阅读说明：本技术 水位获取方法、装置、设备及存储介质 (Water level obtaining method, device, equipment and storage medium ) 是由 吕铮 兰天 何明 于 2020-04-20 设计创作，主要内容包括：本申请公开了一种水位获取方法、装置、设备及存储介质,涉及遥感通信技术领域。该方法包括：获取待测区域对应的目标卫星的目标观测数据；从所述目标观测数据中分解出目标多路径反射信号；根据预设识别条件,从所述目标多路径反射信号中识别出水面反射信号；对所述水面反射信号进行频谱分析,得到所述目标观测数据对应的目标反射高度；根据所述目标反射高度,获取所述待测区域的水位。根据本申请实施例,能够提高获取的水位的准确度。(The application discloses a water level obtaining method, a water level obtaining device, water level obtaining equipment and a storage medium, and relates to the technical field of remote sensing communication. The method comprises the following steps: acquiring target observation data of a target satellite corresponding to a region to be detected; resolving target multipath reflected signals from the target observation data; identifying a water surface reflection signal from the target multipath reflection signal according to a preset identification condition; performing spectrum analysis on the water surface reflection signal to obtain a target reflection height corresponding to the target observation data; and acquiring the water level of the area to be detected according to the target reflection height. According to the embodiment of the application, the accuracy of the acquired water level can be improved.)

1. A water level acquisition method, characterized in that the method comprises:

acquiring target observation data of a target satellite corresponding to a region to be detected;

resolving target multipath reflected signals from the target observation data;

identifying a water surface reflection signal from the target multipath reflection signal according to a preset identification condition;

performing spectrum analysis on the water surface reflection signal to obtain a target reflection height corresponding to the target observation data;

and acquiring the water level of the area to be detected according to the target reflection height.

2. The method of claim 1, wherein said resolving a target multipath reflected signal from said target observation comprises:

extracting a signal-to-noise ratio observation signal from the target observation data;

and performing direct signal removing processing and noise signal removing processing on the signal-to-noise ratio observation signal to obtain the target multipath reflection signal.

3. The method of claim 2, wherein the performing direct signal removal processing and noise signal removal processing on the snr observation signal to obtain the target multipath reflected signal comprises:

removing trend items from the signal-to-noise ratio observation signal to obtain a target signal-to-noise ratio observation signal which does not contain the direct signal;

carrying out empirical mode decomposition on the target signal-to-noise ratio observation signal to obtain a plurality of layers of intrinsic mode signals;

determining principal component components of the intrinsic mode signals of each layer through singular value decomposition;

determining a boundary layer of a noise signal and the target multipath reflection signal by combining the principal component and a preset noise contribution rate;

and taking the eigenmode signal with the layer number behind the boundary layer as the target multipath reflection signal.

4. The method of claim 1, wherein the identifying a surface reflection signal from the target multipath reflection signal according to a predetermined identification condition comprises:

and identifying the water surface reflection signal from the target multipath reflection signal by combining the signal spectrum variation characteristic and the adjacent annual area daily interval period.

5. The method of claim 4, wherein identifying a surface reflection signal from the target multipath reflection signal in combination with the signal spectrum variation characteristics and the adjacent time of year and day interval period comprises:

identifying a first eigenmode signal from the target multipath reflected signal based on signal spectrum variation characteristics of a non-water surface signal;

acquiring a correlation coefficient of the first eigenmode signal and an eigenmode signal in historical multipath reflection signals; the historical multi-path reflected signal is obtained based on observation data which is separated from the target observation data by the adjacent annual product day interval time period;

identifying a second eigenmode signal from the first eigenmode signals based on the correlation coefficient;

and removing the second eigenmode signal from the target multipath reflection signal to obtain the water surface reflection signal.

6. The method of claim 1, wherein the performing a spectral analysis on the water surface reflection signal to obtain a target reflection height corresponding to the target observation data comprises:

carrying out frequency spectrum analysis on the water surface reflection signal to obtain the main frequency of the water surface reflection signal;

and acquiring the target reflection height corresponding to the target observation data by combining the main frequency and the conversion relation between the main frequency and the reflection height.

7. The method of claim 6, wherein the primary frequency to reflection height conversion relationship comprises:

wherein f is_mIs the main frequency, lambda is the carrier wavelength, H_effIs the target reflection height.

8. The method of any one of claims 1-7, wherein the number of target satellites is plural, and accordingly, the number of target observation data is plural, and the target reflection height is plural; wherein the global satellite navigation systems to which the plurality of target satellites belong are different.

