Urban pipe network system resilience key node identification method and system, device and medium

Abstract

The present application relates to the field of drainage system performance evaluation, in particular to a city pipe network system resilience key node identification method and system, equipment and medium, mainly including quantifying system performance based on system total overflow volume and average flooding duration to unify resilience index, obtaining system resilience index; constructing resilience envelope based on system resilience index to screen extreme scenarios, for each failure sequence, at the first failure, calculating the marginal resilience loss caused by the newly added failure unit; based on the criticality scoring function and the marginal resilience loss, the criticality score of each structural unit in all extreme scenarios is calculated. Through the above method, the degradation process of system resilience with failure degree under different structural failure levels is quantified, and the change of system level resilience is allocated to a single pipe segment, and finally the criticality ranking of the whole network pipe segment is formed, so as to identify the key pipe segment.

Description

Technical Field

[0001] This invention relates to the field of drainage system performance evaluation, and more specifically, to a method, system, equipment, and medium for identifying critical nodes of resilience in urban pipe network systems. Background Technology

[0002] Urban drainage networks are critical infrastructure for ensuring urban flood control, drainage, and sewage transport. Their operational status directly affects urban drainage capacity, safety, and environmental quality. With rapid urbanization, problems such as the increasing service life of drainage networks, overloading, environmental corrosion, and structural aging are becoming increasingly prominent. Local structural damage or node failure can easily lead to systemic risks. Under the influence of disturbances such as heavy rainfall, sudden changes in upstream runoff, tidal backwater, and siltation, the failure of a single node can trigger a chain reaction, significantly reducing the overall drainage capacity of the system and causing a series of problems such as increased flooding, prolonged inundation, road traffic disruptions, and facility damage.

[0003] Current technologies lack a critical pipeline identification method that integrates both "fault scale" and "fault duration" dimensions to cover all fault scenarios. This results in a lack of targeted approach in drainage system repair and optimization—it can only perform overall network modifications, making it difficult to precisely target core weak points. This not only increases modification costs but also fails to efficiently improve the overall system resilience and cope with the complex risks of extreme weather and structural failures. Therefore, identifying the critical nodes in the drainage network that have the most significant impact on the overall system performance is of great importance for improving the resilience of drainage systems. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, equipment, and medium for identifying critical nodes of urban pipeline network resilience, in order to solve the above-mentioned problems in the prior art.

[0005] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for identifying critical resilience nodes in urban pipeline systems, including: Define a set of structural units, and generate a random structural failure sequence based on the set of structural units; Several failure sets are constructed based on the failure sequence, and the failure sets are simulated using a drainage system hydraulic model to obtain the total overflow volume and the average inundation duration of all water accumulation nodes under the failure sets. The system performance is quantified using a unified resilience index based on the total overflow volume and average flooding duration of the system, and the system resilience index is obtained. A resilience envelope is constructed based on system resilience indicators to screen extreme scenarios, resulting in a set of extreme scenarios. For each failure sequence, at the 1st When a level failure occurs, calculate the marginal toughness loss caused by the newly added failure element; The criticality score of each structural unit in all extreme scenarios is calculated based on the criticality scoring function and the marginal resilience loss. The criticality scores are then sorted in descending order to obtain a criticality ranking list, which is then output.

[0006] Preferably, the generation of random structural failure sequences based on the set of structural units includes: The set of structural units includes:

[0007] Based on the Monte Carlo method, random sampling without replacement is performed from the set of structural units to generate... Cumulative failure sequence:

[0008] In the formula, A collection of structural units. for Each pipeline structure unit This is a failure sequence. for The current sequence at the is in the ... When a level failure occurs, a new structural unit fails.

[0009] Preferably, the quantification of system performance based on a unified resilience index using the total overflow volume and average flooding duration includes:

[0010] in, For the first The failure sequence is in the first System resilience at level 1 failure For the total overflow volume of the failure set, This refers to the total inflow or total overflow volume under the baseline scenario. The average flooding duration for all water-filled nodes in the failure set. The total simulation duration is the baseline scenario.

