An open-cut deep foundation pit dewatering operation intelligent control system

Abstract

The present application relates to a kind of open deep foundation pit dewatering operation intelligent control system, belong to civil engineering groundwater control and foundation pit construction monitoring automation field, including perception module, calculation processing module and execution feedback module, by the perception foundation pit water level, pore water pressure and surface subsidence data, utilize digital twin engine and neural network to predict groundwater seepage time lag, calculate target drainage power.The system is based on comprehensive dewatering pressure model and surface subsidence threshold envelope, dynamically adjusts submersible pump variable frequency frequency.Meanwhile, introduce the fault early warning and load balancing mechanism based on current harmonic analysis, realize the active collaborative scheduling and fault disaster recovery switching of multiple water pumps.The present application solves the technical problems that traditional dewatering mode response lag, surface subsidence risk is difficult to control in real time and single-point fault affects operation continuity, effectively balances foundation pit water level control and soil structure stability, reduces energy consumption, significantly improves the intelligent level of deep foundation pit dewatering operation and construction safety guarantee ability.

Description

Technical Field

[0001] This invention belongs to the field of automation technology for groundwater control and foundation pit construction monitoring in civil engineering, specifically, it relates to an intelligent control system for dewatering operations in open-cut deep foundation pits. Background Technology

[0002] In the construction of open-cut deep foundation pits, effective control of the groundwater level is crucial to ensuring the safety of the dry working environment and surrounding structures. Currently, common foundation pit dewatering operations often employ a start-stop control mode based on a single-point liquid level threshold. This involves deploying water level gauges within the pit; when the monitored water level reaches the upper limit, the water pump is activated, and when the water level drops to the lower limit, the pump is stopped. However, this control mode has significant limitations in complex geological conditions or deep aquifers. First, groundwater seepage in the soil exhibits a significant time lag effect. From the start of pumping to a significant drop in water level, or from the cessation of pumping to groundwater recharge, the system often cannot accurately predict the water level evolution trend due to the non-uniform distribution of parameters such as soil permeability coefficient and specific yield. This results in dewatering operations lagging behind actual needs. Secondly, traditional dewatering systems lack a direct correlation mechanism with the displacement of the surrounding soil and rock mass during operation. They cannot dynamically adjust the dewatering intensity in real time based on subtle changes in surface subsidence, often leading to excessive pumping intensity, uneven surface subsidence, and even serious engineering safety risks. Furthermore, in multi-well coordinated dewatering scenarios, each pump operates independently, lacking coordinated allocation of load across different wells. This not only easily leads to increased harmonic current in individual wells and uneven motor load, but also lacks proactive monitoring and rapid switching disaster recovery response measures when well filters become clogged or mechanical failures occur. This discrete, passive control mode, lacking environmental constraints, results in low energy efficiency in dewatering operations and fails to adequately address the variable groundwater environment during foundation pit construction, limiting the safety and intelligence level of deep foundation pit dewatering operations. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent control system for dewatering operations in open-cut deep foundation pits, which solves the technical problems of poor stability of the surrounding soil structure, high energy consumption and low system reliability caused by insufficient perception of soil seepage time lag, lack of linkage between dewatering operations and surface settlement feedback, and delayed response to equipment failures in the existing open-cut deep foundation pit dewatering process.

[0004] The objective of this invention can be achieved through the following technical solutions: An intelligent control system for dewatering operations in open-cut deep foundation pits includes: multiple dewatering wells set around the foundation pit and submersible pumps installed in each dewatering well, wherein the submersible pumps are connected to frequency converters; It also includes a perception module, a computation processing module, and an execution feedback module; The sensing module is used to collect real-time data on water levels inside and outside the foundation pit, pore water pressure data, and settlement and displacement data of the ground surface around the foundation pit. The computing module includes a digital twin computing engine, which constructs a dynamic mathematical model of the hydrogeology of the foundation pit and uses a neural network algorithm to calculate the target drainage power curve based on the real-time data and the preset settlement safety threshold of the foundation pit, and converts the target drainage power curve into frequency commands for each frequency converter. The execution feedback module receives frequency commands and drives the frequency converter to adjust the speed of the submersible pump. At the same time, it monitors the rate of change of surface settlement displacement data in real time. If the rate of change of settlement displacement data exceeds a preset threshold, it sends a correction signal to the calculation and processing module. The calculation and processing module recalculates and optimizes the frequency commands based on the correction signal.

