to claim 1, characterized in that: The reflection measurement technology water level monitoring device comprises double channels, each channel being composed of an antenna and a receiver channel and being used for receiving direct signals and reflection signals; The double channels collect two signals through synchronous sampling, and are consistent in time, frequency and code phase.

3. The small reservoir water level retrieval analysis method based on GNSS-R according to claim 2, characterized in that: The code Doppler frequency shift compensation on the received signals comprises real-time sampling point adjustment of the expected signals, and when the sampling point misalignment between the expected signals and the reference signals reaches a certain threshold, data points are actively deleted or inserted, so that the code rate of the expected signals and the reference signals is aligned.

4. The small reservoir water level retrieval analysis method based on GNSS-R according to claim 3, characterized in that: The suppression of the direct signals mixed in the reflection channel through the adaptive filtering algorithm comprises the following steps: the signals collected by the direct channel are input as reference, the reflection channel signals are input as expected output, a transverse structure finite impulse response filter framework is constructed, the filter continuously adjusts the coefficients through iteration, and the direct component in the error signal in the output result is minimized, so that the reflection signal is extracted; The error signal is the residual error of the expected signal and the reference signal.

5. The small reservoir water level retrieval analysis method based on GNSS-R according to claim 4, characterized in that: The correlation processing of the processed reflection signals and the direct signals comprises the following steps: the obtained pure reflection signals are matched through sliding correlation with the direct signals processed through the multipath and clutter components; Code delay, that is, the time difference between the reflection signals and the direct signals, and carrier phase difference, that is, the change of the reflection signal transmission path, are obtained by analyzing the correlation waveform; The code delay and the phase difference together constitute the measurement basis of the path length difference, and reflect the extension of the satellite signal transmission path after the water surface reflection.

6. The small reservoir water level retrieval analysis method based on GNSS-R according to claim 5, characterized in that: The inversion of the water surface height comprises the following steps: according to the difference in the transmission characteristics of different frequency signals in the ionosphere, a linear combination is designed, the ionosphere delay items in the combined observation values are offset, and the sensitive components of the reflection path difference are reserved; The obtained phase difference and code delay are introduced into a geometric transmission model constructed based on the mirror method, and the spatial geometric relationship among the satellite height angle, the antenna installation height and the water surface reflection point is established; Through multi-epoch continuous observation data, an estimation optimization algorithm is used to fit and solve the model, and the vertical distance from the antenna to the water surface is inverted; In combination with the initial elevation of the device installation or historical measurement data, the relationship between the antenna and the water surface height is converted into a standard water level elevation, and continuous water level time series data are formed.

7. The small reservoir water level retrieval analysis method based on GNSS-R according to claim 6, characterized in that: The other multi-source sensing data comprise meteorological, geological, hydrological and dam stress data; The visualization display and analysis through the three-dimensional geographic information system comprises superimposing various monitoring data in the form of icons, heat maps and curves on the three-dimensional scene through layer superimposition, and directly displaying the running state at different positions and different times.

8. A GNSS-R based small reservoir water level inversion analysis system, applying a GNSS-R based small reservoir water level inversion analysis method according to any one of claims 1 to 7, characterized in that: The signal acquisition module, the signal processing module, the inversion module and the visualization analysis module are comprised. The signal acquisition module uses a water level monitoring device based on reflection measurement technology to synchronously receive direct signals and water surface reflection signals from a satellite. The signal processing module performs code Doppler frequency shift compensation on the received signals to realize long-time coherent integration processing, processes multi-path and clutter components in the direct signals, suppresses the direct signals mixed in the reflection channel through an adaptive filtering algorithm to obtain pure reflection signals, and performs correlation processing on the processed reflection signals and the direct signals to extract code delay and phase information. The inversion module constructs a linear model between frequency and antenna height based on three-frequency phase combination observation values, and inversely obtains water surface height. The visualization analysis module integrates the inversion water level results with other multi-source sensing data, and performs visualization display and analysis through a three-dimensional geographic information system. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the small reservoir water level inversion analysis method based on GNSS-R in any one of claims 1 to 7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the small reservoir water level inversion analysis method based on GNSS-R in any one of claims 1 to 7.

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