Water conservancy system intelligent early warning system based on digital twinborn technology

Abstract

The invention discloses a water conservancy system intelligent early warning system based on a digital twinborn technology, and relates to the technical field of water conservancy early warning. Under the unified support of a standardized time sequence data set Dset, a pipe network terrain twinborn model Twin is constructed and runs in real time, so that waterlogging prediction is not limited to traditional single-point ponding monitoring any more, and the water conservancy system intelligent early warning system based on the digital twinborn technology is obtained. Compared with the prior art, the system has the advantages that the area which is most likely to fail can be recognized in advance and the bottleneck set Bset is generated by combining the dynamic comparison of the inflow demand load Qdem and the outflow capacity Qsup, the problem of insufficient coverage of potential waterlogging points in the prior art is effectively solved, meanwhile, the system further takes the bottleneck set Bset as an overflow starting point, and the system has the advantages that the system is simple in structure and convenient to operate. And a ponding depth grid Hmap is generated through calculation and is mapped to form a ponding road section set Aset, so that the ponding position, the ponding depth grade and the duration of a specific road section can be clearly pointed out when the rainstorm occurs, and full-chain coverage from pipe network bearing capacity evaluation to road risk early warning is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water conservancy early warning, in particular to a water conservancy system intelligent early warning system based on digital twin technology. BACKGROUND

[0002] With the continuous integration of information technology and water conservancy engineering, digital twin technology is gradually introduced into the water conservancy system to build a virtual and real interactive simulation model. On a more specific level, the urban waterlogging problem in the water conservancy system is one of the focuses of digital twin application, especially in the process of rapid urbanization, the urban underground drainage pipe network system has gradually become a key facility in water conservancy flood control and disaster reduction. In the context of urban heavy rainfall, it is difficult to accurately predict the development trend of waterlogging by relying solely on traditional water level monitoring of water accumulation points, while through digital twin technology, the coupling relationship between the carrying capacity of the drainage pipe network and the road terrain can be reconstructed in the virtual space, thereby realizing early warning of water accumulation sections.

[0003] At present, in the management of urban drainage and waterlogging prevention, the common method is to install water level meters or water accumulation sensors in some sections, sunken interchanges or key areas to monitor the water depth of single points in real time. This method has certain reference value in obtaining local water conditions, but has obvious defects: first, the monitoring points are limited in coverage and cannot form a global water accumulation distribution map; second, single point data cannot reflect the overall carrying capacity of the drainage pipe network and cannot dynamically simulate the flow of rainwater between different pipe sections; third, when extreme rain occurs, water accumulation points are often not captured in time in the early stage, resulting in a large lag in waterlogging warning. Because of these deficiencies, the existing system can only issue an alarm when water accumulation has occurred and caused traffic congestion, and lacks predictive function, so it is necessary to couple the drainage pipe network and the road terrain through digital twin modeling to form an early prediction of potential water accumulation sections. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a water conservancy system intelligent early warning system based on digital twin technology, which solves the problems mentioned in the background art.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a water conservancy system intelligent early warning system based on digital twin technology, comprising a data acquisition and processing module, a pipe network and terrain twin construction module, a load carrying capacity evaluation module and a water accumulation distribution deduction and early warning module; The data acquisition and processing module is connected to the city meteorological station and the rain monitoring equipment to form a unified standardized time series data set Dset; The pipe network terrain twin construction module establishes a virtual model corresponding to the real urban drainage system in a virtual environment based on the standardized time series dataset Dset, encapsulates the spatial coupling relationship between roads and pipe networks in the virtual system, and obtains a pipe network terrain twin model Twin; The load bearing evaluation module calls the standardized time series dataset Dset and the pipe network terrain twin model Twin, calculates the inflow demand load Qdem and the outflow capacity Qsup, and aggregates to form a bottleneck set Bset; The water accumulation distribution deduction and early warning module takes the bottleneck set Bset as the overflow starting point, performs water flow diffusion and confluence deduction under the framework of the pipe network terrain twin model Twin, gradually calculates the accumulation process of surface water on road units, generates a water accumulation depth grid Hmap, and maps to generate a water accumulation road section set Aset for early warning.

[0006] Preferably, the data acquisition and processing module includes a multi-source hydrological geographic data access unit and a time series data standardization processing unit; The multi-source hydrological geographic data access unit accesses different sources of data sources to form an original dataset; Specifically, it includes: collecting rainfall intensity sequence Rint and rainfall distribution grid Rmap from urban meteorological stations and rainfall monitoring equipment in real time; The rainfall intensity sequence Rint is a rainfall sequence in a unit time; The rainfall distribution grid Rmap represents two-dimensional grid data of rainfall at different spatial points in the urban area; The terrain elevation data Gdem and the road low-lying distribution data Dlow are called from the urban surveying and mapping data; The terrain elevation data Gdem represents the elevation grid data of the urban area; The road low-lying distribution data Dlow represents the road low-lying points and water accumulation prone area set extracted from the terrain elevation data Gdem; The drainage pipe network topology data Tnet, the pipe diameter parameter set Ddia, the slope parameter set Sgra, the roughness parameter set Nman, and the water inlet capacity Cinl are accessed from the municipal drainage department; The drainage pipe network topology data Tnet represents the spatial connection relationship data of pipe sections, nodes, and inspection wells in the urban drainage system; The pipe diameter parameter set Ddia represents a set of inner diameters of each pipe section in the urban drainage system; The slope parameter set Sgra represents the ratio of the longitudinal elevation difference to the length of each pipe section in the urban drainage system; The roughness parameter set Nman represents the pipe wall resistance coefficient, reflecting the degree of water flow resistance; The inlet capacity Cinl refers to the maximum flow that the inlet and inspection well can converge into the pipe network per unit time; At the same time, the water level sequence Wext of the external river is introduced by combining with the monitoring stations of the adjacent river.

