A foundation pit dewatering processing method, system, electronic equipment and storage medium

Abstract

A foundation pit dewatering processing method and system, electronic equipment and storage medium are provided, which relate to the field of foundation pit dewatering processing. In the method, various types of data are obtained from sensors installed at preset positions around the foundation pit and inside the foundation pit, the various types of data are integrated with meteorological data and geological survey data to establish a space-time database; according to the data in the space-time database, a preset LSTM neural network model is used to predict a first water level change in a future preset time, and a machine learning soil parameter inversion algorithm is used to invert soil parameters according to a three-dimensional seepage field dynamic simulation engine; according to the first water level change, a variable frequency pump group collaborative control algorithm is used to collaboratively control the pump group, and the pump group operation parameters are dynamically adjusted for drainage; and according to the second water level change of the foundation pit after drainage and the soil parameters, the recharge pressure and water quality are controlled. The technical solution provided by the present application realizes efficient and intelligent processing of foundation pit dewatering.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of foundation pit dewatering treatment, and in particular to a foundation pit dewatering treatment method and system, an electronic device and a storage medium. BACKGROUND

[0002] In modern construction engineering, with the acceleration of urbanization, the height of buildings is increasing, and the excavation depth of foundation pits is also increasing, which brings unprecedented challenges to groundwater control.

[0003] The current widely used foundation pit dewatering treatment method mainly includes artificial regular inspection, automatic water pump using fixed threshold, and simple remote monitoring platform. Although they can achieve dewatering treatment to a certain extent, they have significant shortcomings in accuracy, real-time performance and adaptability. The existing foundation pit dewatering treatment system lacks intelligent analysis capability and is difficult to accurately predict and respond to sudden groundwater level fluctuations, resulting in excessive extraction of water resources or water accumulation, thereby affecting the construction progress and safety, and also increasing the maintenance cost.

[0004] Therefore, how to realize efficient and intelligent treatment of foundation pit dewatering under the premise of safety has become a problem to be solved. SUMMARY

[0005] The present application provides a foundation pit dewatering treatment method and system, an electronic device and a storage medium, which realizes efficient and intelligent treatment of foundation pit dewatering.

[0006] In a first aspect of the present application, a foundation pit dewatering treatment method is provided, applied to a foundation pit dewatering treatment platform, and the method comprises:

[0007] Obtaining various types of data from sensors installed around the foundation pit and at preset positions inside the foundation pit, the various types of data including water level data, pore water pressure data and deformation data, integrating the various types of data with meteorological data and geological survey data, and establishing a space-time database;

[0008] According to the data in the space-time database, using a preset LSTM neural network model to predict a first water level change in a future preset time, and using a machine learning soil parameter inversion algorithm to invert soil parameters according to a three-dimensional seepage field dynamic simulation engine;

[0009] According to the first water level change, using a variable frequency pump group cooperative control algorithm to cooperatively control the pump group, and dynamically adjusting the pump group operation parameters to drain water;

[0010] According to the second water level change of the foundation pit after drainage and the soil parameters, controlling the recharge pressure and water quality.

[0011] Optionally, the soil body parameter inversion algorithm based on machine learning includes:

[0012] The three-dimensional seepage field dynamic simulation engine is used to simulate the seepage field in the foundation pit based on the finite element method, and dynamic change data of the seepage field is obtained.

[0013] The soil body parameter inversion algorithm based on machine learning is used to analyze and process the dynamic change data of the seepage field.

[0014] By constructing a mapping relationship between the inversion parameters and the displacement, the soil body parameters of the soil body are inverted, including the permeability coefficient and the porosity.

[0015] Optionally, the three-dimensional seepage field dynamic simulation engine is used to simulate the seepage field in the foundation pit based on the finite element method, and dynamic change data of the seepage field is obtained, including:

[0016] The foundation pit area is divided into a plurality of finite element units, and the soil type, permeability coefficient, and porosity of each finite element unit are determined according to the geological survey data and the various types of data.

[0017] The seepage equation of the seepage field is established according to the soil type, permeability coefficient, and porosity of each finite element unit, and the seepage equation is solved by the finite element method to obtain dynamic change data of the foundation pit, including water pressure and flow rate seepage field parameters at different positions.

[0018] The dynamic change data is updated and adjusted in real time according to the water level change, pumping rate, and recharge flow rate during the foundation pit dewatering process.

