A kinetic simulation method for the sedimentation process of sewage sludge in lakes and reservoirs

Abstract

This invention relates to the field of wastewater treatment technology, and more particularly to a method for simulating the dynamics of wastewater and sludge deposition processes in lakes and reservoirs. The method includes the following steps: collecting data from lake and reservoir hydrological stations, filtering outliers and completing missing values ​​to generate a complete hydrological sequence; performing flow regime subdivision to obtain flow regime stratification data; reconstructing multi-layered hydrodynamic fields to form a hydrodynamic field set; performing suspended solids analysis; extracting wastewater and sludge data and deconstructing its transport characteristics to generate transport dynamic data; performing water body stress tensor decomposition to obtain stress distribution data; using this data to simulate material transport on the complete hydrological sequence; establishing a multi-scale deposition dynamics model; collecting real-time water body deposition change data and comparing it with the model; if inconsistencies exist, fine-tuning the model in real time to generate an optimized multi-scale deposition dynamics model. This invention achieves a more accurate method for simulating the dynamics of wastewater and sludge deposition processes in lakes and reservoirs.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a dynamic simulation method for the sludge deposition process in lakes and reservoirs. Background Technology

[0002] Lakes and reservoirs, as important water resources and ecosystems, bear multiple functions, including water quality regulation, ecological protection, and human activities. However, with the acceleration of urbanization and the frequency of human activities, the water environment of lakes and reservoirs is facing increasingly severe pollution problems, especially the increasingly serious phenomenon of sewage and sludge accumulation, which leads to a series of environmental problems such as water quality deterioration, biodiversity loss, and eutrophication. Traditional water quality monitoring and management methods are often unable to reflect the dynamic changes of lakes and reservoirs in a timely and accurate manner, and lack effective prediction and control mechanisms. At present, research on sewage and sludge in lakes and reservoirs is mostly focused on static analysis, lacking an in-depth understanding of hydrodynamics. Research on the relationship between water flow state and material transport is relatively weak. Many studies have failed to fully consider the influence of hydrodynamic fields, resulting in inaccurate simulation and prediction of siltation phenomena. In addition, existing siltation models often cannot be updated in real time and are difficult to adapt to rapid changes in the water environment, leading to lagging and unscientific management measures. Summary of the Invention

[0003] Therefore, it is necessary to provide a dynamic simulation method for the sludge deposition process in lakes and reservoirs to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a dynamic simulation method for the sedimentation process of sewage sludge in lakes and reservoirs is provided, comprising the following steps:

[0005] Step S1: Collect hydrological station data from lakes and reservoirs; filter outliers from the hydrological station data to obtain clean hydrological data; perform missing value completion processing on the clean hydrological data to generate a complete hydrological sequence;

[0006] Step S2: Perform flow regime subdivision on the complete hydrological sequence to obtain flow regime stratified data; reconstruct multi-layer hydrodynamic field from the flow regime stratified data to generate a hydrodynamic field set;

[0007] Step S3: Perform suspended solids analysis on the complete hydrological sequence based on the hydrodynamic field set to obtain sewage sludge data; deconstruct the transport characteristics of the sewage sludge data to generate transport dynamic data;

[0008] Step S4: Perform water body stress tensor decomposition based on transport dynamic data to obtain stress distribution data; simulate material transport on the complete hydrological sequence based on stress distribution data to generate a multi-scale sedimentation dynamics model;

[0009] Step S5: Collect real-time sedimentation change data of water bodies; compare the real-time sedimentation change data of water bodies with the multi-scale sedimentation dynamics model. When the sedimentation changes are inconsistent, fine-tune the multi-scale sedimentation dynamics model in real time based on the real-time sedimentation change data of water bodies to generate an optimized multi-scale sedimentation dynamics model.

[0010] This invention ensures the accuracy and reliability of hydrological data through the collection and outlier filtering of lake and reservoir hydrological stations. The cleaned hydrological data, after filling in missing values ​​to generate a complete hydrological sequence, provides a solid foundation for subsequent analysis. The implementation of flow regime segmentation enables the formation of stratified flow regime data, thereby enhancing the understanding and description of flow characteristics. The reconstruction of multi-layered hydrodynamic fields allows the generation of hydrodynamic field assemblies to reflect hydrodynamic characteristics under different conditions, laying the foundation for subsequent suspended solids analysis. Suspended solids analysis based on hydrodynamic field assemblies reveals the distribution and dynamic changes of sewage sludge. The subsequent deconstruction of transport characteristics allows for in-depth analysis of the characteristics of sewage sludge data. The acquired transport dynamic data provides detailed evidence for the decomposition of water body stress tensor. The acquisition of stress distribution data further enables accurate simulation of water body material transport. The generated multi-scale sedimentation dynamic model provides a scientific explanation and prediction method for sedimentation phenomena. The comparison between real-time collected water body sedimentation change data and the multi-scale sedimentation dynamic model can promptly detect inconsistencies in sedimentation changes, thereby providing data support for real-time fine-tuning of the model. The optimized multi-scale sedimentation dynamic model not only improves the model's adaptability and accuracy, but also provides effective decision support for the management and governance of lakes and reservoirs, and overall promotes the scientification and systematization of lake and reservoir water quality monitoring and environmental protection work.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S11: Collect data from lake and reservoir hydrological stations; reconstruct the time series data from the lake and reservoir hydrological stations to obtain continuous monitoring data;

[0013] Step S12: Perform spatial interpolation calibration on the continuous monitoring data to generate gridded data; perform outlier detection and filtering on the gridded data to obtain cleaning hydrological data;

[0014] Step S13: Perform spatiotemporal constraint network mapping on the cleaned hydrological data to obtain the hydrological spatiotemporal matrix; perform local deviation analysis of adjacent stations based on the hydrological spatiotemporal matrix to obtain the stations with significant deviations;

[0015] Step S14: Fill in the missing values ​​for significantly deviating stations to generate a complete hydrological sequence.

[0016] This invention provides continuous monitoring information through the collection and time-series reconstruction of lake and reservoir hydrological station data. The gridded data generated by spatial interpolation calibration enhances the spatial consistency and comprehensiveness of the data. Outlier detection and filtering ensure the accuracy and reliability of the data. The generation of cleaned hydrological data lays a solid foundation for subsequent analysis. The implementation of spatiotemporal constraint network mapping enables the formation of a hydrological spatiotemporal matrix to better reflect the spatiotemporal characteristics of hydrological phenomena. Local deviation analysis of adjacent stations reveals significantly deviating stations, providing a basis for targeted data repair. The application of segmented missing value completion technology generates a complete hydrological sequence, providing high-quality data support for subsequent dynamic simulation. Overall, this invention improves the accuracy and completeness of lake and reservoir hydrological data, provides strong technical support for the dynamic simulation of sewage and sludge deposition processes, and promotes the scientific and systematic process of lake and reservoir management and governance.

[0017] Preferably, step S2 includes the following steps:

[0018] Step S21: Identify water body flow rate in the complete hydrological sequence to obtain water level and flow rate data; extract hydraulic features from the water level and flow rate data to generate hydrodynamic data;

[0019] Step S22: Perform boundary layer subdivision on the complete hydrological sequence based on hydrodynamic data to obtain flow regime stratification data;

[0020] Step S23: Quantize the turbulence intensity of the flow regime stratification data to obtain turbulent field data; decompose the vortex structure of the turbulent field data to generate vorticity field data;

[0021] Step S24: Perform energy cascade decomposition on the vorticity field data to obtain multi-scale field data; perform hierarchical hydrodynamic field reconstruction based on the multi-scale field data to generate a hydrodynamic field set.

[0022] This invention provides crucial water level and flow rate data through the identification of water body flow in a complete hydrological sequence. The hydrodynamic data generated by extracting hydraulic features provides a foundation for flow regime analysis. The implementation of boundary layer decomposition makes the flow regime stratification data more accurate. The process of quantifying turbulence intensity reveals the turbulent characteristics in the water body. The generated turbulent field data lays the foundation for subsequent structural analysis. The decomposition of vortex structures allows the formation of vorticity field data to reflect flow characteristics in greater detail. The application of energy cascade decomposition promotes the acquisition of multi-scale field data. The implementation of stratified hydrodynamic field reconstruction enables the hydrodynamic field ensemble to comprehensively present hydrodynamic characteristics. Overall, it improves the understanding of the hydrological environment of lakes and reservoirs, provides rich data support and scientific basis for the dynamic simulation of sewage and sludge deposition processes, and promotes the optimization and implementation of relevant management and governance strategies.

