Offshore bridge operation and maintenance action optimization and scheduling management system based on resilience management

Abstract

The present application relates to the technical field of bridge resilience operation and maintenance management, and discloses a near-shore bridge operation and maintenance action optimization and scheduling management system based on resilience management, which comprises: a state and data modeling module, which defines the deterioration state and exposure state of the near-shore bridge components and obtains passive agent observation data; a likelihood and tail distribution module, which constructs an observation likelihood model and establishes a heavy-tailed joint distribution; a shock identification and updating module, which identifies shock events and updates the posterior distribution of the deterioration state and exposure state; a safety index calculation module, which calculates the resistance distribution and demand distribution, evaluates the failure probability and reliability index; an operation optimization and scheduling module, which optimizes the operation action scheme, resource allocation and scheduling; a resilience target configuration module, which calculates the resilience gap score and generates a set of resilience strategy parameters; and a monitoring and auditing module, which splits the task list and calculates the cost deviation, progress deviation and resilience back-off amount. The present application realizes the operation action optimization and efficient resource allocation of the near-shore bridge.

Description

Technical Field

[0001] This invention belongs to the field of bridge resilience operation and maintenance management technology, specifically involving an optimization and scheduling management system for nearshore bridge operation and maintenance activities based on resilience management. Background Technology

[0002] Nearshore bridges operate in environments characterized by high humidity and salinity, strong winds and waves, and complex hydrodynamics. Their components are prone to degradation phenomena such as chloride-induced corrosion of steel bars, localized pitting corrosion, prestress attenuation, and weakening of load-bearing sections. This degradation process is characterized by both long-term, slow accumulation and acceleration triggered by extreme events. Simultaneously, external exposures such as typhoons, near-ship collision risks, and foundation scour exhibit a significant heavy-tailed distribution and interdependent extreme characteristics, causing demand on bridges to be concentrated and amplified under a few high-risk conditions. Current in-service management relies heavily on periodic inspections or single-source monitoring, focusing on static assessments of structural condition. This makes it difficult to fully utilize multi-source proxy information such as passive monitoring, inspection, and maintenance records to form a continuous and updatable risk understanding. At the operation and maintenance level, assessment, decision-making, and scheduling are often processed in segments, lacking a unified action optimization mechanism across components and cycles based on reliability and risk output. Resource allocation relies heavily on experience-based ranking, making it difficult to reflect priority differences under resilience objectives. At the same time, there is a lack of systematic auditing and feedback on cost and schedule deviations during execution, as well as risk improvement and resilience decline before and after actions. This makes it difficult for operation and maintenance plans to continuously approach the target resilience level within the rolling cycle. Summary of the Invention

[0003] This invention provides a near-shore bridge operation and maintenance action optimization and scheduling management system based on resilience management, which solves the technical problem in related technologies that it is difficult to quantify in-service risks under the coupling of multi-source passive observation and extreme exposure, thus leading to a lack of resilience constraints in operation and maintenance action optimization and cross-cycle scheduling.

[0004] This invention provides a nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management, including:

[0005] The State and Data Modeling Module is used to define the effective cross-sectional area, corrosion pit density, and prestress loss as the deterioration state for near-shore bridge components, and define the typhoon severity index, near-ship risk, and scour risk as the exposure state, to form an initial prior and state transition relationship and acquire passive proxy observation data.

[0006] The likelihood and tail distribution module is used to map passive proxy observation data with cross-sectional effective area, corrosion pit density, prestress loss, typhoon severity index, near-ship risk, and scour risk to form an observational likelihood model and establish a heavy-tail joint distribution based on historical data.

[0007] The impact identification and update module is used to identify impact events and determine the posterior distribution of the deteriorated state and the exposed state based on the initial prior and state transition relationship, the observation likelihood model and the heavy-tailed joint distribution.

[0008] The safety index calculation module is used to obtain the resistance distribution and demand distribution based on the posterior distribution, and to calculate the failure probability and reliability index.

[0009] The operation and maintenance optimization scheduling module is used to perform tail sensitivity diagnosis based on failure probability and reliability indicators, and to determine action plans, resource allocation and intervention schedules in observation, re-inspection, repair, hardening and load limiting.

[0010] The resilience target configuration module is used to read the failure probability, reliability index and priority list, calculate the resilience gap score and classify it, and generate a set of resilience strategy parameters.

[0011] The monitoring and auditing module is used to break down action plans and intervention schedules into action task lists, calculate costs and schedule deviations, and recalculate risk improvement and resilience decline.

[0012] The beneficial effects of this invention are as follows: This invention achieves optimized operation and maintenance actions and efficient resource allocation for offshore bridges through a resilience-based operation and maintenance action optimization and scheduling management system. The system uses a state and data modeling module to identify the deterioration and exposure states of components, ensuring data consistency and integrity; a likelihood and tail distribution module uses historical data to establish a heavy-tailed joint distribution, optimizing extreme risk assessment; an impact identification and update module dynamically identifies impact events and updates the posterior distribution, enhancing the system's responsiveness; a safety index calculation module quantitatively assesses failure probability and reliability, providing support for decision-making; an operation and maintenance optimization scheduling module optimizes resource allocation and task scheduling; and a resilience target configuration module combined with a monitoring and auditing module achieves prioritized resource allocation and efficient task execution, ensuring the traceability of the execution process. Overall, this invention improves the scientific rigor, efficiency, and resilience of offshore bridge operation and maintenance, ensuring the long-term safe operation of bridges. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the module of the offshore bridge operation and maintenance action optimization and scheduling management system based on resilience management of the present invention. Detailed Implementation

[0014] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0015] like Figure 1 As shown, the nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management includes:

[0016] The State and Data Modeling Module is used to define the effective cross-sectional area, corrosion pit density, and prestress loss as the deterioration state for near-shore bridge components, and define the typhoon severity index, near-ship risk, and scour risk as the exposure state, to form an initial prior and state transition relationship and acquire passive proxy observation data.

[0017] The likelihood and tail distribution module is used to map passive proxy observation data with cross-sectional effective area, corrosion pit density, prestress loss, typhoon severity index, near-ship risk, and scour risk to form an observational likelihood model and establish a heavy-tail joint distribution based on historical data.

[0018] The impact identification and update module is used to identify impact events and determine the posterior distribution of the deteriorated state and the exposed state based on the initial prior and state transition relationship, the observation likelihood model and the heavy-tailed joint distribution.

[0019] The safety index calculation module is used to obtain the resistance distribution and demand distribution based on the posterior distribution, and to calculate the failure probability and reliability index.

[0020] The operation and maintenance optimization scheduling module is used to perform tail sensitivity diagnosis based on failure probability and reliability indicators, and to determine action plans, resource allocation and intervention schedules in observation, re-inspection, repair, hardening and load limiting.

[0021] The resilience target configuration module is used to read the failure probability, reliability index and priority list, calculate the resilience gap score and classify it, and generate a set of resilience strategy parameters.

