Multi-model coupling water quality optimization method, system and equipment based on digital twin

Abstract

The present invention discloses a multi-model coupling water quality optimization method, system and equipment based on digital twins, and belongs to the field of urban water pollution simulation technology. Existing water quality optimization schemes are mainly applied to single systems, lacking comprehensive consideration of the synergy of multiple systems in complex urban environments, and unable to predict the diffusion of pollutants and optimize the layout scheme of urban underground pipe networks. A multi-model coupling water quality optimization method based on digital twins of the present invention obtains sudden pollution diffusion information by simulating hydrological processes and performing sudden pollution diffusion simulation calculations; at the same time, based on the digital twin mechanism and sudden pollution diffusion information, an optimization scheme for the operation of hydraulic structures is obtained, so that the full process simulation and optimization scheduling of sudden pollution diffusion in the city can be carried out, thereby realizing timely control of sudden pollution diffusion, thereby greatly reducing the lag of manual intervention and decision-making, significantly shortening the scheduling response time, and improving the efficiency of emergency handling.

Description

Technical Field

[0001] The present invention relates to a multi-model coupling water quality optimization method, system and equipment based on digital twins, and belongs to the technical field of urban water pollution simulation. Background Art

[0002] With the acceleration of urbanization, the frequency and scope of sudden water pollution diffusion events in urban underground pipe networks are increasing. Such events usually originate from industrial accidents, traffic accidents, chemical leaks, etc., and are characterized by strong suddenness, rapid diffusion, and wide pollution impact, posing a serious threat to residents' lives, the ecological environment of urban rivers, and public health. However, with the increasing complexity of urban drainage systems and river networks, the diffusion paths of pollutants through underground pipe networks and river networks have become more complex, and the difficulty of pollution control has also increased accordingly.

[0003] Furthermore, a Chinese patent (publication number: CN111639838B) discloses a method for optimizing the layout of water quality monitoring points suitable for a water supply network. The method uses various indicators of monitoring nodes to comprehensively analyze the risk of pollution at each node, and selects some nodes with high pollution risks for pollution diffusion law analysis. Furthermore, based on the above-mentioned pipeline pollution risk analysis and pollution diffusion law analysis, the method proposes an improved multi-objective non-dominated genetic algorithm model for searching for reliable monitoring point layout combinations, using two water quality optimization goals based on minimizing monitoring time and maximizing the monitoring ratio of pollution events; based on the length and diameter of the pipes connected by the nodes, a dual-objective function is constructed by calculating node importance indicators such as the volume of the connected nodes, which expresses the response time of the monitoring point combination to pollution events and the number of pollution events monitored, meeting the main needs of pipeline water quality monitoring.

[0004] Although the above technology can predict the spread of pollutants and optimize the layout plan for sudden pollution incidents, it is mainly applied to a single system, namely the simulation and analysis of the water supply network, and lacks comprehensive consideration of the synergy of multiple systems in a complex urban environment, resulting in the inability to fully simulate the multi-system integrated environment. Therefore, the above technology cannot be applied to the sudden water pollution spread scenario of the urban underground pipeline network, and cannot predict the spread of pollutants and optimize the layout plan for the urban underground pipeline network, making it difficult to effectively respond to sudden pollution incidents in the urban underground pipeline network.

[0005] The information disclosed in this Background Art is only for understanding the background of the inventive concept and therefore it may include information that does not constitute the prior art. Summary of the invention

[0006] In response to the above problem or one of the above problems, an object of the present invention is to provide a multi-model coupling water quality optimization method based on digital twins. By constructing a surface runoff model, a sudden pollution diffusion model, an operation plan generation model, and a plan evaluation model, the hydrological process is simulated, and sudden pollution diffusion simulation calculations are performed to obtain sudden pollution diffusion information; at the same time, based on the digital twin mechanism and sudden pollution diffusion information, an operation optimization plan for hydraulic structures is obtained, which can realize surface runoff simulation and water flow and pollutant diffusion simulation in pipelines and river networks, thereby enabling full-process simulation and optimal scheduling of urban sudden pollution diffusion, and the plan is scientific, reasonable, and feasible.

[0007] In response to the above problem or one of the above problems, the second purpose of the present invention is to provide a multi-model coupled water quality optimization system based on digital twins. By integrating the surface runoff simulation, the sudden pollution diffusion module, the operation plan generation module and the result display module, the surface runoff simulation, the pipe network and river network hydrodynamic simulation, the optimal scheduling and the result display can be realized, so that the sudden pollution diffusion in the multi-system integration scenario can be simulated and optimized in the whole process, so as to achieve timely control of the sudden pollution diffusion, and can greatly reduce the lag of manual intervention and decision-making, significantly shorten the scheduling response time, and improve the efficiency of emergency handling.

[0008] In response to the above problem or one of the above problems, the third object of the present invention is to provide a multi-model coupled water quality optimization method, system and equipment based on digital twins. By comprehensively considering the synergy of multiple systems in a complex urban environment, it is possible to predict the diffusion of pollutants in the urban underground pipeline network and optimize the layout plan, thereby effectively responding to sudden pollution incidents in the urban underground pipeline network.

[0009] To achieve one of the above purposes, the first technical solution of the present invention is:

[0010] A multi-model coupling water quality optimization method based on digital twins includes the following steps:

[0011] Step 1: Through the pre-built surface runoff model, rainfall data, terrain elevation data and land use type data are coupled and processed to simulate urban rainfall and hydrological changes, and obtain the hydrological process entering the urban pipe network nodes and river network nodes;

[0012] Step 2: Using the pre-built sudden pollution diffusion model, according to the pollutant emission location and emission data, and based on the hydrological process, a sudden pollution diffusion simulation calculation is performed to obtain sudden pollution diffusion information;

[0013] Step 3: Use the pre-built operation plan generation model to establish various hydraulic structure operation plans based on the digital twin mechanism and sudden pollution diffusion information;

[0014] Step 4: Use the pre-built scheme evaluation model to set up evaluation indicators, evaluate the operation schemes of various hydraulic structures, obtain the optimal operation scheme of hydraulic structures, and realize multi-model coupling water quality optimization based on digital twins.

[0015] The present invention constructs a surface runoff model, a sudden pollution diffusion model, an operation plan generation model, and a plan evaluation model to simulate the hydrological process and perform sudden pollution diffusion simulation calculations to obtain sudden pollution diffusion information; at the same time, based on the digital twin mechanism and sudden pollution diffusion information, an operation optimization plan for hydraulic structures is obtained, which can realize surface runoff simulation and water flow and pollutant diffusion simulation in pipe networks and river networks, thereby enabling full-process simulation and optimal scheduling of sudden pollution diffusion in cities, and the plan is scientific, reasonable, and feasible.

[0016] Furthermore, the surface runoff model and sudden pollution diffusion model of the present invention can comprehensively consider the synergistic effects of multiple systems in a complex urban environment, and can accurately simulate the multi-system integrated environment in all aspects. Therefore, the present invention is particularly suitable for sudden water pollution diffusion scenarios in urban underground pipelines, and can predict the diffusion of pollutants in urban underground pipelines and optimize the layout plan, thereby effectively responding to sudden pollution incidents in urban underground pipelines.

[0017] Furthermore, the multi-model coupling scheme of the present invention can quickly generate targeted hydraulic structure operation plans for improving water environment and water quality when pollutant concentrations are abnormal, thereby achieving timely control of the spread of sudden pollution. Therefore, it can greatly reduce the lag of human intervention and decision-making, significantly shorten the scheduling response time, and improve the efficiency of emergency handling.