[0023] Preferably, step S24 includes the following steps:

[0024] The vorticity field data is subjected to spectral decomposition to obtain spectral distribution data;

[0025] Energy flux is calibrated from spectral distribution data to generate energy channel data;

[0026] The energy channel data is filtered by scale according to a preset scale range to obtain multi-scale field data;

[0027] Vertical stratification of water bodies is performed on multi-scale field data to obtain interlayer structure data;

[0028] Based on the interlayer structure data, laminar flow relationships are constructed to generate laminar flow connection data;

[0029] The laminar flow data is spliced ​​together to obtain the field strength distribution data.

[0030] Spatiotemporal coordinate registration of field intensity distribution data is performed based on multi-scale field data to generate a hydrodynamic field set.

[0031] This invention provides detailed spectral distribution data through spectral decomposition of eddy field data. The energy channel data generated by energy flux calibration lays the foundation for subsequent energy analysis. The implementation of scale screening ensures the effectiveness and relevance of multi-scale field data. Vertical stratification of water bodies makes the interlayer structure data more accurate. The process of constructing laminar flow relationships reveals the internal flow characteristics of the water body. The field strength distribution data obtained by flow field splicing provides a clear picture of the overall flow state. The application of spatiotemporal coordinate registration ensures the accuracy and consistency of the hydrodynamic field set. Overall, it enhances the dynamic understanding of the lake and reservoir hydrological environment, provides a solid data foundation and scientific support for the accurate simulation of sewage and sludge deposition processes, and promotes the intelligent and systematic process of lake and reservoir management and water quality treatment.

[0032] Preferably, step S3 includes the following steps:

[0033] Step S31: Extract suspended solids from the complete hydrological sequence to obtain suspended solids data; perform concentration identification on the suspended solids data to generate suspended solids concentration parameters;

[0034] Step S32: Perform particle size distribution analysis on the suspended solids concentration parameters to obtain particle characteristic data;

[0035] Step S33: Perform flocculation kinetic analysis on particle characteristic data based on hydrodynamic field ensemble to obtain wastewater sludge data; perform diffusion coefficient tensor decomposition on wastewater sludge data to obtain sludge diffusion characteristics;

[0036] Step S34: Track the transport path of sewage sludge data according to the sludge diffusion characteristics to generate migration path data; perform dynamic inversion on sewage sludge data based on migration path data to generate transport force data.

[0037] This invention provides accurate suspended solids data through the extraction of suspended solids from a complete hydrological sequence. The concentration parameters generated by concentration identification lay the foundation for subsequent analysis. The implementation of particle size distribution analysis makes the acquisition of particle characteristic data more comprehensive. The flocculation kinetic analysis based on the hydrodynamic field set reveals the characteristics of sewage sludge, providing important information for environmental governance. The diffusion coefficient tensor decomposition process helps to deeply understand the diffusion characteristics of sludge. The generation of migration path data provides a clear trajectory for the transport process of sewage sludge. The application of dynamic inversion enables the generation of transport dynamic data to reflect the real dynamics of sewage sludge. Overall, this invention improves the understanding of the sewage sludge deposition process in lakes and reservoirs, provides scientific basis and data support for water quality management and environmental protection, and promotes the intelligent and efficient process of lake and reservoir governance.

[0038] Preferably, step S34 includes the following steps:

[0039] Step S341: Detect the concentration gradient of the wastewater sludge data based on the sludge diffusion characteristics to obtain gradient field data; perform flow direction vector calibration based on the gradient field data to generate sludge flow direction data;

[0040] Step S342: Perform connectivity mapping on the sludge flow direction data to obtain connectivity path data; perform trajectory integration processing on the connectivity path data to generate migration path data;

[0041] Step S343: Perform curvature analysis on the migration path data to obtain curvature distribution data; perform resistance simulation on the curvature distribution data to generate resistance field data;

[0042] Step S344: Apply dynamic inversion to the wastewater and sludge data based on the resistance field data to generate transport force data.

[0043] This invention generates gradient field data by detecting the concentration gradient of sewage sludge data based on sludge diffusion characteristics, providing a foundation for subsequent flow direction analysis. The implementation of flow direction vector calibration makes the sludge flow direction data clearer, helping to understand the movement trend of sludge. Connectivity mapping reveals the flow path of sludge in water bodies. The migration path data generated by trajectory integration provides detailed trajectory information for dynamic simulation. Curvature analysis enables the formation of curvature distribution data, providing an important reference for flow characteristics. The resistance field data generated by resistance simulation provides a deeper understanding of the resistance factors of sludge movement in water bodies. The application of dynamic inversion can accurately reflect the dynamic influence of sewage sludge during the flow process. Overall, it improves the accuracy and reliability of sewage sludge dynamic simulation, provides scientific basis and technical support for lake and reservoir management and water quality treatment, and promotes the efficient and intelligent process of environmental protection.

[0044] Preferably, step S344 includes the following steps:

[0045] Reconstruct the velocity field from the drag field data to generate the velocity distribution.

[0046] Based on the velocity distribution, sludge settling evolution is performed on sewage sludge data to generate sedimentation data;

[0047] Hydrodynamic calculations are performed on the siltation and settlement data based on the velocity distribution to generate impact hydrodynamic parameters.

[0048] Gravity term separation processing was performed on the siltation and settlement data to obtain gravity effect data;

[0049] Based on gravity data, buoyancy terms are derived from sedimentation and settlement data to generate buoyancy field data.

[0050] Force field integration is performed on impact hydrodynamic parameters, gravity data, and buoyancy field data to generate transport force data.

[0051] This invention reconstructs the velocity distribution from the velocity field of the resistance field data, providing crucial data for sludge settling evolution. The implementation of sludge settling evolution data ensures that the generated sedimentation data accurately reflects the deposition process. Hydrodynamic calculations, based on the velocity distribution, yield impact hydrodynamic parameters, providing a quantitative basis for understanding hydrodynamic behavior. Gravity term separation ensures the accuracy of gravity data, facilitating in-depth analysis of gravity's influence on the sedimentation process. The application of buoyancy term derivation generates buoyancy field data from the sedimentation data. The force field integration process combines impact hydrodynamic parameters, gravity data, and buoyancy field data, generating transport dynamic data that comprehensively reflects the influence of various forces on sewage sludge during flow. Overall, this improves the accuracy and effectiveness of dynamic simulation of sewage sludge sedimentation processes in lakes and reservoirs, providing important technical support and scientific basis for water treatment and environmental protection.

[0052] Preferably, step S4 includes the following steps:

[0053] Step S41: Perform shear stress decomposition on the input dynamic data to obtain stress component data;

[0054] Step S42: Locate the principal stress directions of the stress component data to generate principal stress directions; perform stress distribution analysis on the stress component data based on the principal stress directions to obtain stress distribution data;

[0055] Step S43: Perform boundary layer pattern recognition on the stress distribution data to obtain interface feature data;

[0056] Step S44: Simulate material transport on the complete hydrological sequence based on interface characteristic data to generate a multi-scale sedimentation dynamics model.

[0057] This invention generates stress component data through shear stress decomposition of transport dynamic data, providing a foundation for subsequent stress analysis. The implementation of principal stress direction positioning makes the determination of the principal stress direction more accurate, which helps to understand the directionality of stress in flow. Stress distribution analysis based on the principal stress direction generates stress distribution data, providing a quantitative basis for flow characteristics. The boundary layer pattern recognition process reveals the characteristics of the water interface. The generation of interface feature data lays the foundation for material transport simulation. Material transport simulation of the complete hydrological sequence generates a multi-scale sedimentation dynamic model, providing a systematic framework for the dynamic behavior of sewage and sludge. Overall, it improves the understanding and prediction capabilities of sewage and sludge sedimentation processes in lakes and reservoirs, providing important technical support and scientific basis for environmental governance and water quality management, and promoting the intelligent and efficient process of lake and reservoir management.

[0058] Preferably, step S44 includes the following steps:

[0059] Boundary condition calibration is performed on the interface feature data to obtain boundary condition parameters;

[0060] Flow field mapping is performed on the complete hydrological sequence based on boundary condition parameters to generate hydrological flow pattern distribution data;

[0061] Particle motion tracking was performed based on hydrological flow pattern distribution data to obtain the sludge movement trajectory;

[0062] The sludge movement trajectory is reconstructed to recreate the sedimentation process and generate sedimentation process data;

[0063] Multi-scale models were constructed based on the sedimentation process data to generate a multi-scale sedimentation dynamics model.