[0022] The monitoring and auditing module is used to break down action plans and intervention schedules into action task lists, calculate costs and schedule deviations, and recalculate risk improvement and resilience decline.

[0023] In one embodiment of the present invention, the degradation state and the exposure state are defined, the hierarchical prior and time evolution are determined, and the initial prior and state transition relationship are determined, including:

[0024] Step 11: Set the value range of each parameter for the deteriorated state and the exposed state to limit the legal domain of the random variable, and the cross-sectional area constant and the prestress constant at the completion of tensioning, as the benchmark parameters for the effective cross-sectional area and prestress loss; the deteriorated state refers to the key state quantity that can characterize the attenuation of the load-bearing capacity of the component during its service life; the exposed state refers to the key state quantity that reflects the level of external risk exposure of the component; the effective cross-sectional area refers to the effective load-bearing cross-sectional area of ​​the component after considering the weakening of material corrosion; the corrosion pit density refers to the number of corrosion pits per unit area; the prestress loss refers to the difference between the prestress constant at the completion of tensioning and the re-measured value; the typhoon severity index represents the strength quantification index formed by the combination of wind speed, air pressure loss and bridge distance; the near-ship risk refers to the risk quantification value based on the near-ship event of the Automatic Identification System; the scour risk represents the scour intensity quantification value based on water level and flow velocity; the cross-sectional area constant is the initial design cross-sectional area of ​​the component or the intact cross-sectional area determined by benchmark testing; the prestress constant at the completion of tensioning is the prestress level when the component is tensioned or when the benchmark is re-measured.

[0025] Step 12: Set the mean and covariance of each parameter in the deteriorated state and the mean and covariance of each parameter in the exposed state, and generate the initial prior distributions for the deteriorated state and the exposed state respectively to form the initial prior object set; where the mean is used to reflect the central trend of each parameter in the initial scheduling period, and the covariance is used to reflect the statistical correlation and uncertainty intensity between parameters.

[0026] Step 13: Obtain the typhoon severity index, near-ship risk, and scour risk from the previous scheduling cycle. Use these as inputs and update the typhoon severity index, near-ship risk, and scour risk according to the rule that the first-order autoregressive coefficient is greater than 0 and less than 1. This characterizes the inertia and regression characteristics of the exposed state over time. Update the effective area of ​​the cross-section according to a non-increasing rule to prevent unfounded recovery over time. Update the corrosion pit density and prestress loss according to a non-decreasing rule to ensure that pitting corrosion expansion and prestress attenuation follow an irreversible trend. When the update result exceeds the corresponding value range in Step 11, execute the boundary truncation rule to generate a state transition relationship. The state transition relationship is used to characterize the prior prediction mechanism of the deteriorated state and the exposed state from the previous scheduling cycle to the current scheduling cycle. It is the time prior propagation link in the Bayesian sequential update.

[0027] This embodiment unifies the physical meaning of state variables with the data domain by setting the value range and benchmark constant for the deteriorated and exposed states; it quantifies the uncertainty of the starting point of the scheduling cycle by constructing the initial prior distribution of the deteriorated and exposed states; and it generates state transition relationships through first-order autoregression and monotonic evolution rules to achieve stable connection between prior prediction and sequential Bayesian update during the scheduling cycle, ultimately providing a consistent time prior basis for rolling operation and maintenance optimization and scheduling under resilience management.

[0028] In one embodiment of the present invention, passive proxy observation data refers to a set of data obtained through environmental and in-service monitoring, inspection and maintenance records that can indirectly characterize the degradation and exposure level of components, including the metal loss ratio of the resistance corrosion probe, surface chloride ion concentration, ambient temperature, humidity and salinity, corrosion level, near-ship event count, and inspection and maintenance records.

[0029] In one embodiment of the present invention, the formation of the observation likelihood model includes:

[0030] Step 21: Input the passive proxy observation data into the database according to the time index, and align it hourly with the effective area of ​​the cross section, corrosion pit density, prestress loss, typhoon severity index, near-ship risk, and scour risk. After alignment, remove records that exceed their respective value ranges, complete the missing measurement and outlier marking, and ensure that the observation and state variables maintain physical consistency. At the same time, complete the missing measurement records, mark outliers, and register the data source and version identifier to form a traceable observation data foundation. Passive proxy observation data refers to the data set that can indirectly characterize the deterioration and exposure level of components through environmental and in-service monitoring, inspection and maintenance records, including the metal loss ratio of the resistance corrosion probe, surface chloride ion concentration, environmental temperature, humidity and salinity, corrosion level, near-ship event count, and inspection and maintenance records.

[0031] Step 22: Subtract the metal loss ratio from 1, multiply the result by the cross-sectional area constant to obtain a point estimate of the effective cross-sectional area. Use this point estimate as the mean and the measurement error of the resistance corrosion probe as the variance to establish a normal conditional probability distribution of the effective cross-sectional area, characterizing the measurement deviation of the point estimate relative to the actual effective cross-sectional area. Obtain the corrosion level occurrence rate parameter from a table according to the corrosion level, and use it as the intensity parameter for the corrosion pit density to establish a Poisson conditional probability distribution, quantifying the pitting corrosion counting characteristics. The corrosion level is the discrete corrosion degree classification obtained from visual inspection, and the level occurrence rate parameter is the pitting corrosion intensity corresponding to that level. Subtract the prestress remeasurement value from the prestress constant at tension completion to obtain a point estimate of the prestress loss, and use the remeasurement uncertainty as the variance to establish a normal conditional probability distribution of the prestress loss, characterizing the random relationship between the remeasurement error and the actual prestress loss. The prestress remeasurement value is the measured prestress in the inspection and maintenance records.

[0032] Step 23: Select events with a nearest encounter distance less than the nearest encounter distance threshold from the near-ship event count. For each event, sum the weighted event intensity by weighting the speed and heading deviations. Establish a Poisson conditional probability distribution for near-ship risk using the product of the weighted event intensity and a proportionality coefficient, reflecting the count-type risk characteristics of near-ship events. The nearest encounter distance is the minimum spatial distance between the ship and the bridge site during navigation. A point estimate of the typhoon severity index is obtained by weighting and summing the reciprocal of the bridge site distance, the pressure deficit, and the maximum sustained wind speed. A normal conditional probability distribution of the typhoon severity index is established using the measurement error as the variance, reflecting the typhoon's exposure intensity to the bridge. The bridge site distance is the spatial distance between the typhoon center and the bridge site, and the pressure deficit and maximum sustained wind speed are characteristic quantities of typhoon intensity. A point estimate of the scour risk is obtained by weighting and summing the water level and flow velocity according to calibration weights. A normal conditional probability distribution of the scour risk is established using the measurement error as the variance. Water level and flow velocity are fundamental quantities reflecting hydrodynamic scour capacity.

