Data visualization processing method applied to intelligent water conservancy

Abstract

The invention relates to the technical field of data processing, in particular to a data visualization processing method applied to intelligent water conservancy, and the method comprises the steps: collecting data, and generating a hydrological data set; processing the data to generate a rainfall grid matrix, and comparing terrain reflectivity through a verification channel to generate a space credibility mark set; a rainstorm condition is detected based on a mark set, an anti-interference channel is activated, a rainfall sequence is reconstructed through a particle swarm algorithm, a generative adversarial network fills radar space-time cracks, and core parameters of a hydraulic model are controlled; an interpolation strategy is selected based on the projection deviation value, and risk visualization parameters are generated by combining piping probability loading pulsation red ball holographic projection; a dynamic filtering mechanism isolates high-risk operation to a sandbox, and catastrophe scene retrieval is triggered through pulsation distribution clustering; and optimizing the terrain reflectivity reference library based on the retrieval result and feeding back to the verification channel. According to the method, visualization distortion caused by space-time drift of multi-source data is effectively eliminated, and a pollution conduction chain of error data to a model training set is blocked.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a data visualization processing method applied to smart water conservancy. BACKGROUND

[0002] The data visualization of smart water conservancy is based on the continuous collection of real-time hydrological data such as water level, flow rate and rainfall by Internet of Things sensors, and forms a structured information stream through big data processing. The data is visually presented in the form of dynamic charts, geographic information system mapping or heat maps, making the complex water resource monitoring results intuitive and discernible. This process helps water managers to directly identify abnormal patterns such as flood risk accumulation or water quality abnormal areas from the graphical interface, infer potential problem trends, and then guide decision-making to optimize water resource deployment, improve prediction and early warning capabilities and emergency response efficiency, and reduce natural disaster losses.

[0003] The existing data visualization technology of smart water conservancy has the technical pain point of model cognitive collapse caused by the failure of multi-source spatio-temporal field dynamic alignment. Specifically, the hydrological sensor network, satellite remote sensing data stream and artificial control instruction have transmission protocol heterogeneity and timestamp benchmark drift. When the moving speed of a sudden rain cloud exceeds the data collection period, the minute-level delayed data of the rainfall station and the second-level predicted image of the radar will be spatially projected out of position in the visualization interface, forming a purple pseudo-flood rendering artifact. At the same time, the emergency opening control instruction of the gate cannot be synchronized to the water power model due to communication delay, causing the flow simulation contour to be broken and distorted. After the operator implements the scheduling based on the distorted heat map, the error condition data pollutes the deep learning training set in the reverse direction, ultimately leading to the complete failure of the dam-break risk prediction function when a real disaster occurs, causing decision-making lag and scheduling deviation. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a data visualization processing method applied to smart water conservancy, which solves the problems of visualization interface flood pseudo-phenomenon caused by the coupling of multi-source hydrological spatio-temporal field asynchronous drift and sudden environmental change, and the systematic degradation of model performance caused by the transmission and accumulation of error data in the deep learning training set.

[0005] To solve the above technical problems, the present application is as follows: The present application provides a data visualization processing method applied to smart water conservancy, which comprises: Step 1, collect water level pulse signal, flow rate Doppler frequency shift data and rainfall counter pulse, and process the data to generate a hydrological data set, which includes a device status code; Step 2, receive the hydrological data set, process the hydrological data set to generate a rainfall grid matrix, receive remote sensing satellite data, process the remote sensing satellite data to generate a spatial credibility label, and output a spatial credibility label set; Step 3, receive the spatial credibility mark set; detect the heavy rain condition based on the spatial credibility mark set; when the heavy rain condition is detected, reconstruct the rainfall history sequence, fill in the radar space-time gap, control the hydraulic model core parameter, output the reconstructed rainfall history sequence and the radar grid data with the generated value identifier; Step 4, receive the reconstructed rainfall history sequence and the radar grid data with the generated value identifier; obtain the projection bias value from the spatial credibility mark set; select an interpolation strategy based on the projection bias value, input the adjacent spatial credibility mark and the terrain undulation coefficient; load the pulsating red ball holographic projection based on the pipe gushing probability, generate the pulsating red ball diameter distribution and the pulsating frequency parameter; Step 5, receive the pulsating frequency parameter and the pulsating red ball diameter distribution; isolate the high-risk operation to the sandbox, update the filtering rule; output the updated filtering rule and the pulsating red ball diameter distribution; Step 6, receive the updated filtering rule and the pulsating red ball diameter distribution; analyze the pulsating red ball diameter distribution; retrieve the similar disaster scenario, output the retrieval result; Step 7, receive the retrieval result; when the retrieval result meets the preset condition, load the long-term memory parameter; feed back the long-term memory parameter to step 2, optimize the terrain reflectivity benchmark library credibility mark rule.

[0006] Further, the data visualization processing method applied to intelligent water conservancy provided by the application, step 1 comprises: collecting water level pulse signals, flow velocity Doppler frequency shift data and rainfall counter pulses, and generating device state codes; processing the water level pulse signals to generate digital water level parameters; analyzing the flow velocity Doppler frequency shift data to generate device health state codes; integrating the digital water level parameters, flow velocity parameters, rainfall parameters and meteorological radar grid data to generate hydrological data sets; Step 2 comprises: receiving the hydrological data set to generate a rainfall grid matrix; attaching a coordinated universal time stamp to the rainfall grid matrix; receiving remote sensing satellite infrared spectrum data; extracting terrain thermal inertia distribution characteristics from the remote sensing satellite infrared spectrum data to generate interpretation feature data; when the reflectivity feature standard deviation is greater than 0.35, processing the interpretation feature data to generate an enhanced mark value; binding the enhanced mark value to geographic coordinates to generate a spatial credibility mark.

[0007] Further, the data visualization processing method applied to intelligent water conservancy provided by the application, step 3 comprises: Detect the rainstorm migration speed based on the spatial credibility label set; when the rainstorm migration speed is greater than 20 km / h, an adaptability function with a constraint rain gradient deviation of 5 mm / h is constructed; the meteorological fluid mechanics equation is embedded into the adaptability function; the adaptability function is applied to the particle swarm algorithm, the historical disaster event space-time distribution weight is loaded to update the particle position, and a particle position data set is generated; a rain history sequence is generated according to the particle position data set, and the rain history sequence is attached with an entropy value fluctuation index label; the radar space-time gap is filled through the generative adversarial network, and radar grid data with a generated value identifier is generated; the core parameters of the hydraulic model are controlled to enter a semi-frozen state.

[0008] Further, the data visualization processing method applied to intelligent water conservancy, the step 3 further comprises: Generate initial filling data based on the rainfall grid matrix and the terrain relief coefficient, and verify the space-time continuity of the initial filling data; When the verification fails, the gap area is marked and a generated value identifier is attached, and when the confidence of the generated value identifier is less than 0.7, the filling data is regenerated.

[0009] Further, the data visualization processing method applied to intelligent water conservancy, the step 4 comprises: Build a three-dimensional energy equation minimization interpolation calculation, generate initial decision parameters; Detect whether there is a historical dam break point in the interpolation area based on the spatial credibility label set; When there is a historical dam break point, add a landslide displacement constraint term to the initial decision parameters to generate optimized decision parameters, and select an interpolation method based on the projection deviation value and the optimized decision parameters: when the projection deviation value is 500-1000 meters, the Kriging interpolation method is adopted; when the projection deviation value is greater than 1000 meters, the radial basis function interpolation method is adopted.

[0010] Further, the data visualization processing method applied to intelligent water conservancy, the step 4 comprises: Receive the pipe gushing probability matrix output by the hydraulic model and the device state code generated in step 1; Generate a dynamic risk parameter set based on the pipe gushing probability matrix and the device state code; Perform weighted calculation on the dynamic risk parameter set based on the dam break factor coefficient to generate a 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.

[0011] Further, the data visualization processing method applied to intelligent water conservancy, the step 5 comprises: Receive real-time hydrological operation data stream; detect gate opening change and flow rate mutation; When the gate opening changes greater than 30% / min or the flow rate suddenly changes greater than 1.5m / s, high-risk operation data is generated; Isolate the high-risk operation data to the sandbox and attach the error failure label; Calculate the sandbox cleaning period based on the rainstorm migration speed, and clean the data in the sandbox.

[0012] Further, the data visualization processing method applied to intelligent water conservancy comprises the following steps: Receive the historical disaster event database, the hydrological monitoring parameter set and the disaster relief plan library; Establish a node association relationship based on the historical disaster event database, the hydrological monitoring parameter set and the disaster relief plan library, and generate an initial graph structure; Calculate the edge weight value based on the economic loss data and the initial graph structure, and update the connection relationship of the graph structure; Extract the space-time feature vector based on the updated graph structure; perform dimensionality reduction coding on the space-time feature vector to generate a feature embedding representation; and construct a similarity calculation model based on the feature embedding representation.