[0064] This invention generates boundary condition parameters through boundary condition calibration of interface feature data, providing a precise basis for subsequent flow field mapping. The implementation of flow field mapping generates hydrological flow distribution data, which can comprehensively reflect the flow state in the water body. The particle motion tracking process ensures the accurate capture of sludge movement trajectory, providing important information for analyzing sludge behavior. The siltation process data generated by the siltation process reconstruction provides a detailed foundation for the construction of multi-scale models. The multi-scale model construction based on the siltation process data forms a multi-scale siltation dynamic model, providing a systematic framework for the dynamic simulation of sewage and sludge in lakes and reservoirs. Overall, it improves the understanding and prediction capabilities of sewage and sludge siltation processes, provides important technical support and scientific basis for water management and environmental protection, and promotes the efficient and intelligent process of lake and reservoir management.

[0065] Preferably, step S5 includes the following steps:

[0066] Step S51: Conduct multi-point dynamic monitoring of the lake and reservoir water bodies to obtain multi-point monitoring water body data; perform feature layer analysis on the multi-point monitoring water body data to generate layered feature water body data;

[0067] Step S52: Reconstruct the regional dynamic characteristics of the layered water body data to generate real-time sedimentation change data; divide the real-time sedimentation change data of the water body into sections to obtain sedimentation section data;

[0068] Step S53: Perform volume integration processing on the siltation cross-section data to generate siltation quantification data; predict the siltation status based on the multi-scale siltation dynamics model to obtain the predicted siltation amount;

[0069] Step S54: Perform deviation statistics on the siltation quantification data and predicted siltation amount to obtain error distribution data;

[0070] Step S55: When the error distribution data is not zero, perform parameter correction processing on the multi-scale sedimentation dynamics model based on the sedimentation quantification data to obtain the model correction parameters; reconstruct the multi-scale sedimentation dynamics model based on the model correction parameters to generate an optimized multi-scale sedimentation dynamics model.

[0071] This invention generates multi-point monitoring water body data through multi-point dynamic monitoring of lake and reservoir water bodies, providing rich information for subsequent analysis. Feature stratification analysis ensures the formation of stratified characteristic water body data, which can describe the distribution characteristics of water bodies in detail. The implementation of regional dynamic characteristic reconstruction generates real-time sedimentation change data of water bodies, providing real-time basis for dynamic monitoring. The generation of sedimentation cross-section data provides clear spatial division for sedimentation analysis. The sedimentation quantification data generated by volume integral processing provides quantitative basis for sedimentation status. Sedimentation status prediction based on multi-scale sedimentation dynamics model can effectively assess future sedimentation volume. Deviation statistics ensure the generation of error distribution data, providing an important reference for model calibration. Parameter calibration processing when the error distribution data is not zero can optimize the accuracy of the model. The implementation of model reconstruction generates an optimized multi-scale sedimentation dynamics model. Overall, it improves the monitoring and prediction capabilities of lake and reservoir sewage and sludge sedimentation processes, provides scientific basis and technical support for water body management and environmental protection, and promotes the efficient and intelligent process of lake and reservoir management. Attached Figure Description

[0072] Figure 1 A schematic diagram of the steps in a dynamic simulation method for the sedimentation process of sewage sludge in lakes and reservoirs;

[0073] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0074] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0075] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0076] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0077] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0078] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0079] To achieve the above objectives, please refer to Figures 1 to 3 A dynamic simulation method for the sedimentation process of sewage sludge in lakes and reservoirs includes the following steps:

[0080] Step S1: Collect hydrological station data from lakes and reservoirs; filter outliers from the hydrological station data to obtain clean hydrological data; perform missing value completion processing on the clean hydrological data to generate a complete hydrological sequence;

[0081] Step S2: Perform flow regime subdivision on the complete hydrological sequence to obtain flow regime stratified data; reconstruct multi-layer hydrodynamic field from the flow regime stratified data to generate a hydrodynamic field set;

[0082] Step S3: Perform suspended solids analysis on the complete hydrological sequence based on the hydrodynamic field set to obtain sewage sludge data; deconstruct the transport characteristics of the sewage sludge data to generate transport dynamic data;

[0083] Step S4: Perform water body stress tensor decomposition based on transport dynamic data to obtain stress distribution data; simulate material transport on the complete hydrological sequence based on stress distribution data to generate a multi-scale sedimentation dynamics model;

[0084] Step S5: Collect real-time sedimentation change data of water bodies; compare the real-time sedimentation change data of water bodies with the multi-scale sedimentation dynamics model. When the sedimentation changes are inconsistent, fine-tune the multi-scale sedimentation dynamics model in real time based on the real-time sedimentation change data of water bodies to generate an optimized multi-scale sedimentation dynamics model.

[0085] This invention ensures the accuracy and reliability of hydrological data through the collection and outlier filtering of lake and reservoir hydrological stations. The cleaned hydrological data, after filling in missing values ​​to generate a complete hydrological sequence, provides a solid foundation for subsequent analysis. The implementation of flow regime segmentation enables the formation of stratified flow regime data, thereby enhancing the understanding and description of flow characteristics. The reconstruction of multi-layered hydrodynamic fields allows the generation of hydrodynamic field assemblies to reflect hydrodynamic characteristics under different conditions, laying the foundation for subsequent suspended solids analysis. Suspended solids analysis based on hydrodynamic field assemblies reveals the distribution and dynamic changes of sewage sludge. The subsequent deconstruction of transport characteristics allows for in-depth analysis of the characteristics of sewage sludge data. The acquired transport dynamic data provides detailed evidence for the decomposition of water body stress tensor. The acquisition of stress distribution data further enables accurate simulation of water body material transport. The generated multi-scale sedimentation dynamic model provides a scientific explanation and prediction method for sedimentation phenomena. The comparison between real-time collected water body sedimentation change data and the multi-scale sedimentation dynamic model can promptly detect inconsistencies in sedimentation changes, thereby providing data support for real-time fine-tuning of the model. The optimized multi-scale sedimentation dynamic model not only improves the model's adaptability and accuracy, but also provides effective decision support for the management and governance of lakes and reservoirs, and overall promotes the scientification and systematization of lake and reservoir water quality monitoring and environmental protection work.

[0086] In this embodiment of the invention, the method for kinetic simulation of the sludge deposition process in lakes and reservoirs includes the following steps:

[0087] Step S1: Collect hydrological station data from lakes and reservoirs; filter outliers from the hydrological station data to obtain clean hydrological data; perform missing value completion processing on the clean hydrological data to generate a complete hydrological sequence;

[0088] In this embodiment, when collecting data from lake and reservoir hydrological stations, real-time data including water level, flow velocity, flow direction, turbidity, and suspended particle concentration are collected by multi-parameter hydrological sensors installed in the lake and reservoir area. The sensor model selected is the YSI EXO2 multi-parameter detector (YSI EXO2 is a multi-parameter sensing device suitable for water quality monitoring). The data acquisition frequency is set to once per minute. After acquisition, the data is connected to the data storage server via the data interface module for storage. After acquisition, an outlier filtering is performed using an anomaly detection algorithm based on statistical thresholds. Outliers exceeding 3 standard deviations are marked as invalid data. The mean of the data before and after the marked point is replaced using the moving window method. After the outlier filtering is completed, the cleaned hydrological data is transmitted to the missing value completion module. The missing points are completed using the spatiotemporal interpolation method. In the spatiotemporal interpolation method, the Lagrange interpolation algorithm is used in the time direction, and the neighborhood weighted calculation method based on Kriging (spatial interpolation method) is used in the spatial direction. After completion, a complete hydrological sequence data is obtained.

[0089] Step S2: Perform flow regime subdivision on the complete hydrological sequence to obtain flow regime stratified data; reconstruct multi-layer hydrodynamic field from the flow regime stratified data to generate a hydrodynamic field set;

[0090] In this embodiment, after the complete hydrological sequence is imported, the water area is divided into several sub-regions according to the distribution of sampling points using a regional segmentation method. Each sub-region is divided into layers with water depth as the vertical axis. The number of layers is determined according to the water depth range, with a minimum layer height of 1 meter. Using the flow regime feature analysis module, water level changes, velocity gradients, and vorticity distribution are used as input parameters. A segmentation algorithm based on flow regime tensor decomposition is applied to extract the flow characteristics of each water layer, obtaining flow regime layered data. The flow regime layered data is then transferred to the hydrodynamic field reconstruction module. Through a multi-layer coupling algorithm, the flow regime of each water layer is solved using the three-dimensional finite volume method (FVM), calculating the distribution characteristics of velocity field, vorticity field, and pressure field, generating dynamic field data for each layer. Finally, the dynamic field results of each layer are superimposed to obtain the hydrodynamic field set.

