Rice-shrimp breeding area tail water comprehensive utilization simulation method and system based on digital twinning

Abstract

The application discloses a rice-shrimp breeding area tail water comprehensive utilization simulation method and system based on digital twinning, and particularly relates to the technical field of digital twinning and computer simulation, which is used to solve the problem of systematic cognitive bias in simulation results caused by relying on mathematical speculation when the existing digital twinning model simulates a large number of unmonitored data areas in the rice-shrimp breeding area; by obtaining monitoring data and physical topological structure, the simulation area is divided and the key data blank sub-area is identified; then the potential contribution of the state change of each key area to the downstream monitoring data and the monitoring data change difference caused under different hydraulic paths are calculated; the simulation uncertainty level of each key area is evaluated according to the above analysis results; finally, the water quality simulation calculation strategy of the corresponding area in the digital twinning model is adjusted based on the level, and the model is run to output the tail water comprehensive utilization simulation result; the reliability of the overall simulation result and the scientificity of the decision support are improved.

Description

Technical Field

[0001] This invention relates to the field of digital twin and computer simulation technology, and more specifically, to a simulation method and system for the comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins. Background Technology

[0002] In the field of water quality monitoring and wastewater management in rice-shrimp farming areas, the application of digital twin technology is typically based on data-driven models. Existing methods acquire water quality parameters (such as temperature, pH, ammonia nitrogen, dissolved oxygen, and conductivity) by deploying sensors at key locations, and use this discrete monitoring data to drive or calibrate hydrodynamic water quality models. This simulates the migration and transformation of pollutants throughout the region and evaluates wastewater treatment and comprehensive utilization schemes. The core of this approach lies in using real-time data from a limited number of locations to infer and present the continuous water quality status of the entire region, providing visual and quantitative evidence for management decisions.

[0003] However, the aforementioned methods relying on discrete monitoring data to drive regional models have limitations: the reliability of the simulation results for vast areas without sensor deployment is highly dependent on the interpolation and extrapolation capabilities of the mathematical model. Due to the limited number of monitoring points, the model essentially performs speculative calculations in areas with large data gaps. This method of basing regional water quality status on mathematical inference rather than direct perception introduces difficult-to-quantify and systematic cognitive biases when used to assess the overall effectiveness of wastewater comprehensive utilization. In particular, for water quality factors affected by complex biogeochemical processes, their spatial heterogeneity is strong, which may lead to misjudgments of the potential and environmental risks of wastewater resource utilization pathways, thereby affecting the scientific nature of management decisions. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a simulation method and system for the comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A simulation method for comprehensive utilization of wastewater in rice-shrimp farming areas based on digital twins includes the following steps:

[0007] S1. Obtain water quality monitoring data and corresponding physical topology data of each monitoring point in the rice-shrimp farming area;

[0008] S2. Based on the physical topology data, the rice-shrimp farming area is divided into multiple simulated sub-regions, and key data gap sub-regions without monitoring points are identified from them.

[0009] S3. For each key data gap sub-region, calculate the potential contribution of its state change to downstream monitoring data based on water quality monitoring data, and analyze the differences in monitoring data changes caused by state changes through different hydraulic paths.

[0010] S4. Based on the potential contribution and the differences in monitoring data changes, assess the simulation uncertainty level of each key data gap sub-region;

[0011] S5. Adjust the water quality simulation calculation strategy for the corresponding area in the digital twin model based on the simulation uncertainty level, and run the model to output the simulation results of comprehensive utilization of tailwater.

[0012] Furthermore, S1 includes:

[0013] Obtain water quality monitoring data from various monitoring points within the rice-shrimp farming area. The water quality monitoring data includes the monitoring values ​​of at least one water quality parameter collected by sensors at the monitoring points.

[0014] Obtain physical topology data of the rice-shrimp farming area. The physical topology data represents the spatial boundaries of each functional unit and the waterway connections between functional units.

[0015] Furthermore, S2 includes:

[0016] Based on the spatial boundaries of functional units in the physical topology data, the rice-shrimp farming area is divided into multiple simulated sub-regions;

[0017] Based on the spatial location of monitoring points, simulated sub-regions without monitoring points are selected from multiple simulated sub-regions;

[0018] Based on the type attributes of the functional units corresponding to the simulated sub-regions without monitoring points, simulated sub-regions with potential pollution source attributes or key water catchment attributes are identified as key data blank sub-regions.

[0019] Furthermore, S3 includes:

[0020] For each critical data gap sub-region, hypothetical state changes are set for the critical data gap sub-region based on water quality monitoring data;

[0021] Based on physical topology data, determine all possible hydraulic paths from the critical data gap sub-region to at least one downstream monitoring point;

[0022] The theoretical impact of hypothetical state changes on monitoring data when they are transmitted to the monitoring point via each possible hydraulic path is analyzed.

[0023] Based on the theoretical impact of the assumed state changes occurring through all possible hydraulic paths, the potential contribution of state changes in key data gap sub-regions to downstream monitoring data is calculated.

[0024] Based on the differences in the theoretical impacts of hypothetical state changes via different possible hydraulic paths, the differences in monitoring data changes caused by state changes via different hydraulic paths are determined.

[0025] Furthermore, setting hypothetical state changes for key data gap sub-regions includes:

[0026] Based on the type attributes of the functional units corresponding to the blank sub-regions of key data, the typical pollutant types and typical concentration variation ranges under the type attributes are determined.

[0027] Based on typical pollutant types and typical concentration variation ranges, hypothetical state changes are set for key data gap sub-regions.

[0028] Furthermore, S4 includes:

[0029] Based on the potential contribution of changes in the status of key data gap sub-regions to downstream monitoring data, contribution evaluation indicators are determined.

[0030] Based on the differences in monitoring data caused by state changes along different hydraulic paths, determine the path difference assessment index;

[0031] Based on pre-established evaluation rules, contribution evaluation indicators and path difference evaluation indicators are combined to generate simulated uncertainty levels for key data gap sub-regions.