[0033] Step 24: Under the same time index, combine the six conditional probability distributions obtained in Steps 22 and 23 according to the independence combination rule to form an observation likelihood model, and record the time index, data object name, unit, value range, parameter source, and version identifier. The independence combination rule means that, given the deterioration state and the exposure state, when the error terms of each observation channel are independent, their joint likelihood can be obtained by multiplying the conditional probability distributions.

[0034] This embodiment ensures temporal and physical consistency between observation data and state variables by aligning and cleaning passive proxy observation data with component degradation and exposure state variables hourly. It quantifies the measurement errors and uncertainties of these variables by constructing conditional probability distributions for effective cross-sectional area, corrosion pit density, prestress loss, typhoon severity index, near-ship risk, and scour risk. Based on independence combination rules, multiple conditional probability distributions are combined to form an observational likelihood model, ensuring the independence of error terms in each observation channel. Ultimately, this process provides traceable and physically consistent observational data support for constructing a stable Bayesian update framework, contributing to improved accuracy and reliability of in-service safety assessments of near-shore bridges.

[0035] In one embodiment of the present invention, establishing a heavy-tailed joint distribution based on historical data includes:

[0036] Step 31: Organize historical data on typhoon severity index, near-ship risk, and scour risk in the order of the scheduling cycle, perform missing data completion and outlier marking to ensure the continuity and availability of historical data, remove records that are not within their respective value ranges, form a historical dataset for edge modeling, and register the data source, value range, and version identifier; where the scheduling cycle is the discrete time interval division determined in Step 1, and organizing in the order of the scheduling cycle means aligning the three types of historical data hourly according to the time index and maintaining the synchronization within the same time period.

[0037] Step 32: A tail threshold is selected for the typhoon severity index, near-ship risk, and scour risk using the mean excess function and stability test. Excess samples are extracted above the tail threshold. A generalized Pareto distribution is fitted using the maximum likelihood method. The tail threshold, scale parameter, and shape parameter, along with their confidence intervals, are recorded to form three marginal tail distributions. The mean excess function represents the function of the average value of the portion exceeding the threshold changing with the threshold under a given threshold condition. The stability test compares the stable regions of parameter estimates within the candidate threshold interval and determines the threshold accordingly, thus avoiding the introduction of non-tail samples due to an excessively low threshold or insufficient samples due to an excessively high threshold. The tail threshold is the cutoff point for historical data entering the tail statistical model. Excess samples above the tail threshold are the set of samples exceeding the tail threshold. The generalized Pareto distribution is a probability distribution used to construct the tail behavior of samples exceeding the threshold. The scale parameter reflects the tail expansion scale, and the shape parameter reflects the tail thickness and attenuation characteristics. By fitting the data using the maximum likelihood method and recording the confidence intervals, the three marginal tail distributions have definite statistical significance and uncertainty description at the parameter level.

[0038] Step 33: Map the probability values ​​of the three edge tail distributions to the interval between 0 and 1. That is, perform probability transformation on the excess samples of typhoon severity index, near-ship risk, and scour risk to obtain a uniform scale sample that can be used for connectivity modeling. Calculate the rank correlation coefficient to characterize the correlation structure strength of the three exposure states under the tail sample. Construct a candidate set of connectivity models including a symmetric tail connectivity model, an upper tail correlation connectivity model, and a lower tail correlation connectivity model. Estimate the algorithm parameters for each model. Use the value of the Bayesian information criterion as the criterion and select the connectivity model with the smallest Bayesian information criterion as the optimal connectivity model. The smaller the Bayesian information criterion, the better the model's explanation of the tail dependency structure in the candidate set without being overly complex.

[0039] Specifically, the three connection models correspond to different tail dependence morphology assumptions. The symmetrical tail connection model assumes that the typhoon severity index, near-ship risk, and scour risk have approximately the same degree of dependence in the upper and lower tail extreme regions, that is, the correlation between high values ​​and extremes is roughly symmetrical with the correlation between low values ​​and extremes. This type of model is applicable to situations where there are similar linkage mechanisms between the three on both the high and low exposure sides.

[0040] The upper-tail correlation model assumes that the three factors (typhoons, near-ships, and scour) exhibit significant common extrema or amplified linkages in high-value extreme regions, while their correlation is weaker in low-value regions. This model primarily demonstrates tail-end dependence under combined high-exposure scenarios. This type of model is applicable to situations where typhoons, near-ships, and scour are more likely to amplify synchronously under extremely high-risk conditions.

[0041] The lower-tail correlation model assumes that the three factors exhibit stronger common underestimation or synchronous decay in the low-value extreme region, while their correlation is weaker in the high-value region, mainly demonstrating tail dependence under a joint low-exposure scenario. This type of model is suitable for situations where the three factors are constrained by common background factors and exhibit synchronous low levels in the low-risk phase.

[0042] It should be noted that, since the tail linkage mechanism of the three types of exposure states in the nearshore environment may be symmetrical, with only the upper tail being obvious, or only the lower tail being obvious, the candidate sets of the above three types of connection models are fitted with their dependency parameters respectively. Then, the Bayesian information criterion is used as a unified comparison index, and the one with the smallest Bayesian information criterion is selected as the optimal connection model to determine the tail dependency structure that is most consistent with the historical tail samples.

[0043] Step 34: Using the edge tail distribution and the optimal connectivity model as input, generate a uniformly distributed joint sample under the optimal connectivity model. Then, transform each edge tail distribution into a joint sample of typhoon severity index, near-ship risk, and scour risk through the inverse function of each edge tail distribution, forming a heavy-tailed joint distribution. Output the threshold, parameters, and version identifier of the heavy-tailed joint distribution. The uniformly distributed joint sample refers to a joint random sample that satisfies the dependency structure of the optimal connectivity model within the probability interval of 0 to 1. The probability space sample can be restored to a ternary joint sample of the original dimensional space through the inverse function transformation of each edge tail distribution.

[0044] This embodiment ensures data continuity and reliability by filling in missing data and marking outliers in historical data on typhoon severity index, near-ship risk, and scour risk. A tail threshold is selected using the mean excess function and stability test, and a generalized Pareto distribution is fitted using the maximum likelihood method to accurately describe tail behavior. The optimal connectivity model is selected using the Bayesian information criterion, constructing three tail dependency structures: symmetric tail, upper tail correlation, and lower tail correlation, and generating a consistent distribution of joint samples to form a heavy-tailed joint distribution. This method provides reliable statistical evidence and improves the prediction accuracy of near-shore bridge risks under extreme exposure conditions.