[0091] Step S3: Perform suspended solids analysis on the complete hydrological sequence based on the hydrodynamic field set to obtain sewage sludge data; deconstruct the transport characteristics of the sewage sludge data to generate transport dynamic data;

[0092] In this embodiment, the hydrodynamic field set is used as input data to extract the suspended solids concentration characteristics in the dynamic field. Water samples at different depths are collected using a stratified sampling device. The suspended particle concentration is optically analyzed using a HACH DR3900 spectrophotometer (HACH DR3900 is a water quality analyzer) to obtain concentration profile data. Combining the flow velocity and eddy current characteristics in the hydrodynamic field, the transport path of suspended particles is reconstructed using a suspended solids transport characteristic model. By recording the time distribution of suspended particle movement at discrete points, sewage sludge data is generated. The sewage sludge data is transferred to the transport characteristic deconstruction module. The transport equation based on the Euler method is used to simulate and analyze the movement trajectory and deposition rate of sewage sludge, obtaining the energy loss, velocity distribution and deposition characteristics of suspended particle transport, and finally generating transport dynamic data.

[0093] Step S4: Perform water body stress tensor decomposition based on transport dynamic data to obtain stress distribution data; simulate material transport on the complete hydrological sequence based on stress distribution data to generate a multi-scale sedimentation dynamics model;

[0094] In this embodiment, the transport dynamic data is input into the water body stress analysis module. Using a three-dimensional stress distribution calculation tool, the tensor field inside the water body is decomposed based on the stress tensor decomposition method to calculate the principal stress direction and magnitude of the tensor, forming a complete stress field distribution. The stress field distribution result is used as input, combined with the flow regime changes in the complete hydrological sequence, and a transport model based on multi-scale deconstruction is used to simulate material transport. The transport model adopts a multi-layer grid division strategy to calculate the material deposition process at different spatial scales layer by layer, generating the particle size gradient distribution and deposition rate distribution results. Finally, the calculation results are superimposed to obtain a multi-scale sedimentation dynamic model.

[0095] Step S5: Collect real-time sedimentation change data of water bodies; compare the real-time sedimentation change data of water bodies with the multi-scale sedimentation dynamics model. When the sedimentation changes are inconsistent, fine-tune the multi-scale sedimentation dynamics model in real time based on the real-time sedimentation change data of water bodies to generate an optimized multi-scale sedimentation dynamics model.

[0096] In this embodiment, an ultrasonic depth sounder is installed in the lake / reservoir area, and an RDI Teledyne ADCP (Acoustic Doppler Current Profiler) is used to collect real-time data on sedimentation changes, including sedimentation thickness and water depth changes. The collected data is transmitted to the sedimentation change analysis module. Using a comparison method based on differential analysis, the real-time data is compared point-to-point with the predicted data in the multi-scale sedimentation dynamics model. When inconsistencies are found between the real-time data and the model output, the difference features are extracted, and a dynamic parameter adjustment algorithm is used to fine-tune the key parameters of the dynamics model (such as deposition rate, transport path weights, etc.). The adjusted model is then verified to match the real-time data. When the matching degree reaches a preset threshold, the adjusted model is saved as an optimized multi-scale sedimentation dynamics model.

[0097] Preferably, step S1 includes the following steps:

[0098] Step S11: Collect data from lake and reservoir hydrological stations; reconstruct the time series data from the lake and reservoir hydrological stations to obtain continuous monitoring data;

[0099] Step S12: Perform spatial interpolation calibration on the continuous monitoring data to generate gridded data; perform outlier detection and filtering on the gridded data to obtain cleaning hydrological data;

[0100] Step S13: Perform spatiotemporal constraint network mapping on the cleaned hydrological data to obtain the hydrological spatiotemporal matrix; perform local deviation analysis of adjacent stations based on the hydrological spatiotemporal matrix to obtain the stations with significant deviations;

[0101] Step S14: Fill in the missing values ​​for significantly deviating stations to generate a complete hydrological sequence.

[0102] In this embodiment, multiple hydrological monitoring stations are deployed in the lake and reservoir, and high-frequency sampling multi-parameter water quality sensors (such as the YSI EXO2 multi-parameter water quality meter) are used to collect multi-dimensional hydrological data such as water level, water flow velocity, water temperature, and dissolved oxygen. The collection frequency is set to once per hour. After data collection, the data is uploaded to a central data server via a wireless data transmission module (such as a LoRa or NB-IoT module). On the central server, time series analysis tools (such as the Pandas library in Python) are used to reconstruct the time series of the collected hydrological station data. First, the timestamps are parsed and the time axis is aligned at the hourly level. Then, missing data segments are filled using linear interpolation. After filling, the reconstructed time series is smoothed, and a three-point moving average operation is performed on the data using the sliding window method. Finally, continuous monitoring data is generated and imported into a Geographic Information System (GIS) platform. The inverse distance weighting method (IDW) is used. Weighting was used to spatially interpolate station data to generate gridded data. The interpolation resolution was set to 100 meters. The geographic boundary constraint function built into the GIS platform was used to limit the interpolation range to within the lake / reservoir boundary. The standard deviation of the data distribution was calculated point by point for the gridded data generated by interpolation. A threshold of 2 times the standard deviation was set as the outlier detection threshold. Grid points exceeding this threshold were marked as outliers. Then, the spatial neighborhood of the outliers was detected using the K-Nearest Neighbors algorithm. The average value of the normal points in the neighborhood was used to replace the outliers to obtain the cleaning hydrological data. The cleaning hydrological data was constructed into a spatiotemporal data cube, with the time dimension at the hour level and the spatial dimension at the station or grid location. It was imported into a spatiotemporal constraint network model built using a machine learning framework (such as TensorFlow or PyTorch). The model performed spatiotemporal mapping on the cube data to generate a hydrological spatiotemporal matrix. Then, based on this matrix, the Dynamic Time Warping (DTW) algorithm was used. (Warping) calculates the local time series deviation between adjacent stations. For each pair of stations, the deviation value is calculated and compared with a set significance threshold. Stations with deviation values ​​exceeding the threshold are marked as significantly deviating stations. For the data of stations marked as significantly deviating, segmented missing value imputation is performed. First, the time series of deviating stations is segmented, divided into multiple data segments with the time point of anomaly in the station data as the boundary. For each data segment, an Auto-Regressive Integrated Moving Average (ARIMA) model is used for prediction and imputation. The model is trained using historical data from the past 30 days. Based on the trained model, missing values ​​are imputed. The imputed data is then tested again using a time series smoothing algorithm to verify continuity and stability, finally generating a complete hydrological series.The complete hydrological sequence was spatially calibrated with gridded data to ensure that the temporal and spatial data matching met the analytical requirements.

[0103] Preferably, step S2 includes the following steps:

[0104] Step S21: Identify water body flow rate in the complete hydrological sequence to obtain water level and flow rate data; extract hydraulic features from the water level and flow rate data to generate hydrodynamic data;

[0105] Step S22: Perform boundary layer subdivision on the complete hydrological sequence based on hydrodynamic data to obtain flow regime stratification data;

[0106] Step S23: Quantize the turbulence intensity of the flow regime stratification data to obtain turbulent field data; decompose the vortex structure of the turbulent field data to generate vorticity field data;

[0107] Step S24: Perform energy cascade decomposition on the vorticity field data to obtain multi-scale field data; perform hierarchical hydrodynamic field reconstruction based on the multi-scale field data to generate a hydrodynamic field set.