[0032] Furthermore, based on pre-established evaluation rules, the contribution evaluation indicators and path difference evaluation indicators are integrated to include:

[0033] Map contribution assessment indicators and path difference assessment indicators to a predefined assessment matrix;

[0034] Based on the mapping relationship between contribution assessment indicators and path difference assessment indicators in the assessment matrix, the simulation uncertainty level of key data gap sub-regions is determined.

[0035] Furthermore, S5 includes:

[0036] Based on the simulation uncertainty level of the key data blank sub-regions, a predefined water quality simulation calculation strategy is matched for the corresponding key data blank sub-regions;

[0037] Based on the matching results, update the water quality simulation calculation strategy for the corresponding key data gap sub-regions in the digital twin model;

[0038] A digital twin model with an updated water quality simulation calculation strategy was run to simulate the migration, transformation, and utilization process of tailwater in rice-shrimp farming areas, generating simulation results for the comprehensive utilization of tailwater.

[0039] Furthermore, the predefined water quality simulation calculation strategy includes at least one of the following:

[0040] A computational strategy for simulating water quality processes using models of varying complexity;

[0041] A computational strategy employing different time steps for simulation;

[0042] Computational strategies that incorporate varying degrees of prior knowledge or human intervention in simulation calculations.

[0043] On the other hand, the present invention provides a simulation system for the comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins, comprising the following modules:

[0044] The data acquisition module is used to acquire water quality monitoring data and corresponding physical topology data at various monitoring points in the rice-shrimp farming area.

[0045] The region identification module is used to divide the rice-shrimp farming area into multiple simulated sub-regions based on physical topology data, and to identify key data blank sub-regions where no monitoring points have been set up.

[0046] The data analysis module is used to calculate the potential contribution of the state changes of each key data gap sub-region to the downstream monitoring data based on water quality monitoring data, and to analyze the differences in monitoring data changes caused by state changes through different hydraulic paths.

[0047] The rating assessment module is used to assess the simulation uncertainty level of each key data gap sub-region based on potential contribution and differences in monitoring data changes;

[0048] The results output module is used to adjust the water quality simulation calculation strategy for the corresponding area in the digital twin model based on the simulation uncertainty level, and run the model to output the simulation results of comprehensive utilization of tailwater.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. Significantly enhanced the digital twin model's self-awareness and quantitative representation of the reliability of simulation results. By screening out key data gap sub-regions with potential pollution source attributes or key catchment attributes, it achieved accurate identification of high-risk, high-impact blind areas. By calculating the potential contribution of these areas' state changes to downstream monitoring data and analyzing the differences in monitoring data changes that may be caused by their transmission through different hydraulic paths, a specific uncertainty quantitative assessment basis was constructed for each key gap area from two dimensions: the magnitude of the impact and the identifiability of the path. This enabled the model to output not just a single simulation value, but also a clear simulation uncertainty level based on physical and topological logic, thereby transforming the originally vague cognitive biases into quantitative indicators that can be graded, managed, and traced.

[0051] 2. By dynamically linking the simulation uncertainty level with specific simulation calculation strategies, intelligent and efficient allocation of computing resources is achieved. Based on different assessment levels, the digital twin model can automatically match and apply differentiated water quality simulation calculation strategies for different key data gap sub-regions. For example, a more refined model or a smaller time step is used for high uncertainty regions to improve credibility, while a simplified model is used for low uncertainty regions to ensure overall computational efficiency. This differentiated strategy adjustment ensures that limited computing power is prioritized for the simulation links that require the most attention, thereby optimizing the balance between simulation accuracy and computational cost overall. The final output of the tailwater comprehensive utilization simulation results is a comprehensive conclusion based on a clear understanding of the simulation credibility of different regions within it, providing a more scientific, reliable, and interpretable simulation basis for tailwater management decisions. Attached Figure Description

[0052] Figure 1 This is a flowchart of a simulation method for comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins, according to the present invention.

[0053] Figure 2 This is a schematic diagram of the structure of a simulation system for the comprehensive utilization of tailwater in a rice-shrimp farming area based on digital twins, according to the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0055] Example 1: Figure 1 This invention presents a simulation method for the comprehensive utilization of wastewater in rice-shrimp farming areas based on digital twins, which includes the following steps:

[0056] S1. Obtain water quality monitoring data and corresponding physical topology data of each monitoring point in the rice-shrimp farming area;

[0057] S2. Based on the physical topology data, the rice-shrimp farming area is divided into multiple simulated sub-regions, and key data gap sub-regions without monitoring points are identified from them.

[0058] S3. For each key data gap sub-region, calculate the potential contribution of its state change to downstream monitoring data based on water quality monitoring data, and analyze the differences in monitoring data changes caused by state changes through different hydraulic paths.

[0059] S4. Based on the potential contribution and the differences in monitoring data changes, assess the simulation uncertainty level of each key data gap sub-region;

[0060] S5. Adjust the water quality simulation calculation strategy for the corresponding area in the digital twin model based on the simulation uncertainty level, and run the model to output the simulation results of comprehensive utilization of tailwater.

[0061] S1. Obtain water quality monitoring data and corresponding physical topology data at each monitoring point within the rice-shrimp farming area. The specific implementation is as follows:

[0062] Specifically, water quality monitoring data at various monitoring points within the rice-shrimp farming area is obtained through real-time or periodic data collection by water quality sensors deployed at these points. The locations of these monitoring points are determined based on the management needs and hydrological characteristics of the rice-shrimp farming area, typically including the inlet of the farming field, representative points within the field, the outlet of the field, upstream and downstream of connecting ditches, and the final discharge outlet or before the reuse pumping station. Water quality sensors are devices capable of measuring specific physical or chemical parameters of water bodies, such as dissolved oxygen sensors, ammonia nitrogen sensors, pH sensors, or conductivity sensors. The monitored value of at least one water quality parameter collected by the sensors at the monitoring points refers to the digital reading output by each sensor for the specific water quality parameter it is responsible for measuring. For example, a dissolved oxygen sensor continuously measures the dissolved oxygen concentration in the water and outputs the monitoring value in milligrams per liter. Similarly, an ammonia nitrogen sensor measures the ammonia nitrogen content in the water and outputs the monitoring value in milligrams per liter. These monitoring values ​​are aggregated to the data processing center via wired or wireless transmission networks, forming a dataset containing the identification information of each monitoring point, the monitoring timestamp, and the monitoring values ​​of at least one corresponding water quality parameter, thus providing basic data for subsequent analysis.