[0045] In one embodiment of the present invention, identifying impact events and determining the posterior distribution of the deteriorated state and the exposed state includes:

[0046] Step 41: In the initial scheduling period, the initial prior is determined as the predicted prior. In non-initial scheduling periods, integral propagation is performed on the posterior distribution of the previous scheduling period according to the state transition relationship to obtain the predicted prior of the current scheduling period, and the time index and version identifier are recorded. The initial scheduling period is the scheduling period in which the security assessment is performed for the first time, and the initial prior is directly used as the predicted prior. The integral propagation refers to the temporal evolution push of the posterior distribution of the previous scheduling period according to the state transition relationship to obtain the prior prediction of the degradation state and the exposure state for the current scheduling period. Through this step, the predicted prior has a unified source and traceable mark in each scheduling period, providing prior input for subsequent likelihood coupling.

[0047] Step 42: When any data object among the Typhoon Severity Index, Near-Ship Risk, and Scour Risk exceeds its corresponding tail threshold, it is identified as an impact event. An impact event refers to an extreme exposure situation occurring within the current scheduling cycle, where the observed level of the exposure state significantly deviates from the normal range. The baseline noise parameter in the state transition relationship is replaced with an amplified noise parameter, forming a gated state transition relationship. The baseline noise parameter is the noise intensity parameter characterizing the uncertainty of natural time evolution in the state transition relationship, while the amplified noise parameter is a higher uncertainty intensity parameter used during the impact event. The gated state transition relationship refers to the state transition relationship after switching the baseline noise parameter with the impact event as the trigger condition. Through this step, the prior propagation has a more relaxed uncertainty description during the impact event, matching the possible transition characteristics between the deteriorated state and the exposed state under extreme exposure.

[0048] Step 43: The product of the observation likelihood model and the prediction prior is used as the unnormalized posterior, and the integral of this product over the entire state space is used as the normalization constant to normalize the unnormalized posterior, thus obtaining the posterior distribution of the current deteriorated state and the exposed state. Here, the unnormalized posterior is the joint probability obtained by multiplying the observation likelihood model and the prediction prior, which has not yet met the normalization requirement of the probability distribution; the entire state space is the joint value space of the deteriorated state and the exposed state. Through this step, the Bayesian coupling update of the observation evidence and the prediction prior is realized, and the posterior distribution of the current scheduling period is obtained.

[0049] Step 44: The posterior distribution, time index, impact event indication, gating coefficient, tail threshold and version identifier are summarized to generate a posterior object. The integral result of the log ratio of the posterior distribution and the predicted prior in the whole state space is used as the information gain, and the current posterior distribution is determined as the initial prior for the next scheduling period. The system comprises the following components: Impact Event Indicator (PEI) is a binary label representing whether an impact event has occurred in the current scheduling cycle; Gating Coefficient is a constant used to amplify the reference noise parameter, with values ​​corresponding to the PPI; Posterior Object is a structured encapsulation of the current Bayesian update result and its gating state, containing at least the posterior distribution representation, time index, impact event indicator, gating coefficient, tail threshold, and version identifier; Information Gain measures the update magnitude brought about by the observed evidence relative to the predicted prior, obtained by integrating the logarithm ratio of the posterior distribution to the predicted prior over the entire state space; Determining the current posterior distribution as the initial prior for the next scheduling cycle means using the posterior distribution in the posterior object as the starting point input for the prior of subsequent scheduling cycles, thus forming a sequential Bayesian update link across scheduling cycles. This step ensures that the posterior results, impact gating state, and version information are uniformly archived, and that prior inheritance for the next scheduling cycle is achieved.

[0050] This embodiment ensures that the predicted prior has a unified source and traceable marker across all scheduling cycles by integrating the initial prior and state transition relationships, providing reliable prior input for subsequent likelihood coupling. By introducing shock event identification and gating state transition relationships, the looseness of uncertainty description is improved under extreme exposure conditions, ensuring accurate capture of exposure state transition characteristics. Bayesian coupled updates effectively fuse observational evidence with predicted priors to obtain the posterior distribution of the current scheduling cycle, thus providing a basis for prior inheritance in the next cycle and forming a stable cross-cycle update chain. Furthermore, the calculation of information gain enhances the model's sensitivity to changes in observed data, enabling Bayesian updates to reflect actual exposure changes and improving the system's prediction accuracy and resilience management capabilities.

[0051] In one embodiment of the present invention, based on the posterior distributions of the deteriorated state and the exposed state, the resistance distribution and the demand distribution are obtained, and the failure probability and reliability index are calculated, including:

[0052] Step 51: Read the posterior object, extract the posterior distributions of the deteriorated state and the exposed state according to the time index, generate an equal sample set, unify the effective area of ​​the cross section to square millimeters, unify the density of corrosion pits to per square millimeter, unify the prestress loss to megapascals, and unify the typhoon severity index, near-ship risk, and scour risk to dimensionless non-negative quantities, and record the sample size and version identifier; the equal sample set refers to the set of sample pairs simultaneously extracted from the joint posterior distribution of the deteriorated state and the exposed state, used to maintain the correspondence between the two states at the sample level;

[0053] Step 52: Retrieve the nominal resistance strength constant, resistance reduction factor, and strength reduction weight from the material strength parameter table. For each sample, calculate the strength reduction factor based on the corrosion pit density and strength reduction weight, and calculate the prestress reduction factor based on the prestress loss and the prestress constant at the end of tensioning. Multiply the nominal resistance strength constant by the two reduction factors, and then multiply by the effective cross-sectional area and resistance reduction factor to obtain the resistance sample. Summarize these to form the resistance distribution. The material strength parameter table must contain at least the nominal resistance strength constant, resistance reduction factor, and strength reduction weight. The three types of constant parameters are: the nominal strength constant, which is the baseline strength level of the component under no degradation influence; the strength reduction coefficient, which is a proportional coefficient comprehensively reflecting the strength reduction mechanism of the component; and the strength reduction weight, which is a weight parameter for the contribution of corrosion pit density and prestress loss to the strength; the strength reduction factor, which characterizes the reduction ratio of corrosion pit density to the nominal strength constant; and the prestress reduction factor, which characterizes the reduction ratio of prestress loss relative to the prestress constant at the end of tensioning; and the resistance distribution, which describes the uncertainty range of the component's resistance within the current scheduling cycle. Through this step, a probabilistic mapping from the posterior distribution of the degradation state to the resistance distribution is achieved.

[0054] Specifically, the formula for calculating the intensity reduction factor is: , Indicates the intensity reduction factor. Indicates the density of corrosion pits. The formula for calculating the prestress reduction factor is: (The formula is missing from the original text.) , Indicates the prestress reduction factor. Indicates prestress loss. This represents the prestress constant at the completion of tensioning. This indicates the intensity reduction weight.