[0005] Furthermore, the digital twin computing engine specifically employs the following steps to achieve data prediction: Acquire water level fluctuation data and seepage time lag parameters during historical precipitation processes; After normalizing the time-series data, it is input into a pre-defined recurrent neural network model; The model calculates the current seepage time delay compensation value, which serves as an input factor for correcting the target drainage power curve, so that the frequency command execution of each inverter has a time advance.

[0006] Furthermore, the sensing module also includes an array of pore water pressure sensors arranged at different depths for real-time monitoring of the rate of change of pore water pressure in the soil.

[0007] Furthermore, the calculation module calculates the comprehensive precipitation pressure using the following formula: in, For total precipitation pressure, Let be the weighting coefficient for the i-th precipitation well. The pressure change value measured by the pore water pressure sensor around the i-th precipitation well.

[0008] Furthermore, the execution feedback module also includes a load balancing unit for collecting current harmonic data from each submersible pump.

[0009] Furthermore, the load balancing unit analyzes the mechanical load status of each submersible pump based on the current harmonic data. When the current harmonic frequency deviation is greater than the set range, it automatically allocates a higher frequency to the submersible pump with a lower load to achieve multi-well load balancing.

[0010] Furthermore, the calculation and processing module is equipped with a dynamic safety threshold envelope, which is generated based on the surface subsidence rate deviation function.

[0011] Furthermore, when the real-time settlement displacement data approaches the boundary of the envelope, the system forcibly executes a frequency attenuation command, reducing the inverter output frequency to between 40% and 60% of the reference value.

[0012] Furthermore, it also includes a fault early warning module, which collects the frequency of current change of the inverter and performs pattern matching with a preset sand and gravel blockage feature model.

[0013] Furthermore, when the matching degree exceeds 85%, the fault warning module sends a shutdown command to the execution feedback module and switches to the backup dewatering well to maintain the stability of the pit water level.

[0014] The beneficial effects of this invention are:

[0015] 1. By introducing a digital twin computing engine and a recurrent neural network model, the system can calculate the seepage time delay compensation value in real time, and tightly couple the target drainage power with the predicted water level change trend. This not only achieves stable feedforward control of the foundation pit water level, but also significantly reduces the number of frequent start-stop cycles of the submersible pump during operation, effectively reducing mechanical fatigue wear and electrical shock of the equipment.

[0016] 2. The system innovatively adopts real-time calculation of comprehensive precipitation pressure and dynamic safety threshold envelope control of surface settlement, realizing intelligent scheduling from "water level driven" to "soil stress constraint driven". The system can automatically correct the pumping intensity according to the surface settlement change rate, and actively avoid the risk of soil settlement caused by excessive pumping while ensuring water level.

[0017] 3. By combining load balancing technology with a fault early warning model based on current harmonic characteristics, the system not only optimizes the power distribution during the coordinated operation of multiple water pumps and improves the overall dewatering efficiency, but also enables accurate judgment and automatic disaster recovery switching before a single point of failure occurs. While ensuring the continuous stability of the water level in the foundation pit, it significantly improves the robustness and operational continuity of the entire control system under extreme conditions. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a flowchart illustrating the principle of predictive control for dewatering in open-cut deep foundation pits according to the present invention. Figure 2 This is a schematic diagram illustrating the principle of stress coordination and load balancing in foundation pit dewatering according to the present invention. Figure 3 This is the logic diagram for the submersible pump fault mode diagnosis and disaster recovery response of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figure 1-3 As shown; Example