[0007] Preferably, the time series data standardization processing unit, after receiving the original data set, performs time series alignment, spatial unification and missing correction pre-processing on various types of data; The pre-processing specifically includes: By using the time series resampling method, the rainfall intensity sequence Rint, the rainfall distribution grid Rmap and the external river water level sequence Wext are aligned by hour, so that the monitoring frequency and the calculation step are consistent; Then, by using the geographic information system coordinate projection and spatial overlay method, the terrain elevation data Gdem, the road low-lying distribution data Dlow and the drainage pipe network topology data Tnet are unified into the same coordinate system, so that the twin modeling can superimpose spatial information; At the same time, the time series interpolation method and the parameter inversion correction method are used to interpolate and correct the missing and errors of the pipe diameter parameter set Ddia, the slope parameter set Sgra, the roughness parameter set Nman and the inlet capacity Cinl; After the pre-processing is completed, the original data set is normalized by using the minimum-maximum normalization method, so that the data of different sources and different physical dimensions are converted into comparable dimensionless sequences, and a standardized time series data set Dset is obtained.

[0008] Preferably, the pipe network terrain twin construction module includes a spatial coupling modeling unit and a dynamic twin generation unit; After receiving the standardized time series data set Dset, the spatial coupling modeling unit extracts the drainage pipe network topology data Tnet and the terrain elevation data Gdem, reconstructs the drainage pipe network spatial skeleton corresponding to the reality in the virtual environment by using the three-dimensional geographic information modeling method, and establishes a vertical mapping relationship between the ground elevation of the pipe network node and the grid elevation point in the terrain elevation data Gdem through the spatial registration process, so that the pipe network node can form a consistent geometric association with the road surface elevation; Wherein, when three-dimensional modeling is performed based on the drainage pipe network topology data Tnet, the inspection well, the inlet and the catchment well of the pipe network are abstracted as discrete pipe network node objects Nset, and form a pipe network node object Nset layer inside; then based on the pipe connection relationship, all pipe sections are abstracted as pipe section objects Eset, and form a pipe section layer in the form of a set inside the drainage pipe network spatial skeleton; In this process, the road low-lying distribution data Dlow is written into the model as an additional attribute of the road unit, so that the road terrain features can form a calculable spatial coupling with the pipe network overflow point in the virtual environment, and a virtual hydraulic terrain model is obtained.

[0009] Preferably, the dynamic twin generation unit generates a virtual hydraulic terrain model, calls rainfall intensity sequence Rint and rainfall distribution grid Rmap as dynamic boundary conditions, and uses hydrodynamic numerical simulation method to calculate the water level, flow velocity and flow rate of the nodes and road grid points in the pipe network at each time step, so that the static virtual hydraulic terrain model obtains dynamic running characteristics evolving with time; The water exchange results generated in the simulation process are combined with the low-lying area distribution data Dlow to make the virtual hydraulic terrain model quantify the accumulation and dissipation process of overflow water in the low-lying area, and further reflect the two-way hydraulic action relationship between the ground and the pipe network. Then, through the model state assimilation method, the simulation results of the virtual hydraulic terrain model are dynamically corrected by receiving real-time data streams from field sensors and monitoring equipment, so as to realize the state synchronization between the virtual environment and the real system. Then, the virtual hydraulic terrain model with dynamic calculation ability and real-time correction mechanism is obtained; After dynamic driving, overflow evolution and state correction are completed, the virtual hydraulic terrain model is instantiated as a sustainable running digital twin, forming a pipe network terrain twin model Twin.

[0010] Preferably, the load bearing evaluation module includes an inflow demand calculation unit and a bearing determination unit; The inflow demand calculation unit calls the standardized time series data set Dset and the pipe network terrain twin model Twin, and extracts the rainfall intensity sequence Rint, the rainfall distribution grid Rmap and the terrain elevation data Gdem therefrom; First, based on the terrain elevation data Gdem, the entire target warning area is divided into multiple catchment units Hset by using catchment partitioning method, each catchment unit corresponds to an inflow port or road catchment in the pipe network system, and is bound to the corresponding node in the pipe network terrain twin model Twin through spatial mapping relationship; Then, combined with the rainfall intensity sequence Rint and the rainfall distribution grid Rmap, the runoff of multiple catchment units Hset is calculated at each time step by using hydrological runoff calculation method, and the calculation results are assigned to the corresponding pipe network node object Nset to form dynamic attributes; All pipe network node objects Nset jointly constitute the inflow demand load Qdem in the pipe network terrain twin model Twin, and the inflow demand load Qdem is bound to the pipe network node object Nset layer in the form of time series.

[0011] Preferably, the carrying capacity determination unit, after calling the standardized timing data set Dset and the pipe network terrain twin model Twin, uses the Manning hydraulic calculation method to calculate the limit water carrying capacity of each pipe segment at the pipe segment layer by inputting the pipe diameter parameter set Ddia, the slope parameter set Sgra, the roughness parameter set Nman, the inlet capacity Cinl, and the external river water level sequence Wext, to form the outflow capacity Qsup bound to the pipe segment layer; At the same time, the inflow demand load Qdem bound to the pipe network node object Nset layer is automatically transmitted to the adjacent pipe segment according to the topological relationship and the conservation relationship, to obtain the demand flow Qreq bound to the pipe segment layer; Subsequently, the ratio of the outflow capacity Qsup to the demand flow Qreq is calculated, which is defined as the carrying ratio Rcap, and then the overload and potential overflow point marking are performed according to the carrying ratio Rcap, to generate the bottleneck set Bset.

[0012] Preferably, the bottleneck set Bset is generated by the following judgment method: When the carrying ratio Rcap is less than 1, the corresponding pipe network pipe segment is in an overload state, and at the same time, the pipe network node object Nset layer positions associated with the two ends of the pipe segment are marked as potential overflow points; then, all the marked overload pipe segments and potential overflow nodes are aggregated in the entire timing dimension, to generate the bottleneck set Bset; When the carrying ratio Rcap is greater than or equal to 1, the water carrying capacity of the corresponding pipe segment in the time period meets the demand, and no overload or potential overflow point marking is performed.

