Method for identifying unstable state of urban drainage system under composite boundary conditions

Abstract

This invention discloses a method for identifying the instability state of urban drainage systems under complex boundary conditions, comprising: acquiring rainfall sequences, external water levels, and basic data of the drainage system; constructing normalized state parameters characterizing the load level by combining the actual water volume and maximum treatment capacity of functional units; extracting state change quantities and coupling them with the remaining treatment margin to calculate stability parameters characterizing the sensitivity to disturbances; comprehensively considering the external water level backwater effect and the backwater effect of the pipe network topology to perform multi-maintenance positive cascade correction of state parameters on the normalized state parameters; constructing a system-level comprehensive evaluation index based on the above parameters, and determining the instability risk level according to priority. This invention can uniformly quantify the load state of heterogeneous drainage facilities, capture the instability characteristics of the system under high load, and be used to achieve continuous tracking and early warning of urban flooding evolution under complex hydrological conditions.

Description

Technical Field

[0001] This invention relates to the field of urban water and drainage system analysis technology, and in particular to a method for identifying the instability state of urban drainage systems under complex boundary conditions. Background Technology

[0002] In recent years, rapid global warming has led to frequent extreme rainfall events, and the rapid expansion of cities has resulted in an increase in their impermeable surfaces, further exacerbating urban flooding problems. Modern urban drainage processes typically involve a hydrodynamic system comprised of multiple stages, including surface infiltration, pipe network transport, and pumping station discharge. Especially in low-lying cities or those with dense river networks, rising water levels in external rivers increase resistance to drainage, leading to flooding caused by the combined effects of rainfall runoff and high external water levels.

[0003] Currently, the analysis of urban flooding and drainage networks mainly relies on hydrodynamic numerical simulations and drainage capacity design verification. Hydrodynamic simulation methods typically employ one-dimensional pipe networks or one- or two-dimensional coupled models, integrating rainfall runoff over time steps to output the overflow volume at pipe points or the surface inundation depth, thereby evaluating the degree of flooding. Drainage capacity verification methods, based on current standards and specifications, calculate the flow capacity of various pipe sections under full-flow conditions to determine whether they meet the design requirements of the preset return period. Some risk assessment methods also classify the disaster risk under different rainfall scenarios by statistically analyzing historical inundation data.

[0004] However, current methods mostly focus on statistical analysis of flooding results after rainfall ends, or on describing the static hydraulic characteristics of a single time slice. They are difficult to continuously track the dynamic evolution of the system from normal low load to high load congestion or even local failure during the rainfall process. Summary of the Invention

[0005] Purpose of the invention: To provide a method for identifying the instability state of urban drainage systems under complex boundary conditions, in order to solve the above-mentioned problems existing in the prior art.

[0006] Technical solution: A method for identifying the instability state of an urban drainage system under complex boundary conditions, comprising:

[0007] Acquire rainfall sequence data, external water level data, and basic data of the drainage system, including topology and various functional units, for the target area;

[0008] Based on rainfall sequence data and drainage system basic data, the actual water volume of each functional unit is determined, and combined with the maximum processing capacity of each functional unit determined based on drainage system basic data, and based on the actual water volume and maximum processing capacity, normalized state parameters characterizing the current load level are constructed.

[0009] The state change is extracted based on the difference of normalized state parameters between adjacent time steps, and coupled with the remaining processing margin determined based on the normalized state parameters to obtain the stability parameters characterizing the system’s sensitivity to load disturbances.

[0010] Based on external water level data and topological structure characterization, multiple corrections are performed on the normalized state parameters to obtain cascaded corrected state parameters.

[0011] Based on cascaded correction state parameters, state change quantities, and stability parameters, a system-level comprehensive evaluation index is constructed, and the instability risk level of the current drainage system is determined and output according to priority classification rules.

[0012] According to one aspect of this application, the process of constructing normalized state parameters characterizing the current load level includes:

[0013] Based on water regulation behavior, the functional units are divided into infiltration and storage units, transportation and distribution units, and forced discharge units;

[0014] The maximum processing capacity is quantified based on the physical adjustment mechanism corresponding to each type of unit.

[0015] Calculate the ratio of the actual treated water volume to the maximum treated capacity to obtain the normalized state parameters.

[0016] According to one aspect of this application, for a forced discharge unit, the rated discharge flow rate under pre-configured design head conditions is obtained as the maximum processing capacity of the forced discharge unit.

[0017] According to one aspect of this application, the process of obtaining cascaded modified state parameters specifically includes:

[0018] Based on external water level data, the normalized state parameters are scaled to obtain the boundary correction state parameters.

[0019] Based on the connection relationship determined by the topology, the backwater conduction effect generated by the downstream associated functional unit on the upstream functional unit is calculated, and the boundary correction state parameters are superimposed with equivalent loads based on the backwater conduction effect to obtain the cascaded correction state parameters.

[0020] According to one aspect of this application, the process of scaling normalized state parameters based on external water level data to obtain boundary correction state parameters includes:

[0021] Obtain the pre-configured reference water level and the adjustment coefficient used to characterize the amplification intensity of the top support;

[0022] Calculate the relative deviation ratio of external water level data from the reference water level;

[0023] Based on the adjustment coefficient, the relative deviation ratio is weighted to construct an amplified adjustment factor that characterizes the back pressure from the external hydrological environment.

[0024] The normalized state parameters are amplified and corrected using an amplification adjustment factor to obtain the boundary-corrected state parameters.

[0025] According to one aspect of this application, the process of obtaining the cascaded modified state parameters includes:

[0026] Based on the topology, each functional unit is sorted in reverse topology.

[0027] Obtain the cascade activation threshold and the cascade coupling coefficient, which reflects the conduction strength between adjacent functional units;

[0028] Based on the reverse topology sorting, the cascaded correction state parameters of the target functional units are calculated one by one;

[0029] When the cascaded correction state parameters exceed the cascaded activation threshold, the state variables exceeding the threshold are extracted and transformed into new equivalent loads based on the cascaded coupling coefficient; otherwise, the new equivalent loads are set to zero.

[0030] The cascaded corrected state parameters of the target functional unit are obtained by summing the boundary correction state parameters and the newly added equivalent load, and the result does not exceed the full-load state constant 1.

[0031] According to one aspect of this application, the process of obtaining a cascade coupling coefficient reflecting the conduction strength between adjacent functional units includes:

[0032] Based on the basic data of the drainage system, the cross-sectional area of ​​the connection between the target functional unit and the corresponding downstream directly related unit is obtained, as well as the total cross-sectional area of ​​the connection in all outflow directions.

[0033] Calculate the proportion of the area of ​​the connecting cross-section to the total area of ​​the connecting cross-section;

[0034] Obtain the basic coupling strength parameters, correct the ratio, and use the corrected value as the cascade coupling coefficient between the target functional unit and the corresponding downstream directly related unit.

[0035] According to one aspect of this application, the process of constructing a system-level comprehensive evaluation index includes:

[0036] Obtain the catchment area weights that characterize the relative importance of each functional unit;

[0037] Based on the catchment area weight, the cascaded modified state parameters of each functional unit are weighted and summed to obtain the system load index used to characterize the global load level.

