Drainage pipe maintenance linkage system and method based on bridge deck runoff monitoring data

Abstract

The application discloses a drainage pipe maintenance linkage system and method based on bridge deck runoff monitoring data, and relates to the technical field of bridge deck drainage system operation and maintenance.The system comprises a benchmark state construction module, a monitoring data processing module, a performance attenuation backstepping module and a maintenance period calibration module; the benchmark state construction module establishes an initial performance benchmark and a monitoring database, the monitoring data processing module collects data and processes to form an evolution record, the performance attenuation backstepping module backsteps performance attenuation based on multi-parameter coupling, and the calibration module dynamically sets a threshold value and adjusts a maintenance period.The application solves the poor adaptability problem of traditional fixed-period maintenance through the closed loop of "initial benchmark construction-monitoring data processing-performance attenuation backstepping-maintenance period dynamic calibration", improves maintenance accuracy, avoids resource waste, and guarantees long-term stable operation of the drainage pipe.

Description

Technical Field

[0001] This invention relates to the field of bridge deck drainage system operation and maintenance technology, specifically to a drainage pipe maintenance linkage system and method based on bridge deck runoff monitoring data. Background Technology

[0002] In the operation and maintenance of highway bridge drainage systems, the rationality of the drainage pipe maintenance cycle directly affects maintenance costs and pipeline lifespan. Currently, the industry commonly adopts a fixed-cycle maintenance model, such as setting the maintenance interval to 3-6 months based on season or experience. This model fails to consider the dynamic changes in bridge runoff pollution load. The concentration of pollutants such as suspended solids and grease in bridge runoff fluctuates significantly with rainfall intensity, traffic volume, and seasonal climate (e.g., high pollution intensity during the rainy season and slow pollutant accumulation during the dry season), leading to a disconnect between the fixed cycle and the actual wear and tear of the pipeline. When the runoff pollution load is high, the pipeline is prone to rapid siltation, resulting in maintenance delays due to the fixed cycle, causing problems such as reduced pipeline flow capacity and poor drainage. Conversely, when the pollution load is low, fixed-cycle maintenance results in resource waste.

[0003] In existing technologies, although sensors are used to monitor the concentration of pollutants in runoff or the flow velocity in pipelines, a correlation mechanism between "dynamic evolution of runoff parameters - pipeline performance degradation - maintenance cycle" has not been established. Consequently, it is impossible to calibrate the maintenance cycle in reverse based on the actual parameter evolution trend, making it difficult to achieve a precise match between the maintenance cycle and the actual pipeline loss. Therefore, a maintenance cycle calibration scheme that can dynamically adapt to changes in pollution load is urgently needed. Summary of the Invention

[0004] The purpose of this invention is to provide a drainage pipe maintenance linkage system and method based on bridge deck runoff monitoring data, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] The method for coordinated maintenance of drainage pipes based on bridge runoff monitoring data includes the following steps:

[0007] S1. Obtain the initial performance parameters of the drainage pipe and use them as baseline state data; determine the bridge deck runoff parameters and pipe physical parameters to be monitored; and construct a parameter monitoring database to store the monitoring data.

[0008] S2. Collect bridge deck runoff parameters and pipeline physical parameters through monitoring equipment at preset cycles. After processing the collected bridge deck runoff parameters and pipeline physical parameters for effectiveness, update them to the parameter monitoring database according to time series to form a continuous record of parameter evolution.

[0009] S3. Using the baseline state data as a reference, extract monitoring data for continuous periods from the parameter monitoring database, calculate the changing trends of bridge deck runoff parameters and pipeline physical parameters, and based on the correlation between the changing trends of bridge deck runoff parameters and pipeline physical parameters and pipeline performance loss, inversely deduce the pipeline performance degradation state.

[0010] S4. Set a preset threshold for pipeline performance degradation, compare the parameter change trend with the preset threshold, and if the parameter change trend reaches the preset threshold, automatically adjust the original maintenance cycle; if it does not reach the preset threshold, maintain the original maintenance cycle, while continuously updating the monitoring data and entering the next round of analysis, forming a closed-loop linkage between parameter evolution and cycle calibration.

[0011] Furthermore, in S1, the initial performance parameters of the drainage pipe are obtained and used as baseline state data. The specific details are as follows:

[0012] Select n typical test sections before the drainage pipe is put into use, with each section spaced at 1 / n of the total length of the drainage pipe, where n is a positive integer between 3 and 5. For each test section, use pipe performance testing equipment to individually test its flow capacity Q and inner wall roughness Ra. The flow capacity Q is determined by a pipe hydraulic test. A constant flow of clean water is passed through the pipe section of a single test section, and the pipe outlet flow rate corresponding to that section is recorded three times consecutively. The average of the three measurements is taken as the flow capacity Q of that section. The inner wall roughness Ra is determined by a surface roughness meter. Five measuring points are evenly selected along the circumference of a single test section, and the roughness of each measuring point is measured. The average of the five measurements is taken as the inner wall roughness Ra of that section.

[0013] The average values ​​of the initial performance parameters of the drainage pipe are calculated. The formula for calculating the initial flow capacity reference value Q_base is: Q_base=(Q1+Q2+...+Qn) / n, where Q1 is the flow capacity of the first typical test section, Q2 is the flow capacity of the second typical test section, and so on, with Qn being the flow capacity of the nth typical test section. The formula for calculating the initial inner wall roughness reference value Ra_base is: Ra_base=(Ra1+Ra2+...+Ran) / n. Similarly, Ra1 is the inner wall roughness of the first typical test section, Ra2 is the inner wall roughness of the second typical test section, and so on, with Ran being the inner wall roughness of the nth typical test section. The initial flow capacity reference value Q_base and the initial inner wall roughness reference value Ra_base are used as reference state data.

[0014] The bridge deck runoff parameters to be monitored are determined to be suspended solids concentration C_ss and grease concentration C_oil, and the pipeline physical parameters are the inlet and outlet pressure drop ΔP and the fluid velocity v inside the pipe. A parameter monitoring database is constructed, with database fields including monitoring timestamp, suspended solids concentration C_ss, grease concentration C_oil, inlet and outlet pressure drop ΔP, fluid velocity v inside the pipe, and the corresponding initial flow capacity reference value Q_base and initial inner wall roughness reference value Ra_base.

