Method and device for removing temporal noise and spatial noise of hydrological monitoring data

Abstract

The application relates to the technical field of hydrological monitoring and discloses a hydrological monitoring data instantaneous noise and spatial noise cooperative removal method and device. The hydrological monitoring data instantaneous noise and spatial noise cooperative removal method comprises the following steps: extracting instantaneous noise characteristics and spatial noise characteristics in hydrological monitoring data to obtain target instantaneous noise characteristics and target spatial noise characteristics; inputting the target instantaneous noise characteristics, the target spatial noise characteristics and the hydrological monitoring data into a joint optimization model to obtain target denoising parameters; performing denoising processing on the hydrological monitoring data according to the target denoising parameters to obtain first denoised hydrological monitoring data; performing denoising processing on the hydrological monitoring data again by using the first denoised hydrological monitoring data to perform data reconstruction and obtain target hydrological monitoring data. The hydrological monitoring data instantaneous noise and spatial noise are cooperatively removed, and the accuracy of the hydrological monitoring data is improved.

Description

Technical Field

[0001] This invention relates to the field of hydrological monitoring technology, specifically to a method and apparatus for the coordinated removal of instantaneous and spatial noise from hydrological monitoring data. Background Technology

[0002] During hydrological monitoring, monitoring equipment is susceptible to interference from various factors, resulting in noise in the monitoring data. Instantaneous noise is typically caused by sudden electromagnetic interference, brief equipment malfunctions, etc., and is characterized by large amplitude and short duration. Spatial noise arises from the spatial correlation between different monitoring points within the monitoring area and spatial variations in environmental factors, manifesting as spatial discontinuities and abnormal fluctuations in the data. To ensure the accuracy of hydrological monitoring data, noise removal is necessary.

[0003] In related technologies, the processing of instantaneous noise and spatial noise in hydrological monitoring data is often carried out independently. However, removing instantaneous noise alone may destroy the spatial correlation of the data, while simply adjusting spatial noise may retain unidentified instantaneous outliers, resulting in insufficient spatiotemporal consistency of the denoised data. Therefore, it is difficult to cope with the changes in noise characteristics under different hydrological scenarios. Summary of the Invention

[0004] This invention provides a method and apparatus for the coordinated removal of instantaneous and spatial noise from hydrological monitoring data, in order to solve the problem in related technologies that it is difficult to cope with changes in noise characteristics under different hydrological scenarios.

[0005] In a first aspect, the present invention provides a method for the collaborative removal of instantaneous noise and spatial noise from hydrological monitoring data, comprising: acquiring hydrological monitoring data; extracting instantaneous noise features from the hydrological monitoring data to obtain target instantaneous noise features; extracting spatial noise features from the hydrological monitoring data to obtain target spatial noise features; inputting the target instantaneous noise features, target spatial noise features, and hydrological monitoring data into a joint optimization model to obtain target denoising parameters; the joint optimization model is a denoising parameter optimization model constructed with minimizing data reconstruction error and spatial consistency deviation as objective functions and feature constraints of instantaneous noise and spatial noise as constraint conditions; denoising the hydrological monitoring data according to the target denoising parameters to obtain first denoised hydrological monitoring data; updating the hydrological monitoring data using the first denoised hydrological monitoring data; iterating back the steps of extracting instantaneous noise features and spatial noise features from the hydrological monitoring data until a preset stopping condition is reached to obtain second denoised hydrological monitoring data; and reconstructing the second denoised hydrological monitoring data to obtain the collaboratively denoised target hydrological monitoring data.

[0006] This invention discloses a method for the collaborative removal of instantaneous and spatial noise from hydrological monitoring data. The method acquires hydrological monitoring data, extracts instantaneous noise features from the data to obtain target instantaneous noise features, and extracts spatial noise features from the data to obtain target spatial noise features. This accurately identifies and quantifies the core characteristics of instantaneous and spatial noise, achieving differentiated characterization of noise types. The invention inputs the target instantaneous noise features, target spatial noise features, and hydrological monitoring data into a joint optimization model constructed with minimizing data reconstruction error and spatial consistency deviation as objective functions and the characteristic constraints of instantaneous and spatial noise as constraints. This yields target denoising parameters. Minimizing reconstruction error ensures that the data retains the original hydrological information to the greatest extent possible after denoising, while minimizing spatial consistency deviation ensures that the spatial distribution pattern of the hydrological data remains unchanged, aligning with the spatial correlation characteristics of hydrological monitoring. The target denoising parameters obtained through the joint optimization model can collaboratively remove instantaneous and spatial noise. This invention denoises hydrological monitoring data based on target denoising parameters to obtain first denoised hydrological monitoring data. The first denoised hydrological monitoring data is then used to update the existing hydrological monitoring data. The process iteratively repeats steps to extract instantaneous noise features and spatial noise features from the hydrological monitoring data until a preset stopping condition is met, resulting in second denoised hydrological monitoring data. Through multiple noise extractions, parameter optimizations, and iterative denoising, residual instantaneous and spatial noise are gradually weakened, continuously correcting denoising deviations. Instantaneous and spatial noise are co-optimized, taking into account spatial noise adjustments while removing instantaneous noise, and considering the impact of spatial noise on instantaneous noise while processing spatial noise. This fully utilizes the interrelationship between the two, effectively improving the spatiotemporal consistency of the denoised data. This invention then reconstructs the second denoised hydrological monitoring data to obtain co-denoised target hydrological monitoring data, which more accurately reflects the true changing trends of hydrological phenomena. Compared with related technologies, this invention solves the problems of insufficient spatiotemporal consistency and difficulty in adapting to the changes in noise characteristics of different hydrological scenarios caused by processing instantaneous noise and spatial noise separately. By using a collaborative optimization approach, the removal of both types of noise is completed simultaneously. While effectively removing various types of noise, the original true information and spatial distribution patterns of hydrological data are preserved to the greatest extent, thereby improving the overall accuracy and reliability of the denoised hydrological monitoring data.

[0007] In one optional implementation, the instantaneous noise features in the hydrological monitoring data are extracted to obtain target instantaneous noise features, and the spatial noise features in the hydrological monitoring data are extracted to obtain target spatial noise features. This includes: performing deviation analysis on each data point in the hydrological monitoring data, identifying data points with deviations greater than a preset threshold as potential instantaneous noise anomalies, and obtaining a set of potential instantaneous noise; performing wavelet transform analysis on the hydrological monitoring data to obtain the frequency distribution and energy characteristics of the instantaneous noise, and obtaining the target instantaneous noise features based on the set of potential instantaneous noise and the frequency distribution and energy characteristics of the instantaneous noise; analyzing the spatial correlation between different monitoring stations based on the hydrological monitoring data to obtain a spatial correlation index; the spatial correlation index is used to quantify the spatial clustering or dispersion characteristics of the hydrological monitoring data; constructing a correlation model between spatial noise and environmental factors using the spatial noise intensity of the hydrological monitoring data as the dependent variable and environmental factors as the independent variable, and obtaining the target spatial noise features based on the spatial correlation index and the correlation model.