[0038] The absolute values ​​of the state changes of each functional unit are extracted and averaged across the system scale to obtain the state fluctuation index, which characterizes the degree of global operational instability.

[0039] The difference between the full-load state constant 1 and the maximum value of the corrected state parameters of each stage is obtained to obtain the stability margin used to characterize the remaining physical space of the system from the failure state.

[0040] The three parameters obtained, along with the stability parameters of each functional unit, are used as system-level comprehensive evaluation indicators.

[0041] According to one aspect of this application, the process of obtaining stability parameters characterizing the sensitivity of a system to load disturbances includes:

[0042] Calculate the difference between the full-load state constant 1 and the normalized state parameters to determine the remaining treatment margin;

[0043] Obtain the smoothing parameters used to prevent numerical overflow;

[0044] Extract the absolute value of the state change;

[0045] The ratio is calculated using the absolute value of the state change as the numerator and the sum of the smoothing parameter and the remaining treatment margin as the denominator, and this ratio is used as the stability parameter.

[0046] Beneficial effects: It can unify and quantify the load status of heterogeneous drainage facilities, capture the instability characteristics of the system under high load, and enable continuous tracking and early warning of urban flooding evolution under complex hydrological conditions. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method for identifying the instability state of an urban drainage system under composite boundary conditions according to the present invention.

[0048] Figure 2 This is a flowchart of the present invention for constructing normalized state parameters that characterize the current load level.

[0049] Figure 3 This is a flowchart of the process for obtaining boundary correction state parameters according to the present invention.

[0050] Figure 4 This is a flowchart of the construction of a system-level comprehensive evaluation index according to the present invention.

[0051] Figure 5 This is a flowchart of the process for obtaining stability parameters according to the present invention. Detailed Implementation

[0052] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0053] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein are implemented in a sequence other than those illustrated or described herein. Furthermore, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to the process, method, product, or apparatus.

[0054] In addition to the issues mentioned in the background technology, current methods often focus on post-event statistics of the results after rainfall ends, or fail to continuously track the pressure evolution trajectory of the drainage network. Furthermore, in the face of complex working conditions where external backwater levels interact with internal pipe network congestion, existing evaluation systems lack unified measurement standards across facility types, making it difficult to keenly capture the critical turning point characteristics of local overload leading to global collapse at the macro-system level.

[0055] To solve these problems, combined with Figures 1 to 5 The present invention will be specifically described through the following embodiments.

[0056] According to one aspect of this application, this embodiment provides a method for identifying the instability state of an urban drainage system under complex boundary conditions, such as... Figure 1 As shown, it specifically includes:

[0057] Step 101: Obtain rainfall sequence data, external water level data, and basic data of the drainage system including topology and multiple functional units for the target area.

[0058] In this embodiment, the rainfall sequence data consists of an array of rainfall amounts collected by meteorological stations within the study area at a preset time step, for example, setting the time step to 1 hour. The external water level data consists of real-time water level monitoring values ​​at urban drainage outlets controlled by river networks or water systems, used to reflect the backwater boundary conditions during the drainage process. The basic data of the drainage system includes topographic elevation, physical parameters of the pipe network, and the topological connections of various hydraulic structures. The functional unit is an abstract representation of different regulatory behaviors during the drainage process, and the topological structure G is a directed graph structure.

[0059] Step 102: Based on this, determine the actual water volume to be processed by each functional unit and its maximum processing capacity, and construct normalized state parameters characterizing the current load level based on the actual water volume to be processed and the maximum processing capacity. In other words, determine the actual water volume to be processed by each functional unit based on rainfall sequence data and drainage system basic data, and construct normalized state parameters characterizing the current load level by combining the maximum processing capacity of each functional unit determined based on drainage system basic data.

[0060] In this embodiment, the actual water volume processed is the dynamic water volume carried by each functional unit under rainfall-driven conditions. The maximum processing capacity is the processing limit of the functional unit under ideal design conditions. By normalizing the actual water volume processed and the maximum processing capacity, the load level of each unit is uniformly reduced to the range of 0 to 1. When the normalized state parameter approaches 1, it indicates that the unit's load is approaching saturation.

[0061] Step 103: Extract the state change based on the difference of normalized state parameters between adjacent time steps, and perform coupled calculation with the remaining processing margin determined based on the normalized state parameters to obtain the stability parameters characterizing the system's sensitivity to load disturbances.

[0062] In this embodiment, the state change is used to characterize the evolution trend of the load with rainfall. The stability parameter is used to measure the system's sensitivity to small disturbances under high load conditions. In the calculation, the state change is used as the numerator, and the sum of the smoothing parameter and the residual handling margin is used as the denominator. Accordingly, a residual handling margin is introduced so that the same load change can produce a larger stability parameter response when the system is near full load. The corresponding coupling formula is:

[0063] Ψ _j =|D _j | / (ε+(1-S _j ));

[0064] Among them, Ψ _j Let D be the stability parameter of the j-th functional unit. _j S is the state change quantity, ε is the smoothing parameter used to prevent the denominator from being zero and to provide numerical stability. _jFor the current normalized state parameters, 1-S _j Characterizes the remaining processing margin.

[0065] In this embodiment, the state change is extracted based on the difference between the normalized state parameter at the current time step and the normalized state parameter at the previous time step. The state change is then coupled with the remaining processing margin determined based on the normalized state parameter to obtain a stability parameter characterizing the system's sensitivity to load disturbances.

[0066] Step 104: Based on the external water level data representation and the topological structure representation, that is, combining the supporting effect of the external water level data representation and the backwater effect of the topological structure representation, perform multiple corrections on the normalized state parameters to obtain cascaded corrected state parameters.

[0067] Specifically, the backwater effect caused by the external water level will amplify and correct the basic load of the functional unit through the adjustment factor. The influence of the internal topology is reflected through the backwater effect, that is, when the downstream unit is overloaded, the pressure it generates is transmitted upstream along the pipeline topology, increasing the equivalent operating load of the upstream unit.

[0068] The revised cascaded state parameters more accurately reflect the pressure-bearing state of the drainage system under complex hydrological conditions. These parameters are equivalent mappings based on physical loads, characterizing the system's pressure-bearing capacity without altering the mass conservation relationships in the original hydrodynamic processes.

[0069] Step 105: Based on the cascaded correction state parameters, state change quantities, and stability parameters, construct a system-level comprehensive evaluation index, and determine and output the current instability risk level of the drainage system according to the priority classification rules.

[0070] Specifically, the system-level comprehensive evaluation indicators include system load index, state fluctuation index, and stability margin. Pre-defined priority grading rules are used, such as a mutually exclusive priority determination mechanism, starting with the highest failure level and screening downwards level by level. The determination results are displayed as a risk zoning map in the geographic information system interface, providing instability warnings for dispatch and management personnel.

[0071] In some alternative implementations, the determination result can also trigger preset emergency response commands, such as starting a drainage pumping station or adjusting the gate of a storage tank.