[0063] Obtaining the physical topology data of rice-shrimp farming areas is accomplished by integrating spatial and attribute information from multiple sources. The physical topology data includes the spatial boundaries of each functional unit and the waterway connections between them. The spatial boundaries of each functional unit refer to the closed geometric outlines used to distinguish different functional areas. Functional units are independent areas defined according to their role in the rice-shrimp farming and wastewater treatment process, such as contiguous rice-shrimp farming fields, ecological purification ditches, sedimentation ponds, artificial wetlands, or reservoirs. Methods for obtaining this spatial boundary data include manual digital delineation using high-precision satellite remote sensing or aerial imagery, importing pre-existing regional planning or engineering design computer-aided design drawings, or using global navigation satellite system (GNSS) receivers for on-site measurement and boundary calibration. The obtained spatial boundary data is stored in a spatial database in the form of polygonal features, with each polygon associated with its corresponding functional unit type attribute.

[0064] The waterway connections between functional units refer to the channels and connections through which water flows from one functional unit to another in the actual physical space. Obtaining these connections requires on-site surveys, engineering drawings, and analysis of the topography. Specifically, it involves identifying and digitally representing all waterways connecting functional units, such as pipes, culverts, open channels, gates, and pumping stations. For each connection segment, its starting and ending functional units need to be defined, thus constructing a directed connection network. This network information, combined with the spatial boundary data of the functional units, forms a complete physical topology data model. This model reflects the possible paths of water in the rice-shrimp farming area from inflow to final discharge or reuse, serving as the basis for subsequent simulation sub-region division and hydraulic path analysis. All acquired water quality monitoring data and physical topology data must undergo format standardization and consistency checks, such as unifying the spatial coordinate reference system, timestamp format, and data units, to ensure that subsequent steps can directly and accurately use this data.

[0065] S2. Based on the physical topology data, the rice-shrimp farming area is divided into multiple simulated sub-regions, and key data gap sub-regions without monitoring points are identified. The specific implementation is as follows:

[0066] Specifically, the rice-shrimp farming area is divided into multiple simulated sub-regions based on the spatial boundaries of functional units in the physical topology data. This is achieved using geographic information system software or a computing program with spatial analysis capabilities. In this embodiment, the spatial boundaries of the functional units are represented as polygon data stored in a spatial database. The division operation directly uses these polygons as the spatial extent definition of the simulated sub-regions. Each polygon defined by the spatial boundary of a functional unit constitutes a simulated sub-region. This means that there is a one-to-one spatial correspondence between simulated sub-regions and functional units; one functional unit corresponds to one simulated sub-region. For example, if the physical topology data contains ten polygons representing ten independent rice-shrimp farming fields, then ten simulated sub-regions will be generated after division. The purpose of this division method is to ensure that subsequent simulations and analyses can use independent functional units as the basic operational objects, maintaining the independence of the physical process. During the division, it is necessary to ensure that the spatial boundaries of all functional units collectively cover the entire rice-shrimp farming area to be simulated and do not overlap with each other, to avoid omissions or duplicate calculations in the simulation area. After the partitioning is completed, each simulated sub-region is assigned a unique identifier and associated with all attribute information of its source functional unit, including the type attribute of the functional unit, to provide a basis for subsequent steps.

[0067] The process of filtering out simulated sub-regions without monitoring points from multiple simulated sub-regions based on the spatial location of monitoring points is accomplished through spatial location relationship judgment. The spatial location data of the monitoring points comes from the positioning information attached to the water quality monitoring data obtained in step S1, and is usually represented in the form of geographic coordinate points. To determine whether a simulated sub-region has a monitoring point, a spatial inclusion analysis operation is performed in the geographic information system software or computing program. This operation compares the coordinates of the monitoring points with the polygon boundaries of each simulated sub-region. If the coordinates of a monitoring point are located inside the polygon boundary of a simulated sub-region, it is determined that a monitoring point is set up in that simulated sub-region. After traversing all monitoring points and all simulated sub-regions, a list is obtained that contains at least one monitoring point. Simulated sub-regions not appearing in this list are those without monitoring points. For example, assuming fifteen simulated sub-regions are generated, and spatial inclusion analysis reveals that five of them contain monitoring point coordinates, then the remaining ten simulated sub-regions are filtered out and classified as those without monitoring points. This filtering process is the fundamental step in identifying data gaps.

[0068] Based on the type attributes of functional units corresponding to simulated sub-regions without monitoring points, simulated sub-regions with potential pollution source attributes or key catchment attributes are identified as key data gap sub-regions. This process relies on the application of predefined classifications and rules for functional unit type attributes. The type attributes of functional units are predefined descriptive labels associated with each functional unit, characterizing the unit's primary role in the tailwater system of the rice-shrimp farming area. Potential pollution source attributes refer to functional units that are considered major or high-risk sources of pollutants released into the water body during normal operation. Examples of functional unit types with potential pollution source attributes include concentrated feeding areas, areas where aquatic disease prevention and control medications are applied, and low-lying areas or ditch sections that receive initial rainfall runoff that may carry non-point source pollution. Key catchment attributes refer to functional units occupying a critical position in the hydraulic network where multiple upstream water flows converge, or whose water quality has a decisive impact on important downstream water bodies or treatment facilities. Functional unit types with key water catchment attributes include public ecological ditches into which the tailwaters of multiple farming and aquaculture fields flow, water inlet and distribution areas of artificial wetlands, and reservoirs or monitoring ponds where the tailwaters are finally discharged.