[0019] Optionally, the soil body parameter inversion algorithm based on machine learning includes:

[0020] A mapping relationship model between the inversion parameters and the displacement is established based on the dynamic change data of the seepage field, and the mapping relationship model is used to describe the nonlinear relationship between the soil body parameters and the displacement.

[0021] The dynamic change data of the seepage field is converted into predicted values of the soil body parameters using the mapping relationship model.

[0022] Optionally, the soil body parameter inversion algorithm based on machine learning includes:

[0023] According to the first water level change, the amount of water that needs to be discharged in the foundation pit is determined.

[0024] A variable frequency pump group cooperative control algorithm is used to calculate the operation parameters of each pump group according to the required water discharge, the operation parameters including the rotating speed, flow rate and power;

[0025] According to the calculated operation parameters, the operation state of each pump group is dynamically adjusted to realize the cooperative work of the pump groups.

[0026] Optionally, the control of the recharge pressure and water quality according to the second water level change of the foundation pit after the drainage and the soil body parameters comprises:

[0027] Based on the gradient distribution characteristics of the second water level change, a three-dimensional dynamic recharge model is established in combination with the permeability coefficient in the soil body parameters;

[0028] The three-dimensional dynamic recharge model is input into a multi-objective optimization algorithm to calculate the partition pressure threshold and flow rate ratio of each recharge well, with the groundwater level balance as a constraint condition;

[0029] The pressure output of each recharge pump group is dynamically adjusted according to the partition pressure threshold of each recharge well through a fuzzy PID controller.

[0030] Optionally, the control of the recharge pressure and water quality according to the second water level change of the foundation pit after the drainage and the soil body parameters further comprises:

[0031] The turbidity, conductivity and suspended matter concentration indexes are collected by a multi-spectrum water quality sensor array in the recharge pipeline;

[0032] When any index exceeds the allowable threshold of the corresponding stratum in the geological survey data, the suspended matter concentration is reduced by adjusting the backwashing frequency of the recharge well filter, and when the over-standard duration of the conductivity reaches a preset duration, the ion exchange device is started to modify the water quality;

[0033] The processed real-time water quality data is fed back to the three-dimensional dynamic recharge model for parameter compensation.

[0034] In a second aspect of the present application, a treatment system for foundation pit dewatering is provided, comprising a data module, a parameter module, a control module and a recharge module, wherein:

[0035] The data module is configured to obtain various data from sensors installed at the periphery of the foundation pit and the preset positions inside the foundation pit, the various data including water level data, pore water pressure data and deformation data, and the various data are integrated with meteorological data and geological survey data to establish a space-time database;

[0036] a parameter module configured to predict a first water level change in a future preset time using a preset LSTM neural network model based on data in the spatiotemporal database, and to inversely derive soil parameters using a machine learning soil parameter inversion algorithm based on a three-dimensional seepage field dynamic simulation engine;

[0037] a control module configured to use a variable frequency pump group collaborative control algorithm to collaboratively control the pump group based on the first water level change, and to dynamically adjust pump group operation parameters to perform drainage;

[0038] a recharge module configured to control recharge pressure and water quality based on a second water level change of the foundation pit after drainage and the soil parameters.

[0039] In a third aspect of the present application, an electronic device is provided, comprising a processor, a memory, a user interface, and a network interface, the memory being configured to store instructions, the user interface and the network interface both being configured to communicate with other devices, and the processor being configured to execute the instructions stored in the memory to enable the electronic device to perform the method of any one of the above aspects.

[0040] In a fourth aspect of the present application, a computer-readable storage medium is provided, which stores instructions that, when executed, perform the method of any one of the above aspects.