[0008] This invention performs deviation analysis on each data point in hydrological monitoring data, identifying data points with deviations exceeding a preset threshold as potential transient noise anomalies. It quickly identifies potential transient noise points with abnormal amplitudes and uses wavelet transform to analyze the frequency distribution and energy characteristics of the noise, accurately locating high-frequency noise sub-bands. For spatial noise, it analyzes the spatial correlation between different monitoring stations based on hydrological monitoring data, obtaining spatial correlation indices. Using the spatial noise intensity of hydrological monitoring data as the dependent variable and environmental factors as the independent variable, it constructs a correlation model between spatial noise and environmental factors. This not only quantifies the spatial correlation and noise distribution patterns between monitoring stations but also reveals the influence mechanism of environmental factors on spatial noise. It comprehensively covers the temporal domain characteristics of transient noise and the spatial domain and environmental correlation characteristics of spatial noise, providing accurate and comprehensive noise information support for subsequent collaborative denoising and avoiding denoising deviations caused by incomplete noise feature characterization.

[0009] In one optional implementation, the instantaneous noise characteristics of the target, the spatial noise characteristics of the target, and the hydrological monitoring data are input into a joint optimization model to obtain the target denoising parameters. This includes: constructing a joint optimization model with the objective function of minimizing data reconstruction error and spatial consistency deviation, and with the characteristic constraints of instantaneous noise and spatial noise as constraints; inputting the instantaneous noise characteristics of the target, the spatial noise characteristics of the target, and the hydrological monitoring data into the joint optimization model, and solving the joint optimization model to obtain the target denoising parameters.

[0010] In one optional implementation, the hydrological monitoring data is denoised according to the target denoising parameters to obtain the first denoised hydrological monitoring data, including: performing sliding window filtering on the hydrological monitoring data according to the target denoising parameters to obtain preliminary denoised hydrological monitoring data; performing spatial interpolation on the preliminary denoised hydrological monitoring data, using the deviation between the data of each monitoring station and the interpolation result as a spatial noise adjustment amount, and performing spatial denoising on the preliminary denoised hydrological monitoring data to obtain the first denoised hydrological monitoring data.

[0011] In one optional implementation, the second denoised hydrological monitoring data is reconstructed to obtain the target hydrological monitoring data after collaborative denoising, including: using interpolation processing to fill in missing points in the second denoised hydrological monitoring data, and extracting and reconstructing the trend items in the second denoised hydrological monitoring data to obtain the target hydrological monitoring data after collaborative denoising.

[0012] In one optional implementation, before reconstructing the second denoised hydrological monitoring data to obtain the target hydrological monitoring data after collaborative denoising, the method for collaborative removal of instantaneous noise and spatial noise in hydrological monitoring data further includes: evaluating the second denoised hydrological monitoring data based on the mean square error index, the peak signal-to-noise ratio index, and the spatial correlation coefficient index to obtain an evaluation result; the mean square error index is used to reflect the average error between the second denoised hydrological monitoring data and the real data, the peak signal-to-noise ratio index is used to measure the degree of quality improvement of the second denoised hydrological monitoring data, and the spatial correlation coefficient index is used to evaluate the spatial consistency of the second denoised hydrological monitoring data; if the evaluation result does not meet the preset expected standard, the parameters of the joint optimization model are adjusted, and the step of inputting the target instantaneous noise characteristics, the target spatial noise characteristics, and the hydrological monitoring data into the joint optimization model is returned to iteratively.

[0013] The multi-index evaluation system of this invention includes mean square error index, peak signal-to-noise ratio index, and spatial correlation coefficient index. It comprehensively quantifies the denoising effect from both the time and spatial domains, avoiding the one-sidedness of a single index. The denoising parameters are dynamically optimized based on the evaluation results. If the index does not meet expectations, the denoising process is automatically re-executed. It can adapt to the characteristics of different monitoring data without manual intervention, ensuring that the denoising effect always meets the needs of practical applications.

[0014] Secondly, the present invention provides a device for the collaborative removal of instantaneous noise and spatial noise from hydrological monitoring data, comprising: a noise feature extraction module for acquiring hydrological monitoring data, extracting instantaneous noise features from the hydrological monitoring data to obtain target instantaneous noise features, and extracting spatial noise features from the hydrological monitoring data to obtain target spatial noise features; and a denoising parameter determination module for inputting the target instantaneous noise features, target spatial noise features, and hydrological monitoring data into a joint optimization model to obtain target denoising parameters; the joint optimization model is configured to minimize data reconstruction error and spatial consistency deviation as the objective function, and to determine the instantaneous noise parameters as the target spatial noise features. The system employs a denoising parameter optimization model constructed with spatial noise feature constraints as constraints; an iterative solution module is used to denoise the hydrological monitoring data according to the target denoising parameters to obtain the first denoised hydrological monitoring data; the first denoised hydrological monitoring data is used to update the hydrological monitoring data; the steps of extracting instantaneous noise features and spatial noise features from the hydrological monitoring data are iterated until a preset stopping condition is reached to obtain the second denoised hydrological monitoring data; and a data reconstruction module is used to reconstruct the second denoised hydrological monitoring data to obtain the target hydrological monitoring data after collaborative denoising.

[0015] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the above-described method for the coordinated removal of instantaneous noise and spatial noise from hydrological monitoring data in any of the first aspects or corresponding embodiments.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the method for collaborative removal of instantaneous and spatial noise from hydrological monitoring data as described in the first aspect or any corresponding embodiment.

[0017] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the method for collaborative removal of instantaneous and spatial noise from hydrological monitoring data as described in the first aspect or any corresponding embodiment. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the method for collaborative removal of instantaneous noise and spatial noise in hydrological monitoring data according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process of the method for collaborative removal of instantaneous noise and spatial noise in hydrological monitoring data according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the method for collaborative removal of instantaneous noise and spatial noise in hydrological monitoring data according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a device for the coordinated removal of instantaneous and spatial noise from hydrological monitoring data according to an embodiment of the present invention. Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0022] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0023] As an optional application scenario of this invention, such as Figure 1 As shown, the hydrological monitoring data instantaneous noise and spatial noise collaborative removal system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0024] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0025] This invention provides a method for the coordinated removal of instantaneous and spatial noise in hydrological monitoring data. By coordinating the removal of instantaneous and spatial noise in hydrological monitoring data, the accuracy of hydrological monitoring data can be improved.

