Water conservancy cofferdam monitoring method, system, computing device and readable storage medium

Abstract

The application discloses a water conservancy cofferdam monitoring method, system, computing device and readable storage medium, comprising a sensing optical fiber arranged on the surface of a monitoring area, and a demodulator connected with the optical signal of the sensing optical fiber, the demodulator being in communication connection with a host computer and an active thermal excitation module for periodically actively heating the cofferdam monitoring area; further comprising a quantum dot tracing module comprising a plurality of quantum dot release units distributed in a grid shape and carrying spatial coordinate information and a plurality of fluorescent spectrum identification units arranged in a distributed grid shape, wherein the quantum dots stored in different quantum dot release units have unique optical coding characteristics. The application simultaneously utilizes a distributed temperature measurement system and quantum dot tracing technology to realize accurate qualitative and quantitative monitoring of cofferdam seepage, and simultaneously utilizes the monitoring system to realize real-time visualization of cofferdam monitoring, thereby providing accurate data support for cofferdam construction early warning.

Description

Technical Field

[0001] This invention relates to monitoring seepage information of cofferdams, and particularly to monitoring methods, systems, computing devices, and readable storage media for monitoring hydraulic cofferdams. Background Technology

[0002] During the construction of water conservancy facilities, temporary cofferdams are required to prevent water and soil from entering the construction site, allowing construction to proceed within the cofferdam. Monitoring the cofferdam structure is a critical area for the safe operation of water conservancy projects, directly impacting the safety of construction workers and downstream public safety. Accurately understanding the seepage status and deformation trends of the cofferdam not only ensures project safety but also extends its service life, possessing significant engineering application value.

[0003] However, existing monitoring methods often fall short of practical needs in complex environments. Current technologies mostly rely on single types of sensors, which are insufficient to cope with the variability of the internal environment of cofferdams and the complex interference of external conditions. Especially when dynamically monitoring seepage paths and predicting structural deformation, potential risks are often not detected in a timely manner due to insufficient data accuracy or limited coverage.

[0004] Existing technologies include applications of distributed optical fiber temperature measurement, such as CN118033173A, patent titled: "A Device and Method for Monitoring Underground Seepage Velocity and Direction Using an Actively Heated Fiber Bragg Grating." The technical principle is that when no seepage occurs, the device only conducts heat to the surrounding soil and rock, and the temperature field remains relatively stable. When seepage occurs, the seepage carries away heat from the monitoring point through thermal convection. The upstream temperature decreases faster than the downstream temperature, causing a change in the temperature field. This temperature change affects the effective refractive index and period of the fiber Bragg grating, resulting in a shift in the center wavelength of the reflected light, thus enabling temperature measurement at the monitoring point.

[0005] However, the aforementioned seepage monitoring methods are still limited to qualitative analysis in dealing with seepage. They can only roughly determine the area where seepage occurs, but cannot accurately monitor seepage path, flow rate, and velocity. Furthermore, they cannot meet the application requirements for real-time monitoring and simulation of cofferdam seepage in smart construction scenarios. Summary of the Invention

[0006] This application first proposes a monitoring method for hydraulic cofferdams, aiming to solve the problem described in the background art that current seepage monitoring cannot achieve quantitative monitoring.

[0007] A method for monitoring hydraulic cofferdams, the method comprising the following steps:

[0008] Step S101: Collect temperature data of the surface of the cofferdam monitoring area through the distributed optical fiber sensing module according to the set program;

[0009] Step S102: The monitoring area of ​​the cofferdam is periodically and actively heated by the active thermal excitation module according to the set program, so that the distributed optical fiber sensing module can collect the temperature data of the surface of the monitoring area of ​​the cofferdam after active heating.

[0010] Step S103: Establish a steady-state temperature baseline field based on the steady-state temperature information collected by the distributed optical fiber sensing module when active heating has not been started;

[0011] Step S104: Compare the real-time temperature data collected by the distributed optical fiber sensing module with the steady-state temperature baseline field data to obtain the differential temperature field.

[0012] Step S105: Process the differential temperature field data to identify valid suspected leakage areas;

[0013] Step S106: Quantum dot tracking and monitoring are performed using a number of quantum dot release units marked with spatial location information in a grid-like distribution and a number of fluorescence spectral recognition units arranged in a distributed grid-like distribution, wherein the quantum dots stored in different quantum dot release units have unique optical coding characteristics.