[0015] Furthermore, in S2, the collected bridge deck runoff parameters and pipeline physical parameters undergo validity processing, as detailed below:

[0016] According to the preset period T, the suspended solids concentration C_ss and oil concentration C_oil are collected by the runoff sensor, the pressure drop ΔP at the inlet and outlet of the pipeline is collected by the pipeline pressure sensor, and the fluid velocity v in the pipe is collected by the pipeline velocity sensor.

[0017] Calculate the mean μ and standard deviation σ of 10 consecutive data collections for each parameter. If a certain collected value x satisfies |x-μ|>3σ, it is determined to be an outlier and removed. The outlier is filled with the linear interpolation result x'=x1+(t-t1)(x2-x1) / (t2-t1) of two adjacent valid data, where t is the time of outlier collection, t1 and t2 are the times of adjacent valid data collection, and x1 and x2 are the valid data at the corresponding times.

[0018] The suspended solids concentration C_ss, oil and grease concentration C_oil, pipeline inlet and outlet pressure drop ΔP, and fluid velocity v in the pipe are sorted according to time series. Each data point is associated with the collection timestamp and updated to the parameter monitoring database to form a continuous record of parameter evolution.

[0019] Furthermore, the specific details of S3 are as follows:

[0020] Extract monitoring data for m consecutive days from the parameter monitoring database, where m ≥ 30; define the monitoring data for day 1 as: C_ss_start, C_oil_start, ΔP_start, v_start; and the monitoring data for day m as: C_ss_end, C_oil_end, ΔP_end, v_end; where C_ss_start represents the suspended solids concentration on day 1; C_oil_start represents the oil and grease concentration on day 1; ΔP_start represents the pressure drop at the pipe inlet and outlet on day 1; v_start represents the fluid velocity in the pipe on day 1; C_ss_end represents the suspended solids concentration on day m; C_oil_end represents the oil and grease concentration on day m; ΔP_end represents the pressure drop at the pipe inlet and outlet on day m; and v_end represents the fluid velocity in the pipe on day m. Calculate the cumulative change of each parameter over time, where the formula for the cumulative influence coefficient of runoff pollutants K_pollut is:

[0021] K_pollut=(C_ss_end×v_end+C_oil_end×v_end) / (C_ss_start×v_start+C_oil_start×v_start); This coefficient quantifies the scouring-deposition accumulation effect of runoff pollutants on the inner wall of the pipe by the "product of pollutant concentration and flow velocity".

[0022] The rates of change of the pipeline's physical parameters are ΔP_rate and v_rate, and their corresponding calculation formulas are as follows:

[0023] ΔP_rate=(ΔP_end-ΔP_start) / ΔP_start×100%;

[0024] v_rate=(v_end-v_start) / v_start×100%;

[0025] A coupled correlation model of parameter variation and performance loss is established. Based on the initial flow capacity baseline value Q_base and the real-time flow velocity, the flow capacity attenuation rate η_Q of the pipeline is derived, and η_Q=1-(v_end×A) / Q_base, where A is the cross-sectional area of ​​the pipeline. Based on the hydraulic characteristics of pressure drop and flow velocity, the increase in the roughness of the pipeline inner wall η_Ra is derived, and η_Ra=[(Ra_current / Ra_base)-1]×100%, where Ra_current is the current equivalent roughness of the pipeline inner wall, which is obtained by inversion calculation based on the principles of fluid mechanics from the pipeline hydraulic monitoring data.

[0026] Introduce a synergistic attenuation coefficient K_sync, and K_sync=|η_Q-η_Ra| / max(η_Q,η_Ra), where |η_Q-η_Ra| represents the absolute value of the difference between the pipe flow capacity attenuation rate η_Q and the pipe inner wall roughness increase η_Ra, and max(η_Q,η_Ra) represents taking the larger value of η_Q and η_Ra.

[0027] If K_sync≤K0 (indicating that the decline in flow capacity is consistent with the increase in roughness, and the data is reliable) and η_Q≥0, η_Ra≥0, then the pipeline performance is determined to have undergone consistent decline. The comprehensive decline value η_total=α×η_Q+β×η_Ra is calculated, where K0 is a preset threshold, and α and β are the weighting coefficients for the performance impact based on historical bridge runoff monitoring data, α>β and α+β=1; the method for determining the weighting coefficients α and β is as follows:

[0028] By statistically analyzing cases of pipeline performance degradation in historical monitoring databases, the relative importance of flow capacity degradation and internal wall roughness increase on the overall pipeline performance is determined. For example, an objective weighting method based on data dispersion is used for calculation. The basic principle is that the greater the difference in the numerical change of a certain indicator, the greater the weight assigned to that indicator in the comprehensive evaluation. Since the change in flow capacity Q directly reflects the final drainage efficiency of the pipeline, its change is usually more significant than that of internal wall roughness Ra. Therefore, the weight allocation satisfies the relationship that α is greater than β.

[0029] If K_pollut is greater than the preset pollution risk threshold K_p, it is determined that the attenuation is mainly caused by pollutant deposition, and dredging and maintenance early warning information is output; if ΔP_rate is greater than PO and v_rate is less than V0, it is consistent with the performance attenuation conclusion, which enhances the reliability of the judgment, and P0 and V0 are preset physical parameter change thresholds.

[0030] If K_sync > K0, it is determined that there is a data anomaly, and the validity of the monitoring data is re-verified to avoid misjudgment of a single parameter; if the changing trends of ΔP_rate and v_rate show contradictory phenomena that are contrary to the principles of fluid mechanics, or if the value of K_pollut exceeds its effective range, it can provide direction for further data verification.

[0031] Specifically, when K_sync > K0, the following steps are taken to re-verify the validity of the monitoring data: Check the changing trends of the pressure drop rate ΔP_rate at the pipeline inlet and outlet and the fluid velocity rate v_rate within the pipe to confirm whether there are any contradictory phenomena that violate the principles of fluid mechanics; then check whether the value of the cumulative influence coefficient of runoff pollutants K_pollut is within its effective range; through targeted verification of these two key parameters, locate the cause of data anomalies and avoid affecting the pipeline performance degradation judgment result due to misjudgment of a single parameter.

