A data visualization processing method applied to intelligent water conservancy

Abstract

This invention relates to the field of data processing technology, and in particular to a data visualization processing method applied to smart water conservancy. The method includes: collecting data and generating a hydrological dataset; processing the data to generate a rainfall raster matrix; generating a spatial credibility marker set by comparing topographic reflectance through a verification channel; detecting rainstorm conditions based on the marker set and activating an anti-interference channel; reconstructing the rainfall sequence using a particle swarm optimization algorithm; filling the spatiotemporal gaps in radar data using a generative adversarial network; controlling the core parameters of the hydraulic model; selecting an interpolation strategy based on the projection deviation value; combining the piping probability with a pulsating red sphere holographic projection to generate risk visualization parameters; isolating high-risk operations to a sandbox using a dynamic filtering mechanism; triggering disaster scenario retrieval through pulsating distribution clustering; and optimizing the topographic reflectance benchmark library based on the retrieval results and feeding it back to the verification channel. This invention effectively eliminates visualization distortion caused by spatiotemporal drift of multi-source data and blocks the contamination transmission chain of erroneous data to the model training set.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data visualization processing method applied to smart water conservancy. Background Technology

[0002] Smart water management data visualization is based on the continuous collection of real-time hydrological data such as water level, flow velocity, and rainfall by IoT sensors. After big data processing, it forms a structured information flow, which is then visualized through dynamic charts, geographic information system mapping, or heat maps, making complex water resource monitoring results intuitive and identifiable. This process helps water managers directly identify abnormal patterns such as flood risk accumulation or areas with abnormal water quality from a graphical interface, infer potential problem trends, and thus guide decision-making to optimize water resource deployment, improve forecasting and early warning capabilities and emergency response efficiency, and reduce losses from natural disasters.

[0003] Existing smart water conservancy data visualization technologies suffer from technical pain points such as the failure of dynamic alignment of multi-source spatiotemporal fields, leading to model cognitive collapse. Specifically, the heterogeneity of transmission protocols and timestamp drift exist among hydrological sensor networks, satellite remote sensing data streams, and manual control commands. When the movement speed of sudden rainstorm clouds exceeds the data acquisition cycle, the minute-level delay data from rain gauges and the second-level prediction images from radars cause spatial projection misalignment on the visualization interface, creating a false purple floodplain rendering. At the same time, emergency gate opening control commands fail to be synchronized to the hydraulic model due to communication delays, resulting in broken and distorted flow simulation contour lines. When operators implement scheduling based on distorted heat maps, the erroneous operating condition data contaminates the deep learning training set, ultimately causing the dam failure risk prediction function to completely fail when a real disaster occurs, resulting in decision-making lag and scheduling deviations. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a data visualization processing method for smart water conservancy, which solves the problems of flood artifacts on the visualization interface caused by the coupling effect of asynchronous drift of multi-source hydrological spatiotemporal fields and sudden environmental changes, as well as the systematic degradation of model performance caused by the transmission and accumulation of erroneous data in the deep learning training set.

[0005] To solve the above-mentioned technical problems, the specific details of the present invention are as follows:

[0006] This invention provides a data visualization processing method for smart water conservancy, comprising:

[0007] Step 1: Collect water level pulse signals, flow velocity Doppler frequency shift data, and rainfall counting pulses, and process the data to generate a hydrological dataset, which includes equipment status codes.

[0008] Step 2: Receive hydrological dataset, process the hydrological dataset, generate a rainfall raster matrix, receive remote sensing satellite data, process the remote sensing satellite data to generate spatial credibility markers, and output a set of spatial credibility markers.

[0009] Step 3: Receive the spatial credibility tag set; detect rainstorm conditions based on the spatial credibility tag set; when rainstorm conditions are detected, reconstruct the historical rainfall sequence, fill the spatiotemporal gaps in the radar, control the core parameters of the hydraulic model, and output the reconstructed historical rainfall sequence and radar grid data with generated value identifiers.

[0010] Step 4: Receive the reconstructed historical rainfall sequence and radar grid data with generated value identifiers; obtain the projection deviation value from the spatial credibility marker set; select an interpolation strategy based on the projection deviation value, and input the adjacent spatial credibility markers and terrain undulation coefficients; load the pulsating red sphere holographic projection based on the piping probability to generate the pulsating red sphere diameter distribution and pulsation frequency parameters.

[0011] Step 5: Receive the pulse frequency parameters and the pulse red ball diameter distribution; isolate high-risk operations to the sandbox and update the filtering rules; output the updated filtering rules and the pulse red ball diameter distribution.

[0012] Step 6: Receive updated filtering rules and pulsating red ball diameter distribution; parse the pulsating red ball diameter distribution; search for similar disaster scenarios and output the search results;

[0013] Step 7: Receive search results; when the search results meet the preset conditions, load long-term memory parameters; feed back the long-term memory parameters to Step 2 to optimize the credibility labeling rules of the terrain reflectivity benchmark library.

[0014] Furthermore, the data visualization processing method for smart water conservancy described in this invention includes the following steps in step 1: collecting water level pulse signals, flow velocity Doppler frequency shift data, and rainfall counting pulses to generate equipment status codes; processing water level pulse signals to generate digital water level parameters; analyzing flow velocity Doppler frequency shift data to generate equipment health status codes; and integrating digital water level parameters, flow velocity parameters, rainfall parameters, and meteorological radar grid data to generate a hydrological dataset.

[0015] Step 2 includes: receiving hydrological datasets to generate a rainfall raster matrix; attaching a Coordinated Universal Time Stamp (UTC) to the rainfall raster matrix; receiving remote sensing satellite infrared spectral data; extracting topographic thermal inertia distribution features from the remote sensing satellite infrared spectral data to generate interpretation feature data; when the standard deviation of the reflectance feature is greater than 0.35, processing the interpretation feature data to generate enhanced label values; and binding the enhanced label values ​​to geographic coordinates to generate spatial credibility labels.

[0016] Furthermore, in the data visualization processing method for smart water conservancy described in this invention, step 3 includes:

[0017] The migration speed of rainstorms is detected based on a spatial reliability tag set. When the migration speed of rainstorms is greater than 20 km / h, a fitness function is constructed to constrain the rainfall gradient deviation to ≤5 mm / h. Meteorological fluid dynamics equations are embedded into the fitness function. The fitness function is applied to the particle swarm optimization algorithm, and the spatiotemporal distribution weights of historical catastrophic events are loaded to update the particle positions, generating a particle position data set. A historical rainfall sequence is generated based on the particle position data set, and the historical rainfall sequence is labeled with an entropy fluctuation index. The spatiotemporal gaps of radar are filled through adversarial generative networks to generate radar grid data with generation value identifiers. The core parameters of the hydraulic model are controlled to enter a semi-frozen state.

[0018] Furthermore, in the data visualization processing method for smart water conservancy described in this invention, step 3 further includes:

[0019] Initial infill data was generated based on the rainfall raster matrix and terrain relief coefficient, and the spatiotemporal continuity of the initial infill data was verified.

[0020] When the verification fails, the cracked area is identified and a generated value identifier is attached. When the confidence level of the generated value identifier is less than 0.7, the filling data is regenerated.

[0021] Furthermore, in the data visualization processing method for smart water conservancy described in this invention, step 4 includes:

[0022] Construct a three-dimensional energy equation to minimize the interpolation computation and generate initial decision parameters;

[0023] Detection of historical dam failure points in interpolation regions based on spatial credibility tag sets;

[0024] When historical dam failure points exist, the landslide displacement constraint term is added to the initial decision parameters to generate optimized decision parameters. Based on the projection deviation value and the optimized decision parameters, the interpolation method is selected: when the projection deviation value is 500-1000 meters, the Kriging interpolation method is used; when the projection deviation value is greater than 1000 meters, the radial basis function interpolation method is used.

[0025] Furthermore, in the data visualization processing method for smart water conservancy described in this invention, step 4 includes:

[0026] Receive the piping probability matrix output by the hydraulic model and the equipment status code generated in step 1;

[0027] A dynamic risk parameter set is generated based on the piping probability matrix and equipment status codes;

[0028] Based on the dam break factor coefficient, a weighted calculation is performed on the dynamic risk parameter set to generate the pulsating red ball diameter value;

[0029] The diameter values ​​of pulsating red spheres are merged with historical geological disaster frequency layers to generate dynamic rendering results.

[0030] Furthermore, in the data visualization processing method for smart water conservancy described in this invention, step 5 includes:

[0031] Receive real-time hydrological operation data streams; detect changes in gate opening and sudden changes in flow velocity;

[0032] High-risk operation data is generated when the gate opening changes by more than 30% / min or the flow velocity changes by more than 1.5m / s.

[0033] Isolate high-risk operational data into a sandbox and attach error failure flags;

[0034] The sandbox cleanup cycle is calculated based on the migration speed of rainstorms, and the data in the sandbox is cleared.

[0035] Furthermore, in the data visualization processing method for smart water conservancy described in this invention, step 6 includes:

[0036] Receive historical disaster event databases, hydrological monitoring parameter sets, and disaster relief plan databases;

[0037] Establish node relationships based on historical disaster event database, hydrological monitoring parameter set and disaster relief plan database, and generate initial map structure;

[0038] Calculate edge weights based on economic loss data and the initial graph structure, and update the connectivity of the graph structure.