[0108] In this embodiment, time-series data of flow rate and water level are extracted from the complete hydrological sequence using a flow rate identification algorithm and continuous velocity measurement technology. Electromagnetic current meters and differential pressure water level meters are used to record velocity and water level data, respectively. The collected data are imported into a flow rate calculation model, and the relationship between flow rate and water level is analyzed using the control volume method to obtain water level-flow rate data. Hydraulic features are extracted from the water level-flow rate data, and dimensionality reduction methods such as principal component analysis (PCA) are used to extract hydrodynamic characteristic variables, including velocity gradient, shear rate, and pressure change, generating hydrodynamic data. Boundary layer decomposition is then performed based on the hydrodynamic data, first using direct numerical simulation (DNS). The Large Eddy Simulation (LES) technique inputs hydrodynamic data into a boundary layer decomposition model. The model sets calculation conditions for boundary layer thickness, such as the flow velocity reaching 99% of the free velocity. By analyzing the velocity distribution characteristics of the water flow in different boundary layers layer by layer, the resulting flow regime stratification data includes laminar, transition, and turbulent regions. Simultaneously, the velocity profile and turbulence intensity within each region are calculated. Turbulence intensity is quantified using the flow regime stratification data. A three-dimensional particle image velocimetry (PIV) instrument is used to record the turbulent flow field characteristics in the water body. Data processing software is used to calculate turbulence intensity through time-series variations, including the ratio of the variance of fluctuating velocity to the average velocity. The turbulence intensity data is input into a vortex decomposition algorithm for vortex structure decomposition. The Q-criterion and λ²-criterion are used to mark the vortex formation regions. Further decomposition yields vortex field data, which includes the intensity and direction of vorticity and the specific location distribution of vortex nuclei. Energy cascade decomposition is performed on the vortex field data using Large Eddy Simulation (LES). The Simulation method extracts energy transport characteristics from the overall vorticity field and obtains multi-scale field data through decomposition. The multi-scale field data consists of different distributions of large, medium, and small eddies. Based on the multi-scale field data, a hierarchical hydrodynamic field reconstruction is performed. The hierarchical reconstruction algorithm is used to perform regional calculations on the multi-scale data, and the dynamic characteristics of each scale are reconstructed into the hierarchical flow regime to generate a hydrodynamic field set. Finally, a complete data set containing multi-dimensional flow field characteristics and energy distribution features is output. The reconstruction process sets the grid resolution to 50 meters to ensure that the spatial resolution of the hydrodynamic field set matches the actual situation of the lake and reservoir.

[0109] Preferably, step S24 includes the following steps:

[0110] The vorticity field data is subjected to spectral decomposition to obtain spectral distribution data;

[0111] Energy flux is calibrated from spectral distribution data to generate energy channel data;

[0112] The energy channel data is filtered by scale according to a preset scale range to obtain multi-scale field data;

[0113] Vertical stratification of water bodies is performed on multi-scale field data to obtain interlayer structure data;

[0114] Based on the interlayer structure data, laminar flow relationships are constructed to generate laminar flow connection data;

[0115] The laminar flow data is spliced ​​together to obtain the field strength distribution data.

[0116] Spatiotemporal coordinate registration of field intensity distribution data is performed based on multi-scale field data to generate a hydrodynamic field set.

[0117] In this embodiment, spectral decomposition is performed on the eddy field data. The Fast Fourier Transform (FFT) algorithm is used to convert the eddy field data from the time domain to the frequency domain. The main energy distribution characteristics of the eddy field are extracted through spectral analysis. A signal analyzer is used to acquire and decompose signals with frequencies ranging from 0.1 Hz to 10 Hz to obtain spectral distribution data. This spectral distribution data includes the specific correspondence between frequency and energy. The data is stored in a multi-dimensional matrix format. Based on the spectral distribution data, energy flux calibration is performed on the eddy field data. By introducing an energy flux model, the spectral distribution data and the energy flux of the eddy field are matched and calculated. A dynamometer is used to record the energy transfer rate within different frequency ranges of the eddy field. The calibration results are then compared with actual observations to generate energy channel data. This energy channel data contains the energy transfer path and intensity at specific frequencies. The energy channel data is scale-filtered according to a preset scale range, and a data stratification algorithm is used to perform hierarchical processing of the energy channel data, with the filtering range set to 0.The data, ranging from 0.1 meters to 1 meter in eddy current scale, was filtered to remove invalid energy channels below the noise threshold. The filtered data was then reconstructed into a three-dimensional mesh format to obtain multi-scale field data. This multi-scale field data characterizes the energy distribution characteristics across different scale ranges. Vertical stratification of the water body was performed on the multi-scale field data. An Acoustic Doppler Current Profiler (ADCP) was used to record velocity gradients at different depths. The water body was then segmented at different depths with a stratification algorithm, with a depth interval of 2 meters, generating interlayer structure data. This interlayer structure data describes the velocity, eddy current, and pressure distribution characteristics of each water layer. Based on this data, laminar flow relationships were constructed. Interlayer coupling analysis was used to calculate the flow interactions between different layers. The correlation matrix characterizes the intensity and direction of the mutual influence between layers. A matrix factorization algorithm was used to extract interlayer flow patterns, generating laminar flow connection data. This data is stored in a graph structure, where each node represents a single-layer fluid unit, and edges represent interlayer flows. The flow field data of laminar flow relationships are stitched together using a field stitching algorithm combined with spatial interpolation to integrate the flow field data from each layer. The stitching result is optimized by adjusting node positions and edge weights to ensure the continuity and consistency of the overall flow field. After the field intensity distribution data is generated, it is visualized using color images, with each pixel representing the intensity value of a flow field unit. Spatiotemporal coordinate registration is performed on the field intensity distribution data based on multi-scale field data. A three-dimensional spatiotemporal registration algorithm is used to align the temporal and spatial coordinates of the field intensity distribution data. Grid coordinate transformation technology is used to match the data with reference coordinates in a Geographic Information System (GIS) to generate a hydrodynamic field set. This hydrodynamic field set contains comprehensive flow field characteristics across time, space, and energy dimensions.

[0118] Preferably, step S3 includes the following steps:

[0119] Step S31: Extract suspended solids from the complete hydrological sequence to obtain suspended solids data; perform concentration identification on the suspended solids data to generate suspended solids concentration parameters;

[0120] Step S32: Perform particle size distribution analysis on the suspended solids concentration parameters to obtain particle characteristic data;

[0121] Step S33: Perform flocculation kinetic analysis on particle characteristic data based on hydrodynamic field ensemble to obtain wastewater sludge data; perform diffusion coefficient tensor decomposition on wastewater sludge data to obtain sludge diffusion characteristics;

[0122] Step S34: Track the transport path of sewage sludge data according to the sludge diffusion characteristics to generate migration path data; perform dynamic inversion on sewage sludge data based on migration path data to generate transport force data.

[0123] In this embodiment, when extracting suspended solids from a complete hydrological sequence, an optical turbidity sensor combined with membrane filtration technology is used to separate and measure suspended particles in the water. The wavelength range of the optical sensor is set to 780nm to 1100nm. A multi-point sampling device is used to simultaneously sample at different depths and areas of the water. After filtering and drying, the samples are weighed to calculate the mass concentration of suspended solids, ultimately generating suspended solids data. When identifying the concentration of suspended solids data, a particulate matter concentration detector (such as a laser particle counter) is used to detect the samples. The suspended solids mass concentration data is normalized with the water volume to calculate the suspended solids concentration parameter. During the detection process, the sample flow rate must be controlled to 0. With a flow rate of 0.5 L / min and a detection range of 1 mg / L to 500 mg / L, a laser particle size analyzer was used to determine the particle size distribution of suspended solids during particle size spectrum analysis. The suspended solids sample was diluted to 5% by mass before being injected into the detection chamber. The laser wavelength was set to 632.8 nm, and dynamic light scattering technology was used to determine the particle characteristics ranging from 0.01 μm to 100 μm. The particle characteristic data were analyzed, and flocculation kinetics analysis was performed based on the hydrodynamic field ensemble. A flocculation model was introduced to simulate the aggregation behavior of particles in the hydrodynamic field. Combining parameters such as flow velocity, particle concentration, and particle size, a particle tracking algorithm was used to simulate particle collisions, adhesion, and... In the separation process, with a flocculation efficiency of 70%, the simulation results generate wastewater sludge data. This data records particle mass changes in a time-series format. When performing diffusion coefficient tensor decomposition on the wastewater sludge data, a distributed computing model is used to analyze the sludge diffusion characteristics. Spatial frequency distribution features are extracted based on Fourier transform, and the principal axis direction and diffusion coefficient magnitude of the diffusion tensor are calculated by combining the sludge particle concentration gradient and hydrodynamic field intensity, generating sludge diffusion characteristic data. This data includes diffusion velocity, diffusion direction, and corresponding time dimension information. When tracing the transport path of the wastewater sludge data based on these diffusion characteristics, a Lagrange tracing method is used in conjunction with the velocity field information of the hydrodynamic field. The transport path of sludge particles in water bodies is calculated by numerical integration using initial coordinates and time steps. This generates migration path data, which is stored as a sequence of path points, each containing three-dimensional coordinates and time information. When applying dynamic inversion to sewage sludge data based on the migration path data, an inversion algorithm is used to reconstruct the external forces acting on the sludge during migration. By adjusting the spatial displacement and temporal relationship of the migration path data and combining it with the hydrodynamic field set, transport force data is generated step by step. This transport force data is stored in matrix form, containing multi-dimensional data characteristics of time, space, and dynamic intensity. Finally, the transport force information required for the hydrodynamic model is obtained, completing the model construction.