[0069] The identification process is based on a predefined mapping rule table. This table explicitly lists functional unit types considered to have potential pollution source attributes and functional unit types considered to have critical catchment attributes. This rule table can be established based on domain expert knowledge, historical pollution event analysis, or process design documents. During implementation, all simulated sub-regions without monitoring points are traversed, and the type attribute of the functional unit associated with each simulated sub-region is read. This type attribute is then compared with the lists in the mapping rule table. If the type attribute appears in the potential pollution source attribute list, the corresponding simulated sub-region is determined to have a potential pollution source attribute. If the type attribute appears in the critical catchment attribute list, the corresponding simulated sub-region is determined to have a critical catchment attribute. Simulated sub-regions without monitoring points that meet either or both of these conditions are identified as critical data gap sub-regions. For example, a simulated sub-region without monitoring points whose corresponding functional unit type attribute is defined as a feed-farming field, and feed-farming fields are pre-included in the potential pollution source attribute list, is then identified as a critical data gap sub-region with a potential pollution source attribute. For example, another simulated sub-region without monitoring points, whose functional unit type is the terminal collection channel, and which is included in the list of key catchment attributes, is identified as a key data gap sub-region with key catchment attributes. All identified key data gap sub-regions will be recorded and used for specific analysis in subsequent steps. This identification step ensures that limited simulation and analysis resources can be prioritized for areas without monitoring data that have a significant impact on or pose a high risk to the overall system's water quality, thus improving the method's targeting and practicality.

[0070] S3. For each key data gap sub-region, calculate the potential contribution of its state changes to downstream monitoring data based on water quality monitoring data, and analyze the differences in monitoring data changes caused by state changes through different hydraulic paths. The specific implementation is as follows:

[0071] Specifically, for each critical data gap sub-region, hypothetical state changes are set based on water quality monitoring data, which is achieved in the following way. Hypothetical state changes refer to artificially assigning a theoretical water quality parameter change scenario to the region in order to assess the potential impact of the critical data gap sub-region on downstream monitoring data. This setting is not based on actual measurements in the region, as it is a data gap region, but rather on the type attributes of the corresponding functional unit and relevant background knowledge. Based on the type attributes of the functional unit corresponding to the critical data gap sub-region, typical pollutant types and typical concentration variation ranges under each type attribute are determined. The type attributes of the functional unit are as described above, such as aquaculture fields or end-of-pipe collection channels. For each type attribute, by consulting relevant technical literature, analyzing historical monitoring data, or relying on industry experience, the main pollutant types most likely to be generated or transmitted can be summarized, i.e., typical pollutant types. For example, for aquaculture fields, typical pollutant types may include ammonia nitrogen and total phosphorus. For sedimentation tanks mainly responsible for suspended solids settling, typical pollutant types may include suspended solids. Simultaneously, for each typical pollutant type, a reasonable concentration variation range needs to be determined. This range can be obtained by statistically analyzing the concentration fluctuations of similar functional units in historical monitoring data, or by referring to the pollutant concentration increments that such units may produce under typical operating conditions, as given in relevant technical manuals. For example, for ammonia nitrogen in aquaculture farms fed with feed, the typical concentration variation range might be set to increase from 0.5 mg / L to 2.0 mg / L above the background value. Based on the typical pollutant type and typical concentration variation range, hypothetical state changes are set for key data blank sub-regions. Specifically, a representative value within the typical concentration variation range can be selected as the hypothetical change amount. For example, for the aforementioned key data blank sub-region of the aquaculture farm, the hypothetical state change of its ammonia nitrogen concentration can be set to increase by 1.0 mg / L above the background concentration. The background concentration can be referenced from the monitoring concentration of upstream water in the area or the estimated background concentration of the regional water body. When setting hypothetical state changes, it can be done for only one most representative typical pollutant, or the hypothetical change amount can be set for multiple typical pollutants simultaneously, and subsequent analysis can be performed separately for each pollutant.

[0072] Based on physical topology data, all possible hydraulic paths from a critical data gap sub-region to at least one downstream monitoring point are determined. The physical topology data defines the waterway connections between functional units, forming a directed network. Determining possible hydraulic paths requires the use of path search algorithms from graph theory. Each functional unit is abstracted as a network node, and the waterway connections between functional units are abstracted as directed edges, with the direction of water flow being the direction of the edges. The functional unit corresponding to the critical data gap sub-region serves as the starting node of the path, and the functional unit containing the target monitoring point serves as the ending node of the path. Using either depth-first search or breadth-first search algorithms, all reachable path sequences from the starting node to the ending node are searched in the network. Each path sequence consists of a series of sequentially connected functional unit nodes, representing a complete channel through which water flows from the critical data gap sub-region to the target monitoring point. The search process needs to consider actual hydraulic characteristics. For example, for nodes with branching structures, water flow may descend along different branches, resulting in multiple branch paths. For nodes with confluence structures, multiple upstream water paths may merge into a single downstream path. We need to identify all logically feasible, non-cyclical paths. For example, from a field corresponding to a critical data gap to a downstream monitoring point, possible paths include: Path 1, where the field flows directly into the ditch where the monitoring point is located via an open channel; Path 2, where the field first flows into an intermediate sedimentation tank, and then overflows from the sedimentation tank into the ditch where the monitoring point is located. Both paths will be identified.