[0011] Preferably, the extreme scenario set obtained by constructing a resilience envelope based on system resilience indicators to screen extreme scenarios includes: In the same failure level The toughness indices of all failure sequences are statistically analyzed to obtain the maximum toughness at that level. and minimum toughness ; Will and Connect the lines to obtain the upper and lower envelopes of the toughness; At each failure level Based on the minimum resilience, screening conditions are set, and scenarios that meet the screening conditions are selected from all sequences to form a set of extreme scenarios.

[0012] Preferably, the calculation of the marginal toughness loss caused by the newly added failure unit includes:

[0013] In the formula, For marginal resilience loss, For the first The failure sequence is in the first System resilience under level failure.

[0014] Preferably, the calculation of the criticality score of each structural unit under all extreme scenarios based on the criticality scoring function and the marginal resilience loss includes:

[0015] In the formula, For unit structure Keyness score, For hierarchical weights, Let be an index pair, representing the scenario where the r-th random failure sequence fails at level k. Indicates the sequence r at the th k A structural unit that fails under a Level 1 failure scenario.

[0016] Preferably, the step of sorting the criticality scores in descending order to obtain a criticality ranking list and outputting it includes: Set a scoring threshold, and output the attributes of the current structural unit based on different criticality scores and scoring thresholds.

[0017] Secondly, the present invention also provides a system for identifying resilient critical nodes in urban pipeline systems, used to perform the above-described method for identifying resilient critical nodes in urban pipeline systems, including: The model building module is configured to define a set of structural elements, generate random structural failure sequences based on the set of structural elements, construct several failure sets based on the failure sequences, and simulate the failure sets using a drainage system hydraulic model to obtain the total overflow volume and the average inundation duration of all water accumulation nodes under the failure sets; quantify the system performance using a unified resilience index based on the total overflow volume and average inundation duration to obtain the system resilience index; construct a resilience envelope based on the system resilience index to screen extreme scenarios, obtaining a set of extreme scenarios; for each failure sequence, at the... When a level failure occurs, calculate the marginal toughness loss caused by the newly added failure element; The output module is configured to calculate the criticality score of each structural unit in all extreme scenarios based on the criticality scoring function and the marginal resilience loss, sort the criticality scores in descending order, obtain the criticality ranking list, and output it.

[0018] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for identifying resilient key nodes of urban pipeline network systems.

[0019] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for identifying resilient key nodes in urban pipeline network systems.

[0020] The technical solution of the present invention has at least the following advantages and beneficial effects: The method provided by this invention mainly includes: quantifying system performance based on the total overflow volume and average flooding duration using a unified resilience index to obtain the system resilience index; constructing a resilience envelope based on the system resilience index to screen extreme scenarios; and for each failure sequence, [further details on the method are needed]. At level 1 failure, the marginal toughness loss caused by the newly added failed unit is calculated. Based on the criticality scoring function and the marginal toughness loss, the criticality score of each structural unit is calculated in all extreme scenarios. Through the above method, the degradation process of system toughness with the degree of failure under different structural failure levels is quantified, and the system-level toughness change is distributed to individual pipe segments, ultimately forming a criticality ranking of all pipe segments in the network, thereby identifying critical pipe segments. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the process of the present invention.

[0023] Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0025] The independently described modules or sub-modules may or may not be physically separated; they may be implemented in software or hardware, and some modules or sub-modules may be implemented in software, with the processor calling the software to implement the function of these modules or sub-modules, while other modules or sub-modules may be implemented in hardware, such as through hardware circuits. Furthermore, some or all of the modules can be selected to achieve the purpose of this application's solution according to actual needs.