[0039] Spatiotemporal feature vectors are extracted based on the updated graph structure; the spatiotemporal feature vectors are dimensionality-reduced and encoded to generate feature embedding representations; and a similarity calculation model is constructed based on the feature embedding representations.

[0040] Furthermore, in the data visualization processing method for smart water conservancy described in this invention, step 6 further includes:

[0041] Receive pulsed red ball diameter distribution data; cluster the pulsed red ball diameter distribution data through nearest neighbor propagation to generate disaster scenario similarity classification results;

[0042] When the diameter of the pulsating red ball suddenly increases to more than 15cm, similar disaster scenarios are retrieved from the historical disaster event database, and the search scope is limited to the geological structure zone of the same watershed.

[0043] Based on the search results, the associated memory parameters are loaded, the residuals are corrected before loading, and an optimized transfer learning parameter set is generated.

[0044] Furthermore, in the data visualization processing method for smart water conservancy described in this invention, step 7 includes:

[0045] Receive search results; when the similarity among the search results is greater than 80%, load long-term memory parameters;

[0046] The number of conflicts between the transfer learning parameter set and the terrain reflectivity benchmark library is detected; when the number of conflicts exceeds 3, multi-center water conservancy data is fused.

[0047] Generate an updated terrain reflectance benchmark library; transfer the updated terrain reflectance benchmark library to step 2.

[0048] Beneficial effects of this invention;

[0049] This invention systematically eliminates the visualization distortion problem caused by spatiotemporal asynchronous drift of multi-source hydrological data by constructing a spatial credibility marking system and a dynamic anti-interference mechanism. The spatial credibility marking dynamically corrects the projection deviation between satellite and ground sensors by comparing real-time terrain reflectivity with a historical benchmark database. The anti-interference channel uses a particle swarm optimization algorithm to reconstruct historical rainfall sequences and integrates a generative adversarial network to fill the spatiotemporal gaps in radar data, ensuring the physical rationality and spatiotemporal continuity of the data. Quantum annealing decision-making selects interpolation strategies based on projection deviation values, driving pulsating red sphere holographic projection to accurately map the piping risk level and avoid contour line breaks. The dynamic filtering mechanism monitors the gate in real time. Sudden changes in flow rate and surges in flow velocity isolate high-risk operations into a sandbox and attach error failure markers. The cleanup cycle is adjusted according to the migration speed of the rainstorm, preventing the reverse infiltration of erroneous operating condition data into the training set from the source. Cluster analysis of the diameter distribution of pulsating red spheres triggers disaster retrieval of geological structural zones in the same basin, providing decision support for historically similar scenarios. When the transfer learning parameters and the terrain reflectivity benchmark library conflict multiple times, the federated learning mechanism integrates multi-center data to update the benchmark library. A closed-loop feedback is formed through the verification channel to continuously optimize the system's adaptability, thereby significantly improving the accuracy, reliability, and anti-interference ability of smart water conservancy data visualization and reducing the decision-making risks caused by data distortion. Attached Figure Description

[0050] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0051] Figure 1 This is a flowchart illustrating a data visualization processing method for smart water conservancy, provided as an embodiment of the present invention. Detailed Implementation

[0052] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. 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. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0053] Please see Figure 1 The present invention provides a data visualization processing method for smart water conservancy, comprising:

[0054] Step 1: Collect water level pulse signals, flow velocity Doppler frequency shift data, and rainfall counting pulses, and process the data to generate a hydrological dataset, which includes equipment status codes.

[0055] Step 2: Receive hydrological dataset, process the hydrological dataset, generate a rainfall raster matrix, receive remote sensing satellite data, process the remote sensing satellite data to generate spatial credibility markers, and output a set of spatial credibility markers.

[0056] Step 3: Receive the spatial credibility tag set; detect rainstorm conditions based on the spatial credibility tag set; when rainstorm conditions are detected, reconstruct the historical rainfall sequence, fill the spatiotemporal gaps in the radar, control the core parameters of the hydraulic model, and output the reconstructed historical rainfall sequence and radar grid data with generated value identifiers.

[0057] Step 4: Receive the reconstructed historical rainfall sequence and radar grid data with generated value identifiers; obtain the projection deviation value from the spatial credibility marker set; select an interpolation strategy based on the projection deviation value, and input the adjacent spatial credibility markers and terrain undulation coefficients; load the pulsating red sphere holographic projection based on the piping probability to generate the pulsating red sphere diameter distribution and pulsation frequency parameters.

[0058] Step 5: Receive the pulse frequency parameters and the pulse red ball diameter distribution; isolate high-risk operations to the sandbox and update the filtering rules; output the updated filtering rules and the pulse red ball diameter distribution.

[0059] Step 6: Receive updated filtering rules and pulsating red ball diameter distribution; parse the pulsating red ball diameter distribution; search for similar disaster scenarios and output the search results;

[0060] Step 7: Receive search results; when the search results meet the preset conditions, load long-term memory parameters; feed back the long-term memory parameters to Step 2 to optimize the credibility labeling rules of the terrain reflectivity benchmark library.

[0061] In the implementation of the smart water conservancy data visualization processing method, step 1 involves the acquisition and integration of multi-source hydrological data. Water level pulse signals are generated by pressure level gauges, flow velocity Doppler frequency shift data are captured by radar current meters, and rainfall counting pulses originate from tipping bucket rain gauges. The raw signals are converted from analog to digital to generate digital water level parameters, flow velocity parameters, and rainfall parameters. Simultaneously, equipment status codes are added based on equipment operating status analysis to form a structured hydrological dataset. The equipment status codes provide traceability identifiers for data quality, facilitating subsequent verification of data reliability.

[0062] Step 2 processes the hydrological dataset to generate a rainfall raster matrix and adds a Coordinated Universal Time Stamp (UTC) to eliminate clock drift. Remote sensing satellite infrared spectral data is used as input, and interpreted feature data is generated by interpreting topographic thermal inertia distribution characteristics. When anomalies are found in the reflectance features compared with the historical benchmark database, a filtering algorithm is used to enhance credibility, outputting a set of spatial credibility markers bound to geographic coordinates. This step solves the spatial alignment problem between satellite and ground sensor data, providing a foundation for heavy rainfall detection.

[0063] Step 3 detects rainstorm conditions based on a spatial credibility tag set, for example, by analyzing whether the rainstorm migration speed exceeds a threshold. When a rainstorm condition is triggered, anti-disturbance processing is initiated: meteorological fluid dynamics equations are embedded into the fitness function of a particle swarm optimization algorithm to reconstruct the historical rainfall sequence to maintain physical plausibility; terrain features are fused using an adversarial generative network to fill the spatiotemporal gaps in the radar, and generated values ​​are labeled with identifiers; the core parameters of the hydraulic model are simultaneously controlled to enter a semi-frozen state to avoid model distortion caused by sudden data anomalies. Identified radar raster data is output to ensure the traceability of subsequent processing.

[0064] Step 4, after receiving the reconstructed data, extracts the projection deviation value from the spatial credibility marker set and selects an interpolation strategy based on the deviation level. Kriging interpolation is used when the deviation is small, while radial basis function interpolation is switched to when the deviation is large. Adjacent spatial credibility markers and terrain undulation coefficients are introduced to optimize interpolation accuracy. Based on the piping probability, a pulsating red sphere holographic projection is applied to high-risk areas to dynamically generate diameter distribution and frequency parameters, intuitively mapping the piping risk level.

[0065] Step 5 involves monitoring real-time hydrological operation data streams through a dynamic reliability filtering mechanism to identify high-risk operations such as sudden changes in gate opening or surges in flow velocity. High-risk data is isolated to a sandbox storage area and marked with error / failure tags to prevent erroneous decision-making data from back-infiltrating the training set. The sandbox cleanup cycle is dynamically adjusted based on the speed of rainstorm migration to ensure timely removal of contaminated data.

[0066] Step 6 analyzes the diameter distribution of pulsating red spheres and uses nearest-neighbor propagation clustering analysis to generate a similarity classification result for disaster scenarios. When there is an abnormally sudden increase in diameter, similar scenarios within the same watershed geological structure zone are retrieved from the historical disaster event database, and the retrieval results are output to support emergency decision-making. This step utilizes clustering technology to enhance the accuracy of scenario matching.

[0067] Step 7 loads long-term memory parameters when the similarity of the search results meets the conditions, and feeds them back to Step 2 to optimize the terrain reflectivity benchmark database. Simultaneously, it monitors the number of conflicts between the transfer learning parameters and the benchmark database. When the number of conflicts exceeds a threshold, a federated learning mechanism is activated to merge multi-center water conservancy data and update the benchmark database. The updated benchmark database forms a closed-loop feedback through a verification channel, continuously improving the accuracy of spatial credibility labeling.

[0068] Specifically, the data visualization processing method for smart water conservancy described in this invention includes the following steps: Step 1: collecting water level pulse signals, flow velocity Doppler frequency shift data, and rainfall counting pulses to generate equipment status codes; processing water level pulse signals to generate digital water level parameters; analyzing flow velocity Doppler frequency shift data to generate equipment health status codes; and integrating digital water level parameters, flow velocity parameters, rainfall parameters, and meteorological radar grid data to generate a hydrological dataset.