[0073] This study analyzes the theoretical impact of hypothetical state changes transmitted to monitoring points via each possible hydraulic path on the monitoring data. This analysis requires a simplified water transport and transformation model. This model simulates the physicochemical processes, such as dilution, diffusion, degradation, or sedimentation, experienced by pollutants as they migrate from the source to the downstream monitoring point along a specific hydraulic path carried by water flow. For each identified possible hydraulic path, the hypothetical state change of the critical data gap sub-region is used as the model input, i.e., the initial pollutant increment. The model then simulates the changes in pollutants as they pass through each intermediate functional unit along this path sequence. Factors considered by the model include the residence time of water in each functional unit, the volume or flow rate of the water body, and the average removal efficiency or attenuation coefficient of that unit for a specific pollutant. These parameters can be set based on the design parameters of the functional units, historical operating data, or empirical values. After model calculation, the expected change in pollutant monitoring values ​​at the monitoring point when the hypothetical state change of the critical data gap sub-region is transmitted to the downstream monitoring point under this path scenario can be obtained; this change is the theoretical impact. For example, for path one, due to its short length and lack of a dedicated treatment unit, the calculated theoretical impact value may be large. For path two, due to the purification effect of the sedimentation tank, the calculated theoretical impact value may be small. It is necessary to perform such a simulation calculation independently for each possible hydraulic path.

[0074] Based on the theoretical impact of hypothetical state changes occurring via all possible hydraulic paths, the potential contribution of state changes in a critical data gap sub-region to downstream monitoring data is calculated. Potential contribution is a comprehensive indicator used to quantify the possible impact of state changes in a critical data gap sub-region on downstream monitoring data. One method for calculating potential contribution is to aggregate the theoretical impact values ​​of all possible hydraulic paths. A common aggregation method is to take the maximum value among all theoretical impact values, as the maximum value represents the maximum possible impact under the most unfavorable transmission path. Another method is to perform a weighted average of all theoretical impact values, with the weights set according to the probability of occurrence of each path or the proportion of water flow distribution under actual hydrological conditions. For example, if it is known that 70% of the water flow will exit via path one and 30% via path two, the theoretical impact values ​​of the two paths can be weighted and summed according to this proportion. The result is a numerical value representing the potential contribution of the critical data gap sub-region to a specific downstream monitoring point. If there are multiple monitoring points downstream, the potential contribution to each monitoring point can be calculated separately, and then the maximum or average value can be taken as the final potential contribution index for the key data gap sub-region.

[0075] Based on the differences in the theoretical impacts of hypothetical state changes via different possible hydraulic paths, this study determines the differences in monitoring data changes caused by state changes along different hydraulic paths. These differences characterize state changes in critical data gap sub-regions; if propagated via different hydraulic paths, they will exhibit different degrees of manifestation at downstream monitoring points. The greater this difference, the greater the uncertainty when inferring the state of critical data gap sub-regions solely from data changes at monitoring points, as it's impossible to determine which path the change originated from. The method for determining these differences is to calculate the dispersion between the theoretical impact values ​​corresponding to different possible hydraulic paths. A simple calculation method is to obtain the standard deviation of these theoretical impact values. A larger standard deviation indicates a greater difference between the theoretical impact values ​​of each path, i.e., a greater difference in monitoring data changes. Another method is to calculate the difference between the maximum and minimum values. The calculated value is the quantitative indicator of the difference in monitoring data changes. For example, if the theoretical impact of path one is an increase of 1.0 mg / L, and the theoretical impact of path two is an increase of 0.1 mg / L, then the difference of 0.9 mg / L, or the relative difference calculated accordingly, reflects a significant difference in monitoring data changes. Through the above steps, two core parameters, potential contribution and difference in monitoring data changes, are calculated for each key data gap sub-region, providing input for subsequent uncertainty level assessment.

[0076] S4. Based on the potential contribution and the differences in monitoring data changes, assess the simulation uncertainty level of each key data gap sub-region. The specific implementation is as follows:

[0077] Specifically, contribution assessment indicators are determined based on the potential contribution of changes in the status of key data gap sub-regions to downstream monitoring data. The contribution assessment indicator converts the calculated potential contribution value into a standardized indicator for grade evaluation. First, the grade classification method for the contribution assessment indicator needs to be defined. A common approach is to set several contribution thresholds, mapping continuous potential contribution values ​​to discrete contribution levels. For example, two contribution thresholds can be set: a low contribution threshold and a high contribution threshold. The specific values ​​of the low and high contribution thresholds can be determined based on historical simulation data or expert experience. For instance, by statistically analyzing the distribution of the impact of all key data gap sub-regions on downstream monitoring points in historical scenarios, the 33rd quantile of the distribution can be used as the low contribution threshold, and the 67th quantile as the high contribution threshold. After determining the contribution thresholds, the rules for determining the contribution assessment indicator are as follows: if the potential contribution value of a key data gap sub-region is less than the low contribution threshold, its contribution assessment indicator is determined to be at a low level. If the potential contribution value is greater than or equal to the low contribution threshold but less than the high contribution threshold, the contribution assessment indicator is determined to be of medium level. If the potential contribution value is greater than or equal to the high contribution threshold, the contribution assessment indicator is determined to be of high level. In this way, each key data gap sub-region will obtain a categorized contribution assessment indicator, such as low, medium, or high level, which facilitates subsequent comprehensive evaluation.

[0078] Based on the differences in monitoring data caused by state changes along different hydraulic paths, path difference assessment indicators are determined. These indicators convert the calculated differences in monitoring data into standardized metrics for grade evaluation. First, the grade classification method for these indicators needs to be defined. Similar to contribution assessment indicators, the grade classification of path difference indicators also relies on threshold settings. Two path difference thresholds can be set: a small difference threshold and a large difference threshold. The specific values ​​of these thresholds can also be set based on historical data statistical analysis or expert experience. For example, by analyzing the distribution of monitoring data change differences corresponding to all key data gap sub-regions in historical scenarios, the 33rd percentile of the distribution can be used as the small difference threshold, and the 67th percentile as the large difference threshold. After determining the path difference thresholds, the rules for determining the path difference assessment indicators are as follows: If the monitoring data change difference value of a key data gap sub-region is less than the small difference threshold, its path difference assessment indicator is determined to be of a low grade. If the monitoring data change difference value is greater than or equal to the small difference threshold but less than the large difference threshold, the path difference assessment indicator is determined to be of a medium grade. If the difference in monitoring data is greater than or equal to the large difference threshold, the path difference assessment index is determined to be of a high level. Here, a low level of the path difference assessment index means that the difference in downstream monitoring data caused by different hydraulic paths is small, while a high level means that the difference is large. Each key data gap sub-region thus obtains a categorized path difference assessment index, such as low level, medium level, or high level.