9. The method according to claim 8, wherein the obtaining the water level of the area to be measured according to the target reflection height comprises:

acquiring target weight values of each group of target observation data based on the satellite system configuration and the satellite time span;

acquiring the target water level of each group of target observation data based on the target weight and the target reflection height;

and averaging a plurality of target water levels to obtain the water level of the area to be detected.

10. A water level obtaining apparatus, characterized in that the apparatus comprises:

the data acquisition module is used for acquiring target observation data of a target satellite corresponding to the area to be detected;

the decomposition module is used for decomposing a target multipath reflection signal from the target observation data;

the identification module is used for identifying the water surface reflection signal from the target multipath reflection signal according to a preset identification condition;

the frequency spectrum analysis module is used for carrying out frequency spectrum analysis on the water surface reflection signal to obtain a target reflection height corresponding to the target observation data;

and the water level acquisition module is used for acquiring the water level of the area to be detected according to the target reflection height.

11. The apparatus of claim 10, wherein the number of target satellites is plural, and accordingly, the number of target observation data is plural, and the target reflection height is plural; wherein the global satellite navigation systems to which the plurality of target satellites belong are different.

12. A water level obtaining apparatus, characterized in that the apparatus comprises: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements the water level acquisition method of any one of claims 1-9.

13. A computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement the water level acquisition method according to any one of claims 1 to 9.

Technical Field

The application belongs to the technical field of remote sensing communication, and particularly relates to a water level obtaining method, a water level obtaining device, water level obtaining equipment and a storage medium.

Background

The water level monitoring refers to monitoring the water surface depth of a water area to be measured such as a dam, a reservoir, a river and the like. Since ancient times, water level monitoring has always had great significance in the people's county. Taking a dam as an example, the dam is an indispensable means for adjusting regional water resource distribution and spatial layout, can realize functions such as irrigation, hydroelectric power generation and flood control, and generally takes the water level as an important index for monitoring safe operation of the dam in order to reduce major hazards to production, life and property safety of people caused by dam collapse and collapse.

In the prior art, observation data of a water area to be measured is usually acquired by a GNSS-R (Global Navigation Satellite System) remote sensing technology, and then the water level of the water area to be measured is calculated by using the observation data. However, the accuracy of the water level of the water area to be measured obtained by the existing remote sensing signal is often low.

Disclosure of Invention

The embodiment of the application provides a water level obtaining method, a water level obtaining device, water level obtaining equipment and a storage medium, and aims to solve the problem of how to improve the accuracy of the obtained water level.

In order to solve the technical problem, the present application is implemented as follows:

in a first aspect, an embodiment of the present application provides a water level obtaining method, including:

acquiring target observation data of a target satellite corresponding to a region to be detected;

resolving target multipath reflected signals from the target observation data;

identifying a water surface reflection signal from the target multipath reflection signal according to a preset identification condition;

performing spectrum analysis on the water surface reflection signal to obtain a target reflection height corresponding to the target observation data;

and acquiring the water level of the area to be detected according to the target reflection height.

In a second aspect, an embodiment of the present application provides a water level obtaining apparatus, including:

the data acquisition module is used for acquiring target observation data of a target satellite corresponding to the area to be detected;

the decomposition module is used for decomposing a target multipath reflection signal from the target observation data;

the identification module is used for identifying the water surface reflection signal from the target multipath reflection signal according to a preset identification condition;

the frequency spectrum analysis module is used for carrying out frequency spectrum analysis on the water surface reflection signal to obtain a target reflection height corresponding to the target observation data;

and the water level acquisition module is used for acquiring the water level of the area to be detected according to the target reflection height.

In a third aspect, an embodiment of the present application provides a water level obtaining apparatus, including: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements the water level acquisition method as described in the first aspect.

In a fourth aspect, the present application provides a computer storage medium having computer program instructions stored thereon, where the computer program instructions, when executed by a processor, implement the water level obtaining method according to the first aspect.

Compared with the prior art, the method has the following beneficial effects:

in the embodiment of the application, the water level of the area to be measured can be obtained by observing the reflection height obtained by data. Since the reflection height is obtained based on the water surface reflection signal identified from the observation data, the interference caused by the non-water surface reflection signal in the observation data can be eliminated, thereby improving the accuracy of the acquired water level.

