Multi-parameter coordinated underwater backfilling material extrusion process parameter set monitoring method and system

Abstract

The application provides a method and system for intelligently outputting a dredging criterion parameter set in an underwater backfilling material extrusion process, and relates to the technical fields of underwater geotechnical monitoring and dredging control. The method comprises the following steps: synchronously collecting multi-source monitoring data of the extrusion process, taking displacement information as a process alignment reference, and establishing a unified time axis; based on the aligned multi-source data, selecting key response quantities and calculating their change rates, and extracting change rate stability characteristics; taking the condition that the change rate of at least one key response quantity meets the stability condition as a preliminary determination condition, and using other monitoring parameters for mutual verification and checking; when the stability condition is met and the mutual verification and checking are consistent, outputting the dredging criterion parameter set and the dredging determination result. The application can solve the problems in the prior art that the determination of the dredging criterion depends on a single index, the judgment basis is insufficient, and the monitoring results are difficult to comprehensively utilize, and improves the objectivity, reviewability and application range of the dredging determination.

Description

Technical Field

[0001] This invention relates to the field of underwater geotechnical monitoring and dredging control technology, specifically to a multi-parameter collaborative underwater silt removal process parameter set monitoring method and system, which is applied to the determination and monitoring of silt removal criteria in the construction of underwater gravity structure projects such as immersed tunnels, gravity wharves, and caisson structures. Background Technology

[0002] During the construction of underwater gravity structures, a large amount of soft sediment (i.e., backfill material) often accumulates after the foundation trench is excavated. The presence of backfill material directly affects the stability of the structure during lowering and installation. If the residual backfill material is too thick, it may lead to risks such as uneven structural support, additional sinking, or attitude deviation. Therefore, determining whether and to what extent the backfill material needs to be dredged is a key issue to ensure the smooth implementation of the project.

[0003] Currently, the criteria for determining the dredging of backfill material in engineering still mainly rely on experience or a single indicator, such as the thickness of the residual mud surface or a thickness threshold under a certain bulk density condition. Because the physical and mechanical properties of backfill material vary significantly from sea area to sea, even if the bulk density is similar, their yield and rheological characteristics, as well as their extrusion behavior under load, may differ. Therefore, relying on a single indicator is prone to misjudgment, leading to unnecessary dredging and increased costs, or insufficient dredging and creating quality and safety hazards.

[0004] On the other hand, existing technologies for gravity-based sludge removal often rely on single monitoring indicators, commonly focusing only on displacement or local pressure, lacking a mechanism for simultaneously acquiring and comprehensively judging information such as extrusion volume, flow characteristics, and morphological evolution. The lack of a unified time benchmark and cross-verification logic among multi-source monitoring data makes it difficult to objectively determine whether the sludge removal process has entered a stable / terminated state, leading to sludge removal decisions relying more on manual experience and offline analysis.

[0005] There is an urgent need for a method and its supporting monitoring system that can utilize a large-scale experimental model to simultaneously acquire multi-parameter data, extract key response change characteristics, and output a dredging criterion parameter set and state determination results through multi-parameter cross-verification and verification during the dredging process of gravity-type structure lowering. The judgment result information output by this system can be fed back to the construction site and calibrated with the pressure, displacement and other data that can be measured on site, thereby achieving the goal of standardizing the dredging criterion parameter set and dredging state determination results, so as to improve the objectivity, verifiability and engineering applicability of dredging judgment. Summary of the Invention

[0006] This invention aims to overcome the shortcomings of the prior art and provide a multi-parameter collaborative underwater silt removal process parameter set monitoring method and system to solve the problems in the prior art, such as reliance on a single indicator for silt removal criteria, insufficient judgment basis, and difficulty in comprehensively utilizing monitoring results.

[0007] A multi-parameter coordinated underwater sediment backfilling process parameter set monitoring method, the monitoring method includes the following steps: 1) Synchronous acquisition and time alignment of multi-source data: Collect multi-source monitoring data consisting of displacement response, pressure response, flow characteristic parameters, extrusion quantity characteristic parameters and morphological characteristic parameters during the sludge squeezing process; use the displacement response as the time reference to establish a unified time axis, and perform time alignment and process segmentation on the multi-source monitoring data to obtain time-consistent multi-source monitoring data. 2) Construction of key response quantities and their rate of change characteristics: Select at least one parameter that can characterize the siltation state from the aligned multi-source monitoring data as the key response quantity. After denoising, smoothing and outlier identification processing of the key response quantity, calculate the rate of change of the key response quantity with time or with displacement respectively. Within a preset time window, construct the stability characteristics of the rate of change of the key response quantity, and determine the moment when the key response quantity enters a stable state, so as to reduce the risk of misjudgment caused by instantaneous disturbances or single indicator anomalies. 3) Multi-parameter cross-verification and consistency check: The change rate of key response quantities meets the stability condition as the initial judgment basis. Cross-verification and consistency check are performed using at least one of the pressure response quantity, flow characteristic parameters, extrusion quantity characteristic parameters, and morphological characteristic parameters. When multiple parameters jointly characterize the sludge extrusion process as tending to enter a stable state, the sludge extrusion criterion is confirmed to be valid. When there are obvious contradictions or anomalies among the parameters, the corresponding data segments are marked or removed, and the judgment is re-evaluated. 4) Intelligent output of dredging criterion parameter set and judgment result: When the key response quantity meets the conditions for entering a stable state, and after the cross-verification of multiple parameters is consistent, the dredging process is judged to have entered a stable or ended state; the dredging criterion parameter set is output, which includes at least: key response quantity type, change rate threshold, stable duration, cross-verification parameters, and characteristic value corresponding to the stable time.