[0021] An intelligent control system for dewatering operations in open-cut deep foundation pits includes multiple dewatering wells set around the foundation pit and submersible pumps installed in each well. The submersible pumps are connected to frequency converters. The system also includes a sensing module, a computing and processing module, and an execution feedback module. The sensing module collects real-time data on water levels inside and outside the foundation pit, pore water pressure data, and settlement displacement data of the surrounding surface. The computing and processing module includes a digital twin computing engine. This engine constructs a dynamic mathematical model of the foundation pit's hydrogeology and uses a neural network algorithm to calculate a target drainage power curve based on the real-time data and a preset settlement safety threshold for the foundation pit. The target drainage power curve is then converted into frequency commands for each frequency converter. The execution feedback module receives the frequency commands and drives the frequency converters to adjust the speed of the submersible pumps. Simultaneously, it monitors the rate of change of surface settlement displacement data in real time. If the rate of change of settlement displacement data exceeds a preset threshold, a correction signal is sent to the computing and processing module. The computing and processing module then recalculates and optimizes the frequency commands based on the correction signal. The digital twin computing engine specifically employs the following steps to achieve data prediction: acquiring water level fluctuation data and seepage time delay parameters during historical precipitation processes; normalizing the time-series data and inputting it into a preset recurrent neural network model; calculating the current seepage time delay compensation value through the model, which serves as an input factor for correcting the target drainage power curve, enabling the frequency command execution of each inverter to have a time lead.

[0022] The system architecture consists of a perception layer, a data processing layer, and an execution layer. The perception layer comprises multiple pore water pressure sensors, non-contact radar level gauges, and surface displacement monitoring optical fibers buried deep within the foundation pit, all positioned around the pit. These sensors convert raw physical signals into electrical signals, which are then transmitted via an industrial fieldbus to an embedded industrial computer serving as the computing module. The digital twin computing engine runs within this industrial computer, and its core task is to simulate the interaction between groundwater flow and soil structure in real time. To achieve this simulation, the geological parameters around the foundation pit must first be digitally modeled, treating the pit as a porous media seepage field. Hydrogeological parameters such as permeability coefficient and specific yield are used to describe the movement of groundwater within the aquifer. The digital twin computing engine utilizes a recurrent neural network as its core algorithm unit. This network is specifically designed for time-series data and can effectively capture the seepage lag effect present in dewatering operations. Specifically, when a submersible pump starts pumping water, the formation of the dewatering funnel within the soil is not instantaneous but exhibits a non-linear changing trend as groundwater seeps into the soil layer. To quantify this process, the computational engine was trained using historical precipitation data on water level fluctuations and real-time collected seepage time delay parameters.

[0023] Define the input vector as ,in Indicates the current monitored water level. This represents the current surface subsidence rate. Let be the seepage delay at the current moment. The hidden layer state update formula of the recurrent neural network is:

[0024] in In hidden state, For activation function, and Here, are the hidden layer weight matrix and the input layer weight matrix, respectively, and b is the bias term. Through iterative calculation of the above parameters, the model outputs a predicted water level change trend and calculates the current seepage time lag compensation value. The compensation value The physical meaning of this value lies in representing the time difference required for groundwater pressure to be transmitted to the boundary of the foundation pit. By introducing this compensation value into the control loop, the system can advance the execution time of the frequency command when adjusting the submersible pump speed. The target drainage power curve is derived by comparing the predicted water level with the safe water level benchmark value. Its calculation logic is as follows:

[0025] in For the target drainage power, The preset constant water level target, The water level predicted by the model for the next moment. K represents the water level drop rate, and K is the proportional control coefficient. After calculating the target drainage power, the feedback module maps it to the drive frequency of the frequency converter. The frequency is linearly proportional to the drainage power, thus driving the submersible pump installed in the dewatering well to change its rotation speed. During system operation, the computational processing module continuously monitors the rate of change of surface settlement displacement data and compares the monitored rate of change with a preset safety threshold in real time. If the rate of change of settlement displacement exceeds the safety threshold, the system corrects the above model parameters in real time through correction signals. Specifically, this is manifested in automatically adjusting the proportional control coefficient K and the compensation weight, causing the drainage power curve to shift downward, thereby reducing the pumping intensity and preventing the surrounding soil from becoming unstable or the ground from settling excessively due to excessive precipitation intensity. This closed-loop control method based on predictive compensation changes the passive mode of traditional dewatering systems that only switch pumps on and off based on the current water level, transforming the dewatering process into a dynamic continuous process with feedforward adjustment capabilities. Since the submersible pump speed adjustment is based on the smoothed power curve optimized by the recurrent neural network in the computational processing module, the number of start-ups and shutdowns of each submersible pump is significantly reduced, effectively reducing mechanical losses and electrical shocks during frequent start-ups. The entire control process, through deep interaction between the sensing module and the execution module, ensures the stable evolution of the water level inside the foundation pit, avoids drastic fluctuations in the water level during the pumping process, and achieves the control of surface settlement displacement within the allowable range of the project while ensuring a dry working environment in the foundation pit.