[0072] In view of the heterogeneous internal structure of the drainage system, based on the above embodiments, this embodiment abstracts various hydraulic facilities into functional units of a unified dimension, and establishes quantitative models of maximum processing capacity for each, specifically including:

[0073] Step 201: Based on water regulation behavior, the functional units are divided into infiltration and storage units, transportation and distribution units, and forced discharge units.

[0074] In this embodiment, infiltration and retention units refer to physical facilities whose main function is to reduce or delay the total amount of surface runoff, that is, to absorb water through soil infiltration or artificial temporary storage, such as permeable paved areas, sunken green spaces, bioretention facilities, and regulating ponds.

[0075] Transport and distribution units refer to facilities that use gravity to transport runoff from the catchment area to the downstream, that is, to realize the spatial transport of water through hydraulic gradient, such as stormwater pipes, open channels and box culverts.

[0076] Forced discharge units refer to facilities that use mechanical lifting to discharge water from the system, that is, to overcome water level differences through external energy input, such as drainage pumping stations.

[0077] The three types of units can be categorized based on their water volume regulation behavior. In some embodiments, when the storage tank is equipped with an overflow outlet, its main body can be classified as an infiltration and retention unit. The overflow capacity of the overflow outlet is then separated and incorporated into a transport and distribution unit.

[0078] Step 201a: Obtain the pre-configured attribute parameters of the infiltration and storage unit. The pre-configured attribute parameters include the stable infiltration rate, the effective infiltration area, and the available storage volume. Based on the available storage volume and the preset calculation time step, calculate the storage equivalent flow rate. Add the product of the stable infiltration rate and the effective infiltration area to the storage equivalent flow rate to obtain the maximum processing capacity of the infiltration and storage unit.

[0079] In this embodiment, the quantification of the maximum processing capacity based on area infiltration and volumetric storage is expressed by the following formula:

[0080] Q _1 max =f _s ×A _inf ×10 －3 +V _st / Δt;

[0081] Among them, Q _1 max f represents the maximum processing capacity of infiltration retention units. _s To stabilize the infiltration rate, A _inf To achieve effective infiltration area, V _st V represents the available storage capacity, Δt is the preset calculation time step, and V _st The / Δt term represents the equivalent flow rate of static capacity distribution within Δt.

[0082] In obtaining the pre-configured attribute parameters, the stable infiltration rate is determined based on the soil hydrological characteristics of the area where the facility is located. The calculation time step is consistent with the time resolution of the external hydrodynamic model. Furthermore, when the preset facility only has infiltration function and not physical storage capacity, V... _st When set to 0, its maximum processing capacity is determined by the infiltration product term.

[0083] Step 201b: Obtain the pre-configured hydraulic parameters of each pipe segment or channel that makes up the conveying and distribution unit; determine the flow capacity of each pipe segment or channel under full flow conditions based on the pre-configured hydraulic parameters; select the minimum flow capacity among the flow capacities of each pipe segment according to the bottleneck principle as the maximum processing capacity of the conveying and distribution unit.

[0084] For transport and distribution units, the Manning formula under full pipe or full channel flow conditions is used to calculate the flow capacity of the pipe section. Pre-configured hydraulic parameters include the Manning roughness coefficient, cross-sectional area, hydraulic radius, and the slope of the pipe or channel bottom. Based on these parameters, the extreme flow values ​​of each individual pipe section are solved.

[0085] Since the transport and distribution unit comprises a topological subnet consisting of multiple pipe segments connected in series, this embodiment uses the bottleneck principle to determine the overall capacity. Specifically, the set of pipe segment flow capacities for each pipe segment within the subnet is extracted, and the minimum value of this set is obtained. The pipe segment with the minimum flow capacity is set as the maximum processing capacity of the transport and distribution unit, thereby reflecting the limiting node effect of the pipeline system under gravity flow transport conditions.

[0086] Step 201c: Obtain the rated drainage flow rate under the pre-configured design head conditions as the maximum processing capacity of the forced discharge unit.

[0087] In this embodiment, the rated parameters calibrated by the pumping station equipment at the factory are obtained, the rated drainage flow rate under the design head condition is extracted, and a value is assigned.

[0088] Optionally, if the forced discharge unit includes multiple pumps operating in parallel, the rated discharge flow rate of each pump is obtained, and scalar summation is performed, with the summation result used as the maximum processing capacity of the forced discharge unit.

[0089] Step 202: Quantify the corresponding maximum processing capacity based on the physical regulation mechanism of each type of unit; calculate the ratio of the actual processed water volume to the maximum processing capacity to obtain the normalized state parameters.

[0090] After quantifying the maximum processing capacity of all functional units, this capacity is converted into parameters that allow for homogeneous evaluation of the system. Specifically, the actual processed water volume output by the hydrodynamic model within the same calculation time step is divided by the maximum processing capacity of the corresponding functional unit. By performing this calculation, the status of all facilities can be uniformly mapped to a range of 0 to 1.

[0091] Unlike existing technologies that use a fixed absolute load as the evaluation benchmark, this embodiment introduces residual processing margin as a driving term in the denominator, thereby enhancing the system's ability to sense load disturbances as the load level increases. Specifically, this includes:

[0092] Step 301: Determine the remaining processing margin by subtracting the full-load state constant 1 from the normalized state parameters.

[0093] In this embodiment, the full-load state constant 1 represents the theoretical limit state when the functional unit reaches its maximum physical processing capacity. The normalized state parameter characterizes the proportion of the processing load currently occupied by the functional unit, taking a value within a closed interval of 0 to 1. The remaining processing margin is obtained by calculating the difference between the constant 1 and the current normalized state parameter. This remaining processing margin characterizes the remaining available physical space or processing capacity of the functional unit before overflow or hydraulic failure occurs. That is, when the drainage system is in a high-load operation phase, the normalized state parameter approaches 1, and the remaining processing margin approaches 0.

[0094] Step 302: Obtain the pre-configured smoothing parameters to prevent numerical overflow.

[0095] The smoothing parameter is a very small constant value pre-stored in the system configuration library. In stability parameter division operations, when rainfall reaches extreme conditions, the functional unit may be fully loaded, resulting in zero remaining processing margin. Therefore, a smoothing parameter is introduced to ensure the denominator is always greater than zero, preventing division-by-zero errors and program crashes when the computer kernel executes the division instruction. In other words, the smoothing parameter ensures the denominator is always positive, and its magnitude needs to be much smaller than the variation of the normalized state parameter under typical operating conditions, ensuring that this value does not interfere with the physical judgment. For example, setting the smoothing parameter to 0.01 is much smaller than the typical order of magnitude of the daily fluctuations of the normalized state parameter.

[0096] Step 303: Extract the absolute value of the state change.

[0097] Specifically, the state change of the j-th functional unit within the k-th calculation time step is calculated as follows:

[0098] D _j (k)=S _j (k)-S _j (k-1);

[0099] Where S_j (k) represents the normalized state parameter at step k, S _j (k-1) represents the normalized state parameter from the previous step.

[0100] The state change is the difference in normalized state parameters of a preset functional unit within two adjacent time steps. The positive amplitude of this difference is extracted using an absolute value function to avoid interference from sign differences in the monotonically increasing or decreasing direction of the state parameters. This absolute amplitude characterizes the incremental impact intensity of surface runoff on groundwater infrastructure within a unit time window.