[0055] Step 53: Retrieve the typhoon severity index coefficient, near-ship risk coefficient, scour risk coefficient, and cross-section influence coefficient from the equivalent internal force coefficient table. For each sample, obtain the equivalent internal force base value by linearly combining the three coefficients with the typhoon severity index, near-ship risk, and scour risk. Then, correct the base value according to the relative loss of effective cross-sectional area and the cross-sectional influence coefficient to obtain the demand sample, which is then summarized to form the demand distribution. The equivalent internal force coefficient table contains at least four types of constant parameters: typhoon severity index coefficient, near-ship risk coefficient, scour risk coefficient, and cross-section influence coefficient. The relative loss of effective cross-sectional area refers to the proportion of loss of effective cross-sectional area relative to the constant cross-sectional area. The cross-sectional influence coefficient is used to characterize the amplification or correction effect of changes in effective cross-sectional area on the equivalent internal force on the demand side. The demand distribution is used to describe the uncertainty range of component demand within the current scheduling cycle. Through this step, the probabilistic mapping from the exposed state posterior distribution and its coupling information with the deteriorated state to the demand distribution is realized.

[0056] Step 54: For each sample, compare the demand sample with the resistance sample. Calculate the proportion of samples where the demand sample is greater than the resistance sample as the failure probability. Use the failure probability to inversely calculate the reliability index using the standard normal distribution function. The standard normal distribution function inverse calculation refers to mapping the failure probability to a reliability index that is consistent with the structural reliability, so that the reliability index and the failure probability maintain a statistical correspondence.

[0057] This embodiment derives the resistance and demand distributions based on the posterior distributions of the deteriorated and exposed states, and then calculates the failure probability and reliability indices, achieving a quantitative assessment of near-shore bridge operation and maintenance action optimization and scheduling management based on Bayesian updates. By extracting samples using a unified time index and applying material strength parameter tables and equivalent internal force coefficient tables, the strength reduction factor and prestress reduction factor are accurately calculated, reflecting the impact of deterioration and exposure on the component's resistance and demand. Based on this, the failure probability is calculated and the reliability index is obtained through inverse calculation using a standard normal distribution, ensuring the accuracy and consistency of risk and reliability measurements in the operation and maintenance decision-making process. This method provides data-driven, precise action optimization and scheduling decision support for the resilience management of near-shore bridges, improving the system's predictive ability for bridge safety under extreme exposure conditions and enhancing operation and maintenance efficiency.

[0058] In one embodiment of the present invention, tail sensitivity diagnosis is performed based on failure probability and reliability indicators, and action plans, resource allocation, and intervention schedules are determined through observation, re-inspection, repair, reinforcement, and load limiting, including:

[0059] Step 61: Read the resistance distribution, demand distribution, failure probability, and reliability index, and read the heavy-tailed joint distribution. Under the preset confidence level, calculate the high quantile values ​​of the typhoon severity index, near-ship risk, and scour risk respectively to form the exposure state high quantile vector. The preset confidence level is a pre-set tail diagnosis probability level, which is used to determine the extreme quantile positions of the exposure state. The high quantile value refers to the quantile estimate distributed in the corresponding edge tail. The exposure state high quantile vector is used to uniformly represent the joint extreme exposure situation.

[0060] Step 62: Under the high quantile vector of the exposed state, the effective cross-sectional area, corrosion pit density, and prestress loss are reduced by a preset perturbation amplitude, respectively, and the failure probability increment is recalculated. The three failure probability increments are then weighted and summed to obtain the comprehensive tail sensitivity. The components are then sorted in descending order to generate a priority list. The preset perturbation amplitude is a small change set for the effective cross-sectional area, corrosion pit density, and prestress loss, respectively, to characterize the sensitivity of the risk to the degradation state perturbation under the tail condition while maintaining physical feasibility. By using the resistance distribution and demand distribution formation and failure probability calculation method from steps 51 to 54, the corresponding failure probability is obtained, and then the failure probability increment is obtained.

[0061] Step 63: Set action parameters for observation, re-inspection, repair, reinforcement, and load limitation. The uncertainty scaling factor for re-inspection, the increase in effective cross-sectional area and the decrease in corrosion pit density for repair, the upward adjustment of the resistance reduction factor for reinforcement, and the demand coefficient for load limitation are all preset constants. Under each candidate action, the failure probability is recalculated based on the resistance distribution and demand distribution. The expected total cost is determined by multiplying the failure probability and the failure loss constant, and then adding the implementation cost. The action with the minimum expected total cost is selected as the optimal action for a single component. Observation, re-inspection, repair, reinforcement, and load limitation are five candidate action types for the evaluated component, corresponding to different status confirmations, information supplements, or engineering interventions. Methods; Action parameters are a set of deterministic parameters used to describe the impact of each candidate action on the distribution of resistance force, demand distribution, or posterior uncertainty; the uncertainty scaling factor for re-inspection is used to proportionally scale the uncertainty magnitude of the posterior distribution; the increase in effective cross-sectional area and the decrease in corrosion pit density are used to deterministically correct the effective cross-sectional area and corrosion pit density in the deteriorated state; the upward adjustment value of the resistance reduction factor for reinforcement is used to deterministically increase the resistance reduction factor in the resistance mapping; the demand factor for load limiting is used to proportionally scale the demand sample; the failure loss constant is a pre-set component failure cost parameter; the implementation cost is the direct cost parameter corresponding to each candidate action;

[0062] Step 64: Under a given budget constraint and scheduling period set, determine the action choices for components and periods with the target of the sum of expected risk reductions. Constraint that action choices for the same component within the same period are mutually exclusive, forming an action plan and intervention schedule. The budget constraint is the upper limit of the total cost available for action implementation across all evaluated components; the scheduling period set is a pre-defined discrete execution period within the current and subsequent scheduling periods; the expected risk reduction is the decrease in the probability of failure risk cost of a component after taking a candidate action compared to not taking action; the mutual exclusion of action choices for the same component within the same period ensures that a single component executes only one type of action within a single period; the action plan and intervention schedule are structured outputs containing component number, action type, execution period, and corresponding cost parameters, used to lock in action implementation plans across components and periods.

[0063] This embodiment quantifies the sensitivity of different risk factors to the degradation state of components by performing tail sensitivity diagnosis based on failure probability and reliability indicators, combined with the high quantile vector of the exposure state and the perturbation amplitude. By calculating the failure probability increment and generating a priority list, priority treatment of high-risk components is ensured. Based on the principle of minimizing expected total cost, action plans such as observation, re-inspection, repair, reinforcement, and load limiting are optimized, and resource allocation and intervention scheduling are carried out under budget constraints and scheduling cycles. Through this process, the present invention can achieve comprehensive resilience management across components and cycles, improve the optimization decision-making capability of near-shore bridge operation and maintenance, enable the system to flexibly respond to extreme exposure situations, reduce costs and improve resource utilization efficiency, and provide scientific decision support for the long-term safe operation of bridges.