[0014] Step S107: Trigger a specific quantum dot release unit located upstream of the effective suspected leakage area, the quantum dot release unit releases quantum dots with specific optical encoding, and records the release time T1;

[0015] Step S108: Monitor the fluorescence spectrum by using multiple fluorescence spectral recognition units arranged in a distributed grid, identify the quantum dot signal with the specific optical encoding, and determine the spatial location of the first and subsequent recognition units that detect the quantum dot signal and the corresponding signal arrival time T2;

[0016] Step S109: Calculate the seepage velocity V based on the equivalent seepage path length L between the quantum dot release unit and the first fluorescence spectroscopy recognition unit that detected the signal, and the time difference ΔT=T2-T1;

[0017] Step S1010: Based on the preset fitting model of the relationship between the peak quantum dot signal intensity and the seepage flow rate Q, and the peak quantum dot signal intensity I detected by the fluorescence spectroscopy recognition unit that detected the signal, the estimated value of the seepage flow rate Q is obtained.

[0018] In the above or some embodiments, in step S103, the differential temperature field data is reconstructed into a three-dimensional data volume, and the DBSCAN clustering algorithm is used to identify the set of low temperature anomalies; and the characteristic parameters of each anomaly region are calculated. When the area and average temperature difference constant value of the region exceed the preset threshold, it is determined to be a valid suspected leakage region.

[0019] In the above or some embodiments, in step S104, the geometric center coordinates of the effective suspected leakage area are calculated according to a pre-stored mapping table containing the spatial coordinates of all quantum dot release units and their corresponding quantum dot optical codes; the nearest quantum dot release unit located upstream of the geometric center coordinates is found in the mapping table and triggered.

[0020] In the above or some embodiments, the fitting model of the relationship between the peak quantum dot signal intensity and the percolation flow rate Q in step S107 can simulate the dilution degree of a specific optically encoded quantum dot under experimental conditions by different percolation flow rates, detect the corresponding peak fluorescence signal intensity, establish a calibration relationship between the peak signal intensity and the percolation flow rate, and determine the fitting model of the relationship between the peak quantum dot signal intensity and the percolation flow rate Q based on the calibration relationship.

[0021] In the above or some embodiments, the seepage path can be delineated in reverse based on the spatial position of the first and subsequent fluorescence spectral recognition units that detect quantum dot signals; the data of the effective suspected leakage area, the seepage path, the seepage velocity V, and the seepage flow rate Q are fused and visualized.

[0022] This application further proposes a cofferdam seepage monitoring system, the purpose of which is to solve the problem described in the background art that current seepage monitoring cannot achieve quantitative monitoring and quantitative analysis.

[0023] A monitoring system for hydraulic cofferdams, the technical solution of which is as follows:

[0024] The distributed optical fiber sensing module is used to collect temperature data in the monitoring area of ​​the cofferdam. It includes sensing optical fibers deployed on the surface of the monitoring area and a demodulator connected to the optical signal of the sensing optical fibers. The demodulator is communicatively connected to the host computer.

[0025] The active thermal excitation module for periodically heating the monitoring area of ​​the cofferdam includes several heating elements arranged in a grid and a heating controller. The heating controller receives instructions from the host to enable the specified heating elements.

[0026] The quantum dot tracer module includes a plurality of quantum dot release units arranged in a grid and carrying spatial coordinate information, and a plurality of fluorescence spectral recognition units arranged in a distributed grid. Each quantum dot release unit stores quantum dots with unique optical coding characteristics. Each quantum dot release unit includes a sealed container for storing a quantum dot solution and an electrically controlled flow control device connected to the sealed container, forming a structure where the quantum dot solution is released at a constant flow rate controlled by an electrically controlled signal. The electrically controlled flow control device is controlled by a host computer or other controller to perform switching actions. The fluorescence spectral recognition units are communicatively connected to the host computer.

[0027] It also includes a data processing and analysis module for receiving and processing data from each module and executing the above-mentioned hydraulic cofferdam seepage monitoring method; and a data fusion and visualization module for fusing multi-source data and generating a visual monitoring interface.

[0028] It also includes a power supply module that supplies power to the distributed optical fiber sensing module, the active thermal excitation module, and the quantum dot tracer module.

[0029] In the above or some embodiments, the quantum dot release unit and the fluorescence spectral recognition unit are deployed on the back slope of the cofferdam, the toe of the slope, the drainage body and the observation well at potential seepage overflows or locations that can be monitored.

[0030] In the above or some embodiments, the quantum dot release unit is a waterproof sealed container with openings at both the upper and lower ends. The lower opening is connected to the electronically controlled flow control device. The fluorescence spectroscopy recognition unit includes a fluorescence spectrometer, an excitation light source, and a plurality of optical probes arranged in a grid pattern. Each optical probe is connected to the fluorescence spectrometer via optical signal communication. The excitation light source controls the on / off state of the excitation light source transmitted to each optical probe through an optical switch.