[0026] The system described in this embodiment uses an embedded industrial computer as a central processing unit, connected to a frequency converter controller, and achieves precise frequency control of the physical water pump through software algorithms. This technical solution can operate stably, and the functions of each module support each other, ensuring that those skilled in the art can completely reproduce the precipitation control system without additional creative labor.

[0027] Example 2 An intelligent control system for dewatering operations in open-cut deep foundation pits includes multiple dewatering wells set around the foundation pit and submersible pumps installed in each well. The submersible pumps are connected to a frequency converter. The system also includes a sensing module, a computational processing module, and an execution feedback module. The sensing module collects real-time data on water levels inside and outside the foundation pit, pore water pressure data, and settlement displacement data of the surrounding ground surface. The computational processing module includes a digital twin computing engine, which constructs a dynamic mathematical model of the foundation pit's hydrogeology and uses a neural network algorithm to calculate the target drainage target based on the real-time data and a preset settlement safety threshold for the foundation pit. The water power curve is generated and the target drainage power curve is converted into frequency commands for each frequency converter. The execution feedback module receives the frequency commands and drives the frequency converters to adjust the speed of the submersible pump. At the same time, it monitors the rate of change of surface settlement displacement data in real time. If the rate of change of settlement displacement data exceeds a preset threshold, a correction signal is sent to the calculation and processing module. The calculation and processing module recalculates and optimizes the frequency commands based on the correction signal. The sensing module also includes an array of pore water pressure sensors arranged at different depths to monitor the rate of change of soil pore water pressure in real time. The calculation and processing module calculates the comprehensive precipitation pressure using the following formula: ,in, For total precipitation pressure, For the first The weighting coefficient of each precipitation well. For the first The pressure change values ​​measured by pore water pressure sensors around each dewatering well; the execution feedback module also includes a load balancing unit for collecting current harmonic data from each submersible pump; the load balancing unit analyzes the mechanical load status of each submersible pump based on the current harmonic data, and automatically allocates a higher frequency to the submersible pump with a lower load when the current harmonic frequency deviation is greater than a set range, so as to achieve load balance among multiple wells; the calculation and processing module is equipped with a dynamic safety threshold envelope, which is generated based on the surface settlement rate deviation function; when the real-time settlement displacement data approaches the boundary of the envelope, the system forcibly executes a frequency attenuation command, reducing the inverter output frequency to between 40% and 60% of the reference value.

[0028] This system integrates deep pore water pressure monitoring, dynamic surface settlement constraints, and variable frequency pump load balancing technology to establish a highly coordinated control architecture for the stability of foundation pit soil and the efficiency of dewatering equipment. The pore water pressure sensor array in the sensing module is arranged in layers along the depth of the foundation pit, enabling it to capture the seepage pressure gradient of groundwater in different soil layers. The comprehensive dewatering pressure calculation logic running within the calculation and processing module essentially quantifies the impact of different dewatering wells on the stress field of the surrounding strata. The formula...