[0079] Based on pre-established evaluation rules, contribution evaluation indicators and path difference evaluation indicators are integrated to generate the simulated uncertainty level of key data gap sub-regions. The simulated uncertainty level is a comprehensive evaluation level of the credibility or risk of the simulation results when performing digital twin simulations of this region. The integration process relies on a pre-established evaluation rule that defines the simulated uncertainty level corresponding to different combinations of contribution evaluation indicators and path difference evaluation indicators. Mapping the contribution evaluation indicators and path difference evaluation indicators to a predefined evaluation matrix is ​​a specific method to achieve this integration. The predefined evaluation matrix is ​​a two-dimensional table, where rows typically correspond to different levels of contribution evaluation indicators, and columns correspond to different levels of path difference evaluation indicators. The content filled in each cell of the matrix is ​​the simulated uncertainty level determined for that combination. For example, a simple evaluation matrix can be designed with three rows and three columns, because both contribution evaluation indicators and path difference evaluation indicators can have three levels: low, medium, and high. The specific content of the matrix needs to be predefined; for example, the following rule can be defined: when both the contribution evaluation indicator and the path difference evaluation indicator are at a low level, the simulated uncertainty level is determined to be low. When the contribution assessment indicator is low and the path difference assessment indicator is medium, the simulation uncertainty level is determined to be low. When the contribution assessment indicator is low and the path difference assessment indicator is high, the simulation uncertainty level is determined to be medium. When the contribution assessment indicator is medium and the path difference assessment indicator is low, the simulation uncertainty level is determined to be low. When the contribution assessment indicator is medium and the path difference assessment indicator is medium, the simulation uncertainty level is determined to be medium. When the contribution assessment indicator is medium and the path difference assessment indicator is high, the simulation uncertainty level is determined to be high. When the contribution assessment indicator is high and the path difference assessment indicator is low, the simulation uncertainty level is determined to be medium. When the contribution assessment indicator is high and the path difference assessment indicator is medium, the simulation uncertainty level is determined to be high. When the contribution assessment indicator is high and the path difference assessment indicator is high, the simulation uncertainty level is determined to be high. Based on the mapping relationship between the contribution assessment indicator and the path difference assessment indicator in the assessment matrix, the simulation uncertainty level of the key data blank sub-region is determined. In practice, for a given critical data gap sub-region, its established contribution assessment indicators and path difference assessment indicators are first obtained. Then, in a predefined assessment matrix, the cell corresponding to the intersection of the row label for the contribution assessment indicator and the column label for the path difference assessment indicator is found. The level entered in this cell is the final simulated uncertainty level for the critical data gap sub-region. For example, if the contribution assessment indicator for a critical data gap sub-region is determined to be at a medium level and its path difference assessment indicator is determined to be at a high level, according to the rules of the example matrix, its simulated uncertainty level is determined to be high.Through this process, each key data gap sub-region is assigned a clear simulation uncertainty level, such as low, medium, or high. This level comprehensively reflects the region's potential impact on downstream monitoring data and the identifiability of this impact along its propagation path, providing a direct decision-making basis for subsequently adjusting the simulation strategy of the digital twin model. The entire assessment rules, thresholds, and assessment matrix can be adjusted and configured before implementation according to the specific characteristics and management needs of the rice-shrimp farming area, but the core mapping and overall logic remain consistent.

[0080] S5. Adjust the water quality simulation calculation strategy for the corresponding area in the digital twin model based on the simulation uncertainty level, and run the model to output the simulation results of comprehensive utilization of tailwater. The specific implementation is as follows:

[0081] Based on the simulation uncertainty level of the key data gap sub-regions, predefined water quality simulation calculation strategies are matched to the corresponding key data gap sub-regions. This matching process relies on a pre-defined strategy mapping table. This strategy mapping table explicitly specifies one or more recommended water quality simulation calculation strategies for each simulation uncertainty level. The simulation uncertainty level is determined in the previous step, for example, low, medium, or high. A water quality simulation calculation strategy refers to the set of specific methods, models, or parameter settings used to calculate the water quality change process in a specific area during digital twin simulation. The principle of matching different strategies for different levels is generally as follows: For key data gap sub-regions with a low simulation uncertainty level, it indicates that the simulation results are relatively reliable or the risk is low, and a simulation strategy with higher computational efficiency but potentially lower simplification can be used. For key data gap sub-regions with a high simulation uncertainty level, it indicates that the simulation results have high uncertainty and a large impact on the system, and a more refined, conservative simulation strategy or one that introduces more auxiliary information should be used to improve the credibility of the simulation results in this area or to manage risks. For key data gap sub-regions with a medium simulation uncertainty level, a simulation strategy between the low and high level strategies is matched. For example, the following mapping relationship can be defined: When the simulation uncertainty level is low, strategy A is matched. When the simulation uncertainty level is medium, strategy B is matched. When the simulation uncertainty level is high, strategy C is matched. Strategy A, strategy B, and strategy C are specific instances of predefined water quality simulation calculation strategies.