[0101] Step 304: Calculate the ratio using the absolute value of the state change as the numerator and the sum of the smoothing parameter and the remaining treatment margin as the denominator, and use the ratio as the stability parameter.

[0102] Specifically, the specific computational logic executed by the microprocessor is as follows:

[0103] Ψ _j =|D _j | / (ε+(1-S _j ));

[0104] Among them, Ψ _j Let |D be the stability parameter of the j-th functional unit. _j | represents the absolute value of the state change, ε is the pre-configured smoothing parameter, and S _j For normalized state parameters, 1-S _j The overall characterization of the remaining processing margin.

[0105] For example, in the early stages of rainfall, a drainage pipe unit only carries low-intensity surface runoff, and the normalized state parameters are in a low range. Even if the rainfall briefly intensifies within a single time step, the response value of the stability parameters remains relatively limited, and the system has strong resistance to disturbances. However, when continuous heavy rainfall increases the pipe load or approaches the full-flow critical state, the stability parameter response caused by the same amount of rainfall increment will be amplified, far exceeding the response value in the low-load stage. This embodiment can achieve deep coupling between state fluctuation and the system's ultimate carrying capacity.

[0106] In some optional implementations, for preset operating conditions where the drainage system is in an extremely low load range, the system invokes an alternative monitoring logic oriented towards initial response in parallel. Specifically, the absolute value of the state change is used as the numerator, and the sum of the smoothing parameter and the normalized state parameter is used as the denominator to calculate a ratio. This alternative ratio measures the marginal growth rate of the system's current load. When this marginal growth rate exceeds a preset start-up threshold, the system kernel generates an early wake-up flag for that functional unit. This alternative monitoring mechanism, together with the remaining processing margin determination mechanism, can cooperate in different load ranges to achieve continuous feature tracking of the entire cycle of urban flooding evolution.

[0107] This embodiment calculates the normalized state parameters of the functional units and introduces residual processing margin to quantify the system's sensitivity to minor state disturbances under high-load critical conditions, thereby identifying early-stage waterlogging instability. The stability parameters are applicable to instability sensitivity identification during medium-to-high load operation. For operating conditions in lower load ranges, the initial disturbance identification logic can be switched to achieve continuous monitoring at different operating stages.

[0108] After obtaining the normalized state parameters of the unit's foundation, the combined amplification effect of external high water level backwater and downstream pipe network congestion on the equivalent load of the current functional unit is quantified. In one possible implementation, the following steps are included:

[0109] Step 401: Obtain the pre-configured reference water level and the adjustment coefficient used to characterize the amplification intensity of the top backwater; calculate the relative deviation ratio of the external water level data from the reference water level; weight the relative deviation ratio based on the adjustment coefficient to construct an amplification adjustment factor characterizing the top backwater pressure of the external hydrological environment; use the amplification adjustment factor to amplify and correct the normalized state parameters to obtain the boundary correction state parameters.

[0110] In this embodiment, the external boundary correction is applicable to urban drainage systems affected by external rivers, lakes, reservoirs or tidal boundaries. For areas where there is no external boundary support, the boundary influence coefficient can be set to zero.

[0111] External water level data is obtained from sensing devices at the system boundary, with the reference water level being a preset constant water level value without top support. By constructing an amplification adjustment factor, the hydraulic resistance exerted by the external hydrological environment is converted into an increase in the internal load of the system; the corresponding calculation formula is as follows:

[0112] S _j adj =S _j ×(1+α×(H _ext -H _ref ) / H _ref );

[0113] Among them, S _j adj S is the boundary correction state parameter for the j-th functional unit. _j Here, H represents the normalized state parameters, α is the pre-configured adjustment coefficient, and H... _ext For external water level data, H _ref For reference water level, item (H) _ext -H _ref ) / H _ref Represents the relative deviation ratio, 1+α×(H) _ext -H _ref ) / H_ref The overall representation is the amplification adjustment factor.

[0114] In practical applications, the backwater boundary conditions characterized by external water level data typically occur at H _ext ≥H _ref In this case, the amplification adjustment factor is always greater than 1. To ensure numerical stability, after performing the amplification correction, the result is maximized so that S... _j adj Not less than 0, that is:

[0115] S _j adj =max(0,S _j ×(1+α×(H _ext -H _ref ) / H _ref )).

[0116] The adjustment coefficient α reflects the hydraulic coupling strength between the external water body and the drainage outlet. It can be determined by regression analysis of the measured load data of the drainage facilities during the period when the external water level exceeds the reference water level, based on the historical flood event records of the target area. When historical records are lacking, those skilled in the art can make adaptive adjustments based on the degree of hydraulic connectivity between the drainage outlet and the external receiving water body.

[0117] Step 402: Based on the topology, perform a reverse topology sorting of all functional units in the system from downstream to upstream; obtain the pre-configured cascade activation threshold and the cascade coupling coefficient reflecting the conduction strength between adjacent functional units.

[0118] To simulate the backflow effect, the execution order of calculations needs to be determined. Specifically, based on the directed connections within the topology, the end-discharge nodes are identified, and all functional units are traversed in the reverse direction of the water flow to generate a one-dimensional calculation execution sequence. A pre-configured cascade activation threshold is obtained to define the triggering conditions for congestion propagation. That is, during normal system operation, the cascade activation threshold is higher than the peak load level of each functional unit but lower than the full-flow critical state; congestion is only triggered when downstream units experience blockage. For example, when the state parameters of a functional unit are lower than the cascade activation threshold, it is considered normal outflow, and no upstream backflow propagation occurs.

[0119] In this embodiment, historical operational data of the target area can be retrieved and analyzed, and the cascade activation threshold value can be determined based on the state parameter level corresponding to the actual backwater conduction in the pipeline.

[0120] Step 403: Obtain the connection cross-sectional area of ​​the target functional unit flowing to the corresponding downstream directly associated unit recorded in the basic data of the drainage system, as well as the total connection cross-sectional area in all outflow directions of the target functional unit; calculate the ratio of the connection cross-sectional area to the total connection cross-sectional area; obtain the pre-configured basic coupling strength parameter, and use the basic coupling strength parameter to correct the ratio, and use the corrected value as the cascade coupling coefficient between the target functional unit and the corresponding downstream directly associated unit.

[0121] In this embodiment, the basic coupling strength parameter is used to characterize the overall hydraulic connectivity of the pipe network. The cascade coupling coefficient is used to distribute the congestion reaction force of multiple downstream nodes on the same upstream node. Based on the hydraulic cross-sectional flow splitting principle, the ratio of the pipe cross-sectional area of ​​the target unit j flowing to the preset downstream unit i to the sum of the cross-sectional areas of all branches is calculated. Combined with the pre-configured basic coupling strength parameter, the allocation weight is established, and the relevant formula is as follows:

[0122] β _ji =(A _ji / A _total )×β _0 ;

[0123] Where, β _ji A is the cascade coupling coefficient. _ji A represents the cross-sectional area of ​​the connection to the corresponding downstream unit. _total β is the total cross-sectional area of ​​the connection. _0 Based on the basic coupling strength parameter.