[0029] In This represents the weighting coefficient of each precipitation well's contribution to a specific monitoring area. This coefficient is dynamically generated based on the spacing between precipitation wells and the real-time discharge rate of each pump. Representing the The deviation of the measured pore water pressure around each dewatering well relative to the initial static water level is calculated. By summing these deviations, the system obtains the total dewatering pressure, reflecting the overall hydraulic equilibrium of the foundation pit. This indicator is directly related to the effective stress experienced by the soil skeleton. To ensure surface stability, the control logic introduces a dynamic safety threshold envelope, which is constructed based on the surface settlement rate deviation function, expressed as follows: ,in For real-time monitoring of surface subsidence rate. The system sets a baseline settlement rate for the geological model of the foundation pit. As construction progresses, this envelope evolves in real time based on the soil's rheological properties. Once the monitored real-time settlement data approaches the boundary of the envelope, it indicates that the current precipitation intensity is nearing the critical state of uneven soil settlement. At this point, the system triggers protection logic, sending a frequency attenuation command to forcibly correct the inverter's output frequency to 40% to 60% of the baseline value, thus smoothly reducing the precipitation rate and alleviating the stress release rate in the soil. Simultaneously, the load balancing unit, relying on the communication bus within the industrial control computer, collects the current harmonic signals of each submersible pump in real time during operation. Due to factors such as well filter blockage, pump mechanical wear, or differences in hydraulic conditions during pumping, the operating state of each motor exhibits inconsistent current harmonic characteristics, manifested as amplitude fluctuations in different frequency components. The calculation and processing module performs fast Fourier transform analysis on the current harmonic data to extract characteristic frequencies representing the mechanical load state. When the monitored current harmonic frequency deviation exceeds the set normal range, the system automatically executes a load redistribution command. By adjusting the inverter output parameters, it allocates the additional power demand to the submersible pumps with lower loads. This not only optimizes the overall system's energy distribution efficiency but also avoids motor overheating and shortened lifespan caused by excessive load on a single well. The system's hardware implementation includes a logic controller connected to each inverter. The controller executes the aforementioned algorithm code, maintaining a dynamic balance between dewatering pressure and current harmonic characteristics through a PID control loop. The system's operation covers the entire cycle from the initial stage of foundation pit dewatering to continuous operation. Its core logic lies in abstracting the discrete dewatering pump group into a controlled dynamic system. By adjusting the individual operating parameters of each submersible pump, the overall control of the entire site's dewatering pressure field is achieved. This collaborative scheduling method completely changes the independent operation mode of pumps in traditional dewatering schemes, elevating foundation pit dewatering to an active control engineering based on soil stress feedback and mechanical load perception. By processing pore water pressure array data in real time, the system can accurately identify areas where pore water pressure in the soil changes drastically during dewatering and adjust the frequency converter of the corresponding pump in that area accordingly, rather than broadly controlling the total amount of dewatering for the entire foundation pit. This refined management approach not only effectively reduces the risk of large-scale ground subsidence caused by excessive pumping but also extends the service life of submersible pumps in harsh hydrological environments through load balancing technology. The system integrates various functional modules through an embedded industrial control unit, ensuring real-time response between algorithm logic and hardware execution. In actual engineering operations, the system dynamically adapts to various changes in geological conditions through a closed-loop path of continuous monitoring, calculation, comparison, and adjustment, providing an intelligent system solution with engineering implementation value for achieving long-term stability of open-cut foundation pits and efficient dewatering operations.The technical means and logical flow described in this embodiment, through the collaborative work of various sensing hardware and control software, ensure that the technical features of each claim can be fully reflected and supported. Based on this description, those skilled in the art can completely reproduce the foundation pit dewatering system with soil stability control and load balancing functions.

[0030] Example 3 An intelligent control system for dewatering operations in open-cut deep foundation pits includes multiple dewatering wells set around the foundation pit and submersible pumps installed in each well. The submersible pumps are connected to a frequency converter. The system also includes a sensing module, a computational processing module, and an execution feedback module. The sensing module collects real-time data on water levels inside and outside the foundation pit, pore water pressure data, and settlement displacement data of the surrounding surface. The computational processing module includes a digital twin computing engine. This engine constructs a dynamic mathematical model of the foundation pit's hydrogeology and uses a neural network algorithm to calculate a target drainage power curve based on the real-time data and a preset settlement safety threshold for the foundation pit. The target drainage power curve is then converted into... The frequency commands are converted into frequency commands for each inverter. The execution feedback module receives the frequency commands and drives the inverters to adjust the speed of the submersible pump. At the same time, it monitors the rate of change of surface settlement displacement data in real time. If the rate of change of settlement displacement data exceeds a preset threshold, it sends a correction signal to the calculation and processing module. The calculation and processing module recalculates and optimizes the frequency commands based on the correction signal. The system also includes a fault early warning module. The fault early warning module collects the frequency of current change of the inverters and performs pattern matching with a preset sand and gravel blockage feature model. When the matching degree exceeds a set threshold, the fault early warning module sends a shutdown command to the execution feedback module and switches to the backup dewatering well to maintain the stability of the pit water level.