[0069] Step 2 includes: receiving hydrological datasets to generate a rainfall raster matrix; attaching a Coordinated Universal Time Stamp (UTC) to the rainfall raster matrix; receiving remote sensing satellite infrared spectral data; extracting topographic thermal inertia distribution features from the remote sensing satellite infrared spectral data to generate interpretation feature data; when the standard deviation of the reflectance feature is greater than 0.35, processing the interpretation feature data to generate enhanced label values; and binding the enhanced label values ​​to geographic coordinates to generate spatial credibility labels.

[0070] In step 1 of the smart water conservancy data visualization processing method, the head pulse signal collected by the pressure level gauge is processed by the analog-to-digital conversion module to generate digital water level parameters with physical check codes. The Doppler frequency shift data captured by the radar current meter is analyzed using a frequency domain analysis algorithm to determine the flow velocity value, and combined with indicators such as equipment operating temperature and signal strength to generate equipment health status codes. The rainfall counting pulses output by the tipping bucket rain gauge are converted into minute-level rainfall intensity data through cumulative calculation. Heterogeneous data are aligned using timestamps and spatially registered with meteorological radar grid data, ultimately integrating into a structured hydrological dataset including equipment status codes. The equipment status codes use binary encoding, where the high-order byte identifies the equipment type and the low-order byte records the signal quality level, providing a traceability basis for subsequent data reliability assessment.

[0071] In step 2, the hydrological dataset processing stage, the central server first performs spatiotemporal consistency verification upon receiving the data. The rainfall raster matrix generation employs an inverse distance weighted interpolation algorithm to convert discrete station data into 500-meter resolution grid data, and adds a Coordinated Universal Time Stamp (UTC) synchronized with the BeiDou satellite. Remote sensing satellite infrared spectral data is processed using an atmospheric correction model to eliminate cloud interference. Principal component analysis is used to extract topographic thermal inertia distribution characteristics, generating interpretation feature data including surface material information. When abnormal fluctuations are observed in reflectance characteristics compared to historical benchmarks, the Kalman filter algorithm predicts reflectance change trends using state-space equations, dynamically corrects signal distortion caused by atmospheric disturbances, and outputs enhanced marker values. The geographic coordinate binding process uses the WGS84 coordinate system, overlaying the marker values ​​with the digital elevation model to generate a spatial reliability marker layer.

[0072] This layered processing mechanism demonstrates significant effectiveness in real-world watershed monitoring scenarios. Taking mountain reservoir monitoring as an example, the sampling frequency of the water level pulse signal is set to 1Hz, and the analog-to-digital conversion uses 16-bit precision to ensure that the water level measurement error is controlled within the centimeter level. Flow velocity Doppler frequency shift data is used to extract flow velocity features through Fast Fourier Transform, and the device health status code reflects the sensor's operating status in real time. A terrain shadow correction algorithm is introduced when generating the rainfall raster matrix to effectively eliminate the occlusion effect of mountains on radar echoes. During the interpretation of satellite infrared spectral data, a mapping relationship is established between thermal inertia characteristics and soil moisture content, providing early indicators for flood risk warning. The spatial credibility marker layer is presented as a semi-transparent overlay on the GIS platform, with color depth intuitively reflecting the data credibility level, assisting managers in quickly identifying abnormal areas.

[0073] The data processing chain places particular emphasis on temporal correlation. The timestamps for generating device status codes are strictly synchronized with the data acquisition time to avoid matching errors caused by latency. The temporal resolution of the rainfall raster matrix is ​​set to 5 minutes to synchronize with the radar data update cycle. During the generation of spatial reliability markers, the terrain reflectivity benchmark library is updated using a sliding window mechanism, with reflectivity data from the most recent 30 days participating in the benchmark calculation. This dynamic benchmark maintenance method effectively adapts to seasonal land cover changes and avoids interference from vegetation growth cycles on reflectivity characteristics. When binding marker values ​​to geographic coordinates, a bilinear interpolation algorithm is used to achieve pixel-level alignment between satellite data and ground observation data at different resolutions.

[0074] At the technical implementation level, the key challenge of multi-source data fusion was addressed. A unified data dictionary was established during the integration of hydrological datasets, clearly defining the physical units and value ranges of each parameter. Equipment status codes adopted a hierarchical coding structure: the first layer identifies the sensor type, the second records the power supply status, and the third indicates communication quality. During the generation of the rainfall raster matrix, a terrain correction coefficient was introduced to address radar beam obstruction, and the beam obstruction angle was calculated based on a digital elevation model. Data from neighboring stations was used to compensate for obstructed areas. The enhancement processing of spatial reliability labeling employed an adaptive filtering algorithm, with the filtering coefficients dynamically adjusted according to the signal-to-noise ratio, ensuring data accuracy while avoiding excessive smoothing that could lead to feature loss.

[0075] Specifically, in the data visualization processing method for smart water conservancy described in this invention, step 3 includes:

[0076] The migration speed of rainstorms is detected based on a spatial reliability tag set. When the migration speed of rainstorms is greater than 20 km / h, a fitness function is constructed to constrain the rainfall gradient deviation to ≤5 mm / h. Meteorological fluid dynamics equations are embedded into the fitness function. The fitness function is applied to the particle swarm optimization algorithm, and the spatiotemporal distribution weights of historical catastrophic events are loaded to update the particle positions, generating a particle position data set. A historical rainfall sequence is generated based on the particle position data set, and the historical rainfall sequence is labeled with an entropy fluctuation index. The spatiotemporal gaps of radar are filled through adversarial generative networks to generate radar grid data with generation value identifiers. The core parameters of the hydraulic model are controlled to enter a semi-frozen state.

[0077] In step 3, the spatial reliability marker set serves as the input data source. The migration speed of the rainstorm is detected by analyzing the spatiotemporal variation patterns of reflectivity characteristics within the marker set. Specifically, a cross-correlation algorithm is used to calculate the displacement vector of continuous temporal radar echo sequences, and vector field analysis is used to obtain the trajectory and movement speed of the rainstorm cloud. When the detected rainstorm migration speed exceeds a set threshold, the system automatically triggers an anti-interference mechanism.

[0078] The disturbance rejection process first constructs a fitness function, which uses rainfall gradient deviation as a constraint and embeds the mass conservation equation from meteorological fluid dynamics as a physical constraint into the objective function. In practical applications, such as for mountainous rainstorm monitoring scenarios, the fitness function considers the rainfall enhancement phenomenon caused by topographic lifting, and improves the physical rationality of sequence reconstruction by introducing an elevation correction factor. In the particle swarm optimization algorithm initialization phase, each particle position vector represents a potential historical rainfall sequence scheme, and the particle dimension corresponds to the length of the historical time window.

[0079] During the particle swarm optimization (PSO) algorithm iteration, the spatiotemporal distribution weights of historical disaster events are loaded in matrix form, with weight values ​​allocated based on the spatial density and temporal frequency of the disaster records. When updating particle positions, the fitness function simultaneously evaluates the physical consistency of the sequence and the matching degree of historical disasters, ensuring that the reconstructed sequence conforms to both fluid dynamics principles and reflects regional disaster characteristics. The updated particle position dataset is then used to select the optimal solution through cluster analysis, generating a historical rainfall sequence labeled with an additional entropy fluctuation index. The entropy index quantifies the randomness of the rainfall process by calculating the information entropy of the sequence, providing a quantitative basis for subsequent risk warnings.

[0080] The radar spatiotemporal gap filling method employs a conditional generative adversarial network (GAN) architecture. The generator takes a rainfall raster matrix and terrain undulation coefficient as conditional inputs and learns the spatial distribution pattern of normal radar echoes through an encoder-decoder structure. The discriminator integrates a spatiotemporal continuity verification module, using a 3D convolutional neural network to detect the continuity of the generated data in both time and space dimensions. When a gap region is identified, the system labels the generated value with an identifier, which includes metadata such as generation time, confidence level, and repair range. In a real-world river monitoring case, this mechanism effectively repairs radar scan blind spots caused by mountain obstruction.

[0081] The semi-frozen control of core hydraulic model parameters employs a parameter grouping strategy, dividing model parameters into a dynamic response group and a steady-state hold group. When the system enters disturbance rejection mode, the learning rate of the parameters in the dynamic response group is adjusted to one-tenth of the normal value, while the parameters in the steady-state hold group are completely frozen. This hierarchical control method preserves the model's responsiveness to normal hydrological changes while avoiding the impact of sudden abnormal data on core parameters. For example, under sudden gate control conditions, the model can maintain the stability of the core flow calculation algorithm while allowing for moderate adjustments to boundary condition parameters.

[0082] The anti-disturbance processing forms a closed-loop quality control mechanism. The generated value identifier is fed back to the spatial reliability labeling system to update the confidence weights of the topographic reflectivity benchmark library. When high-frequency data repair is detected in the same area for multiple consecutive time periods, the system automatically increases the basic reliability level of that area and initiates a special verification process. This dynamic weight adjustment mechanism enables the system to gradually adapt to changes in regional surface characteristics, improving the accuracy of long-term monitoring.

[0083] Specifically, in the data visualization processing method for smart water conservancy described in this invention, step 3 further includes:

[0084] Initial infill data was generated based on the rainfall raster matrix and terrain relief coefficient, and the spatiotemporal continuity of the initial infill data was verified.