[0008] Furthermore, the flow characteristic parameters include the flow velocity and flow state within the siltation zone and at the extrusion end; the extrusion quantity characteristic parameters include the amount of silt extruded, the interface position, or the amount of morphological evolution; and the morphological characteristic parameters are obtained through image acquisition and are used to reflect the morphological changes in the siltation zone.

[0009] Furthermore, in step 2), the specific process of constructing stability features of the rate of change of key response quantities within a preset time window and determining the moment when the key response quantities enter a stable state, in order to reduce the risk of misjudgment caused by instantaneous disturbances or abnormalities of a single indicator, is as follows: Within a preset time window, stability features are constructed that include at least the absolute value of the rate of change, the amplitude of the rate of change fluctuation, the standard deviation of the rate of change, and the trend of the rate of change. The absolute value of the rate of change is used to characterize the strength of the change, the amplitude of the rate of change fluctuation is used to characterize the degree of disturbance, the standard deviation of the rate of change is used to characterize the degree of discrete convergence, and the trend of the rate of change is used to characterize the trend convergence state. The stability features can be compared with the corresponding thresholds for initial screening. Preferably, at least two stability features are used for joint judgment, combined with the continuous window consistency criterion, to determine the moment when the key response quantities enter a stable state, so as to reduce the risk of misjudgment caused by instantaneous disturbances or abnormalities of a single indicator.

[0010] Moreover, the method for determining when the rate of change of the response quantity enters a stable state in step 2) is as follows: the absolute value of the rate of change decreases to below a preset threshold and remains stable within a continuous preset time window; or the fluctuation range of the rate of change within the preset time window is less than a preset range, thereby determining that the silt removal process has entered a stable stage from the dynamic change stage.

[0011] Furthermore, the specific steps for verifying the condition using other monitoring parameters in the multi-parameter mutual verification step are as follows: when it is initially determined that the siltation is stabilizing, the changing trends of characteristic parameters other than the key response quantity among the pressure response quantity, flow characteristic parameters, extrusion quantity characteristic parameters, and morphological characteristic parameters are compared simultaneously; if all parameters involved in the verification are stable or have consistent changing trends, the stability determination is confirmed to be valid; if there are abnormal fluctuations or contradictory trends, the current time period is determined to be invalid and removed, and the stability determination is re-executed.

[0012] Furthermore, the specific process of cross-validation and consistency verification in step 3) is as follows: Cross-validation includes aligning the parameters to be verified within the same window as or corresponding to the key response quantity, and comparing the stability indicators, change trends, or threshold states of each parameter to determine whether they meet the conditions of synchronous stability, trend consistency, or state non-conflict within the preset tolerance range. Cross-validation can also be combined with rule matching, correlation analysis, or trend comparison after time delay compensation. Consistency verification includes at least window synchronization verification, change trend coordination verification, and anomaly priority investigation to verify temporal consistency, state evolution consistency, and suppress abnormal data interference, respectively.

[0013] A multi-parameter collaborative underwater sediment backfilling and dissipation process parameter set monitoring system, the monitoring system comprising the following modules: 1) Displacement monitoring unit: used to acquire the displacement response during the lowering process of the gravity structure of the underwater siltation system, and to serve as the reference signal for multi-source data time alignment and process segmentation; 2) Pressure monitoring unit: used to acquire the pressure response and its distribution characteristics during the siltation process; 3) Flow characteristic monitoring unit: used to acquire flow characteristic parameters in the squeezing zone and the extrusion end region, including flow velocity and flow state characteristics; 4) Extrusion volume monitoring unit: used to acquire the extrusion volume of sludge or characteristic parameters of the extrusion volume, such as the interface position and morphology, which can characterize the extrusion volume as the process evolves; 5) Morphological monitoring unit: used to collect morphological characteristic parameters of the siltation area, including morphological evolution images and state characteristics; 6) Data Synchronization Acquisition and Intelligent Fusion Criterion Output Unit: Connected to the above-mentioned displacement monitoring unit, pressure monitoring unit, flow characteristic monitoring unit, extrusion quantity monitoring unit and morphology monitoring unit, it is used to synchronously acquire, time-align, construct the stability characteristics of the change rate of key response quantities, and cross-verify multiple parameters of multi-source data such as pressure response quantity, flow characteristic parameters, extrusion quantity characteristic parameters and morphology characteristic parameters, and output the dredging criterion parameter set and dredging status judgment result.

[0014] Furthermore, the displacement monitoring unit consists of a set of wire displacement sensors, positioned at both ends of the immersed tube model; the pressure monitoring unit is a set of earth pressure cells, positioned on the lower surface of the immersed tube model's bottom plate; the flow characteristic monitoring unit is a set of ultrasonic flow meters, positioned in the middle of the mud-squeezing test trench and 0.5-1.5m outside the extrusion end of the test trench's backfill material; the extrusion volume monitoring unit is a coordinate grid set on the tempered glass side; the morphology monitoring unit consists of a set of pinhole cameras and a wide-angle camera, positioned on one side of the tempered glass of the test trench; and the data synchronous acquisition and intelligent fusion criterion output unit adopts an industrial control-grade processing system.