[0124] For example, the initial estimated values ​​for the following three types of pipe networks can be adaptively adjusted based on the actual as-built drawings and operation and maintenance records: for standard municipal stormwater pipe networks with regular cross-sectional shapes and no siltation; for old pipe networks or those with local blockages; and for newly built pipe networks with larger diameters and smoother hydraulic gradients.

[0125] Step 404: Calculate the cascaded correction state parameters of the target functional unit one by one according to the reverse topology sorting: When the cascaded correction state parameters of the downstream directly related units of the target functional unit exceed the cascaded activation threshold, extract the state variables that exceed the threshold, and convert the state variables that exceed the threshold upstream into the new equivalent load of the target functional unit based on the cascade coupling coefficient; otherwise, set the new equivalent load to zero.

[0126] The boundary correction state parameters of the target functional unit are summed with the newly added equivalent load, and the cascade correction state parameters of the target functional unit are obtained on the premise that the summation result does not exceed the upper limit of the full load state, i.e., the full load state constant 1.

[0127] Specifically, each unit is processed sequentially according to a predetermined countercurrent order, so that when the current unit is being calculated, the cascaded correction state parameters of all its downstream units have been calculated.

[0128] Accordingly, the overflow difference of downstream units exceeding the cascade activation threshold is extracted, multiplied by the corresponding cascade coupling coefficient, and accumulated to generate a new equivalent load. The corresponding correction formula is as follows:

[0129] S _j cas =min(1,S _j adj +S _add );

[0130] Among them, S _j cas For the cascaded correction of state parameters of the target functional unit, the min function is used to perform the upper limit truncation operation, where the value 1 represents the upper limit value of the full load state, S _j adj To correct the state parameters at the boundary, S _add To add an equivalent load.

[0131] In some alternative implementations, for functional units located at the physical end of the drainage system, since there are no associated facilities defined by the topology downstream, the system directly sets its cascaded corrected state parameter to equal its boundary corrected state parameter, i.e., S. _add It equals 0.

[0132] After obtaining the cascaded correction state parameters and state changes of each functional unit, a comprehensive evaluation index is constructed to elevate it to the global system dimension, and mutually exclusive multi-level risk assessment rules with strict priorities are executed. In one possible implementation, it also includes:

[0133] Step 501: Obtain the pre-configured catchment area weights that characterize the relative importance of each functional unit; calculate the weighted sum of the cascaded modified state parameters of each functional unit based on the catchment area weights to obtain the system load index used to characterize the global load level.

[0134] In this embodiment, the system load index is used to measure the hydraulic pressure state of the entire drainage network. Since different pipe networks or pumping stations serve different areas, directly calculating the state mean is insufficient to reflect the impact of local overload on the overall system. Therefore, a catchment area weight is introduced for weighted calculation.

[0135] Specifically, pre-configured catchment area weights are obtained to characterize the relative importance of each functional unit. This includes retrieving basic data of the drainage system to obtain the total catchment area covered by the drainage system and the local catchment area independently served by each functional unit; calculating the ratio of each local catchment area to the total catchment area; and using this ratio as the catchment area weight of the corresponding functional unit, so that the sum of the catchment area weights of all functional units in the drainage system satisfies the normalization condition.

[0136] After obtaining the normalized weights, the formula for calculating the system load index is:

[0137] SLI=∑(w _j ×S _j cas );

[0138] Where SLI is the system load index, w _j S represents the catchment area weight of the j-th functional unit. _j cas The cascaded correction state parameters for this unit.

[0139] Step 502: Extract the absolute value of the state change of each functional unit and perform cross-system-scale averaging to obtain the state fluctuation index used to characterize the degree of global operational instability.

[0140] In this embodiment, the state fluctuation index is used to characterize the degree of state change of the system within the current time step. To enable horizontal comparison of this index across different drainage systems, the system needs to perform cross-system-scale averaging, and the corresponding calculation logic is as follows:

[0141] SVI=(1 / n)×∑|D _j |;

[0142] Where SVI is the state fluctuation index, n is the total number of functional units in the system, and |D _j | represents the absolute value of the state change of the j-th functional unit.

[0143] Step 503: Based on the difference between the full-load state constant 1 and the maximum value of all cascaded modified state parameters in the system, the stability margin used to characterize the remaining physical space of the system from the failure state is obtained.

[0144] In this embodiment, the stability margin is used to evaluate the system's baseline capability to withstand extreme flooding. The cascaded modified state parameters of all functional units are iterated, and the maximum value is extracted. This maximum value is then subtracted from the full-load state constant 1 to obtain the stability margin, as shown in the following formula:

[0145] SM = 1 - max(S) cas );

[0146] Where SM is the stability margin, max(S) cas This represents the maximum value among all cascaded correction state parameters. When a local node approaches full load, this stability margin will decrease rapidly, thus providing an early warning signal from a global perspective.

[0147] Step 504: The system load index, state fluctuation index, stability margin, and stability parameters of each functional unit are used as system-level comprehensive evaluation indicators.

[0148] Step 505: Obtain the pre-configured multi-level judgment thresholds; compare the cascaded correction state parameters of each functional unit with the pre-configured full load judgment sub-thresholds in the multi-level judgment thresholds, and count the number of functional units exceeding the full load judgment sub-thresholds; according to the priority from high to low, execute the following mutually exclusive judgment rules to determine the instability risk level.

[0149] Read the pre-configured threshold matrix, which includes boundary values ​​for multiple different indicators. To prevent logical conflicts when multiple indicators are used for judgment, a mutually exclusive execution process must be followed, starting from the highest risk level and proceeding downwards.

[0150] Step 506: When the system load index, stability margin, or number of fully loaded functional units is lower than the highest level of failure judgment threshold, the instability risk level is determined to be the failure zone.

[0151] This step is the highest priority judgment. For example, the failure threshold for the system load index is set to 0.90, the failure threshold for the stability margin is set to 0.05, and the full load quantity threshold is set to 2.

[0152] If the current SLI is greater than 0.90; or SM is less than 0.05; or the number of functional units in the system with a state parameter greater than or equal to 0.95 is not less than 2, the system will immediately output an instability risk level of "failure zone" and terminate the judgment of subsequent levels.

[0153] Step 507: If the failure zone conditions are not met, when the system load index or stability margin exceeds the second-highest critical judgment threshold, or when the stability parameter of any functional unit exceeds the stability threshold, the instability risk level is determined to be in the critical zone.

[0154] If the failure zone conditions are not met, a secondary priority judgment is initiated. For example, critical judgment thresholds are configured, such as setting the critical threshold for system load index to 0.70 and the critical threshold for stability margin to 0.20. Correspondingly, a stability parameter is also introduced at this level. If the current SLI is greater than 0.70; or SM is less than 0.20; or at least one functional unit in the system has a stability parameter greater than the stability threshold, a critical zone is output if any of these conditions are met. Through this process, even if the SLI is not high, a warning is still triggered as long as there is a high sensitivity to state changes in a localized area.

[0155] Step 508: If the system load index or stability margin exceeds the intermediate load development judgment threshold without meeting the critical zone conditions, the instability risk level is determined to be in the development zone.