[0064] In one embodiment of the present invention, a toughness notch score is calculated and graded to generate a set of toughness strategy parameters, including:

[0065] Step 71: Read the failure probability, reliability index and priority list, align the three according to the same time index and verify the value range, remove records that are not within the value range and complete the missing test to form a resilience input set, to ensure consistency within the same time period, as well as the completeness and accuracy of the input data;

[0066] Step 72: Set a lower limit for the reliability index and an upper limit for the failure probability. For each component in the toughness input set, calculate the difference between the lower limit for the reliability index and the reliability index of that component. If the difference is positive, record the difference as a reliability index gap; if the difference is not positive, record the reliability index gap as zero. Calculate the difference between the failure probability of that component and the upper limit for the failure probability. If the difference is positive, record the difference as a failure probability gap; if the difference is not positive, record the failure probability gap as zero. This step is used to quantify the deviation of each component from the preset safety threshold, providing a numerical basis for subsequent analysis.

[0067] Step 73: The reliability index gap and failure probability gap are weighted and summed according to the preset gap weights to obtain the toughness gap score. The higher the toughness gap score, the more severe the component's toughness deficiency. The toughness gap score is compared with the preset grading threshold to determine the toughness gap level. For example, the set high, medium and low three-level thresholds determine the high gap level, medium gap level and low gap level. The high gap level corresponds to severe toughness deficiency, the medium gap level corresponds to moderate toughness deficiency, and the low gap level corresponds to slight toughness deficiency. Each component is assigned a priority number according to the priority list. The higher the priority, the smaller the number. The priority number is converted into a priority weight according to the monotonic mapping rule. The priority weight is coupled with the toughness gap level to generate a toughness penalty weight, which is used to reflect the importance of the component and the severity of the toughness deficiency.

[0068] Step 74: The reliability index lower limit, failure probability upper limit, resilience penalty weight, budget constraint and scheduling cycle set are summarized and encapsulated to form a resilience strategy parameter set, and the time index and version identifier are bound to the output.

[0069] This embodiment accurately quantifies the reliability and failure probability of components by calculating and classifying toughness gap scores. By introducing a priority list and a toughness penalty weight mechanism, it ensures that components with high toughness gaps are addressed first when resources are limited, thereby maximizing resource utilization efficiency and effectively avoiding high-risk components. Furthermore, the generated toughness strategy parameter set provides a traceable and clear basis for subsequent operation and maintenance decisions, enabling the bridge to maintain efficient operation and maintenance management and reasonable resource allocation when facing long-term service and extreme exposure scenarios, thus ensuring the safety and toughness level of the bridge structure.

[0070] In one embodiment of the present invention, the action plan and intervention schedule are broken down into a list of action tasks, and costs and schedule deviations are calculated, and the amount of risk improvement and resilience decline are recalculated, including:

[0071] Step 81: Read the action plan and intervention schedule, break them down by components and schedule cycle to obtain a list of action tasks. Each action task includes action type, component identifier, plan implementation cycle, plan implementation cost and plan risk reduction amount, and is bound to version identifier;

[0072] Step 82: Read the execution feedback data corresponding to the action task list according to the time index. The execution feedback data includes task status, actual implementation cost and actual completion time. Validate the value range of the execution feedback data, remove abnormal data that exceeds the reasonable range, use interpolation to complete the missing data, and accurately align the preprocessed execution feedback data with the action task list according to component identification and planned implementation cycle to ensure that the planned information of each task matches the corresponding actual execution information one by one.

[0073] Step 83: For each action task, calculate the difference between the actual implementation cost and the planned implementation cost to obtain the cost deviation; calculate the difference between the actual completion time and the corresponding completion time of the planned implementation cycle to obtain the schedule deviation; for completed action tasks, read the failure probability and reliability index before the action, and recalculate the failure probability and reliability index after the action based on the new round of passive agent observation data under the same time index; calculate the difference between the failure probability before and after the action to obtain the risk improvement amount; the larger the risk improvement amount, the more obvious the improvement effect; calculate the difference between the reliability index after the action and the reliability index before the action to obtain the toughness decline amount; a positive toughness decline amount indicates that the action has improved the toughness level of the component, while a negative amount indicates that the toughness of the component has declined, and the problem of insufficient toughness should be guarded against.

[0074] Step 84: Summarize the action task list, cost deviation, schedule deviation, risk improvement amount, resilience decline amount, time index and version identifier to generate action audit records, and write them into the audit chain according to the time index.

[0075] This embodiment further quantifies the effectiveness of each task by breaking down the action plan and intervention schedule into a detailed list of action tasks and calculating cost and schedule deviations based on execution feedback data. By comparing the differences between actual costs and schedules and planned values, deviations in the operation and maintenance plan can be identified and adjusted in a timely manner. By recalculating the amount of risk improvement and resilience reduction, the actual effectiveness of each operation and maintenance task in reducing risk and improving resilience is effectively evaluated. In addition, the generation of action audit records and the construction of audit chains enable the precise tracking of the execution status of each operation and maintenance task, further improving the transparency and traceability of the decision-making process. Through this process, the present invention can improve the scientific nature, accuracy, and resilience of near-shore bridge operation and maintenance management, ensuring reliability and safety under extreme conditions.

[0076] In one embodiment of the present invention, the action plan is determined after resource quota stratification based on resilience gap level, including:

[0077] Step 91: Read the toughness gap level, toughness penalty weight and priority list, filter the high gap component set according to the toughness gap level, and then extract the components with the first preset ranking from the high gap component set according to the priority list to form a locked component set; the components in this set are dual key components with severe toughness deficiency and high structural importance, and are the core guarantee objects for toughness improvement.

[0078] Step 92: Under budget constraints, set rigid resource quotas for the locked component set, and the remaining budget forms variable resource quotas to meet the resilience improvement needs of the remaining components; the components and cycles of the rigid resource quotas cannot be removed by subsequent scheduling adjustments; the budget constraint refers to the upper limit of the total resource amount that can be invested in operation and maintenance.

[0079] Step 93: Within the rigid resource quota, directly execute step 63 to determine the optimal action for a single component within the locked component set, and fix its action type and implementation cycle;

[0080] Step 94: Execute step 64 on the remaining components within the variable resource quota to form action plans and intervention schedules for the remaining components, and summarize them with the fixed actions of the locked component set into action plans and intervention schedules for all components.

[0081] This embodiment effectively optimizes resource allocation by stratifying and allocating resources based on the resilience gap level and priority list of components. This ensures that components with high resilience gaps are addressed first, thereby reducing the overall risk of the system. By setting rigid resource quotas for locked components, it ensures that high-risk components can be processed as planned, avoiding risk escalation due to insufficient resources. By combining budget constraints with optimized resource allocation based on scheduling cycles, a complete action plan and intervention schedule are formed, ensuring efficient resource utilization and timely execution of maintenance tasks. This improves the scientific nature and resilience of near-shore bridge operation and maintenance management, ensuring the long-term stable operation of the bridge.

[0082] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0083] The embodiments of the present invention have been described above, but the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of the present embodiments, all of which are within the protection scope of the present embodiments.