[0015] The beneficial effects of this invention are as follows: 1. The multi-parameter collaborative underwater silt removal process parameter set monitoring method and system of the present invention, based on a large-scale test model, realizes the simultaneous acquisition of multi-parameter data and extraction of key response quantity change characteristics during the gravity structure lowering and silt removal process. Through multi-parameter mutual verification and verification, the system outputs the silt removal criterion parameter set and state judgment results. The judgment result information output by the system can be fed back to the construction site and calibrated with the pressure, displacement and other data that can be measured on site, thereby achieving the purpose of standardizing the silt removal criterion parameter set and silt removal state judgment results, so as to improve the objectivity, verifiability and engineering applicability of silt removal judgment.

[0016] 2. The multi-parameter collaborative underwater siltation process parameter set monitoring method and system of the present invention achieves comprehensive monitoring of the siltation process through multi-source data synchronous acquisition and time alignment. Using displacement information as the process alignment benchmark, a unified time axis is established to complete data alignment and process segmentation, thereby ensuring the comparability of monitoring data from different sources.

[0017] 3. The multi-parameter coordinated underwater siltation process parameter set monitoring method and system of the present invention, based on aligned multi-source data, selects at least one data that can characterize the state of the siltation process as a key response quantity, calculates its rate of change with time or displacement, and further extracts the stability feature of the rate of change to characterize when the rate of change of the response quantity enters a stable state. By reducing the absolute value of the rate of change to below a preset threshold and maintaining it within a preset time window, or by reducing the fluctuation range of the rate of change within a preset time window to within a preset range, the transition of the siltation process from significant change to a stable stage can be accurately identified.

[0018] 4. The multi-parameter collaborative underwater silt removal process parameter set monitoring method and system of the present invention uses the stability condition of the change rate of at least one key response quantity as a preliminary judgment condition, and uses other monitoring parameters to cross-verify this condition. Based on the silt removal criterion of stable change rate and multi-parameter cross-verification, the silt removal process is objectively determined to have entered a stable / terminated state. When multiple parameters consistently indicate that the silt removal process tends to be stable, the silt removal criterion parameter set is established. If inconsistencies or anomalies occur, the corresponding data segments are marked or removed, thereby effectively avoiding misjudgments caused by single parameter anomalies. The core form of the silt removal criterion is that the change rate of the key response quantity enters a stable state, and a multi-parameter cross-verification mechanism is introduced to reduce the uncertainty caused by judging based on a single indicator or experience.

[0019] 5. The multi-parameter collaborative underwater silt removal process parameter set monitoring method and system of the present invention outputs a dredging criterion parameter set and dredging judgment result after the stability conditions are met and the cross-verification is consistent. The dredging criterion parameter set includes at least the selected key response quantity, the rate of change threshold, the duration, the parameter items used in the cross-verification, and the key response quantity characteristic value corresponding to the stability / end time. Through multi-parameter collaborative monitoring and cross-verification, the reliability of the judgment is improved. Multiple types of information such as displacement, pressure, flow characteristics, extrusion volume, and morphology can be acquired simultaneously. Cross-verification avoids misjudgment caused by a single monitoring anomaly or local disturbance. The judgment process is traceable and verifiable, improving the consistency and efficiency of dredging decision-making.

[0020] 6. The multi-parameter collaborative underwater silt removal process parameter set monitoring method and system of the present invention intelligently outputs the dredging criterion parameter set and judgment results, which is convenient for recording, verification and comparison with similar working conditions. By forming the dredging criterion parameter set and outputting the judgment results, the judgment process is traceable and verifiable, thereby improving the consistency and efficiency of dredging decision-making.

[0021] 7. The multi-parameter collaborative underwater silt removal process parameter set monitoring method and system of the present invention improves the problem of difficulty in synchronizing and uniformly analyzing multi-source data in the silt removal process, solves the problem of lacking objective criteria for identifying the silt removal process entering a stable / ending state, overcomes the defect of insufficient reliability of single parameter judgment, realizes intelligent output of silt removal criterion parameter set and state results, and significantly improves the objectivity and verifiability of silt removal judgment.

[0022] 8. The multi-parameter collaborative underwater siltation process parameter set monitoring method and system of the present invention has a modular design and strong adaptability: each monitoring unit and criterion output unit adopts modular configuration, and the monitoring parameter type and quantity can be flexibly selected according to different engineering and experimental needs. It is applicable to siltation monitoring and dredging criterion evaluation of gravity-type structures of different scales.

[0023] 9. The multi-parameter collaborative underwater silt removal process parameter set monitoring method and system of the present invention realizes the method and supporting monitoring system for simultaneously acquiring multi-parameter data, extracting key response quantity change characteristics, and outputting dredging criterion parameter set and state judgment results through multi-parameter mutual verification during the silt removal process of gravity structure lowering. This improves the objectivity, verifiability and applicability of dredging judgment. Attached Figure Description

[0024] Figure 1 This is a flowchart of the multi-parameter coordinated underwater silt removal process parameter set monitoring method of the present invention; Figure 2 This is a schematic diagram of the multi-parameter coordinated underwater silt removal process parameter set monitoring system of the present invention; Figure 3 This is a schematic diagram of the installation location of the pressure monitoring unit in the multi-parameter coordinated underwater silt removal process parameter set monitoring system of the present invention; Figure 4 This is a schematic diagram of the installation location of the flow characteristic monitoring unit in the multi-parameter coordinated underwater siltation process parameter set monitoring system of the present invention. Figure 5 This is a schematic diagram of the installation location of the morphological monitoring unit in the multi-parameter collaborative underwater silt removal process parameter set monitoring system of the present invention. Figure 6 This is a complete displacement curve of the immersed tube in an embodiment of the present invention.

