Water-rich tunnel drainage pressure reduction regulation method and system

Abstract

The application relates to the technical field of tunnel water discharge pressure reduction control, and discloses a water-rich tunnel water discharge pressure reduction regulation and control method and system, which comprises an information sensing unit, an intelligent analysis unit and an execution control unit. The information sensing unit collects water pressure and drainage flow data behind the tunnel lining in real time; the intelligent analysis unit generates a control signal based on a preset logic rule and realizes forward-looking adjustment through a trend prediction module; and the execution control unit accurately controls the drainage flow through an intelligent adjustment device to maintain the water pressure in a safe range. The system adopts multi-source data fusion and machine learning technology, solves the hysteresis problem of the traditional water discharge system, avoids energy waste and environmental impact, and provides reliable protection for the safe operation of the water-rich tunnel.

Description

Technical Field

[0001] This invention belongs to the field of tunnel drainage and pressure reduction control technology, specifically a method and system for regulating drainage and pressure reduction in water-rich tunnels. Background Technology

[0002] During the construction and operation of water-rich tunnels, the water pressure in the surrounding rock behind the tunnel lining is a critical safety factor. Excessive water pressure can lead to deformation, cracking, or even instability of the lining structure, thus affecting the long-term safety and stability of the tunnel. Traditionally, to reduce the water pressure behind the lining, piezometers and drainage facilities are typically used for depressurization. However, these methods largely rely on real-time monitoring feedback mechanisms, and their control decisions are based on single-dimensional instantaneous water pressure data, lacking the ability to predict water pressure change trends. This makes the system difficult to adapt to dynamically changing hydrogeological conditions.

[0003] Furthermore, existing drainage systems often exhibit lag in response to sudden heavy rainfall or changes in upstream water sources. Because the system's control is limited to whether the instantaneous pressure exceeds a preset threshold, it may fail to initiate drainage measures in a timely manner when pressure rises rapidly, increasing the risk of the lining structure being subjected to excessive pressure. Simultaneously, frequent starting and stopping of drainage equipment unnecessarily not only wastes energy but may also cause a drop in the regional groundwater level, leading to uneven soil consolidation and ground subsidence, potentially impacting the surrounding environment.

[0004] Therefore, there is an urgent need for an intelligent drainage control system that can integrate multi-source information and achieve forward-looking prediction in order to improve the safety of tunnel structures and take into account both energy efficiency and environmental protection requirements. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for regulating water discharge and depressurization in water-rich tunnels, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides a water-rich tunnel drainage and pressure reduction control system for regulating the water pressure in the surrounding rock behind the tunnel lining. The system includes an information sensing unit configured to acquire real-time water pressure data behind the tunnel lining and real-time drainage flow data from a drainage pipe laid within the tunnel; an intelligent analysis unit connected to the information sensing unit via wired or wireless communication, configured to receive the real-time water pressure data and real-time drainage flow data, and to parse and judge the received data based on preset logical rules to generate corresponding control signals; and an execution control unit connected to the intelligent analysis unit via a control signal link, configured to receive and respond to the control signals to precisely adjust the drainage flow of the drainage pipe, thereby maintaining the water pressure behind the tunnel lining within a preset safe range.

[0007] The information sensing unit includes at least one set of pressure detection devices, whose sensing ends are placed inside the tunnel lining by embedded installation or surface fixing, so as to directly contact the surrounding rock water environment behind the lining, thereby continuously monitoring and outputting the real-time water pressure data; and at least one set of flow detection devices, which are installed on the drainage pipe by flange connection or clamp fixing, so as to continuously monitor and output the instantaneous drainage flow rate through the execution control unit as the real-time drainage flow rate data.

[0008] The intelligent analysis unit is functionally divided into a signal processing module and a decision calculation module. The signal processing module is configured to receive raw sensor data from the information sensing unit and perform digital filtering and validity verification on the raw sensor data to generate stable and reliable real-time pressure and flow values. The decision calculation module is configured to acquire the processed real-time pressure and flow values ​​from the signal processing module and perform logical analysis on these values ​​based on preset parameter thresholds to generate the control signal.

[0009] The signal processing module uses wavelet transform or adaptive filtering algorithms to perform digital filtering on the received raw sensor data in order to eliminate the impact of on-site noise interference and signal fluctuations on measurement accuracy.