Drawings

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 of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic illustration of a signal reflection from a dam provided by an embodiment of the present application;

FIG. 2 is a diagram of an application scenario provided by another embodiment of the present application;

fig. 3 is a schematic flow chart of a water level obtaining method according to another embodiment of the present application;

fig. 4 is a schematic structural diagram of a water level obtaining system according to another embodiment of the present application;

FIG. 5 is a schematic structural diagram of a software foreground in the water level obtaining system according to another embodiment of the present application;

fig. 6 is a schematic structural diagram of a software settlement background in the water level acquisition system according to another embodiment of the present application;

fig. 7 is a schematic structural diagram of a water level obtaining apparatus according to another embodiment of the present application;

fig. 8 is a schematic structural diagram of a water level obtaining apparatus according to still another embodiment of the present application.

Detailed Description

Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The water level monitoring refers to monitoring the water surface depth of a water area to be measured, such as a dam, a reservoir, a river and the like, generally, the observation data of the water area to be measured is collected through a global navigation satellite system remote sensing technology, and then the water level of the water area to be measured is calculated by using the observation data.

The applicant finds that the environment around the water area to be detected is often complex, and the components of remote sensing signals received by the GNSS antenna arranged in the water area to be detected are complex. Taking a dam as an example, as shown in fig. 1, the GNSS antenna receives not only the direct signal and the surface reflection signal from the water surface, but also the reflection signals of other reflection surfaces, such as the top reflection signal of the top of the dam. Among these signals, only the water surface reflection signal can be used to accurately calculate the water level. However, in the prior art, the multipath reflected signal including the water surface reflected signal is only separated from the remote sensing signal, and then the water level of the water area to be measured is calculated based on the multipath reflected signal, resulting in the calculated water level, which is often low in accuracy.

In order to solve the problem of the prior art, embodiments of the present application provide a water level obtaining method, apparatus, device, and storage medium. First, a water level obtaining method provided in an embodiment of the present application will be described below.

The execution subject of the water level obtaining method may be a water level obtaining device, such as a server or a service cluster composed of a plurality of servers. As shown in fig. 2, the water level obtaining device 200 may obtain observation data corresponding to the region to be measured, and then may identify the water surface reflection signal from the observation data, so as to obtain the water level of the region to be measured through the water surface reflection signal.

As shown in fig. 3, the water level obtaining method provided in the embodiment of the present application includes the following steps:

s301, acquiring target observation data of a target satellite corresponding to the area to be measured.

S302, target multipath reflected signals are separated from target observation data.

And S303, identifying the water surface reflection signal from the target multipath reflection signal according to a preset identification condition.

S304, carrying out frequency spectrum analysis on the water surface reflection signal to obtain a target reflection height corresponding to the target observation data.

S305, acquiring the water level of the area to be measured according to the target reflection height.

Specific implementations of the above steps will be described in detail below.

In the embodiment of the application, the water level of the area to be measured can be obtained by observing the reflection height obtained by data. Since the reflection height is obtained based on the water surface reflection signal identified from the observation data, the interference caused by the non-water surface reflection signal in the observation data can be eliminated, thereby improving the accuracy of the acquired water level.

Specific implementations of the above steps are described below.

First, a specific implementation of S301 will be described. In an example embodiment, a satellite receiver may be deployed in the area under test, and the satellite receiver may receive target observations of respective target satellites via a GNSS antenna. Thus, the water level acquisition device can acquire target observation data from the satellite receiver.

The above is a specific implementation of S301, and a specific implementation of S302 is described below.

In an example embodiment, after acquiring the target observation data, the water level acquiring device may perform decomposition processing on the target observation data to obtain a target multipath reflected signal.

Considering that the multipath reflected signal is usually located in the snr observed signal, the specific processing of S302 may be as follows: extracting a signal-to-noise ratio observation signal from target observation data; and performing direct signal removing processing and noise signal removing processing on the signal-to-noise ratio observation signal to obtain a target multipath reflection signal.

In an example embodiment, after acquiring the target observation data, the water level acquiring device may extract a Signal-to-Noise Ratio (SNR) observation Signal from the target observation data, and then may perform direct Signal removal processing and Noise Signal removal processing on the SNR observation Signal to obtain a target multipath reflected Signal.

Optionally, the target multipath reflected signal may be obtained from the signal-to-noise ratio observation signal by removing trend term processing and empirical mode decomposition, and the corresponding processing may be as follows: removing trend items from the signal-to-noise ratio observation signal to obtain a target signal-to-noise ratio observation signal which does not contain the direct signal; carrying out empirical mode decomposition on the target signal-to-noise ratio observation signal to obtain a plurality of layers of intrinsic mode signals; determining principal component components of the intrinsic mode signals of each layer through singular value decomposition; determining a boundary layer of a noise signal and the target multipath reflection signal by combining the principal component and a preset noise contribution rate; and taking the eigenmode signal with the layer number behind the boundary layer as the target multipath reflection signal.