[0013] Further, the data visualization processing method applied to intelligent water conservancy comprises the following steps: Receive the pulsating red ball diameter distribution data; and cluster the pulsating red ball diameter distribution data by the near neighbor propagation to generate a disaster scene similarity classification result; When the pulsating red ball diameter suddenly increases by more than 15cm, search for similar disaster scenes from the historical disaster event library, and limit the search range to the same river basin geological structure belt; Load the associated memory parameters based on the search result, correct the residual error before loading, and generate an optimized transfer learning parameter set.

[0014] Further, the data visualization processing method applied to intelligent water conservancy comprises the following steps: Receive the search result; when the similarity in the search result is greater than 80%, load the long-term memory parameters; Detect the number of conflicts between the transfer learning parameter set and the terrain reflectivity benchmark library; when the number of conflicts exceeds 3 times, 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.

[0015] The present application has the following advantages: The application systematically eliminates the visualization distortion problem caused by the time-space asynchronous drift of multi-source hydrological data by constructing a space credibility marking system and a dynamic anti-interference mechanism. The space credibility marking dynamically corrects the projection deviation of satellite and ground sensors by comparing the real-time terrain reflectivity with the historical benchmark library. The anti-interference channel reconstructs the rainfall history sequence using a particle swarm algorithm and fills the radar time-space gaps using a generative adversarial network to ensure data physical rationality and time-space continuity. The quantum annealing decision makes a hierarchical selection of interpolation strategies based on the projection deviation value, drives the pulsating red ball holographic projection to accurately map the pipe gushing risk level, and avoids contour line breakage. The dynamic filtering mechanism monitors the gate opening mutation and flow rate surge in real time, isolates high-risk operations to the sandbox and adds error failure markers, adjusts the cleaning period according to the storm migration speed, and blocks the reverse infiltration of error working condition data into the training set from the source. The pulsating red ball diameter distribution clustering analysis triggers the disaster retrieval of the same geological structure zone in the basin and provides historical similar scene decision support. The federated learning mechanism updates the benchmark library with multi-center data when the migration learning parameters and the terrain reflectivity benchmark library collide multiple times, forms a closed-loop feedback through the verification channel, and continuously optimizes the system adaptability, thereby significantly improving the accuracy, reliability and anti-interference ability of the intelligent water conservancy data visualization, and reducing the decision risk caused by data distortion. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the drawings.

[0017] Figure 1 A flowchart of a data visualization processing method applied to intelligent water conservancy provided by an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the technical solutions of the present application clearer, the following will combine the specific embodiments of the present application and the corresponding drawings to clearly and completely describe the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. The following will combine the drawings to specifically describe the present application provided by the embodiments of the present application. In order to better understand the purpose of the present application, the following will further describe the present application in detail.

[0019] Please refer to Figure 1 The present application provides a data visualization processing method applied to intelligent water conservancy, which comprises: Step 1, collect water level pulse signal, flow Doppler frequency shift data and rain gauge count pulse, and process the data to generate hydrological data set, the hydrological data set includes device state code; Step 2, receive the hydrological data set, process the hydrological data set, generate the rainfall grid matrix, receive the remote sensing satellite data, process the remote sensing satellite data to generate the spatial reliability mark, and output the spatial reliability mark set; Step 3, receive the spatial reliability mark set; detect the heavy rain condition based on the spatial reliability mark set; when the heavy rain condition is detected, reconstruct the rainfall history sequence, fill the radar space-time gap, control the hydraulic model core parameter, output the reconstructed rainfall history sequence and the radar grid data with the generated value identifier; Step 4, receive the reconstructed rainfall history sequence and the radar grid data with the generated value identifier; obtain the projection deviation value from the spatial reliability mark set; select the interpolation strategy based on the projection deviation value, input the adjacent spatial reliability mark and the terrain undulation coefficient; load the pulsating red ball holographic projection based on the pipe gushing probability, generate the pulsating red ball diameter distribution and the pulsating frequency parameter; Step 5, receive the pulsating frequency parameter and the pulsating red ball diameter distribution; isolate the high-risk operation to the sandbox, update the filtering rule; output the updated filtering rule and the pulsating red ball diameter distribution; Step 6, receive the updated filtering rule and the pulsating red ball diameter distribution; analyze the pulsating red ball diameter distribution; retrieve similar disaster scenarios, output the retrieval result; Step 7, receive the retrieval result; when the retrieval result meets the preset condition, load the long-term memory parameter; feed back the long-term memory parameter to step 2, optimize the terrain reflectivity benchmark library reliability mark rule.

[0020] In the implementation of the intelligent water conservancy data visualization processing method, step 1 involves the collection and integration of multi-source hydrological data. The water level pulse signal is generated by a pressure type water level gauge, the flow Doppler frequency shift data is captured by a radar flowmeter, and the rain gauge count pulse is derived from a tipping bucket rain gauge. The original signal is converted into digital water level parameters, flow parameters and rainfall parameters through analog-to-digital conversion, and at the same time, device state codes are added based on the analysis of device operating status to form a structured hydrological data set. The device state code provides traceability identification for data quality, facilitating the verification of data reliability in subsequent steps.

[0021] Step 2 processes the hydrological data set to generate a rainfall grid matrix, and adds coordinated universal time stamps to eliminate clock drift. The remote sensing satellite infrared spectrum data is used as input, and the interpreted feature data is generated by interpreting the terrain heat inertia distribution characteristics. When the reflectivity characteristics are compared with the historical benchmark library and an anomaly occurs, a filtering algorithm is used for reliability enhancement, and a spatial reliability mark set bound to geographic coordinates is output. This step solves the spatial alignment problem between satellite and ground sensor data, providing a basis for heavy rain detection.

[0022] Step 3 detects the rainstorm condition based on the spatial credibility label set, for example, by analyzing whether the rainstorm migration speed exceeds a threshold value. When the rainstorm condition is triggered, the anti-interference processing is started: the meteorological fluid mechanics equation is embedded into the fitness function of the particle swarm algorithm, the rainfall history sequence is reconstructed to maintain physical reasonableness; the terrain features are fused by the generative adversarial network to fill in the radar space-time gaps, and the generated value identifier is labeled; the core parameters of the hydraulic model are synchronously controlled into a semi-frozen state to avoid model distortion caused by sudden data anomalies. The output of the radar grid data with the identifier ensures the traceability of subsequent processing.

[0023] Step 4 extracts the projection deviation value from the spatial credibility label set after receiving the reconstructed data, and selects an interpolation strategy according to the deviation degree. When the deviation is small, the Kriging interpolation method is used, and when the deviation is large, the radial basis function interpolation method is switched to, and the adjacent spatial credibility labels and the terrain relief coefficient are introduced to optimize the interpolation accuracy. Based on the pipe gully probability, the pulsating red ball holographic projection is loaded on the high-risk area to dynamically generate the diameter distribution and frequency parameters, and intuitively map the pipe gully risk level.

[0024] Step 5 monitors real-time hydrological operation data streams through a dynamic credibility filtering mechanism to identify high-risk operations such as gate opening mutations or flow rate surges. High-risk data is isolated to a sandbox storage area and attached with an error failure label to prevent error decision data from penetrating the training set in reverse. The sandbox cleaning period is dynamically adjusted according to the rainstorm migration speed to achieve timely cleaning of contaminated data.

[0025] Step 6 analyzes the pulsating red ball diameter distribution and uses the nearest neighbor propagation clustering analysis to generate a disaster scenario similarity classification result. When the diameter abnormally increases, similar scenarios within the same geological structure zone of the flow field are retrieved from the historical disaster event library, and the retrieval results support emergency decision-making. This step uses clustering technology to enhance the accuracy of scenario matching.

[0026] Step 7 loads long-term memory parameters when the similarity of the retrieval results meets the conditions, and feeds back to Step 2 to optimize the terrain reflectivity benchmark library. At the same time, the number of conflicts between the migration learning parameters and the benchmark library is monitored, and when the conflicts exceed a threshold value, a federated learning mechanism is started to fuse multi-center water conservancy data to update the benchmark library. The updated benchmark library forms a closed-loop feedback through a truth verification channel to continuously improve the accuracy of the spatial credibility label.

[0027] Specifically, the data visualization processing method applied to smart water conservancy according to the application comprises the following steps: collecting water level pulse signals, flow rate Doppler frequency shift data and rainfall counter pulses to generate device state codes; processing the water level pulse signals to generate digital water level parameters; analyzing the flow rate Doppler frequency shift data to generate device health state codes; integrating the digital water level parameters, flow rate parameters, rainfall parameters and meteorological radar grid data to generate a hydrological data set; Step 2 includes: receiving hydrological data set to generate rainfall grid matrix; attaching coordinated universal time stamp to rainfall grid matrix; receiving remote sensing satellite infrared spectrum data; extracting terrain thermal inertia distribution characteristics from remote sensing satellite infrared spectrum data to generate interpretation feature data; when reflectance feature standard deviation is greater than 0.35, processing interpretation feature data to generate enhanced label value; binding enhanced label value to geographic coordinates to generate spatial credibility label.