[0041] In summary, the one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0042] 1. Using an LSTM neural network model to predict a first water level change in a future preset time can effectively process time series data, capture long-term dependencies of water level changes, and improve the accuracy of prediction. The LSTM model can adaptively adjust the flow of information through its internal gating mechanism, such as the forget gate, input gate, and output gate, to better handle complex patterns and dynamic changes in water level data;

[0043] 2. Combined with the three-dimensional seepage field dynamic simulation engine, the dynamic changes of the seepage field in the foundation pit can be more accurately simulated, providing more accurate data support for the inversion of soil parameters. By using the finite element method to numerically simulate the seepage field in the foundation pit, more detailed seepage field parameters such as water pressure and flow rate at different locations can be obtained, thereby improving the accuracy of soil parameter inversion;

[0044] 3. According to the predicted first water level change, the variable frequency pump group collaborative control algorithm is used to collaboratively control the pump group, and the pump group operation parameters are dynamically adjusted to discharge water, which can realize efficient collaborative work of the pump group and improve the drainage efficiency. By real-time monitoring of the water level change and seepage field dynamic change data in the foundation pit, the operation parameters of the pump group such as speed, flow and power are dynamically adjusted, so as to realize the collaborative work of the pump group and improve the drainage efficiency;

[0045] 4. According to the second water level change of the foundation pit after drainage and the soil body parameters, the recharge pressure and water quality are controlled, which can maintain the hydraulic balance in the foundation pit and prevent environmental problems such as ground subsidence. By establishing a three-dimensional dynamic recharge model, combining a multi-objective optimization algorithm and a fuzzy PID controller, the pressure output of the recharge pump group is dynamically adjusted, so that the actual recharge pressure curve tracks the partition pressure threshold, thereby realizing accurate control of the recharge process;

[0046] 5. A multi-spectral water quality sensor array is deployed in the recharge pipeline to real-time collect turbidity, conductivity and suspended solids concentration indicators, which can timely monitor the water quality change of the recharge water and provide real-time data support for water quality control. When the water quality indicators exceed the allowable threshold, a two-stage processing mechanism is triggered to adjust the backwashing frequency of the recharge well filter or start the ion exchange device for water quality modification, so as to ensure that the water quality of the recharge water meets the requirements;

[0047] 6. A recharge efficiency evaluation matrix is established, including stratum water absorption decay coefficient, chemical compatibility index and hydraulic fracturing risk value, which can real-time evaluate the efficiency of the recharge system, timely find potential problems and take corresponding measures. When the decay coefficient exceeds the warning value, a well group rotation scheme is automatically generated to maintain system efficiency by switching active recharge well combination; when the risk value reaches the critical state, a three-dimensional seepage field dynamic simulation engine is linked to verify the hydraulic gradient, and a viscoelastic constitutive model is introduced to correct the upper limit of the recharge pressure, thereby enhancing the adaptability and stability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 is a flowchart of the processing method for foundation pit dewatering disclosed by the embodiments of the present application;

[0049] Figure 2 is a module schematic diagram of the processing system for foundation pit dewatering disclosed by the embodiments of the present application;

[0050] Figure 3 is a structural schematic diagram of an electronic device disclosed by the embodiments of the present application.

[0051] Mark explanation: 201, data module; 202, parameter module; 203, control module; 204, recharge module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0052] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0053] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0054] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0055] This embodiment discloses a method for treating foundation pit dewatering, applied to a foundation pit dewatering treatment platform. Figure 1 This is a schematic flowchart of the method for treating foundation pit dewatering disclosed in the embodiments of this application, as shown below. Figure 1 As shown, the method includes the following steps:

[0056] S101. Obtain various types of data from sensors installed at preset locations around and inside the foundation pit. These data include water level data, pore water pressure data, and deformation data. Integrate these data with meteorological data and geological survey data to establish a spatiotemporal database.

[0057] S102. Based on the data in the spatiotemporal database, a preset LSTM neural network model is used to predict the first water level change within a preset time period in the future, and the soil parameters are inverted using a machine learning soil parameter inversion algorithm based on the three-dimensional seepage field dynamic simulation engine.

[0058] S103. Based on the first water level change, use the variable frequency pump group collaborative control algorithm to perform collaborative control of the pump group and dynamically adjust the pump group operating parameters to carry out drainage.

[0059] S104、According to the second water level change of the foundation pit after drainage and the soil parameters, the recharge pressure and water quality are controlled.