[0082] Predefined water quality simulation strategies include at least one of the following: simulation strategies using water quality process models of varying complexity; simulation strategies using different time steps; and simulation strategies incorporating varying degrees of prior knowledge or human intervention. The simulation strategy using water quality process models of varying complexity refers to selecting water quality dynamic models with different levels of mathematical detail to simulate the migration and transformation processes of pollutants within key data gap sub-regions. Lower-complexity models may use a zero-dimensional completely mixed reactor model, considering only the average concentration and overall attenuation coefficient within the region, resulting in fast computation. Higher-complexity models may use one-dimensional or two-dimensional hydrodynamic-water quality coupled models, capable of simulating the spatial distribution of concentrations and more detailed biochemical reaction processes within the region, offering higher computational accuracy but longer computation time. For example, strategy A, corresponding to low-level uncertainty, can specify the use of a zero-dimensional water quality model. Strategy C, corresponding to high-level uncertainty, can specify the use of a one-dimensional water quality model. The simulation strategy using different time steps refers to using different time-step intervals for calculations related to key data gap sub-regions during dynamic simulation calculations using a digital twin model. Larger time steps allow for faster calculations but may overlook rapidly changing processes. Smaller time steps provide more detailed calculations but have a higher computational load. For example, strategy A could use a 1-hour time step as the basic simulation time step, while strategy C could use a 0.5-hour time step. Introducing different levels of prior knowledge or human intervention into the simulation strategy refers to using additional information to constrain or guide the simulation process for key data gap sub-regions. For regions with low uncertainty, the model may be able to perform calculations entirely on its own. For regions with high uncertainty, prior knowledge can be introduced, such as using the possible range of effluent concentrations in that region as constraints on the model calculations, or allowing human interaction to be triggered according to rules during the simulation process, allowing operators to make reasonable judgments on the intermediate simulation results or fine-tune parameters based on their experience. After matching according to the strategy mapping table, each key data gap sub-region has a clearly defined water quality simulation calculation strategy.

[0083] Based on the matching results, the water quality simulation calculation strategy for the corresponding key data gap sub-regions is updated in the digital twin model. The digital twin model is a software system for mathematical modeling and simulation of the entire rice-shrimp farming wastewater system. Its internal water quality simulation calculation methods for different spatial parts are configurable. The update operation refers to setting the calculation method parameters of the model sub-modules corresponding to each key data gap sub-region in the model configuration according to the matched predefined water quality simulation calculation strategy. For example, for a key data gap sub-region matched with a one-dimensional water quality model strategy, the original water quality calculation module for that region needs to be replaced or parameterized into a one-dimensional water quality model instance in the digital twin model, and the corresponding mesh parameters, boundary conditions, and reaction kinetic parameters need to be configured. For a key data gap sub-region matched with a 0.5-hour time step strategy, 0.5 hours needs to be specified as the local time step for all calculation processes related to that region in the model's time step control settings. For key data gap sub-regions that have been matched with a priori concentration range constraint strategy, inequality constraints for the effluent concentration in that region need to be added to the model's optimization or data assimilation module. These update operations are achieved by modifying the model's configuration file, input parameter file, or dynamically adjusting model parameters through the application programming interface. After the update, the simulation calculation method within the digital twin model for different key data gap sub-regions has been differentiated according to its simulation uncertainty level.

[0084] A digital twin model with an updated water quality simulation strategy was run to simulate the migration, transformation, and utilization of wastewater from a rice-shrimp farming area, generating simulation results for comprehensive wastewater utilization. Running the model involves activating the digital twin model's simulation engine, inputting initial conditions, boundary conditions, driving data, and updated configuration parameters, and executing numerical calculations. The simulation of wastewater migration, transformation, and utilization in the rice-shrimp farming area specifically includes calculating water flow throughout the system network, the transport of pollutants in each functional unit and connecting waterways, the biochemical degradation, transformation, precipitation, and adsorption processes within each unit, and simulating recycling or compliant discharge scenarios of wastewater along different pathways. Because a computational strategy adapted to the uncertainty level was applied to key data gap sub-regions, the model's simulation behavior in these regions was specifically adjusted. For example, for high uncertainty regions, the simulation process consumes more computational resources due to the use of a more refined model and a smaller time step, but theoretically yields more detailed or conservative results. For low uncertainty regions, a simplified model improves overall simulation efficiency. Generating simulation results for comprehensive wastewater utilization refers to the various quantitative data and charts output after the model calculations are completed. These results can include water quality concentration variation curves at the system outlet throughout the simulation period, simulated water quality status of key data gap sub-regions at different time points, analysis of the amount of wastewater reused for irrigation and its compliance with water quality standards, and comparison of pollutant load reduction effects under different scheduling schemes. These simulation results comprehensively reflect the simulation assessment conclusions regarding the comprehensive utilization potential and environmental impact of wastewater in the entire rice-shrimp farming area after considering simulation uncertainties in key areas and implementing differentiated strategy adjustments, providing information support for management decisions. The entire simulation process can be executed on high-performance computers or servers to handle potentially increased computational complexity. The final simulation result is a digital twin system output integrating spatially differentiated simulation strategies, aiming to obtain a more reliable and practical simulation of regional wastewater system behavior under limited monitoring data conditions through intelligent uncertainty management and resource allocation.

[0085] Example 2: Figure 2 A schematic diagram of a digital twin-based simulation system for the comprehensive utilization of wastewater from rice-shrimp farming areas is provided. This system includes the following modules:

[0086] The data acquisition module is used to acquire water quality monitoring data and corresponding physical topology data at various monitoring points in the rice-shrimp farming area.

[0087] The region identification module is used to divide the rice-shrimp farming area into multiple simulated sub-regions based on physical topology data, and to identify key data blank sub-regions where no monitoring points have been set up.

[0088] The data analysis module is used to calculate the potential contribution of the state changes of each key data gap sub-region to the downstream monitoring data based on water quality monitoring data, and to analyze the differences in monitoring data changes caused by state changes through different hydraulic paths.

[0089] The rating assessment module is used to assess the simulation uncertainty level of each key data gap sub-region based on potential contribution and differences in monitoring data changes;

[0090] The results output module is used to adjust the water quality simulation calculation strategy for the corresponding area in the digital twin model based on the simulation uncertainty level, and run the model to output the simulation results of comprehensive utilization of tailwater.