In an exemplary embodiment, a process of obtaining a target multipath reflection signal is specifically described by taking an Empirical Mode Decomposition (CEEMD) as an example.

Firstly, trend item removing processing can be carried out on the signal-to-noise ratio observation signal to obtain a target signal-to-noise ratio observation signal which does not contain the direct signal.

Thereafter, the CEEMD may be used to perform empirical Mode decomposition on the target snr observation signal to obtain a series of signal components ranging from high frequency to low frequency, which may be referred to as Intrinsic Mode Function (IMF) signals. Specifically, for example, taking the first eigenmode IMF1 signal as an example, a unit variance white noise with a mean value of zero I times can be added to the sequence X of the original decomposed signal, i.e., the target snr observed signal, to generate I groups of different signalsThen, to the signalPerforming empirical mode decomposition, and then performing mode decomposition on the first layer according to the formula (1)(group I) were averaged to obtain the first eigenmode IMF1 signal.

Next, the remaining remainder is denoted by r1, i.e., r₁＝X-IMF₁Using operator E_j(. cndot.) represents the EMD decomposition process for the SNR observed signal sequence X to obtain the j-th eigenmode signal. Specifically, r can be represented by the formula (2)₁The following treatments were carried out:

r_k+1＝r_k+β_k×E_k(ω^j) (2)

n, j 1 … I, β is the signal-to-noise ratio, and ω is the white noise with zero mean unit variance. EMD decomposition is carried out on the added I group of noises respectively to obtain the kth intrinsic mode signal, and r is added respectively_kWhen k is 1, the compound is obtained₂。

Then, r is aligned again₂EMD decomposition is performed, and then the first layer mode is averaged according to formula (3) to obtain a second intrinsic mode IMF₂A signal.

It is worth noting that after adding the kth-order residual term into the kth-order mode after EMD decomposition of Gaussian white noise, EMD decomposition is carried out and the first-layer mode is taken, and so on can be carried out until the residual term of the analytic signal can not be decomposed. At this time, the signal-to-noise ratio observation signal sequence X may be as shown in formula (4):

where K represents the number of decomposition layers.

Therefore, by combining different intrinsic mode IMF signals, a high-pass filter, a band-pass filter and a low-pass filter can be formed, and the signal extraction effect is achieved. Because the multi-path signal presents low-frequency characteristics and exists in a high-order mode, a principal component analysis method can be adopted, principal component components of intrinsic mode signals of each layer are determined through singular value decomposition, then boundary layers of noise signals and target multi-path reflection signals are determined by combining the principal component components and preset noise contribution rates, and then the intrinsic mode signals with the layer number behind the boundary layers are used as the target multi-path reflection signals, so that high-frequency noise is eliminated or weakened.

In an exemplary embodiment, after obtaining the plurality of eigenmode signals, considering that the eigenmode signals are orthogonal to each other and arranged from high to low in terms of frequency, and the plurality of eigenmode signals mainly include a noise signal having a high-frequency random characteristic and a multipath reflection signal having a low-frequency characteristic, it is critical to determine a boundary layer between the noise signal and the multipath reflection signal. The singular value decomposition of the equations (5) and (6) can be utilized to determine the principal component of the eigenmode signal of each layer, and then the boundary layer of the noise signal and the target multipath reflection signal is determined by combining the principal component and the preset noise contribution rate.

[V^T,S,Q]＝SVD(Y)(5)

Y_m×n>>Q_m×nS_n×nV_n×n(6)

Wherein, Y represents a matrix formed by original signals, namely intrinsic mode signals IMF; q represents a feature vector matrix, and each column of Q represents a feature vector; each row of V is orthogonal and represents a principal component associated with the eigenmode signal IMF; s denotes a diagonal matrix, and S is diag (S)₁,s₂,s₃,s₄,×××,s_n). At this time, if Y is an invertible matrix, the diagonal matrix S has characteristic values of Y on the diagonal and is arranged from large to small.

Then, the number of stages l, i.e. the boundary layer, can be found according to equation (7), and the eigenmode signals before stage l can be used as noise signals, and the signals after stage l are added together to be used as multipath reflection signals.

Where D represents the noise contribution rate.