[0018] As the preferred technical measures:

[0019] Step 1: Through the pre-built surface runoff model, the rainfall data, terrain elevation data and land use type data are coupled and processed to simulate urban rainfall and hydrological changes. The method of obtaining the hydrological process entering the urban pipe network nodes and river network nodes is as follows:

[0020] Based on terrain elevation data, the grid rainfall method is used to process rainfall data to take into account the unevenness of rainfall and obtain the average rainfall in a certain area or a certain basin;

[0021] Using the rainfall-runoff calculation model, and according to the land use type data and hydrological conditions, the average rainfall is processed to obtain the urban flow process, which is used to simulate the urban rainfall process, evaporation and infiltration process, water storage process and confluence process;

[0022] According to the urban flow production and sink process, the hydrological process entering the urban pipe network nodes and river network nodes is obtained.

[0023] As the preferred technical measures:

[0024] Based on terrain elevation data, the grid rainfall method is used to process rainfall data to obtain the average rainfall in a certain area or a certain basin as follows:

[0025] According to the terrain elevation data, the Thiessen polygon method is used to connect all adjacent rainfall stations to obtain several triangle information;

[0026] According to the triangle information, draw the perpendicular bisectors of each side of each triangle to obtain the perpendicular bisector data;

[0027] According to the perpendicular bisector data, multiple perpendicular bisectors around each rainfall station are obtained;

[0028] Connect multiple perpendicular bisectors to obtain several polygonal areas, each of which contains a unique rainfall station;

[0029] Couple several polygonal areas to form a polygonal network, and calculate the area of ​​each polygonal area and the area of ​​the polygonal network;

[0030] Calculate the ratio of the area of ​​each polygonal region to the area of ​​the polygonal network to obtain the weight coefficient of each polygonal region;

[0031] Get the rainfall of the rain gauges contained in the polygonal area;

[0032] The weight coefficient of each polygonal area and the corresponding rainfall are multiplied and accumulated to obtain the average rainfall in a certain area or a certain basin.

[0033] As the preferred technical measures:

[0034] Step 2: Using the pre-built sudden pollution diffusion model, according to the pollutant emission location and emission data, and based on the hydrological process, a sudden pollution diffusion simulation calculation is performed to obtain the sudden pollution diffusion information as follows:

[0035] Based on the topological structure of the urban pipe network and river network, spatial discretization is performed to divide the pipe network and river into several nodes and units;

[0036] According to the location, amount and time of pollutant discharge, combined with the hydrological process entering several nodes and units under rainfall conditions, the spatiotemporal changes of the hydrodynamic elements of the urban pipe network and river network and the diffusion path of pollutants are simulated;

[0037] Based on the diffusion path, the velocity and direction of the water flow, the connectivity of the nodes in the pipe network and the river network are analyzed to deduce the concentration distribution of pollutants at different time points, and the dynamic changes of pollutant concentrations in each section are obtained to monitor the changes in water quality in each section;

[0038] Combined with the dynamic changes in pollutant concentrations in each section, the number of controlled sections that meet water quality standards is determined to obtain information on sudden pollution spread.

[0039] As the preferred technical measures:

[0040] The method of constructing the sudden pollution diffusion model is as follows:

[0041] Based on gravity acceleration, water flow cross-sectional area, static water head, pipe length, time, flow rate, friction resistance, pressure term, gravity term, convection acceleration, friction resistance term, flow change term in and out of the control unit body, and water volume change term, momentum equation and continuity equation for hydraulic control are established; among which, friction resistance is calculated according to Manning's formula;

[0042] The node control equation is established based on the node free surface area and the flow in and out of the node;

[0043] According to the pollutant concentration, longitudinal velocity component, longitudinal diffusion coefficient, reaction rate term, longitudinal distance and time, the mass conservation law equation is established to describe the transport process of pollutants along the pipe network and river network;

[0044] The continuity equation, momentum equation, node control equation and mass conservation equation are coupled to form a sudden pollution diffusion model, which is used to calculate the hydrodynamic elements and pollutant distribution of each pipe section and river network, and to simulate the water flow evolution process and pollutant diffusion and transportation process in urban pipe networks and river networks.

[0045] As the preferred technical measures:

[0046] Step 3: Using the pre-built operation plan generation model, the method of establishing operation plans for various hydraulic structures based on the digital twin mechanism and sudden pollution diffusion information is as follows:

[0047] Based on the digital twin mechanism, hydraulic structures are modeled and the initial operation plan of hydraulic structures is obtained;

[0048] Based on the operation mechanism of hydraulic structures, the water quality compliance rate of the monitoring control section is taken as the optimization target, and the gate valve group optimization scheduling method is established, and the gate control parameters of the hydraulic structures are set; the gate control parameters at least include the opening adjustment speed of each gate in the initial state, the current opening state of each gate, the gate opening change speed and the optimal opening setting of each gate;

[0049] The water quality compliance rate is the ratio of the number of monitoring and control sections that meet the water quality standards to the total number of monitoring and control sections. It is used to characterize the improvement of water quality in pipe networks and river networks after sudden pollution spreads.

[0050] Based on the gate control parameters, a fitness function is established;

[0051] According to the sudden pollution diffusion information, the initial operation plan of the hydraulic structure is adjusted by using the fitness function, and a variety of hydraulic structure operation plans are obtained.

[0052] As the preferred technical measures:

[0053] Step 4: Use the pre-built scheme evaluation model to set up evaluation indicators, evaluate the operation schemes of various hydraulic structures, and obtain the hydraulic structure operation optimization scheme. The method for realizing multi-model coupling water quality optimization based on digital twins is as follows:

[0054] Set evaluation indicators to evaluate the water quality compliance of the monitoring and control sections;

[0055] Call the sudden pollution diffusion model to calculate various hydraulic structure operation plans and obtain some new sudden pollution diffusion information;

[0056] Based on some new sudden pollution diffusion information, determine the hydraulic structure operation plan with the largest number of control sections that meet the water quality standards, that is, the hydraulic structure operation optimization plan;

[0057] According to the hydraulic structure operation optimization plan, the operating status of the hydraulic structure is adjusted to obtain the flow, water level and pollutant concentration of the urban pipe network and river network, which are used to characterize the water quality status of the monitoring and control section, thereby realizing multi-model coupling water quality optimization based on digital twins.

[0058] To achieve one of the above purposes, the second technical solution of the present invention is:

[0059] A multi-model coupling water quality optimization method based on digital twins, including the following:

[0060] Couple rainfall data, terrain elevation data and land use type data to simulate urban rainfall and hydrological changes, and obtain the hydrological process entering the urban pipe network nodes and river network nodes;

[0061] Using the pre-built sudden pollution diffusion model, according to the pollutant emission location and emission data, and based on the hydrological process, sudden pollution diffusion simulation calculation is carried out to obtain sudden pollution diffusion information;

[0062] Based on the sudden pollution diffusion information, the gate valve group optimization scheduling algorithm is used to call the sudden pollution diffusion model in real time, calculate the operation plans of different hydraulic structures, and evaluate the water quality compliance of the monitored control section according to the evaluation indicators. After repeated iterations, we can finally obtain the operation optimization plan of hydraulic structures that can improve the urban water environment and water quality, and realize multi-model coupling water quality optimization based on digital twins.