[0032] Furthermore, S4 includes the following:

[0033] A dynamic threshold system for pipeline performance degradation is constructed, including the flow capacity degradation threshold η_Qth, the internal wall roughness increase threshold η_Rath, and the comprehensive early warning threshold η_th. The formula for calculating the flow capacity degradation threshold η_Qth is: η_Qth=1-(Q_min / Q_base), where Q_min is the minimum allowable flow capacity in the design. The formula for calculating the internal wall roughness increase threshold η_Rath is: η_Rath=(Ra_max-Ra_base) / Ra_base×100%, where Ra_max is the maximum allowable roughness provided by the pipeline material manufacturer. The formula for calculating the comprehensive early warning threshold η_th is: η_th=α×η_Qth+β×η_Rath; where α1 and β1 are weighting coefficients used to quantify the relative importance of the flow capacity degradation threshold η_Qth and the internal wall roughness increase threshold η_Rath in pipeline maintenance early warning judgment, and α1+β1=1. The specific values ​​need to be dynamically determined based on the actual application scenario of the pipeline, historical monitoring data, material characteristics, and risk assessment results.

[0034] When η_total ≥ η_th, the adjusted maintenance cycle T_new is calculated as follows: T_new = T0 × e^(-k × η_total / η_th), where T0 is the original maintenance cycle, and k is the attenuation sensitivity coefficient. The specific value needs to be dynamically determined based on the comprehensive attenuation value η_total of at least three complete operating cycles of the pipeline, the performance recovery rate after maintenance, historical data, pipeline material characteristic test data, and risk assessment results of actual application scenarios. When η_total < η_th, the attenuation acceleration a is calculated as a = (η_total - η_prev) / Δt, where η_prev is the previous attenuation value. The comprehensive value of periodic decay, where Δt is the number of days between two periods; if a > 0 (accelerated decay) and η_total + a × T0 ≥ η_th (predicted threshold to be reached within the original period), then adjust in advance to T_new = 0.8 × T0 (preventatively shortening by 20%); if a ≤ 0 (slower decay), maintain the original period T0; store the calculated η_total, η_th, T_new and the adjustment basis (such as decay acceleration a) along with the timestamp in the parameter monitoring database as the historical benchmark for the next period analysis; the next day, data is re-collected according to the preset period, and the analysis process is repeated to form a closed-loop linkage mechanism of parameter-threshold-period.

[0035] The drainage pipe maintenance linkage system based on bridge runoff monitoring data includes: a baseline state construction module, a monitoring data processing module, a performance degradation back calculation module, and a maintenance cycle calibration module.

[0036] The baseline state construction module acquires the initial performance parameters of the drainage pipe and generates baseline state data, determines the bridge deck runoff parameters and pipe physical parameters to be monitored, and constructs a parameter monitoring database.

[0037] The monitoring data processing module periodically collects parameter data through monitoring equipment, performs validity processing on the collected data, updates the parameter monitoring database according to the time series, and forms a parameter evolution record.

[0038] The performance degradation inference module is used to extract monitoring data over a continuous period of time, calculate the trend of parameter changes, and infer the performance degradation status of the pipeline based on the coupled correlation model between parameter changes and pipeline performance loss.

[0039] The maintenance cycle calibration module is used to construct a dynamic threshold system for pipeline performance degradation, compare the degradation status with the preset threshold and adjust the maintenance cycle to form a closed-loop linkage of parameters, thresholds and cycles.

[0040] Furthermore, the baseline state construction module includes an initial performance parameter acquisition unit and a monitoring parameter system construction unit;

[0041] The initial performance parameter acquisition unit selects a typical test section of the drainage pipe before it is put into use. The flow capacity and inner wall roughness of each section are detected by the pipe performance testing equipment. After calculating the average value, the initial flow capacity reference value and the initial inner wall roughness reference value are generated.

[0042] Furthermore, the monitoring data processing module includes a parameter periodic acquisition unit and a data validity processing unit;

[0043] The parameter periodic acquisition unit collects bridge deck runoff parameters and pipeline physical parameters through runoff sensors, pipeline pressure sensors, and pipeline flow velocity sensors at preset intervals.

[0044] The data validity processing unit calculates the mean and standard deviation of the continuously collected data for each parameter, removes outliers and fills in missing data through linear interpolation, and updates the processed data to the parameter monitoring database by associating time series with timestamps.

[0045] Furthermore, the performance degradation back-calculation module includes a parameter change trend calculation unit and a performance degradation coupled back-calculation unit;

[0046] The parameter change trend calculation unit extracts monitoring data from the parameter monitoring database within a continuous detection period and calculates the cumulative influence coefficient of runoff pollutants and the rate of change of pipeline physical parameters.

[0047] The performance degradation coupled reverse calculation unit derives the pipeline flow capacity degradation rate and inner wall roughness increase based on the initial reference value and real-time parameters. It introduces a degradation synergy coefficient to verify the reliability of the data and calculates the comprehensive degradation value to determine the pipeline performance degradation status. If the data is abnormal, it triggers the re-verification of the monitoring data.

[0048] Furthermore, the maintenance cycle calibration module includes a dynamic threshold system construction unit and a maintenance cycle dynamic adjustment unit;

[0049] The dynamic threshold system construction unit combines drainage pipe design specifications and pipe material characteristics to calculate the flow capacity attenuation threshold and the inner wall roughness increase threshold, and generates a comprehensive early warning threshold through weighted fusion.

[0050] The maintenance cycle dynamic adjustment unit dynamically adjusts the maintenance cycle based on the comparison between the comprehensive attenuation value and the comprehensive early warning threshold, and stores the adjustment results and associated timestamps in the parameter monitoring database for analysis in the next cycle, forming a closed-loop linkage.