[0013] Preferably, the waterlogging distribution deduction and early warning module comprises a waterlogging deduction unit and an early warning generation unit. The waterlogging deduction unit takes the overload and overflow nodes identified in the bottleneck set Bset as the starting point, calls the road low-lying distribution data Dlow in the dynamic running environment of the pipe network terrain twin model Twin, and uses the surface water flow diffusion calculation method to deduce the migration, accumulation, and dissipation process of the overflow water on the road unit at each time. In the deduction process, the channel and retention position of the water flow are determined according to the road low-lying distribution data Dlow, so as to dynamically calculate the spatial distribution of the surface water on the road surface and obtain the waterlogging depth grid Hmap.

[0014] Preferably, the early warning generation unit, after receiving the waterlogging depth grid Hmap, maps and matches the grid data with the road space unit, divides the risk levels based on the waterlogging depth of each road unit, converts different waterlogging depth values into corresponding waterlogging levels, and integrates the waterlogging information in the timing dimension. A set of flooded road sections Aset is formed, which includes road signs, flooding levels and corresponding time periods. The flooded road section set Aset identifies the affected road sections and the severity of flooding, and is used for the communication content of the water conservancy system to issue waterlogging risk warnings to the outside world.

[0015] The present invention provides an intelligent early warning system for water conservancy systems based on digital twin technology, which has the following beneficial effects: (1) By constructing a pipe network terrain twin model Twin and running it in real time under the unified support of the standardized time series data set Dset, urban flooding prediction is no longer limited to traditional single-point waterlogging monitoring. Instead, it can combine the dynamic comparison of inflow demand load Qdem and outflow capacity Qsup to identify the areas most prone to failure in advance and generate a bottleneck set Bset, effectively solving the problem of insufficient coverage of potential urban flooding points in existing technologies. At the same time, this system further uses the bottleneck set Bset as the overflow starting point, calculates and generates the waterlogging depth grid Hmap and maps it to form a waterlogging section set Aset, so that the waterlogging location, waterlogging depth level and duration of specific road sections can be clearly pointed out when heavy rain occurs, achieving full chain coverage from pipe network carrying capacity assessment to road risk warning.

[0016] (2) The calculated outflow capacity Qsup is compared section by section at the pipe level to generate a bottleneck set Bset containing overload points and potential overflow points. This mechanism enables the failure risk of the drainage system to be located at the specific pipe section and its associated network nodes before the water overflows the surface, thus avoiding the delayed situation in traditional drainage management where problems are only discovered after obvious water accumulation occurs on the surface.

[0017] (3) In the Twin model of the pipeline network terrain, starting from the overload or overflow nodes marked by the bottleneck set Bset, the migration and accumulation of overflow water on the road unit is deduced hourly in combination with the road low-lying distribution data Dlow. This generates a water depth grid Hmap that accurately describes the evolution of surface waterlogging. This is further mapped to the road level to form a set of flooded road sections Aset that includes road signs, waterlogging levels, and corresponding time periods. Unlike existing methods that can only issue fuzzy warnings based on single-point water level monitoring, this system can directly convert internal failure information of the pipeline network into waterlogging warning results for specific roads, thereby achieving an intuitive transfer of risks from underground pipeline networks to surface roads. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic block diagram of an intelligent early warning system for water conservancy systems based on digital twin technology in the present invention. DETAILED DESCRIPTION

[0019] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the present application.

[0020] Embodiment 1

[0021] The present application provides a water conservancy system intelligent early warning system based on digital twin technology, please refer to Figure 1 , comprising a data acquisition and processing module, a pipe network terrain twin construction module, a load bearing evaluation module and a water accumulation distribution deduction and early warning module. The data acquisition and processing module is connected with the city meteorological station and the rain monitoring equipment to form a unified standardized time series data set Dset. The pipe network terrain twin construction module establishes a virtual model corresponding to the real city drainage system in a virtual environment based on the standardized time series data set Dset, encapsulates the spatial coupling relationship between the road and the pipe network in the virtual system, and obtains a pipe network terrain twin model Twin. The load bearing evaluation module calls the standardized time series data set Dset and the pipe network terrain twin model Twin, calculates the inflow demand load Qdem and the outflow capacity Qsup, and aggregates to form a bottleneck set Bset. The water accumulation distribution deduction and early warning module takes the bottleneck set Bset as the overflow starting point, performs water flow diffusion and confluence deduction under the framework of the pipe network terrain twin model Twin, gradually calculates the accumulation process of surface water on the road unit, generates a water accumulation depth grid Hmap, and maps to generate a water accumulation road section set Aset for early warning.

[0022] In this embodiment, by constructing the pipe network terrain twin model Twin under the unified support of the standardized time series dataset Dset and running it in real time, the waterlogging prediction is no longer limited to the traditional single-point waterlogging monitoring, but can combine the dynamic comparison of the inflow demand load Qdem and the outflow capacity Qsup, identify the most vulnerable areas in advance and generate the bottleneck set Bset, effectively solving the problem of insufficient coverage of potential waterlogging points in the prior art. At the same time, the system further takes the bottleneck set Bset as the overflow starting point, calculates the waterlogging depth grid Hmap and maps the waterlogging section set Aset, so as to be able to clearly point out the waterlogging position, waterlogging depth level and duration of specific road sections when a rainstorm occurs, and realize the full-chain coverage from pipe network carrying capacity evaluation to road risk warning. Thus, not only does it overcome the limitation of the prior art that "only waterlogging that has occurred can be monitored", but it can also give early warning information that has direct guiding significance for road traffic and emergency drainage before waterlogging occurs, for example, before waterlogging occurs on a city trunk road, the risk level can be released through the waterlogging section set Aset, providing a basis for the traffic management department to implement control and drainage scheduling in advance, thereby realizing proactive prediction and precise intervention of urban waterlogging risk.