[0026] This invention provides a method for identifying critical resilience nodes in urban pipeline systems, including: Define a set of structural units, and generate a random structural failure sequence based on the set of structural units; Structural failure refers to the state in which a structural unit is considered to have "lost its normal function" in a hydraulic model. For example, the flow capacity of a pipe section is reduced to a very low level or even completely blocked to simulate real-world situations such as blockage, collapse, or valve closure. By gradually adding failed units to the model, the evolution of a pipe network from a healthy state to a degraded state can be simulated.

[0027] Several failure sets are constructed based on the failure sequence, and the failure sets are simulated using a drainage system hydraulic model to obtain the total overflow volume and the average inundation duration of all water accumulation nodes under the failure sets. For each failure sequence In the The failure set corresponding to a level failure is defined as follows:

[0028] in, This represents the baseline scenario with no failures. for The first Failure sequence at level 1 failure For the set of failures, for The maximum value.

[0029] In each failure set Next, a complete simulation was performed using the drainage system hydraulic model (SWMM model) to obtain... (Total system overflow volume) and Indicators characterizing system performance include (average flooding duration of all water accumulation nodes).

[0030] In the baseline scenario ( Simulations were performed under conditions of no structural failure, and the results were as follows:

[0031] in, Indicates the baseline total inflow rate or total overflow volume. Indicates the total duration of the baseline simulation. Indicates the reference duration under the baseline scenario. This indicates the reference total inflow or total overflow volume under the baseline scenario.

[0032] In the Failure sequence number 1 Simulations under a set of level failures yield the total overflow volume and average flooding duration of the system. The former characterizes how much rainwater cannot be discharged through the pipe network and thus overflows, while the latter characterizes the duration of water accumulation. Together, they reflect the system's drainage performance.

[0033] The baseline scenario refers to a situation where all structural elements are intact and only given rainfall and boundary conditions exist. Running the hydraulic model under this scenario yields... , as well as Equal benchmark quantities are used for subsequent normalization and comparison of performance indicators under different failure scenarios.

[0034] The system performance is quantified using a unified resilience index based on the total overflow volume and average flooding duration of the system, and the system resilience index is obtained. A resilience envelope is constructed based on system resilience indicators to screen extreme scenarios, resulting in a set of extreme scenarios. Drainage system resilience refers to the comprehensive ability of a drainage system to maintain or quickly restore its flood control capacity and service functions in the face of disturbances such as pipe section failure, extreme rainfall, and siltation. In this method, let the first... Failure sequence number The system's toughness at level 1 failure is Its value ranges from [0,1]. In the case of no overflow and light water accumulation, the resilience value is close to 1, indicating that the system function is well maintained; in the case of large-scale overflow or long-term water accumulation, the resilience value approaches 0, indicating that the system function is severely degraded or basically failed.

[0035] For each failure sequence, at the 1st When a level failure occurs, calculate the marginal toughness loss caused by the newly added failure element; The criticality score of each structural unit in all extreme scenarios is calculated based on the criticality scoring function and the marginal resilience loss. The criticality scores are then sorted in descending order to obtain a criticality ranking list, which is then output.

[0036] The method provided by this invention mainly includes: quantifying system performance based on the total overflow volume and average flooding duration using a unified resilience index to obtain the system resilience index; constructing a resilience envelope based on the system resilience index to screen extreme scenarios; and for each failure sequence, [further details on the method are needed]. At level 1 failure, the marginal toughness loss caused by the newly added failed unit is calculated. Based on the criticality scoring function and the marginal toughness loss, the criticality score of each structural unit is calculated in all extreme scenarios. Through the above method, the degradation process of system toughness with the degree of failure under different structural failure levels is quantified, and the system-level toughness change is distributed to individual pipe segments, ultimately forming a criticality ranking of all pipe segments in the network, thereby identifying critical pipe segments.

[0037] An exemplary embodiment of the present invention, generating a random structural failure sequence based on a set of structural units, includes: In hydraulic models, each pipe segment or node is considered a "structural unit." All structural units in the entire network constitute a set of structural units. , recorded as ,in, This represents a specific pipe segment or node. From 1 to , for In practical applications, each pipe segment is usually treated as a structural unit in order to sort and evaluate the criticality of each segment.