[0031] This application also proposes a monitoring and calculation device for hydraulic cofferdams, which aims to solve the problem described in the background art that current seepage monitoring cannot achieve quantitative monitoring and quantitative analysis.

[0032] The technical solution for the monitoring and calculation equipment of hydraulic cofferdams is as follows:

[0033] It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the above-described method for monitoring seepage in hydraulic cofferdams.

[0034] This application also proposes a readable storage medium for monitoring hydraulic cofferdams, aiming to solve the problem described in the background art that current seepage monitoring cannot achieve quantitative monitoring and quantitative analysis.

[0035] The monitoring readable storage medium for hydraulic cofferdams stores a computer program that, when executed by a processor, implements the steps of the aforementioned hydraulic cofferdam seepage monitoring method.

[0036] This invention employs a distributed temperature measurement system and utilizes quantum dot tracer technology to achieve precise qualitative and quantitative monitoring of cofferdam seepage. Furthermore, the monitoring system based on this invention enables real-time visualization of cofferdam monitoring, providing accurate data support for early warning during cofferdam construction. This significantly improves the monitoring accuracy and real-time performance of cofferdam seepage and deformation, effectively identifies potential risk points and predicts deformation trends, providing reliable technical support for the safe operation of cofferdams. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the seepage monitoring method for hydraulic cofferdams in this invention.

[0038] Figure 2 This is a topology diagram of an embodiment of the hydraulic cofferdam seepage monitoring system of the present invention.

[0039] Figure 3 This is a topological diagram of the fluorescence spectral recognition unit in one embodiment of the hydraulic cofferdam seepage monitoring system of the present invention.

[0040] Figure 4 This is a schematic diagram of the arrangement of the composite optical cable in this invention. Detailed Implementation

[0041] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0042] This invention can be applied to steel plate cofferdams, earth-rock cofferdams, concrete cofferdams, etc. Taking concrete cofferdam monitoring as an example, the implementation process of this invention is explained in detail.

[0043] A cofferdam seepage monitoring system includes a distributed optical fiber sensing module 200 for collecting temperature data of the cofferdam monitoring area; the distributed optical fiber sensing module 200 includes a sensing optical fiber 201 and a demodulator 202; an active thermal excitation module 300 for periodically and actively heating the cofferdam monitoring area; the active thermal excitation module 300 includes a heating element 301 and a heating controller 302; and a quantum dot tracer module 400, including a plurality of quantum dot release units 401 and a plurality of fluorescence spectral recognition units 402, wherein the quantum dot release units 401 and the plurality of fluorescence spectral recognition units 402 cover the cofferdam monitoring area in a grid pattern, and the quantum dots stored in different quantum dot release units 401 have... The unique optical coding feature of the quantum dot release unit 401 includes a sealed container for storing the quantum dot solution and an electrically controlled flow control device connected to the sealed container, forming a structure for constant flow release of the quantum dot solution controlled by an electrically controlled signal; the data processing and analysis module 500 is used to receive and process data from various modules and perform cofferdam seepage monitoring; the data fusion and visualization module 600 is used to fuse multi-source data and generate a visual monitoring interface; the data processing and analysis module 500 and the data fusion and visualization module 600 run a data processing instruction set through the processor unit to realize data processing and visualization operations, and display it through the Three.js Web visualization platform.

[0044] In the above or some embodiments, the sensing fiber 201 is integrated into the armored composite optical cable 100, which is attached to the cofferdam monitoring area in a serpentine or mesh-like manner. The armored composite optical cable 100 also includes power lines and signal lines for powering and transmitting signals to the heating element 301, the heating controller 302, the quantum dot release unit 401, and the fluorescence spectroscopy recognition unit 402. The heating unit may be made of sheet-like flexible silicone rubber. Each heating element 301, quantum dot release unit 401, and fluorescence spectroscopy recognition unit 402 is connected to the power line and signal line connectors in a quick-connect manner.

[0045] In the above or some embodiments, the armored composite optical cable 100 includes an outer armor layer 101 made of insulating and corrosion-resistant rubber. The armor layer 101 is provided with mounting seats 102 at intervals. The mounting seats 102 are provided with symmetrical fixing holes, which are used to fix the mounting seats 102 to the surface of the cofferdam monitoring area by means of plugging, screw fixing or other methods.