[0063] To achieve one of the above purposes, the third technical solution of the present invention is:

[0064] A multi-model coupled water quality optimization system based on digital twins, which includes a surface runoff generation module, a sudden pollution diffusion module, an operation plan generation module and a result display module:

[0065] The surface runoff module is used to couple rainfall data, terrain elevation data and land use type data, simulate urban rainfall and hydrological changes, and obtain the hydrological process entering the urban pipe network nodes and river network nodes;

[0066] The sudden pollution diffusion module is used to simulate and calculate the sudden pollution diffusion according to the pollutant emission location and emission data and based on the hydrological process to obtain the sudden pollution diffusion information;

[0067] The operation plan generation module is used to establish operation plans for various hydraulic structures based on the digital twin mechanism and sudden pollution diffusion information; and evaluate the operation plans of various hydraulic structures according to the evaluation indicators to obtain the optimal operation plan for hydraulic structures;

[0068] The result display module is used to visualize the optimization results of the hydraulic structure operation optimization plan, including pollutant diffusion paths and water quality improvement data.

[0069] The system of the present invention can realize surface runoff simulation, pipe network and river network hydrodynamic simulation, optimized scheduling and result display by integrating a surface runoff generation and convergence module, a sudden pollution diffusion module, an operation plan generation module and a result display module, so as to perform full-process simulation and optimized scheduling of sudden pollution diffusion in a multi-system integration scenario, thereby achieving timely control of sudden pollution diffusion, greatly reducing the lag of manual intervention and decision-making, significantly shortening the scheduling response time, and improving the efficiency of emergency handling.

[0070] To achieve one of the above purposes, the fourth technical solution of the present invention is:

[0071] A multi-model coupling water quality optimization device based on digital twin, comprising:

[0072] one or more processors;

[0073] A storage device for storing one or more programs;

[0074] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned multi-model coupling water quality optimization method based on digital twins.

[0075] Compared with the prior art solutions, the present invention has the following beneficial effects:

[0076] The present invention constructs a surface runoff model, a sudden pollution diffusion model, an operation plan generation model, and a plan evaluation model to simulate the hydrological process and perform sudden pollution diffusion simulation calculations to obtain sudden pollution diffusion information; at the same time, based on the digital twin mechanism and sudden pollution diffusion information, an operation optimization plan for hydraulic structures is obtained, which can realize surface runoff simulation and water flow and pollutant diffusion simulation in pipe networks and river networks, thereby enabling full-process simulation and optimal scheduling of sudden pollution diffusion in cities, and the plan is scientific, reasonable, and feasible.

[0077] Furthermore, the surface runoff model and sudden pollution diffusion model of the present invention can comprehensively consider the synergistic effects of multiple systems in a complex urban environment, and can accurately simulate the multi-system integrated environment in all aspects. Therefore, the present invention is particularly suitable for sudden water pollution diffusion scenarios in urban underground pipelines, and can predict the diffusion of pollutants in urban underground pipelines and optimize the layout plan, thereby effectively responding to sudden pollution incidents in urban underground pipelines.

[0078] Furthermore, the system of the present invention can realize surface runoff simulation, pipe network and river network hydrodynamic simulation, optimal scheduling and result display by integrating the surface runoff production and convergence module, the sudden pollution diffusion module, the operation plan generation module and the result display module, so as to perform full-process simulation and optimal scheduling of sudden pollution diffusion in multi-system integration scenarios, thereby achieving timely control of sudden pollution diffusion, greatly reducing the lag of manual intervention and decision-making, significantly shortening the scheduling response time, and improving the efficiency of emergency handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A schematic diagram of a process of the multi-model coupling water quality optimization method of the present invention;

[0080] Figure 2 Another schematic flow chart of the multi-model coupling water quality optimization method of the present invention. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0082] On the contrary, the present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the following detailed description of the present invention. Those skilled in the art can fully understand the present invention without the description of these details.

[0083] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention pertains. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0084] like Figure 1 As shown, the first specific embodiment of the multi-model coupling water quality optimization method based on digital twins of the present invention is:

[0085] A multi-model coupling water quality optimization method based on digital twins includes the following steps:

[0086] Step 1: Through the pre-built surface runoff model, rainfall data, terrain elevation data and land use type data are coupled and processed to simulate urban rainfall and hydrological changes, and obtain the hydrological process entering the urban pipe network nodes and river network nodes;

[0087] Step 2: Using the pre-built sudden pollution diffusion model, according to the pollutant emission location and emission data, and based on the hydrological process, a sudden pollution diffusion simulation calculation is performed to obtain sudden pollution diffusion information;

[0088] Step 3: Use the pre-built operation plan generation model to establish various hydraulic structure operation plans based on the digital twin mechanism and sudden pollution diffusion information;

[0089] Step 4: Use the pre-built scheme evaluation model to set up evaluation indicators, evaluate the operation schemes of various hydraulic structures, obtain the optimal operation scheme of hydraulic structures, and realize multi-model coupling water quality optimization based on digital twins.

[0090] The second specific embodiment of the multi-model coupling water quality optimization method based on digital twin of the present invention:

[0091] A multi-model coupling water quality optimization method based on digital twins, including the following:

[0092] Through the pre-built surface runoff model, rainfall data, terrain elevation data and land use type data are coupled and processed to simulate urban rainfall and hydrological changes, and obtain the hydrological process entering the urban pipe network nodes and river network nodes;

[0093] Using the pre-built sudden pollution diffusion model, according to the pollutant emission location and emission data, and based on the hydrological process, sudden pollution diffusion simulation calculation is carried out to obtain sudden pollution diffusion information;

[0094] A pre-built operation plan generation model is used. Based on the sudden pollution diffusion information, the gate valve group optimization scheduling algorithm is used to call the sudden pollution diffusion model in real time, calculate the operation plans of different hydraulic structures, and evaluate the water quality compliance of the monitored control section according to the evaluation indicators. After repeated iterations, the operation optimization plan of the hydraulic structure that can improve the urban water environment and water quality is finally obtained, realizing multi-model coupling water quality optimization based on digital twins.

[0095] like Figure 2 As shown, the third specific embodiment of the multi-model coupling water quality optimization method based on digital twins of the present invention is:

[0096] A multi-model coupling water quality optimization method based on digital twins includes the following steps:

[0097] S1: Based on terrain elevation data, land use type and other data, a surface runoff model is established for the city. The rainfall data from different rain gauges are connected, and the grid rainfall method is used to consider the unevenness of rainfall. In combination with the rainfall runoff calculation model, the urban rainfall, evaporation and infiltration, water storage, and runoff processes are simulated in detail. The urban runoff flow process is calculated to obtain the hydrological process entering the urban pipe network nodes and river network nodes.

[0098] S2: Based on the topological structure of the pipeline network and river network, pipeline network parameters, river section data, hydraulic structure data, and pollutant emission location and emission data, numerical simulation technology is used to numerically model the urban underground pipeline network and river network. It can support the simulation calculation of sudden pollution diffusion under specific rainfall conditions, sudden pollution emission scenarios and current hydraulic structure operation plans.

[0099] S3: Based on the number of monitoring and control sections that meet the water quality standards calculated by S2 and the current operation plan of hydraulic structures, the gate valve group optimization scheduling algorithm is used to call the sudden pollution diffusion model in real time, calculate the operation plans of different hydraulic structures, establish evaluation indicators, evaluate the water quality compliance of the monitoring and control sections, iterate repeatedly, and finally obtain the best operation plan for hydraulic structures to improve the urban water environment and water quality.

[0100] S4: Based on the optimal operation plan of hydraulic structures obtained in S3, it displays numerical cloud maps of urban pipe networks, river network flow, water level, pollutant concentration, etc., lists the water quality conditions after optimization of monitoring and control sections, and provides a comprehensive evaluation for urban water environment management.