Claims

1. A nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management, characterized in that, include: The State and Data Modeling Module is used to define the effective cross-sectional area, corrosion pit density, and prestress loss as the deterioration state for near-shore bridge components, and define the typhoon severity index, near-ship risk, and scour risk as the exposure state. It forms an initial prior and state transition relationship and acquires passive proxy observation data, where the passive proxy observation data represents a set of data that can indirectly characterize the deterioration and exposure levels of the components. The likelihood and tail distribution module is used to map passive proxy observation data with cross-sectional effective area, corrosion pit density, prestress loss, typhoon severity index, near-ship risk, and scour risk to form an observational likelihood model and establish a heavy-tail joint distribution based on historical data. The impact identification and update module is used to identify impact events and determine the posterior distribution of the deteriorated state and the exposed state based on the initial prior and state transition relationship, the observation likelihood model and the heavy-tailed joint distribution. The safety index calculation module is used to obtain the resistance distribution and demand distribution based on the posterior distribution, and to calculate the failure probability and reliability index. The operation and maintenance optimization scheduling module is used to perform tail sensitivity diagnosis based on failure probability and reliability indicators, and to determine action plans, resource allocation and intervention schedules in observation, re-inspection, repair, reinforcement and load limiting; among them, tail sensitivity diagnosis is used to quantify the sensitivity of different risk factors to the degradation state of components. The toughness target configuration module is used to read the failure probability, reliability index and priority list, calculate the toughness gap score and classify it, and generate a toughness strategy parameter set; among which, the toughness gap score represents the degree of component toughness insufficiency; The monitoring and auditing module is used to break down action plans and intervention schedules into action task lists, calculate costs and schedule deviations, and recalculate risk improvement and resilience decline.

2. The nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management according to claim 1, characterized in that, Passive agent observation data includes the metal loss ratio of the electrical corrosion probe, surface chloride ion concentration, ambient temperature, humidity and salinity, corrosion level, near-ship event count, and inspection and maintenance records. Define the degraded state and the exposed state, and determine the relationship between the initial prior and the state transition, including: Step 11: Set the value range of each parameter in the deteriorated state and the exposed state, as well as the cross-sectional area constant and the prestress constant when tensioning is completed; Step 12: Set the mean and covariance of each parameter in the deteriorated state and the mean and covariance of each parameter in the exposed state, and generate the initial prior distributions for the deteriorated state and the exposed state respectively to form the initial prior object set; Step 13: Obtain the typhoon severity index, near-ship risk, and scour risk from the previous scheduling cycle. Use these as inputs and update the typhoon severity index, near-ship risk, and scour risk according to the rule that the first-order autoregressive coefficient is greater than 0 and less than 1. Update the effective area of ​​the cross section according to the non-increasing rule and update the corrosion pit density and prestress loss according to the non-decreasing rule. When the update result exceeds the corresponding value range, execute the boundary truncation rule to generate the state transition relationship.

3. The nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management according to claim 1, characterized in that, The formation of the observation likelihood model includes: Step 21: Input the passive proxy observation data into the database according to the time index, and align it with the effective area of ​​the cross section, corrosion pit density, prestress loss, typhoon severity index, near-ship risk, and scour risk. Remove records that exceed their respective value ranges to complete the missing measurement and outlier marking. Step 22: Subtract the metal loss ratio from 1, multiply the result by the cross-sectional area constant to obtain a point estimate of the effective cross-sectional area, and establish a normal conditional probability distribution of the effective cross-sectional area using this point estimate as the mean and the measurement error of the resistance corrosion probe as the variance; obtain the grade occurrence rate parameter according to the corrosion grade table, and establish a Poisson conditional probability distribution as the strength parameter of the corrosion pit density; obtain a point estimate of the prestress loss by subtracting the prestress remeasurement value from the prestress constant when tensioning is completed, and establish a normal conditional probability distribution of the prestress loss using the remeasurement uncertainty as the variance. Step 23: Filter events with a nearest encounter distance less than the nearest encounter distance threshold from the near-ship event count. Calculate the weighted event intensity for each event by summing the weights. Establish a Poisson conditional probability distribution for near-ship risk using the product of the weighted event intensity and the proportionality coefficient. Calculate the point estimate of the typhoon severity index by summing the reciprocal of the bridge distance, the pressure deficit, and the maximum sustained wind speed. Establish a normal conditional probability distribution of the typhoon severity index using the measurement error as the variance. Calculate the point estimate of the scour risk by summing the water level and flow velocity according to the calibration weights. Establish a normal conditional probability distribution of the scour risk using the measurement error as the variance. Step 24: Under the same time index, combine the six conditional probability distributions obtained in Step 22 and Step 23 according to the independence combination rule to form the observation likelihood model.

4. The nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management according to claim 1, characterized in that, Establish a heavy-tailed joint distribution based on historical data, including: Step 31: Organize the historical data of typhoon severity index, near-ship risk, and scour risk in the order of scheduling cycle, perform missing data filling and outlier marking, remove records that are not within their respective value ranges, form a historical dataset for edge modeling, and register the data source, value range and version identifier. Step 32: The mean excess function and stability test are used to select the tail threshold for the typhoon severity index, near-ship risk and scour risk. Excess samples are extracted above the tail threshold. The maximum likelihood method is used to fit the generalized Pareto distribution. The tail threshold, scale parameter and shape parameter and their confidence intervals are recorded to form three marginal tail distributions. Step 33: Map the probability values ​​of the three edge tail distributions to the interval between 0 and 1, calculate the rank correlation coefficient, construct a candidate set of connection models including the symmetric tail connection model, the upper tail correlation connection model, and the lower tail correlation connection model, estimate the algorithm parameters respectively, and select the connection model with the smallest Bayesian information criterion as the optimal connection model based on the value of the Bayesian information criterion. Step 34: Using the edge tail distribution and the optimal connection model as input, generate a joint sample with a consistent distribution under the optimal connection model, and convert it into a joint sample of typhoon severity index, near-ship risk and scour risk one by one through the inverse function of each edge tail distribution, forming a heavy-tailed joint distribution, and output the threshold, parameters and version identifier of the heavy-tailed joint distribution.

5. The nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management according to claim 1, characterized in that, Identify shock events and determine the posterior distributions of the degradation and exposure states, including: Step 41: In the initial scheduling period, the initial prior is determined as the predicted prior. In non-initial scheduling periods, the posterior distribution of the previous scheduling period is integrally propagated according to the state transition relationship to obtain the predicted prior of the current scheduling period. Step 42: When any data object among the typhoon severity index, near-ship risk, and scour risk exceeds the corresponding tail threshold, it is determined to be an impact event, and the reference noise parameter in the state transition relationship is replaced with the amplified noise parameter to form a gated state transition relationship. Step 43: The product of the observation likelihood model and the prediction prior is used as the unnormalized posterior, and the integral of the product over the whole state space is used as the normalization constant to normalize the unnormalized posterior, so as to obtain the posterior distribution of the current deteriorated state and the exposed state. Step 44: The posterior distribution, time index, impact event indication, gating coefficient, tail threshold and version identifier are summarized to generate a posterior object. The integral result of the log ratio of the posterior distribution and the predicted prior in the whole state space is used as the information gain, and the current posterior distribution is determined as the initial prior for the next scheduling period.