[0085] When the verification fails, the cracked area is identified and a generated value identifier is attached. When the confidence level of the generated value identifier is less than 0.7, the filling data is regenerated.

[0086] In step 3, the system generates initial infill data based on the rainfall raster matrix and terrain undulation coefficient. The generation process employs a spatial interpolation algorithm, comprehensively considering the influence of terrain features such as elevation and slope on rainfall distribution. Missing areas in the radar data are filled using inverse distance weighted interpolation or the Kriging method. The initial infill data aims to restore data gaps caused by cloud cover or sensor malfunctions. For example, in mountainous terrain, the algorithm adjusts the interpolation weights according to the undulation patterns of valleys and ridges, making the infill results more consistent with the actual spatial distribution of precipitation.

[0087] When verifying the spatiotemporal continuity of the initial imputation data, the system checks the data's coherence over time and its smoothness in the spatial dimension. Verification methods include analyzing the consistency of rainfall movement trajectories and calculating the gradient changes in values ​​of adjacent grid cells to identify anomalous abrupt changes. For example, optical flow is used to track the movement vector of rainstorm clouds; if the imputation data deviates significantly from the movement pattern, the verification fails. The verification process also involves comparing the imputation values ​​with historical data from the same period to evaluate their reasonableness.

[0088] When verification fails, the system identifies the gap region and appends a generated value identifier. The gap region is located through difference analysis, which compares the residual distribution of the original radar echo with that of the filled data. The identifier records the filling timestamp, geographic coordinates, and confidence score. The confidence score is calculated based on the historical accuracy of the filling algorithm and data consistency metrics, reflecting the reliability of the generated data.

[0089] If the confidence level of the generated value identifier is lower than a preset threshold, the system regenerates the imputation data. The regeneration process may incorporate more advanced algorithms, such as generative adversarial network-based models, fusing multi-source meteorological data as constraints. During the iterative optimization phase, algorithm parameters are adjusted or training samples are increased to improve the imputation quality. Multiple iterations are performed until the confidence level meets the requirements, thereby improving data usability.

[0090] Specifically, in the data visualization processing method for smart water conservancy described in this invention, step 4 includes:

[0091] Construct a three-dimensional energy equation to minimize the interpolation computation and generate initial decision parameters;

[0092] Detection of historical dam failure points in interpolation regions based on spatial credibility tag sets;

[0093] When historical dam failure points exist, the landslide displacement constraint term is added to the initial decision parameters to generate optimized decision parameters. Based on the projection deviation value and the optimized decision parameters, the interpolation method is selected: when the projection deviation value is 500-1000 meters, the Kriging interpolation method is used; when the projection deviation value is greater than 1000 meters, the radial basis function interpolation method is used.

[0094] In the quantum annealing decision-making process, the system first constructs a three-dimensional energy equation to optimize interpolation computation efficiency. The energy equation comprehensively considers spatial coordinates, time dimension, and data density distribution, transforming interpolation path planning into an energy minimization problem. Initial decision parameters are generated by solving the ground-state solution of the energy equation, with each parameter representing the balance between computational cost and accuracy for different interpolation strategies. For example, in river topography modeling, the energy equation adjusts the weighting coefficients based on the riverbed undulation characteristics, prioritizing the extension of the interpolation path along the water flow direction.

[0095] When detecting the presence of historical dam break points in an interpolated area based on a spatial reliability tag set, the system calls upon a geological hazard database for spatial matching analysis. The detection process employs a sliding window scanning technique, performing polygon overlay analysis between the current interpolated area and historical dam break point records. When geological fault zones or areas of loose soil are identified, the system marks potential risk areas. This detection mechanism is particularly crucial in mountainous reservoir monitoring, enabling early identification of geologically vulnerable zones caused by historical landslide accumulation.

[0096] When historical dam failure points exist, landslide displacement constraints are added to the initial decision parameters. These constraints are constructed using displacement monitoring data, including geomechanical parameters such as soil creep rate and potential sliding surface dip angle. The optimization decision parameter generation process employs the Lagrange multiplier method, embedding the constraints into the objective function for multi-objective optimization. In practical applications, such as for earth-rock dam areas that have experienced previous failures, the constraints force the interpolation path to avoid deformation-sensitive areas of the dam body, reducing the risk of secondary disasters.

[0097] The interpolation method selection mechanism is dynamically adjusted based on the projection bias value and optimized decision parameters. The projection bias value is obtained by comparing the geographic coordinate differences between satellite imagery and ground measurement data. When the bias value is within a moderate range, the Kriging interpolation method is activated. This method utilizes a variogram to analyze spatial autocorrelation, making it particularly suitable for handling plains areas with continuously changing terrain. When the bias value exceeds a threshold, the system switches to radial basis function interpolation, fitting a nonlinear spatial distribution through a Gaussian kernel function to effectively address projection distortion caused by abrupt changes in mountainous terrain. The risk weight coefficient in the decision parameters further optimizes the shape of the interpolation kernel function, for example, by adding anisotropic adjustment factors in river bends.

[0098] The quantum annealing decision-making process establishes an adaptive optimization mechanism. The solution results of the energy equation are fed back to the spatial reliability labeling system to update the terrain complexity assessment indicators. When multiple consecutive interpolation calculations point to a specific risk area, the system automatically upgrades the monitoring level of that area and initiates a special geological exploration process. This dynamic adjustment strategy enables the interpolation algorithm to gradually adapt to changes in regional geological characteristics, improving the reliability of long-term predictions.

[0099] Specifically, in the data visualization processing method for smart water conservancy described in this invention, step 4 includes:

[0100] Receive the piping probability matrix output by the hydraulic model and the equipment status code generated in step 1;

[0101] A dynamic risk parameter set is generated based on the piping probability matrix and equipment status codes;

[0102] Based on the dam break factor coefficient, a weighted calculation is performed on the dynamic risk parameter set to generate the pulsating red ball diameter value;

[0103] The diameter values ​​of pulsating red spheres are merged with historical geological disaster frequency layers to generate dynamic rendering results.

[0104] In step 4, the system first receives the piping probability matrix output by the hydraulic model and the equipment status codes generated in step 1. The piping probability matrix is ​​generated by the hydraulic model through simulation of water flow seepage and soil stability calculations, representing the probability level of piping disasters in different areas. The equipment status codes originate from the hydrological data acquisition stage, including sensor operating status and data quality indicators. The receiving process achieves time alignment through a data interface protocol, ensuring that the probability matrix and status codes are synchronized in timestamps, providing a consistent data foundation for subsequent risk integration.

[0105] When generating a dynamic risk parameter set based on the piping probability matrix and equipment status codes, the system employs a multi-source data fusion algorithm. The piping probability matrix provides the spatially distributed risk probabilities, while the equipment status codes contribute data reliability weights. The generation process uses weighted superposition calculations to combine the probability values ​​with the reliability coefficients represented by the status codes, forming a comprehensive risk score. The dynamic risk parameter set is updated in real time, reflecting changes in the risk level of various areas under current hydrological conditions. For example, in river monitoring, the parameter set will mark high-probability piping areas and their corresponding data source reliability.

[0106] The diameter of the pulsating red sphere is generated by performing a weighted calculation on the dynamic risk parameter set based on the dam break factor coefficient. The dam break factor coefficient is extracted from a historical disaster database and includes geomechanical parameters such as soil shear strength and permeability coefficient. The weighted calculation uses a linear combination model, with the dynamic risk parameter set as the input vector and the dam break factor coefficient as the weight matrix. The diameter value is output through a dot product operation. The diameter value quantifies the degree of piping risk, and its magnitude is positively correlated with the risk level. Normalization is introduced during the calculation process to eliminate the influence of dimensional differences on the results.

[0107] The diameter values ​​of pulsating red spheres are fused with a historical geological hazard frequency layer to generate a dynamic rendering result. The historical geological hazard frequency layer is loaded from a geographic information system (GIS) and records the frequency of historical collapses, landslides, and other events in the region. The fusion operation employs spatial overlay analysis, mapping the real-time calculated diameter values ​​to corresponding geographic coordinates and combining them with historical frequency data using a color blending algorithm. The rendering engine generates a heatmap-style visualization output based on the fusion result, with the dynamic changes in the diameter of the pulsating red spheres presented through color depth and flicker frequency, providing an intuitive mapping of risk levels. The dynamic rendering result supports real-time updates, providing a basis for water conservancy management decisions.

[0108] Specifically, in the data visualization processing method for smart water conservancy described in this invention, step 5 includes:

[0109] Receive real-time hydrological operation data streams; detect changes in gate opening and sudden changes in flow velocity;

[0110] High-risk operation data is generated when the gate opening changes by more than 30% / min or the flow velocity changes by more than 1.5m / s.

[0111] Isolate high-risk operational data into a sandbox and attach error failure flags;

[0112] The sandbox cleanup cycle is calculated based on the migration speed of rainstorms, and the data in the sandbox is cleared.

[0113] During step 5, the system receives real-time hydrological operation data streams via an IoT protocol interface. These data streams include gate opening sensor readings and flow velocity meter readings. The detection module uses a differential algorithm to calculate the gate opening change rate and flow velocity change per unit time, and analyzes the gradient characteristics of the data sequence using a sliding time window. When the gate opening change rate exceeds 30% per minute or the flow velocity change exceeds 1.5 meters per second, the system triggers a risk assessment mechanism. This detection method is designed for scenarios involving sudden scheduling operations or drastic flow changes caused by flood impacts in water conservancy projects, such as emergency gate openings or flow velocity surges due to heavy rain.