[0026] According to an embodiment of the present invention, a method for the coordinated removal of instantaneous noise and spatial noise in hydrological monitoring data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] This embodiment provides a method for the coordinated removal of instantaneous and spatial noise in hydrological monitoring data, which can be achieved using computer equipment. Figure 2 This is a flowchart of the first method for collaborative removal of instantaneous and spatial noise from hydrological monitoring data according to an embodiment of the present invention, as follows: Figure 2 As shown, the process includes the following steps: Step S201: Obtain hydrological monitoring data, extract instantaneous noise features from the hydrological monitoring data to obtain target instantaneous noise features, and extract spatial noise features from the hydrological monitoring data to obtain target spatial noise features.

[0028] Among them, hydrological monitoring data refers to the raw temporal spatial grid monitoring data collected by hydrological stations, including water level, flow rate, flow velocity, and rainfall; target instantaneous noise characteristics are the characteristics of instantaneous noise extracted from hydrological monitoring data caused by sudden electromagnetic interference, brief equipment failures, etc. For example, target instantaneous noise characteristics include potential instantaneous noise sets, frequency distribution and energy characteristics of instantaneous noise, etc.; target spatial noise characteristics are the characteristics of spatial noise extracted from hydrological monitoring data caused by spatial correlation between different monitoring points in the monitoring area and spatial changes in environmental factors, etc. For example, target spatial noise characteristics include spatial correlation indicators, correlation models between spatial noise and environmental factors, etc.

[0029] Step S202: Input the instantaneous noise characteristics of the target, the spatial noise characteristics of the target, and the hydrological monitoring data into the joint optimization model to obtain the target denoising parameters. The joint optimization model is a denoising parameter optimization model constructed with the objective function of minimizing data reconstruction error and spatial consistency deviation, and the characteristic constraints of instantaneous noise and spatial noise as constraints.

[0030] The joint optimization model is an optimization model that simultaneously constrains instantaneous noise and spatial noise, taking into account both temporal accuracy and spatial consistency. The target denoising parameters are parameters used for noise reduction. For example, the target denoising parameters include the instantaneous noise removal threshold, the wavelet decomposition layer number / threshold, the spatial weight matrix attenuation coefficient, the iterative convergence threshold, and the sliding window size, etc.

[0031] In some optional implementations, the data reconstruction error is the deviation between the denoised data and the real hydrological signal; the spatial consistency deviation is the degree of deviation of the spatial distribution pattern of hydrological data of each station in the denoised area; the objective function is the core minimization index of model optimization; the constraints are rules that limit the noise processing boundary to avoid excessive or insufficient noise reduction; for example, the characteristic constraints of instantaneous noise and spatial noise include: statistical anomaly threshold constraints, high-frequency subband energy / kurtosis / variance constraints, spatial autocorrelation coefficient constraints, spatial distribution continuity constraints, environmental association model constraints, etc.

[0032] Step S203: Denoise the hydrological monitoring data according to the target denoising parameters to obtain the first denoised hydrological monitoring data. Update the hydrological monitoring data using the first denoised hydrological monitoring data. Iterate through the steps of extracting instantaneous noise features and spatial noise features from the hydrological monitoring data until the preset stopping condition is met to obtain the second denoised hydrological monitoring data.

[0033] Among them, the first denoised hydrological monitoring data is the preliminary hydrological data after a single denoising process; the second denoised hydrological monitoring data is the optimized data after multiple iterations of denoising, when the noise tends to be stable.

[0034] In some alternative implementations, updating the hydrological monitoring data using the first denoised hydrological monitoring data includes replacing the hydrological monitoring data with the first denoised hydrological monitoring data.

[0035] In some alternative implementations, the preset stopping condition can be that the data reconstruction error and spatial consistency deviation reach a preset threshold.

[0036] Step S204: Reconstruct the second denoised hydrological monitoring data to obtain the target hydrological monitoring data after collaborative denoising.

[0037] Among them, data reconstruction is used to complete the temporal integrity of the noise-reduced data, repair spatial distribution distortion, and restore the true hydrological change pattern; the target hydrological monitoring data is the final hydrological data that has been simultaneously removed from instantaneous and spatial noise and conforms to the hydrological pattern in both time and space.

[0038] This embodiment provides a method for the collaborative removal of instantaneous and spatial noise from hydrological monitoring data. It acquires hydrological monitoring data, extracts instantaneous noise features from the data to obtain target instantaneous noise features, and extracts spatial noise features to obtain target spatial noise features. This accurately identifies and quantifies the core characteristics of instantaneous and spatial noise, achieving differentiated characterization of noise types. In this embodiment, the target instantaneous noise features, target spatial noise features, and hydrological monitoring data are input into a joint optimization model constructed with minimizing data reconstruction error and spatial consistency deviation as objective functions and the characteristic constraints of instantaneous and spatial noise as constraints. This yields target denoising parameters. Minimizing reconstruction error ensures that the data retains the original hydrological information to the greatest extent possible after denoising, while minimizing spatial consistency deviation ensures that the spatial distribution pattern of the hydrological data remains unchanged, aligning with the spatial correlation characteristics of hydrological monitoring. The target denoising parameters obtained through the joint optimization model can collaboratively remove instantaneous and spatial noise. This invention denoises hydrological monitoring data according to target denoising parameters to obtain first denoised hydrological monitoring data. The first denoised hydrological monitoring data is then used to update the existing hydrological monitoring data. The process iterates through the steps of extracting instantaneous noise features and spatial noise features from the hydrological monitoring data until a preset stopping condition is met, resulting in second denoised hydrological monitoring data. Through multiple noise extractions, parameter optimizations, and iterative denoising, residual instantaneous and spatial noise are gradually weakened, continuously correcting denoising deviations. Instantaneous and spatial noise are co-optimized, taking into account spatial noise adjustments while removing instantaneous noise, and considering the impact of spatial noise on instantaneous noise while processing spatial noise. This fully utilizes the interrelationship between the two, effectively improving the spatiotemporal consistency of the denoised data. This invention also reconstructs the second denoised hydrological monitoring data to obtain co-denoised target hydrological monitoring data, which more accurately reflects the true changing trends of hydrological phenomena. Compared with related technologies, the embodiments of the present invention solve the problems of insufficient spatiotemporal consistency and difficulty in adapting to the changes in noise characteristics of different hydrological scenarios caused by processing instantaneous noise and spatial noise separately. By using a collaborative optimization method, the removal of the two types of noise is completed simultaneously. While effectively removing various types of noise, the original true information and spatial distribution patterns of hydrological data are preserved to the greatest extent, thereby improving the overall accuracy and reliability of the denoised hydrological monitoring data.