[0051] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention breaks through the limitations of traditional fixed-cycle maintenance of drainage pipes. Through a complete closed-loop logic of "initial benchmark construction - monitoring data processing - performance degradation back-calculation - dynamic calibration of maintenance cycle", it achieves precise matching between the maintenance cycle and the actual performance loss state of the pipeline, effectively avoiding the waste of operation and maintenance resources or maintenance delays caused by traditional fixed-cycle maintenance; This invention constructs an initial performance benchmark through multi-section testing, which significantly improves the representativeness and reliability of the initial benchmark data compared with the single or two-end section testing methods in existing technologies, providing a precise reference for subsequent performance degradation analysis; At the same time, it adopts multi-parameter... The coupled correlation model, combined with attenuation synergy coefficient verification, overcomes the limitations of existing technologies that rely on a single parameter to determine performance degradation, significantly improving the accuracy of pipeline performance degradation determination and reducing misjudgments caused by abnormal single parameters. This invention employs a standardized data validity processing procedure to remove outliers and fill in missing values ​​in monitoring data, ensuring the continuity and reliability of the monitoring data and providing high-quality data support for performance degradation back-calculation and periodic adjustments. The overall method and logic of this invention have good automation adaptability, can stably support drainage pipe operation and maintenance work for a long time, effectively ensure the continuous and stable operation of bridge deck drainage systems, and reduce manual intervention costs and operation and maintenance complexity. Attached Figure Description

[0052] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0053] Figure 1 This is a schematic diagram of the drainage pipe maintenance linkage system based on bridge runoff monitoring data of the present invention. Detailed Implementation

[0054] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figure 1 The present invention provides the following technical solution:

[0056] The drainage pipe maintenance linkage system based on bridge runoff monitoring data includes: a baseline state construction module, a monitoring data processing module, a performance degradation back calculation module, and a maintenance cycle calibration module.

[0057] The baseline state construction module acquires the initial performance parameters of the drainage pipe and generates baseline state data, determines the bridge deck runoff parameters and pipe physical parameters to be monitored, and constructs a parameter monitoring database.

[0058] The monitoring data processing module periodically collects parameter data through monitoring equipment, performs validity processing on the collected data, updates the parameter monitoring database according to the time series, and forms a parameter evolution record.

[0059] The performance degradation inference module is used to extract monitoring data over a continuous period of time, calculate the trend of parameter changes, and infer the performance degradation status of the pipeline based on the coupled correlation model between parameter changes and pipeline performance loss.

[0060] The maintenance cycle calibration module is used to construct a dynamic threshold system for pipeline performance degradation, compare the degradation status with the preset threshold and adjust the maintenance cycle to form a closed-loop linkage of parameters, thresholds and cycles.

[0061] The baseline state construction module includes an initial performance parameter acquisition unit and a monitoring parameter system construction unit;

[0062] The initial performance parameter acquisition unit selects a typical test section of the drainage pipe before it is put into use. The flow capacity and inner wall roughness of each section are detected by the pipe performance testing equipment. After calculating the average value, the initial flow capacity reference value and the initial inner wall roughness reference value are generated.

[0063] The monitoring data processing module includes a parameter periodic acquisition unit and a data validity processing unit;

[0064] The parameter periodic acquisition unit collects bridge deck runoff parameters and pipeline physical parameters through runoff sensors, pipeline pressure sensors, and pipeline flow velocity sensors at preset intervals.

[0065] The data validity processing unit calculates the mean and standard deviation of the continuously collected data for each parameter, removes outliers and fills in missing data through linear interpolation, and updates the processed data to the parameter monitoring database by associating time series with timestamps.

[0066] The performance degradation back-calculation module includes a parameter change trend calculation unit and a performance degradation coupled back-calculation unit;

[0067] The parameter change trend calculation unit extracts monitoring data from the parameter monitoring database within a continuous detection period and calculates the cumulative influence coefficient of runoff pollutants and the rate of change of pipeline physical parameters.

[0068] The performance degradation coupled reverse calculation unit derives the pipeline flow capacity degradation rate and inner wall roughness increase based on the initial reference value and real-time parameters. It introduces a degradation synergy coefficient to verify the reliability of the data and calculates the comprehensive degradation value to determine the pipeline performance degradation status. If the data is abnormal, it triggers the re-verification of the monitoring data.

[0069] The maintenance cycle calibration module includes a dynamic threshold system construction unit and a maintenance cycle dynamic adjustment unit;

[0070] The dynamic threshold system construction unit combines drainage pipe design specifications and pipe material characteristics to calculate the flow capacity attenuation threshold and the inner wall roughness increase threshold, and generates a comprehensive early warning threshold through weighted fusion.

[0071] The maintenance cycle dynamic adjustment unit dynamically adjusts the maintenance cycle based on the comparison results of the comprehensive attenuation value and the comprehensive early warning threshold, and stores the adjustment results and the associated timestamps in the parameter monitoring database for analysis in the next cycle, forming a closed-loop linkage.

[0072] The method for coordinated maintenance of drainage pipes based on bridge runoff monitoring data includes the following steps:

[0073] S1. Obtain the initial performance parameters of the drainage pipe and use them as baseline state data; determine the bridge deck runoff parameters and pipe physical parameters to be monitored; and construct a parameter monitoring database to store the monitoring data.

[0074] S2. Collect bridge deck runoff parameters and pipeline physical parameters through monitoring equipment at preset cycles. After processing the collected bridge deck runoff parameters and pipeline physical parameters for effectiveness, update them to the parameter monitoring database according to time series to form a continuous record of parameter evolution.

[0075] S3. Using the baseline state data as a reference, extract monitoring data for continuous periods from the parameter monitoring database, calculate the changing trends of bridge deck runoff parameters and pipeline physical parameters, and based on the correlation between the changing trends of bridge deck runoff parameters and pipeline physical parameters and pipeline performance loss, inversely deduce the pipeline performance degradation state.

[0076] S4. Set a preset threshold for pipeline performance degradation, compare the parameter change trend with the preset threshold, and if the parameter change trend reaches the preset threshold, automatically adjust the original maintenance cycle; if it does not reach the preset threshold, maintain the original maintenance cycle, while continuously updating the monitoring data and entering the next round of analysis, forming a closed-loop linkage between parameter evolution and cycle calibration.

[0077] In S1, the initial performance parameters of the drainage pipe are obtained and used as baseline state data. The specific details are as follows:

[0078] Select n typical test sections before the drainage pipe is put into use, with each section spaced at 1 / n of the total length of the drainage pipe, where n is a positive integer between 3 and 5. For each test section, use pipe performance testing equipment to individually test its flow capacity Q and inner wall roughness Ra. The flow capacity Q is determined by a pipe hydraulic test. A constant flow of clean water is passed through the pipe section of a single test section, and the pipe outlet flow rate corresponding to that section is recorded three times consecutively. The average of the three measurements is taken as the flow capacity Q of that section. The inner wall roughness Ra is determined by a surface roughness meter. Five measuring points are evenly selected along the circumference of a single test section, and the roughness of each measuring point is measured. The average of the five measurements is taken as the inner wall roughness Ra of that section.