[0031] This system introduces a fault early warning and automated disaster recovery response mechanism to improve the robustness and continuity of the foundation pit dewatering system under complex and harsh construction environments. The fault early warning module, as a crucial component of the computational processing module, focuses on real-time diagnosis of the submersible pump's operating conditions. In open-cut deep foundation pit dewatering operations, submersible pumps are constantly exposed to high-sand-content water flow environments, making them highly susceptible to abnormalities such as filter clogging or impeller jamming. This solution uses high-frequency sensors to monitor the current fluctuation signal at the submersible pump drive end in real time, converting the collected current time series into frequency domain features. The fault early warning module pre-defines a sand and gravel clogging characteristic model. This model constructs the current harmonic spectrum before and after sand and gravel clogging by pre-normalizing the submersible pump operating current under laboratory conditions and different field conditions. A current characteristic matching function is defined.

[0032] in The current frequency component is measured in real time by the system. W represents the standard frequency component in the preset feature model, and W is the weighting coefficient for the corresponding frequency point, used to highlight specific low-frequency harmonic fluctuations caused by sand and gravel blockage. When the calculated matching evaluation coefficient M exceeds the preset judgment threshold, the system determines that the dewatering well is in a fault-induced state. In the specific processing flow, the above algorithm is executed by the embedded industrial control unit, which is connected to the current feedback port of the frequency converter through a data acquisition card. Once the fault judgment is triggered, the system enters the automated disaster recovery response program. The calculation and processing module first sends an emergency stop command to the frequency converter corresponding to the faulty pump, using the soft stop function of the frequency converter to prevent instantaneous voltage fluctuations. Then, based on the water level distribution in the foundation pit, it calls the control interface of the backup dewatering wells deployed around the foundation pit. The startup of the backup dewatering well follows the smooth loading logic, linearly increasing the speed of the frequency converter to prevent sudden changes in the groundwater level gradient caused by abrupt changes in pumping capacity. The load distribution of the backup well follows the water level compensation principle, determined by the calculation and processing module based on the deviation between the real-time water level in the foundation pit and the water level predicted by the model.

[0033] The system dynamically adjusts compensation power to ensure that the overall water level fluctuation in the foundation pit remains within a safe range during the switching process. To achieve full-process adaptability, the system automatically uploads fault logs and corresponding characteristic parameters to the monitoring terminal while identifying faults, facilitating maintenance personnel to quickly locate blockages. This disaster recovery management mode combines fault early warning with multi-well collaborative control, avoiding the risk of sudden water level changes caused by the lag in traditional manual inspections, and realizing unmanned emergency handling of dewatering operations. The system's hardware logic connects all dewatering units via an industrial bus, with the computing and processing module acting as the central command brain, continuously executing logic scanning cycles. When a deviation in the current harmonic characteristics of any dewatering well is detected, the system can complete fault determination and execute disaster recovery logic within one second. This method, based on a combination of physical feature matching and closed-loop control, ensures that even if one or more dewatering wells fail, the water level control of the entire foundation pit can still remain within the preset target fluctuation range. This embodiment provides a clear implementation blueprint for those skilled in the art through detailed algorithm definitions and hardware interface descriptions between modules. By accurately acquiring current change frequency, comparing feature models in real time, and automatically triggering disaster recovery logic, it upgrades the foundation pit dewatering system from preventative monitoring to proactive emergency support, ensuring the safety and stability of the entire dewatering operation lifecycle. For complex hydrogeological environments, the system adjusts the weight coefficients of the feature models. This allows for adaptation to different types of blockages, and the parameterized design ensures the versatility and engineering feasibility of the solution. Through the synergy of the aforementioned integrated hardware and software, this solution can completely resolve the risk of soil collapse caused by a sudden rise in water level due to pump failure in foundation pit engineering.