[0046] In the above or some embodiments, the sealed container can be made of stainless steel with openings at the top and bottom. The lower opening of the stainless steel bottle is connected to an electrically controlled flow control device, which can be an electrically controlled constant flow valve, specifically a miniature diaphragm-type micro-constant flow valve. In the above scheme, the electrically controlled constant flow valve and the active heating unit can share a single controller for control. The controller can be a programmable logic controller (PLC). The PLC (e.g., a Siemens S7-1500 series PLC) serves as the field-level control core. It is connected to a solid-state relay in the heating circuit via an extended digital output module to execute a preset periodic on / off strategy; simultaneously, it is connected to the electrically controlled flow control device within the quantum dot release unit 401 via an extended analog output module or a high-speed pulse output port.

[0047] In the above scheme, when the algorithm determines that a certain quantum dot release unit 401 needs to be triggered, it sends a command (e.g., 'trigger pump 3, run for 10 seconds') to the programmable logic controller via Ethernet. Upon receiving the command, the controller immediately sends a control signal (e.g., output a 10-second 5V pulse signal or a 4-20mA current signal) to the designated micro-pump through the corresponding output channel, driving the electronically controlled flow control device to operate at a constant flow rate for a precise time, thereby completing the release of a fixed volume of quantum dot solution. Quantum dot encoding: the emission wavelength (color) of a quantum dot is determined by the size of its core material. Larger sizes result in longer emission wavelengths (redshift); smaller sizes result in shorter emission wavelengths (blueshift). By precisely controlling the synthesis process, quantum dots with emission peaks ranging from blue to infrared can be prepared. Therefore, quantum dot solutions with different emission wavelengths can be synthesized from quantum dots in different spatial locations. In one embodiment, quantum dot solutions with different emission peak wavelengths are configured for release units deployed in different spatial locations, with the peak wavelength difference not less than 20nm, so that downstream fluorescence spectrometers can distinguish them by identifying characteristic peak wavelengths. The data processing and analysis module has a built-in coding library that records the mapping relationship between the geographical location of each release unit and its quantum dot optical coding characteristics. The module matches the detected optical characteristics with the coding library by running spectral peak finding or intensity ratio calculation algorithms, thereby achieving accurate tracing of the seepage source.

[0048] In the above or some embodiments, coordinates are constructed for the cofferdam monitoring area, which includes all potential seepage overflows or locations that can be monitored, such as the back slope, slope toe, drainage body, and observation wells. Quantum dot release units 401 and fluorescence spectroscopy recognition units 402 are distributed in a grid, forming a quantum dot release unit 401 grid and a fluorescence spectroscopy recognition unit 402 grid, with each quantum dot release unit 401 and fluorescence spectroscopy recognition unit 402 assigned a spatial coordinate. Through the distributed recognition units, the system can effectively capture the outflow point of seepage water. When a quantum dot release unit 401 is triggered, its corresponding quantum dot signal will be detected sequentially by one or more recognition units in chronological order. Based on the spatial coordinates of the recognition units that detected the signal, the system can reversely delineate the main path of seepage in the medium and estimate the seepage velocity in different sections based on the signal arrival time difference.

[0049] In the above or some embodiments, the fluorescence spectroscopy identification unit 402 includes a fluorescence spectrometer 402.1, an excitation light source 402.2, and a plurality of optical probes 402.4 arranged in a grid. Each optical probe 402.4 is optically connected to the fluorescence spectrometer 402.1 for communication. The excitation light source 402.2 is controlled by an optical switch 402.3 to switch the transmission of the excitation light source 402.2 to each optical probe 402.4. The excitation light source 402.2 is a highly stable semiconductor laser, which is installed together with the main unit of the fluorescence spectrometer 402.1 in a central computer room. The output end of the laser is connected to the common end of a computer-controlled 1×N fiber optic switch 402.3. Each output end of the optical switch 402.3 is connected to the trunk optical cable leading to the optical probes 402.4 at different monitoring points via fiber optic patch cords, thereby realizing time-division and cyclic distribution of excitation light.

[0050] The host control manages the channel switching timing of optical switch 402.3 and ensures that the acquisition channel of the fluorescence spectrometer 402.1 is synchronized with the output channel of optical switch 402.3. When optical switch 402.3 switches to a certain channel, the laser is transmitted to the corresponding field optical probe 402.4 to excite the quantum dots to generate fluorescence. Simultaneously, the fluorescence signal collected by this probe is returned to the spectrometer host for analysis. This setup places the core sensitive equipment in a favorable environment and reduces system cost and complexity by sharing the laser and optical switch 402.3, while ensuring the stability and reliability of the excitation source 402.2.