[0124] Preferably, step S34 includes the following steps:

[0125] Step S341: Detect the concentration gradient of the wastewater sludge data based on the sludge diffusion characteristics to obtain gradient field data; perform flow direction vector calibration based on the gradient field data to generate sludge flow direction data;

[0126] Step S342: Perform connectivity mapping on the sludge flow direction data to obtain connectivity path data; perform trajectory integration processing on the connectivity path data to generate migration path data;

[0127] Step S343: Perform curvature analysis on the migration path data to obtain curvature distribution data; perform resistance simulation on the curvature distribution data to generate resistance field data;

[0128] Step S344: Apply dynamic inversion to the wastewater and sludge data based on the resistance field data to generate transport force data.

[0129] In this embodiment, when detecting the concentration gradient of wastewater sludge data based on sludge diffusion characteristics, a two-dimensional grid division technique is used to divide the entire study area into 100m × 100m cells. Sludge particle concentration data is collected within each cell. Based on the concentration distribution, the concentration gradient between each cell is calculated. The gradient calculation formula is used to calculate the ratio of the concentration difference to the spatial distance between the centers of adjacent cells, generating gradient field data. The gradient field data is stored in vector form, with each vector recording the starting grid coordinates, ending grid coordinates, and gradient magnitude. When calibrating the flow direction vector based on the gradient field data, flow field analysis tools are used to normalize the directions of all vectors in the gradient field data to unit vectors. Statistical methods are then used to... Spatial interpolation of the vector directions of adjacent grids generates a continuous flow direction vector field. The density of the flow direction vector field is set to 100 vectors per square kilometer. The final output is sludge flow direction data, stored in a three-column record table containing the vector start-point coordinates, end-point coordinates, and vector direction angle. When performing connectivity mapping on the sludge flow direction data, Dijkstra's shortest path algorithm is used to calculate the path connectivity between any two points. The flow direction vector data is used to determine the consistency of the flow direction on each path. Paths with good connectivity are output as connected path data, stored as a three-dimensional list. Each path records the point sequence, total length, and direction consistency coefficient. Trajectory integration is performed on the connected path data. During the migration path simulation, cubic spline interpolation is used to fit discrete points along the path, generating continuous trajectory lines. Numerical integration is then used to accumulate the length and time of each trajectory line, generating migration path data. This data includes the time series, spatial series, and cumulative distance for each path. For curvature analysis, the path curvature is calculated using the arc length method based on three-dimensional path coordinates. Data analysis software is used with a sampling interval of 5 meters to generate curvature distribution data. This data is stored with a correspondence between path numbers and curvature values. The path curvature values ​​reflect the degree of bending and shape changes of the path. When simulating resistance using the curvature distribution data, a resistance model is established by combining fluid dynamics parameters. The hydrodynamic resistance along the path is calculated using path curvature and flow velocity. The fluid viscosity is set to 0.001 kg / (m·s). The resistance values ​​are mapped one-to-one with the spatial coordinates of the path to generate resistance field data. The resistance field data is stored in the form of a three-dimensional tensor, containing the three-dimensional coordinates of the path points and the corresponding resistance values. When applying dynamic inversion to sewage sludge data based on the resistance field data, the inversion algorithm combined with the sewage dynamic field model is used to calculate the force on the sludge particles. By dynamically adjusting the resistance and flow velocity parameters, the historical trajectory of particle motion is gradually reconstructed to generate transport force data. The transport force data records the dynamic intensity, direction, and spatial position at each moment in a time series, ultimately forming a complete description of the sludge dynamic characteristics.

[0130] Preferably, step S344 includes the following steps:

[0131] Reconstruct the velocity field from the drag field data to generate the velocity distribution.

[0132] Based on the velocity distribution, sludge settling evolution is performed on sewage sludge data to generate sedimentation data;

[0133] Hydrodynamic calculations are performed on the siltation and settlement data based on the velocity distribution to generate impact hydrodynamic parameters.

[0134] Gravity term separation processing was performed on the siltation and settlement data to obtain gravity effect data;

[0135] Based on gravity data, buoyancy terms are derived from sedimentation and settlement data to generate buoyancy field data.

[0136] Force field integration is performed on impact hydrodynamic parameters, gravity data, and buoyancy field data to generate transport force data.

[0137] In this embodiment, when reconstructing the velocity field from the resistance field data, fluid dynamics analysis tools are used to calculate the resistance field data point by point. The velocity values ​​at each point are obtained in a gridded distribution, with a grid size of 50 meters × 50 meters. An interpolation algorithm is used to calculate the velocity values ​​in uncovered areas. The velocity values ​​are represented in vector form, including both magnitude and direction. The velocity distribution is displayed as a two-dimensional vector field graph. When performing sludge settling evolution analysis on the wastewater sludge data based on the velocity distribution, a particle dynamics model is established, with an input particle density of 1.5 g / cm³. 3 Numerical calculations were performed using particle settling simulation software, with settling distribution data output every 30 minutes. The settling distribution data was stored in a three-dimensional matrix, where each matrix element recorded the settling thickness at a specific location. When performing hydrodynamic calculations on the sedimentation settling data based on velocity distribution, a Lagrange-based hydrodynamic analysis tool was used. Settling thickness and velocity field data were input to calculate the hydrodynamic impact force in the sedimentation area. The water viscosity was set to 0.001 kg / (m·s) and the time step to 10 seconds. The generated impact hydrodynamic parameters included the hydrodynamic intensity and direction at each grid point. The impact parameters were recorded in CSV format, with each row containing the coordinate point location and corresponding hydrodynamic vector information. When performing gravity term separation processing on the sedimentation settling data, the gravitational effect in the settling data was extracted using static decomposition technology. A high-precision topographic model (DEM) was used in conjunction with the sedimentation data to calculate the gravity value at each point, with a particle density set to 1.5 g / cm³. 3 The acceleration due to gravity is 9.8 m / s². 2 The generated gravity data recorded the magnitude and distribution of gravity at each point. When deriving the buoyancy term from the sedimentation data based on this gravity data, Archimedes' principle was used, with a fluid density of 1.0 g / cm³.3 The assumption is that the buoyancy at each point is calculated, and the buoyancy value is obtained by calculating point by point and recorded as buoyancy field data. The buoyancy field data is represented in the form of a vector field, which includes the buoyancy magnitude and direction at each point. When integrating the impact hydrodynamic parameters, gravity data and buoyancy field data, a force field integration analysis tool is used to superimpose the three force field data point by point according to their positions to obtain comprehensive force field data. During the superposition calculation, the consistency of vector direction and magnitude is ensured. The output of each data point includes the comprehensive force magnitude and direction vector.

[0138] Preferably, step S4 includes the following steps:

[0139] Step S41: Perform shear stress decomposition on the input dynamic data to obtain stress component data;

[0140] Step S42: Locate the principal stress directions of the stress component data to generate principal stress directions; perform stress distribution analysis on the stress component data based on the principal stress directions to obtain stress distribution data;

[0141] Step S43: Perform boundary layer pattern recognition on the stress distribution data to obtain interface feature data;

[0142] Step S44: Simulate material transport on the complete hydrological sequence based on interface characteristic data to generate a multi-scale sedimentation dynamics model.