[0034] In the description of this specification, the references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0035] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

Claims

1. An intelligent control system for dewatering operations in open-cut deep foundation pits, comprising: The system comprises multiple dewatering wells located around the foundation pit and submersible pumps installed in each dewatering well, wherein the submersible pumps are connected to frequency converters; its characteristic is that... It also includes a perception module, a computation processing module, and an execution feedback module; The sensing module is used to collect real-time data on water levels inside and outside the foundation pit, pore water pressure data, and settlement and displacement data of the ground surface around the foundation pit. The computing module includes a digital twin computing engine, which constructs a dynamic mathematical model of the hydrogeology of the foundation pit and uses a neural network algorithm to calculate the target drainage power curve based on the real-time data and the preset settlement safety threshold of the foundation pit, and converts the target drainage power curve into frequency commands for each frequency converter. The execution feedback module receives frequency commands and drives the frequency converter to adjust the speed of the submersible pump. Simultaneously, it monitors the rate of change of surface settlement displacement data in real time. If the rate of change of settlement displacement data exceeds a preset threshold, it sends a correction signal to the calculation and processing module. The calculation and processing module recalculates and optimizes the frequency commands based on the correction signal.

2. The intelligent control system for dewatering operations in open-cut deep foundation pits according to claim 1, characterized in that, The digital twin computing engine specifically employs the following steps to achieve data prediction: Acquire water level fluctuation data and seepage time lag parameters during historical precipitation processes; After normalizing the time-series data, it is input into a pre-defined recurrent neural network model; The model calculates the current seepage time delay compensation value, which serves as an input factor for correcting the target drainage power curve, so that the frequency command execution of each inverter has a time advance.

3. The intelligent control system for dewatering operations in open-cut deep foundation pits according to claim 1, characterized in that, The sensing module also includes an array of pore water pressure sensors arranged at different depths for real-time monitoring of the rate of change of pore water pressure in the soil.

4. The intelligent control system for dewatering operations in open-cut deep foundation pits according to claim 3, characterized in that, The calculation module calculates the overall precipitation pressure using the following formula: in, For total precipitation pressure, Let be the weighting coefficient for the i-th precipitation well. The pressure change value measured by the pore water pressure sensor around the i-th precipitation well.

5. The intelligent control system for dewatering operations in open-cut deep foundation pits according to claim 1, characterized in that, The execution feedback module also includes a load balancing unit for collecting current harmonic data from each submersible pump.

6. The intelligent control system for dewatering operations in open-cut deep foundation pits according to claim 5, characterized in that, The load balancing unit analyzes the mechanical load status of each submersible pump based on the current harmonic data. When the current harmonic frequency deviation is greater than the set range, it automatically allocates a higher frequency to the submersible pump with a lower load to achieve load balancing across multiple wells.

7. The intelligent control system for dewatering operations in open-cut deep foundation pits according to claim 1, characterized in that, The calculation and processing module is equipped with a dynamic safety threshold envelope, which is generated based on the surface subsidence rate deviation function.

8. The intelligent control system for dewatering operations in open-cut deep foundation pits according to claim 7, characterized in that, When the real-time settlement displacement data approaches the boundary of the envelope, the system forcibly executes a frequency attenuation command, reducing the inverter output frequency to between 40% and 60% of the reference value.

9. The intelligent control system for dewatering operations in open-cut deep foundation pits according to claim 1, characterized in that, It also includes a fault early warning module, which collects the frequency of current change of the inverter and performs pattern matching with a preset sand and gravel blockage feature model.

10. The intelligent control system for dewatering operations in open-cut deep foundation pits according to claim 9, characterized in that, When the matching degree exceeds 85%, the fault warning module sends a shutdown command to the execution feedback module and switches to the backup dewatering well to maintain the stability of the pit water level.

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