[0039] This embodiment provides a method for the coordinated removal of instantaneous and spatial noise in hydrological monitoring data, which can be achieved using computer equipment. Figure 3This is a second flowchart of the method for collaborative removal of instantaneous and spatial noise from hydrological monitoring data according to an embodiment of the present invention, as follows: Figure 3 As shown, the process includes the following steps: Step S301: Obtain hydrological monitoring data, extract instantaneous noise features from the hydrological monitoring data to obtain target instantaneous noise features, and extract spatial noise features from the hydrological monitoring data to obtain target spatial noise features.

[0040] Specifically, step S301 includes: Step S3011: Perform deviation analysis on each data point in the hydrological monitoring data, and identify data points with deviations greater than a preset threshold as potential transient noise anomalies to obtain a set of potential transient noise.

[0041] Among them, a method based on statistical analysis and signal processing is used to analyze the hydrological monitoring data point by point. By calculating the mean, standard deviation and kurtosis statistical characteristics of the data, outliers in the hydrological monitoring data are identified. These outliers form a set of potential transient noise.

[0042] Specifically, the hydrological time series data of a single station in the hydrological monitoring data is obtained, and the mean and standard deviation of the data in each window are calculated segment by segment using a sliding window. The deviation of each data point is obtained based on the difference between the mean and the standard deviation. When the deviation is greater than a preset threshold, the data point is judged as a potential instantaneous noise anomaly. The preset threshold is dynamically determined through the statistical distribution characteristics of historical data. For example, the preset threshold can be 2.8.

[0043] In some optional implementations, the method for determining potential transient noise anomalies can also be as follows: Data points within each sliding window are sorted from smallest to largest using a sliding window to obtain the 25th and 75th quantiles. The interquartile range is obtained based on the difference between the 75th and 25th quantiles. The interquartile range is multiplied by 1.5 to obtain the product. The lower bound of normal data is obtained based on the difference between the 25th quantile and the product. The upper bound of normal data is obtained based on the sum of the 75th quantile and the product. If a single data point is greater than the upper bound of normal data, the deviation is the difference between the data point and the upper bound of normal data. If a single data point is less than the lower bound of normal data, the deviation is the difference between the lower bound of normal data and the data point. If a single data point is greater than or equal to the lower bound of normal data and less than or equal to the upper bound of normal data, the deviation is 0. When the deviation is greater than a preset threshold, the data point is determined as a potential transient noise anomaly. The preset threshold is dynamically determined based on the statistical distribution characteristics of historical data. For example, the preset threshold can be 3.2.

[0044] Step S3012: Perform wavelet transform analysis on the hydrological monitoring data to obtain the frequency distribution and energy characteristics of the instantaneous noise. Based on the potential instantaneous noise set and the frequency distribution and energy characteristics of the instantaneous noise, obtain the target instantaneous noise characteristics.

[0045] Among them, wavelet transform signal processing technology is used to analyze the characteristics of data at different frequency scales, and further determine the frequency distribution and energy characteristics of instantaneous noise, providing a basis for subsequent removal operations.

[0046] Specifically, when using wavelet transform for signal processing, multi-level multi-resolution analysis is adopted. Discrete wavelet transform or continuous wavelet transform is performed on hydrological monitoring data. By analyzing the energy distribution of different frequency sub-bands, high-frequency sub-bands containing instantaneous noise are identified, and the kurtosis and variance of each high-frequency sub-band are extracted as frequency characteristic parameters of instantaneous noise.

[0047] In some optional implementations, single-station hydrological time-series data from hydrological monitoring data are acquired. Discrete wavelet transform is performed on the single-station time-series hydrological data, and multi-scale decomposition is carried out. The low-frequency approximate component indicates the normal hydrological trend signal, while the high-frequency detail component is the high-frequency abrupt signal. Based on the potential instantaneous noise set, the scale corresponding to each noise point in each wavelet high-frequency layer is found. The smaller the scale, the higher the corresponding frequency. The main distribution levels of all noise points are statistically analyzed to obtain the instantaneous noise frequency distribution range. For each noise point, the energy characteristics of the corresponding high-frequency layer wavelet coefficients are calculated. The larger the energy characteristics, the stronger the noise interference intensity.

[0048] In some alternative implementations, the target transient noise feature is composed of a set of potential transient noises and the frequency distribution and energy characteristics of the transient noises.

[0049] Step S3013: Analyze the spatial correlation of data between different monitoring stations based on hydrological monitoring data to obtain spatial correlation indicators; spatial correlation indicators are used to quantify the spatial clustering or dispersion characteristics of hydrological monitoring data.

[0050] Among them, based on geographic information system technology and spatial statistics methods, the spatial correlation of data between different monitoring stations is analyzed, and indicators such as spatial autocorrelation coefficient and variability function are calculated to characterize the spatial distribution pattern and intensity characteristics of spatial noise.

[0051] Specifically, the global spatial autocorrelation coefficient is used to calculate the spatial clustering / dispersion characteristics based on the constructed spatial weight matrix (considering the Euclidean distance or geographical adjacency of the sites), and the variogram is used to characterize the degree of variation of spatial noise at different spatial distances.

[0052] In some optional implementations, hydrological data of each hydrological monitoring station at the same time is obtained from the hydrological monitoring data to construct a spatial dataset. A spatial weight matrix is ​​constructed based on the Euclidean distance between every two hydrological monitoring stations. Alternatively, if the hydrological monitoring stations have a common boundary (i.e., are adjacent), the weight is set to 1 if there is an adjacency relationship and 0 if there is no adjacency relationship. A spatial weight matrix is ​​constructed, and the spatial autocorrelation coefficient is calculated based on the spatial weight matrix and the spatial dataset to obtain the spatial correlation index.

[0053] In constructing the spatial weight matrix based on the Euclidean distance between every two hydrological monitoring stations, an adaptive distance decay function is used. The weight values ​​decrease exponentially or Gaussianly as the Euclidean distance between the monitoring stations increases. The decay coefficient is determined by fitting the semi-variogram of the spatial autocorrelation coefficient. The adaptive distance decay function is expressed as follows:

[0054]

[0055] in, Hydrological monitoring stations corresponding to the number decay function With hydrological monitoring stations Spatial weights, The attenuation coefficient is determined by fitting the semivariogram of the spatial autocorrelation coefficient. Hydrological monitoring stations With hydrological monitoring stations The Euclidean distance between them Hydrological monitoring stations corresponding to the Gaussian decay function With hydrological monitoring stations Spatial weights.