[0101] In this embodiment, the method of step S1: calculating the urban production and sink flow process and obtaining the hydrological process of entering the urban pipe network node and the river network node is as follows:

[0102] S1.1: Gridded rainfall refers to the use of Thiessen polygon method to process grid surface rainfall, that is, all adjacent rain gauges are connected into triangles, and the perpendicular bisectors of the sides of these triangles are drawn. Then, the perpendicular bisectors around each rain gauge form a polygon, and the rainfall of a unique rain gauge contained in this polygon is used to represent the rainfall in this polygon area. This polygon is the Thiessen polygon. Therefore, multiple rain gauge networks in a certain area or basin constitute the Thiessen polygon network. The weight coefficient of the polygon area calculation of each rain gauge in the sub-basin is calculated, and then the weight coefficient of each polygon in the polygon network is multiplied by the rainfall of each rain gauge station and then accumulated to obtain the average rainfall in the area or basin. The formula is as follows:

[0103]

[0104] Where: represents the average rainfall of each subcatchment at different time points. represents the weight of the nth rainfall station in the mth subcatchment; Represents the rainfall at the nth rain gauge at the kth time point.

[0105] S1.2: Use the rainfall-runoff calculation model to calculate surface runoff and quickly estimate runoff based on land use data and hydrological conditions. Regardless of the complex process between rainfall and runoff, it only needs to access the gridded rainfall data of each sub-basin.

[0106] when When , the calculation formula of surface runoff generation and convergence principle is as follows:

[0107]

[0108]

[0109] Where: Q is runoff; P is rainfall; S is the potential maximum retention loss; is the initial loss; CN is the flow-generating capacity of the basin. The larger the value, the stronger the flow-generating capacity of the basin.

[0110] In this embodiment, step S2: according to the topological structure of the pipe network and the river network, pipe network parameters, river section data, hydraulic structure data, and information such as the location and data of pollutant discharge, the urban underground pipe network and the river network are numerically modeled using numerical simulation technology to obtain a sudden pollution diffusion model for the simulation calculation of sudden pollution diffusion, which includes the following contents:

[0111] The sudden pollution diffusion model is based on the topological structure of the urban pipe network and river network, and performs spatial discretization. The pipe network and river channel are divided into several nodes and units, and the temporal changes of pollutants are discretized using a time step that adapts to sudden pollution events. The model solves the spatiotemporal changes of pollutant concentrations in the pipe network and river network through the finite difference method.

[0112] The sudden pollution diffusion model built simulates the spatiotemporal changes of the hydrodynamic elements of the urban pipe network and river network and the diffusion path of pollutants in it based on the input data such as the location, amount and time of pollutant discharge, combined with the hydrological process of entering the urban pipe network and river network nodes under specific rainfall conditions. By analyzing the speed and direction of the water flow, the connectivity of the nodes in the pipe network and river network, the concentration distribution of pollutants at different time points is calculated.

[0113] The model monitors the water quality changes of each section in real time during the pollutant diffusion simulation according to the set monitoring and control sections and their water quality indicators. Combined with the dynamic changes in the pollutant concentrations of each section, the number of monitoring and control sections that meet the water quality standards is determined.

[0114] The sudden pollution diffusion model can simulate the spatiotemporal distribution and diffusion process of pollutants in urban pipe networks and river networks under different rainfall conditions, discharge scenarios and hydraulic structure operations, providing support for water quality control in sudden pollution events.

[0115] The sudden pollution diffusion model uses the continuity equation, momentum equation and pollution diffusion equation of water flow in pipelines and river networks to obtain the hydrodynamic elements and pollutant distribution of each pipe section and river network to simulate the evolution of water flow and the diffusion and transportation of pollutants in urban pipe networks and river networks. The hydraulic control equations calculated by the model are as follows:

[0116] The momentum equation can be expressed as follows:

[0117]

[0118] The expression of the continuity equation is as follows:

[0119]

[0120] Where: is the acceleration due to gravity; is the cross-sectional area of ​​water flow; is the static pressure head; For the head of the tube; For time; For flow; is the friction resistance; are the pressure and gravity terms; is the convection acceleration; is the friction resistance term; is the flow change item entering and leaving the control unit body; Controls the change in water volume in the cell.

[0121] The friction resistance is calculated by the Manning formula, which is as follows:

[0122]

[0123]

[0124] Where: Speed ​​plus absolute value It means that the direction of friction force is opposite to the direction of water flow, R is the hydraulic radius, and n is the roughness.

[0125] The node control equation is as follows:

[0126]

[0127] Where: is the node free surface area, is the traffic in and out of the node.

[0128] The pollution diffusion equation used in the model calculation is as follows:

[0129] The transport process of pollutants along the pipe network and river network can be described by the law of conservation of mass, which is expressed as follows:

[0130]

[0131] Where: is the pollutant concentration; is the longitudinal velocity component; is the longitudinal diffusion coefficient; is the reaction rate term; is the longitudinal distance; For time.

[0132] The first term in this equation represents advective transport, where the contaminant moves along the pipe at the same speed as the fluid. The second term represents longitudinal dispersion, where the contaminant mixes with the fluid due to velocity and concentration gradients. The last term represents other reactions that change the contaminant concentration, unrelated to fluid motion.

[0133] In this embodiment, the step S3: based on the number of monitoring and control sections that meet the water quality standards calculated in S2 and the current hydraulic structure operation plan, the gate valve group optimization scheduling algorithm is used to call the sudden pollution diffusion model in real time, calculate the operation plans of different hydraulic structures, set evaluation indicators, evaluate the water quality compliance status of the monitoring and control sections, iterate repeatedly, and finally obtain the method of the best operation plan of hydraulic structures to improve the urban water environment water quality as follows:

[0134] S31: Based on the beetle swarm optimization algorithm, a gate valve group optimization algorithm is constructed, and the sudden water pollution diffusion model is called to obtain the operation rules of hydraulic structures that meet the water quality indicators of the monitored control section.

[0135] Based on the water quality of the monitored control section, the gate valve group optimization algorithm will call the sudden pollution diffusion model established by S2 and then find n initial feasible solutions within the scope of the hydraulic structure operation regulations. , that is, the initial opening state of the gate, and the current opening state of the gate , which is expressed as follows:

[0136]

[0137] Then the initial feasible solution is assigned the opening adjustment speed of each gate in the initial state , get the gate opening change speed , which is expressed as follows:

[0138]

[0139] When iterating, the current opening state of the gate needs to be updated , Gate opening change speed , Optimal gate opening setting , which is expressed as follows:

[0140]

[0141]

[0142]

[0143] After completing the fitness function calculation, update the optimal opening setting of the gate valve group , which is expressed as follows:

[0144]

[0145] Where: It is a component in the optimal opening setting vector of the gate valve group, representing the optimal value of the gate valve group in the Nth dimension.

[0146] The formula for updating the gate valve group opening status is as follows:

[0147]

[0148]

[0149]

[0150]

[0151] Where: is the gate at The opening state at the time of the iteration; is the gate at The opening speed at the iteration; is the weight between the gate opening speed and the opening state, which is generally a constant value; For the The change in gate opening when the i-th iteration is updated; For the The gate opening step value at the iteration; For the The spacing value between the two search directions of the gate in the iteration; For the The opening state of the i-th gate in the right search direction at the iteration; For the The opening state of the i-th gate in the left search direction at the iteration; f(x) is the fitness function.

[0152] The gate valve group opening speed update formula is as follows:

[0153]

[0154] Where: is the inertia weight when the velocity is updated, , are the individual learning factor and valve group learning factor respectively; , is a random number between 0 and 1. For the The optimal value of the individual gate at the i-th iteration.

[0155] The calculation formulas for other parameters are as follows:

[0156]

[0157]

[0158]

[0159] Where: is the maximum inertia weight value; is the minimum inertia weight value, is the maximum number of iterations; is the attenuation factor, which is generally taken as 0.95. is the number of iterations.

[0160] Calculate and update the opening combination of each hydraulic structure according to the above formula.