[0010] The preset parameter thresholds in the decision calculation module include pressure upper limit threshold, target pressure value, pressure lower limit threshold, and flow anomaly threshold; the thresholds are preset based on the tunnel geological survey report, surrounding rock stability assessment, and lining structure mechanical performance analysis.

[0011] The logic rules executed by the decision calculation module specifically include the following: when the real-time pressure value is determined to be greater than or equal to the upper pressure threshold, a signal is generated to drive the execution control unit to fully open; when the real-time pressure value is determined to be less than the upper pressure threshold but greater than the target pressure value, a fuzzy control algorithm is activated, and using the target pressure value as a reference value, a precise adjustment signal is calculated to smoothly adjust the real-time pressure value to the target pressure value; when the real-time pressure value is determined to be less than the lower pressure threshold, a signal is generated to drive the execution control unit to reduce its opening degree or completely close.

[0012] The core component of the execution control unit is an intelligent adjustment device. The structure of the intelligent adjustment device includes a main body with a threaded interface for connecting to the drainage pipe; an adjustment element disposed inside the main body for adjusting the cross-section of the water flow; and a drive component linked to the adjustment element for driving the adjustment element to perform precise positioning according to the control signal.

[0013] The regulating element adopts a multi-stage throttling structure. Multiple sets of radially distributed micro-channels are machined on the outer wall of the structure. By axially moving the regulating element, these micro-channels are opened or closed in stages to achieve graded control of the total pressure drop. Furthermore, the key throttling surfaces of the regulating element that come into contact with the water flow are coated with a high-hardness, wear-resistant coating through a thermal spraying process to resist the scouring and corrosion of the high-pressure water flow.

[0014] The drive component is an integrated intelligent electric actuator, which integrates a stepper motor, a gear transmission mechanism, and a position sensor. The position sensor is used to detect the actual displacement of the adjustment element in real time and feed the displacement information back to the intelligent analysis unit to form a closed-loop position control for adjusting the opening.

[0015] A method for controlling water pressure reduction in a water-rich tunnel includes the following steps: Real-time information acquisition: Real-time water pressure data and real-time drainage flow data are continuously acquired at a preset sampling period using a pressure detection device deployed behind the tunnel lining and a flow detection device installed on the drainage pipe; State analysis and signal generation: The collected and processed real-time pressure and flow values ​​are continuously compared logically and analyzed with preset upper pressure threshold, target pressure value, and lower pressure threshold, and a control signal is generated accordingly; Signal execution: The control signal is sent to an intelligent regulating device installed on the drainage pipe, driving the intelligent regulating device to adjust its opening to change the drainage flow; Feedback and loop: The steps of real-time information acquisition, state analysis and signal generation, and signal execution are continuously and cyclically executed to form a closed-loop feedback control, so as to dynamically maintain the water pressure behind the tunnel lining within the ideal safe range defined by the target pressure value and the lower pressure threshold.

[0016] A further technical feature of this invention is that the intelligent analysis unit further includes a trend prediction module. This trend prediction module is configured to integrate multi-source heterogeneous data from inside and outside the tunnel, including but not limited to real-time water pressure data, real-time drainage flow data, and surrounding rock seepage pressure data inside the tunnel, as well as meteorological forecast data (such as rainfall in the next 72 hours) and surrounding hydrogeological data outside the tunnel. The trend prediction module models and analyzes the above data using machine learning algorithms to predict the water pressure change trend over a future period and generate corresponding forward-looking control signals.

[0017] The trend prediction module uses Long Short-Term Memory (LSTM) or Support Vector Machine (SVM) algorithms to model multi-source data in order to capture the time-series characteristics of water pressure changes and the nonlinear relationship between external influencing factors.

[0018] The specific generation rules for the forward-looking control signal include the following: when the prediction result shows that the future water pressure value may exceed the upper pressure threshold, a signal is generated in advance to drive the execution control unit to gradually increase; when the prediction result shows that the future water pressure value may be lower than the lower pressure threshold, a signal is generated in advance to drive the execution control unit to gradually decrease or shut down.

[0019] The information sensing unit also includes an auxiliary detection device, which is configured to monitor the temperature, humidity, and groundwater chemical composition of the environment surrounding the tunnel to supplement the integrity of multi-source data; the auxiliary detection device establishes a data connection with the intelligent analysis unit via wireless communication.