[0023] Embodiment 2

[0024] Specifically, the data acquisition and processing module includes a multi-source hydrological geographic data access unit and a time series data standardization processing unit. The multi-source hydrological geographic data access unit accesses different sources of data sources to form an original dataset. Specifically, it includes: collecting rainfall intensity sequence Rint and rainfall distribution grid Rmap in real time from city meteorological stations and rainfall monitoring equipment; The rainfall intensity sequence Rint is the rainfall sequence in a unit time, usually measured in millimeters / minute or millimeters / hour; The rainfall distribution grid Rmap represents two-dimensional grid data of rainfall at different spatial points in the city area, which is specifically obtained through meteorological radar inversion and rainfall station interpolation analysis; The terrain elevation data Gdem and the road low-lying distribution data Dlow are called from city surveying and mapping data; The terrain elevation data Gdem represents the elevation grid data of the city area, which is specifically obtained by extracting city surveying and mapping departments, remote sensing image data and unmanned aerial vehicle surveying and mapping results; The road low-lying distribution data Dlow represents the road low-lying points and waterlogging-prone area set extracted from the terrain elevation data Gdem, which is specifically obtained by GIS terrain analysis and DEM (Digital Elevation Model) extraction; accessing a sewer network topology data Tnet, a pipe diameter parameter set Ddia, a slope parameter set Sgra, a roughness parameter set Nman and an inlet capacity Cinl from a municipal drainage department; The sewer network topology data Tnet represents the spatial connection relationship data of pipe sections, nodes and inspection wells in the urban drainage system, which is obtained through municipal drainage design drawings and underground pipe network survey; The pipe diameter parameter set Ddia represents the set of inner diameters of each pipe section in the urban drainage system, which is specifically obtained through urban drainage system design drawings, completion materials and pipe detection robots; The slope parameter set Sgra represents the ratio of the longitudinal elevation difference to the length of each pipe section in the urban drainage system, which is specifically obtained through sewer network mapping and urban drainage system completion materials extraction; The roughness parameter set Nman represents the pipe wall resistance coefficient, reflecting the degree of water flow resistance, which is specifically calibrated by the design specifications of each pipe section in the urban drainage system, including different materials such as concrete, PVC and steel pipes; The inlet capacity Cinl refers to the maximum flow that a rainwater inlet and an inspection well can collect into the pipe network per unit time, which is theoretically limited by the diameter of the connected pipe section (i.e., the pipe diameter parameter set Ddia), but in actual operation, Cinl is also affected by factors such as the opening area of the well mouth, the grid accumulation, the road slope and the water depth at the well mouth, so the actual value of Cinl is usually smaller than the water carrying capacity corresponding to Ddia; At the same time, the external river water level sequence Wext is imported from the monitoring station of the adjacent river, which specifically represents the hourly water level observation sequence of the adjacent river or the drainage river section.

[0025] The time series data standardization processing unit, after receiving the original data set, performs time series alignment, spatial unification and missing correction preprocessing on various data to ensure data integrity, calculability and consistency across modules; The preprocessing specifically includes: By using the time series resampling method, the uniform time step processing of different source data is realized to ensure that each monitoring data can be directly compared and calculated under the same time scale, and the hourly alignment of the rainfall intensity sequence Rint, the rainfall distribution grid Rmap and the external river water level sequence Wext is performed to make the monitoring frequency and the calculation step consistent; Then, by using the geographic information system coordinate projection and spatial overlay method, the terrain and pipe network data of different sources are unified to the same coordinate system and grid resolution to ensure the spatial consistency in twin modeling, and the terrain elevation data Gdem, the road low-lying distribution data Dlow and the sewer network topology data Tnet are unified to the same coordinate system, so that the twin modeling can superimpose spatial information; At the same time, the time series interpolation method and the parameter inversion correction method are used to complete and correct the missing data and abnormal values, and the pipe diameter parameter set Ddia, the slope parameter set Sgra, the roughness parameter set Nman and the inlet capacity Cinl are supplemented and corrected to avoid the influence of single point observation deviation on the overall calculation. After the pretreatment is completed, the original data set is normalized using the minimum-maximum normalization method, different sources and different physical dimensions of data are converted into comparable dimensionless sequences, the standardized time series data set Dset is obtained, and it is used as the only result of cross-module interaction for the pipe network terrain twin construction module and the load bearing evaluation module, so that the simplicity, traceability and stability of the whole system in the calculation process are ensured.

[0026] In this embodiment, the originally dispersed rainfall intensity sequence Rint, rainfall distribution grid Rmap, terrain elevation data Gdem, road low-lying distribution data Dlow, drainage pipe network topology data Tnet, pipe diameter parameter set Ddia, slope parameter set Sgra, roughness parameter set Nman, inlet capacity Cinl and river water level sequence Wext and other multi-source data are unified into standardized time series data set Dset, so that the problems of inconsistent monitoring frequency, different coordinate systems, parameter missing and error are effectively eliminated. In the rainstorm weather, the traditional monitoring system often causes the rain monitoring data to be minute level and the river water level data to be hour level, which leads to the difficulty in direct connection between pipe network calculation and river dispatching. However, through the time series resampling and minimum-maximum normalization method, the data with different time steps and different dimensions have comparability in the same calculation framework, so that the stability of the twin calculation and the seamless fusion of cross-department data are ensured. Therefore, the municipal management department can directly carry out waterlogging simulation and drainage dispatching under the support of the unified standardized time series data set Dset, avoid the delay or error caused by the data format and unit difference in the past, and quickly compare the waterlogging risk of the low-lying point in the city and the change trend of the river water level in the early stage of the rainstorm, so as to provide real-time executable data basis for cross-department joint drainage.