[0114] When generating high-risk operational data, the system creates structured data objects that encapsulate timestamps, geographic coordinates, risk types, and quantitative indicators. Risk level labels are attached to the data objects; sudden changes in gate opening are marked as mechanical operational risks, and sudden changes in flow velocity are marked as hydraulic impact risks. The generation process is linked to equipment status codes and operational command sources to ensure data traceability. Under high-risk conditions, such as uncontrolled rapid gate opening, the system records the operational sequence context, providing a complete information chain for subsequent analysis.

[0115] High-risk operational data is transferred to a sandbox storage area, which employs physically isolated storage partitions and logical access control policies. When data is written to the sandbox, an error failure marker is automatically attached, including a risk confidence score, the data failure time point, and a contamination propagation path identifier. This isolation mechanism prevents high-risk data from participating in the model training process, avoiding the infiltration of erroneous operational information into the learning algorithm. For example, when a flow rate sensor generates abnormal readings due to impact from floating objects, the relevant data is immediately isolated.

[0116] When calculating the sandbox cleanup cycle based on the migration speed of rainstorms, the system queries cloud movement speed data provided by weather radar and dynamically adjusts the cleanup interval using an inverse proportional function. The faster the rainstorm moves, the shorter the sandbox data retention period, ensuring timely cleanup of temporarily stored contaminated data. The cleanup operation executes a secure erasure protocol to completely delete expired data records from the sandbox. This mechanism is particularly crucial in scenarios with rapidly passing rainstorms, effectively shortening the lifespan of high-risk data and reducing the risk of the system being exposed to distorted data.

[0117] Specifically, in the data visualization processing method for smart water conservancy described in this invention, step 6 includes:

[0118] Receive historical disaster event databases, hydrological monitoring parameter sets, and disaster relief plan databases;

[0119] Establish node relationships based on historical disaster event database, hydrological monitoring parameter set and disaster relief plan database, and generate initial map structure;

[0120] Calculate edge weights based on economic loss data and the initial graph structure, and update the connectivity of the graph structure.

[0121] Spatiotemporal feature vectors are extracted based on the updated graph structure; the spatiotemporal feature vectors are dimensionality-reduced and encoded to generate feature embedding representations; and a similarity calculation model is constructed based on the feature embedding representations.

[0122] During step 6, the system receives a historical disaster event database, a hydrological monitoring parameter set, and a disaster relief plan database via a data interface protocol. The historical disaster event database includes time and location records of disaster events such as floods and dam breaks; the hydrological monitoring parameter set provides real-time monitoring data such as water level and flow velocity; and the disaster relief plan database stores emergency response procedures and resource allocation schemes. The receiving module performs data format standardization processing, converting heterogeneous data into a unified time-series format, achieving timestamp alignment and spatial coordinate matching. For example, in watershed flood control analysis, the system integrates flood records from the past ten years with current hydrological station observations, providing a multi-dimensional data foundation for map construction.

[0123] When establishing node relationships based on a historical disaster event database, hydrological monitoring parameter set, and disaster relief plan database, the system uses a graph database model to construct the initial graph structure. Node types include disaster event entities, monitoring station objects, and plan action units. Edge relationships define spatiotemporal association logic, such as the causal relationship between disaster events and monitoring data, and the response association between plans and disaster types. The graph generation algorithm uses entity parsing and relation extraction techniques to identify co-occurrence patterns and dependencies in the data, forming an initial graph with attribute labels. Taking a mountain flood case as an example, the system connects specific rain gauge data with downstream dam failure event nodes and associates them with corresponding evacuation plan nodes.

[0124] Edge weights are calculated based on economic loss data and the initial graph structure to update the connectivity relationships within the graph. Economic loss data, extracted from post-disaster assessment reports, includes quantitative indicators such as property damage and relief costs. Edge weights are calculated using a weighted algorithm, considering factors such as the scope of event impact, temporal proximity, and spatial distance, and the connection strength between nodes is optimized through gradient descent. The update process dynamically corrects the graph topology, strengthening edge weights in high-loss disaster chains and weakening statistically irrelevant connections. For example, a multi-point dam failure sequence caused by consecutive torrential rain events is assigned higher weights, forming a visual representation of the risk transmission path.

[0125] Based on the updated graph structure, spatiotemporal feature vectors are extracted. The system employs a graph neural network to traverse the neighborhood of nodes and aggregate multi-hop connection information to generate high-dimensional feature representations. The spatiotemporal feature capture module combines time sliding window analysis to identify event sequence patterns and integrates geospatial interpolation algorithms to extract regional distribution characteristics. The feature vectors encode event evolution trends and spatial clustering characteristics, providing structured input for downstream analysis. In river network monitoring scenarios, the feature vectors reflect the time difference of flood peak propagation and the influence of watershed topography.

[0126] When generating feature embedding representations by dimensionality reduction encoding of spatiotemporal feature vectors, the system applies principal component analysis or autoencoder algorithms to compress the vector dimension while retaining key discriminative information. The dimensionality reduction process eliminates data redundancy, improves computational efficiency, and maintains the relative distance relationships between features. The feature embedding representation maps to a low-dimensional latent space, allowing similar catastrophic patterns to cluster in the embedding space, facilitating subsequent retrieval and matching. For example, different types of piping events will form distinct clusters in the embedding space.

[0127] A similarity calculation model is constructed based on feature embedding representations. The system employs metric learning algorithms to train distance functions, such as cosine similarity or Euclidean distance. The model optimizes the embedding space structure through contrastive learning, minimizing the feature embedding distance for similar types of disasters and maximizing the distance for heterogeneous events. The similarity calculation model supports real-time queries; by inputting real-time monitoring data feature embeddings, historical similar cases can be retrieved, and matching scores and confidence levels are output. When applied to flood control scheduling, this model can quickly match current hydrological conditions with historical disaster patterns, providing a reference for emergency decision-making.

[0128] Specifically, in the data visualization processing method for smart water conservancy described in this invention, step 6 further includes:

[0129] Receive pulsed red ball diameter distribution data; cluster the pulsed red ball diameter distribution data through nearest neighbor propagation to generate disaster scenario similarity classification results;

[0130] When the diameter of the pulsating red ball suddenly increases to more than 15cm, similar disaster scenarios are retrieved from the historical disaster event database, and the search scope is limited to the geological structure zone of the same watershed.

[0131] Based on the search results, the associated memory parameters are loaded, the residuals are corrected before loading, and an optimized transfer learning parameter set is generated.

[0132] In the supplementary processing of step 6, the system receives diameter distribution data generated by the holographic projection of pulsating red spheres, with each data point representing the piping risk level at different spatial locations. The nearest neighbor propagation clustering algorithm is applied to the pulsating red sphere diameter distribution data. By calculating the similarity matrix between data points and iteratively updating the cluster centers, it identifies disaster patterns with common characteristics. The clustering process groups similar risk scenarios based on the spatial distribution and temporal trend of diameter values, outputting disaster scenario similarity classification results. The classification results are categorized into risk levels according to cluster distance, such as low-risk, medium-risk, and high-risk clusters, providing structured input for subsequent retrieval.

[0133] When a sudden increase in the diameter of a pulsating red sphere exceeds a set threshold, the system triggers a historical disaster event retrieval mechanism. The diameter surge detection module uses a sliding window to analyze time-series data, calculating the diameter change rate at consecutive time points. When the change rate consistently exceeds a critical value and the duration reaches the window length, it is determined to be a surge event. The retrieval process is limited to a database of historical disaster events within the same watershed geological structure zone. Spatial geocoding is used to match the watershed boundaries and geological features of the current monitoring point with those of historical events. The retrieval algorithm uses a spatiotemporal similarity metric to compare the current diameter distribution pattern with the spatiotemporal distribution characteristics of historical events, outputting a list of disaster scenarios with the highest similarity scores.

[0134] When loading associated memory parameters based on search results, the system extracts memory parameters related to similar disaster scenarios from the knowledge base. These parameters include historical response strategy model parameters and environmental feature codes. Before loading, a residual correction process is performed, using the least squares method to fit the residual distribution of current and historical data, eliminating systematic errors and random noise. The corrected memory parameters are then fused with real-time monitoring data, and a gradient descent algorithm is used to optimize the transfer learning parameter set, enabling the model to quickly adapt to changes in the current environment. The optimized transfer learning parameter set is used to update the risk prediction model, improving the inference accuracy and response speed in sudden disaster scenarios.

[0135] Specifically, in the data visualization processing method for smart water conservancy described in this invention, step 7 includes:

[0136] Receive search results; when the similarity among the search results is greater than 80%, load long-term memory parameters;

[0137] The number of conflicts between the transfer learning parameter set and the terrain reflectivity benchmark library is detected; when the number of conflicts exceeds 3, multi-center water conservancy data is fused.

[0138] Generate an updated terrain reflectance benchmark library; transfer the updated terrain reflectance benchmark library to step 2.