[0020] The main body of the intelligent regulating device is made of corrosion-resistant material and coated with an epoxy resin coating on its outer surface to enhance its corrosion resistance; the key throttling surface of the regulating element is coated with a high-hardness alloy coating through laser cladding process to improve its erosion resistance.

[0021] The gear transmission mechanism of the intelligent electric actuator adopts a precision helical gear design to reduce friction loss and noise during transmission; the stepper motor is equipped with a temperature control protection circuit to prevent damage caused by overload or overheating.

[0022] Compared with the prior art, the beneficial effects of the present invention are: This invention introduces multi-source data fusion and machine learning techniques to construct an intelligent control system capable of predicting future water pressure changes. This system not only solves the problem of the lag in the passive response of traditional drainage systems but also avoids energy waste and environmental impact caused by excessive drainage, providing a reliable guarantee for the safe operation of water-rich tunnels. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall structure of the system of the present invention.

[0024] Figure 2 This is a schematic diagram of the composition of an information sensing unit.

[0025] Figure 3 This is a functional module diagram of the intelligent analysis unit.

[0026] Figure 4 A schematic diagram of the core components of the control unit.

[0027] Figure 5 This is a flowchart of the method of the present invention. Detailed Implementation

[0028] This invention relates to a method and system for controlling water drainage and pressure reduction in water-rich tunnels. It achieves dynamic control of the water pressure in the surrounding rock behind the tunnel lining through the coordinated operation of an information sensing unit, an intelligent analysis unit, and an execution control unit. The following is in conjunction with the appendix... Figure 1 To be continued Figure 5 The specific embodiments of the present invention will be described in detail below.

[0029] In practical applications, the information sensing unit includes at least one set of pressure detection devices and at least one set of flow detection devices. The sensing ends of the pressure detection devices are embedded or surface-mounted inside the tunnel lining, directly contacting the surrounding rock water environment behind the lining, for continuous monitoring and outputting real-time water pressure data. The flow detection devices are installed on the drainage pipe via flange connections or clamp fixation, for continuous monitoring and outputting instantaneous drainage flow data as real-time drainage flow data. Each device in the information sensing unit transmits the collected data to the intelligent analysis unit via wired or wireless communication to achieve centralized data processing and analysis.

[0030] The intelligent analysis unit is divided into three functional modules: signal processing module, decision calculation module, and trend prediction module. The signal processing module receives raw sensor data from the information sensing unit and performs digital filtering on the received raw data using wavelet transform algorithm or adaptive filtering algorithm to eliminate the influence of on-site noise interference and signal fluctuations, thereby generating stable and reliable real-time pressure and flow values.

[0031] The decision calculation module obtains the real-time pressure and flow values ​​processed by the signal processing module, and performs logical analysis on these values ​​based on the preset upper pressure threshold, target pressure value, lower pressure threshold, and flow anomaly threshold.

[0032] When the real-time pressure value is greater than or equal to the upper pressure threshold, the decision calculation module generates a signal to drive the execution control unit to fully open; when the real-time pressure value is less than the upper pressure threshold but greater than the target pressure value, the fuzzy control algorithm is activated, and the target pressure value is used as a reference to calculate a precise adjustment signal to smoothly adjust the real-time pressure value to the target pressure value; when the real-time pressure value is less than the lower pressure threshold, a signal is generated to drive the execution control unit to reduce its opening or close it completely.

[0033] In addition, the trend prediction module integrates multi-source heterogeneous data from inside and outside the tunnel, including but not limited to real-time water pressure data, real-time drainage flow data, and surrounding rock seepage pressure data inside the tunnel, as well as meteorological forecast data and surrounding hydrogeological data outside the tunnel. The module uses Long Short-Term Memory Network (LSTM) or Support Vector Machine (SVM) algorithms to model and analyze the above data in order to predict the trend of water pressure changes in the future and generate corresponding forward-looking control signals.

[0034] The core component of the control unit is an intelligent regulating device. Its main body is made of corrosion-resistant material and coated with an epoxy resin layer to enhance corrosion resistance. The main body of the intelligent regulating device has threaded interfaces at both ends for connecting to drainage pipes. The regulating element is located inside the main body, and its outer wall is machined with multiple sets of radially distributed microporous channels. These microporous channels are opened or closed in stages by axial movement to achieve graded control of the total pressure drop.