[0027] Embodiment 3

[0028] Specifically, the pipe network terrain twin construction module comprises a space coupling modeling unit and a dynamic twin generation unit. After receiving the standardized time series data set Dset, the space coupling modeling unit extracts the drainage pipe network topology data Tnet and the terrain elevation data Gdem, reconstructs the drainage pipe network space skeleton corresponding to the reality in the virtual environment by using the three-dimensional geographic information modeling method, and establishes a vertical mapping relationship between the ground elevation of the pipe network node and the grid elevation point in the terrain elevation data Gdem through the space registration process, so that the pipe network node can form consistent geometric association with the road surface elevation. In the process of three-dimensional modeling based on the drainage pipe network topology data Tnet, the inspection wells, gullies and catchment wells of the pipe network are abstracted as discrete pipe network node objects Nset, and a pipe network node object Nset layer is formed inside; based on the pipe connection relationship, all pipe sections are abstracted as pipe network pipe section objects Eset, and a pipe section layer is formed in the form of a set inside the drainage pipe network spatial skeleton, so as to establish a double-layer structure of nodes and pipe sections of the drainage system in the twin body; In this process, the road low-lying distribution data Dlow is combined as an additional attribute of the road unit and written into the model, so that the road terrain features can form a calculable spatial coupling with the overflow points of the pipe network in the virtual environment, and a virtual hydraulic terrain model is obtained, which provides the basis for the interaction between the terrain and the pipe network for dynamic twin calculation; It should be noted that the pipe network node object Nset in the model is used as an inflow port or an exchange port in space to receive the inflow demand load calculated by the catchment unit Hset, and to transfer water at the connection with the pipe section; the pipe section layer is used to represent the hydraulic bearing unit of each pipe in the drainage pipe network, each pipe network pipe section object Eset is connected with the pipe network node objects Nset at both ends and inherits the geometric parameters (including the pipe diameter parameter set Ddia, the slope parameter set Sgra and the roughness parameter set Nman), while the boundary conditions such as the water inlet capacity Cinl and the external river water level sequence Wext can be added, in the running process, the pipe section layer not only receives the inflow demand load Qdem transferred from the pipe network node object Nset layer, but also stores the outflow capacity Qsup obtained by hydraulic calculation, so as to become a direct calculation layer for bearing capacity determination and bottleneck identification.

[0029] Based on the virtual hydraulic terrain model generated by the dynamic twin generation unit, the rainfall intensity sequence Rint and the rainfall distribution grid Rmap are called as dynamic boundary conditions input, and the water dynamics numerical simulation method is used to calculate the water level, flow velocity and flow variation of the pipe network nodes and road grid points at each time, so that the static virtual hydraulic terrain model obtains the dynamic running characteristics evolving with time; The water flow exchange results generated in the simulation process are combined with the road low-lying distribution data Dlow to make the virtual hydraulic terrain model quantify the accumulation and dissipation process of overflow water in the low-lying area, and then reflect the bidirectional hydraulic action relationship between the ground and the pipe network, and then receive real-time data streams from field sensors and monitoring equipment, and dynamically correct the simulation results of the virtual hydraulic terrain model through the model state assimilation method, to realize the state synchronization between the virtual environment and the real system; and then obtain the virtual hydraulic terrain model with dynamic calculation ability and real-time correction mechanism; After the dynamic driving, overflow evolution and state correction are completed, the virtual hydraulic terrain model is instantiated as a sustainable running digital twin to form a pipe network terrain twin model Twin. Through this process, the pipe network terrain twin model Twin has the characteristics of "static spatial relationship + dynamic hydraulic driving + real-time state synchronization", and realizes the gradual generation process from a static virtual skeleton to a dynamic running twin, providing a calculable and traceable running environment for the subsequent load bearing evaluation module and waterlogging evolution and early warning module. It should be clear that: Virtual hydraulic terrain model: static level, only used to describe the spatial coupling relationship between roads and pipe networks, is a skeleton framework in a digital environment; Pipe network terrain twin model Twin: a running model generated by superimposing dynamic simulation mechanism, monitoring data assimilation mechanism and running instantiation mechanism on the basis of virtual hydraulic-terrain model, which can evolve in real time with rainfall input and map the running state of the real drainage system.

[0030] In this embodiment, under the support of the standardized time series data set Dset, the drainage pipe network topology data Tnet and the terrain elevation data Gdem are unified into a three-dimensional virtual environment, a double-layer spatial skeleton composed of pipe network node object Nset layer and pipe segment layer is established, and further driven by rainfall intensity sequence Rint and rainfall distribution grid Rmap, the virtual hydraulic terrain model has dynamic calculation ability, and combined with the model state assimilation method to access real-time monitoring data, the pipe network terrain twin model Twin which can map the real running state is finally generated. This process not only guarantees the geometric coupling relationship between the drainage pipe network and the road terrain, but also makes the hydraulic interaction between the pipe network and the road complete and quantifiable, thereby solving the common problem of "static modeling results cannot be dynamically tracked" in existing drainage simulation. In actual rainstorm process, when a certain rainwater inlet in the city is limited by the inlet capacity Cinl and causes local waterlogging, the pipe network terrain twin model Twin of the system can reflect the dynamic changes of the pipe segment water carrying capacity and the road low point catchment in real time, and then help the management department to identify the risk position before the waterlogging spreads, for example, when waterlogging appears in the dense roads of the business district, deploy the waterlogging vehicles and temporary pumping stations in advance, and realize the practical effect of changing from post-incident emergency to pre-incident intervention.