[0056] Step S3014: Using the spatial noise intensity of hydrological monitoring data as the dependent variable and environmental factors as the independent variable, construct a correlation model between spatial noise and environmental factors. Based on the spatial correlation index and the correlation model, obtain the target spatial noise characteristics.

[0057] Among them, by combining environmental factors such as topography and meteorology, a correlation model between spatial noise and environmental factors is established to gain a deeper understanding of the generation mechanism of spatial noise and provide spatial dimension information for collaborative noise reduction.

[0058] Specifically, when establishing the correlation model between spatial noise and environmental factors, geographically weighted regression or ordinary least squares method is adopted. Environmental factors such as terrain slope, altitude, precipitation, and wind speed are used as independent variables, and spatial noise intensity is used as the dependent variable. A spatial heterogeneity regression model is constructed to obtain the correlation model. When the spatial heterogeneity regression model adopts geographically weighted regression, the bandwidth selection is optimized by modifying the Akaike Information Criterion to balance the model fitting accuracy and spatial smoothness.

[0059] In some alternative implementations, the target spatial noise features are composed of spatial correlation indices and correlation models.

[0060] Step S302: Input the instantaneous noise characteristics of the target, the spatial noise characteristics of the target, and the hydrological monitoring data into the joint optimization model to obtain the target denoising parameters; the joint optimization model is a denoising parameter optimization model constructed with the minimization of data reconstruction error and spatial consistency deviation as the objective function and the characteristic constraints of instantaneous noise and spatial noise as the constraint conditions.

[0061] Specifically, step S302 includes: Step S3021: With the minimization of data reconstruction error and spatial consistency deviation as the objective function and the characteristic constraints of instantaneous noise and spatial noise as the constraint conditions, a joint optimization model is constructed.

[0062] A joint optimization model is established, which treats instantaneous noise removal and spatial noise removal as two sub-objectives for collaborative optimization. The model takes minimizing data reconstruction error and spatial consistency deviation as the objective function, while considering the characteristic constraints of instantaneous noise and spatial noise.

[0063] Specifically, the objective function can be expressed as:

[0064] in, To minimize, Let be the objective function. For data reconstruction error, As the first weighting coefficient, This is due to spatial consistency deviation. This is the second weighting coefficient.

[0065] In some optional implementations, instantaneous noise characteristic constraints include: statistical anomaly threshold constraints, high-frequency subband energy / kurtosis / variance constraints; spatial noise characteristic constraints include: spatial autocorrelation coefficient constraints, spatial distribution continuity constraints, and environmental correlation model constraints.

[0066] Step S3022: Input the instantaneous noise characteristics of the target, the spatial noise characteristics of the target, and the hydrological monitoring data into the joint optimization model, solve the joint optimization model, and obtain the target denoising parameters.

[0067] By solving this joint optimization model, the optimal denoising parameters are obtained, which enables the removal of instantaneous noise while effectively adjusting spatial noise and maintaining the spatial consistency of the data.

[0068] Specifically, the target denoising parameters include the instantaneous noise removal threshold, the number of wavelet decomposition layers / threshold, the spatial weight matrix decay coefficient, the iterative convergence threshold, and the sliding window size.

[0069] In some alternative implementations, iterative algorithms are used to solve the joint optimization model.

[0070] Step S303: Denoise the hydrological monitoring data according to the target denoising parameters to obtain the first denoised hydrological monitoring data. Update the hydrological monitoring data using the first denoised hydrological monitoring data. Iterate through the steps of extracting instantaneous noise features and spatial noise features from the hydrological monitoring data until the preset stopping condition is met to obtain the second denoised hydrological monitoring data.

[0071] Specifically, step S303 includes: Step S3031: Based on the target denoising parameters, perform sliding window filtering on the hydrological monitoring data to obtain preliminary denoised hydrological monitoring data.

[0072] Specifically, based on the target denoising parameters, a sliding window filter is applied to the data sequence of each monitoring station, and the window size is dynamically adjusted according to the time scale of the instantaneous noise.

[0073] In some optional implementations, the sliding window size is set. For example, the sliding window size for short-term instantaneous noise is 3-6 time steps, and the sliding window size for longer burst noise is 12-24 time steps. Based on the target denoising parameters (wavelet decomposition level / threshold / statistical filter weight), wavelet threshold filtering or mean filtering is used to slide the window point by point along the time series of water level / flow at a single station, filtering the data within the window, removing instantaneous noise from single-point abrupt changes, and obtaining preliminary denoised hydrological monitoring data.

[0074] Step S3032: Spatial interpolation processing is performed on the preliminary denoised hydrological monitoring data. The deviation between the data of each monitoring station and the interpolation result is used as the spatial noise adjustment amount. Spatial denoising processing is performed on the preliminary denoised hydrological monitoring data to obtain the first denoised hydrological monitoring data.

[0075] Among them, Kriging interpolation or inverse distance weighting is used to spatially interpolate the data after preliminary denoising, and the deviation between the data of each station and the interpolation result is calculated as the spatial noise adjustment amount.

[0076] In some optional implementations, Kriging interpolation or inverse distance weighted interpolation is performed on the preliminary denoised hydrological monitoring data to obtain a spatial interpolation reference value. The deviation between the preliminary denoised hydrological monitoring data and the spatial interpolation reference value is used as a spatial noise adjustment amount. The spatial noise adjustment amount is used to correct the station data, eliminate regional synchronization offset and spatial dispersion deviation between stations, and complete the spatial noise removal. Kriging interpolation is to generate the optimal spatial reference value for each station in the region by combining the spatial autocorrelation characteristics of the stations, and inverse distance weighted interpolation is to calculate the interpolated predicted value for each station based on the Euclidean distance weighting of the stations.

[0077] Step S3033: Update the hydrological monitoring data using the first denoised hydrological monitoring data, and iterate through the steps of extracting instantaneous noise features and spatial noise features from the hydrological monitoring data until the preset stopping condition is met to obtain the second denoised hydrological monitoring data.

[0078] In each iteration, the instantaneous noise is first initially removed based on the current denoising parameters. Then, based on the result of removing the instantaneous noise, the spatial noise is adjusted. Through continuous iteration, the denoising parameters are gradually optimized until the data reconstruction error and spatial consistency deviation reach the preset threshold. This iterative algorithm can make full use of the relationship between instantaneous noise and spatial noise to achieve the coordinated removal of the two.

[0079] Step S304: Reconstruct the second denoised hydrological monitoring data to obtain the target hydrological monitoring data after collaborative denoising.