[0156] After excluding the failure zone and critical zone, the system is assessed to determine if it is in the load ramp-up phase. For example, a system load index threshold of 0.40 and a stability margin threshold of 0.50 are set. If the SLI is greater than 0.40 or the SM is less than 0.50, it is determined to be in the development zone.

[0157] Step 509: If none of the above conditions are met, the instability risk level is determined to be in the stable zone.

[0158] When all preceding higher-order logic returns false, the current drainage network is determined to be in a low-risk stable zone, thus completing the judgment loop for the entire evaluation cycle.

[0159] For example, the system is configured to include four catchment area transport pipeline units (U1-U4), one regulating reservoir unit (U5), and one pumping station unit (U6). In the early stages of typhoon landfall (t=1h), the normalized state parameters of each unit are below 0.3, indicating the system is in a stable zone. As rainfall intensity increases, and the water level of the external receiving water body rises above the reference level due to tidal surge, the backwater effect causes the boundary correction state parameters of each unit to rise overall. At t=3h, downstream congestion occurs in the pipeline where U2 is located, and the cascaded correction state parameters exceed the activation threshold. Through topological reverse flow, the equivalent load of U1 increases accordingly, and the comprehensive evaluation index SLI exceeds 0.40, indicating the system enters the development zone. At t=4h, the stability parameters of U2 exceed the pre-calibrated stability threshold, triggering the critical zone determination. The system issues an early warning approximately one time step before actual surface water accumulation, providing a decision-making window for dispatchers to initiate emergency drainage measures.

[0160] Based on the above embodiments, for utilizing a data-driven mechanism to objectively extract judgment thresholds by reverse-calculating the physical processes of historically known flooding events, pre-configured parameters are provided for risk assessment in online systems. One possible implementation includes the following steps:

[0161] Step 601: Obtain the pre-stored sequence of rainfall events that are historically known to have occurred as actual flooding events.

[0162] In this embodiment, the system reads a pre-built historical meteorological and water management operation database. The database stores real meteorological characteristics and disaster records collected in the target area during historical operating cycles. A data query command is executed to extract the data set corresponding to a preset time period for waterlogging records. In terms of time span, the rainfall process sequence covers the entire process from the start of rainfall to the occurrence of surface waterlogging failure, and its data sampling frequency is consistent with the time resolution of the online early warning system.

[0163] Step 602: Based on the rainfall process sequence and basic data of the drainage system, calculate the historical stability parameters of each functional unit on the historical time axis.

[0164] The rainfall process sequence is used as the external driving boundary and input into the simulation computing environment. In offline mode, the system-level state calculus logic is invoked to sequentially execute hydrodynamic simulation, capacity normalization calculation, and sensitivity index verification. Specifically, the normalized state parameters and state changes of each functional unit within each discrete time step are calculated, and the remaining processing margin is calculated in combination with pre-configured smoothing parameters, followed by ratio calculation. Through this reverse solution process, the load evolution trajectory of each node within the drainage network before historical flooding can be reconstructed, generating a set of historical stability parameters in time series format.

[0165] Step 603: Extract the maximum value of the historical stability parameter that occurred within the calculation time step before the actual flooding event, and use it as the pre-calibrated stability threshold.

[0166] After reconstructing the time series of parameters, the system performs a timeline-based location analysis. The physical model for flooding is a hydraulic overflow at a preset node, with the critical abrupt change occurring within the preceding time window of the overflow; that is, the flooding hazard will suddenly appear just before the water overflows. The system locates the recorded moment of the actual flooding event and extracts a data slice corresponding to a calculation time step preceding that moment. Accordingly, it performs a comparison operation on the historical stability parameters of all functional units within this data slice, selecting the parameter with the largest value. This maximum value represents the critical sensitivity level at which the system experiences functional failure in the real physical world. This maximum value is converted into a pre-calibrated stability threshold and written to the configuration library for use in the critical section determination logic during online runtime.

[0167] In some optional implementations, when it is difficult to obtain complete historical flooding event records for the target pipe network area due to sensor deficiencies, the system provides a local parameter configuration interface. In this implementation scenario, the stability threshold can be set by receiving external configuration commands. The basis for limiting this threshold range is that when the stability parameters of the water system fall within this range, its corresponding physical hydraulic state is usually characterized by the pipe network load approaching the full flow limit, and a backflow oscillation response to minor surface inflow disturbances, which conforms to the physical critical instability characteristics of general urban underground drainage systems.

[0168] According to another aspect of this application, this embodiment also proposes a method for identifying the instability state of an urban drainage system under complex boundary conditions. This method abstractly models the urban drainage process, treating the entire drainage system as a dynamic system composed of multiple functional units. Each functional unit corresponds to different types of water regulation behaviors, including infiltration and storage behavior, transport and distribution behavior, and forced discharge behavior.

[0169] Specifically, basic data for the study area are acquired, including rainfall sequence data R(t); topographic elevation data Z(x,y); drainage network structure data G; and external water level data H. _ext .

[0170] A coupled hydrodynamic model is established based on the input data to simulate the water transport process of the system under different rainfall conditions, thereby obtaining the actual processing capacity of each functional unit under different operating conditions. Here, j is the functional unit number, and Q... _j R represents the actual amount of water processed, and R represents the input rainfall.

[0171] After completing the hydrodynamic calculations, a state function is introduced to provide a unified and quantitative description of the system's operating state. Specifically, the actual processing volume of each functional unit is normalized to its maximum processing capacity, and a state function is constructed as follows:

[0172] S _j (k)=Q _j (k) / Q _j max ;

[0173] Among them, Q _j max S represents the maximum processing power of the j-th functional unit under ideal conditions. _j (k) ranges from 0 to 1 and is used to represent the system load level. When S _j (k) When it is small, it indicates that the system is operating under low load; when it is close to 1, it indicates that the system is approaching its processing limit.

[0174] Furthermore, to characterize the dynamic process of system state changes with rainfall, this embodiment introduces a state change quantity to simulate the trend of system state changes with rainfall, namely:

[0175] D _j (k)=S _j (k)-S _j (k-1).

[0176] This change in state can reflect the rate of increase in system load. When its absolute value is large, it indicates that the system is rapidly approaching its operating limit.

[0177] In another embodiment, to further identify the stability of the system, a stability discrimination model is also constructed, with the corresponding stability function being:

[0178] Ψ _j (k)=|D _j (k)| / (S _j (k)+ε);

[0179] Among them, when Ψ _j When (k)≤1, the system is in a stable state; when Ψ _j When (k)>1, the system enters an unstable state. This discrimination method couples state changes with the current load level, making the system more sensitive to changes under high load conditions.

[0180] Stability parameters, as unit-level sensitivity indicators, together with system load index and stability margin, constitute a multi-dimensional comprehensive evaluation framework. A comprehensive grading rule is used to determine the overall system instability risk level. When the stability parameter of any functional unit exceeds a pre-calibrated stability threshold, a critical zone determination is triggered. Compared to traditional methods based on fixed thresholds, this method allows parameters to be calibrated based on historical operating data, model calculation results, or regional hydrodynamic characteristics.