[0139] During step 7, the system receives the search results from step 6 via a data interface. These results include similarity scores between historical disaster scenarios and current monitoring data. The receiving module performs data format validation and timestamp alignment to ensure the search results maintain temporal consistency with real-time hydrological monitoring data. When the similarity score in the search results exceeds a set threshold, the system triggers a long-term memory parameter loading mechanism. Long-term memory parameters are extracted from the knowledge base, and each parameter encodes historical disaster response strategies and environmental characteristic patterns, providing experiential references for current decision-making.

[0140] After loading long-term memory parameters, the system detects the number of conflicts between the transfer learning parameter set and the terrain reflectivity benchmark library. Conflict detection is achieved by comparing the deviation between the predicted values ​​output by the transfer learning parameter set and the actual measured values ​​in the terrain reflectivity benchmark library. When the number of consecutively detected conflicts exceeds a preset limit, the system determines that there is a significant difference between the current model and the benchmark library. The conflict count uses a sliding window counting algorithm to ensure the timeliness and accuracy of the detection results.

[0141] When the number of conflicts reaches the trigger condition, the system initiates a multi-center water resources data fusion process. This fusion process integrates hydrological data from different monitoring centers, including satellite remote sensing information, ground sensor readings, and manual inspection records. The data fusion algorithm employs a weighted average method, assigning weights based on the reliability of the data sources to generate a more consistent comprehensive dataset. The fused data is then used to correct outliers or expired records in the topographic reflectivity benchmark database.

[0142] Based on the fusion results, the system generates an updated topographic reflectivity benchmark database. The update process employs an incremental learning algorithm, preserving historically valid features of the benchmark database while incorporating new data patterns. The updated benchmark database undergoes integrity verification and consistency checks to ensure data quality meets subsequent processing requirements. Finally, the system feeds back the updated topographic reflectivity benchmark database to the spatial reliability label generation stage in step 2 via an encrypted data transmission channel, forming a closed-loop optimization mechanism to continuously improve the system's adaptability to complex hydrological environments.

[0143] This invention systematically addresses the technical challenges caused by the coupling of asynchronous drift and sudden anomalies in multi-source hydrological data by constructing a multi-level collaborative processing mechanism. At the data acquisition level, device status codes are injected into all hydrological sensor data to form a traceability chain. Spatial projection deviations between satellite remote sensing data and ground monitoring data are dynamically corrected through spatial reliability markers in the verification channel. When radar echoes and rain gauge data experience positional differences due to clock drift, the spatial reliability marker set compares real-time terrain reflectivity with a historical benchmark database to eliminate false flooding rendering caused by rainstorm cloud movement exceeding the data acquisition cycle.

[0144] In response to sudden environmental anomalies, the anti-interference channel employs a particle swarm optimization algorithm to reconstruct historical rainfall sequences, embedding meteorological fluid dynamics equations as physical constraints into the fitness function to ensure the reconstructed sequences conform to fluid motion laws. An adversarial generative network (GDN) integrates terrain features to fill spatiotemporal gaps in radar data and synchronously triggers semi-frozen control of core hydraulic model parameters to prevent the disruption of flow simulation contour lines due to gate control command delays. This approach demonstrates significant performance in mountainous reservoir monitoring scenarios; when torrential rain clouds pass rapidly, the system effectively repairs radar scanning blind spots caused by mountain obstruction, maintaining the spatiotemporal continuity of the visualization interface.

[0145] To address the systemic degradation of model performance, a three-tiered defense chain is established. A dynamic filtering mechanism isolates high-risk operations in a sandbox environment and attaches error / failure markers by real-time monitoring of sudden gate opening changes and flow velocity surges. The data cleanup cycle is dynamically calculated based on the speed of rainstorm migration, preventing erroneous operational data from seeping into the training set at the source. Pulsating red sphere distribution clustering analysis triggers a retrieval of disaster patterns within the same watershed. When an abnormal surge occurs in the visualized indicators of piping risk, the system matches disaster patterns from the historical disaster database for similar geological structures, providing a reference for decision-making.

[0146] The federated learning mechanism forms the core of the closed-loop optimization. When transfer learning parameters conflict repeatedly with the topographic reflectivity benchmark library, the system integrates data from multiple monitoring centers to update the benchmark library. The optimization results are then fed back to the spatial credibility label generation stage through a verification channel. This feedback mechanism enables the system to continuously adapt to changes in regional surface characteristics. In the river management case, the system continuously learns the topographic reflectivity characteristics of different river sections, gradually reducing projection bias caused by seasonal land cover changes, forming a self-evolving visualization processing system. Throughout the process, data lineage is tracked using device status codes, repair areas are marked using generated value identifiers, and contaminated data is isolated using error and failure markers, ultimately achieving collaborative governance of visualization distortion and error propagation in the model.

[0147] In an embodiment of the mountain reservoir monitoring scenario, the present invention specifically implements a pressure-type water level gauge installed on the reservoir dam to collect water head pulse signals at a frequency of once per second, a radar current meter to capture Doppler frequency shift data, and a tipping bucket rain gauge to record rainfall count pulses per minute. All data are transmitted to edge nodes via an IoT module, where analog-to-digital conversion is performed to generate digital water level parameters. Simultaneously, the operating status of the equipment, such as power supply voltage and signal strength, is analyzed to generate equipment status codes. The integrated hydrological dataset is registered with meteorological radar raster data to form a data stream with a Coordinated Universal Time Stamp (UTC). After receiving the data, the central platform uses an inverse distance weighting algorithm to generate a 500-meter resolution rainfall raster matrix. Simultaneously, remote sensing satellite infrared spectral data undergoes atmospheric correction, and topographic thermal inertia distribution characteristics are extracted. When the standard deviation of the reflectance characteristics exceeds 0.35, a Kalman filter algorithm is used to enhance signal reliability, outputting a set of spatial reliability markers bound to geographic coordinates. When the system detects that the migration speed of the rainstorm cloud reaches 25 km / h, the anti-interference channel is activated: the particle swarm optimization algorithm reconstructs the historical sequence with the constraint that the rainfall gradient deviation does not exceed 5 mm / h; the adversarial generative network fuses the terrain undulation coefficient to fill the gaps in the radar data; the confidence threshold of the generated value identifier is set to 0.7; and the core parameters of the hydraulic model enter a semi-frozen state. The quantum annealing decision module selects the interpolation method based on the projection deviation value. When the projection deviation is 800 meters, Kriging interpolation is used, and the confidence marker of the adjacent space and the terrain undulation coefficient are input. The piping probability matrix and equipment status codes generate a dynamic risk parameter set. The dam failure factor coefficient is used to calculate the diameter value of the pulsating red ball. When the diameter suddenly increases by 18 cm, the historical disaster retrieval of the geological structure zone in the same watershed is triggered. The dynamic filtering mechanism monitors the gate opening changes in real time. When the opening change rate exceeds 30% per minute or the flow velocity suddenly changes to 2 meters per second, the high-risk operation is isolated to the sandbox, an error failure mark is attached, and the sandbox clearing cycle is calculated according to the rainstorm migration speed. When the similarity of the search results reaches 85%, long-term memory parameters are loaded. When the number of conflicts between the transfer learning parameters and the terrain reflectivity benchmark database reaches four, the benchmark database is updated by fusing multi-center water conservancy data and fed back to the verification channel. This implementation effectively eliminates the misalignment between radar and ground data caused by mountainous terrain obstruction, avoids the rendering of purple false floodplains, and prevents the erroneous transmission of incorrect scheduling instructions to the model training set.

[0148] In an embodiment of flood control scheduling in a plain river channel, this invention is applied to densely networked river areas. A set of hydrological sensors is deployed every kilometer along the river channel to collect water level, flow velocity, and rainfall data. Device status codes are generated in real time and appended to the dataset. The central system processes and generates a rainfall raster matrix, adds a Coordinated Universal Time Stamp (UTC), and verifies it synchronously with satellite remote sensing data. When the detected rainfall migration speed is 22 km / h, the system triggers anti-interference processing: a particle swarm optimization algorithm is embedded into meteorological fluid dynamics equations to reconstruct the rainfall sequence, constraining the gradient deviation to no more than 5 mm / h; a generative adversarial network fills the spatiotemporal gap in the radar; and the confidence threshold for the generated value identifier is set to 0.7. In quantum annealing decision-making, radial basis function interpolation is used when the projection deviation is 1200 meters, fusing adjacent confidence markers and terrain undulation coefficients. A pulsating red sphere diameter distribution is generated based on the piping probability matrix. When the diameter suddenly increases by 17 cm, nearest neighbor propagation clustering analysis generates a disaster scenario similarity classification result, and historical disaster events in the same watershed are retrieved. In the real-time operational data stream, when the gate opening change rate is monitored to be 35% per minute or the flow velocity changes abruptly by 1.8 meters per second, it is identified as a high-risk operation, isolated to a sandbox, and marked as faulty. The sandbox clearing cycle is dynamically adjusted according to the rainstorm migration speed. When the similarity of the search results reaches 82%, long-term memory parameters are loaded. After five conflicts between the transfer learning parameters and the benchmark library are detected, federated learning is initiated to fuse multi-center data and update the terrain reflectivity benchmark library. This implementation solves the problem of contour line breaks caused by differences in data collection cycles in plain areas, improves the accuracy and response efficiency of flood control scheduling, and blocks error propagation paths in the model.