[0028] In the implementation process of step 1 of the intelligent water conservancy data visualization processing method, the water head pulse signal collected by the pressure type water level gauge is processed by the analog-digital conversion module to generate digital water level parameters with physical check code. The Doppler frequency shift data captured by the radar flowmeter is analyzed by the frequency domain analysis algorithm to obtain the flow rate value, and the device health status code is generated by combining the device operating temperature, signal strength and other indicators. The rain gauge count pulse output by the tipping bucket rain gauge is converted into minute-level rainfall intensity data through accumulation calculation. After the isomerization data are aligned by time stamp, they are spatially registered with the weather radar grid data, and finally integrated into a structured hydrological data set including device status code. The device status code is encoded in binary, wherein the high byte identifies the device type and the low byte records the signal quality level, providing a traceability basis for subsequent data credibility evaluation.

[0029] In the hydrological data set processing link of step 2, the central server first performs spatiotemporal consistency verification after receiving the data. The rainfall grid matrix is generated by using the inverse distance weighted interpolation algorithm to convert the discrete site data into 500-meter resolution grid data, and the coordinated universal time stamp of the Beidou satellite timing is attached. The remote sensing satellite infrared spectrum data is corrected by the atmospheric correction model to eliminate cloud interference, and the principal component analysis method is used to extract the terrain thermal inertia distribution characteristics to generate interpretation feature data including surface material information. When the reflectance feature deviates abnormally compared with the historical benchmark library, the Kalman filter algorithm predicts the reflectance trend through the state space equation to dynamically correct the signal distortion caused by atmospheric disturbance and outputs the enhanced label value. The geographic coordinate binding process uses the WGS84 coordinate system to superimpose the label value and the digital elevation model to generate a spatial credibility label layer.

[0030] Such hierarchical processing mechanism is significant in actual river basin monitoring scenarios. Taking mountain reservoir monitoring as an example, the water level pulse signal sampling frequency is set to 1 Hz, and the analog-to-digital conversion adopts 16-bit precision to ensure that the water level measurement error is controlled within centimeter level. The Doppler frequency shift data of flow velocity is extracted by fast Fourier transform to extract the flow velocity characteristics, and the device health status code reflects the working state of the sensor in real time. The terrain shadow correction algorithm is introduced when generating the rainfall grid matrix, which effectively eliminates the shielding effect of mountains on radar echoes. In the interpretation process of satellite infrared spectral data, the thermal inertia characteristics and soil moisture content are mapped to provide early indicators for flood risk warning. The spatial reliability marking layer is presented in the form of a semi-transparent overlay layer on the GIS platform, and the color depth directly reflects the data reliability level, which helps managers quickly identify abnormal areas.

[0031] The time sequence correlation is particularly emphasized in the data processing chain. The device status code generation timestamp is strictly synchronized with the data acquisition time to avoid matching errors caused by time delay. The time resolution of the rainfall grid matrix is set to 5 minutes, which is synchronized with the radar data update period. In the generation process of the spatial reliability mark, the terrain reflectivity reference library is updated using a sliding window mechanism, and the reflectivity data of the last 30 days is involved in the reference calculation. This dynamic reference maintenance method effectively adapts to seasonal changes in surface coverage and avoids interference caused by vegetation growth cycle on reflectivity characteristics. When binding the mark value with geographic coordinates, a bilinear interpolation algorithm is used to align satellite data and ground observation data at pixel level.

[0032] The technical implementation level focuses on solving the problem of multi-source data fusion. A unified data dictionary is established when integrating hydrological data sets, and the physical units and value ranges of each parameter are clearly defined. The device status code adopts a hierarchical coding structure, the first layer identifies the sensor type, the second layer records the power supply state, and the third layer marks the communication quality. In the generation process of the rainfall grid matrix, terrain correction coefficients are introduced to deal with the radar beam blocking phenomenon, and the beam blocking angle is calculated according to the digital elevation model, and the adjacent site data is used to compensate for the shaded area. The enhanced processing of the spatial reliability mark uses an adaptive filtering algorithm, and the filtering coefficient is dynamically adjusted according to the signal-to-noise ratio to ensure data accuracy while avoiding feature loss caused by excessive smoothing.

[0033] Specifically, the data visualization processing method applied to smart water conservancy according to the present application, step 3 comprises: The rainstorm migration speed is detected based on a spatial credibility label set; when the rainstorm migration speed is greater than 20 km / h, an adaptability function with a constraint of a rainfall gradient deviation of less than 5 mm / h is constructed; a meteorological fluid mechanics equation is embedded in the adaptability function; the adaptability function is applied to a particle swarm algorithm, a historical disaster event space-time distribution weight is loaded to update a particle position, and a particle position data set is generated; a rainfall history sequence is generated according to the particle position data set, and the rainfall history sequence is attached with an entropy value fluctuation index label; a radar space-time gap is filled through a generative adversarial network, and radar grid data with a generated value identifier is generated; and a hydraulic model core parameter is controlled to enter a semi-frozen state.

[0034] In the implementation process of step 3, the spatial credibility label set is used as an input data source, and the rainstorm migration speed is detected by analyzing the space-time change pattern of the reflectivity characteristics in the label set. In the specific implementation, a cross-correlation algorithm is used to calculate the displacement vector of the continuous time-phase radar echo sequence, and the motion trajectory and moving speed of the rainstorm cloud cluster are obtained through vector field analysis. When the rainstorm migration speed is detected to be greater than a set threshold, the system automatically triggers the anti-interference processing mechanism.

[0035] The anti-interference processing first constructs an adaptability function, which takes the rainfall gradient deviation as a constraint condition and embeds the mass conservation equation in meteorological fluid mechanics as a physical constraint into the objective function. In actual application, for example, in the monitoring scene of mountain rainstorm, the adaptability function considers the rainfall enhancement phenomenon caused by the terrain lifting effect, and improves the physical rationality of sequence reconstruction by introducing an elevation correction factor. In the initialization stage of the particle swarm algorithm, each particle position vector represents a potential rainfall history sequence scheme, and the particle dimension corresponds to the length of the historical time window.

[0036] In the iteration process of the particle swarm algorithm, the historical disaster event space-time distribution weight is loaded in the form of a matrix, and the weight value is distributed according to the spatial density and time frequency of the disaster record. When the particle position is updated, the adaptability function simultaneously evaluates the physical consistency and historical disaster matching degree of the sequence, so that the reconstructed sequence not only conforms to the fluid mechanics law but also reflects the regional disaster characteristics. The updated particle position data set is screened through cluster analysis to obtain the optimal solution, and a rainfall history sequence with an additional entropy value fluctuation index label is generated. The entropy index quantifies the randomness characteristics of the rainfall process by calculating the information entropy of the sequence, providing a quantitative basis for subsequent risk warning.

[0037] The radar space-time crack filling adopts a conditional generative adversarial network architecture. The generator takes the rainfall grid matrix and the terrain relief coefficient as conditional input, and learns the spatial distribution pattern of normal radar echoes through an encoder-decoder structure. The discriminator integrates a space-time continuity verification module, which uses a three-dimensional convolutional neural network to detect the continuity of the generated data in the time and space dimensions. When a crack region is identified, the system will label a generated value identifier, which includes metadata such as generation time, confidence, and repair range. In actual river monitoring cases, this mechanism can effectively repair radar scan blind area data caused by mountain shielding.

[0038] The semi-frozen control of the core parameters of the hydraulic model adopts a parameter grouping strategy, which divides the model parameters into dynamic response groups and steady-state maintenance groups. When the system enters the anti-disturbance 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 maintenance group are completely frozen. This hierarchical control method not only retains the model's response ability to normal hydrological changes, but also avoids the impact of sudden abnormal data on core parameters. For example, in the case of sudden gate regulation, the model can maintain the stability of the flow calculation core algorithm, while allowing the boundary condition parameters to be adjusted moderately.

[0039] The anti-disturbance processing process forms a closed-loop quality control mechanism, and the generated value identifier is fed back to the spatial credibility marking system to update the confidence weight of the terrain reflectivity benchmark library. When the same region is detected to have high-frequency data repair in multiple consecutive time periods, the system will automatically upgrade the basic credibility level of that region and start a special verification process. This dynamic weight adjustment mechanism enables the system to gradually adapt to regional surface feature changes and improve the accuracy of long-term monitoring.

[0040] Specifically, the data visualization processing method applied to intelligent water conservancy according to the present application, step 3 further comprises: Generate initial filling data based on the rainfall grid matrix and the terrain relief coefficient, and verify the space-time continuity of the initial filling data; When the verification fails, identify the crack region and attach a generated value identifier. When the confidence of the generated value identifier is less than 0.7, regenerate the filling data.