[0181] The discrimination logic is a threshold expression of the stability parameter in a single dimension. In the comprehensive evaluation of the system, the stability parameter serves as one of the reference conditions for determining the critical zone, and together with the system load index and stability margin, it constitutes a multi-dimensional judgment system.

[0182] Furthermore, a state correction mechanism is introduced. By constructing a corrected state function, the influence of external water level on the system's operating state is incorporated into the analysis process. The corresponding formula is as follows:

[0183] S _j corr (k)=S _j (k)×(1+α×(H _ext (k)-H _ref ) / H _ref );

[0184] Among them, H _ext (k) represents the external water level, H _ref The reference water level is a positive reference value and is not zero. It is used to characterize the boundary water level benchmark under normal operating conditions of the system, and α is the adjustment coefficient.

[0185] After completing the state calculations and corrections for each functional unit, a comprehensive system evaluation index is constructed to quantitatively analyze the overall operational status. Among these, the System Load Index (SLI) is used to characterize the overall system load level.

[0186] SLI=Σ _j=1 n w _j ×S _j ;

[0187] Wherein, SLI represents the system load index, which is the weighted sum of the state values ​​of all functional units and is used to reflect the overall load level of the system; n represents the total number of functional units, the value of which can be determined according to the system structure or experience, and satisfies the weight normalization condition.

[0188] The state fluctuation index is used to characterize the instability of a system's operation, and the corresponding formula is:

[0189] SVI=Σ j=1 n |D _j (k)|;

[0190] Wherein, SVI represents the state fluctuation index, which is the sum of the absolute values ​​of the state changes of each functional unit, and n is the total number of functional units in the system.

[0191] Stability margin measures the remaining space of a system before it fails, and the corresponding formula is:

[0192] SM = 1 - max(S) _j );

[0193] Here, SM represents stability margin, which is defined as 1 minus the maximum state value in the system, and is used to represent the remaining capacity space of the system before failure.

[0194] By analyzing the above indicators, the system operating state can be divided into the stable zone (low load), the development zone (load increases), the critical zone (approaching instability), and the failure zone (system instability), thus realizing the phased identification of the system operation process.

[0195] Accordingly, for transport and distribution units, Q _2 max The flow capacity under full pipe or full channel flow conditions is calculated using Manning's formula:

[0196] Q_2 max =(1 / n _M )×A _c ×R _h (2 / 3) ×S _0 (1 / 2) ;

[0197] Where n _M Let A be the Manning roughness coefficient. _c R is the cross-sectional area of ​​the pipe or channel through which water flows. _h S is the hydraulic radius. _0 This refers to the slope of the pipe bottom or channel bottom. For a transport unit composed of multiple pipe sections, the pipe section with the smallest flow capacity is taken as the Q of that unit. _2 max This is the bottleneck principle.

[0198] For mandatory emission units, Q _3 max The rated drainage flow rate of the pumping station under the design head condition is determined directly from the design parameters or factory nameplate data of the pumping station.

[0199] Weighting coefficient w _j To reflect the relative importance of each functional unit to the overall system operation, allocation can be based on the proportion of the catchment area served by each unit to the total catchment area. The corresponding formula is:

[0200] w _j =A _j / (∑ _(j=1) n A _j );

[0201] Where A _j Let w be the catchment area corresponding to the j-th functional unit. It satisfies the weight normalization condition ∑w _j =1.

[0202] According to one aspect of this application, before calculating the comprehensive index, a correction for the cascaded coupling state of functional units based on the pipeline topology is also included.

[0203] In practical drainage systems, functional units are interconnected through a pipe network topology. When a downstream unit approaches or reaches full load, a backflow effect occurs, obstructing the effective discharge channel of the upstream unit and increasing its equivalent operating load. Therefore, a cascaded coupling state correction model based on the pipe network topology is constructed, specifically including:

[0204] Based on the drainage network structure data G, directed connections between functional units are established, and the set of downstream connected units D(j) for each unit is determined. For the j-th functional unit, D(j) represents the set of downstream functional units directly connected to it. If the j-th functional unit is a terminal unit of the drainage system, such as a water outlet or a terminal pumping station, then D(j) = ∅.

[0205] Following the reverse flow direction of the pipeline topology or from downstream to upstream, the cascaded corrected state values ​​are calculated unit by unit. For the terminal unit, since it is not affected by the return water from downstream units, its cascaded state value is equal to the boundary corrected state value, specifically:

[0206] S _j cas (R _k )=S _j adj (R _k ).

[0207] For non-terminal cells, the formula for calculating their cascaded correction state values ​​is:

[0208] S _j cas (R _k )=min(1,S _j adj (R _k )+∑ _(i∈D(j)) β _ji ×[S _i cas (R _k )-θ _c ] + );

[0209] Where, β _ji θ is the cascade coupling coefficient between unit j and its downstream unit i, reflecting the intensity of the impact of downstream congestion on the upstream; _c This is the cascading activation threshold, indicating that the downstream unit's load level will only affect the upstream return water flow if it exceeds this threshold; [•] + =max(•,0) indicates that cascading propagation is triggered only when the downstream unit exceeds the threshold, R _k This represents the rainfall amount at the k-th calculation time step.

[0210] When the cascade state value S of downstream unit i _i cas Exceeding the threshold θ _c When, the excess portion is calculated according to the coupling coefficient β _ji The load is propagated upstream to cell j, increasing the equivalent load on the upstream cell. The truncation function ensures that the corrected value does not exceed 1.

[0211] Coupling coefficient β_ji The method for determining this is based on the cross-sectional area A of the pipe connection from unit j to unit i. _ji The ratio of the total cross-sectional area of ​​all outflow connections in element j to the total cross-sectional area is determined, i.e.:

[0212] β _ji =A _ji / (∑ _(i∈D(j)) A _ji )×β _0 ;

[0213] Where, β _0 This is the basic coupling strength parameter. For a single outflow direction element, β _ji =β _0 .

[0214] Since the cascading transmission direction is from downstream to upstream, the calculation requires topological sorting of the pipeline network topology. The calculation order for each unit is determined by starting from the downstreammost point (outlet) and proceeding upstream layer by layer. This ensures that the S of the upstream unit is calculated... _j cas At that time, the downstream unit S referenced _i cas The calculations have been completed.

[0215] At the software implementation level, the method in this embodiment is executed step by step according to a time sequence.

[0216] In practical applications, the method of this invention can be used for real-time monitoring and early warning of drainage systems in coastal cities. For example, during rainfall, by inputting rainfall data and external water level data in real time, the system state changes can be continuously calculated, and an early warning signal can be issued before the system enters a critical state, providing decision support for drainage scheduling. Correspondingly, this method can also be used for simulation analysis under different rainfall scenarios to identify critical intervals of system instability, providing a basis for drainage system planning.

[0217] As an example, this embodiment can also be used in the following technical and product scenarios in practical applications.

[0218] For example, in the field of urban drainage system planning and design, it can be used to analyze the changes in the operating status of different design schemes under various rainfall scenarios and external water level conditions, identify key operating conditions that may cause system instability, and provide quantitative basis for drainage network scale design, pump station capacity configuration and storage facility layout.