[0143] In this embodiment, when performing shear stress decomposition on the input force data, a multidimensional stress analysis tool is used to calculate the input force data point by point, decomposing the input force vector at each point into tangential and normal components. The tangential component is extracted as the principal component of the shear stress. A three-dimensional mesh is used during the analysis, with each mesh cell measuring 50m × 50m × 5m. The input data includes the magnitude and direction of the input force at each point. The output stress component data is stored as a three-dimensional matrix containing shear stress values. Each element of the matrix records the magnitude of the shear stress at the corresponding mesh point. When locating the principal stress direction of the stress component data, tensor calculations are performed on the shear stress components based on the principal stress direction algorithm. The principal stress direction at each point is obtained using the tensor decomposition method. The principal stress direction is represented as a three-dimensional vector, including spatial angle information. During the analysis, the angle resolution is set to 1 degree, and a layer-by-layer analysis method is used. Statistical averaging of the layered data is performed to ensure overall directional consistency. The generated principal stress direction data is stored in CSV format. Based on the principal stress direction... When analyzing stress distribution using stress component data, a stress distribution statistical tool is used to calculate the spatial distribution characteristics of stress point by point. When performing boundary layer pattern recognition on stress distribution data, a machine learning classification model is used to extract boundary features based on the boundary layer recognition algorithm. The model input includes stress distribution data and training samples of known boundary layer patterns. The classification model is a multi-classifier based on support vector machine (SVM). After training, the stress distribution data of the entire domain is classified. The output interface feature data includes boundary layer pattern categories and feature parameters (such as boundary layer thickness and stress gradient values). When simulating material transport on a complete hydrological sequence based on interface characteristic data, a multi-scale hydrodynamic simulation tool is used. The input interface characteristic data and complete hydrological sequence data are used. The hydrological sequence data includes lake and reservoir water level changes, flow velocity changes, and particle distribution information. During the simulation, a three-dimensional finite difference method is used for numerical solution. The time step is set to 1 hour, and the spatial resolution is consistent with the interface characteristic data. The output simulation results include material transport paths, velocity fields, and settlement thickness distribution.

[0144] Preferably, step S44 includes the following steps:

[0145] Boundary condition calibration is performed on the interface feature data to obtain boundary condition parameters;

[0146] Flow field mapping is performed on the complete hydrological sequence based on boundary condition parameters to generate hydrological flow pattern distribution data;

[0147] Particle motion tracking was performed based on hydrological flow pattern distribution data to obtain the sludge movement trajectory;

[0148] The sludge movement trajectory is reconstructed to recreate the sedimentation process and generate sedimentation process data;

[0149] Multi-scale models were constructed based on the sedimentation process data to generate a multi-scale sedimentation dynamics model.

[0150] In this embodiment, when calibrating the boundary conditions of the interface feature data, a three-dimensional boundary condition calibration tool is used to analyze and process the feature parameters such as boundary layer thickness and stress gradient in the interface feature data. During analysis, the boundary type is determined based on the boundary layer pattern classification results, including laminar boundary, turbulent boundary, etc., and calibration is performed in conjunction with actual hydrological measurement data such as velocity field and water level change data. During the calibration process, the gradient descent method is used to optimize the boundary parameters to ensure the global consistency of the calculation results. The final boundary condition parameters are stored in JSON format, including boundary type, boundary location, and time variation characteristics. When mapping the flow field of the complete hydrological sequence based on the boundary condition parameters, the flow field calculation module is used to perform flow field calculation on the hydrological sequence. The velocity, direction, and time variations in the data were interpolated point-by-point using a bilinear interpolation algorithm to ensure accurate matching between boundary conditions and the flow field data. A three-dimensional unsteady-state flow field model was used for the calculations, with a spatial resolution of 25 m × 25 m × 1 m and a temporal resolution of 1 hour. The mapping results were output as hydrological flow regime distribution data, saved in a three-dimensional vector file format, including the magnitude and direction information of the velocity vector. When tracking particle motion based on the hydrological flow regime distribution data, a Lagrange particle motion model was used to simulate particle trajectories. The input parameters for the model included particle size distribution (1 micrometer to 100 micrometers) and density range (1.05 to 2.65 grams). The tracking algorithm uses the fourth-order Runge-Kutta method to iteratively calculate the particle trajectory with a time step of 10 minutes, taking into account the particle's settling velocity, rising velocity, and lateral diffusion characteristics in the fluid. When reconstructing the sludge movement trajectory for the sedimentation process, a three-dimensional mesh reconstruction technique is used to spatially reconstruct the trajectory data. The reconstruction process determines the sedimentation thickness distribution by calculating the particle residence time and deposition probability in the bottom region. The reconstruction mesh used is consistent with the flow field mesh, with a spatial resolution of 25 m × 25 m × 1 m. Data processing is accelerated using CUDA to improve computational efficiency. The reconstruction results are output as sedimentation process data in a three-dimensional mesh format. The data file contains information on the deposition thickness, sediment particle size distribution, and temporal variation of the grid cells. When constructing a multi-scale model for the deposition process data, based on the hierarchical multi-scale model method, the deposition data is first decomposed according to the temporal and spatial scales. The temporal scales are divided into three categories: short-term (1 day), medium-term (1 month), and long-term (1 year). The spatial scales are divided into three categories: local (less than 1 square kilometer), regional (1 to 10 square kilometers), and global (greater than 10 square kilometers). The model construction process uses a multi-level linear regression algorithm to perform correlation analysis on the data at different scales. The generated multi-scale deposition dynamics model includes the changes in deposition thickness, material transport paths, and deposition rate distribution at each scale.

[0151] Preferably, step S5 includes the following steps:

[0152] Step S51: Conduct multi-point dynamic monitoring of the lake and reservoir water bodies to obtain multi-point monitoring water body data; perform feature layer analysis on the multi-point monitoring water body data to generate layered feature water body data;

[0153] Step S52: Reconstruct the regional dynamic characteristics of the layered water body data to generate real-time sedimentation change data; divide the real-time sedimentation change data of the water body into sections to obtain sedimentation section data;

[0154] Step S53: Perform volume integration processing on the siltation cross-section data to generate siltation quantification data; predict the siltation status based on the multi-scale siltation dynamics model to obtain the predicted siltation amount;

[0155] Step S54: Perform deviation statistics on the siltation quantification data and predicted siltation amount to obtain error distribution data;

[0156] Step S55: When the error distribution data is not zero, perform parameter correction processing on the multi-scale sedimentation dynamics model based on the sedimentation quantification data to obtain the model correction parameters; reconstruct the multi-scale sedimentation dynamics model based on the model correction parameters to generate an optimized multi-scale sedimentation dynamics model.

[0157] In this embodiment, when conducting multi-point dynamic monitoring of the lake / reservoir water body, an automated water quality monitoring buoy system is deployed at different locations within the lake / reservoir. The monitoring points are rationally distributed according to the size of the lake / reservoir and its topographical features. Each monitoring point is equipped with a multi-parameter water quality sensor, monitoring parameters including temperature, pH value, dissolved oxygen, and suspended solids concentration. Monitoring data is transmitted wirelessly to a central data processing server at 5-minute intervals. During processing, the original monitoring data undergoes time-series correction and missing value completion to generate complete multi-point monitoring water body data. When performing feature-layered analysis on the multi-point monitoring water body data, a layered analysis algorithm is used to vertically stratify the water body based on characteristics such as water depth, temperature gradient, and suspended solids concentration. The stratification process is based on… Using hierarchical clustering, the location of the stratification interface is determined by analyzing the data change patterns at different depths of each monitoring point. Key feature parameters are extracted from the water body data of each layer, including the average temperature, average suspended solids concentration, and layer thickness within the stratification range. When reconstructing the regional dynamic characteristics of the stratified water body data, based on the time series and spatial distribution characteristics of the stratified feature data, spatial interpolation and time-weighted smoothing algorithms are used to reconstruct the dynamic changes of the entire lake and reservoir water stratification. The grid resolution is set to 50m × 50m during reconstruction, and the dynamic water quality feature data of each grid cell are output. Subsequently, the changes in suspended solids concentration in each grid cell are analyzed based on the reconstruction results, real-time sedimentation change trends are extracted, and real-time sedimentation change data of the water body is generated. Topographic elevation data is used to divide the siltation area into sections. When performing volume integration on the siltation section data, a three-dimensional section model is used. Section boundaries are determined by surface fitting of the section shape. Integration calculations are performed on each section using topographic data and real-time siltation thickness data. The calculation results include the siltation volume and particle size distribution for each section. The generated quantified siltation data is stored in JSON format. Simultaneously, a multi-scale siltation dynamics model is called, inputting real-time siltation change data of the water body. A prediction algorithm based on principal component analysis is used to predict the siltation status at different time scales in the future and generate predicted siltation amounts. When performing deviation statistics on the quantified siltation data and predicted siltation amounts, a two-sample t-test is used to compare the quantified data and predicted data. Statistical bias analysis was conducted. First, the normality of the two sets of data was tested to ensure the statistical basis of the bias analysis. Then, the bias values ​​for each time point and region were calculated, and error distribution data were generated. When the error distribution data was not zero, the parameters of the multi-scale sedimentation dynamics model were corrected based on the sedimentation quantification data. First, the main sources of bias in the model parameters were identified through the analysis of the error distribution data. Then, Bayesian optimization methods were used to adjust key parameters of the model, such as the sedimentation rate coefficient and particle settling velocity. After parameter correction, the model was rerun to verify the correction effect. Finally, the multi-scale sedimentation dynamics model was reconstructed based on the corrected parameters. The reconstruction process involved global optimization of the model parameters and spatial distribution characteristics.