[0038] Based on the Monte Carlo method, random sampling without replacement is performed from the set of structural units to generate... Cumulative failure sequence:

[0039] In the formula, This is a failure sequence. for The current sequence at the is in the ... When a level failure occurs, a new structural unit fails.

[0040] Each sequence This represents a structural degradation path that gradually evolves from "completely intact network" to "completely failed network": when the sequence length is... At that time, all structural units had failed; sequence prefix ( ,..., ) indicates "previous The structural state is characterized by "one unit has failed, while the rest remain intact." This corresponds to the baseline scenario with no failures.

[0041] An exemplary embodiment of the present invention, which quantifies system performance using a unified resilience index based on the total overflow volume and average flooding duration of the system, includes:

[0042] in, For the first The failure sequence is in the first System resilience at level 1 failure For the total overflow volume of the failure set, This refers to the total inflow or total overflow volume under the baseline scenario. The average flooding duration for all water-filled nodes in the failure set. The total duration of the baseline simulation is given.

[0043] A value close to 1 indicates that the system still has strong drainage capacity and functional integrity at the current failure level; a value close to 0 indicates that the system's functions have severely degraded or are basically ineffective.

[0044] In the baseline scenario Below, there are:

[0045] The system resilience under the baseline scenario characterizes the baseline resilience of the system in an intact state.

[0046] In one exemplary embodiment of the present invention, a resilience envelope is constructed based on system resilience indices to screen extreme scenarios, resulting in a set of extreme scenarios including: In the same failure level The toughness indices of all failure sequences are statistically analyzed to obtain the maximum toughness at that level. and minimum toughness ； Will and Connect the lines to obtain the upper and lower envelopes of the toughness, which are used to characterize the best and worst states that the system may reach under different failure levels; At each failure level Based on the minimum resilience, screening conditions are set, and scenarios that meet the screening conditions are selected from all sequences to form a set of extreme scenarios.

[0047] Specifically, to highlight the "worst-case scenario," at each failure level... Above, with minimum toughness Centered on this set of extreme scenarios, a set of scenarios whose resilience is close to the minimum is selected from all sequences. The scenarios in this set typically correspond to the most severe structural failure combinations and are the focus of subsequent analysis for identifying critical pipe sections.

[0048] The filtering criteria are as follows:

[0049] For small tolerance, For index pairs, indicating the first... The random failure sequence in the 1st Scenario of level failure.

[0050] In one exemplary embodiment of the present invention, for each failure sequence In the When a level failure occurs, a new failure unit is added. The resulting marginal resilience loss is defined as:

[0051] In the formula, For marginal resilience loss, For the first The failure sequence is in the first System resilience under level failure.

[0052] when When, explain the first The addition of a single failure element further reduces the system's resilience, and its absolute value The larger the value, the more significant the structural unit's impact on the system's toughness.

[0053] In practical applications, only the aforementioned set of extreme scenarios can be considered. In calculate This reduces computational load and emphasizes the analysis of worst-case scenarios.

[0054] In one exemplary embodiment of the present invention, for each structural unit ∈ Based on its performance across all extreme scenarios, the criticality scoring function is defined as follows:

[0055] In the formula, For unit structure Keyness score, For hierarchical weights, For index pairs, indicating the first... The random failure sequence in the 1st Scenario of Level 1 failure Indicates in sequence The k A structural unit that fails under a Level 1 failure scenario.

[0056] Among them, hierarchical weight To reflect the impact of "early or late failure" on criticality, inversely proportional weights or exponentially decaying weights can be selected, for example:

[0057]

[0058] This is a calculation coefficient, without a fixed value, and is generally determined by engineering experience or calibration tests. It is a dimensionless, adjustable parameter. If greater emphasis is placed on the contribution of early-stage failures to criticality, The value can be too large, but the weight will drop rapidly; a value of 1.0 is recommended. If you want to emphasize the impact of failures in the later stages, The value can be relatively small, and the weight decreases more slowly. A value of 0.1 is recommended.