[0080] Specifically, step S304 includes: Step S3041: Use interpolation to fill in missing points in the second denoised hydrological monitoring data, extract and reconstruct the trend terms in the second denoised hydrological monitoring data, and obtain the target hydrological monitoring data after collaborative denoising.

[0081] In this process, after removing instantaneous and spatial noise, the second denoised hydrological monitoring data is used for data reconstruction. Appropriate data interpolation and fitting methods are employed to supplement missing data points, restoring the integrity and continuity of the data. The reconstructed data can more accurately reflect the true changing trends of hydrological phenomena, providing a high-quality data foundation for subsequent data analysis and applications.

[0082] In some alternative implementations, spatiotemporal kriging interpolation is used for missing data points during data reconstruction, while considering the autocorrelation of the time series and the correlation of spatial location. For the trend term of the data series, multinomial fitting or empirical mode decomposition is used for trend extraction and reconstruction.

[0083] In some optional implementations, interpolation processing is used to supplement missing points in the second denoised hydrological monitoring data, including: expanding each data point of the second denoised hydrological monitoring data into a four-dimensional sample, and simultaneously incorporating the temporal dimension (historical data before and after this station) and the spatial dimension (data from neighboring stations at the same time), constructing a spatiotemporal variability function, and for missing data points, using the effective spatiotemporal neighboring data to interpolate and calculate the missing point's supplementary value, and using the supplementary value to supplement the missing points.

[0084] In some optional implementations, trend terms in the second denoised hydrological monitoring data are extracted and reconstructed. For the complete time series data after denoising, a polynomial model is constructed, the coefficients are solved using the least squares method, long-term trend terms are extracted, noise residuals are removed, and a smooth and continuous hydrological trend sequence is reconstructed.

[0085] In some optional implementations, before reconstructing the second denoised hydrological monitoring data to obtain the target hydrological monitoring data after collaborative denoising, the method for collaborative removal of instantaneous noise and spatial noise in hydrological monitoring data further includes: evaluating the second denoised hydrological monitoring data based on the mean square error index, peak signal-to-noise ratio index, and spatial correlation coefficient index to obtain evaluation results; the mean square error index is used to reflect the average error between the second denoised hydrological monitoring data and the real data, the peak signal-to-noise ratio index is used to measure the degree of quality improvement of the second denoised hydrological monitoring data, and the spatial correlation coefficient index is used to evaluate the spatial consistency of the second denoised hydrological monitoring data; if the evaluation results do not meet the preset expected standards, the parameters of the joint optimization model are adjusted, and the step of inputting the target instantaneous noise characteristics, target spatial noise characteristics, and hydrological monitoring data into the joint optimization model is returned to iteratively.

[0086] Among them, the spatial correlation coefficient index adopts Spearman's rank correlation coefficient or Kendall's harmony coefficient to evaluate the spatial synchronicity of data sequences from different monitoring stations after denoising. The peak signal-to-noise ratio index is measured by calculating the ratio of the maximum possible signal power to the noise power of the denoised data and the real data.

[0087] In some optional implementations, the mean square error index reflects the average deviation between the second denoised hydrological monitoring data and the real hydrological data. The smaller the mean square error index, the more accurate the time series fitting. The mean square error index is obtained by calculating the mean square error of each data point in the second denoised hydrological monitoring data.

[0088] In some alternative implementations, the peak signal-to-noise ratio (PSNR) measures the degree of improvement in the quality of the second denoised hydrological monitoring data. The higher the PSNR, the better the denoising effect and the more effective information is retained. The PSNR is obtained by taking the base-10 logarithm of the quotient of the square of the maximum value of the hydrological data and the mean square error index, and then multiplying it by 10.

[0089] In some alternative implementations, the spatial correlation coefficient index is used to evaluate the spatial clustering consistency of hydrological data from regional stations after denoising. The closer the spatial correlation coefficient index is to the normal range, the more thorough the spatial noise removal. By comprehensively analyzing these indicators, the effectiveness and reliability of the denoising method can be accurately determined.

[0090] In some optional implementations, an adaptive adjustment mechanism is established based on the evaluation results of the denoising effect. If the evaluation indicators do not meet the expected standards, the system will automatically adjust the parameters of the denoising algorithm and perform the denoising operation again. This adaptive adjustment mechanism can automatically optimize the denoising process according to the characteristics of different hydrological monitoring data, ensuring that the denoising effect always meets the actual needs.

[0091] In some optional implementations, the data before and after denoising are visualized. By drawing various charts such as time series plots and spatial distribution maps, the changes in the data before and after denoising can be compared intuitively, helping users to clearly understand the denoising effect. At the same time, using geographic information system technology, hydrological monitoring data can be visualized on a map, intuitively presenting the spatial distribution characteristics of the data, and providing intuitive support for water resource management and decision-making.

[0092] The method for collaborative removal of instantaneous and spatial noise from hydrological monitoring data provided in this embodiment analyzes the deviation of each data point in the hydrological monitoring data. Data points with deviations greater than a preset threshold are identified as potential instantaneous noise anomalies, quickly identifying potential instantaneous noise points with abnormal amplitudes. Wavelet transform is used to analyze the frequency distribution and energy characteristics of the noise, accurately locating high-frequency noise sub-bands. For spatial noise, the spatial correlation between different monitoring stations is analyzed based on the hydrological monitoring data to obtain spatial correlation indices. Using the spatial noise intensity of the hydrological monitoring data as the dependent variable and environmental factors as the independent variable, a correlation model between spatial noise and environmental factors is constructed. This not only quantifies the spatial correlation and noise distribution patterns between monitoring stations but also reveals the influence mechanism of environmental factors on spatial noise. It comprehensively covers the temporal domain characteristics of instantaneous noise and the spatial domain and environmental correlation characteristics of spatial noise, providing accurate and comprehensive noise information support for subsequent collaborative denoising and avoiding denoising deviations caused by incomplete noise feature characterization. The multi-index evaluation system includes mean square error, peak signal-to-noise ratio, and spatial correlation coefficient, comprehensively quantifying the denoising effect in both the time and spatial domains. This avoids the limitations of a single index and dynamically optimizes denoising parameters based on the evaluation results. If an index fails to meet expectations, the denoising process is automatically re-executed, adapting to the characteristics of different monitoring data without manual intervention, ensuring that the denoising effect always meets the needs of practical applications. Through spatiotemporal kriging interpolation combined with polynomial fitting or empirical mode decomposition reconstruction, not only is the integrity and continuity of the data restored, but the true changing trends of hydrological phenomena are also preserved. This provides a high-quality data foundation for subsequent hydrological simulation and predictive analysis. Visualization intuitively presents the differences before and after denoising, helping users quickly understand the denoising effect and clearly grasp the spatial distribution characteristics of hydrological data. Ultimately, the high-quality denoised data and intuitive visualization results can provide precise support for decision-making in water resource allocation, flood warning, and water environment management, enhancing the practical application value of hydrological monitoring data.