6. The nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management according to claim 1, characterized in that, Based on the posterior distributions of the deteriorated and exposed states, the resistance distribution and demand distribution are obtained, and the failure probability and reliability indices are calculated, including: Step 51: Read the posterior object, extract the posterior distribution of the degraded state and the exposed state according to the time index, generate an equal sample set, and record the sample size and version identifier; Step 52: Read the nominal resistance strength constant, resistance reduction factor and strength reduction weight from the material strength parameter table. For each sample, calculate the strength reduction factor based on the corrosion pit density and strength reduction weight, and calculate the prestress reduction factor based on the prestress loss and the prestress constant when tensioning is completed. Multiply the nominal resistance strength constant by the two reduction factors, and multiply by the effective area of ​​the cross section and the resistance reduction factor to obtain the resistance sample. Summarize them to form the resistance distribution. Step 53: Read the typhoon severity index coefficient, near-ship risk coefficient, scour risk coefficient and cross-section influence coefficient from the equivalent internal force coefficient table. For each sample, obtain the equivalent internal force base value by linear combination of the three coefficients with the typhoon severity index, near-ship risk and scour risk, and correct it according to the relative loss of effective cross-section area and cross-section influence coefficient to obtain the demand sample and summarize it to form the demand distribution. Step 54: For each sample, compare the demand sample with the resistance sample, and calculate the proportion of the demand sample that is greater than the resistance sample as the failure probability. The reliability index is then obtained by back-calculating the failure probability using the standard normal distribution function.

7. The nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management according to claim 1, characterized in that, Tail sensitivity diagnosis is performed based on failure probability and reliability indicators. Action plans, resource allocation, and intervention schedules are determined through observation, re-inspection, repair, reinforcement, and load limiting, including: Step 61: Read the resistance distribution, demand distribution, failure probability and reliability index, and read the heavy-tailed joint distribution. Under the preset confidence level, calculate the high quantile values ​​of typhoon severity index, near-ship risk and scour risk respectively, and form the exposure state high quantile vector. Step 62: Under the high quantile vector of the exposed state, reduce the effective area of ​​the cross section, increase the density of corrosion pits, and increase the prestress loss according to the preset perturbation amplitude, respectively, recalculate the failure probability increment, and weight the three failure probability increments to obtain the comprehensive tail sensitivity, and sort the components in descending order to generate a priority list. Step 63: Set action parameters for observation, re-inspection, repair, reinforcement, and load limiting. Under each candidate action, recalculate the failure probability based on the resistance distribution and demand distribution. Determine the expected total cost by multiplying the failure probability and the failure loss constant, and then adding the implementation cost. Select the action with the minimum expected total cost as the optimal action for a single component. Step 64: Under the given budget constraints and scheduling cycle set, determine the action choices for components and cycles with the sum of expected risk reduction as the objective, constrain the action choices of the same component in the same cycle to be mutually exclusive, and form an action plan and intervention schedule.

8. The nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management according to claim 1, characterized in that, Calculate and classify the resilience gap score, and generate a set of resilience strategy parameters, including: Step 71: Read the failure probability, reliability index and priority list, align the three according to the same time index and verify the value range, remove records that are not within the value range and complete the missing test to form a resilience input set; Step 72: Set the lower limit of reliability index and the upper limit of failure probability. For each component in the toughness input set, calculate the difference between the lower limit of reliability index and the reliability index of the component. If the difference is positive, record the difference as the reliability index gap; if the difference is not positive, record the reliability index gap as zero. Calculate the difference between the failure probability of the component and the upper limit of failure probability. If the difference is positive, record the difference as the failure probability gap; if the difference is not positive, record the failure probability gap as zero. Step 73: The reliability index gap and the failure probability gap are weighted and summed according to the preset gap weight to obtain the toughness gap score. The toughness gap score is compared with the preset grading threshold to determine the toughness gap level. Each component is assigned a priority number according to the priority list. The priority number is converted into a priority weight according to the monotonic mapping rule. The priority weight is coupled with the toughness gap level to generate the toughness penalty weight. Step 74: The reliability index lower limit, failure probability upper limit, resilience penalty weight, budget constraint and scheduling cycle set are summarized and encapsulated to form a resilience strategy parameter set, and the time index and version identifier are bound to the output.

9. The nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management according to claim 1, characterized in that, The action plan and intervention schedule are broken down into a list of action tasks, and costs and schedule deviations are calculated, along with a recalculation of risk improvement and resilience decline, including: Step 81: Read the action plan and intervention schedule, break them down by components and schedule cycle to obtain a list of action tasks. Each action task includes action type, component identifier, plan implementation cycle, plan implementation cost and plan risk reduction amount, and is bound to version identifier; Step 82: Read the execution feedback data corresponding to the action task list according to the time index. The execution feedback data includes task status, actual implementation cost and actual completion time. Validate the value range of the execution feedback data, fill in the missing test data and align it with the action task list item by item. Step 83: For each action task, calculate the difference between the actual implementation cost and the planned implementation cost to obtain the cost deviation; calculate the difference between the actual completion time and the corresponding completion time of the planned implementation cycle to obtain the schedule deviation; for completed action tasks, read the failure probability and reliability index before the action, and recalculate the failure probability and reliability index after the action based on the new round of passive agent observation data under the same time index; calculate the difference between the failure probability before and after the action to obtain the risk improvement amount; calculate the difference between the reliability index after the action and the reliability index before the action to obtain the resilience decline amount. Step 84: Summarize the action task list, cost deviation, schedule deviation, risk improvement amount, resilience decline amount, time index and version identifier to generate action audit records, and write them into the audit chain according to the time index.

10. The nearshore bridge operation and maintenance action optimization and scheduling management system based on resilience management according to claim 7, characterized in that, Action plans will be determined based on resource quota stratification driven by resilience gap levels, including: Step 91: Read the toughness notch level, toughness penalty weight and priority list, filter the high notch component set according to the toughness notch level, and then extract the components with the previous preset ranking from the high notch component set according to the priority list to form a locked component set. Step 92: Set rigid resource quotas for the locked component set under budget constraints, and form variable resource quotas with the remaining budget; the components and periods of the rigid resource quotas cannot be removed by subsequent scheduling adjustments; Step 93: Within the rigid resource quota, directly execute step 63 to determine the optimal action for a single component within the locked component set, and fix its action type and implementation cycle; Step 94: Execute step 64 on the remaining components within the variable resource quota to form action plans and intervention schedules for the remaining components, and summarize them with the fixed actions of the locked component set into action plans and intervention schedules for all components.

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