[0079] The average values ​​of the initial performance parameters of the drainage pipe are calculated. The formula for calculating the initial flow capacity reference value Q_base is: Q_base=(Q1+Q2+...+Qn) / n, where Q1 is the flow capacity of the first typical test section, Q2 is the flow capacity of the second typical test section, and so on, with Qn being the flow capacity of the nth typical test section. The formula for calculating the initial inner wall roughness reference value Ra_base is: Ra_base=(Ra1+Ra2+...+Ran) / n. Similarly, Ra1 is the inner wall roughness of the first typical test section, Ra2 is the inner wall roughness of the second typical test section, and so on, with Ran being the inner wall roughness of the nth typical test section. The initial flow capacity reference value Q_base and the initial inner wall roughness reference value Ra_base are used as reference state data.

[0080] The bridge deck runoff parameters to be monitored are determined to be suspended solids concentration C_ss and grease concentration C_oil, and the pipeline physical parameters are the inlet and outlet pressure drop ΔP and the fluid velocity v inside the pipe. A parameter monitoring database is constructed, with database fields including monitoring timestamp, suspended solids concentration C_ss, grease concentration C_oil, inlet and outlet pressure drop ΔP, fluid velocity v inside the pipe, and the corresponding initial flow capacity reference value Q_base and initial inner wall roughness reference value Ra_base.

[0081] In this embodiment, for a PVC drainage pipe (total length L=150m) of a cross-river bridge, assuming n=3, the test sections are located at L1=50m, L2=100m, and L3=150m respectively.

[0082] Flow capacity test: A constant flow of clean water was introduced into the pipe section where each section was located, and the flow rates were recorded as Q1=18m³ / h, Q2=17.8m³ / h, and Q3=18.2m³ / h. The flow rate was calculated according to the formula Q_base=(18+17.8+18.2) / 3=18m³ / h.

[0083] Inner wall roughness test: Measure Ra at 5 points on each cross section, Ra1=0.02mm, Ra2=0.021mm, Ra3=0.019mm, calculate Ra_base=(0.02+0.021+0.019) / 3=0.02mm;

[0084] In S2, the collected bridge deck runoff parameters and pipeline physical parameters undergo validity processing, as detailed below:

[0085] According to the preset period T, the suspended solids concentration C_ss and oil concentration C_oil are collected by the runoff sensor, the pressure drop ΔP at the inlet and outlet of the pipeline is collected by the pipeline pressure sensor, and the fluid velocity v in the pipe is collected by the pipeline velocity sensor.

[0086] Calculate the mean μ and standard deviation σ of 10 consecutive data collections for each parameter. If a certain collected value x satisfies |x-μ|>3σ, it is determined to be an outlier and removed. The outlier is filled with the linear interpolation result x'=x1+(t-t1)(x2-x1) / (t2-t1) of two adjacent valid data, where t is the time of outlier collection, t1 and t2 are the times of adjacent valid data collection, and x1 and x2 are the valid data at the corresponding times.

[0087] The suspended solids concentration C_ss, oil and grease concentration C_oil, pipeline inlet and outlet pressure drop ΔP, and fluid velocity v in the pipe are sorted according to time series. Each data point is associated with the collection timestamp and updated to the parameter monitoring database to form a continuous record of parameter evolution.

[0088] In this embodiment, assuming a preset period T = 12 hours (collected daily at 9:00 and 21:00), during a certain collection of C_ss, the mean of 10 consecutive data points is μ = 50 mg / L, the standard deviation is σ = 5 mg / L, and a certain collected value is x = 70 mg / L (|70-50| = 20 > 3 × 5 = 15), which is determined to be an outlier. The adjacent valid data points are t1 = 9:00 (x1 = 48 mg / L) and t2 = 21:00 (x2 = 52 mg / L). The outlier collection time is t = 15:00. According to the formula, x' = 48 + (15-9) × (52-48) / (21-9) = 50 mg / L. After completion, the data is updated to the database according to the time series.

[0089] The specific details of S3 are as follows:

[0090] Extract monitoring data for m consecutive days from the parameter monitoring database, where m ≥ 30; define the monitoring data for day 1 as: C_ss_start, C_oil_start, ΔP_start, v_start; and the monitoring data for day m as: C_ss_end, C_oil_end, ΔP_end, v_end; where C_ss_start represents the suspended solids concentration on day 1; C_oil_start represents the oil and grease concentration on day 1; ΔP_start represents the pressure drop at the pipe inlet and outlet on day 1; v_start represents the fluid velocity in the pipe on day 1; C_ss_end represents the suspended solids concentration on day m; C_oil_end represents the oil and grease concentration on day m; ΔP_end represents the pressure drop at the pipe inlet and outlet on day m; and v_end represents the fluid velocity in the pipe on day m. Calculate the cumulative change of each parameter over time, where the formula for the cumulative influence coefficient of runoff pollutants K_pollut is:

[0091] K_pollut=(C_ss_end×v_end+C_oil_end×v_end) / (C_ss_start×v_start+C_oil_start×v_start); This coefficient quantifies the scouring-deposition accumulation effect of runoff pollutants on the inner wall of the pipe by the "product of pollutant concentration and flow velocity".

[0092] The rates of change of the pipeline's physical parameters are ΔP_rate and v_rate, and their corresponding calculation formulas are as follows:

[0093] ΔP_rate=(ΔP_end-ΔP_start) / ΔP_start×100%;

[0094] v_rate=(v_end-v_start) / v_start×100%;

[0095] A coupled correlation model of parameter variation and performance loss is established. Based on the initial flow capacity baseline value Q_base and the real-time flow velocity, the flow capacity attenuation rate η_Q of the pipeline is derived, and η_Q=1-(v_end×A) / Q_base, where A is the cross-sectional area of ​​the pipeline. Based on the hydraulic characteristics of pressure drop and flow velocity, the increase in the roughness of the pipeline inner wall η_Ra is derived, and η_Ra=[(Ra_current / Ra_base)-1]×100%, where Ra_current is the current equivalent roughness of the pipeline inner wall, which is obtained by inversion calculation based on the principles of fluid mechanics from the pipeline hydraulic monitoring data.