[0093] This embodiment provides a method for the coordinated removal of instantaneous and spatial noise in hydrological monitoring data, which can be achieved using computer equipment. Figure 4 This is a third flowchart of the method for collaborative removal of instantaneous and spatial noise from hydrological monitoring data according to an embodiment of the present invention, as follows: Figure 4 As shown, the process includes the following steps: Noise feature extraction and analysis; collaborative denoising algorithm processing; denoising effect evaluation; data reconstruction and visualization processing.

[0094] The noise feature extraction and analysis includes instantaneous noise feature extraction and spatial noise feature extraction; the collaborative denoising algorithm processing includes joint optimization model construction and iterative solution algorithm; the denoising effect evaluation includes multi-index evaluation system construction and adaptive adjustment mechanism; and the data reconstruction and visualization processing includes data reconstruction and visualization display.

[0095] This invention establishes a joint optimization model and an iterative solution algorithm, treating instantaneous noise and spatial noise as sub-objectives for collaborative optimization. When removing instantaneous noise, it also considers the adjustment of spatial noise, and when processing spatial noise, it considers the impact on instantaneous noise. By making full use of the relationship between the two, it effectively improves the spatiotemporal consistency of the denoised data, overcomes the problem that separate processing may destroy the spatial correlation of data or retain unidentified instantaneous outliers, and can better cope with the changes in noise characteristics under different hydrological scenarios.

[0096] This embodiment also provides a device for the coordinated removal of instantaneous and spatial noise from hydrological monitoring data. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0097] This embodiment provides a device for the coordinated removal of instantaneous and spatial noise from hydrological monitoring data, such as... Figure 5 As shown, it includes: The noise feature extraction module 501 is used to acquire hydrological monitoring data, extract instantaneous noise features from the hydrological monitoring data to obtain target instantaneous noise features, and extract spatial noise features from the hydrological monitoring data to obtain target spatial noise features.

[0098] The denoising parameter determination module 502 is used to input the instantaneous noise characteristics of the target, the spatial noise characteristics of the target, and the hydrological monitoring data into the joint optimization model to obtain the target denoising parameters. The joint optimization model is a denoising parameter optimization model constructed with the minimization of data reconstruction error and spatial consistency deviation as the objective function and the characteristic constraints of instantaneous noise and spatial noise as the constraint conditions.

[0099] The iterative solution module 503 is used to denoise the hydrological monitoring data according to the target denoising parameters to obtain the first denoised hydrological monitoring data. The first denoised hydrological monitoring data is used to update the hydrological monitoring data. The steps of extracting instantaneous noise features and spatial noise features from the hydrological monitoring data are iterated until the preset stopping condition is reached to obtain the second denoised hydrological monitoring data.

[0100] The data reconstruction module 504 is used to reconstruct the second denoised hydrological monitoring data to obtain the target hydrological monitoring data after collaborative denoising.

[0101] In some optional implementations, the noise feature extraction module 501 includes: The anomaly extraction unit is used to perform deviation analysis on each data point in the hydrological monitoring data, and to identify data points with deviations greater than a preset threshold as potential transient noise anomalies, thereby obtaining a set of potential transient noise.

[0102] The wavelet transform analysis unit is used to perform wavelet transform analysis on hydrological monitoring data to obtain the frequency distribution and energy characteristics of instantaneous noise. Based on the potential instantaneous noise set and the frequency distribution and energy characteristics of instantaneous noise, the target instantaneous noise characteristics are obtained.

[0103] The correlation analysis unit is used to analyze the spatial correlation of data between different monitoring stations based on hydrological monitoring data to obtain spatial correlation indicators; the spatial correlation indicators are used to quantify the spatial clustering or dispersion characteristics of hydrological monitoring data.

[0104] The environmental correlation unit is used to construct a correlation model between spatial noise and environmental factors, using the spatial noise intensity of hydrological monitoring data as the dependent variable and environmental factors as the independent variable. Based on the spatial correlation index and the correlation model, the target spatial noise characteristics are obtained.

[0105] In some optional implementations, the denoising parameter determination module 502 includes: The joint optimization model building unit is used to construct a joint optimization model with the objective function of minimizing data reconstruction error and spatial consistency deviation, and with the characteristic constraints of instantaneous noise and spatial noise as constraints.

[0106] The optimization model solving unit is used to input the instantaneous noise characteristics of the target, the spatial noise characteristics of the target, and the hydrological monitoring data into the joint optimization model, solve the joint optimization model, and obtain the target denoising parameters.

[0107] In some optional implementations, the iterative solution module 503 includes: The instantaneous noise removal unit is used to perform sliding window filtering on hydrological monitoring data according to the target denoising parameters to obtain preliminary denoised hydrological monitoring data.

[0108] The spatial noise removal unit is used to perform spatial interpolation processing on the preliminary denoised hydrological monitoring data. The deviation between the data of each monitoring station and the interpolation result is used as the spatial noise adjustment amount to perform spatial denoising processing on the preliminary denoised hydrological monitoring data to obtain the first denoised hydrological monitoring data.

[0109] In some alternative implementations, the data reconstruction module 504 includes: The data reconstruction unit is used to supplement missing points in the second denoised hydrological monitoring data by interpolation processing, extract and reconstruct the trend terms in the second denoised hydrological monitoring data, and obtain the target hydrological monitoring data after collaborative denoising.

[0110] In some optional embodiments, the device for coordinated removal of instantaneous and spatial noise from hydrological monitoring data further includes: The data evaluation module is used to evaluate the second denoised hydrological monitoring data based on the mean square error index, peak signal-to-noise ratio index, and spatial correlation coefficient index to obtain the evaluation results. The mean square error index is used to reflect the average error between the second denoised hydrological monitoring data and the real data, the peak signal-to-noise ratio index is used to measure the degree of quality improvement of the second denoised hydrological monitoring data, and the spatial correlation coefficient index is used to evaluate the spatial consistency of the second denoised hydrological monitoring data.

[0111] The parameter adjustment module is used to adjust the parameters of the joint optimization model if the evaluation results do not meet the preset expected standards. It returns to the steps of inputting the target instantaneous noise characteristics, target spatial noise characteristics and hydrological monitoring data into the joint optimization model for iterative iteration.

[0112] The hydrological monitoring data instantaneous noise and spatial noise collaborative removal device provided in this embodiment of the invention can execute the hydrological monitoring data instantaneous noise and spatial noise collaborative removal method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0113] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0114] The following is a detailed reference. Figure 6 This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0115] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0116] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the method for collaborative removal of instantaneous noise and spatial noise from hydrological monitoring data according to embodiments of the present invention.