Further, the noise contribution rate D can be verified by KS (Kolmogorov-Smirnov) test. Since the white noise distribution conforms to the gaussian distribution, the power spectrum of the extracted high-frequency signal can be obtained, then the gaussian distribution parameters σ and u are inversely calculated, and the KS test is performed on the frequency spectrum of the gaussian distribution data conforming to the same parameters. If the KS test results in acceptance, it indicates that the residual (i.e., the sum of the quantities prior to class l) is Gaussian distributed, i.e., the selected D value is acceptable. In the experiment, since the noise content in the data is unknown, the D value can be set to a large value.

It should be noted that, when the noise contribution rate D is verified by KS, the acquired high-frequency signal before l layers will usually contain part of the multipath reflected signal due to the large value of D, so that the KS check will not pass.

The above is a specific implementation of S302, and a specific implementation of S303 is described below.

In an example embodiment, the water level obtaining device may identify the water surface reflection signal from the target multipath reflection signal according to a preset identification condition after resolving the target multipath reflection signal.

Optionally, the signal spectrum variation characteristic and the adjacent inter-year-day interval period may be combined to identify the water surface reflection signal from the target multipath reflection signal, and the corresponding processing may be as follows: identifying a first eigenmode signal from the target multipath reflected signal based on signal spectrum variation characteristics of the non-water surface signal; acquiring a correlation coefficient of the first eigenmode signal and an eigenmode signal in the historical multipath reflection signal; historical multi-path reflected signals are obtained based on observation data which are separated from target observation data by adjacent annual product day interval time periods; identifying a second eigenmode signal from the first eigenmode signals based on the correlation coefficient; and removing the second eigenmode signal from the target multipath reflection signal to obtain a water surface reflection signal.

In an exemplary embodiment, since the ground track that the satellite signal passes through is generally fixed, and only in a few cases will orbit, if there is a reflecting surface on such a fixed ground track, the resulting multipath reflected signal will exhibit a certain regular characteristic. Therefore, potential non-surface reflection signals, namely the first eigenmode signals, can be identified from the target multipath reflection signals based on the signal spectrum variation characteristics of the non-surface signals. Then, a correlation coefficient of the first eigenmode signal and an eigenmode signal in a historical multipath reflected signal, which can be obtained based on the observation data separated from the target observation data by the adjacent annual product day interval period, can be obtained by equation (8).

Wherein, A, phi and f respectively represent the amplitude, phase and frequency of the signal l; l_dRepresenting the signals of the eigenmodes of the day,/_d+1+d_tRepresenting the eigenmode signals after the misalignment correction,representing the correlation coefficient between the intrinsic mode signals of two adjacent days.

From the potential non-surface reflection signals, true non-surface reflection signals, i.e. second eigenmode signals,

the above is a specific implementation of S303, and a specific implementation of S304 is described below.

In an example embodiment, after the water level obtaining device identifies the water surface reflection signal, the water level obtaining device may perform spectrum analysis on the water surface reflection signal to obtain a target reflection height corresponding to the target observation data, and the target reflection height may be used as the water level of the area to be measured corresponding to the target observation data.

Optionally, the target reflection height may be obtained based on a conversion relationship between the main frequency and the reflection height of the water surface reflection signal, and the corresponding processing may be as follows: carrying out frequency spectrum analysis on the water surface reflection signal to obtain the main frequency of the water surface reflection signal; and acquiring the target reflection height corresponding to the target observation data by combining the main frequency and the conversion relation between the main frequency and the reflection height.

In an example embodiment, a method of L-S (Lomb-scale periodic analysis) spectrum analysis may be adopted to perform spectrum analysis on the water surface reflection signal, that is, according to an analysis result, an amplitude and a frequency signal of the water surface reflection signal may be obtained, and then a main frequency signal may be obtained from the frequency signal.

Specifically, a frequency conversion formula shown in formula (9) may be adopted to calculate the target reflection height corresponding to the target observation data:

wherein f is_mIs the main frequency, lambda is the carrier wavelength, H_effIs the target reflection height.

The above is a specific implementation of S304, and a specific implementation of S305 is described below.

In an example embodiment, the area to be measured may correspond to a plurality of target satellites, and correspondingly, the water level obtaining device may obtain target observation data of a plurality of groups of different target satellites, and the global satellite navigation systems to which the target satellites belong may be the same or different. And then the water level acquisition device can acquire a plurality of target reflection heights. Therefore, the water level obtaining device can obtain the final water level of the water area to be measured in a mode corresponding to the quantity of the obtained target observation data of the target satellite. If the water level obtaining device only obtains the target observation data of a group of target satellites, the target reflection height of the target observation data can be used as the final water level of the area to be measured. If the water level obtaining device obtains target observation data of multiple groups of target satellites, weighting processing can be performed on target reflection heights corresponding to the multiple groups of target observation data to obtain the final water level of the area to be measured.