[0031] Embodiment 4

[0032] Specifically, the load bearing evaluation module includes an inflow demand calculation unit and a bearing determination unit; The inflow demand calculation unit calls the standardized time series data set Dset and the pipe network terrain twin model Twin, and extracts the rainfall intensity sequence Rint, the rainfall distribution grid Rmap and the terrain elevation data Gdem therefrom; Firstly, the entire target early warning area is divided into multiple catchment units Hset based on terrain elevation data Gdem using catchment partitioning method, each catchment unit corresponds to an inflow port or road catchment in the pipe network system, and is bound to the corresponding node in the pipe network terrain twin model Twin through spatial mapping relationship; Subsequently, combined with the rainfall intensity sequence Rint and the rainfall distribution grid Rmap, the runoff of multiple catchment units Hset is calculated by hydrological runoff calculation method, and the calculation results are assigned to the corresponding pipe network node object Nset to form dynamic attributes; All pipe network node objects Nset jointly constitute inflow demand load Qdem in the pipe network terrain twin model Twin, which is bound to the pipe network node object Nset layer in the form of time sequence, and is compared with the outflow capacity node by node in the subsequent bearing determination.

[0033] The bearing determination unit calls the standardized time series data set Dset and the pipe network terrain twin model Twin, takes the pipe diameter parameter set Ddia, the slope parameter set Sgra, the roughness parameter set Nman, the inlet capacity Cinl and the river water level sequence Wext as input, and uses the Manning hydraulic calculation method to calculate the limit water carrying capacity of each pipe section at each time, forming the outflow capacity Qsup bound to the pipe section layer; At the same time, the inflow demand load Qdem bound to the pipe network node object Nset layer is automatically transmitted to the adjacent pipe section according to the topological relationship and the conservation relationship, obtaining the demand flow Qreq bound to the pipe section layer, so that the demand and capacity at each time step can be directly compared at the same spatial level; Subsequently, the ratio of the outflow capacity Qsup and the demand flow Qreq is calculated, defined as the bearing ratio Rcap, and then the bearing ratio Rcap is used to judge the overload and the marking of potential overflow points to generate the bottleneck set Bset.

[0034] The bottleneck set Bset is generated by the following judgment method: When the bearing ratio Rcap<1, the corresponding pipe section is in an overload state, and at the same time, the pipe network node object Nset layer position associated with the two ends of the pipe section is marked as a potential overflow point; then all the marked overload pipe sections and potential overflow nodes are aggregated in the entire time sequence dimension to generate the bottleneck set Bset; When the bearing ratio Rcap≥1, the water carrying capacity of the corresponding pipe section in the time period meets the demand, and no overload or potential overflow point marking is performed.

[0035] In this embodiment, the rainfall intensity sequence Rint, the rainfall distribution grid Rmap, and the terrain elevation data Gdem are converted into inflow demand load Qdem bound to the pipe network node object Nset layer, and the demand flow Qreq is transmitted under the topological relationship to form the demand flow Qreq. Then, the outflow capacity Qsup calculated based on the pipe diameter parameter set Ddia, the slope parameter set Sgra, the roughness parameter set Nman, the inlet capacity Cinl, and the external river water level sequence Wext is compared segment by segment at the pipe segment layer to generate the bottleneck set Bset containing overload points and potential overflow points. This mechanism enables the failure risk of the drainage system to be located to specific pipe segments and their associated pipe network nodes before the water flow overflows the ground, thereby avoiding the lagging situation in traditional drainage management that "problems are discovered only after obvious water accumulation appears on the ground". In real-world applications, for example, when an old drainage pipe network in a certain area cannot withstand stormwater due to the small pipe diameter parameter set Ddia, the system can directly mark the overload of the pipe segment and generate the bottleneck set Bset through the determination of the bearing ratio Rcap when the water level is still below the wellhead, enabling the municipal department to deploy pumping equipment or open a diversion measure in advance, thereby significantly reducing the risk of forced closure of the entire road due to sudden overflow of the pipe segment.

[0036] Embodiment 5

[0037] Specifically, the water accumulation distribution deduction and early warning module includes a water accumulation deduction unit and an early warning generation unit. The water accumulation deduction unit takes the overload and overflow nodes identified in the bottleneck set Bset as the starting point, calls the road low-lying distribution data Dlow in the dynamic running environment of the pipe network terrain twin model Twin, and uses a surface water flow diffusion calculation method to deduce the migration, accumulation, and dissipation process of the overflow water on the road unit at each time step. During the deduction process, the channel and retention position of the water flow are determined according to the road low-lying distribution data Dlow, so as to dynamically calculate the spatial distribution of the surface water on the road surface and obtain the water accumulation depth grid Hmap, which records the water accumulation depth value at different positions at each time step and is the direct input for subsequent risk early warning.

[0038] The early warning generation unit maps and matches the grid data with the road space unit after receiving the water accumulation depth grid Hmap, converts different water accumulation depth values into corresponding water accumulation grades based on the water accumulation depth of each road unit, and integrates the water accumulation information in the time sequence dimension. The risk grade division method sets multiple threshold values according to the influence degree on road traffic and pedestrian safety based on the water accumulation depth value in the water accumulation depth grid Hmap. When the water depth is less than or equal to 0.10 m, the water is classified as low-grade water, converted to water grade I, indicating that vehicles and pedestrians can still pass, and only a caution is given; When the water depth is greater than 0.30 m, the water is classified as high-grade water, converted to water grade III, indicating that most vehicles cannot pass, and traffic control needs to be implemented; When the water depth is greater than 0.30 m, the water is classified as high-grade water, converted to water grade III, indicating that most vehicles cannot pass, and traffic control needs to be implemented; When the water depth is greater than 0.50 m, the water is classified as severe water, converted to water grade IV, indicating that the road is completely disabled, and needs to be closed urgently and emergency drainage is carried out; A waterlogged road section set Aset containing road signs, water grades and corresponding time periods is formed, which identifies the affected road sections and the severity of waterlogging, and is used for the communication content of the water conservancy system to issue internal flooding risk warnings to the outside.