[0161] In addition, it is necessary to set constraints to limit the calculation results of the gate valve group to ensure the rationality of the calculation results. The constraints are as follows:

[0162]

[0163]

[0164]

[0165]

[0166] in, is the flow velocity of the ith river or pipe section; is the designed maximum flow rate; is the water level of the ith river network section; It is the ecological water level of the section, that is, the minimum water level to ensure the normal water ecological environment; is the safe water level of the section; is the flow rate of the ith river or pipe section; is the designed maximum flow rate; is the opening of the jth hydraulic structure; is the minimum opening of the jth hydraulic structure; is the maximum opening of the jth hydraulic structure.

[0167] After screening the constraints, the optimal operation mode of the hydraulic structure is obtained, and the water quality compliance rate of key sections is the highest.

[0168] S32: The gate valve group optimization algorithm takes the water quality compliance rate of key sections as the optimization target. The water quality compliance rate is the ratio of the number of key sections that meet the water quality standards to the total number of key sections. It is used to characterize the improvement of the water quality of the pipe network and river network after the sudden pollution spreads, and reflects the comparison between the pollutant concentration of each monitoring and control section and the water quality target value after calling the optimization algorithm. The higher the water quality compliance rate, the better the effect of the optimization scheduling algorithm on improving the water environment. The calculation formula is as follows:

[0169]

[0170] Among them, R represents the water quality compliance rate of the monitoring and control section obtained by the optimization scheduling algorithm; It indicates the number of monitoring and control sections that meet the water quality standards, and N indicates the total number of monitoring and control sections.

[0171] S33: When the intergenerational change of the opening of the hydraulic structure is less than 0.000005 and the maximum number of iterations reaches 1300, the optimization algorithm of the gate valve group stops.

[0172] This embodiment adopts the gate valve group optimization algorithm, takes each hydraulic structure in the pipe network and river network as a dispatching unit, and realizes multi-point collaborative control. Based on the consideration of the health of the water ecological environment, this method minimizes the concentration of water environment pollutants, improves the water quality compliance rate of the urban monitoring and control section, and achieves a balance between pollution control and ecological protection, so that the urban water environment can be improved in a timely and efficient manner, and the intelligent management of urban water environment governance can be promoted. The present invention can access rainfall data, pollutant discharge location, concentration and other data in different regions in real time, deduce the hydrodynamic water quality of the urban pipe network and river network, and use the gate valve group optimization algorithm for automatic optimization according to the water quality of the monitoring and control section. The algorithm takes into account the water transfer capacity of the actual pipe network and river network, the operation constraints of hydraulic structures and other principles, optimizes the iterative hydraulic structure operation plan, automatically evaluates the water quality improvement effect, and outputs the optimal water quality improvement hydraulic structure operation plan, thereby realizing the intelligent management of urban water environment governance.

[0173] In this embodiment, the step S4: based on the optimal operation plan of the hydraulic structure obtained in step S3, displays the numerical cloud diagrams of the flow, water level, pollutant concentration, etc. of the urban pipe network and river network, and lists the water quality conditions of the optimized monitoring and control sections, so as to provide a comprehensive assessment for urban water environment management. The specific operations are as follows:

[0174] According to the gate valve group optimization algorithm in S3, the best operation plan combination of each hydraulic structure is obtained, and the sudden pollution diffusion model in S2 is run to simulate the spatiotemporal distribution of water level, flow, pollutant concentration, etc. in the optimized pipeline and river network in the whole city under sudden pollution.

[0175] At the same time, it generates a display of key evaluation indicators such as water quality classification statistics and compliance rate of the monitoring control section. Through these visualization results, it provides a scientific basis for evaluating the water quality improvement effect under the sudden pollution diffusion scenario and promotes the automation and efficiency of urban water environment governance.

[0176] Furthermore, the present invention comprehensively integrates the physical data of urban pipe networks and river networks, including characteristic parameters of hydraulic structures, topological structures of pipe networks and river networks, pipe network size information, and river network cross-section data, and establishes a complete solution for improving water quality in the event of sudden pollution diffusion. The solution can respond to pollution diffusion events in real time, use each hydraulic structure in the pipe network and river network as a scheduling unit, and formulate optimal scheduling plans for different sudden pollution sources. The system accesses real-time rainfall data and pollutant emission locations and concentrations in different regions, and performs simulation calculations of the entire process of water quality improvement in the event of sudden pollution diffusion, covering rainfall runoff calculations, surface runoff calculations, hydrodynamic and water quality deduction calculations in pipe networks and river networks, and optimal scheduling of hydraulic structures. The physical system is combined with a virtual simulation model through digital technology, thereby achieving accurate modeling, monitoring, prediction, and optimization of the physical system.

[0177] Therefore, the present invention can achieve urban water environment improvement, which covers the entire process from digital modeling, automatic optimization scheduling to standardized output, aiming to promote the automated development of urban water environment improvement management.

[0178] The first specific embodiment of the multi-model coupling water quality optimization system based on digital twin of the present invention:

[0179] A multi-model coupled water quality optimization system based on digital twins, which includes a surface runoff generation module, a sudden pollution diffusion module, an operation plan generation module and a result display module:

[0180] The surface runoff module is used to couple rainfall data, terrain elevation data and land use type data, simulate urban rainfall and hydrological changes, and obtain the hydrological process entering the urban pipe network nodes and river network nodes;

[0181] The sudden pollution diffusion module is used to simulate and calculate the sudden pollution diffusion according to the pollutant emission location and emission data and based on the hydrological process to obtain the sudden pollution diffusion information;

[0182] The operation plan generation module is used to establish operation plans for various hydraulic structures based on the digital twin mechanism and sudden pollution diffusion information; and evaluate the operation plans of various hydraulic structures according to the evaluation indicators to obtain the optimal operation plan for hydraulic structures;

[0183] The result display module is used to visualize the optimization results of the hydraulic structure operation optimization plan, including pollutant diffusion paths and water quality improvement data.

[0184] The second specific embodiment of the multi-model coupling water quality optimization system based on digital twin of the present invention:

[0185] A multi-model coupled water quality optimization system based on digital twins integrates surface runoff module, sudden pollution diffusion module, operation plan generation module and result display module, aiming to achieve water quality management and optimal scheduling under urban sudden pollution diffusion scenarios.

[0186] The surface runoff module is used to take into account the unevenness of rainfall, simulate the runoff generation and convergence process of surface runoff based on gridded rainfall data, accurately predict the amount of rainwater flowing into the pipe network and river network and its time process, and provide input conditions for subsequent hydrodynamic and water quality simulations.

[0187] The sudden pollution diffusion module is used to simulate the hydrodynamic process of underground pipe networks and river networks and the migration and diffusion process of pollutants. By comprehensively analyzing the transmission path and concentration changes of pollutants in pipe networks and river networks, the water quality of key sections can be evaluated in real time.

[0188] The operation plan generation module uses the automatic optimization algorithm of gate valve groups to automatically dispatch hydraulic structures (such as pump stations, gates, weirs, etc.) in the pipe network and river network. The algorithm optimizes the operation plan of each hydraulic structure through dynamic prediction of pollutant diffusion and analysis of water quality in key monitoring sections, maximizes the water quality compliance rate in key sections, and reduces the occurrence of pollution exceeding the standard.

[0189] The result display module is used to provide a visual display of simulation and optimization results, including pollutant diffusion paths, water quality improvement effects, etc. Through an intuitive interface, the implementation effects of different scheduling schemes are displayed to help decision makers conduct more scientific emergency response and water quality management.