[0149] The technical features of this invention are explained below:

[0150] In step 2, the inverse distance weighted interpolation algorithm processes the hydrological dataset to generate a rainfall raster matrix. Based on discretely distributed hydrological monitoring station data, the algorithm calculates the reciprocal of the spatial distance between the interpolation point and known stations as the weight coefficient, converting point observations into continuous spatially distributed grid data. At the same time, it combines topographic elevation data to correct the weights, giving higher weights to stations located in low-lying areas such as river valleys, thereby accurately reflecting the influence of topography on rainfall distribution. The generated rainfall raster matrix provides a basic data layer for subsequent spatial credibility labeling, and its interpolation accuracy directly affects the accuracy of rainstorm condition detection in step 3.

[0151] In step 3, the particle swarm optimization (PSO) algorithm is used to reconstruct the historical rainfall sequence. The PSO algorithm treats each possible rainfall sequence as a particle in a multi-dimensional space and searches for the optimal solution by iteratively updating the particle position and velocity. The meteorological fluid dynamics equations embedded in the fitness function ensure that the reconstructed sequence conforms to the law of conservation of mass, while the spatiotemporal distribution weights of historical disaster events guide the particles to converge toward the disaster mode. The PSO algorithm and the spatial credibility tag set generated in step 2 form a closed loop. The spatial tags provide parameters for the rainstorm migration velocity, and the algorithm adjusts its search strategy accordingly. The reconstructed sequence is output to step 4 for calculating the projection deviation value.

[0152] The meteorological fluid dynamics equations are embedded as physical constraints in the fitness function of the particle swarm optimization algorithm in step 3. The equations include continuity equations and motion equations to ensure that the reconstructed rainfall sequence maintains the rationality of fluid motion in the spatiotemporal dimensions. When the system detects an abnormal migration velocity of rainstorms, the convection term in the equations will strengthen the spatiotemporal correlation of the sequence to prevent physically unreasonable abrupt changes. The meteorological fluid dynamics equations are linked with the calculation of the projection deviation value in step 4. The equations ensure the physical consistency of the sequence, while the projection deviation value reflects the spatial fit between the sequence and the measured data. Together, they ensure the reconstruction quality.

[0153] In step 3, the adversarial generative network undertakes the task of filling the spatiotemporal gaps in the radar. The generator takes the rainfall raster matrix and terrain undulation coefficient as conditional inputs and learns the spatial distribution pattern of normal radar echoes through an encoder-decoder structure. The discriminator integrates a spatiotemporal continuity verification module and uses a three-dimensional convolutional neural network to detect the continuity of the generated data in the time and space dimensions. When a gap region is identified, the system marks the generated value with an identifier. The radar raster data with the identifier output by the adversarial generative network is passed to step 4 for interpolation strategy selection based on the projection deviation value.

[0154] In step 4, the Kriging interpolation method is selected based on the projection deviation value. When the projection deviation value is in the range of 500-1000 meters, the method is activated. The Kriging interpolation method uses the variogram function to analyze spatial autocorrelation and performs unbiased optimal estimation under the condition of considering the topographic relief constraint. It is particularly suitable for handling plain areas with continuous topographic changes. The interpolation process introduces the adjacent spatial confidence label and the topographic relief coefficient to optimize the accuracy. The results are used to generate the pulsating red ball diameter distribution and pulsation frequency parameters, providing input for the dynamic filtering in step 5.

[0155] In step 4, the radial basis function interpolation method replaces the Kriging interpolation method when the projection deviation value is greater than 1000 meters. The radial basis function interpolation method uses a Gaussian kernel function to fit the nonlinear spatial distribution, which effectively addresses the projection distortion caused by abrupt changes in mountainous terrain. During the interpolation process, the shape of the kernel function is dynamically adjusted according to the projection deviation value and the optimization decision parameters. For example, an anisotropic adjustment factor is added in the river bend section. The output of the radial basis function interpolation method is similar to that of the Kriging interpolation method and is used to drive the pulsating red sphere holographic projection. The generated risk visualization parameters support the disaster scenario retrieval in step 6.

[0156] In step 4, the quantum annealing algorithm processes the projection deviation value to optimize the interpolation decision path. The quantum annealing algorithm first constructs a three-dimensional energy equation to transform the interpolation path planning into an energy minimization problem, and generates initial decision parameters by solving the ground state solution. Then, it detects whether there are historical dam break points in the interpolation area based on the spatial credibility label set. When a dam break point is identified, the landslide displacement constraint term is incorporated into the decision parameters to form optimized decision parameters. Finally, the interpolation method is selected according to the range of projection deviation value. When the deviation value is between 500 and 1000 meters, Kriging interpolation is activated, and when the deviation value exceeds 1000 meters, it is switched to radial basis function interpolation. This process ensures that the interpolation strategy meets both computational efficiency and adapts to complex terrain conditions through the efficient search capability of quantum annealing.

[0157] In step 6, the nearest neighbor propagation clustering algorithm analyzes the distribution data of pulsating red sphere diameters to achieve disaster pattern recognition. The nearest neighbor propagation clustering algorithm automatically determines the cluster center by iteratively calculating the similarity matrix between data points, dividing the spatial distribution characteristics of diameter values ​​into clusters of different risk levels. When a sudden increase in diameter exceeding 15 cm is detected in real time, the clustering result triggers the retrieval of historical disaster events in the same watershed geological structure zone. The retrieval process narrows the search range based on the similarity classification results generated by clustering. The clustering output provides a classification basis for subsequent loading of associated memory parameters, improving the accuracy of disaster scenario matching.

[0158] In step 6, the metric learning algorithm constructs a similarity calculation model based on the dimensionality-reduced feature embedding representation. The metric learning algorithm optimizes the feature space structure through the triplet loss function, minimizing the distance between similar catastrophic events and maximizing the distance between dissimilar events in the embedding space. The trained model can calculate the similarity score between real-time monitoring data and historical cases in real time, and the output results directly support the long-term memory parameter loading decision in step 7. The metric learning process effectively improves the accuracy of catastrophic event retrieval and avoids misjudgments caused by feature representation mismatch.

[0159] In step 7, the federated learning mechanism addresses the conflict between transfer learning parameters and the terrain reflectivity benchmark library. When more than three conflicts are detected, the mechanism initiates a multi-center data fusion process. Each local node trains model parameters based on local water conservancy data and only uploads the model increment to the central server for secure aggregation. The aggregated parameters are used to update the terrain reflectivity benchmark library and fed back to the spatial credibility label generation stage in step 2 through an encrypted channel. Federated learning ensures data privacy while achieving knowledge sharing and continuously optimizes the system's generalization ability.

[0160] In step 2, the Kalman filter algorithm enhances the reliability of remote sensing satellite data. It uses a state-space equation to predict and correct infrared spectral data in a loop, dynamically estimating the true reflectance value and eliminating atmospheric disturbance noise. When the standard deviation of the reflectance of the interpreted feature data exceeds 0.35, the filtering result is used to generate enhanced label values. These label values ​​are then bound to geographic coordinates to form a spatial reliability label set. The Kalman filter effectively improves the alignment accuracy between satellite and ground data, providing a clean data source for subsequent heavy rain detection.

[0161] In step 2, principal component analysis (PCA) processes remote sensing satellite infrared spectral data to achieve feature dimensionality reduction. PCA transforms high-dimensional spectral data into a few principal components through orthogonal transformation, preserving key variance information of topographic thermal inertia distribution. The dimensionality-reduced interpreted feature data is used to generate spatial credibility markers, reducing data redundancy while highlighting the differences in surface material characteristics. PCA not only improves data processing efficiency but also ensures the accuracy of topographic reflectance comparison in subsequent steps.

[0162] In step 1, the Fast Fourier Transform (FFT) processes the flow velocity Doppler frequency shift data, extracting flow velocity feature values ​​from the time-domain signal through frequency domain transformation. The algorithm analyzes the spectral components of the Doppler frequency shift to accurately calculate the water flow velocity. Simultaneously, it combines equipment operating status parameters to generate equipment health status codes. The transformed frequency domain data provides a reliable velocity parameter basis for subsequent integration of hydrological datasets, ensuring the accuracy and real-time performance of flow velocity measurements.

[0163] In step 3, the optical flow method verifies the spatiotemporal continuity of the initial filling data. The optical flow method tracks the movement trajectory and velocity changes of the rainstorm cloud in space by calculating the motion vector field of the pixels in the continuous time-series radar image. If there is a significant deviation between the filling data and the movement pattern analyzed by the optical flow method, the spatiotemporal continuity verification is deemed to have failed, thereby triggering the process of regenerating the filling data to ensure the consistency between the radar data and the actual situation.

[0164] In step 6, the gradient descent algorithm optimizes the edge weights of the graph structure. By iteratively calculating the gradient of the loss function and updating the edge weight parameters along the negative gradient direction, the gradient descent algorithm minimizes the difference between the graph connectivity and historical economic loss data. In each iteration, the weights are adjusted to strengthen the correlation of high-loss disaster events, gradually optimizing the graph topology and improving the accuracy of disaster scenario similarity calculation.

[0165] In step 6, the least squares method performs a residual correction process. The least squares method solves for the optimal fitting parameters by linearly fitting the sum of squared residuals between the current pulsating red ball diameter distribution data and the historical disaster event data to eliminate systematic errors and random noise. The corrected residuals are used to adjust the loading values ​​of the associated memory parameters to ensure that the transfer learning parameter set can more accurately adapt to real-time environmental changes.