[0039] In this embodiment, in the pipe network terrain twin model Twin, the overloaded or overflow node marked by the bottleneck set Bset is taken as the starting point, and the migration and accumulation process of the overflow water on the road unit is deduced in combination with the road low-lying distribution data Dlow, to generate a water depth grid Hmap accurately describing the evolution of surface water, and further map it to the road level, to form a waterlogged road section set Aset containing road signs, water grades and corresponding time periods. Unlike the existing method of issuing a vague warning based only on single-point water level monitoring, the system can directly convert the internal failure information of the pipe network into waterlogging warning results of specific roads, thereby realizing the intuitive transmission from the underground pipe network risk to the surface road risk. In real-world applications, when heavy rainfall causes the water in the low-lying sections of the urban trunk road to gradually deepen, the system not only gives a fine spatial distribution through the water depth grid Hmap, but also clearly labels the waterlogging grade and possible time period of the road in the waterlogged road section set Aset, so that the traffic management department can issue detour instructions or implement temporary closures in advance before the road is severely flooded, avoiding the situation of vehicles being stranded and traffic being paralyzed.

[0040] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent early warning system for water conservancy systems based on digital twin technology, characterized by: It includes data acquisition and processing module, pipeline network terrain twin construction module, load bearing assessment module and water accumulation distribution deduction and early warning module; The data acquisition and processing module forms a unified standardized time series data set Dset by connecting to urban meteorological stations and rainfall monitoring equipment; The pipeline network terrain twin construction module establishes a virtual model corresponding to the real urban drainage system in a virtual environment based on the standardized time series dataset Dset, encapsulates the spatial coupling relationship between roads and pipeline networks in the virtual system, and obtains the pipeline network terrain twin model Twin; The load carrying assessment module calls the standardized time series data set Dset and the pipeline network terrain twin model Twin to calculate the inflow demand load Qdem and outflow capacity Qsup, and aggregates them to form a bottleneck set Bset; The waterlogging distribution deduction and warning module uses the bottleneck set Bset as the overflow starting point, and performs water flow diffusion and confluence deduction under the framework of the pipeline terrain twin model Twin, gradually calculates the accumulation process of surface water on the road unit, generates the waterlogging depth grid Hmap, and maps the generated waterlogged road section set Aset for early warning.

2. The intelligent early warning system for water conservancy systems based on digital twin technology according to claim 1 is characterized by: The data acquisition and processing module includes a multi-source hydrological and geographic data access unit and a time series data standardization processing unit; The multi-source hydrological and geographic data access unit forms an original data set by connecting to data sources from different sources; Specifically, it includes: collecting rainfall intensity series Rint and rainfall distribution grid Rmap in real time from urban meteorological stations and rainfall monitoring equipment; The rainfall intensity sequence Rint is a sequence of rainfall amounts per unit time; The rainfall distribution grid Rmap represents two-dimensional grid data of rainfall at different spatial grid points within the urban area; Call terrain elevation data Gdem and road low-lying distribution data Dlow from urban surveying and mapping data; The terrain elevation data Gdem represents elevation grid data of the urban area; The road low-lying distribution data Dlow represents a set of road low-lying points and waterlogging-prone areas extracted from the terrain elevation data Gdem; Access the drainage network topology data Tnet, pipe diameter parameter set Ddia, slope parameter set Sgra, roughness parameter set Nman and inlet capacity Cinl from the municipal drainage department; The drainage network topology data Tnet represents the spatial connection relationship data of pipe sections, nodes, and inspection wells in the urban drainage system; The pipe diameter parameter set Ddia represents the set of inner diameter dimensions of each pipe section in the urban drainage system; The slope parameter set Sgra represents the ratio of the longitudinal elevation difference to the length of each pipe section in the urban drainage system; The roughness parameter set Nman represents the pipe inner wall resistance coefficient, reflecting the degree of friction resistance of water flow; The water inlet capacity Cinl refers to the maximum flow that can be collected into the pipe network by the rainwater inlet and the inspection well in unit time; At the same time, the external river water level sequence Wext is imported in combination with the monitoring stations of the adjacent river.

3. The intelligent early warning system for water conservancy systems based on digital twin technology according to claim 2 is characterized by: After receiving the original data set, the time series data standardization processing unit performs preprocessing of time series alignment, spatial unification and missing correction on various types of data; Preprocessing specifically includes: The rainfall intensity series Rint, rainfall distribution grid Rmap and external river water level series Wext are aligned hour by hour using the time series resampling method to make the monitoring frequency consistent with the calculation step size. Then, the terrain elevation data Gdem, road low-lying distribution data Dlow, and drainage network topology data Tnet are unified into the same coordinate system using the geographic information system coordinate projection and spatial overlay method, so that the twin modeling can superimpose spatial information. At the same time, the time series interpolation method and parameter inversion correction method are used to perform missing interpolation and error correction on the pipe diameter parameter set Ddia, slope parameter set Sgra, roughness parameter set Nman and inlet capacity Cinl. After the preprocessing is completed, the minimum-maximum normalization method is used to normalize the original data set, converting data from different sources and different physical dimensions into comparable dimensionless sequences to obtain the standardized time series data set Dset.

4. The intelligent early warning system for water conservancy systems based on digital twin technology according to claim 3 is characterized by: The pipeline network terrain twin construction module includes a spatial coupling modeling unit and a dynamic twin generation unit; After receiving the standardized time series data set Dset, the spatial coupling modeling unit extracts the drainage network topology data Tnet and the terrain elevation data Gdem, and uses a three-dimensional geographic information modeling method to reconstruct the drainage network spatial skeleton corresponding to the reality in a virtual environment. Through a spatial registration process, the ground elevation of the network nodes is vertically mapped to the grid elevation points in the terrain elevation data Gdem, so that the network nodes can form a consistent geometric association with the road surface elevation. When performing 3D modeling based on the drainage network topology data Tnet, the inspection wells, rainwater inlets, and water collection wells of the network are abstracted into discrete network node objects Nset, and a network node object Nset layer is constructed internally. Based on the pipeline connectivity, all pipe segments are abstracted into network segment objects Eset, and a segment layer is constructed in the form of a collection within the drainage network spatial skeleton. In this process, the road low-lying distribution data Dlow is combined and written into the model as an additional attribute of the road unit, so that the road terrain characteristics can form a computable spatial coupling with the pipe network overflow points in the virtual environment to obtain a virtual hydraulic terrain model.