[0158] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0159] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method of simulating the process dynamics of lake reservoir sewage sludge accretion, characterized by, Includes the following steps: Step S1: Collect data from lake and reservoir hydrological stations; Outlier filtering is performed on the hydrological data from lake and reservoir stations to obtain clean hydrological data; Missing values ​​in the cleaned hydrological data are filled in to generate a complete hydrological sequence; Step S2: Perform flow regime segmentation on the complete hydrological sequence to obtain flow regime stratification data; Multi-layer hydrodynamic field reconstruction is performed on the flow regime stratification data to generate a hydrodynamic field set; wherein, step S2 includes the following steps: Step S21: Identify water body flow rate in the complete hydrological sequence to obtain water level and flow rate data; extract hydraulic features from the water level and flow rate data to generate hydrodynamic data; Step S22: Perform boundary layer subdivision on the complete hydrological sequence based on hydrodynamic data to obtain flow regime stratification data; Step S23: Quantize the turbulence intensity of the flow regime stratification data to obtain turbulent field data; decompose the vortex structure of the turbulent field data to generate vorticity field data; Step S24: Perform energy cascade decomposition on the vorticity field data to obtain multi-scale field data; reconstruct the multi-layer hydrodynamic field based on the multi-scale field data to generate a hydrodynamic field set; wherein, step S24 includes the following steps: The vorticity field data is subjected to spectral decomposition to obtain spectral distribution data; Energy flux is calibrated from spectral distribution data to generate energy channel data; The energy channel data is filtered by scale according to a preset scale range to obtain multi-scale field data; Vertical stratification of water bodies is performed on multi-scale field data to obtain interlayer structure data; Based on the interlayer structure data, laminar flow relationships are constructed to generate laminar flow connection data; The laminar flow data is spliced ​​together to obtain the field strength distribution data. Spatiotemporal coordinate registration of field intensity distribution data is performed based on multi-scale field data to generate a hydrodynamic field set; Step S3: Perform suspended solids analysis on the complete hydrological sequence based on the hydrodynamic field set to obtain sewage sludge data; deconstruct the transport characteristics of the sewage sludge data to generate transport dynamic data; Step S4: Perform water body stress tensor decomposition based on transport dynamic data to obtain stress distribution data; simulate material transport on the complete hydrological sequence based on stress distribution data to generate a multi-scale sedimentation dynamics model; Step S5: Collect real-time sedimentation change data of water bodies; compare the real-time sedimentation change data of water bodies with the multi-scale sedimentation dynamics model. When the sedimentation changes are inconsistent, fine-tune the multi-scale sedimentation dynamics model in real time based on the real-time sedimentation change data of water bodies to generate an optimized multi-scale sedimentation dynamics model.

2. The process dynamics simulation method of lake reservoir sewage sludge accumulation according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Collect data from lake and reservoir hydrological stations; reconstruct the time series data from the lake and reservoir hydrological stations to obtain continuous monitoring data; Step S12: Perform spatial interpolation calibration on the continuous monitoring data to generate gridded data; perform outlier detection and filtering on the gridded data to obtain cleaning hydrological data; Step S13: Perform spatiotemporal constraint network mapping on the cleaned hydrological data to obtain the hydrological spatiotemporal matrix; perform local deviation analysis between adjacent stations based on the hydrological spatiotemporal matrix to obtain the stations with significant deviations; Step S14: Fill in the missing values ​​for significantly deviating stations to generate a complete hydrological sequence.

3. The dynamic simulation method for the sludge deposition process in lakes and reservoirs according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Extract suspended solids from the complete hydrological sequence to obtain suspended solids data; perform concentration identification on the suspended solids data to generate suspended solids concentration parameters; Step S32: Perform particle size distribution analysis on the suspended solids concentration parameters to obtain particle characteristic data; Step S33: Perform flocculation kinetic analysis on particle characteristic data based on hydrodynamic field ensemble to obtain wastewater sludge data; perform diffusion coefficient tensor decomposition on wastewater sludge data to obtain sludge diffusion characteristics; Step S34: Track the transport path of sewage sludge data according to the sludge diffusion characteristics to generate migration path data; perform dynamic inversion on sewage sludge data based on migration path data to generate transport force data.

4. The dynamic simulation method for the sludge deposition process in lakes and reservoirs according to claim 3, characterized in that, Step S34 includes the following steps: Step S341: Detect the concentration gradient of the wastewater sludge data based on the sludge diffusion characteristics to obtain gradient field data; perform flow direction vector calibration based on the gradient field data to generate sludge flow direction data; Step S342: Perform connectivity mapping on the sludge flow direction data to obtain connectivity path data; perform trajectory integration processing on the connectivity path data to generate migration path data; Step S343: Perform curvature analysis on the migration path data to obtain curvature distribution data; perform resistance simulation on the curvature distribution data to generate resistance field data; Step S344: Apply dynamic inversion to the wastewater and sludge data based on the resistance field data to generate transport force data.

5. The dynamic simulation method for the sludge deposition process in lakes and reservoirs according to claim 4, characterized in that, Step S344 includes the following steps: Reconstruct the velocity field from the drag field data to generate the velocity distribution. Based on the velocity distribution, sludge settling evolution is performed on sewage sludge data to generate sedimentation data; Hydrodynamic calculations are performed on the siltation and settlement data based on the velocity distribution to generate impact hydrodynamic parameters. Gravity term separation processing was performed on the siltation and settlement data to obtain gravity effect data; Based on gravity data, buoyancy terms are derived from sedimentation and settlement data to generate buoyancy field data. Force field integration is performed on impact hydrodynamic parameters, gravity data, and buoyancy field data to generate transport force data.

6. The method for kinetic simulation of sludge deposition process in lakes and reservoirs according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Perform shear stress decomposition on the input dynamic data to obtain stress component data; Step S42: Locate the principal stress directions of the stress component data to generate principal stress directions; perform stress distribution analysis on the stress component data based on the principal stress directions to obtain stress distribution data; Step S43: Perform boundary layer pattern recognition on the stress distribution data to obtain interface feature data; Step S44: Simulate material transport on the complete hydrological sequence based on interface characteristic data to generate a multi-scale sedimentation dynamics model.

7. The dynamic simulation method for the sludge deposition process in lakes and reservoirs according to claim 6, characterized in that, Step S44 includes the following steps: Boundary condition parameters are obtained by calibrating the interface feature data; Flow field mapping is performed on the complete hydrological sequence based on boundary condition parameters to generate hydrological flow pattern distribution data; Particle motion tracking was performed based on hydrological flow distribution data to obtain the sludge movement trajectory; The sludge movement trajectory is reconstructed to recreate the sedimentation process and generate sedimentation process data; Multi-scale models were constructed based on the sedimentation process data to generate a multi-scale sedimentation dynamics model.

8. The method for kinetic simulation of sludge deposition process in lakes and reservoirs according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Conduct multi-point dynamic monitoring of the lake and reservoir water bodies to obtain multi-point monitoring water body data; perform feature layer analysis on the multi-point monitoring water body data to generate layered feature water body data; Step S52: Reconstruct the regional dynamic characteristics of the layered water body data to generate real-time sedimentation change data; divide the real-time sedimentation change data of the water body into sections to obtain sedimentation section data; Step S53: Perform volume integration processing on the siltation cross-section data to generate siltation quantification data; predict the siltation status based on the multi-scale siltation dynamics model to obtain the predicted siltation amount; Step S54: Perform deviation statistics on the siltation quantification data and predicted siltation amount to obtain error distribution data; Step S55: When the error distribution data is not zero, perform parameter correction processing on the multi-scale sedimentation dynamics model based on the sedimentation quantification data to obtain the model correction parameters; reconstruct the multi-scale sedimentation dynamics model based on the model correction parameters to generate an optimized multi-scale sedimentation dynamics model.

Images