[0059] The criticality scores of all structural units are sorted in descending order to obtain a criticality ranking list:

[0060] in, ; Score the criticality of several structural units. for A unit structure sorted in descending order based on keyness scores. .

[0061] In one exemplary embodiment of the present invention, the key scores are sorted in descending order to obtain a key score ranking list, which is then output as follows: Set a scoring threshold, and output the attributes of the current structural unit based on different criticality scores and scoring thresholds.

[0062] Specifically, selection rules are set according to actual needs to obtain a set of key pipe sections. :

[0063] Used to identify the most critical pipe sections, The first unit structure in the sorting ; or

[0064] Used for identification before A key pipe section; or

[0065] in, To preset the scoring threshold, To be before sorting results Unit structure .

[0066] By using any of the above methods or a combination thereof, at least one critical pipe segment or several critical pipe segments can be identified, providing a basis for the reinforcement, renovation, monitoring and operation and maintenance of drainage pipe networks.

[0067] like Figure 1-2 As shown, the present invention also provides a specific example to further illustrate the above method: Suppose there is a simple drainage network consisting of only 5 pipe segments, which are considered as structural units and denoted as follows: , , , , In the SWMM model, the geometric parameters, roughness, connection relationships, and corresponding catchment areas of these five pipe segments have been fully defined, and the given design rainfall duration curve and downstream boundary conditions are ready.

[0068] This embodiment takes a 2-hour rainstorm as an example, and selects the overflow volume of the nodes and the submersion time of the water accumulation nodes as system performance indicators to calculate the resilience of the drainage system.

[0069] S1: Define the set of network management structure units: The five pipe sections are treated as a single structural unit, forming a structural unit set. :

[0070] in, This can be understood as an upstream branch pipe. It is the central water catchment main. The outlet pipe is the one connected to the external system; the remaining pipe sections are branch pipes or main pipes at different locations. In practical applications, it is only necessary to ensure that each structural unit has a unique ID corresponding to it in the model.

[0071] S2: Generate random structure failure sequences (example: 3 sequences): In real-world engineering projects, a large number of random failure sequences can be generated. For ease of explanation, this embodiment uses only three Monte Carlo random sequences. The failure sequence is denoted as Its elements represent structural units that fail sequentially.

[0072]

[0073]

[0074]

[0075] For any sequence, the first The failure set at level 1 failure is defined as the previous failure set. A set consisting of failure units:

[0076] in, This corresponds to the baseline scenario with no failures.

[0077] S3: Construct the failure hierarchy and perform SWMM hydraulic simulation: First, in the baseline scenario (i.e. Run the SWMM model under conditions of no structural failure to obtain the baseline total inflow or total overflow volume. Total simulation duration and reference flooding duration .

[0078] Subsequently, in sequence For example, construct in sequence 、 、…、 In the model, the corresponding pipe segment is set to the failure state, and a complete simulation is run under each failure level to obtain the results. and For the sequence and The same procedure was followed to obtain the overflow volume and flooding duration of the three sequences at each failure level.

[0079] S4: Quantify system performance using a unified resilience index, and calculate the resilience index for each scenario: After obtaining all Context and Then, the toughness index is calculated according to a unified formula:

[0080] when At times, there are usually This represents the baseline toughness of the system in an intact state; as the failure level increases... Increase and Generally, increasing makes The gradual decline reflects the degradation of the system's drainage capacity and service level.

[0081] S5: Construct a resilience envelope and screen for extreme scenarios: At each failure level The above analysis of the toughness indices of the three failure sequences yielded the following results:

[0082]

[0083] Point pair and Connecting these lines yields the upper and lower envelopes of toughness, which characterize the best and worst states the system can reach under the same number of failures. for The maximum value, for The minimum value.