5. The intelligent early warning system for water conservancy systems based on digital twin technology according to claim 4 is characterized by: Based on the virtual hydraulic terrain model generated by the dynamic twin generation unit, the rainfall intensity sequence Rint and the rainfall distribution grid Rmap are called as dynamic boundary condition inputs, and the water level, flow velocity and flow rate changes of the pipe network nodes and road grids are calculated hourly using the hydrodynamic numerical simulation method, so that the static virtual hydraulic terrain model obtains dynamic operation characteristics that evolve over time; By combining the water flow exchange results generated during the simulation with the road low-lying distribution data Dlow, the virtual hydraulic terrain model quantifies the accumulation and dissipation process of overflow water in low-lying areas, thereby reflecting the two-way hydraulic interaction relationship between the surface and the pipe network. By receiving real-time data streams from on-site sensors and monitoring equipment, the simulation results of the virtual hydraulic terrain model are dynamically corrected through the model state assimilation method to achieve state synchronization between the virtual environment and the real system. The resulting virtual hydraulic terrain model is endowed with dynamic calculation capabilities and real-time correction mechanisms. After completing dynamic driving, overflow evolution and state correction, the virtual hydraulic terrain model is instantiated into a sustainably operating digital twin, forming a pipeline network terrain twin model Twin.

6. The intelligent early warning system for water conservancy systems based on digital twin technology according to claim 5 is characterized by: The load bearing assessment module includes an inflow demand calculation unit and a load determination unit; The inflow demand calculation unit calls the standardized time series data set Dset and the pipe network terrain twin model Twin, and extracts the rainfall intensity sequence Rint, the rainfall distribution grid Rmap and the terrain elevation data Gdem from them; First, based on the terrain elevation data Gdem, the entire target warning area is decomposed into multiple water catchment units Hset using the water catchment zoning method. Each water catchment unit corresponds to an inlet or road water collection point in the pipe network system and is bound to the corresponding node in the pipe network terrain twin model Twin through spatial mapping. Then, combining the rainfall intensity sequence Rint with the rainfall distribution grid Rmap, the hydrological runoff calculation method is used to calculate the runoff of multiple water catchment units Hset hourly, and the calculation results are assigned to the corresponding pipe network node objects Nset to form dynamic attributes; All the pipe network node objects Nset together constitute the inflow demand load Qdem in the pipe network terrain twin model Twin, and the inflow demand load Qdem is bound to the pipe network node object Nset layer in the form of a time series.

7. The intelligent early warning system for water conservancy systems based on digital twin technology according to claim 6 is characterized by: After calling the standardized time series data set Dset and the pipe network terrain twin model Twin, the load determination unit takes the pipe diameter parameter set Ddia, the slope parameter set Sgra, the roughness parameter set Nman, the inlet capacity Cinl and the external river water level sequence Wext as input, and uses the Manning hydraulic calculation method to calculate the ultimate water delivery capacity of each pipe section at the pipe section layer hour by hour, forming the outflow capacity Qsup bound to the pipe section layer; At the same time, the inflow demand load Qdem bound to the pipe network node object Nset layer is automatically transferred to the adjacent pipe segments according to the topological relationship and conservation relationship, and the demand flow Qreq bound to the pipe segment layer is obtained; Subsequently, the ratio of the flow capacity Qsup to the demand flow Qreq is calculated and defined as the carrying ratio Rcap. The overload and potential overflow points are then marked based on the carrying ratio Rcap to generate the bottleneck set Bset.

8. The intelligent early warning system for water conservancy systems based on digital twin technology according to claim 7 is characterized by: The bottleneck set Bset is generated by the following judgment method: When the carrying ratio Rcap is less than 1, the corresponding pipe network section is in an overloaded state, and the Nset layer positions of the pipe network node objects associated with both ends of the pipe section are marked as potential overflow points; Then, all marked overloaded pipe sections and potential overflow nodes are aggregated in the entire time series dimension to generate the bottleneck set Bset; When the load ratio Rcap ≥ 1, the water delivery capacity of the corresponding pipe section within the time period meets the demand, and no overload or potential overflow point is marked.

9. The intelligent early warning system for water conservancy systems based on digital twin technology according to claim 8 is characterized by: The waterlogging distribution deduction and early warning module includes a waterlogging deduction unit and an early warning generation unit; The water accumulation deduction unit takes the overload and overflow nodes identified in the bottleneck set Bset as the starting point, calls the road low-lying distribution data Dlow in the dynamic operation environment of the pipeline network terrain twin model Twin, and uses the surface water flow diffusion calculation method to deduce the migration, accumulation and dissipation process of overflow water on the road unit hour by hour; During the simulation process, the channels and retention locations of water flow are determined based on the road low-lying distribution data Dlow, so as to dynamically calculate the spatial distribution of surface water on the road surface and obtain the water accumulation depth grid Hmap.

10. The intelligent early warning system for water conservancy systems based on digital twin technology according to claim 9, characterized in that: After receiving the waterlogging depth grid Hmap, the warning generation unit maps the grid data to the road space unit. Based on the waterlogging depth of each road unit, the unit uses a risk level classification method to convert different waterlogging depth values ​​into corresponding waterlogging levels and integrates the waterlogging information in the time series dimension. A set of flooded road sections Aset is formed, which includes road signs, flooding levels and corresponding time periods. The flooded road section set Aset identifies the affected road sections and the severity of flooding, and is used for the communication content of the water conservancy system to issue waterlogging risk warnings to the outside world.

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