[0219] For example, a real-time monitoring platform can be embedded in an urban flood risk assessment and early warning system. By accessing real-time rainfall data and external water level data, the operating status of the drainage system can be continuously calculated. When the system enters a critical or unstable state, it can automatically output early warning information, thereby improving the timeliness and accuracy of flood warnings.

[0220] For example, in a smart water management and digital twin system, it can be used as a status assessment module, integrated with hydrodynamic models and monitoring data, to achieve dynamic simulation and visualization of the operation process of the urban drainage system, providing support for scheduling decisions.

[0221] For example, in the field of emergency dispatch and operation management, it can also be used to identify critical instability phases of the system under extreme rainfall conditions, thereby guiding the formulation of pump station start-up and shutdown strategies, gate dispatching schemes and emergency drainage measures, and improving the safety and stability of system operation.

[0222] Based on this, the present invention can also be extended to fields such as sponge city construction assessment, comprehensive urban flood control, and coastal city drainage system analysis, and has good versatility and promotion value.

[0223] An electronic device includes: one or more processors and a memory, wherein the one or more processors are coupled to the memory, the memory is used to store computer program code, the computer program code including computer instructions, and the electronic device performs the method of the above embodiments when the one or more processors execute the computer instructions.

[0224] A chip, characterized in that it includes: a processor and a communication interface, wherein the processor calls a computer program or instruction through the communication interface to execute the method of the above embodiments.

[0225] A computer program product, when run on a computer, in which the computer performs the method described in the above embodiments.

[0226] A computer-readable storage medium storing a computer program, wherein when the computer program is run on a computer, the computer performs the method of the above embodiments.

[0227] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A method for identifying the instability state of an urban drainage system under complex boundary conditions, characterized in that, include: Acquire rainfall sequence data, external water level data, and basic data of the drainage system, including topology and various functional units, for the target area; Based on this, the actual water volume and maximum processing capacity of each functional unit are determined, and normalized state parameters characterizing the current load level are constructed based on the actual water volume and maximum processing capacity. The state change is extracted based on the difference of normalized state parameters between adjacent time steps, and coupled with the remaining processing margin determined based on the normalized state parameters to obtain stability parameters characterizing the system’s sensitivity to load disturbances. Based on external water level data and topological structure characterization, multiple corrections are performed on the normalized state parameters to obtain cascaded corrected state parameters. Based on this, a system-level comprehensive evaluation index is constructed, and the current instability risk level of the drainage system is determined and output according to the priority classification rules.

2. The method according to claim 1, characterized in that, The process of constructing normalized state parameters characterizing the current load level includes: Based on water regulation behavior, the functional units are divided into infiltration and storage units, transportation and distribution units, and forced discharge units; The maximum processing capacity is quantified based on the physical adjustment mechanism corresponding to each type of unit. Calculate the ratio of the actual treated water volume to the maximum treated capacity to obtain the normalized state parameters.

3. The method according to claim 2, characterized in that, For infiltration and retention units, the corresponding maximum processing capacity is quantified, including: Obtain the pre-configured attribute parameters of the infiltration and storage unit. The pre-configured attribute parameters include the stable infiltration rate, effective infiltration area, and available storage volume. Based on the available storage capacity and the preset calculation time step, the equivalent storage flow is calculated. The maximum treatment capacity of an infiltration-retention unit is obtained by multiplying the stable infiltration rate by the effective infiltration area and adding it to the equivalent flow rate of the storage unit.

4. The method according to claim 2, characterized in that, For forced discharge units, the rated drainage flow rate under the pre-configured design head conditions is obtained as the maximum processing capacity of the forced discharge unit.

5. The method according to claim 1, characterized in that, The process of obtaining cascaded correction state parameters specifically includes: Based on external water level data, the normalized state parameters are scaled to obtain the boundary correction state parameters. Based on the connection relationship determined by the topology, the backwater conduction effect of the downstream associated functional unit on the upstream functional unit is calculated, and the boundary correction state parameters are superimposed with equivalent loads based on the backwater conduction effect to obtain the cascaded correction state parameters.

6. The method according to claim 5, characterized in that, The process of scaling down the normalized state parameters based on external water level data to obtain the boundary correction state parameters includes: Obtain the pre-configured reference water level and the adjustment coefficient used to characterize the amplification intensity of the top support; Calculate the relative deviation ratio of external water level data from the reference water level; Based on the adjustment coefficient, the relative deviation ratio is weighted to construct an amplified adjustment factor that characterizes the back pressure from the external hydrological environment. The normalized state parameters are amplified and corrected using an amplification adjustment factor to obtain the boundary-corrected state parameters.

7. The method according to claim 5, characterized in that, The process of obtaining the cascaded correction state parameters includes: Based on the topology, each functional unit is sorted in reverse topology. Obtain the cascade activation threshold and the cascade coupling coefficient, which reflects the conduction strength between adjacent functional units; Based on the reverse topology sorting, the cascaded correction state parameters of the target functional units are calculated one by one; When the cascaded correction state parameters exceed the cascaded activation threshold, the state variables exceeding the threshold are extracted and transformed into new equivalent loads based on the cascaded coupling coefficients; otherwise, the new equivalent loads are set to zero. The cascaded corrected state parameters of the target functional unit are obtained by summing the boundary correction state parameters and the newly added equivalent load, and the result does not exceed the full-load state constant 1.

8. The method according to claim 7, characterized in that, The process of obtaining the cascade coupling coefficient, which reflects the conduction strength between adjacent functional units, includes: Based on the basic data of the drainage system, the cross-sectional area of ​​the connection between the target functional unit and the corresponding downstream directly related unit is obtained, as well as the total cross-sectional area of ​​the connection in all outflow directions. Calculate the proportion of the area of ​​the connecting cross-section to the total area of ​​the connecting cross-section; Obtain the basic coupling strength parameters, correct the ratio, and use the corrected value as the cascade coupling coefficient between the target functional unit and the corresponding downstream directly related unit.

9. The method according to claim 1, characterized in that, The process of constructing a system-level comprehensive evaluation index includes: Obtain the catchment area weights that characterize the relative importance of each functional unit; Based on the catchment area weight, the cascaded modified state parameters of each functional unit are weighted and summed to obtain the system load index used to characterize the global load level. The absolute values ​​of the state changes of each functional unit are extracted and averaged across the system scale to obtain the state fluctuation index, which characterizes the degree of global operational instability. The difference between the full-load state constant 1 and the maximum value of the corrected state parameters of each stage is obtained to obtain the stability margin used to characterize the remaining physical space of the system from the failure state. The three parameters obtained, along with the stability parameters of each functional unit, are used as system-level comprehensive evaluation indicators.

10. The method according to claim 1, characterized in that, The process of obtaining stability parameters characterizing the system's sensitivity to load disturbances includes: Calculate the difference between the full-load state constant 1 and the normalized state parameters to determine the remaining treatment margin; Obtain the smoothing parameters used to prevent numerical overflow; Extract the absolute value of the state change; The ratio is calculated using the absolute value of the state change as the numerator and the sum of the smoothing parameter and the remaining treatment margin as the denominator, and this ratio is used as the stability parameter.

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