[0084] To emphasize the "worst-case scenario," at each failure level Above, with minimum toughness A small tolerance is introduced around the center. Construct a set of extreme scenarios:

[0085] In the example with 5 pipelines, due to the small number of sequences, typically only one scenario is selected into the extreme scenario set at each failure level.

[0086] S6: Calculate the marginal toughness loss of each failed element: For each failure sequence, the marginal impact of "newly added failure elements" is characterized by the toughness difference between adjacent failure levels:

[0087] like This indicates that in the first Level 1 newly added failure structural unit This leads to a decrease in system resilience, in its absolute value The larger the value, the more significant the damage to the system by that unit under that scenario. In actual calculations, only the set of extreme scenarios needs to be considered. The scenario calculations and records of marginal resilience loss highlight the most dangerous failure combinations.

[0088] S7: Calculate criticality scores and identify critical pipe sections: For each structural unit To synthesize its marginal resilience loss across all extreme scenarios, a keyness scoring function is introduced. :

[0089] In the formula, For unit structure Keyness score, For hierarchical weights, For index pairs, indicating the first... The random failure sequence in the 1st Scenario of Level 1 failure Indicates in sequence r A structural unit fails under the k-th level failure scenario.

[0090] in, For the first The weight for level-one failure can be selected in an inverse proportional form. This demonstrates that the earlier the failure occurs, the more critical it is; an exponential decay model can also be selected. Through parameters Control the relative importance of different levels.

[0091] Calculated arrive Keyness rating 、 、…、 Next, sort the five pipe segments according to their criticality scores from highest to lowest to obtain the criticality ranking result of the "small drainage network of five pipes". As needed, you can: (1) Select only the pipe segment with the highest criticality to form the smallest critical pipe segment set for key reinforcement and monitoring; (2) Select the top few sections (such as the first two sections) as key targets for rectification; (3) Set a criticality threshold and select all pipe segments with scores not lower than the threshold to form a "high-risk pipe segment set".

[0092] By following the steps above, the critical pipe segment identification process based on GRA can be fully implemented in a small drainage network containing only 5 pipe segments, providing an intuitive example and operational paradigm for its subsequent promotion and application in actual urban pipe network systems.

[0093] Secondly, the present invention also provides a system for identifying resilient critical nodes in urban pipeline systems, used to perform the above-described method for identifying resilient critical nodes in urban pipeline systems, including: The model building module is configured to define a set of structural units, generate random structural failure sequences based on the set of structural units, construct several failure sets based on the failure sequences, and simulate the failure sets using a drainage system hydraulic model to obtain the total overflow volume and the average inundation duration of all water accumulation nodes under the failure sets; quantify the system performance using a unified resilience index based on the total overflow volume and average inundation duration to obtain the system resilience index; construct a resilience envelope based on the system resilience index to screen extreme scenarios, obtaining a set of extreme scenarios; for each failure sequence, at the k-th level of failure, calculate the marginal resilience loss caused by the newly added failure unit; The output module is configured to calculate the criticality score of each structural unit in all extreme scenarios based on the criticality scoring function and the marginal resilience loss, sort the criticality scores in descending order, obtain the criticality ranking list, and output it.

[0094] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0095] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. This computer software product, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying critical nodes in the resilience of urban pipeline networks, characterized in that, include: Define a set of structural units, and generate a random structural failure sequence based on the set of structural units; Several failure sets are constructed based on the failure sequence, and the failure sets are simulated using a drainage system hydraulic model to obtain the total overflow volume and the average inundation duration of all water accumulation nodes under the failure sets. The system performance is quantified using a unified resilience index based on the total overflow volume and average flooding duration of the system, and the system resilience index is obtained. A resilience envelope is constructed based on system resilience indicators to screen extreme scenarios, resulting in a set of extreme scenarios. For each failure sequence, at the 1st When a level failure occurs, calculate the marginal toughness loss caused by the newly added failure element; The criticality score of each structural unit in all extreme scenarios is calculated based on the criticality scoring function and the marginal resilience loss. The criticality scores are sorted in descending order to obtain a criticality ranking list and output it. The calculation of the criticality score for each structural unit in all extreme scenarios includes: In the formula, For unit structure Keyness score, For hierarchical weights, For index pairs, indicating the first... The random failure sequence in the 1st Scenario of Level 1 failure Indicates in sequence The A structural element fails under a Level 1 failure scenario. This represents a loss of marginal resilience. or , To calculate the coefficients.