[0035] The key throttling surfaces of the regulating element are coated with a high-hardness alloy using laser cladding to enhance their erosion resistance. The drive assembly is an integrated intelligent electric actuator, which incorporates a stepper motor, gear transmission mechanism, and position sensor. The gear transmission mechanism employs a precision helical gear design to reduce friction loss and noise during transmission, and the stepper motor is equipped with a temperature control protection circuit to prevent damage due to overload or overheating.

[0036] The position sensor is used to detect the actual displacement of the regulating element in real time and feed the displacement information back to the intelligent analysis unit to form a position closed-loop control for adjusting the opening.

[0037] The workflow of this invention is as follows: First, the information sensing unit continuously acquires real-time water pressure data and real-time drainage flow data according to a preset sampling period using pressure and flow detection devices, and transmits the data to the intelligent analysis unit. The signal processing module in the intelligent analysis unit performs digital filtering and validity verification on the received raw data, generating real-time pressure and flow values.

[0038] Subsequently, the decision calculation module performs logical analysis on the real-time pressure and flow values ​​based on preset parameter thresholds, generating control signals. If forward-looking control is required, the trend prediction module combines multi-source data to predict future water pressure change trends and generates corresponding forward-looking control signals. The generated control signals are sent to the execution control unit via the control signal link, driving the intelligent regulating device to adjust its opening to change the drainage flow.

[0039] Throughout the process, the regulating element of the intelligent regulating device opens or closes the micro-orifice channels in stages through axial movement, thereby achieving precise control of the drainage flow. Simultaneously, the position sensor in the drive assembly detects the actual displacement of the regulating element in real time and feeds the displacement information back to the intelligent analysis unit to ensure the accuracy of the adjustment. These steps are continuously and cyclically executed, forming a closed-loop feedback control to dynamically maintain the water pressure behind the tunnel lining within the ideal safe range defined by the target pressure value and the lower pressure threshold.

[0040] The information sensing unit also includes auxiliary detection devices, which monitor the temperature, humidity, and groundwater chemical composition of the environment surrounding the tunnel to supplement the completeness of multi-source data. These auxiliary detection devices establish a data connection with the intelligent analysis unit via wireless communication, providing it with more comprehensive data support. The trend prediction module in the intelligent analysis unit uses machine learning algorithms to model and analyze this multi-source data, capturing the time-series characteristics of water pressure changes and the nonlinear relationship between external influencing factors, thereby generating more accurate forward-looking control signals.

[0041] This invention constructs an intelligent control system capable of predicting future water pressure changes by introducing multi-source data fusion and machine learning techniques. This system not only solves the problem of the lag in the passive response of traditional drainage systems, but also avoids energy waste and environmental impact caused by excessive drainage, providing a reliable guarantee for the safe operation of water-rich tunnels.

[0042] The present invention will be further described below with reference to two embodiments.

[0043] Example 1: A newly constructed mountain tunnel traverses a water-rich sandstone layer. During construction at kilometer marker K18+200, a concentrated water inrush occurred, causing a sudden surge in water pressure behind the lining. The maximum measured water pressure reached 1.2 MPa. Failure to promptly control the pressure could lead to deformation and cracking of the initial support. The area is characterized by frequent heavy rainfall during the rainy season, with continuous rainfall forecast for the next 72 hours. Dynamic control is needed to stabilize the water pressure below 0.5 MPa to ensure construction safety.

[0044] Six pressure detection devices were embedded in the tunnel arch, arch waist and sidewalls, with a sampling period of 10 seconds, to monitor the water pressure behind the lining in real time; two electromagnetic flow detection devices were connected to the 300mm diameter main drainage pipe through flanges to collect drainage flow simultaneously.

[0045] An auxiliary detection device is added to monitor the temperature of the surrounding rock, the pH value of the groundwater, and the surrounding rainfall. The data is transmitted wirelessly to the intelligent analysis unit.

[0046] The signal processing module uses wavelet transform algorithm to filter the raw water pressure data, eliminate construction vibration interference, and generate stable real-time pressure and flow values.

[0047] The decision calculation module has preset parameter thresholds: upper pressure limit 0.8MPa, target pressure 0.5MPa, and lower pressure limit 0.3MPa. When the real-time pressure rises to 0.85MPa, a full-open signal is generated for the execution control unit; when it drops to 0.6MPa, the fuzzy control algorithm is activated to calculate the opening adjustment.