[0060] Various types of data, including water level data, pore water pressure data, and deformation data, are obtained from sensors installed around the foundation pit and at predetermined locations within the foundation pit. These sensors can monitor the water level changes, pore water pressure changes, and deformation of the soil around the foundation pit in real time, providing basic data for subsequent analysis. The obtained sensor data is integrated with meteorological data and geological survey data to establish a spatio-temporal database. Meteorological data includes rainfall, evaporation, air temperature, etc., and geological survey data includes soil structure, soil type, permeability coefficient, etc. By integrating these data, the hydrogeological conditions and environmental factors of the foundation pit can be comprehensively understood, providing rich data support for subsequent water level prediction and soil parameter inversion. Based on the data in the spatio-temporal database, a pre-set LSTM (Long Short-Term Memory) neural network model is used to predict the first water level changes in a pre-set future time. The LSTM neural network model can effectively process time series data and capture the long-term dependence of water level changes, thus accurately predicting future water levels. According to the three-dimensional seepage field dynamic simulation engine, the soil parameter inversion algorithm based on machine learning is used to invert the soil parameters. The three-dimensional seepage field dynamic simulation engine uses the finite element method to numerically simulate the seepage field within the foundation pit, obtaining dynamic change data of the seepage field. The machine learning-based soil parameter inversion algorithm analyzes and processes the dynamic change data of the seepage field to construct the mapping relationship between the inversion parameters and displacement, and to invert the permeability coefficient, porosity, and other parameters of the soil. According to the predicted first water level changes, a variable frequency pump group collaborative control algorithm is used to collaboratively control the pump group. The variable frequency pump group collaborative control algorithm calculates the operating parameters of each pump group, including speed, flow rate, and power, based on the required water discharge. By dynamically adjusting the operating state of each pump group, the collaborative work of the pump group is realized, improving the drainage efficiency and saving energy. Based on the collaborative control of the pump group, the operating parameters of the pump group are dynamically adjusted for drainage. By monitoring the drainage effect in real time, the pump group operating parameters are adjusted according to the actual drainage conditions to ensure the efficiency and stability of the drainage process. According to the second water level changes and soil parameters after drainage, the recharge pressure is controlled. By establishing a three-dimensional dynamic recharge model, combining the gradient distribution characteristics of the second water level changes and the permeability coefficient in the soil parameters, and inputting a multi-objective optimization algorithm, the partition pressure threshold and flow ratio of each recharge well are calculated as the constraint condition of groundwater level equilibrium. The fuzzy PID (Proportional-Integral-Derivative) controller dynamically adjusts the pressure output of the recharge pump group, making the actual recharge pressure curve track the partition pressure threshold, ensuring the rationality and safety of the recharge process. A multi-spectral water quality sensor array is deployed in the middle of the recharge pipeline to collect turbidity, conductivity, and suspended solids concentration indicators in real time. When any indicator exceeds the allowable threshold of the corresponding stratum in the geological survey data, a two-stage processing mechanism is triggered.The primary treatment reduces the concentration of suspended solids by adjusting the backwash frequency of the recharge well filter. If the conductivity continues to exceed the standard, the secondary treatment activates the ion exchange device to modify the water quality. Real-time water quality data after treatment is fed back to the three-dimensional dynamic recharge model for parameter compensation to ensure that the recharge water quality meets the requirements.

[0061] Optionally, the soil parameters are inverted by a machine learning soil parameter inversion algorithm based on the three-dimensional seepage field dynamic simulation engine, and the soil parameters include:

[0062] The three-dimensional seepage field dynamic simulation engine is used to numerically simulate the seepage field in the foundation pit based on the finite element method, and dynamic change data of the seepage field is obtained.

[0063] The machine learning soil parameter inversion algorithm is used to analyze and process the dynamic change data of the seepage field.

[0064] The soil parameters of the soil, including the permeability coefficient and the porosity, are inverted by constructing a mapping relationship between the inversion parameters and the displacement.

[0065] The three-dimensional seepage field dynamic simulation engine numerically simulates the seepage field in the foundation pit based on the finite element method. The finite element method divides the foundation pit area into multiple finite element units, and the soil type, permeability coefficient, and porosity of each unit are determined according to the geological survey data and sensor data. By solving the seepage equation, the dynamic change data of the water pressure and flow rate of the seepage field at different positions in the foundation pit are obtained. These data reflect the changes in the hydraulic gradient and water flow path in the foundation pit, providing basic data for subsequent soil parameter inversion. The machine learning soil parameter inversion algorithm analyzes and processes the dynamic change data of the seepage field. These data include the changes in water pressure, flow rate, and other seepage field parameters in time and space. The algorithm learns the characteristics and rules of these data and extracts feature information related to soil parameters. For example, by analyzing the trend of water pressure and the distribution characteristics of flow rate, the permeability and pore structure of the soil can be preliminarily judged. By constructing a mapping relationship model between the inversion parameters and the displacement, the dynamic change data of the seepage field is converted into predicted values of the soil parameters. The mapping relationship model describes the nonlinear relationship between the soil parameters and the displacement. For example, based on the dynamic change data of the seepage field, a mapping relationship model between the inversion parameters and the displacement can be established to predict the permeability coefficient and the porosity of the soil. The permeability coefficient reflects the permeability of the soil to water flow, and the porosity reflects the proportion of pores in the soil. These parameters are of great significance for foundation pit dewatering treatment and soil stability analysis.