[0096] Introduce a synergistic attenuation coefficient K_sync, and K_sync=|η_Q-η_Ra| / max(η_Q,η_Ra), where |η_Q-η_Ra| represents the absolute value of the difference between the pipe flow capacity attenuation rate η_Q and the pipe inner wall roughness increase η_Ra, and max(η_Q,η_Ra) represents taking the larger value of η_Q and η_Ra.

[0097] If K_sync≤K0 (indicating that the decline in flow capacity is consistent with the increase in roughness, and the data is reliable) and η_Q≥0, η_Ra≥0, then the pipeline performance is determined to have undergone consistent decline. The comprehensive decline value η_total=α×η_Q+β×η_Ra is calculated, where K0 is a preset threshold, and α and β are the weighting coefficients for the performance impact based on historical bridge runoff monitoring data, α>β and α+β=1; the method for determining the weighting coefficients α and β is as follows:

[0098] By statistically analyzing cases of pipeline performance degradation in historical monitoring databases, the relative importance of flow capacity degradation and internal wall roughness increase on the overall pipeline performance is determined. For example, an objective weighting method based on data dispersion is used for calculation. The basic principle is that the greater the difference in the numerical change of a certain indicator, the greater the weight assigned to that indicator in the comprehensive evaluation. Since the change in flow capacity Q directly reflects the final drainage efficiency of the pipeline, its change is usually more significant than that of internal wall roughness Ra. Therefore, the weight allocation satisfies the relationship that α is greater than β.

[0099] If K_pollut is greater than the preset pollution risk threshold K_p, it is determined that the attenuation is mainly caused by pollutant deposition, and dredging and maintenance early warning information is output; if ΔP_rate is greater than PO and v_rate is less than V0, it is consistent with the performance attenuation conclusion, which enhances the reliability of the judgment, and P0 and V0 are preset physical parameter change thresholds.

[0100] If K_sync > K0, it is determined that there is a data anomaly, and the validity of the monitoring data is re-verified to avoid misjudgment of a single parameter; if the changing trends of ΔP_rate and v_rate show contradictory phenomena that are contrary to the principles of fluid mechanics, or if the value of K_pollut exceeds its effective range, it can provide direction for further data verification.

[0101] Specifically, when K_sync > K0, the following steps are taken to re-verify the validity of the monitoring data: Check the changing trends of the pressure drop rate ΔP_rate at the pipeline inlet and outlet and the fluid velocity rate v_rate within the pipe to confirm whether there are any contradictory phenomena that violate the principles of fluid mechanics; then check whether the value of the cumulative influence coefficient of runoff pollutants K_pollut is within its effective range; through targeted verification of these two key parameters, locate the cause of data anomalies and avoid affecting the pipeline performance degradation judgment result due to misjudgment of a single parameter.

[0102] S4 includes the following:

[0103] A dynamic threshold system for pipeline performance degradation is constructed, including the flow capacity degradation threshold η_Qth, the internal wall roughness increase threshold η_Rath, and the comprehensive early warning threshold η_th. The formula for calculating the flow capacity degradation threshold η_Qth is: η_Qth=1-(Q_min / Q_base), where Q_min is the minimum allowable flow capacity in the design. The formula for calculating the internal wall roughness increase threshold η_Rath is: η_Rath=(Ra_max-Ra_base) / Ra_base×100%, where Ra_max is the maximum allowable roughness provided by the pipeline material manufacturer. The formula for calculating the comprehensive early warning threshold η_th is: η_th=α×η_Qth+β×η_Rath; where α1 and β1 are weighting coefficients used to quantify the relative importance of the flow capacity degradation threshold η_Qth and the internal wall roughness increase threshold η_Rath in pipeline maintenance early warning judgment, and α1+β1=1. The specific values ​​need to be dynamically determined based on the actual application scenario of the pipeline, historical monitoring data, material characteristics, and risk assessment results.

[0104] When η_total ≥ η_th, the adjusted maintenance cycle T_new is calculated as follows: T_new = T0 × e^(-k × η_total / η_th), where T0 is the original maintenance cycle, and k is the attenuation sensitivity coefficient. The specific value needs to be dynamically determined based on the comprehensive attenuation value η_total of at least three complete operating cycles of the pipeline, the performance recovery rate after maintenance, historical data, pipeline material characteristic test data, and risk assessment results of actual application scenarios. When η_total < η_th, the attenuation acceleration a is calculated as a = (η_total - η_prev) / Δt, where η_prev is the previous attenuation value. The comprehensive value of periodic decay, where Δt is the number of days between two periods; if a > 0 (accelerated decay) and η_total + a × T0 ≥ η_th (predicted threshold to be reached within the original period), then adjust in advance to T_new = 0.8 × T0 (preventatively shortening by 20%); if a ≤ 0 (slower decay), maintain the original period T0; store the calculated η_total, η_th, T_new and the adjustment basis (such as decay acceleration a) along with the timestamp in the parameter monitoring database as the historical benchmark for the next period analysis; the next day, data is re-collected according to the preset period, and the analysis process is repeated to form a closed-loop linkage mechanism of parameter-threshold-period.