2. The method for identifying critical nodes of urban pipeline network system resilience according to claim 1, characterized in that, The generation of random structural failure sequences based on the set of structural units includes: The set of structural units includes: Based on the Monte Carlo method, random sampling without replacement is performed from the set of structural units to generate... Cumulative failure sequence: In the formula, A collection of structural units. for Each pipeline structure unit This is a failure sequence. for The current sequence at the is in the ... When a level failure occurs, a new structural unit fails.

3. The method for identifying critical nodes of urban pipeline network system resilience according to claim 2, characterized in that, The quantification of system performance using a unified resilience index based on the total overflow volume and average flooding duration includes: in, For the first The failure sequence is in the first System resilience at level 1 failure For the total overflow volume of the failure set, This refers to the total inflow or total overflow volume under the baseline scenario. The average flooding duration for all water-filled nodes in the failure set. This represents the total simulation duration under the baseline scenario; The baseline scenario refers to a situation where all structural elements are intact and only given rainfall and boundary conditions exist.

4. The method for identifying critical nodes of urban pipeline network system resilience according to claim 3, characterized in that, The method of constructing a resilience envelope based on system resilience indicators to screen extreme scenarios yields a set of extreme scenarios, including: In the same failure level The toughness indices of all failure sequences are statistically analyzed to obtain the maximum toughness at that level. and minimum toughness ; Will and Connect the lines to obtain the upper and lower envelopes of the toughness; At each failure level Based on the minimum resilience, screening conditions are set, and scenarios that meet the screening conditions are selected from all sequences to form a set of extreme scenarios.

5. The method for identifying critical nodes of urban pipeline network system resilience according to claim 4, characterized in that, The calculation of the current marginal toughness loss caused by the newly added failed units includes: In the formula, For marginal resilience loss, For the first The failure sequence is in the first System resilience under level failure.

6. The method for identifying critical nodes of urban pipeline network system resilience according to claim 5, characterized in that, The process of sorting the criticality scores in descending order to obtain a criticality ranking list and outputting it includes: Set a scoring threshold, and output the attributes of the current structural unit based on different criticality scores and scoring thresholds.

7. A system for identifying critical nodes of urban pipeline network resilience, used to execute the method for identifying critical nodes of urban pipeline network resilience as described in any one of claims 1-6, characterized in that, include: The model building module is configured to define a set of structural units and generate a random structural failure sequence based on the set of structural units. Several failure sets are constructed based on the failure sequence, and the failure sets are simulated using a drainage system hydraulic model to obtain the total overflow volume and the average inundation duration of all water accumulation nodes under the failure sets. System performance is quantified using a unified resilience index based on the total overflow volume and average flooding duration, resulting in a system resilience index. A resilience envelope is constructed based on this index to screen extreme scenarios, yielding a set of extreme scenarios. For each failure sequence, at the [missing information]... When a level failure occurs, calculate the marginal toughness loss caused by the newly added failure element; The output module is configured to calculate the criticality score of each structural unit in all extreme scenarios based on the criticality scoring function and the marginal resilience loss, sort the criticality scores in descending order, obtain the criticality ranking list, and output it.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for identifying resilient key nodes of urban pipeline network systems as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the method for identifying resilient key nodes of urban pipeline network systems as described in any one of claims 1-6.

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