Specifically, the target weight of each set of target observation data may be obtained based on the satellite system configuration and the satellite time span, and then the target water level of each set of target observation data may be obtained based on the target weight and the target reflection height. Then, the average value of the multiple target water levels can be obtained to obtain an average target water level, which is the final water level of the region to be measured, and the processing mode can be as shown in formula (10):

wherein f is₁，f₂...f_kIs the target weight, f₁+f₂+…f_k＝n，Is an estimated value of the water level of different target satellites in different time spans, namely the target water level t₁，t₂...t_kAre a different span of time that is,is the final water level of the area to be measured.

In addition, H is₀The acquisition of the water level needs a signal-to-noise ratio observation signal with a certain arc length, but the Beidou navigation system and the GPS system have different space configurations, different satellite orbits, different satellite carrier frequencies and the like, and are based on BDS (BeiDou Navigat)For example, when the water level is estimated by using a joint estimation algorithm, different weights can be given according to the BDS and GPS configurations, different weights are given to satellites at different time spans from the water level estimation time point, and finally H is taken as an example₀The weighted average of (a) is used as the water level of the last region to be measured.

The water level is estimated through the joint estimation algorithm, and the accuracy and precision of water level estimation can be effectively improved.

In this embodiment, on the one hand, the CEEMD decomposition and identification algorithm is adopted, so that the interference of the non-water surface reflecting surface can be effectively solved, and the environmental influence is weakened. Due to the complex environment of the dam, the obtained multipath target signals are interference mixed signals of reflection signals from a plurality of surfaces such as the water surface and the dam crest, and the water level estimation algorithm is based on the premise that the reflection signals from the water storage surface are obtained, so that the water surface reflection signals need to be separated. In order to solve the problems, a CEEMD method is introduced, based on the CEEMD method, the method can be used for analyzing non-stationary signals, has good filtering and denoising capabilities and good signal reconstruction effects, and greatly overcomes the problem of modal aliasing.

On the other hand, in order to solve the problem of simplification of a satellite system and effectively cope with a complex environment, a BDS/GPS satellite system can be adopted, then differentiation analysis is carried out on each target satellite in the system, the influence of factors such as signal frequency, altitude angle range and arc section length on a water level estimation result is researched and analyzed, a combined water level estimation algorithm is adopted in combination with the actual observation condition of the dam, a corresponding constraint condition is determined according to prior information, and the final dam water level change monitoring is realized, so that the applicability, reliability and effectiveness of the GNSS-R technology applied to dam water level change monitoring are enhanced.

Optionally, in order to better implement the water level obtaining method, a water level monitoring system is provided below, and the water level monitoring system may be used to obtain the water level of the area to be detected, for example, monitoring the water level of dams such as power station dams, high arch dams, concrete dams, and the like.

In an exemplary embodiment, taking dam water level monitoring as an example, a logical model of the system can be established by combining the actual complex environment and the monitoring requirement of the dam, the model is temporarily independent of the physical factors of the system, such as a computer and a database management system, and is an intermediate structure of the system requirement and the physical implementation, referring to table one, a system organization table showing dam water level monitoring is shown, in table one, each component subsystem (module) of the water level monitoring system is designed and described, and the relationship between the subsystem and the business function.

Watch 1

As shown in fig. 4, the water level monitoring system may include a software foreground, a software calculation background, and a database, wherein the software foreground is mainly used for invoking various programs, configuring stations, interacting data configuration, displaying graphics, and the like.

As shown in fig. 5, the software foreground may include system management, configuration management, station management, signal processing, water level analysis, and help functions. There are 4 links in data processing and data interaction layer, respectively are data management, signal decomposition and identification, water level joint estimation and result output, wherein:

and the data management is to configure corresponding data into a database, wherein the corresponding data comprises certain variable attribute configuration, the received GNSS terminal data and process variables and result variables involved in the water level estimation algorithm process.

The signal decomposition and identification is based on multi-channel signal decomposition and identification of CEEMD, and spectrum analysis is carried out on the denoised GNSS water surface reflection signal to obtain spectrum characteristics.

And (4) water level joint estimation, namely acquiring the water level by using a joint estimation algorithm according to the acquired BDS and GPS SNR data, and finally outputting the result.