[0105] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0106] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for coordinated maintenance of drainage pipes based on bridge deck runoff monitoring data, characterized in that: The method includes the following steps: S1. Obtain the initial performance parameters of the drainage pipe and use them as baseline state data; determine the bridge deck runoff parameters and pipe physical parameters to be monitored; and construct a parameter monitoring database to store the monitoring data. S2. Collect bridge deck runoff parameters and pipeline physical parameters through monitoring equipment at preset cycles. After processing the collected bridge deck runoff parameters and pipeline physical parameters for effectiveness, update them to the parameter monitoring database according to time series to form a continuous record of parameter evolution. S3. Using the baseline state data as a reference, extract monitoring data for continuous periods from the parameter monitoring database, calculate the changing trends of bridge deck runoff parameters and pipeline physical parameters, and based on the correlation between the changing trends of bridge deck runoff parameters and pipeline physical parameters and pipeline performance loss, inversely deduce the pipeline performance degradation state. The specific content of S3 is as follows: Extract monitoring data for m consecutive days from the parameter monitoring database, where m ≥ 30; define the monitoring data for day 1 as: C_ss_start, C_oil_start, ΔP_start, v_start; and the monitoring data for day m as: C_ss_end, C_oil_end, ΔP_end, v_end; where C_ss_start represents the suspended solids concentration on day 1; C_oil_start represents the oil and fat concentration on day 1; ΔP_start represents the pressure drop at the inlet and outlet of the pipeline on day 1; v_start represents the fluid velocity in the pipe on day 1; C_ss_end represents the suspended solids concentration on day m; C_oil_end represents the oil and fat concentration on day m; ΔP_end represents the pressure drop at the inlet and outlet of the pipeline on day m; and v_end represents the fluid velocity in the pipe on day m. Calculate the cumulative change of each parameter over time, where the formula for calculating the cumulative impact coefficient of runoff pollutants, K_pollut, is: K_pollut=(C_ss_end×v_end+C_oil_end×v_end) / (C_ss_start×v_start+C_oil_start×v_start); The rates of change of the pipeline's physical parameters are ΔP_rate and v_rate, and their corresponding calculation formulas are as follows: ΔP_rate=(ΔP_end-ΔP_start) / ΔP_start×100%; v_rate=(v_end-v_start) / v_start×100%; A coupled correlation model of parameter variation and performance loss is established. Based on the initial flow capacity baseline value Q_base and the real-time flow velocity, the flow capacity attenuation rate η_Q of the pipeline is derived, where η_Q = 1 - (v_end × A) / Q_base, and A is the cross-sectional area of ​​the pipeline. Based on the hydraulic characteristics of pressure drop and flow velocity, the increase in the roughness of the pipeline inner wall η_Ra is derived, where η_Ra = [(Ra_current / Ra_base) - 1] × 100%, where Ra_current is the current equivalent roughness of the pipeline inner wall, which is calculated by inversion based on fluid dynamics principles from pipeline hydraulic monitoring data; Ra_base represents the initial inner wall roughness baseline value. Introduce a synergistic attenuation coefficient K_sync, and K_sync=|η_Q-η_Ra| / max(η_Q,η_Ra), where |η_Q-η_Ra| represents the absolute value of the difference between the pipe flow capacity attenuation rate η_Q and the pipe inner wall roughness increase η_Ra, and max(η_Q,η_Ra) represents taking the larger value of η_Q and η_Ra. If K_sync≤K0 and η_Q≥0 and η_Ra≥0, then the pipeline performance is determined to have undergone consistent degradation. The comprehensive degradation value η_total=α×η_Q+β×η_Ra is calculated, where K0 is a preset threshold, α and β are the allocation coefficients for the performance impact weight based on historical bridge runoff monitoring data, α>β and α+β=1; if K_pollut is greater than the preset pollution risk threshold K_p, then the degradation is determined to be mainly caused by pollutant deposition, and dredging and maintenance early warning information is output; if ΔP_rate is greater than PO and v_rate is less than V0, then it corroborates the performance degradation conclusion, enhancing the reliability of the judgment, and P0 and V0 are preset physical parameter change thresholds; If K_sync > K0, it is determined that there is a data anomaly, and the validity of the monitoring data is re-verified to avoid misjudgment of a single parameter; if the changing trends of ΔP_rate and v_rate show a contradictory phenomenon that goes against the principles of fluid mechanics, or if the value of K_pollut exceeds its effective range, it can provide direction for further data verification. S4. Set a preset threshold for pipeline performance degradation, compare the parameter change trend with the preset threshold, and if the parameter change trend reaches the preset threshold, automatically adjust the original maintenance cycle; if it does not reach the preset threshold, maintain the original maintenance cycle, while continuously updating the monitoring data and entering the next round of analysis, forming a closed-loop linkage between parameter evolution and cycle calibration.

2. The drainage pipe maintenance linkage method based on bridge deck runoff monitoring data according to claim 1, characterized in that: In step S1, the initial performance parameters of the drainage pipe are obtained and used as baseline state data. The specific details are as follows: Select n typical test sections before the drainage pipe is put into use, with each section spaced at 1 / n of the total length of the drainage pipe, where n is a positive integer between 3 and 5. For each test section, use pipe performance testing equipment to individually test its flow capacity Q and inner wall roughness Ra. The flow capacity Q is determined by a pipe hydraulic test. A constant flow of clean water is passed through the pipe section of a single test section, and the pipe outlet flow rate corresponding to that section is recorded three times consecutively. The average of the three measurements is taken as the flow capacity Q of that section. The inner wall roughness Ra is determined by a surface roughness meter. Five measuring points are evenly selected along the circumference of a single test section, and the roughness of each measuring point is measured. The average of the five measurements is taken as the inner wall roughness Ra of that section. The average values ​​of the initial performance parameters of the drainage pipe are calculated. The initial flow capacity reference value Q_base is calculated using the formula: Q_base = (Q1 + Q2 + ... + Qn) / n, where Q1 is the flow capacity of the first typical test section, Q2 is the flow capacity of the second typical test section, and so on, with Qn being the flow capacity of the nth typical test section. The initial inner wall roughness reference value Ra_base is calculated using the formula: Ra_base = (Ra1 + Ra2 + ... + Ran) / n, where Ra1 is the inner wall roughness of the first typical test section, Ra2 is the inner wall roughness of the second typical test section, and so on, with Ran being the inner wall roughness of the nth typical test section. The initial flow capacity reference value Q_base and the initial inner wall roughness reference value Ra_base are used as reference state data. The bridge deck runoff parameters to be monitored are determined to be suspended solids concentration C_ss and grease concentration C_oil, and the pipeline physical parameters are the inlet and outlet pressure drop ΔP and the fluid velocity v inside the pipe. A parameter monitoring database is constructed, with database fields including monitoring timestamp, suspended solids concentration C_ss, grease concentration C_oil, inlet and outlet pressure drop ΔP, fluid velocity v inside the pipe, and the corresponding initial flow capacity reference value Q_base and initial inner wall roughness reference value Ra_base.