[0190] The system of the present invention can realize surface runoff simulation, pipe network and river network hydrodynamic simulation, optimal scheduling and result display by integrating a surface runoff generation and convergence module, a sudden pollution diffusion module, an operation plan generation module and a result display module, thereby enabling full-process simulation and optimal scheduling of sudden pollution diffusion in cities.

[0191] Furthermore, the present invention takes into account the spatial heterogeneity of rainfall, uses gridded rainfall data to drive the surface runoff model, and accurately reflects the hydrological process after urban rainfall, thereby accurately simulating the surface runoff process.

[0192] At the same time, the present invention integrates multiple models to simulate the water flow and pollutant diffusion in the pipe network and river network, predicts the concentration changes of pollutants in different sections, provides early warning of water quality exceeding the standard, and can realize real-time hydrodynamics and water quality simulation.

[0193] Furthermore, the present invention utilizes the gate valve group optimization algorithm to optimize the operation strategies of the pipe network and river network hydraulic structures, reduce the number of sections exceeding the standard, and improve the urban water quality compliance rate.

[0194] The third specific embodiment of the multi-model coupling water quality optimization system based on digital twin of the present invention:

[0195] A multi-model coupled water quality optimization system based on digital twins, including a surface runoff module, a sudden pollution diffusion module, an operation plan generation module and a result display module, forming an urban multi-model coupled sudden pollution diffusion and water quality optimization system based on digital twin technology.

[0196] The surface runoff module includes the following:

[0197] This module processes terrain elevation data and land use data to analyze the runoff characteristics of the city. By using the rainfall runoff calculation model combined with rainfall data from different regions, the urban surface runoff process is calculated, and the hydrological process of entering the urban pipe network nodes and river network nodes is predicted and simulated.

[0198] This module uses the written code to process terrain elevation data and land use data, identify water flow direction and water catchment path, analyze the runoff characteristics of the city, and determine the sub-catchment area of ​​each pipe network node and river network node in the city, as well as the runoff capacity of each sub-catchment area. For the rainfall in each sub-catchment area, the code uses the grid rainfall method to calculate, that is, the Thiessen polygon method is used to process the grid surface rainfall.

[0199] The specific data processing steps are as follows: First, connect all adjacent rain gauges into triangles and draw the perpendicular bisectors of each triangle edge. The area enclosed by these bisectors is the Thiessen polygon. The rainfall in each polygon area is represented by the rain gauge data contained in the area.

[0200] After constructing the Thiessen polygon mesh covering the entire city, the weight coefficient of each rain gauge is calculated based on the polygon area ratio corresponding to the rain gauge in each subcatchment. Subsequently, the rainfall of all rain gauges in each subcatchment is multiplied by its corresponding weight coefficient and accumulated to obtain the average rainfall of the subcatchment. The formula used in the code is as follows:

[0201]

[0202] Where: represents the average rainfall of each subcatchment at different time points. represents the weight of the nth rainfall station in the mth subcatchment; Represents the rainfall at the nth rain gauge at the kth time point.

[0203] The surface rainfall data P of each sub-catchment area, the runoff CN value of each sub-catchment area, and the potential maximum retention loss S of each sub-catchment area calculated by the gridding method are input into the rainfall runoff calculation model. The urban surface runoff calculation is performed in the rainfall runoff calculation model to obtain the hydrological process entering each pipe network node and river network node. The principle formula for surface runoff calculation is:

[0204] when When , the calculation formula of surface runoff generation and convergence principle is as follows:

[0205]

[0206]

[0207] Where: CN is the runoff capacity of the basin. The larger the value, the stronger the runoff capacity of the basin. This value can be obtained based on the land use type of the city. Different land use types such as cultivated land, forest, and city have different CN values; S is the potential maximum retention loss; P is the rainfall. The rainfall in each sub-catchment area of ​​the city is obtained based on the grid rainfall calculation formula; is the initial rainfall loss value, usually 20% of the maximum retention loss, i.e. 0.2S; Q is the flow rate of each sub-catchment area, that is, the hydrological process of each pipe network node and river network node, which can be obtained according to the surface runoff formula.

[0208] The sudden pollution diffusion module includes the following:

[0209] This module comprehensively considers the information of hydraulic structures, pipe networks, and river sections, and uses numerical simulation technology to comprehensively model the complex topological structures of urban pipe networks and river networks. Based on the current operation plans of hydraulic structures, it truly reflects the sudden pollution emissions and pollutant diffusion processes.

[0210] By combining the gate valve group optimization algorithm, the optimal hydraulic structure operation plan is obtained to improve the water environment and water quality, and the pollutant diffusion is simulated based on the plan. Based on the hydrological process of entering the urban pipe network nodes and river network nodes provided by the surface runoff module, combined with real-time pollutants, real-time discharge locations and discharge data, the diffusion and transportation process of pollutants in the urban pipe network and river network is simulated.

[0211] This module further counts the water quality compliance rate of the monitored control section. Based on these statistical results, the system starts the gate valve group optimization algorithm to optimize the operation plan of hydraulic structures, thereby effectively improving the water environment and water quality.

[0212] Since the spread of sudden urban pollution covers many hydraulic structures in the pipeline and river networks, including pump stations and interceptors in the pipeline networks, gates and dams in the river networks, etc., the algorithm function that is consistent with the actual function of the hydraulic structures is selected in the system based on the design drawings of the urban hydraulic structures.

[0213] Taking the pump station in the pipe network as an example, it is necessary to input the pump station operation rules and pump station operation characteristic curve into the system. The pump station flow formula is:

[0214]

[0215] in, is the flow rate of the pumping station; is the power of the pump; is the density of the fluid, which is generally 1000kg / m³ for water; is the acceleration due to gravity; It is the pump head.

[0216] If the weirs in the river network are used as columns, it is necessary to set the equivalent weir flow coefficient and weir width and use the standard rectangular weir formula to calculate the flow. The calculation formula is as follows:

[0217]

[0218] in, is the weir flow coefficient; is the crest length of the equivalent weir; is the water head during the water inlet stage; is the offset elevation of the outlet.

[0219] Run the solution generation module, including the following:

[0220] This module is based on the water quality compliance of the monitored control section obtained by the sudden pollution diffusion module, activates the gate valve group optimization algorithm, and takes the current operation plan of hydraulic structures as the basis. By setting optimization objectives, constraints and optimization stop conditions, it finally generates the optimal operation plan of hydraulic structures under the situation of sudden pollution diffusion, and realizes efficient improvement of urban water environment water quality.

[0221] Taking into account the safety of urban water environment and water quality, the water quality compliance rate of the monitored control section is used as the optimization target and as the core indicator for evaluating water environment and water quality improvement plans.

[0222] Furthermore, the ratio of the number of monitoring and control sections that meet the water quality standards to the total number of monitoring and control sections, the higher the water quality compliance rate, the better the effect of the optimization scheduling algorithm on improving the water environment, and its calculation formula is as follows:

[0223]

[0224] Among them, R represents the water quality compliance rate of the monitoring and control section obtained by the optimization scheduling algorithm; It indicates the number of monitoring and control sections that meet the water quality standards, and N indicates the total number of monitoring and control sections.

[0225] This formula can be used to determine the water quality compliance of all control sections at each moment. The expression is as follows:

[0226]

[0227] In the case of sudden urban pollution diffusion, the optimal opening combination of each hydraulic structure is used to describe the best operation plan for improving the water environment and water quality. At each event point, the operation status of the hydraulic structure can be represented by a matrix, and its matrix expression is as follows:

[0228]

[0229] in: Indicates the mth time point Next, the operating status of the nth hydraulic structure.