[0025] In the picture: 1-Wire displacement sensor, 2-Earth pressure cell, 3-Coordinate grid, 4-Ultrasonic flow meter, 5-Pinhole camera, 6-Wide-angle camera, 7-Industrial control-grade processing system, 8-Test tank, 9-Loading support, 10-Steel frame, 11-Submerged tube model. Detailed Implementation

[0026] The present invention will now be described in more detail through specific embodiments. These embodiments are intended to help better understand and explain the present invention and are illustrative in nature, and do not constitute any limitation on the scope of protection of the present invention.

[0027] This invention addresses the dredging assessment needs during the siltation process in immersed tunnel foundation trenches. It constructs a multi-parameter collaborative monitoring system to simultaneously acquire information on displacement, pressure, flow characteristics, extrusion volume, and morphological evolution during the lowering process. Based on the dredging criterion rules of this invention, which feature stable rates of change and cross-verification of multiple parameters, it outputs a set of dredging criterion parameters and dredging status assessment results to support experimental analysis and engineering decision-making. The following section describes specific embodiments of the method and system of this invention using a large-scale model test of siltation in an immersed tunnel foundation trench. First, the composition of the multi-parameter collaborative underwater siltation process parameter set monitoring system of this invention is explained.

[0028] A multi-parameter collaborative underwater sediment flushing process parameter set monitoring system, such as Figure 1 , Figure 2 As shown, the monitoring system includes the following modules: 1) Displacement monitoring unit: used to acquire the displacement response during the lowering process of the gravity structure of the underwater siltation system, and to serve as a reference signal for multi-source data alignment and process segmentation.

[0029] In this embodiment, a large-scale model test of siltation in the foundation trench of an immersed tunnel is conducted. The test system includes a test trench 8, an immersed tunnel model 11, a steel frame 10, and a loading support 9. The test trench 8 is L-shaped and includes a long trench section and a short trench section. The long trench section is closed on three sides and has a mud discharge port on one side, which connects to the short trench section. The immersed tunnel model 11 is suspended above the test trench 8 by a hoisting device, corresponding to the long trench section. A counterweight is placed on the upper part of the loading support 9 according to the simulated working conditions. The hoisting device is used to lift and lower the immersed tunnel model 11. The counterweight is used to adjust the initial negative buoyancy of the immersed tunnel model 11 so that the base pressure of the immersed tunnel model 11 reaches the design requirements and remains within the target range during the lowering process.

[0030] The displacement monitoring unit consists of a set of wire displacement sensors 1. In this embodiment, there are two wire displacement sensors 1. The wire ends of the wire displacement sensors 1 are set at both ends of the immersed tube model 11. The housing end of the wire displacement sensor 1 is fixed to the steel frame 10. It is used to measure the vertical displacement of the immersed tube model 11 in real time during the lowering process. The displacement data serves as a reference signal for multi-source data alignment and process segmentation, and is also used to reflect the overall response characteristics of the lowering process.

[0031] 2) Pressure Monitoring Unit: Used to acquire the pressure response and its distribution characteristics during the siltation process; the pressure monitoring unit is a set of earth pressure cells 2, which are arranged below the bottom plate of the immersed tube model 11 to measure the base pressure. The measuring points are evenly distributed along the length of the immersed tube model 11, such as... Figure 3 As shown, eight measuring points were set up at 1m intervals. By collecting the changes in pressure at each measuring point during the descent process and its spatial distribution characteristics, the data were used to characterize the siltation driving state, spatial non-uniformity, and possible back pressure / local blockage, and served as an important basis for multi-parameter cross-verification.

[0032] 3) Flow Characteristic Monitoring Unit: Used to acquire flow characteristic parameters within the sludge squeezing zone and the extrusion end region, including flow velocity and flow state characteristics; the flow characteristic monitoring unit is a set of ultrasonic flow meters 4; the ultrasonic flow meters 4 are used to measure the flow velocity of the returned sludge during the sludge squeezing process, with two measurement points set up: one measurement point is located in the middle of the sludge squeezing zone, and the other measurement point is located approximately 1m outside the extrusion end of the returned sludge, such as... Figure 4 As shown in the figure. This unit is used to obtain the flow velocity change characteristics within the siltation zone and the extrusion end region, providing a basis for the identification and cross-verification of the siltation process stages.

[0033] 4) Extrusion Volume Monitoring Unit: This unit is used to acquire the extrusion volume of silt or characteristic parameters of the interface position and morphology that characterize the extrusion volume as it evolves during the process. The extrusion volume monitoring unit is a coordinate grid 3 set on the tempered glass side. A coordinate grid 3 is set on the tempered glass side of the test tank 8. By recording the interface position and morphology of the silt in the extrusion end area, the extrusion volume and its evolution characteristics during the process are calculated. The extrusion volume change information obtained by this unit is used to characterize the degree of silt removal and is used in conjunction with parameters such as displacement, pressure, and flow velocity for cross-verification of silt removal criteria.