3. The drainage pipe maintenance linkage method based on bridge deck runoff monitoring data according to claim 2, characterized in that: In step S2, the collected bridge deck runoff parameters and pipeline physical parameters undergo validity processing, as detailed below: According to the preset period T, the suspended solids concentration C_ss and oil concentration C_oil are collected by the runoff sensor, the pressure drop ΔP at the inlet and outlet of the pipeline is collected by the pipeline pressure sensor, and the fluid velocity v in the pipe is collected by the pipeline velocity sensor. Calculate the mean μ and standard deviation σ of 10 consecutive data collections for each parameter. If a certain collected value x satisfies |x-μ|>3σ, it is determined to be an outlier and removed. The outlier is filled with the linear interpolation result x'=x1+(t-t1)(x2-x1) / (t2-t1) of two adjacent valid data, where t is the time of outlier collection, t1 and t2 are the times of adjacent valid data collection, and x1 and x2 are the valid data at the corresponding times. The suspended solids concentration C_ss, oil and grease concentration C_oil, pipeline inlet and outlet pressure drop ΔP, and fluid velocity v in the pipe are sorted according to time series, and each data point is associated with the collection timestamp and updated to the parameter monitoring database to form a continuous record of parameter evolution.

4. The drainage pipe maintenance linkage method based on bridge deck runoff monitoring data according to claim 1, characterized in that: S4 includes the following: A dynamic threshold system for pipeline performance degradation is constructed, including the flow capacity degradation threshold η_Qth, the internal wall roughness increase threshold η_Rath, and the comprehensive early warning threshold η_th. The formula for calculating the flow capacity degradation threshold η_Qth is: η_Qth=1-(Q_min / Q_base), where Q_min is the minimum allowable flow capacity in the design. The formula for calculating the internal wall roughness increase threshold η_Rath is: η_Rath=(Ra_max-Ra_base) / Ra_base×100%, where Ra_max is the maximum allowable roughness provided by the pipeline material manufacturer. The formula for calculating the comprehensive early warning threshold η_th is: η_th=α1×η_Qth+β1×η_Rath, where α1 and β1 are weighting coefficients, and α1+β1=1. When η_total ≥ η_th, the adjusted maintenance cycle T_new is calculated as follows: T_new = T0 × e^(-k × η_total / η_th), where T0 is the original maintenance cycle and k is the attenuation sensitivity coefficient. When η_total < η_th, the attenuation acceleration a = (η_total - η_prev) / Δt is calculated, where η_prev is the comprehensive attenuation value of the previous cycle and Δt is the number of days between the two cycles. If a > 0 and η_total + a × T0 ≥ η_th, then T_new is adjusted to 0.8 × T0 in advance. If a ≤ 0, the original cycle T0 is maintained. The calculated η_total, η_th, T_new and the timestamp associated with the adjustment are stored in the parameter monitoring database as the historical benchmark for the next cycle analysis. The data is re-collected according to the preset cycle the next day, and the analysis process is repeated to form a closed-loop linkage mechanism of parameter-threshold-cycle.

5. A drainage pipe maintenance linkage system based on bridge deck runoff monitoring data, wherein the system applies the drainage pipe maintenance linkage method based on bridge deck runoff monitoring data as described in any one of claims 1-4, characterized in that: The system includes: a baseline state construction module, a monitoring data processing module, a performance degradation back-calculation module, and a maintenance cycle calibration module; The baseline state construction module acquires the initial performance parameters of the drainage pipe and generates baseline state data, determines the bridge deck runoff parameters and pipe physical parameters to be monitored, and constructs a parameter monitoring database. The monitoring data processing module periodically collects parameter data through the monitoring equipment, processes the collected data for validity, updates the parameter monitoring database according to the time series, and forms a parameter evolution record. The performance degradation inference module is used to extract monitoring data over a continuous period of time, calculate the parameter change trend, and infer the pipeline performance degradation status based on the coupled correlation model between parameter change and pipeline performance loss. The maintenance cycle calibration module is used to construct a dynamic threshold system for pipeline performance degradation, compare the degradation state with the preset threshold and adjust the maintenance cycle to form a closed-loop linkage of parameters, thresholds and cycles.

6. The drainage pipe maintenance linkage system based on bridge deck runoff monitoring data according to claim 5, characterized in that: The baseline state construction module includes an initial performance parameter acquisition unit and a monitoring parameter system construction unit; The initial performance parameter acquisition unit selects a typical test section of the drainage pipe before it is put into use, and uses pipeline performance testing equipment to detect the flow capacity and inner wall roughness of each section. After calculating the average value, it generates the initial flow capacity benchmark value and the initial inner wall roughness benchmark value.

7. The drainage pipe maintenance linkage system based on bridge deck runoff monitoring data according to claim 5, characterized in that: The monitoring data processing module includes a parameter periodic acquisition unit and a data validity processing unit; The parameter periodic acquisition unit collects bridge deck runoff parameters and pipeline physical parameters through runoff sensors, pipeline pressure sensors, and pipeline flow velocity sensors at preset intervals. The data validity processing unit calculates the mean and standard deviation of the continuously collected data for each parameter, removes outliers and fills in missing data through linear interpolation, and updates the processed data to the parameter monitoring database by associating the time series with timestamps.

8. The drainage pipe maintenance linkage system based on bridge deck runoff monitoring data according to claim 5, characterized in that: The performance degradation back-calculation module includes a parameter change trend calculation unit and a performance degradation coupled back-calculation unit; The parameter change trend calculation unit extracts monitoring data from the parameter monitoring database within a continuous detection period and calculates the cumulative influence coefficient of runoff pollutants and the change rate of pipeline physical parameters. The performance degradation coupling reverse calculation unit derives the pipeline flow capacity degradation rate and inner wall roughness increase based on the initial benchmark value and real-time parameters. It introduces a degradation synergy coefficient to verify the reliability of the data, calculates the comprehensive degradation value to determine the pipeline performance degradation status, and triggers a re-verification of the monitoring data if the data is abnormal.

9. The drainage pipe maintenance linkage system based on bridge deck runoff monitoring data according to claim 5, characterized in that: The maintenance cycle calibration module includes a dynamic threshold system construction unit and a maintenance cycle dynamic adjustment unit; The dynamic threshold system construction unit combines drainage pipe design specifications and pipe material characteristics to calculate the flow capacity attenuation threshold and the inner wall roughness increase threshold, and generates a comprehensive early warning threshold through weighted fusion. The maintenance cycle dynamic adjustment unit dynamically adjusts the maintenance cycle based on the comparison between the comprehensive attenuation value and the comprehensive early warning threshold, and stores the adjustment results and the associated timestamps in the parameter monitoring database for analysis of the next cycle, forming a closed-loop linkage.

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