[0230] Based on the combination of operation schemes of the hydraulic structure group, other specific operation parameters of each hydraulic structure at each moment, such as flow rate, can be further obtained. The expression is as follows:

[0231] Q=

[0232] The result display module includes the following contents:

[0233] Based on the optimal operation plan of hydraulic structures generated by the operation plan generation module, numerical cloud maps of urban pipe networks, river network flow, water level, pollutant concentration, etc. are displayed, and the water quality status of the optimized monitoring and control sections are listed, providing a comprehensive evaluation for urban water environment management. In the actual scenario of sudden pollution spread in the city, the system outputs the spatiotemporal distribution field of numerical cloud maps of water level, flow, pollutant concentration, etc. of pipe networks and river networks throughout the city every half an hour. At the same time, the water quality of the monitoring and control sections is classified and counted, and key evaluation indicators such as the water quality compliance rate of the monitoring and control sections are displayed. Through these results, the improvement effect of the urban water environment under the condition of sudden pollution spread can be intuitively evaluated, the effectiveness of the water quality treatment plan can be standardized, and the automated development of water environment management can be promoted.

[0234] Therefore, the system of the present invention covers the calculation of surface runoff, hydrodynamics and water quality in pipe networks and river networks, as well as water quality improvement links such as the optimization and scheduling of hydraulic structures. By comprehensively considering multi-source real-time monitoring data and combining water ecological health, digital twin technology is used to build an automatic water quality improvement system for urban sudden pollution diffusion water environment. The monitoring data includes at least the water level and flow of the river hydrological monitoring station, the water level and flow of the pipe network, the discharge location and concentration of pollutants, etc.

[0235] Furthermore, through the optimization algorithm, this system can automatically generate targeted hydraulic structure operation plans for improving water environment and water quality when pollutant concentrations are abnormal, thus achieving global control over the spread of sudden pollution. This automated solution greatly reduces manual intervention and decision-making lags, significantly shortens dispatch response time, and improves emergency response efficiency.

[0236] At the same time, through the simulation of future sudden pollution spread scenarios and the display of water environment and water quality improvement plans, key evaluation indicators such as the water quality compliance rate of the monitored control section are intuitively displayed, providing advanced technical support for urban water environment management and promoting water environment management to develop in a more scientific, efficient and automated direction.

[0237] An embodiment of a device applying the method of the present invention:

[0238] An electronic device comprising:

[0239] one or more processors;

[0240] A storage device for storing one or more programs;

[0241] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned multi-model coupling water quality optimization method based on digital twins.

[0242] A computer medium embodiment using the method of the present invention:

[0243] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned multi-model coupling water quality optimization method based on digital twins.

[0244] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, and computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program codes.

[0245] The present application is described by flowcharts or / and block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each process or / and block in the flowchart or / and block diagram and the combination of the processes or / and blocks in the flowchart or / and block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart or / and block diagram. Figure 1 Process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0246] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 Process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0247] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0248] The model in this application is an object that objectively describes the morphological structure with the help of physical or virtual representation. The object is not equal to the object and is not limited to physical and virtual. It can be a data processing function, software program, processing mode, usage method, operation method, workflow, application process, electronic hardware, circuit module, processing system, system imitation or simulation object.

[0249] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field can still modify or replace the specific implementation methods of the present invention with equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A multi-model coupling water quality optimization method based on digital twin, characterized by: The following steps are involved: Step 1: Through the pre-built surface runoff model, rainfall data, terrain elevation data and land use type data are coupled and processed to simulate urban rainfall and hydrological changes, and obtain the hydrological process entering the urban pipe network nodes and river network nodes; Step 2: Using the pre-built sudden pollution diffusion model, according to the pollutant emission location and emission data, and based on the hydrological process, a sudden pollution diffusion simulation calculation is performed to obtain sudden pollution diffusion information; Step 3: Use the pre-built operation plan generation model to establish various hydraulic structure operation plans based on the digital twin mechanism and sudden pollution diffusion information; Step 4: Use the pre-built scheme evaluation model to set up evaluation indicators, evaluate the operation schemes of various hydraulic structures, obtain the hydraulic structure operation optimization scheme, and realize multi-model coupling water quality optimization based on digital twins; it specifically includes the following contents: According to the number of water quality standards in the monitoring and control sections, the gate valve group optimization scheduling algorithm is used to call the sudden pollution diffusion model in real time, calculate various hydraulic structure operation plans, set up evaluation indicators, evaluate the water quality standards of the monitoring and control sections, and iterate repeatedly to finally obtain the method of optimizing the operation of hydraulic structures to improve the urban water environment and water quality: S31: Based on the beetle swarm optimization algorithm, the gate valve group optimization algorithm is constructed, and the sudden water pollution diffusion model is called to obtain the hydraulic structure operation rules that meet the water quality indicators of the monitoring control section; Based on the water quality of the monitored control section, the gate valve group optimization algorithm will call the sudden pollution diffusion model and find n initial feasible solutions within the scope of the hydraulic structure operation regulations. , that is, the initial opening state of the gate, and the current opening state of the gate , which is expressed as follows: Then the initial feasible solution is assigned the opening adjustment speed of each gate in the initial state , get the gate opening change speed , which is expressed as follows: When iterating, update the current opening state of the gate , Gate opening change speed , Optimal gate opening setting , which is expressed as follows: After completing the fitness function calculation, update the optimal opening setting of the gate valve group , which is expressed as follows: Where: It is a component in the optimal opening setting vector of the gate valve group, representing the optimal value of the gate valve group in the Nth dimension; The formula for updating the gate valve group opening status is as follows: Where: is the gate at The opening state at the time of the iteration; is the gate at The opening speed at the iteration; is the weight between the gate opening speed and the opening state, which is a constant value; For the The change in gate opening when the i-th iteration is updated; For the The gate opening step value at the iteration; For the The spacing value between the two search directions of the gate in the iteration; For the The opening state of the i-th gate in the right search direction at the iteration; For the The opening state of the i-th gate in the left search direction at the iteration; f(x) is the fitness function; The gate valve group opening speed update formula is as follows: Where: is the inertia weight when the velocity is updated, , are the individual learning factor and valve group learning factor respectively; , is a random number between 0 and 1. For the The optimal value of the individual gate at the i-th iteration; The remaining parameter calculation formula is as follows: Where: is the maximum inertia weight value; is the minimum inertia weight value, is the maximum number of iterations; is the attenuation factor, is the number of iterations; Calculate and update the opening combination of each hydraulic structure according to the above formula; In addition, it is necessary to set constraints to limit the calculation results of the gate valve group to ensure the rationality of the calculation results; the constraints are as follows: in, is the flow velocity of the ith river or pipe section; is the designed maximum flow rate; is the water level of the ith river network section; It is the ecological water level of the section, that is, the minimum water level to ensure the normal water ecological environment; is the safe water level of the section; is the flow rate of the ith river or pipe section; is the designed maximum flow rate; is the opening of the jth hydraulic structure; is the minimum opening of the jth hydraulic structure; is the maximum opening of the j-th hydraulic structure; After the constraint conditions are screened, the optimal operation mode of hydraulic structures is obtained, which leads to the highest water quality compliance rate in key sections; S32: The optimization algorithm of the gate valve group takes the water quality compliance rate of key sections as the optimization target. The water quality compliance rate is the ratio of the number of key sections that meet the water quality standards to the total number of key sections. It is used to characterize the improvement of the water quality of the pipe network and river network after the sudden pollution spreads, and reflects the comparison between the pollutant concentration of each monitoring and control section and the water quality target value after calling the optimization algorithm. The calculation formula of the water quality compliance rate is as follows: Among them, R represents the water quality compliance rate of the monitoring and control section obtained by the optimization scheduling algorithm; It indicates the number of monitoring and control sections that meet the water quality standards, and N indicates the total number of monitoring and control sections; S33: When the intergenerational change of the hydraulic structure opening is less than 0.000005 and the maximum number of iterations reaches 1300, the gate valve group optimization algorithm stops searching and outputs the optimal hydraulic structure operation plan for water quality improvement.