[0034] 5) Morphological Monitoring Unit: Used to acquire morphological evolution information and state characteristics parameters during the siltation process; the morphological monitoring unit consists of a set of pinhole cameras 5 and a wide-angle camera 6, such as... Figure 5As shown, a camera system was installed on one side of the tempered glass of the test tank. A wide-angle camera 6 was used to observe the overall model, while pinhole cameras 5 provided detailed observations of the sludge discharge port and the middle of the squeezing zone, enabling full-process video observation of the squeezing process and preserving the test records. Morphological monitoring information was used to extract state characteristics such as the extrusion end interface position and local flow morphology changes, serving as an auxiliary source of information for cross-verification.

[0035] 6) Data Synchronous Acquisition and Intelligent Fusion Criterion Output Unit: Connected to the aforementioned displacement monitoring unit, pressure monitoring unit, flow characteristic monitoring unit, extrusion quantity monitoring unit, and morphology monitoring unit, this unit synchronously acquires, aligns, constructs stability features of key response quantity change rates, and verifies multiple parameters from multiple sources, including pressure response quantities, flow characteristic parameters, extrusion quantity characteristic parameters, and morphology characteristic parameters. It then outputs a dredging criterion parameter set and dredging status determination results. The data synchronous acquisition and intelligent fusion criterion output unit employs an embedded or industrial control-grade processing system with multi-channel synchronous acquisition, unified time axis calibration, feature calculation, logical criterion execution, and result visualization capabilities.

[0036] This embodiment employs a multi-channel data acquisition and processing method to synchronously record and uniformly store monitoring data such as displacement, pressure, and flow velocity. Displacement data is used as the process alignment benchmark to align the time axis and segment the process for various monitoring data. Based on this, the stability of the rate of change of key response quantities is determined according to the dredging criterion rules proposed in this invention, and cross-verification is performed using other monitoring parameters. When the stability / termination criteria are met, the dredging criterion parameter set and dredging status determination results are output, forming a traceable process record for reference in experimental and engineering decision-making.

[0037] A multi-parameter coordinated underwater sediment flushing process parameter set monitoring method, such as... Figure 1 As shown, the monitoring method includes the following steps: 1) Synchronous acquisition and time alignment of multi-source data: Collect multi-source monitoring data consisting of displacement response, pressure response, flow characteristic parameters, extrusion quantity characteristic parameters and morphological characteristic parameters during the sludge squeezing process; use the displacement response as the time reference to establish a unified time axis, and perform time alignment and process segmentation on the multi-source monitoring data to obtain time-consistent multi-source monitoring data.

[0038] The flow characteristic parameters include the flow velocity and flow state within the siltation zone and at the extrusion end; the extrusion quantity characteristic parameters include the amount of silt extruded, the interface position, or the amount of morphological evolution; the morphological characteristic parameters are obtained through image acquisition and are used to reflect the morphological changes in the siltation zone.

[0039] Displacement information can be gravity-based structures, such as the displacement response generated by the immersed tube model 11 during vertical lowering. It has a clear starting point, a monotonically increasing trend, and process uniqueness, making it suitable as a physical anchor point for time synchronization of the entire system. Establishing a unified time axis involves resampling and interpolating the original timestamp data collected by each monitoring unit based on the characteristic points of the displacement signal, such as the moment when the displacement begins to change, the inflection point of the displacement rate, and the moment when the displacement reaches a preset threshold, so that all parameter sequences have a one-to-one correspondence under the same time index. Process segmentation can be based on the displacement evolution law to divide the initial stage of siltation, the process stage, and the stable stage, providing process semantic boundaries for subsequent key response quantity analysis.

[0040] In this embodiment, the data of each channel is truncated and time offset is corrected according to the starting time of the displacement signal change in order to achieve synchronous alignment.

[0041] Taking the moment when the displacement value in the displacement sequence first exceeds 0.5 mm for three consecutive frames as t0, all other monitoring channel data are aligned according to t0 and resampled to a uniform sampling rate of 10Hz to form aligned multi-source data with consistent time axis.

[0042] 2) Construction of key response quantities and their rate of change characteristics: Select at least one parameter that can characterize the siltation state from the aligned multi-source monitoring data as the key response quantity, and calculate the rate of change of the key response quantity with time or with displacement; determine the moment when the key response quantity enters a stable state based on whether the absolute value, fluctuation amplitude, standard deviation or trend of the rate of change meets the threshold condition within the preset time window.

[0043] The method to determine when the rate of change of the response quantity enters a steady state is as follows: the absolute value of the rate of change decreases below a preset threshold and remains within a preset time window, or the fluctuation amplitude / standard deviation of the rate of change within the preset time window decreases to within a preset range, or the slope of the rate of change fitting approaches zero and continues to converge, which is used to characterize the transition of the siltation process from a significant change to a stable stage.

[0044] The key response quantities are selected from one or more combinations of displacement response quantities, pressure response quantities, and extrusion quantity characteristic parameters.

[0045] The specific steps for constructing key response quantities and their rate of change characteristics are as follows: a. Data preprocessing: After time alignment of the original sequences of key response quantities, noise reduction is performed using 5-point sliding median or Savitzky-Golay smoothing; Hampel sliding window anomaly identification is used to identify abrupt jumps, and the anomalies are marked and replaced or repaired by neighborhood median values.