[0041] In the supplementary processing of step 3, the system generates initial filling data based on the rainfall grid matrix and the terrain relief coefficient. The generation process uses a spatial interpolation algorithm, which takes into account the influence of terrain elevation, slope and other features on rainfall distribution, and fills in the missing areas in the radar data through inverse distance weighting interpolation or Kriging method. The initial filling data aims to restore the data cracks caused by cloud shielding or sensor failure. For example, in mountainous terrain, the algorithm will adjust the interpolation weight according to the relief pattern of valleys and ridges, so that the filling result is more consistent with the actual rainfall spatial distribution.

[0042] When verifying the spatiotemporal continuity of the initial filled data, the system checks the consistency of the data in the time series and the smoothness in the spatial dimension. The verification method includes analyzing the consistency of the rainfall movement trajectory and calculating the gradient change of the values of adjacent grid cells to identify abnormal mutation points. For example, the optical flow method is used to track the movement vector of the storm cloud cluster, and if the filled data deviates significantly from the movement pattern, it is determined that the verification fails. The verification process also involves comparing historical contemporaneous data to assess the reasonableness of the filled values.

[0043] When the verification fails, the system identifies the crack area and generates a value identifier. The crack area is located through difference analysis, i.e., comparing the residual distribution of the original radar echo and the filled data, and the identifier records the filling timestamp, geographic coordinates, and confidence score. The confidence score is calculated based on the historical accuracy rate of the filling algorithm and the data consistency index, reflecting the reliability of the generated data.

[0044] If the confidence score of the generated value identifier is lower than the preset threshold, the system regenerates the filled data. The regeneration process may introduce more advanced algorithms, such as a generative adversarial network-based model, which integrates multi-source meteorological data as constraint conditions. The iterative optimization phase adjusts algorithm parameters or increases training samples to improve the filling quality. Multiple iterations are performed until the confidence score meets the requirements, thereby improving data availability.

[0045] Specifically, the data visualization processing method applied to intelligent water conservancy according to the present application, step 4 comprises: Constructing a three-dimensional energy equation minimization interpolation calculation amount to generate initial decision parameters; Detecting whether there is a historical dam break point in the interpolation area based on a set of spatial confidence markers; When there is a historical dam break point, adding a landslide displacement constraint term to the initial decision parameters to generate optimized decision parameters, and selecting an interpolation method based on the projection deviation value and the optimized decision parameters: when the projection deviation value is 500-1000 meters, using the Kriging interpolation method; when the projection deviation value is greater than 1000 meters, using the radial basis function interpolation method.

[0046] In the quantum annealing decision-making process, the system first constructs a three-dimensional energy equation to optimize the interpolation calculation efficiency. The energy equation considers spatial coordinates, time dimension, and data density distribution, and converts the interpolation path planning into an energy minimization problem. The initial decision parameters are generated by solving the ground state solution of the energy equation, and each parameter represents the calculation overhead and accuracy balance point of different interpolation strategies. For example, in river terrain modeling, the energy equation adjusts the weight coefficient according to the riverbed undulation characteristics, so that the interpolation path extends preferentially along the water flow direction.

[0047] When detecting whether there is a historical dam break point in the interpolation region based on the spatial credibility label set, the system calls the geological disaster database for spatial matching analysis. The detection process uses a sliding window scanning technique to perform polygon overlay analysis on the current interpolation region and historical dam break point records. When a geological fault zone or loose soil area is identified, the system will mark the potential risk area. This detection mechanism is particularly critical in mountain reservoir monitoring, as it can identify geological weak zones caused by historical landslide accumulation in advance.

[0048] When there is a historical dam break point, a landslide displacement constraint term is added to the initial decision parameter. The constraint term is constructed from displacement monitoring data, including geomechanical parameters such as soil creep rate and potential sliding surface inclination. The optimization decision parameter generation process uses the Lagrange multiplier method to embed the constraint condition into the objective function for multi-objective optimization. In practical applications, for example, in the area of a dam that has experienced a dam break, the constraint term will force the interpolation path to avoid the dam deformation sensitive area, reducing the risk of secondary disasters.

[0049] The interpolation method selection mechanism is based on the projection deviation value and the dynamic adjustment of the optimized decision parameters. The projection deviation value is obtained by comparing the geographic coordinate differences between satellite images and ground measurement data. When the deviation value is in the moderate range, the Kriging interpolation method is activated, which uses the variogram to analyze spatial autocorrelation, especially suitable for handling continuous changes in terrain in plain areas. When the deviation value exceeds the threshold, the system switches to radial basis function interpolation, which fits the nonlinear spatial distribution through a Gaussian kernel function, effectively addressing the projection distortion caused by sudden changes in mountain terrain. The risk weight coefficient in the decision parameter will further optimize the shape of the interpolation kernel function, such as increasing the anisotropic adjustment factor in the curved section of the river.

[0050] The quantum annealing decision-making process forms an adaptive optimization mechanism. The solution of the energy equation is fed back to the spatial credibility labeling system to update the terrain complexity evaluation index. When continuous multiple interpolation calculations point to a specific risk area, the system will automatically upgrade the monitoring level of that area and start the special geological exploration process. This dynamic adjustment strategy enables the interpolation algorithm to gradually adapt to changes in regional geological features, improving the reliability of long-term predictions.

[0051] Specifically, the data visualization processing method applied to smart water conservancy according to the present application, step 4 comprises: receiving the piping probability matrix output by the hydraulic model and the device state code generated in step 1; generating a dynamic risk parameter set based on the piping probability matrix and the device state code; performing weighted calculation on the dynamic risk parameter set based on the dam break factor coefficient to generate a 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.

[0052] In the implementation process of step 4, the system first receives the piping probability matrix output by the hydraulic model and the device state code generated in step 1. The piping probability matrix is generated by the hydraulic model through simulation of water seepage and soil stability calculation, representing the possibility of piping disaster in different areas. The device state code is derived from the hydrological data acquisition link, including sensor operation status and data quality indicators. The receiving process realizes time alignment through data interface protocol, ensuring that the probability matrix and the state code are synchronized in time stamp, providing a consistent data basis for subsequent risk integration.

[0053] When generating dynamic risk parameter set based on piping probability matrix and device state code, the system adopts multi-source data fusion algorithm. The piping probability matrix provides spatial distribution of risk probability, and the device state code contributes data credibility weight. The generation process calculates by weighted superposition, combining probability value and reliability coefficient represented by state code to form comprehensive risk score. The dynamic risk parameter set is updated in real time, reflecting the risk level change of each area under current hydrological conditions. For example, in river monitoring, the parameter set will mark the high probability piping area and the corresponding data source credibility.

[0054] Based on the dam-break factor coefficient, weighted calculation is performed on the dynamic risk parameter set to generate the pulsating red ball diameter value. The dam-break factor coefficient is extracted from the historical disaster database, including soil shear strength, permeability coefficient and other geomechanical parameters. The weighted calculation adopts 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 pulsating red ball diameter value is output through point multiplication operation. The diameter value quantifies the piping risk level, and the numerical value is positively correlated with the risk level. Normalization is introduced in the calculation process to eliminate the influence of dimension difference on the result.

[0055] The pulsating red ball diameter value is fused with the historical geological disaster frequency layer to generate a dynamic rendering result. The historical geological disaster frequency layer is loaded from the geographic information system, recording the frequency of historical collapse, landslide and other events in the area. The fusion operation adopts spatial overlay analysis, mapping the real-time calculated diameter value to the corresponding geographic coordinates, and combining with the historical frequency data through color mixing algorithm. The rendering engine generates a heat map style visualization output based on the fusion result, with the pulsating red ball diameter dynamically changing through color depth and flicker frequency, realizing intuitive mapping of risk level. The dynamic rendering result supports real-time updating, providing decision basis for water conservancy management.

[0056] Specifically, the data visualization processing method applied to smart water conservancy according to the application comprises the following steps: Receive real-time hydrological operation data stream; detect gate opening change and flow rate mutation; When the gate opening change is greater than 30% / min or the flow rate mutation is greater than 1.5 m / s, generate high-risk operation data; Isolate high-risk operation data to sandbox, attach error invalidation label; Calculate sandbox cleaning period based on storm migration speed, clean data in sandbox.

[0057] In the implementation process of step 5, the system receives a real-time hydrological operation data stream through an Internet of Things protocol interface, and the data stream includes gate opening degree sensor readings and flow velocity meter monitoring values. The detection module uses a difference algorithm to calculate the gate opening degree change rate and the flow velocity change amount per unit time, and analyzes the gradient characteristics of the data sequence through a sliding time window. When the gate opening degree change rate exceeds 30% per minute or the flow velocity change amount exceeds 1.5 meters per second is detected, the system triggers the risk judgment mechanism. This detection method is aimed at sudden dispatch operation or flood impact caused by dramatic flow changes in water conservancy projects, such as emergency opening of the gate or rapid increase of flow velocity caused by heavy rain.