[0117] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0118] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the method for collaborative removal of instantaneous noise and spatial noise in hydrological monitoring data shown in the above embodiments is implemented.

[0119] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0120] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for the coordinated removal of instantaneous noise and spatial noise from hydrological monitoring data, characterized in that, The method includes: Hydrological monitoring data is acquired, and instantaneous noise features are extracted from the hydrological monitoring data to obtain target instantaneous noise features. Spatial noise features are also extracted from the hydrological monitoring data to obtain target spatial noise features. The instantaneous noise characteristics of the target, the spatial noise characteristics of the target, and the hydrological monitoring data are input into the joint optimization model to obtain the target denoising parameters. The joint optimization model is a denoising parameter optimization model constructed with the minimization of data reconstruction error and spatial consistency deviation as the objective function and the characteristic constraints of instantaneous noise and spatial noise as the constraint conditions. The hydrological monitoring data is denoised according to the target denoising parameters to obtain first denoised hydrological monitoring data. The hydrological monitoring data is updated using the first denoised hydrological monitoring data. The steps of extracting instantaneous noise features and spatial noise features from the hydrological monitoring data are iterated until a preset stopping condition is reached to obtain second denoised hydrological monitoring data. The second denoised hydrological monitoring data is reconstructed to obtain the target hydrological monitoring data after collaborative denoising.

2. The method according to claim 1, characterized in that, The instantaneous noise features in the hydrological monitoring data are extracted to obtain the target instantaneous noise features, and the spatial noise features in the hydrological monitoring data are extracted to obtain the target spatial noise features, including: Deviation analysis is performed on each data point in the hydrological monitoring data. Data points with deviations greater than a preset threshold are identified as potential transient noise anomalies, thus obtaining a set of potential transient noise. Wavelet transform analysis is performed on the hydrological monitoring data to obtain the frequency distribution and energy characteristics of the instantaneous noise. Based on the potential instantaneous noise set and the frequency distribution and energy characteristics of the instantaneous noise, the target instantaneous noise characteristics are obtained. Based on the hydrological monitoring data, the spatial correlation between different monitoring stations is analyzed to obtain a spatial correlation index; the spatial correlation index is used to quantify the spatial clustering or dispersion characteristics of the hydrological monitoring data. Using the spatial noise intensity of hydrological monitoring data as the dependent variable and environmental factors as the independent variable, a correlation model between spatial noise and environmental factors is constructed. Based on the spatial correlation index and the correlation model, the target spatial noise characteristics are obtained.

3. The method according to claim 1 or 2, characterized in that, The step of inputting the instantaneous noise characteristics of the target, the spatial noise characteristics of the target, and the hydrological monitoring data into the joint optimization model to obtain the target denoising parameters includes: The joint optimization model is constructed with the objective function of minimizing data reconstruction error and spatial consistency deviation, and with the characteristic constraints of instantaneous noise and spatial noise as constraints. The instantaneous noise characteristics of the target, the spatial noise characteristics of the target, and the hydrological monitoring data are input into the joint optimization model, and the joint optimization model is solved to obtain the target denoising parameters.

4. The method according to claim 1 or 2, characterized in that, The step of denoising the hydrological monitoring data according to the target denoising parameters to obtain the first denoised hydrological monitoring data includes: Based on the target denoising parameters, the hydrological monitoring data is subjected to sliding window filtering to obtain preliminary denoised hydrological monitoring data; Spatial interpolation is performed on the preliminary denoised hydrological monitoring data. The deviation between the data of each monitoring station and the interpolation result is used as the spatial noise adjustment amount. Spatial denoising is then performed on the preliminary denoised hydrological monitoring data to obtain the first denoised hydrological monitoring data.

5. The method according to claim 1 or 2, characterized in that, The step of reconstructing the second denoised hydrological monitoring data to obtain the target hydrological monitoring data after collaborative denoising includes: Interpolation processing is used to fill in missing points in the second denoised hydrological monitoring data, and trend extraction and reconstruction are performed on the trend items in the second denoised hydrological monitoring data to obtain the target hydrological monitoring data after collaborative denoising.

6. The method according to claim 1 or 2, characterized in that, Before reconstructing the second denoised hydrological monitoring data to obtain the collaboratively denoised target hydrological monitoring data, the method further includes: The second denoised hydrological monitoring data is evaluated based on the mean square error index, peak signal-to-noise ratio index, and spatial correlation coefficient index to obtain the evaluation results. The mean square error index is used to reflect the average error between the second denoised hydrological monitoring data and the real data. The peak signal-to-noise ratio index is used to measure the degree of quality improvement of the second denoised hydrological monitoring data. The spatial correlation coefficient index is used to evaluate the spatial consistency of the second denoised hydrological monitoring data. If the evaluation result does not meet the preset expected standard, the parameters of the joint optimization model are adjusted, and the process of inputting the target instantaneous noise characteristics, the target spatial noise characteristics, and the hydrological monitoring data into the joint optimization model is repeated iteratively.

7. A device for the coordinated removal of instantaneous noise and spatial noise from hydrological monitoring data, characterized in that, The device includes: The noise feature extraction module is used to acquire hydrological monitoring data, extract instantaneous noise features from the hydrological monitoring data to obtain target instantaneous noise features, and extract spatial noise features from the hydrological monitoring data to obtain target spatial noise features. The denoising parameter determination module is used to input the target instantaneous noise characteristics, the target spatial noise characteristics, and the hydrological monitoring data into the joint optimization model to obtain the target denoising parameters; the joint optimization model is a denoising parameter optimization model constructed with minimizing data reconstruction error and spatial consistency deviation as the objective function and the characteristic constraints of instantaneous noise and spatial noise as the constraint conditions; The iterative solution module is used to denoise the hydrological monitoring data according to the target denoising parameters to obtain first denoised hydrological monitoring data, update the hydrological monitoring data using the first denoised hydrological monitoring data, and iterate through the steps of extracting instantaneous noise features and spatial noise features from the hydrological monitoring data until a preset stopping condition is reached to obtain second denoised hydrological monitoring data. The data reconstruction module is used to reconstruct the second denoised hydrological monitoring data to obtain the target hydrological monitoring data after collaborative denoising.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for coordinated removal of instantaneous and spatial noise from hydrological monitoring data as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the method for coordinated removal of instantaneous and spatial noise from hydrological monitoring data as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The method includes computer instructions for causing a computer to execute the method for coordinated removal of instantaneous and spatial noise from hydrological monitoring data as described in any one of claims 1 to 6.

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