[0046] b. Rate of change calculation: Select the displacement response, pressure response, or extrusion quantity characteristic parameters as key response quantities, and calculate their rate of change with time or displacement process; when the sampling interval is not uniform, use the differential ratio and normalize it according to the actual time interval or displacement increment.

[0047] c. Stability Feature Extraction and Determination: Within a sliding window, the absolute value of the rate of change, the moving range, the standard deviation, and the slope of the linear fit are extracted as stability features. A preferred method is a joint determination that "at least two parameters satisfy the threshold and two consecutive windows are valid" to determine the stable moment. The steps for extracting the rate of change stability features to characterize when the rate of change of the response quantity enters a stable state are as follows: The stability of the displacement response is determined by the following criteria: First, the standard deviation of the rate of change of the displacement response with respect to the incremental step (i.e., the relative settling rate) is less than 0.02 mm / step within 10 consecutive displacement increments; second, the stability is determined by whether the rate of change of the extrusion quantity characteristic parameter with respect to time t (i.e., the extrusion rate) remains below 0.3 cm / s for more than 10 s, or the fluctuation amplitude within a 10 s window is no greater than 0.08 cm / s. Third, the stability is determined by whether the moving range of the pressure response rate of change drops below 0.1 kPa / s and remains constant within a 10 s sliding window, or the standard deviation within the window is less than 0.08 kPa / s. This application, based on a combination of at least two of the above methods, obtains the stability characteristics of the rate of change of key response quantities that characterize the transition from dynamic to static processes in the sludge extrusion process.

[0048] 3) Multi-parameter cross-verification: The stability condition of the change rate of at least one key response quantity is used as the initial judgment condition, and other monitoring parameters are used to cross-verify the condition; when the multi-parameter consistent characterization of the sludge squeezing process tends to be stable, the sludge dredging criterion parameters are integrated; if inconsistencies or abnormalities occur, the corresponding data segments are marked, downweighted or removed to improve the reliability of the judgment.

[0049] The stability condition of the rate of change of key response quantities is used as the initial judgment basis. At least one of the pressure response quantity, flow characteristic parameters, extrusion quantity characteristic parameters, and morphological characteristic parameters is used for cross-validation and consistency verification. When multiple parameters jointly characterize the sludge extrusion process and tend to be stable, the sludge dredging criterion is confirmed to be effective. When there are obvious contradictions or anomalies among the parameters, the corresponding data segments are marked, downweighted or removed, and re-judged.

[0050] The specific steps for cross-verification of this condition using other monitoring parameters in the multi-parameter cross-verification process are as follows: a. Window alignment and time lag compensation: When the extrusion amount is the key response quantity, the displacement response quantity, pressure response quantity, flow characteristic parameters, and morphological characteristic parameters constitute the parameter items used for mutual verification. First, synchronous comparison is performed within the same 10s sliding window. If there is a lag in the parameter response, the time lag corresponding to the main peak is estimated by cross-correlation, preferably not exceeding ±3s, and then a second verification is performed within the compensated window.

[0051] b. Window synchronization check: Determine whether multiple parameters simultaneously meet their respective stable thresholds within the same window or the corresponding compensated window; if the time difference in reaching stability does not exceed one sampling window or does not exceed the estimated time delay tolerance, then the timing is considered consistent.

[0052] c. Check the synergy of change trends: compare the signs of the rate of change of each parameter, the direction of the slope of the linear fit, and the trend of amplitude decay; when the parameters show convergence in the same direction and the amplitude decays synchronously, the synergistic response is considered to be valid.

[0053] d. Prioritize anomaly detection: Prioritize identifying data segments with sudden jumps, drifts, short-term missing measurements, morphological parameter distortions caused by image occlusion, and obvious violations of physical constraints. For example, if the extrusion rate stabilizes but the pressure change rate is abnormally amplified, mark the data segment first and reduce its weight or remove it, and then perform mutual verification judgment.

[0054] After the extrusion rate of change reaches a stable state, simultaneously check whether the standard deviation of the pressure response rate of change is less than 0.08 kPa / s within the same 10 s window, whether the amplitude of the flow characteristic parameter change rate decreases by more than 50% within the same period, and whether the pixel grayscale gradient change rate of the sludge discharge port area in the morphological characteristic parameter slows down synchronously. If all three conditions are met, it is confirmed that the multi-parameter consistent characterization of the stable state is confirmed, and the dredging criterion parameters are integrated. If only two conditions are met, the time period corresponding to the inconsistent parameters is marked as pending verification and reduced in weight, and is not included in the final criterion output for the time being. If sensor abnormalities or image quality abnormalities occur, abnormal data is removed first and then the judgment is reviewed.

[0055] 4) Intelligent output of dredging criterion parameter set and judgment result: When the stability condition is met and the results are consistent after cross-verification of multiple parameters, the dredging process is determined to have entered a stable or terminated state; the dredging criterion parameter set is output, which includes at least: key response quantity type, rate of change threshold, stability duration, cross-verification parameters, and characteristic value corresponding to the stable moment.

[0056] Once the stability condition of the extrusion rate change rate and the mutual verification of displacement, pressure, flow, and morphological parameters are all satisfied, the system automatically generates a set of dredging criterion parameters, including: the selected key response quantity is the extrusion rate characteristic parameter, the change rate threshold is 0.25 cm / s, the duration is 10 s, the parameters used for mutual verification are displacement response quantity, pressure response quantity, flow characteristic parameter, and morphological characteristic parameter, and the characteristic value of the key response quantity corresponding to the stable time is 28.7 cm; at the same time, the judgment result is output: the dredging process enters a stable / end state 428 s after the start of the test, and it is recommended to terminate the dredging operation.