[0091] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0092] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0093] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0094] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0097] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A simulation method for comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins, characterized in that, Includes the following steps: S1. Obtain water quality monitoring data and corresponding physical topology data of each monitoring point in the rice-shrimp farming area; S2. Based on the physical topology data, the rice-shrimp farming area is divided into multiple simulated sub-regions, and key data gap sub-regions without monitoring points are identified from them. S3. For each key data gap sub-region, calculate the potential contribution of its state changes to downstream monitoring data based on water quality monitoring data, and analyze the differences in monitoring data changes caused by state changes through different hydraulic paths, including: For each critical data gap sub-region, hypothetical state changes are set for the critical data gap sub-region based on water quality monitoring data; Based on physical topology data, determine all possible hydraulic paths from the critical data gap sub-region to at least one downstream monitoring point; The theoretical impact of hypothetical state changes on monitoring data when they are transmitted to the monitoring point via each possible hydraulic path is analyzed. Based on the theoretical impact of the assumed state changes occurring through all possible hydraulic paths, the potential contribution of state changes in key data gap sub-regions to downstream monitoring data is calculated. Based on the differences in the theoretical effects of hypothetical state changes via different possible hydraulic paths, the differences in monitoring data changes caused by state changes via different hydraulic paths are determined. S4. Based on the potential contribution and the differences in monitoring data changes, assess the simulation uncertainty level of each key data gap sub-region; S5. Adjust the water quality simulation calculation strategy for the corresponding area in the digital twin model based on the simulation uncertainty level, and run the model to output the simulation results of comprehensive utilization of tailwater.

2. The simulation method for comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins according to claim 1, characterized in that, S1 includes: Obtain water quality monitoring data from various monitoring points within the rice-shrimp farming area. The water quality monitoring data includes the monitoring values ​​of at least one water quality parameter collected by sensors at the monitoring points. Obtain physical topology data of the rice-shrimp farming area. The physical topology data represents the spatial boundaries of each functional unit and the waterway connections between functional units.

3. The simulation method for comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins according to claim 1, characterized in that, S2 include: Based on the spatial boundaries of functional units in the physical topology data, the rice-shrimp farming area is divided into multiple simulated sub-regions; Based on the spatial location of monitoring points, simulated sub-regions without monitoring points are selected from multiple simulated sub-regions; Based on the type attributes of the functional units corresponding to the simulated sub-regions without monitoring points, simulated sub-regions with potential pollution source attributes or key water catchment attributes are identified as key data blank sub-regions.

4. The simulation method for comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins according to claim 1, characterized in that, Setting hypothetical state changes for key data blank sub-regions includes: Based on the type attributes of the functional units corresponding to the key data blank sub-regions, the typical pollutant types and typical concentration variation ranges under the type attributes are determined. Based on typical pollutant types and typical concentration variation ranges, hypothetical state changes are set for key data gap sub-regions.

5. The simulation method for comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins according to claim 1, characterized in that, S4 includes: Based on the potential contribution of changes in the status of key data gap sub-regions to downstream monitoring data, contribution evaluation indicators are determined. Based on the differences in monitoring data caused by state changes along different hydraulic paths, path difference assessment indicators are determined. Based on pre-established evaluation rules, contribution evaluation indicators and path difference evaluation indicators are combined to generate simulated uncertainty levels for key data gap sub-regions.

6. The simulation method for comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins according to claim 5, characterized in that, Based on pre-established evaluation rules, contribution evaluation indicators and path difference evaluation indicators are integrated, including: Map contribution assessment indicators and path difference assessment indicators to a predefined assessment matrix; Based on the mapping relationship between contribution evaluation indicators and path difference evaluation indicators in the evaluation matrix, the simulation uncertainty level of key data gap sub-regions is determined.

7. A simulation method for comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins according to claim 1, characterized in that, S5 include: Based on the simulation uncertainty level of the key data blank sub-regions, a predefined water quality simulation calculation strategy is matched for the corresponding key data blank sub-regions; Based on the matching results, update the water quality simulation calculation strategy for the corresponding key data gap sub-regions in the digital twin model; A digital twin model with an updated water quality simulation calculation strategy was run to simulate the migration, transformation, and utilization process of tailwater in rice-shrimp farming areas, generating simulation results for the comprehensive utilization of tailwater.

8. The simulation method for comprehensive utilization of tailwater in rice-shrimp farming areas based on digital twins according to claim 7, characterized in that, Predefined water quality simulation strategies include at least one of the following: A computational strategy for simulating water quality processes using models of varying complexity; A computational strategy employing different time steps for simulation; Computational strategies that incorporate varying degrees of prior knowledge or human intervention in simulation calculations.

9. A simulation system for comprehensive utilization of wastewater from rice-shrimp farming areas based on digital twins, used to implement the simulation method for comprehensive utilization of wastewater from rice-shrimp farming areas based on digital twins as described in any one of claims 1-8, characterized in that, Includes the following modules: The data acquisition module is used to acquire water quality monitoring data and corresponding physical topology data at various monitoring points in the rice-shrimp farming area. The region identification module is used to divide the rice-shrimp farming area into multiple simulated sub-regions based on physical topology data, and to identify key data blank sub-regions where no monitoring points have been set up. The data analysis module is used to calculate the potential contribution of the state changes of each key data gap sub-region to the downstream monitoring data based on water quality monitoring data, and to analyze the differences in monitoring data changes caused by state changes through different hydraulic paths. The rating assessment module is used to assess the simulation uncertainty level of each key data gap sub-region based on potential contribution and differences in monitoring data changes; The results output module is used to adjust the water quality simulation calculation strategy for the corresponding area in the digital twin model based on the simulation uncertainty level, and run the model to output the simulation results of comprehensive utilization of tailwater.

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