The graphic function support layer has 4 aspects, namely a station distribution diagram, a signal decomposition diagram, a water level joint estimation diagram and an analysis report diagram.

The station distribution diagram displays a plurality of GNSS receivers arranged at different positions on the top of the dam, and the station distribution diagram is displayed, so that screening and analysis of the stations for water level estimation are facilitated.

The signal exploded view is used for displaying intrinsic mode functions of all levels of IMFs, non-water surface signals and water surface signals, and is convenient for result analysis by combining water level data in the later period.

The water level joint estimation graph can display water level results of BDS, GPS and BDS/GPS joint estimation, and can visually display water level estimation precision, comparison of water level estimation precision of each system and water level change trend.

The analysis report graph is used for displaying analysis reports and analysis images of links such as GNSS signal-to-noise ratio observation signal decomposition identification and BDS/GPS joint estimation.

As shown in FIG. 6, the software solution background may include 6 components, namely RINEX file data acquisition, data preprocessing, CEEMD decomposition identification, L-S spectrum analysis, GPS/BDS joint water level estimation and water level analysis. Wherein:

the RINEX file data acquisition refers to acquiring SNR, ELE, AZI, EPOCH and other data information from the GNSS RINEX file for subsequent data processing.

The data preprocessing comprises the elimination of data gross errors, the pre-analysis of a satellite distribution diagram and the determination of a Fresnel reflection area.

The method comprises the steps of decomposing and identifying signal-to-noise ratio multipath reflection signals by adopting a CEEMD algorithm, preliminarily decomposing the signals and removing high-frequency noise according to function characteristics of IMF eigenmodes and noise contribution rates, then removing non-water surface signals according to a priori determination strategy and an adjacent verification algorithm, and finally obtaining useful reflection signals of a water storage surface.

And L-S spectrum analysis, namely resampling and unitizing the reflected signal data, setting relevant model constraint conditions, and obtaining the main frequency by using an L-S spectrum analysis method.

And by utilizing the constructed combined water level estimation algorithm, estimating the water level of the dam by using the satellite signal-to-noise ratio data of the GPS + BDS system, acquiring a water level change result, and carrying out validity check and subsequent water level analysis on the water level change.

The database support comprises data storage, data acquisition and data management, and the database is designed to meet the functional requirements in an auxiliary mode and mainly stores foreground configuration information of software and background data resolving results. The database design meets the following aspects according to requirements: meeting current needs, separating subject and adjunct, appropriate redundancy, coping with new needs that may arise, coping with large data volumes.

In this way, a complete dam water level monitoring solution is formed. In conclusion, the water level monitoring system constructed and designed mainly comprises a software foreground, a software resolving background and a database part, wherein the software foreground is mainly used for calling various programs, configuring a measuring station, interacting data configuration, displaying graphs and the like. The data processing background program comprises a RINEX file data acquisition part, a CEEMD decomposition identification part, an L-S spectrum analysis part, a GPS/BDS combined water level estimation part and the like. The database part supports data storage, data acquisition and data management. The water level monitoring system can be used for monitoring the water level of dams such as power station dams, high arch dams, concrete dams and the like.

Based on the water level obtaining method provided by the above embodiment, correspondingly, the application further provides a specific implementation manner of the water level obtaining device. Please see the examples below.

Referring to fig. 7 first, a water level obtaining apparatus provided in an embodiment of the present application includes the following modules:

a data obtaining module 701, configured to obtain target observation data of a target satellite corresponding to a region to be detected;

a decomposition module 702, configured to decompose a target multipath reflected signal from the target observation data;

an identifying module 703, configured to identify a water surface reflection signal from the target multipath reflection signal according to a preset identifying condition;

the spectrum analysis module 704 is configured to perform spectrum analysis on the water surface reflection signal to obtain a target reflection height corresponding to the target observation data;

a water level obtaining module 705, configured to obtain the water level of the area to be measured according to the target reflection height.

Through the matching processing of the modules, the water level of the area to be measured can be obtained through the reflection height obtained by observing data. Since the reflection height is obtained based on the water surface reflection signal identified from the observation data, the interference caused by the non-water surface reflection signal in the observation data can be eliminated, thereby improving the accuracy of the acquired water level.

Optionally, in order to obtain the target multipath reflected signal, the decomposition module 702 is specifically configured to:

extracting a signal-to-noise ratio observation signal from the target observation data;

and performing direct signal removing processing and noise signal removing processing on the signal-to-noise ratio observation signal to obtain the target multipath reflection signal.