[0057] Experimental procedure: 1. Preparations before the experiment: Before the test, various sensing devices, including displacement sensors, earth pressure cells 2, ultrasonic flow meters 4, and camera systems, were installed and inspected. Initial state recording and necessary verification were completed. The state of the silt in the test trench 8 was prepared to ensure that the preset working conditions were met. Simultaneously, the data acquisition and recording process was integrated and debugged to ensure that all data and video recordings corresponded to the same test process. The simulated working condition in this embodiment is 12.6 kN / m. 3 The bulk density of the silt was set to a base pressure of 1.3 kPa.

[0058] 2. Experimental process and results: During the silt-dissipating process of the immersed tube model 11, each monitoring unit synchronously acquires and transmits / records process data. After alignment with the displacement reference, pressure, flow velocity, extrusion volume, and morphological information can be compared and analyzed under the same process coordinates. The silt-dissipating process typically undergoes an evolution from significant changes to a stable phase: 1) Initial stage of silt squeezing: When the immersed tube model 11 begins to contact the silt and is lowered, the displacement response gradually increases, the base pressure is established and spatial distribution differences appear; the position of the extrusion end interface begins to change, the extrusion amount gradually accumulates, and the flow response can also be observed at the flow velocity monitoring point. 2) Sludge removal stage: As the discharge rate continues to increase, the interface position and morphology continue to evolve, and parameters such as pressure and flow rate change with the stage of the process; different monitoring parameters can form mutually corroborating process correlations, which can be used to identify local disturbances and overall trends; 3) Stabilization Stage of Squeezing: When the squeezing process gradually stabilizes, the rate of change of at least one key response quantity enters a stable state (e.g., the growth of extrusion volume slows down, the change in displacement slows down, the change in pressure / flow rate decreases, etc.), and after verification and consistency with other parameters, it can be determined that the squeezing process has entered a stable / terminated state. Figure 6 As shown, in the simulated working conditions of this embodiment, the initial silt thickness is 30cm, the final sinking displacement of the submerged pipe is 29cm, and the cumulative extrusion amount accounts for approximately 97% of the initial total. This can be combined with... Figure 6The complete displacement change curve shown, along with the corresponding pressure, flow rate, and extrusion volume records, is used to determine the stabilization / end point and output the dredging criterion parameter set and state conclusion.

[0059] This embodiment demonstrates that by using a multi-parameter collaborative monitoring and intelligent output method for dredging criterion parameter sets, it is possible to make a verifiable determination of the stable / end state of the dredging process without relying on a single indicator, and to provide a basis for comparison of similar working conditions and engineering dredging decisions.

[0060] Although the embodiments and accompanying drawings of this invention are disclosed for illustrative purposes, those skilled in the art will understand that various substitutions, changes, and modifications can be made without departing from the basic spirit and scope of this invention and its appended claims. Therefore, the scope of protection of this invention is not limited to the content shown in the embodiments and drawings.

Claims

1. A method for monitoring a parameter set of underwater sediment backfilling and squeezing process using a multi-parameter coordinated approach, characterized in that: The monitoring method includes the following steps: 1) Synchronous acquisition and time alignment of multi-source data: Collect multi-source monitoring data consisting of displacement response, pressure response, flow characteristic parameters, extrusion quantity characteristic parameters and morphological characteristic parameters during the sludge squeezing process; use the displacement response as the time reference to establish a unified time axis, and perform time alignment and process segmentation on the multi-source monitoring data to obtain time-consistent multi-source monitoring data. 2) Construction of key response quantities and their rate of change characteristics: Select at least one parameter that can characterize the siltation state from the aligned multi-source monitoring data as the key response quantity. After denoising, smoothing and outlier identification processing of the key response quantity, calculate the rate of change of the key response quantity with time or with displacement respectively. Within a preset time window, construct the stability characteristics of the rate of change of the key response quantity, and determine the moment when the key response quantity enters a stable state, so as to reduce the risk of misjudgment caused by instantaneous disturbances or single indicator anomalies. 3) Multi-parameter cross-verification and consistency check: The change rate of key response quantities meets the stability condition as the initial judgment basis. At least two of the pressure response quantity, flow characteristic parameters, extrusion quantity characteristic parameters, and morphological characteristic parameters are used for cross-verification and consistency check. When multiple parameters jointly characterize the sludge extrusion process to enter a stable state, the sludge dredging criterion is confirmed to be valid. When there are obvious contradictions or anomalies among the parameters, the corresponding data segments are marked or removed and re-judged. The specific process of cross-validation and consistency verification in step 3) is as follows: Cross-validation includes aligning the parameters to be verified within the same window as or corresponding to the key response quantity, and comparing the stability indicators, change trends, or threshold states of each parameter to determine whether they meet the conditions of synchronous stability, trend consistency, or state non-conflict within the preset tolerance range. Cross-validation can also be combined with rule matching, correlation analysis, or trend comparison after time delay compensation. Consistency verification includes at least window synchronization verification, change trend coordination verification, and anomaly priority investigation to verify temporal consistency, state evolution consistency, and suppress abnormal data interference, respectively. 4) Intelligent output of dredging criterion parameter set and judgment results: When the key response quantity meets the conditions for entering a stable state, and after the consistency of the multi-parameter mutual verification, the dredging process is judged to have entered a stable or ended state. Output a set of dredging criteria parameters, which includes at least: key response quantity type, rate of change threshold, stable duration, mutual verification parameters, and feature values ​​corresponding to the stable time.