[0051] In the above or some embodiments, optical probes 402.4 are arranged in a grid pattern on the back slope of the cofferdam at elevation intervals of 2 meters and horizontal intervals of 15 meters; an optical probe 402.4 is arranged every 20 meters in the drainage ditch at the toe of the slope; and an optical probe 402.4 is arranged at different depths in the three observation wells of the key section. This constitutes a three-dimensional monitoring network, realizing all-round capture of the seepage path.

[0052] A fitting model was established to establish the relationship between the peak quantum dot signal intensity and the seepage flow rate Q. According to the dilution principle, after a fixed dose (M) of quantum dots is released, it is diluted by the water flow in the seepage path. The concentration C at the downstream detection point is inversely proportional to the seepage flow rate Q: C∝M / Q; while the detected fluorescence signal intensity peak I, within a certain concentration range, is directly proportional to the quantum dot concentration C at the detection point: I∝C.

[0053] First, in the laboratory, a seepage device filled with the same medium as the actual cofferdam was built. By precisely controlling different known flow rates Q0 and injecting a fixed dose of specific coded quantum dots, the peak intensity I of the downstream fluorescence signal was measured simultaneously to obtain multiple sets of (Q0, I) data pairs. The least squares method was used to perform quadratic polynomial fitting on the data to establish the basic calibration model of the coded quantum dots Q=a*I^2+b*I+c.

[0054] On-site, monitoring points are periodically selected, and their actual flow rate Q1 is measured using traditional methods such as the volumetric method, while the peak value I1 of the quantum dot signal is recorded simultaneously. I1 is then substituted into the basic calibration model to obtain an estimated value Q2. The correction factor K = Q1 / Q2 is calculated to calibrate the basic model, resulting in the final on-site model Q1. final =K*(a*I^2+b*I+c).

[0055] The data processing and analysis module 500 has a built-in seepage estimation model. When it receives peak fluorescence signal intensity data, it automatically calls the corresponding coded model parameters to perform calculations and outputs the estimated seepage flow rate.

[0056] The data fusion and visualization module 600 is used to fuse multi-source data and generate a visual monitoring interface. The data fusion and visualization module 600 is developed based on a B / S architecture, uses the Three.js engine to build a web-based 3D visualization scene, and uses a time-series database and a relational database to store monitoring data and system metadata, respectively.

[0057] In the above or some embodiments, firstly, distributed fiber optic sensing data, quantum dot tracer data, and equipment status data are fused under a unified spatiotemporal reference. In the 3D model of the cofferdam, the temperature field distribution is rendered as a heatmap, and the seepage path is rendered as dynamic particle streamlines, where streamline thickness is proportional to seepage flow rate and particle velocity is proportional to seepage velocity. Simultaneously, a real-time dashboard, historical data trend curves, and alarm information list are provided in a 2D panel. This module achieves unified management, deep fusion, and intuitive display of multi-source monitoring data, providing comprehensive decision support for the assessment and early warning of cofferdam seepage safety status.

[0058] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, performs the following steps:

[0059] Temperature data of the surface of the cofferdam monitoring area is collected by the distributed optical fiber sensing module 200 according to the set program.

[0060] Step S102. The monitoring area of ​​the cofferdam is periodically and actively heated by the active thermal excitation module 300 according to the set program, so that the distributed optical fiber sensing module 200 can collect the temperature data of the surface of the monitoring area of ​​the cofferdam after active heating.

[0061] Based on the steady-state temperature information collected by the distributed optical fiber sensing module 200 when active heating is not activated, a steady-state temperature baseline field is established; the real-time temperature data collected by the distributed optical fiber sensing module 200 is compared with the steady-state temperature baseline field data to obtain a differential temperature field; the differential temperature field data is processed to identify effective suspected leakage areas.

[0062] Quantum dot tracking and monitoring are performed using a grid-distributed array of quantum dot release units 401 marked with spatial location information and a distributed grid of fluorescence spectral recognition units 402. Each quantum dot stored in a different quantum dot release unit 401 has a unique optical coding feature. A specific quantum dot release unit 401 located upstream of a suspected leakage area is triggered to release a quantum dot with a specific optical coding, and the release time T1 is recorded.

[0063] By monitoring the fluorescence spectrum through multiple fluorescence spectral recognition units 402 arranged in a distributed grid, a quantum dot signal with a specific optical encoding is identified, and the spatial location of the first and subsequent recognition units that detect the signal and the corresponding signal arrival time T2 are determined.

[0064] The seepage velocity V is calculated based on the equivalent seepage path length L between the quantum dot release unit 401 and the first fluorescence spectral recognition unit 402 that detects the signal, and the time difference ΔT=T2-T1.