Optionally, in order to obtain the target multipath reflected signal, the decomposition module 702 is further configured to:

removing trend items from the signal-to-noise ratio observation signal to obtain a target signal-to-noise ratio observation signal which does not contain the direct signal;

carrying out empirical mode decomposition on the target signal-to-noise ratio observation signal to obtain a plurality of layers of intrinsic mode signals;

determining principal component components of the intrinsic mode signals of each layer through singular value decomposition;

determining a boundary layer of a noise signal and the target multipath reflection signal by combining the principal component and a preset noise contribution rate;

and taking the eigenmode signal with the layer number behind the boundary layer as the target multipath reflection signal.

Optionally, in order to identify the water surface reflection signal, the identifying module 703 is specifically configured to:

and identifying the water surface reflection signal from the target multipath reflection signal by combining the signal spectrum variation characteristic and the adjacent annual area daily interval period.

Optionally, in order to identify the water surface reflection signal, the identifying module 703 is further configured to:

identifying a first eigenmode signal from the target multipath reflected signal based on signal spectrum variation characteristics of a non-water surface signal;

acquiring a correlation coefficient of the first eigenmode signal and an eigenmode signal in historical multipath reflection signals; the historical multi-path reflected signal is obtained based on observation data which is separated from the target observation data by the adjacent annual product day interval time period;

identifying a second eigenmode signal from the first eigenmode signals based on the correlation coefficient;

and removing the second eigenmode signal from the target multipath reflection signal to obtain the water surface reflection signal.

Optionally, in order to obtain a target reflection height corresponding to the target observation data, the spectrum analysis module 704 is specifically configured to:

carrying out frequency spectrum analysis on the water surface reflection signal to obtain the main frequency of the water surface reflection signal;

and acquiring the target reflection height corresponding to the target observation data by combining the main frequency and the conversion relation between the main frequency and the reflection height.

Optionally, the relationship between the main frequency and the reflection height includes:

wherein f is_mIs the main frequency, lambda is the carrier wavelength, H_effIs the target reflection height.

Optionally, the number of the target satellites is multiple, correspondingly, the number of the target observation data is multiple groups, and the number of the target reflection heights is multiple; wherein the global satellite navigation systems to which the plurality of target satellites belong are different.

Optionally, in order to obtain the water level of the area to be measured, the water level obtaining module 705 is specifically configured to:

acquiring target weight values of each group of target observation data based on the satellite system configuration and the satellite time span;

acquiring the target water level of each group of target observation data based on the target weight and the target reflection height;

and averaging a plurality of target water levels to obtain the water level of the area to be detected.

Each module in the water level obtaining apparatus provided in fig. 7 has a function of implementing each step in the embodiment shown in fig. 3, and achieves the same technical effect as the water level obtaining method shown in fig. 3, and for brevity, no further description is given here.

Fig. 8 is a schematic diagram of a hardware structure of a water level obtaining apparatus for implementing various embodiments of the present application.

The water level acquisition device may comprise a processor 801 and a memory 802 in which computer program instructions are stored.

Specifically, the processor 801 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 802 may include mass storage for data or instructions. By way of example, and not limitation, memory 802 may include a Hard Disk Drive (HDD), a floppy Disk Drive, flash memory, an optical Disk, a magneto-optical Disk, a tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 802 may include removable or non-removable (or fixed) media, where appropriate. The memory 802 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 802 is a non-volatile solid-state memory. In a particular embodiment, the memory 802 includes Read Only Memory (ROM). Where appropriate, the ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or a combination of two or more of these.

The processor 801 reads and executes the computer program instructions stored in the memory 802 to implement any of the water level obtaining methods in the above embodiments.

In one example, the water level acquisition device may further include a communication interface 803 and a bus 810. As shown in fig. 8, the processor 801, the memory 802, and the communication interface 803 are connected via a bus 810 to complete communication therebetween.

The communication interface 803 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present application.

The bus 810 includes hardware, software, or both to couple the components of the water level acquisition device to each other. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hypertransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 810 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.

The water level obtaining apparatus may perform the water level obtaining method in the embodiment of the present application, thereby implementing the water level obtaining method and apparatus described in conjunction with fig. 3 and 7.

An embodiment of the present application further provides a computer-readable storage medium, where the computer storage medium has computer program instructions stored thereon; when executed by the processor, the computer program instructions implement the processes of the above-mentioned water level obtaining method embodiments, and can achieve the same technical effects, and are not described herein again to avoid repetition.

It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.

The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.

It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.

As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.