[0058] When high-risk operation data is generated, the system creates a structured data object, encapsulating the timestamp, geographic coordinates, risk type, and quantitative indicators. The data object is attached with a risk level label, where gate opening degree mutation is marked as mechanical operation risk, and flow velocity mutation is marked as hydraulic impact risk. The generation process associates device status codes and operation instruction sources to ensure data traceability. In high-risk working conditions, such as uncontrolled rapid opening of the gate, the system records the operation sequence context to provide a complete information chain for subsequent analysis.

[0059] High-risk operation data is transmitted to the sandbox storage area, which uses physically isolated storage partitions and logical access control strategies. When data is written to the sandbox, an error invalidation label is automatically attached, including risk confidence score, data invalidation time point, and pollution propagation path identification. The isolation mechanism prevents high-risk data from participating in model training processes, preventing the penetration of false working condition information into learning algorithms. For example, when a flow velocity sensor produces abnormal readings due to floating object impact, the related data will be immediately isolated.

[0060] When calculating the sandbox cleaning period based on storm migration speed, the system queries cloud movement speed data provided by a weather radar and dynamically adjusts the cleaning time interval through an inverse function. The faster the storm moves, the shorter the sandbox data retention period, ensuring that contaminated data stored temporarily is cleaned in a timely manner. The cleaning operation performs a secure erase protocol to completely delete expired data records in the sandbox. This mechanism is particularly critical in fast-moving storm scenarios, effectively reducing the survival time of high-risk data and reducing the risk of exposing the system to distorted data.

[0061] Specifically, the data visualization processing method applied to smart water conservancy according to the application comprises the following steps: Receive a historical disaster event database, a hydrological monitoring parameter set, and a disaster relief plan library; establish node association based on historical disaster event database, hydrological monitoring parameter set and disaster relief plan library, generate initial graph structure; calculate edge weight value based on economic loss data and initial graph structure, update connection relationship of graph structure; extract spatio-temporal feature vector based on updated graph structure; generate feature embedding representation by dimensionality reduction coding on spatio-temporal feature vector; construct similarity calculation model based on feature embedding representation.

[0062] In the implementation process of step 6, the system receives historical disaster event database, hydrological monitoring parameter set and disaster relief plan library through data interface protocol. The historical disaster event database includes the time and place records of disasters such as floods and dam failures, the hydrological monitoring parameter set provides real-time monitoring data such as water level and flow rate, and the disaster relief plan library stores emergency response process resource allocation scheme. The receiving module performs data format standardization processing, converts heterogeneous data into a unified time series format, and realizes timestamp alignment and spatial coordinate matching. For example, in the analysis of basin flood control, the system will integrate the past ten years of flood records and the current hydrological station observation values to provide multi-dimensional data basis for graph construction.

[0063] When establishing node association based on historical disaster event database, hydrological monitoring parameter set and disaster relief plan library, the system adopts graph database model to construct initial graph structure. Node types include disaster event entity, monitoring station object and preplan action unit, and edge relationship defines spatio-temporal association logic, such as the causal relationship between disaster event and monitoring data, and the response association between preplan and disaster type. Graph generation algorithm identifies co-occurrence patterns and dependency relationships in data through entity analysis and relationship extraction technology, forming an initial graph with attribute labels. Taking a mountain flood case as an example, the system will connect the specific rainfall station data with the downstream dam failure event node, and associate the corresponding evacuation plan node.

[0064] Based on economic loss data and initial graph structure, the edge weight value is calculated, and the connection relationship of the graph structure is updated. Economic loss data is extracted from post-disaster evaluation report, including quantitative indicators such as property loss and disaster relief cost. The edge weight calculation adopts a weighted algorithm, considering factors such as event influence range, time proximity and spatial distance, and adjusts the connection strength between nodes through gradient descent optimization. The update process dynamically corrects the graph topology, strengthens the edge weight of high-loss disaster chain, and weakens the statistical irrelevant connection. For example, a series of multi-point dam failures caused by consecutive rainstorm events will be assigned a higher weight, forming a visual representation of risk transmission path.

[0065] Based on the updated graph structure, the spatio-temporal feature vector is extracted, and the system uses graph neural network to traverse the node neighborhood and aggregate multi-hop connection information to generate high-dimensional feature representation. The spatio-temporal feature capture module combines the time sliding window to analyze the event sequence pattern, and fuses the geographical space interpolation algorithm to extract the regional distribution characteristics. The feature vector encodes the event evolution trend and spatial aggregation characteristics, providing structured input for downstream analysis. In the river network monitoring scene, the feature vector reflects the flood peak propagation time difference and the influence factor of the basin topography.

[0066] When the spatio-temporal feature vector is dimensionally encoded to generate a feature embedding representation, the system applies principal component analysis or autoencoder algorithm to compress the vector dimension, retaining key discriminative information. The dimension reduction process eliminates data redundancy, improves computational efficiency, and maintains the relative distance relationship between features. The feature embedding representation is mapped to a low-dimensional latent space, so that similar disaster patterns are clustered in the embedding space, facilitating subsequent retrieval and matching. For example, different types of piping events will form distinct cluster distribution in the embedding space.

[0067] Based on the feature embedding representation, a similarity calculation model is constructed, and the system uses metric learning algorithm to train the distance function, such as cosine similarity or Euclidean distance metric. The model optimizes the embedding space structure through contrastive learning, minimizing the feature embedding distance of the same type of disaster event and maximizing the distance of heterogeneous events. The similarity calculation model supports real-time query, and the input real-time monitoring data feature embedding can retrieve historical similar cases, output matching score and confidence. When applied to flood control scheduling, the model can quickly match the current hydrological conditions with historical disaster patterns, providing reference for emergency decision-making.

[0068] Specifically, the data visualization processing method applied to smart water conservancy according to the present application, the step 6 further comprises: Receiving pulsating red ball diameter distribution data; clustering pulsating red ball diameter distribution data by nearest neighbor propagation to generate disaster scene similarity classification results; When the pulsating red ball diameter suddenly increases by more than 15 cm, retrieve similar disaster scenes from the historical disaster event library, and limit the retrieval range to the same basin geological structure belt; Based on the retrieval results, load the associated memory parameters, correct the residual error before loading, and generate an optimized set of transfer learning parameters.

[0069] In the supplementary processing of step 6, the system receives the diameter distribution data generated by the pulsating red ball holographic projection, each data representing the risk level of piping in different spatial locations. The affinity propagation clustering algorithm is applied to the pulsating red ball diameter distribution data, which identifies disaster patterns with common characteristics by calculating the similarity matrix between data points and iteratively updating the cluster centers. The clustering process groups similar risk scenarios based on the spatial distribution and temporal trend of diameter values, and outputs the similarity classification results of disaster scenarios. The classification results are divided into risk levels according to the clustering distance, such as low-risk, medium-risk and high-risk clusters, providing structured input for subsequent retrieval.

[0070] When the real-time monitoring of the sudden increase of the pulsating red ball diameter exceeds the set threshold, the system triggers the historical disaster event retrieval mechanism. The diameter surge detection module uses a sliding window to analyze time series data, calculates the diameter change rate at consecutive time points, and determines a surge event when the change rate is continuously higher than the critical value and the duration reaches the window length. The retrieval process is limited to the historical disaster event library within the same geological structure belt of the river basin, and matches the current monitoring point with the river basin boundary and geological features of the historical events through spatial geographic coding. The retrieval algorithm uses a spatiotemporal similarity measure to compare the current diameter distribution pattern with the spatiotemporal distribution characteristics of historical events, and outputs a list of disaster scenarios with the highest similarity score.

[0071] Based on the retrieval results, the system extracts memory parameters associated with similar disaster scenarios from the knowledge base, each parameter including historical coping strategy model parameters and environmental feature encodings. Before loading, perform residual correction process, fit the residual distribution of current data and historical data by least squares method, eliminate system error and random noise. The corrected memory parameters are fused with real-time monitoring data, and the gradient descent algorithm is used to optimize the transfer learning parameter set, so that the model quickly adapts to the current environmental changes. 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.

[0072] Specifically, the data visualization processing method applied to smart water conservancy according to the application comprises the following steps: receiving the retrieval results; when the similarity in the retrieval results is greater than 80%, loading the long-term memory parameters; detecting the number of conflicts between the transfer learning parameter set and the terrain reflectivity benchmark library; when the number of conflicts exceeds 3 times, fusing multi-center water conservancy data; generating an updated terrain reflectivity benchmark library; transmitting the updated terrain reflectivity benchmark library to step 2.

[0073] In the implementation process of step 7, the system receives the retrieval results from step 6 through the data interface, and the retrieval results include the similarity scores of historical disaster scenarios and current monitoring data. The receiving module performs data format verification and timestamp alignment to ensure that the retrieval results are consistent with the real-time hydrological monitoring data in time sequence. When the similarity score in the retrieval result exceeds the set threshold, the system triggers the long-term memory parameter loading mechanism. The long-term memory parameters are extracted from the knowledge base, and each parameter encodes the historical disaster response strategy and environmental feature mode to provide experience reference for current decision-making.