[0166] The hydraulic model is constructed based on fluid dynamics equations and soil mechanical parameters. It calculates the piping probability by simulating the water flow seepage process and dam stability. The core parameters of the model include the permeability coefficient and shear strength index. In step 3, when the system detects rainstorm conditions, the core parameters of the hydraulic model enter a semi-frozen state to maintain stability. Then, in step 4, the piping probability matrix is ​​output. This matrix is ​​fused with the equipment status code to generate a dynamic risk parameter set, which provides input for the pulsating red sphere projection.

[0167] The pulsating red sphere holographic projection model is constructed using piping probability and terrain features. Through holographic rendering technology, the risk level is mapped to the dynamic changes in the diameter and frequency of the red sphere. In step 4, a dynamic risk parameter set is generated based on the piping probability matrix and equipment status code. The dam failure factor coefficient is applied to perform weighted calculation to output the diameter value of the pulsating red sphere. Finally, it is fused with the historical geological disaster frequency layer to generate a dynamic rendering result, thereby realizing risk visualization.

[0168] The similarity calculation model uses a metric learning algorithm to train feature embedding representations and evaluates the matching degree between historical and current disaster scenarios by optimizing feature space distance functions such as cosine similarity. In step 6, spatiotemporal feature vectors are extracted from the updated graph structure, and feature embedding representations are generated through dimensionality reduction encoding. Based on this, a similarity calculation model is constructed to compare current monitoring data with historical cases in real time and output similarity scores to support disaster retrieval decisions.

[0169] The long-term memory model loads historical disaster response strategies and environmental feature parameters from the knowledge base and encodes them as long-term memory parameters for experience transfer. In step 7, when the similarity of the search results exceeds the threshold, the loading mechanism is triggered, and the long-term memory parameters are fed back to the spatial credibility label generation stage in step 2 to optimize the labeling rules of the terrain reflectivity benchmark library and improve the system adaptability through closed-loop feedback.

[0170] The topographic reflectance benchmark library is constructed by integrating historical satellite remote sensing data and multi-center ground observations, storing topographic reflectance benchmark values ​​from different periods for credibility calibration. In step 2, the real-time topographic reflectance is compared with the benchmark library to generate a set of spatial credibility labels. In step 7, when the transfer learning parameters conflict with the benchmark library, the multi-source data is fused to update the benchmark library, and the optimization results are fed back to the label generation process through the verification channel to ensure the accuracy of data projection.

Claims

1. A data visualization processing method applied to smart water conservancy, characterized in that, include: Step 1: Collect water level pulse signals, flow velocity Doppler frequency shift data, and rainfall counting pulses, and process the data to generate a hydrological dataset, which includes equipment status codes. Step 2: Receive hydrological dataset, process the hydrological dataset, generate a rainfall raster matrix, receive remote sensing satellite data, process the remote sensing satellite data to generate spatial credibility markers, and output a set of spatial credibility markers. Step 3: Receive the spatial confidence tag set; detect rainstorm conditions based on the spatial confidence tag set; when rainstorm conditions are detected, reconstruct the historical rainfall sequence, fill the spatiotemporal gaps in the radar, control the core parameters of the hydraulic model, and output the reconstructed historical rainfall sequence and radar grid data with generated value identifiers. The identifiers record the filling timestamp, geographic coordinates, and confidence score; the generated value identifiers are fed back to the spatial confidence tag system to update the confidence weights of the terrain reflectivity benchmark library. Step 4: Receive the reconstructed historical rainfall sequence and radar raster data with generated value identifiers; obtain the projection deviation value from the spatial credibility marker set; select an interpolation strategy based on the projection deviation value, and input adjacent spatial credibility markers and terrain undulation coefficients; receive the piping probability matrix output by the hydraulic model and the equipment status code generated in Step 1; generate a dynamic risk parameter set based on the piping probability matrix and equipment status code; perform weighted calculation on the dynamic risk parameter set based on the dam failure factor coefficient to generate the pulsating red ball diameter value; fuse the pulsating red ball diameter value with the historical geological disaster frequency layer to generate a dynamic rendering result. Step 5: Receive pulsation frequency parameters and pulsation red ball diameter distribution; receive real-time hydrological operation data stream; detect gate opening changes and flow velocity abrupt changes; when the gate opening change is greater than 30% / min or the flow velocity abrupt change is greater than 1.5m / s, generate high-risk operation data; isolate the high-risk operation data to a sandbox and attach error / failure flags; calculate the sandbox clearing cycle based on the rainstorm migration speed and clear the data in the sandbox; update the filtering rules; output the updated filtering rules and pulsation red ball diameter distribution. Step 6: Receive updated filtering rules and pulsating red ball diameter distribution; parse the pulsating red ball diameter distribution; search for similar disaster scenarios and output the search results; Step 7: Receive the search results; when the search results meet the preset conditions, load the long-term memory parameters; each parameter encodes historical disaster response strategies and environmental feature patterns; detect the number of conflicts between the transfer learning parameter set and the terrain reflectivity benchmark library; when the number of conflicts exceeds 3, fuse the multi-center water conservancy data; generate an updated terrain reflectivity benchmark library; and transmit the updated terrain reflectivity benchmark library to Step 2.

2. The data visualization processing method for smart water conservancy according to claim 1, wherein Step 1 includes: acquiring water level pulse signals, flow velocity Doppler frequency shift data, and rainfall counting pulses to generate equipment status codes; processing water level pulse signals to generate digital water level parameters; analyzing flow velocity Doppler frequency shift data to generate equipment health status codes; and integrating digital water level parameters, flow velocity parameters, rainfall parameters, and meteorological radar grid data to generate a hydrological dataset. Step 2 includes: receiving hydrological datasets to generate a rainfall raster matrix; adding a Coordinated Universal Time Stamp (UTC) to the rainfall raster matrix; receiving remote sensing satellite infrared spectral data; extracting topographic thermal inertia distribution features from the remote sensing satellite infrared spectral data to generate interpretation feature data; when the standard deviation of the reflectance feature is greater than 0.35, processing the interpretation feature data to generate enhanced label values; and binding the enhanced label values ​​to geographic coordinates to generate spatial credibility labels.

3. The data visualization processing method for smart water conservancy as described in claim 2, characterized in that, Step 3 includes: The migration speed of rainstorms is detected based on a spatial reliability tag set. When the migration speed of rainstorms is greater than 20 km / h, a fitness function is constructed to constrain the rainfall gradient deviation to ≤5 mm / h. Meteorological fluid dynamics equations are embedded into the fitness function. The fitness function is applied to the particle swarm optimization algorithm, and the spatiotemporal distribution weights of historical catastrophic events are loaded to update the particle positions, generating a particle position data set. A historical rainfall sequence is generated based on the particle position data set, and the historical rainfall sequence is labeled with an entropy fluctuation index. The spatiotemporal gaps of radar are filled through adversarial generative networks to generate radar grid data with generation value identifiers. The core parameters of the hydraulic model are controlled to enter a semi-frozen state.

4. The data visualization processing method for smart water conservancy as described in claim 3, characterized in that, Step 3 also includes: Initial infill data was generated based on the rainfall raster matrix and terrain relief coefficient, and the spatiotemporal continuity of the initial infill data was verified. When the verification fails, the cracked area is identified and a generated value identifier is attached. When the confidence level of the generated value identifier is less than 0.7, the filling data is regenerated.

5. The data visualization processing method for smart water conservancy as described in claim 4, characterized in that, Step 4 includes: Construct a three-dimensional energy equation to minimize the interpolation computation and generate initial decision parameters; Detection of historical dam failure points in interpolation regions based on spatial credibility tag sets; When historical dam failure points exist, the landslide displacement constraint term is added to the initial decision parameters to generate optimized decision parameters. Based on the projection deviation value and the optimized decision parameters, the interpolation method is selected: when the projection deviation value is 500-1000 meters, the Kriging interpolation method is used; when the projection deviation value is greater than 1000 meters, the radial basis function interpolation method is used.

6. The data visualization processing method for smart water conservancy as described in claim 5, characterized in that, Step 6 includes: Receive historical disaster event databases, hydrological monitoring parameter sets, and disaster relief plan databases; Establish node relationships based on historical disaster event database, hydrological monitoring parameter set and disaster relief plan database, and generate initial map structure; Calculate edge weights based on economic loss data and the initial graph structure, and update the connectivity of the graph structure. Spatiotemporal feature vectors are extracted based on the updated graph structure; the spatiotemporal feature vectors are dimensionality-reduced and encoded to generate feature embedding representations; and a similarity calculation model is constructed based on the feature embedding representations.

7. The data visualization processing method for smart water conservancy as described in claim 6, characterized in that, Step 6 also includes: Receive pulsed red ball diameter distribution data; cluster the pulsed red ball diameter distribution data through nearest neighbor propagation to generate disaster scenario similarity classification results; When the diameter of the pulsating red ball suddenly increases to more than 15cm, similar disaster scenarios are retrieved from the historical disaster event database, and the search scope is limited to the geological structure zone of the same watershed. Based on the search results, the associated memory parameters are loaded, the residuals are corrected before loading, and an optimized transfer learning parameter set is generated.

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