[0065] Based on the preset fitting model of the relationship between the peak quantum dot signal intensity and the seepage flow rate Q, and the peak quantum dot signal intensity I detected by the fluorescence spectroscopy recognition unit 402 that detected the signal, the estimated value of the seepage flow rate Q is obtained.

[0066] In the above or some embodiments, the process of establishing the steady-state temperature baseline field includes: during the initial deployment or periodic calibration of the system, the active thermal excitation module 300 is turned off, allowing the system to run continuously for at least 72 hours without external thermal interference; the distributed fiber optic sensing module 200 continuously collects temperature data at a normal cycle, such as every 5-15 minutes, and after preprocessing such as bad pixel removal and wavelet transform denoising, the data within a specific time window in the early morning of each day is arithmetically averaged to finally generate a steady-state temperature baseline field that is only related to spatial location and independent of time. After the calculation is completed, it is stored as a static array in a database or configuration file. The baseline field is not permanent. The above process should be repeated periodically (e.g., quarterly) or after large-scale environmental changes (e.g., significant changes in water level) to update the baseline field and adapt to long-term seasonal ground temperature changes. The preprocessed real-time temperature data T(x,t) is subtracted point by point from the steady-state temperature baseline field. Preprocessing methods include bad pixel removal through spatial domain filtering and denoising through time domain + spatial domain filtering. After point-by-point subtraction, in the normal zone with no leakage, ΔT≈0, and the real-time temperature in this area is consistent with the background temperature. In the actively heated zone, ΔT>>0 during heating, forming a significant positive anomaly "hot spot." In the seepage zone, the water flow carries away heat during the heating phase, causing the temperature rise in this area to be much smaller than the surrounding area, manifesting as a relative negative anomaly ("cold spot") in the differential field ΔT. During the cooling phase, the thermal inertia of the water causes the cooling rate in this area to be slower than the surrounding area, manifesting as a relative positive anomaly in the differential field ΔT. Since both the steady-state temperature baseline field and the real-time temperature data contain the same slowly changing seasonal background ground temperature, these common-mode interferences are greatly suppressed after subtraction, highlighting the weak seepage anomaly signal. By calculating the differential temperature field, common-mode interference from the environmental background temperature is effectively suppressed, highlighting the local temperature anomalies caused by active heating and seepage, providing a high-quality data foundation for subsequent intelligent identification of suspected leakage areas.

[0067] In the above or some embodiments, determining the effective suspected leakage area includes: reconstructing the differential temperature field data into a two-dimensional image; performing cluster analysis on the pixels in the image using a density-based spatial clustering algorithm, where pixel features include their spatial coordinates and temperature difference constants; calculating the area and average temperature difference constant for each connected region identified by the clustering algorithm; if the area of ​​the region is greater than a first threshold and the average temperature difference constant is lower than a second threshold, then the region is determined to be an effective suspected leakage area. The density-based spatial clustering algorithm is the DBSCAN algorithm, which can automatically identify abnormal regions of arbitrary shapes and exclude sparse noise points. The first threshold is set based on the minimum leakage scale, and the second threshold is set based on the temperature difference abnormal fluctuation range of the non-leakage region. Further, the method also calculates the shape factor of the effective suspected leakage area to assist in determining the leakage type, where a roundness close to 1 indicates point leakage characteristics, and a smaller roundness indicates linear or crack leakage characteristics.

[0068] In the above or some embodiments, one-dimensional fiber optic data points are reconstructed into a two-dimensional temperature field image based on their preset spatial coordinates. The value of each pixel in the image is ΔT at that point. A slight Gaussian blur (e.g., 3x3 kernel, σ=0.5) is applied to the image to smooth out minor random noise while preserving meaningful anomaly boundaries. Each pixel's (x, y, ΔT) in the temperature field image is treated as a three-dimensional data point. The DBSCAN algorithm is used to output multiple clusters, each cluster being a connected set of anomaly points, i.e., a potential leakage region. Points not belonging to any cluster are marked as noise. The features of the clusters are extracted. This includes area characteristics, average temperature anomalies, and shape factor. The number of pixels contained in the cluster × the actual area represented by the pixel (for example, if the pixel pitch is 0.5m, then one pixel represents 0.25m²). The arithmetic mean of the ΔT values ​​of all pixels in the cluster, which is usually a significantly negative value in the seepage zone under active heating mode. The shape factor is used to distinguish the shape of the patch. For example, taking Circularity = 4π*Area / Perimeter² as an example, the closer the value is to 1, the closer the shape is to a circle (possibly a point leak), and the smaller the value, the more complex and elongated the shape (possibly a crack or linear leak). Based on the extracted features, a threshold is set to distinguish between real leakage anomalies and false alarms. For example, if the area is greater than 3 square meters and the average temperature difference is less than -2℃, the area is determined to be a valid suspected leakage area. Otherwise, the area is discarded and regarded as environmental noise or irrelevant disturbance. All valid suspected leakage areas that meet the conditions will have their contour coordinates (pixel set) and all calculated feature values ​​recorded. The arithmetic mean of all point coordinates is calculated based on the contour coordinates and a coordinate value is output. For compact clusters, the result is very close to the centroid.