[0074] After loading the 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 value output by the transfer learning parameter set and the actual measured value of the terrain reflectivity benchmark library. When the number of continuous conflicts detected exceeds the preset limit value, the system determines that there is a significant difference between the current model and the benchmark library. The number of conflicts is counted using a sliding window counting algorithm to ensure the timeliness and accuracy of the detection results.

[0075] When the number of conflicts reaches the trigger condition, the system starts the multi-center water conservancy data fusion process. The 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 uses a weighted average method to assign weights according to the credibility of the data source to generate a more consistent comprehensive data set. The fused data is used to correct abnormal values or outdated records in the terrain reflectivity benchmark library.

[0076] Based on the fusion results, the system generates an updated terrain reflectivity benchmark library. The update process uses an incremental learning algorithm to retain the historical effective features of the benchmark library while incorporating new data patterns. The updated benchmark library is subjected to integrity verification and consistency checking to ensure that the data quality meets the requirements of subsequent processing. Finally, the system feeds back the updated terrain reflectivity benchmark library to the spatial credibility label generation link in step 2 through an encrypted data transmission channel, forming a closed-loop optimization mechanism to continuously improve the system's adaptability to complex hydrological environments.

[0077] The present application systematically deals with the technical problems caused by the coupling of multi-source hydrological data asynchronous drift and sudden changes through the construction of a multi-level collaborative processing mechanism. At the data collection level, device status codes are injected into all hydrological sensor data to form a traceability chain. The spatial credibility label in the verification channel dynamically corrects the spatial projection deviation of satellite remote sensing data and ground monitoring data. When radar echo and rainfall station data have a positional difference due to clock drift, the spatial credibility label set eliminates the false flood area rendering phenomenon caused by the movement speed of the rainstorm cloud exceeding the data collection period by comparing the real-time terrain reflectivity with the historical benchmark library.

[0078] In the face of sudden environmental changes, the anti-interference channel uses particle swarm optimization to reconstruct the rainfall history sequence, and embeds the meteorological fluid mechanics equation as a physical constraint into the fitness function to ensure that the reconstructed sequence conforms to the fluid motion law. The GAN network fills the radar spatiotemporal gaps by combining terrain features, and synchronously triggers the semi-frozen control of the hydraulic model core parameters to prevent the fracture of the flow simulation contour caused by delayed gate control instructions. This processing method performs significantly in mountainous reservoir monitoring scenarios. When a rainstorm cloud passes quickly, the system can effectively repair the radar scanning blind area caused by mountain shielding and maintain the spatiotemporal continuity of the visualization interface.

[0079] To address the systematic degradation of model performance, the system establishes a three-level defense chain. The dynamic filtering mechanism monitors the sudden change in gate opening and the rapid increase in flow rate in real time, isolates high-risk operations in a sandbox environment, and adds an error failure label. Based on the migration speed of the rainstorm, the system dynamically calculates the data removal period to block the penetration of error working condition data into the training set from the source. The pulsating red ball distribution clustering analysis triggers the same basin disaster retrieval. When the pipe surge risk visualization identifier abnormally increases, the system matches the disaster mode of similar geological structure zones from the historical disaster library to provide reference for decision-making.

[0080] The federated learning mechanism constitutes the core of the closed-loop optimization. When the transfer learning parameters and the terrain reflectivity benchmark library conflict multiple times, the system updates the benchmark library by integrating data from multiple monitoring centers and feeds back the optimization results to the spatial credibility label generation link through the verification channel. This feedback mechanism enables the system to continuously adapt to changes in regional surface features. In the case of river management, the system gradually reduces the projection deviation caused by seasonal changes in surface coverage by continuously learning the terrain reflection characteristics of different river sections, forming a visualization processing system with self-evolutionary ability. Throughout the process, the system traces the data lineage through device status codes, marks the repaired areas through generated value identifiers, and isolates contaminated data through error failure labels, ultimately achieving coordinated governance of visualization distortion and model error transmission.

[0081] In the embodiment of mountain reservoir monitoring scene, the pressure type water level gauge installed on the reservoir dam collects water head pulse signals at a frequency of 1 per second, the radar flowmeter captures Doppler frequency shift data, and the tipping bucket rain gauge records rainfall count pulses per minute. Each data is transmitted to the edge node through the Internet of Things module, and the digital quantity water level parameter is generated through analog-digital conversion, and the device state code is generated by analyzing the device running state such as power supply voltage and signal strength. The integrated hydrological data set is registered with the weather radar grid data to form a data stream with coordinated universal time stamp. After the center platform receives the data, it generates a 500-meter resolution rainfall grid matrix using the inverse distance weighting algorithm. Simultaneously, the remote sensing satellite infrared spectrum data is corrected by atmosphere, and the terrain heat inertia distribution characteristics are extracted. When the reflectivity characteristic standard deviation exceeds 0.35, the Kalman filter algorithm enhances the signal credibility, and outputs the spatial credibility label set bound to geographic coordinates. When the system detects that the rainstorm cloud cluster migration speed reaches 25 kilometers per hour, the anti-interference channel is activated: the particle swarm algorithm reconstructs the historical sequence with the rainfall gradient deviation not exceeding 5 millimeters per hour as the constraint, and the generative network fills the radar data gap by integrating the terrain relief coefficient, with the value identifier confidence threshold set to 0.7, and the hydraulic model core parameters enter the semi-frozen state. The quantum annealing decision module selects the interpolation method based on the projection deviation value, and uses Kriging interpolation when the projection deviation is 800 meters, and inputs the adjacent spatial credibility label and terrain relief coefficient. The pipe gushing probability matrix and device state code generate a dynamic risk parameter set, and the dam break factor coefficient weightedly calculates the pulsating red ball diameter value. When the diameter suddenly increases by 18 centimeters, the historical disaster retrieval of the same geological structure zone in the basin is triggered. The dynamic filtering mechanism monitors the gate opening change in real time, and when the opening change rate exceeds 30% per minute or the flow rate suddenly changes by 2 meters per second, the high-risk operation is isolated to the sandbox, an error failure label is added, and the sandbox cleaning period is calculated according to the rainstorm migration speed. When the retrieval result similarity reaches 85%, the long-term memory parameters are loaded, and when the migration learning parameters conflict with the terrain reflectivity benchmark library for 4 times, the multi-center water conservancy data is fused to update the benchmark library, and feedback is fed back to the verification channel. This implementation effectively eliminates the misalignment of radar and ground data caused by mountain terrain shielding, avoids purple pseudo-hongfeng area rendering, and blocks the error transmission of false scheduling instructions to the model training set.

[0082] In the embodiment of flood control scheduling scene of plain river channel, the application is applied to the area with dense river network. A group of hydrological sensors is deployed along the river channel every kilometer to collect water level, flow rate and rainfall data, and the device state code is generated in real time and attached to the data set. The center system processes to generate a rainfall grid matrix, and adds a coordinated universal time stamp for synchronous verification with satellite remote sensing data. When the rainstorm migration speed is detected to be 22 kilometers per hour, the system triggers the anti-interference processing: the particle swarm algorithm is embedded into the meteorological fluid mechanics equation to reconstruct the rainfall sequence, the gradient deviation is constrained to be not more than 5 millimeters per hour, the generated network is filled to resist the temporal and spatial cracks of radar, and the value identifier confidence threshold is set to 0.7. In the quantum annealing decision, the projection deviation value 1200 meters is interpolated by using the radial basis function, and the adjacent confidence labels and the terrain undulation coefficient are fused. Based on the piping probability matrix, the pulsating red ball diameter distribution is generated, when the diameter suddenly increases by 17 centimeters, the near neighbor propagation clustering analysis generates the disaster scenario similarity classification result, and the historical disaster events of the same river basin are retrieved. In the real-time operation data stream, when the gate opening degree change rate is monitored to be 35% per minute or the flow rate suddenly changes by 1.8 meters per second, it is determined that the operation is high risk, isolated to the sandbox and attached with an error failure label. The sandbox cleaning period is dynamically adjusted according to the rainstorm migration speed. When the similarity of the retrieval result reaches 82%, the long-term memory parameters are loaded, and after detecting that the transfer learning parameters conflict with the benchmark library 5 times, the federal learning is started to fuse the multi-center data, and the terrain reflectivity benchmark library is updated. This implementation solves the problem of contour fracture caused by the difference in data collection period in the plain area, improves the accuracy and response efficiency of flood control scheduling, and blocks the error propagation path in the model.