[0048] The trend prediction module integrates real-time water pressure and flow data with rainfall forecasts for the next 72 hours. Using LSTM algorithm modeling, it predicts that the water pressure may rise to 0.9 MPa in 12 hours and generates a forward-looking control signal to gradually increase the opening.

[0049] After receiving the control signal, the intelligent regulating device drives the stepper motor of the drive component to move the regulating element axially via a precision helical gear transmission. The regulating element adopts a three-stage throttling structure, regulating the flow rate in stages by opening radial micro-orifice channels: the flow rate reaches 180 m³ / h when fully open, and during the fuzzy control stage, it is adjusted in gradients of 0.05 MPa / cycle, eventually stabilizing at 120 m³ / h. The position sensor provides real-time feedback on the displacement of the regulating element, forming a closed-loop control of the opening degree to ensure that the flow rate regulation error is <5%.

[0050] Continuous monitoring showed that the water pressure dropped to 0.55 MPa 3 hours after the adjustment, and rose to 0.6 MPa 12 hours later due to rainfall. The system proactively increased the opening to 80% in advance, and finally stabilized the water pressure at 0.48 MPa without triggering an over-limit warning.

[0051] Compared with traditional manual valve regulation, the system reduces the response delay from 30 minutes to 1 minute, and controls the water pressure fluctuation within ±0.05MPa, avoiding excessive stress on the lining structure and reducing ineffective start-stop of drainage equipment.

[0052] Example 2: A subway line 3 tunnel in a certain city passes through a layer of water-rich fine sand. After 5 years of operation, localized water leakage occurred, with the water pressure behind the lining consistently maintained at 0.6-0.9 MPa, far exceeding the design allowable value. Given the high daily passenger flow in this tunnel, precise water pressure control is required without affecting operations, while also avoiding excessive drainage that could lead to ground subsidence.

[0053] Ten pressure testing devices were fixed on the surface of the existing lining structure, and four ultrasonic flow testing devices were installed on the drainage branch pipes in the section using clamps. The sampling period was set to 30 seconds.

[0054] By accessing data from the subway operation monitoring system and integrating auxiliary information such as temperature and humidity inside the tunnel and train vibration frequency, a multi-source database is constructed.

[0055] The signal processing module uses an adaptive filtering algorithm to filter out high-frequency interference caused by train passage, thereby improving the signal-to-noise ratio of water pressure data to over 30dB.

[0056] The decision-making calculation module dynamically adjusts the threshold: during the morning peak hours, to reduce equipment noise, the upper limit of the pressure threshold is temporarily relaxed to 0.6 MPa; during off-peak hours, it reverts to the 0.5 MPa standard. When the real-time pressure value is in the range of 0.5-0.6 MPa, a fuzzy control algorithm is used to achieve smooth flow regulation.

[0057] The trend prediction module trains an SVM model based on operational data from the past three months to predict water pressure changes under different rainfall amounts: when the forecast daily rainfall is >20mm, pre-drainage is initiated 2 hours in advance to reduce peak water pressure.

[0058] The key surfaces of the regulating elements in the intelligent regulating device are coated with a Ni60 alloy using laser cladding to resist sand erosion. The drive assembly is equipped with a temperature control protection circuit, which automatically reduces the speed when the motor temperature exceeds 60℃ to avoid overload damage. Combined with flow feedback, when the drainage flow rate is below 20m³ / h, a signal to reduce the opening is generated, maintaining a minimum opening of 10% to maintain a small amount of drainage and prevent pipe blockage.

[0059] Data from three months of continuous operation shows that the water pressure is stable at 0.45-0.55MPa, the average daily drainage time is reduced from 12 hours in the traditional system to 8 hours, and the monthly power saving is about 200kWh; the ground settlement monitoring value is <1mm / month, which meets the safety requirements.

[0060] The system achieves unattended operation and precise control during the operation period, increasing the water pressure compliance rate from 65% to 98%. At the same time, through forward-looking control and energy-saving logic, it balances structural safety and energy consumption, and solves the problem of the lag in traditional manual inspection.