2. The method for monitoring the parameter set of the underwater siltation process using multi-parameter coordinated methods according to claim 1, characterized in that: The flow characteristic parameters include the flow velocity and flow state within the siltation zone and at the extrusion end; the extrusion quantity characteristic parameters include the amount of silt extruded, the interface position, or the amount of morphological evolution; the morphological characteristic parameters are obtained through image acquisition and are used to reflect the morphological changes in the siltation zone.

3. The method for monitoring the parameter set of the underwater siltation process according to claim 1, characterized in that: Step 2) involves constructing stability features of the rate of change of key response quantities within a preset time window to determine the moment when the key response quantities enter a stable state, thereby reducing the risk of misjudgment caused by instantaneous disturbances or abnormalities in a single indicator. The specific process is as follows: Within the preset time window, stability features are constructed that include at least the absolute value of the rate of change, the amplitude of the rate of change fluctuation, the standard deviation of the rate of change, and the trend of the rate of change. The absolute value of the rate of change characterizes the strength of the change, the amplitude of the rate of change fluctuation characterizes the degree of disturbance, the standard deviation of the rate of change characterizes the degree of discrete convergence, and the trend of the rate of change characterizes the state of trend convergence. These stability features can be initially screened by comparing them individually with corresponding thresholds. At least two stability features are used in conjunction with a continuous window consistency criterion to determine the moment when the key response quantities enter a stable state, thereby reducing the risk of misjudgment caused by instantaneous disturbances or abnormalities in a single indicator.

4. The method for monitoring the parameter set of the underwater siltation process according to claim 1, characterized in that: In step 2), the method for determining when the rate of change of the response quantity enters a stable state is as follows: the absolute value of the rate of change decreases to below a preset threshold and remains stable within a continuous preset time window; or the fluctuation range of the rate of change within the preset time window is less than a preset range, thereby determining that the siltation process has entered a stable stage from the dynamic change stage.

5. The method for monitoring the parameter set of the underwater siltation process according to claim 1, characterized in that: The specific steps for verifying the condition using other monitoring parameters in the multi-parameter mutual verification step are as follows: when it is initially determined that the siltation tends to be stable, the changing trends of characteristic parameters other than the key response quantity among the pressure response quantity, flow characteristic parameters, extrusion quantity characteristic parameters, and morphological characteristic parameters are compared simultaneously; if all parameters involved in the verification tend to be stable or have the same changing trend, the stability determination is confirmed to be valid. If there are abnormal fluctuations or contradictory trends, the current time period is deemed invalid and removed, and the stability determination is re-executed.

6. A monitoring system for a multi-parameter coordinated underwater sediment backfilling process parameter set monitoring method based on any one of claims 1-5, characterized in that: The monitoring system includes the following modules: 1) Displacement monitoring unit: used to acquire the displacement response during the lowering process of the gravity structure of the underwater siltation system, and to serve as the reference signal for multi-source data time alignment and process segmentation; 2) Pressure monitoring unit: used to acquire the pressure response and its distribution characteristics during the siltation process; 3) Flow characteristic monitoring unit: used to acquire flow characteristic parameters in the squeezing zone and the extrusion end region, including flow velocity and flow state characteristics; 4) Extrusion volume monitoring unit: used to acquire the extrusion volume of sludge or characteristic parameters of the extrusion volume, such as the interface position and morphology, which can characterize the extrusion volume as the process evolves; 5) Morphological monitoring unit: used to collect morphological characteristic parameters of the siltation area, including morphological evolution images and state characteristics; 6) Data Synchronization Acquisition and Intelligent Fusion Criterion Output Unit: Connected to the above-mentioned displacement monitoring unit, pressure monitoring unit, flow characteristic monitoring unit, extrusion quantity monitoring unit and morphology monitoring unit, it is used to synchronously acquire, time-align, construct the stability characteristics of the change rate of key response quantities, and cross-verify multiple parameters of multi-source data such as pressure response quantity, flow characteristic parameters, extrusion quantity characteristic parameters and morphology characteristic parameters, and output the dredging criterion parameter set and dredging status judgment result.

7. The monitoring system of the multi-parameter coordinated underwater siltation process parameter set monitoring method according to claim 6, characterized in that: The displacement monitoring unit consists of a set of wire displacement sensors (1) and is set at both ends of the immersed tube model (11); the pressure monitoring unit is a set of earth pressure cells (2) and is set on the lower surface of the bottom plate of the immersed tube model (11); the flow characteristic monitoring unit is a set of ultrasonic flow meters (4) and is set in the middle of the mud squeezing test tank (8) and 0.5-1.5m outside the extrusion end of the test tank backfill material; the extrusion amount monitoring unit is a coordinate grid (3) set on the tempered glass side; the morphology monitoring unit consists of a set of pinhole cameras (5) and wide-angle cameras (6) and is set on the tempered glass side of the test tank; the data synchronous acquisition and intelligent fusion criterion output unit adopts an industrial control level processing system (7).

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