[0083] The technical features of the application are explained as follows: The inverse distance weighted interpolation algorithm processes the hydrological data set to generate a rainfall grid matrix in step 2. The inverse distance weighted interpolation algorithm is based on the data of discrete distributed hydrological monitoring stations. By calculating the reciprocal of the spatial distance between the to-be-interpolated point and the known station as the weight coefficient, the point observation value is converted into continuous spatial distribution grid data. At the same time, the weight is corrected by combining with the terrain elevation data, so that the stations located in the valley and other low-lying areas obtain higher weight, thereby accurately reflecting the influence of terrain on rainfall distribution. The generated rainfall grid matrix provides a basic data layer for subsequent spatial confidence labels, and the interpolation accuracy directly affects the accuracy of the rainstorm condition detection in step 3.

[0084] The particle swarm algorithm is used in step 3 to reconstruct the rainfall history sequence. The particle swarm algorithm regards each possible rainfall sequence as a particle in a multidimensional space, and searches for the optimal solution by iteratively updating the particle position and velocity. The meteorological fluid mechanics equation embedded in the fitness function ensures that the reconstructed sequence complies with the law of conservation of mass, and the historical disaster event spatiotemporal distribution weight guides the particle to converge to the disaster mode. The particle swarm algorithm forms a closed loop with the spatial credibility label set generated in step 2, which provides the storm migration speed parameter, and the algorithm adjusts the search strategy accordingly. The reconstructed sequence is output to step 4 for projection bias value calculation.

[0085] The meteorological fluid mechanics equation is embedded in the fitness function of the particle swarm algorithm in step 3 as a physical constraint. The equation includes the continuity equation and the motion equation, which ensures that the reconstructed rainfall sequence maintains the reasonableness of fluid motion in the spatiotemporal dimension. When the system detects an abnormal storm migration speed, the convection term in the equation will strengthen the spatiotemporal correlation of the sequence, preventing the occurrence of physically unreasonable abrupt points. The meteorological fluid mechanics equation is linked with the projection bias value calculation in step 4, which ensures the physical consistency of the sequence, while the projection bias value reflects the spatial agreement of the sequence with the measured data, both of which ensure the reconstruction quality.

[0086] The generative adversarial network undertakes the task of filling the radar spatiotemporal gaps in step 3. The generator takes the rainfall grid matrix and the terrain relief coefficient as conditional input, learns the spatial distribution pattern of normal radar echoes through an encoder-decoder structure, and the discriminator integrates a spatiotemporal continuity verification module to detect the continuity of generated data in time and space dimensions using a three-dimensional convolutional neural network. When identifying a gap region, the system labels the generated value identifier. The radar grid data with identifier output by the generative adversarial network is passed to step 4 for interpolation strategy selection based on the projection bias value.

[0087] The Kriging interpolation method is used in step 4 based on the projection bias value selection. When the projection bias value is within the range of 500-1000 meters, this method is activated. The Kriging interpolation method uses the variogram to analyze spatial autocorrelation and performs unbiased optimal estimation under the consideration of terrain relief constraints. It is particularly suitable for handling flat areas with continuous terrain changes. The interpolation process introduces adjacent spatial credibility labels and terrain relief coefficients to optimize precision, and the results are used to generate the pulsating red ball diameter distribution and pulsating frequency parameters, providing input for the dynamic filtering in step 5.

[0088] The radial basis function interpolation method replaces the Kriging interpolation method in step 4 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, effectively dealing with the projection distortion caused by the sudden change of mountainous terrain, and dynamically adjusts the shape of the kernel function during the interpolation process according to the projection deviation value and the optimization decision parameter, for example, increasing the anisotropic adjustment factor in the curved section of the river, the result output by the radial basis function interpolation method is similar to the Kriging interpolation method, which is used to drive the pulsating red ball holographic projection, and the generated risk visualization parameter supports the catastrophe scenario retrieval in step 6.

[0089] The quantum annealing algorithm processes the projection deviation value in step 4 to optimize the interpolation decision path, the quantum annealing algorithm first constructs a three-dimensional energy equation to convert the interpolation path planning into an energy minimization problem, and generates initial decision parameters by solving the ground state solution; then based on the spatial credibility label set, it detects whether there is a historical dam failure point in the interpolation area, when the dam failure point is identified, the landslide displacement constraint term is integrated into the decision parameter to form the optimized decision parameter; finally, according to the projection deviation value range, select the interpolation method, when the deviation value is 500-1000 meters, activate the Kriging interpolation, when the deviation value exceeds 1000 meters, switch to the radial basis function interpolation, this process ensures that the interpolation strategy meets the calculation efficiency and adapts to complex terrain conditions through the efficient search ability of quantum annealing.

[0090] The nearest neighbor propagation clustering algorithm analyzes the pulsating red ball diameter distribution data in step 6 to realize the disaster mode recognition, the nearest neighbor propagation clustering algorithm automatically determines the cluster center by iteratively calculating the similarity matrix between data points, and divides the diameter value spatial distribution characteristics into different risk level clusters; when the diameter suddenly increases more than 15 centimeters is monitored in real time, the clustering result triggers the retrieval of historical disaster events in the same geological structure belt of the basin, 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 associated memory parameter loading, improving the accuracy of disaster scenario matching.

[0091] The metric learning algorithm constructs a similarity calculation model based on the feature embedding representation after dimension reduction in step 6, the metric learning algorithm optimizes the feature space structure through a triple loss function, so that the same type of disaster event minimizes the distance in the embedding space and the different type of event maximizes the distance; the trained model can calculate the similarity score of real-time monitoring data and historical cases in real time, the output result directly supports the long-term memory parameter loading decision in step 7; the metric learning process effectively improves the accuracy of disaster retrieval, avoiding false judgments caused by mismatched feature representations.

[0092] The federal learning mechanism handles the conflict between the migration learning parameters and the terrain reflectivity benchmark library in step 7. When the number of conflicts detected exceeds 3 times, the mechanism starts the multi-center data fusion process. Each local node trains model parameters based on local water conservancy data and uploads only the model increment to the central server for secure aggregation. The aggregated parameters are used to update the terrain reflectivity benchmark library and are fed back to the spatial credibility label generation link in step 2 through an encrypted channel. Federal learning ensures data privacy and knowledge sharing, continuously optimizing the system's generalization ability.

[0093] The Kalman filter algorithm enhances the credibility of remote sensing satellite data in step 2. The Kalman filter algorithm performs prediction and correction cycles on infrared spectrum data through state space equations, dynamically estimates the true reflectivity value, and eliminates atmospheric disturbance noise. When the reflectivity standard deviation of the interpreted feature data exceeds 0.35, the filtered results are used to generate enhanced label values. The label values, after being bound to geographic coordinates, form a spatial credibility label set. Kalman filtering effectively improves the alignment accuracy of satellite and ground data, providing a clean data source for subsequent heavy rain detection.

[0094] The principal component analysis method processes remote sensing satellite infrared spectrum data in step 2 to achieve feature dimension reduction. The principal component analysis method converts high-dimensional spectrum data into a few principal components through orthogonal transformation, retaining key variance information of terrain heat inertia distribution. The reduced interpreted feature data is used to generate spatial credibility labels, reducing data redundancy while highlighting surface material difference features. Principal component analysis not only improves data processing efficiency but also ensures the accuracy of terrain reflectivity comparison in subsequent steps.

[0095] Fast Fourier transform processes flow velocity Doppler shift data in step 1. Time domain signals are converted to frequency domain to extract flow velocity feature values. The algorithm analyzes the frequency spectrum components of the Doppler shift to accurately calculate the water flow velocity. Combined with device operating state parameters, a device health status code is generated. The converted frequency domain data provides a reliable speed parameter basis for subsequent integration of hydrological data sets, ensuring the accuracy and real-time performance of flow velocity measurement.

[0096] Optical flow method verifies the temporal and spatial continuity of the initial filling data in step 3. Optical flow method calculates the motion vector field of consecutive time series radar image pixels, tracks the movement trajectory and speed change of the rainstorm cloud cluster in space. If the filling data significantly deviates from the movement pattern analyzed by the optical flow method, the temporal and spatial continuity verification fails, triggering the process of generating new filling data, ensuring the consistency of radar data with actual conditions.

[0097] The gradient descent algorithm optimizes the edge weight values of the graph structure in step 6. The gradient descent algorithm minimizes the difference between the graph connection and the historical economic loss data by iteratively calculating the gradient of the loss function and updating the edge weight parameters in the negative gradient direction. Each iteration adjusts the weight to strengthen the correlation strength of high-loss disaster events, gradually optimizes the graph topology, and improves the accuracy of the disaster scenario similarity calculation.

[0098] The least squares method performs a residual correction process in step 6. The least squares method solves the optimal fitting parameters by linearly fitting the residual sum of squares of the current fluctuating red ball diameter distribution data and the historical disaster event data to eliminate systematic errors and random noise. The corrected residual is used to adjust the loading value of the associated memory parameters to ensure that the transfer learning parameter set can better adapt to real-time environmental changes.