[0061] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "inclusion" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0062] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A water drainage and pressure reduction control system for water-rich tunnels, used to regulate the water pressure in the surrounding rock behind the tunnel lining, characterized in that, The system includes: An information sensing unit is configured to acquire real-time water pressure data behind the tunnel lining and real-time drainage flow data in the drainage pipes laid inside the tunnel. The intelligent analysis unit establishes a data connection with the information sensing unit via wired or wireless communication. The intelligent analysis unit is configured to receive the real-time water pressure data and real-time drainage flow data, and to parse and judge the received data based on preset logic rules in order to generate corresponding control signals. The intelligent analysis unit is divided into a signal processing module and a decision calculation module. The signal processing module is configured to receive raw sensing data from the information sensing unit and perform digital filtering and validity verification on the raw sensing data to generate stable and reliable real-time pressure and flow values. The decision calculation module is configured to obtain the real-time pressure and flow values ​​processed by the signal processing module and perform logical analysis on these values ​​according to preset parameter thresholds to generate the control signal. The intelligent analysis unit also includes a trend prediction module, which is configured to integrate multi-source heterogeneous data from inside and outside the tunnel, including real-time water pressure data, real-time drainage flow data, and surrounding rock seepage pressure data inside the tunnel, as well as meteorological forecast data and surrounding hydrogeological data outside the tunnel. The trend prediction module uses machine learning algorithms to model and analyze the above data to predict the trend of water pressure changes in the future and generate corresponding forward-looking control signals. The specific generation rules for the forward-looking control signal include the following: when the prediction result shows that the future water pressure value may exceed the upper pressure threshold, a signal is generated in advance to drive the execution control unit to gradually increase; when the prediction result shows that the future water pressure value may be lower than the lower pressure threshold, a signal is generated in advance to drive the execution control unit to gradually decrease or shut down. The preset parameter thresholds in the decision calculation module include pressure upper limit threshold, target pressure value, pressure lower limit threshold, and flow anomaly threshold. The logical rules executed by the decision calculation module specifically include the following: When it is determined that the real-time pressure value is greater than or equal to the pressure upper limit threshold, a signal is generated to drive the execution control unit to be fully open; When it is determined that the real-time pressure value is less than the pressure upper limit threshold and greater than the target pressure value, the fuzzy control algorithm is activated, and the target pressure value is used as a reference value to calculate a precise adjustment signal for smoothly adjusting the real-time pressure value to the target pressure value. When it is determined that the real-time pressure value is less than the lower pressure threshold, a signal is generated to drive the execution control unit to reduce the opening degree or completely close it. The core component of the execution control unit is an intelligent adjustment device, the structure of which includes a main body having a threaded interface for connecting the drainage pipe; An adjusting element disposed inside the main body for adjusting the cross-section of the water flow; A drive component that is linked to the adjustment element and is used to drive the adjustment element to perform precise positioning according to the control signal; The execution control unit is connected to the intelligent analysis unit through a control signal link. The execution control unit is configured to receive and respond to the control signal to precisely adjust the drainage flow of the drainage pipe, thereby maintaining the water pressure behind the tunnel lining within a preset safe range.

2. The system according to claim 1, characterized in that, The information sensing unit includes: At least one pressure detection device, whose sensing end is placed inside the tunnel lining by means of embedded installation or surface fixing, is used to directly contact the surrounding rock water environment behind the lining, thereby continuously monitoring and outputting the real-time water pressure data; At least one flow detection device is installed on the drainage pipe by means of flange connection or clamp fixation, for continuously monitoring and outputting the instantaneous drainage flow rate through the execution control unit as the real-time drainage flow rate data.

3. The system according to claim 1, characterized in that, The signal processing module uses wavelet transform or adaptive filtering algorithms to perform digital filtering on the received raw sensor data.

4. The system according to claim 1, characterized in that, The regulating element adopts a multi-stage throttling structure. Multiple sets of radially distributed micro-channels are machined on the outer wall of the structure. The micro-channels are opened or closed in stages by the axial movement of the regulating element to achieve graded control of the total pressure drop. The key throttling surface of the regulating element that comes into contact with the water flow is coated with a high-hardness, wear-resistant coating through a thermal spraying process.

5. The system according to claim 1, characterized in that, The drive component is an integrated intelligent electric actuator, which integrates a stepper motor, a gear transmission mechanism, and a position sensor. The position sensor is used to detect the actual displacement of the adjustment element in real time and feed the displacement information back to the intelligent analysis unit to form a closed-loop position control for adjusting the opening.

Images