[0069] In the above or some embodiments, the seepage velocity V is calculated by calculating the time difference ΔT based on the quantum dot release time T1 and the time T2 when the downstream identification unit first detects the signal; the velocity is calculated according to the formula V=L / ΔT based on the equivalent seepage path length L between the release unit and the identification unit, where the equivalent seepage path length L is determined by multiplying the straight distance by the tortuosity coefficient based on the characteristics of the cofferdam medium.

[0070] In the above or some embodiments, reverse delineation of the seepage path includes: based on the spatial location of the identification unit that detected the quantum dot signal and the temporal order of the signal detection, combined with the hydraulic gradient model of the cofferdam, generating one or more inferred dominant seepage paths from the release unit location to the location of the earliest detected identification unit, and visually rendering them in a 3D model. The location P of the triggered release unit... re (x_r, y_r, z_r), the positions of all recognition units that detected this coded signal {P de 1,P de 2,...}, and the order in which they detected the signals [T1,T2,...]; sort all the identification units that detected the signals in chronological order from earliest to latest. This results in an ordered sequence [S1,S2,S3]. The starting point of the path is a fixed release unit, and the ending point is the position of the earliest detected identification unit S1. S2, S3, etc., are points that may be passed through later in the path. The path tends to flow along the direction of the maximum hydraulic gradient, i.e., from high to low. In the 3D model, starting from the starting point, a streamline is generated along the direction of the terrain gradient descent, ultimately pointing to the identification unit that detected the signal. This generated streamline is the "outlined" seepage path.

[0071] This invention employs a distributed temperature measurement system and utilizes quantum dot tracer technology to achieve precise qualitative and quantitative monitoring of cofferdam seepage. Furthermore, the monitoring system based on this invention enables real-time visualization of cofferdam monitoring, providing accurate data support for early warning during cofferdam construction. This significantly improves the monitoring accuracy and real-time performance of cofferdam seepage and deformation, effectively identifies potential risk points and predicts deformation trends, providing reliable technical support for the safe operation of cofferdams.

Claims

1. A monitoring method for hydraulic cofferdams, the method comprising the following steps: Step S101: Collect temperature data of the surface of the cofferdam monitoring area through the distributed optical fiber sensing module (200) according to the set program; Step S102: The monitoring area of ​​the cofferdam is periodically heated by the active thermal excitation module (300) according to the set program so that the distributed optical fiber sensing module (200) can collect the temperature data of the surface of the monitoring area of ​​the cofferdam after active heating. Its characteristic is that it further includes the following steps: Step S103: Based on the steady-state temperature information collected by the distributed optical fiber sensing module (200) when active heating has not been started, establish a steady-state temperature baseline field; Step S104: Compare the real-time temperature data collected by the distributed optical fiber sensing module (200) with the steady-state temperature baseline field data to obtain the differential temperature field; Step S105: Process the differential temperature field data to identify valid suspected leakage areas; Step S106: Quantum dot tracking and monitoring are performed using a number of quantum dot release units (401) marked with spatial location information in a grid-like distribution and a number of fluorescence spectral recognition units (402) arranged in a distributed grid-like distribution. The quantum dots stored in different quantum dot release units (401) have unique optical coding characteristics. Step S107: Trigger a specific quantum dot release unit (401) located upstream of the effective suspected leakage area, the quantum dot release unit (401) releases quantum dots with specific optical encoding, and records the release time T1; Step S108: By monitoring the fluorescence spectrum through multiple fluorescence spectral recognition units (402) arranged in a distributed grid, the quantum dot signal with the specific optical encoding is identified, and the spatial position of the first and subsequent recognition units that detect the quantum dot signal and the corresponding signal arrival time T2 are determined. Step S109: Calculate the seepage velocity V based on the equivalent seepage path length L between the quantum dot release unit (401) and the first fluorescence spectral recognition unit (402) that detected the signal, and the time difference ΔT=T2-T1; Step S1010: Based on the preset fitting model of the relationship between the peak quantum dot signal intensity and the seepage flow rate Q, and the peak quantum dot signal intensity I detected by the fluorescence spectroscopy recognition unit (402) that detected the signal, the estimated value of the seepage flow rate Q is obtained.