[0099] The hydraulic model is based on fluid mechanics equations and soil mechanics parameters. It calculates the piping probability by simulating the water flow penetration process and dam stability. The core parameters of the model include the permeability coefficient and the shear strength index. In step 3, when the system detects a rainstorm condition, 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, which is fused with the device state code to generate a dynamic risk parameter set, providing input for the fluctuating red ball projection.

[0100] The fluctuating red ball holographic projection model is constructed using piping probability and terrain features. It uses holographic rendering technology to map risk levels to dynamic changes in red ball diameter and frequency. In step 4, based on the piping probability matrix and device state code, a dynamic risk parameter set is generated, and a weighted calculation is performed using the dam break factor coefficient to output the fluctuating red ball diameter value. Finally, it is fused with the historical geological disaster frequency layer to generate a dynamic rendering result, realizing risk visualization.

[0101] The similarity calculation model uses a metric learning algorithm to train feature embedding representations. It optimizes the feature space distance function, such as cosine similarity, to evaluate the matching degree of historical and current disaster scenarios. In step 6, the spatiotemporal feature vector is extracted from the updated graph structure, and the feature embedding representation is generated after dimensionality reduction encoding. Based on this, a similarity calculation model is constructed to compare the current monitoring data with historical cases in real time and output a similarity score, supporting disaster retrieval decisions.

[0102] 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 retrieval result exceeds the threshold, the loading mechanism is triggered, and the long-term memory parameters are fed back to the spatial credibility label generation link in step 2 to optimize the labeling rules of the terrain reflectivity benchmark library, improving the adaptability of the system through closed-loop feedback.

[0103] The terrain reflectance benchmark library is constructed by integrating historical satellite remote sensing data and multi-center ground observations, and stores terrain reflectance benchmark values in different periods for credibility calibration; in step 2, the spatial credibility label set is generated by comparing the real-time terrain reflectance with the benchmark library, in step 7, the benchmark library is updated by fusing multi-source data when the migration learning parameters conflict with the benchmark library, and the optimization results are fed back to the label generation process through the verification channel, ensuring the accuracy of data projection.

Claims

1. A data visualization processing method applied to smart water conservancy, characterized in that, The method comprises the following steps: Step 1, collecting water level pulse signal, flow rate Doppler frequency shift data and rain gauge counting pulse, and processing the data to generate a hydrological data set, which includes a device status code; Step 2, receiving the hydrological data set, processing the hydrological data set to generate a rainfall grid matrix, receiving remote sensing satellite data, and processing the remote sensing satellite data to generate a spatial reliability label, and outputting a set of spatial reliability labels; Step 3, receiving the set of spatial reliability labels; detecting a heavy rain condition based on the set of spatial reliability labels; when the heavy rain condition is detected, reconstructing a rainfall history sequence, filling in the radar spatiotemporal gaps, controlling the core parameters of the hydraulic model, and outputting the reconstructed rainfall history sequence and radar grid data with a generated value identifier; Step 4, receiving the reconstructed rainfall history sequence and radar grid data with a generated value identifier; obtaining a projection bias value from the set of spatial reliability labels; selecting an interpolation strategy based on the projection bias value, inputting adjacent spatial reliability labels and terrain relief coefficients; loading a pulsating red ball holographic projection based on the pipe gushing probability to generate pulsating red ball diameter distribution and pulsating frequency parameters; Step 5, receiving the pulsating frequency parameters and pulsating red ball diameter distribution; isolating high-risk operations to a sandbox and updating filtering rules; outputting the updated filtering rules and pulsating red ball diameter distribution; Step 6, receiving the updated filtering rules and pulsating red ball diameter distribution; analyzing the pulsating red ball diameter distribution; retrieving similar disaster scenarios and outputting the retrieval results; Step 7, receiving the retrieval results; when the retrieval results meet the preset conditions, loading long-term memory parameters; The long-term memory parameters are fed back to Step 2 to optimize the terrain reflectivity benchmark library reliability label rules.

2. The data visualization processing method for smart water conservancy according to claim 1, wherein The step 1 comprises: collecting water level pulse signal, flow rate Doppler frequency shift data and rain gauge counting pulse to generate a device status code; processing the water level pulse signal to generate a digital water level parameter; analyzing the flow rate Doppler frequency shift data to generate a device health status code; integrating the digital water level parameter, flow rate parameter, rainfall parameter and weather radar grid data to generate a hydrological data set; The step 2 comprises: receiving the hydrological data set to generate a rainfall grid matrix; adding coordinated universal time stamps to the rainfall grid matrix; receiving remote sensing satellite infrared spectrum data; extracting terrain thermal inertia distribution characteristics from the remote sensing satellite infrared spectrum data to generate interpretation feature data; when the reflectivity feature standard deviation is greater than 0.35, processing the interpretation feature data to generate an enhanced label value; binding the enhanced label value to geographic coordinates to generate a spatial reliability label.

3. The data visualization processing method for smart water conservancy according to claim 2, wherein The step 3 comprises: Detect the storm migration speed based on the spatial credibility label set; when the storm migration speed is greater than 20 km / h, construct an adaptability function with a constraint of a rainfall gradient deviation of less than or equal to 5 mm / h; embed the meteorological fluid mechanics equation into the adaptability function; apply the adaptability function to the particle swarm algorithm, load the historical disaster event space-time distribution weight to update the particle position, and generate a particle position data set; generate a rainfall history sequence according to the particle position data set, and the rainfall history sequence is attached with an entropy value fluctuation index label; fill in the radar space-time gap through the generative adversarial network to generate radar grid data with a generated value identifier; control the hydraulic model core parameters to enter a semi-frozen state.

4. The data visualization processing method for smart water conservancy according to claim 3, characterized in that, The step 3 further comprises: Generate initial filling data based on the rainfall grid matrix and the terrain relief coefficient, and verify the space-time continuity of the initial filling data; When the verification fails, identify the gap area and attach a generated value identifier, and when the generated value identifier confidence is less than 0.7, regenerate the filling data.

5. The data visualization processing method for smart water conservancy according to claim 4, wherein The step 4 comprises: Construct a three-dimensional energy equation minimization interpolation calculation amount to generate initial decision parameters; Detect whether there is a historical dam breach point in the interpolation area based on the spatial credibility label set; When there is a historical dam breach point, add a landslide displacement constraint term to the initial decision parameters to generate optimized decision parameters, and select an interpolation method based on the projection deviation value and the optimized decision parameters: when the projection deviation value is 500-1000 meters, use the Kriging interpolation method; when the projection deviation value is greater than 1000 meters, use the radial basis function interpolation method.

6. The data visualization processing method for smart water conservancy according to claim 5, wherein The step 4 comprises: Receive the pipe gushing probability matrix output by the hydraulic model and the device state code generated in step 1; Generate a dynamic risk parameter set based on the pipe gushing probability matrix and the device state code; Perform weighted calculation on the dynamic risk parameter set based on the dam break factor coefficient to generate a 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.

7. The data visualization processing method for smart water conservancy according to claim 6, wherein, The step 5 comprises: Receive real-time hydrological operation data stream; detect gate opening change and flow rate mutation; When the gate opening change is greater than 30% / min or the flow rate mutation is greater than 1.5 m / s, generate high-risk operation data; Isolate the high-risk operation data to a sandbox and attach an error failure label; Calculate the sandbox cleaning period based on the storm migration speed and clean the data in the sandbox.

8. The data visualization processing method for smart water conservancy according to claim 7, wherein, The step 6 comprises: Receive the historical disaster event database, the hydrological monitoring parameter set, and the disaster relief plan library; Establish a node association relationship based on the historical disaster event database, the hydrological monitoring parameter set, and the disaster relief plan library to generate an initial graph structure; Calculate the edge weight value based on the economic loss data and the initial graph structure, and update the connection relationship of the graph structure; Extract the space-time feature vector based on the updated graph structure; perform dimensionality reduction coding on the space-time feature vector to generate a feature embedding representation; and construct a similarity calculation model based on the feature embedding representation.

9. The data visualization processing method for smart water conservancy according to claim 8, wherein, The step 6 further comprises: Receive the pulsating red ball diameter distribution data; perform near neighbor propagation clustering on the pulsating red ball diameter distribution data to generate a disaster scenario similarity classification result; When the pulsating red ball diameter suddenly increases by more than 15 cm, a similar disaster scenario is retrieved from the historical disaster event library, and the retrieval range is limited to the same river basin geological structure belt; Based on the retrieval result, load the associated memory parameters, correct the residual error before loading, and generate an optimized transfer learning parameter set.

10. The data visualization processing method for smart water conservancy according to claim 9, wherein, The step 7 comprises: Receiving the retrieval result; when the similarity in the retrieval result is greater than 80%, load the long-term memory parameters; Detecting the number of conflicts between the transfer learning parameter set and the terrain reflectivity benchmark library; when the number of conflicts exceeds 3 times, fuse multi-center water conservancy data; Generating an updated terrain reflectivity benchmark library; and transmitting the updated terrain reflectivity benchmark library to step 2.

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