2. A multi-model coupling water quality optimization method based on digital twins as claimed in claim 1, characterized in that: Step 1: Through the pre-built surface runoff model, the rainfall data, terrain elevation data and land use type data are coupled and processed to simulate urban rainfall and hydrological changes. The method of obtaining the hydrological process entering the urban pipe network nodes and river network nodes is as follows: Based on terrain elevation data, the grid rainfall method is used to process rainfall data to take into account the unevenness of rainfall and obtain the average rainfall in a certain area or a certain basin; Using the rainfall-runoff calculation model, and according to the land use type data and hydrological conditions, the average rainfall is processed to obtain the urban flow process, which is used to simulate the urban rainfall process, evaporation and infiltration process, water storage process and confluence process; According to the urban flow production and sink process, the hydrological process entering the urban pipe network nodes and river network nodes is obtained.

3. A multi-model coupling water quality optimization method based on digital twins as claimed in claim 2, characterized in that: Based on terrain elevation data, the grid rainfall method is used to process rainfall data to obtain the average rainfall in a certain area or a certain basin as follows: According to the terrain elevation data, the Thiessen polygon method is used to connect all adjacent rainfall stations to obtain several triangle information; According to the triangle information, draw the perpendicular bisectors of each side of each triangle to obtain the perpendicular bisector data; According to the perpendicular bisector data, multiple perpendicular bisectors around each rainfall station are obtained; Connect multiple perpendicular bisectors to obtain several polygonal areas, each of which contains a unique rainfall station; Couple several polygonal areas to form a polygonal network, and calculate the area of ​​each polygonal area and the area of ​​the polygonal network; Calculate the ratio of the area of ​​each polygonal region to the area of ​​the polygonal network to obtain the weight coefficient of each polygonal region; Get the rainfall of the rain gauges contained in the polygonal area; The weight coefficient of each polygonal area and the corresponding rainfall are multiplied and accumulated to obtain the average rainfall in a certain area or a certain basin.

4. A multi-model coupling water quality optimization method based on digital twins according to claim 1, characterized in that: Step 2: Using the pre-built sudden pollution diffusion model, according to the pollutant emission location and emission data, and based on the hydrological process, a sudden pollution diffusion simulation calculation is performed to obtain the sudden pollution diffusion information as follows: Based on the topological structure of the urban pipe network and river network, spatial discretization is performed to divide the pipe network and river into several nodes and units; According to the location, amount and time of pollutant discharge, combined with the hydrological process entering several nodes and units under rainfall conditions, the spatiotemporal changes of the hydrodynamic elements of the urban pipe network and river network and the diffusion path of pollutants are simulated; Based on the diffusion path, the velocity and direction of the water flow, the connectivity of the nodes in the pipe network and the river network are analyzed to deduce the concentration distribution of pollutants at different time points, and the dynamic changes of pollutant concentrations in each section are obtained to monitor the changes in water quality in each section; Combined with the dynamic changes in pollutant concentrations in each section, the number of controlled sections that meet water quality standards is determined to obtain information on sudden pollution spread.

5. A multi-model coupling water quality optimization method based on digital twins as claimed in claim 4, characterized in that: The method of constructing the sudden pollution diffusion model is as follows: Based on gravity acceleration, water flow cross-sectional area, static water head, pipe length, time, flow rate, friction resistance, pressure term, gravity term, convection acceleration, friction resistance term, flow change term in and out of the control unit body, and water volume change term, momentum equation and continuity equation for hydraulic control are established; among which, friction resistance is calculated according to Manning's formula; The node control equation is established based on the node free surface area and the flow in and out of the node; According to the pollutant concentration, longitudinal velocity component, longitudinal diffusion coefficient, reaction rate term, longitudinal distance and time, the mass conservation law equation is established to describe the transport process of pollutants along the pipe network and river network; The continuity equation, momentum equation, node control equation and mass conservation equation are coupled to form a sudden pollution diffusion model, which is used to calculate the hydrodynamic elements and pollutant distribution of each pipe section and river network, and to simulate the water flow evolution process and pollutant diffusion and transportation process in urban pipe networks and river networks.

6. The multi-model coupling water quality optimization method based on digital twin according to claim 1, characterized in that: Step 3: Using the pre-built operation plan generation model, the method of establishing operation plans for various hydraulic structures based on the digital twin mechanism and sudden pollution diffusion information is as follows: Based on the digital twin mechanism, hydraulic structures are modeled and the initial operation plan of hydraulic structures is obtained; Based on the operation mechanism of hydraulic structures, the water quality compliance rate of the monitoring control section is taken as the optimization target, and the gate valve group optimization scheduling method is established, and the gate control parameters of the hydraulic structures are set; the gate control parameters at least include the opening adjustment speed of each gate in the initial state, the current opening state of each gate, the gate opening change speed and the optimal opening setting of each gate; The water quality compliance rate is the ratio of the number of monitoring and control sections that meet the water quality standards to the total number of monitoring and control sections. It is used to characterize the improvement of water quality in pipe networks and river networks after sudden pollution spreads. Based on the gate control parameters, a fitness function is established; According to the sudden pollution diffusion information, the initial operation plan of the hydraulic structure is adjusted by using the fitness function, and a variety of hydraulic structure operation plans are obtained.

7. The multi-model coupling water quality optimization method based on digital twin according to claim 1, characterized in that: Step 4: Use the pre-built scheme evaluation model to set up evaluation indicators, evaluate the operation schemes of various hydraulic structures, and obtain the hydraulic structure operation optimization scheme. The method for realizing multi-model coupling water quality optimization based on digital twins is as follows: Set evaluation indicators to evaluate the water quality compliance of the monitoring and control sections; Call the sudden pollution diffusion model to calculate various hydraulic structure operation plans and obtain some new sudden pollution diffusion information; Based on some new sudden pollution diffusion information, determine the hydraulic structure operation plan with the largest number of control sections that meet the water quality standards, that is, the hydraulic structure operation optimization plan; According to the hydraulic structure operation optimization plan, the operating status of the hydraulic structure is adjusted to obtain the flow, water level and pollutant concentration of the urban pipe network and river network, which are used to characterize the water quality status of the monitoring and control section, thereby realizing multi-model coupling water quality optimization based on digital twins.

8. A multi-model coupled water quality optimization system based on digital twins, characterized by: Applying a multi-model coupling water quality optimization method based on digital twins as described in any one of claims 1 to 7; It includes surface runoff generation and convergence module, sudden pollution diffusion module, operation plan generation module and result display module: The surface runoff module is used to couple rainfall data, terrain elevation data and land use type data, simulate urban rainfall and hydrological changes, and obtain the hydrological process entering the urban pipe network nodes and river network nodes; The sudden pollution diffusion module is used to simulate and calculate the sudden pollution diffusion according to the pollutant emission location and emission data and based on the hydrological process to obtain the sudden pollution diffusion information; The operation plan generation module is used to establish operation plans for various hydraulic structures based on the digital twin mechanism and sudden pollution diffusion information; and evaluate the operation plans of various hydraulic structures according to the evaluation indicators to obtain the optimal operation plan for hydraulic structures; The result display module is used to visualize the optimization results of the hydraulic structure operation optimization plan, including pollutant diffusion paths and water quality improvement data.

9. A multi-model coupling water quality optimization device based on digital twin, characterized by: It includes: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a multi-model coupling water quality optimization method based on digital twins as described in any one of claims 1-7.

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