2. The monitoring method for hydraulic cofferdams according to claim 1, characterized in that, In step S103, the differential temperature field data is reconstructed into a three-dimensional data volume, and the DBSCAN clustering algorithm is used to identify the set of low-temperature anomalies. The characteristic parameters of each anomaly region are calculated. When the area and average temperature difference of the region exceed the preset threshold, it is determined to be a valid suspected leakage region.

3. The monitoring method for hydraulic cofferdams according to claim 1, characterized in that, In step S104, the geometric center coordinates of the effective suspected leakage area are calculated according to a pre-stored mapping table containing the spatial coordinates of all quantum dot release units (401) and their corresponding quantum dot optical codes; the nearest quantum dot release unit (401) located upstream of the geometric center coordinates is found in the mapping table and triggered.

4. The monitoring method for hydraulic cofferdams according to claim 1, characterized in that, The fitting model for the relationship between the peak quantum dot signal intensity and the percolation flow rate Q described in step S107 can simulate the dilution degree of a specific optically encoded quantum dot under experimental conditions by different percolation flow rates, detect the corresponding peak fluorescence signal intensity, establish a calibration relationship between the peak signal intensity and the percolation flow rate, and determine the fitting model for the relationship between the peak quantum dot signal intensity and the percolation flow rate Q based on the calibration relationship.

5. The monitoring method for hydraulic cofferdams according to claim 1, characterized in that, The seepage path can be delineated in reverse based on the spatial position of the first and subsequent fluorescence spectral recognition units (402) that detect quantum dot signals; the data of the effective suspected leakage area, the seepage path, the seepage velocity V and the seepage flow rate Q can be fused and visualized.

6. A monitoring system for hydraulic cofferdams used to perform the method of any one of claims 1-5, characterized in that: The distributed optical fiber sensing module (200) is used to collect temperature data of the cofferdam monitoring area, including a sensing optical fiber (201) laid on the surface of the monitoring area, and a demodulator (202) connected to the optical signal of the sensing optical fiber (201), and the demodulator (202) is communicatively connected to the host. The active thermal excitation module (300) for periodically and actively heating the monitoring area of ​​the cofferdam includes a plurality of heating elements (301) arranged in a grid and a heating controller (302). The heating controller (302) receives instructions from the host to enable the specified heating elements (301). The quantum dot tracer module (400) includes a plurality of quantum dot release units (401) arranged in a grid and carrying spatial coordinate information, and a plurality of fluorescence spectral recognition units (402) arranged in a distributed grid. The quantum dots stored in different quantum dot release units (401) have unique optical coding features. The quantum dot release unit (401) includes a sealed container for storing quantum dot solution, and also includes an electrically controlled flow control device connected to the sealed container, forming a structure in which the quantum dot solution is released at a constant flow rate controlled by an electrically controlled signal. The electrically controlled flow control device is controlled by a host or other controller to realize switching action. The fluorescence spectral recognition unit (402) is communicatively connected to the host. It also includes a data processing and analysis module (500) for receiving and processing data from each module and performing the method described in any one of claims 1-5; and a data fusion and visualization module (600) for fusing multi-source data and generating a visual monitoring interface. It also includes a power supply module that supplies power to the distributed optical fiber sensing module (200), the active thermal excitation module (300), and the quantum dot tracer module (400).

7. The system according to claim 6, characterized in that, The quantum dot release unit (401) and the fluorescence spectral recognition unit (402) are deployed on the back slope of the cofferdam, the toe of the slope, the drainage body and the observation well at locations where potential seepage or overflow can be monitored.

8. The system according to claim 7, characterized in that, The quantum dot release unit (401) is a waterproof sealed container with openings at both the upper and lower ends. The lower opening is connected to the electronically controlled flow control device. The fluorescence spectroscopy recognition unit (402) includes a fluorescence spectrometer (402.1), an excitation light source (402.2), and a number of optical probes (402.4) arranged in a grid. Each optical probe (402.4) is connected to the fluorescence spectrometer (402.1) via optical signal communication. The excitation light source (402.2) controls the on / off state of the excitation light source (402.2) transmitted to each optical probe (402.4) through an optical switch (402.3).

9. A monitoring and computing device for hydraulic cofferdams, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1-5.

10. A computer-readable storage medium for monitoring hydraulic cofferdams, wherein the computer-readable storage medium stores a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-5.

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