[0066] The three-dimensional seepage field dynamic simulation engine is used to numerically simulate the seepage field in the foundation pit based on the finite element method, and the dynamic change data of the seepage field can be obtained. These data include water pressure, flow rate and other seepage field parameters, which provide rich information for soil parameter inversion. Machine learning-based soil parameter inversion algorithms, such as particle swarm optimization combined with multi-output least squares support vector regression (PSO-MLSSVR) or BP neural network, are used to analyze and process the dynamic change data of the seepage field. Machine learning algorithms can automatically learn patterns and rules in the data, improving the accuracy and reliability of the inversion. By constructing a mapping relationship between the inversion parameters and the displacement, the dynamic change data of the seepage field is linked to the soil parameters. This mapping relationship can more accurately reflect the relationship between soil parameters and seepage field, further improving the accuracy of the inversion. Accurate soil parameters can support the foundation pit dewatering processing platform to make more scientific decisions. For example, during the drainage process, the operation parameters of the pump set can be reasonably adjusted based on the soil parameters and water level changes to achieve efficient drainage; during the recharge process, the recharge pressure and water quality can be controlled based on the soil parameters and water level changes to maintain hydraulic balance in the foundation pit. Through accurate soil parameter inversion, the seepage field in the foundation pit can be better understood, and the dewatering process can be optimized. For example, the operation parameters of the pump set can be reasonably adjusted to improve drainage efficiency and save energy; at the same time, by controlling the recharge pressure and water quality, problems such as ground subsidence or tilting of surrounding buildings caused by improper recharge can be avoided.

[0067] Optionally, the three-dimensional seepage field dynamic simulation engine is used to numerically simulate the seepage field in the foundation pit based on the finite element method, and the dynamic change data of the seepage field includes:

[0068] The foundation pit area is divided into multiple finite element units, and the soil type, permeability coefficient and porosity of each finite element unit are determined based on the geological survey data and the various types of data;

[0069] The seepage equation of the seepage field is established based on the soil type, permeability coefficient and porosity of each finite element unit, and the seepage equation is solved by the finite element method to obtain the dynamic change data of the foundation pit. The dynamic change data includes water pressure and flow rate seepage field parameters at different positions;

[0070] The dynamic change data is updated and adjusted in real time based on the water level changes, pumping rate and recharge flow during the foundation pit dewatering process.

[0071] Based on the geological survey data and various monitoring data, the soil types in the foundation pit area are analyzed in detail. Different types of soil have different physical and mechanical properties, such as permeability coefficient and porosity, which have important influence on the distribution and change of seepage field. For example, the permeability coefficient of sand is usually larger, while the permeability coefficient of clay is relatively smaller. Based on the analysis results of soil properties, the foundation pit area is divided into multiple finite element units. The size and shape of each finite element unit can be adjusted according to the actual situation to ensure the accuracy of simulation and the efficiency of calculation. In the process of division, the non-homogeneous and anisotropic properties of soil, as well as the geological boundary conditions around the foundation pit, need to be considered. For each finite element unit, the soil type, permeability coefficient, and porosity, etc. parameters are determined according to the geological survey data and various monitoring data. These parameters are the basis of seepage field numerical simulation, and accurate parameter values can improve the reliability of simulation results. For example, through drilling and sampling analysis, the permeability coefficient and porosity data of soil at different depths and locations can be obtained. According to the soil type, permeability coefficient, and porosity, etc. parameters of each finite element unit, the seepage equation of seepage field is established. The seepage equation is usually based on Darcy's law and continuity equation, which describes the motion law of water flow in soil. For example, for saturated soil, Darcy's law can be used to describe the relationship between the seepage velocity and hydraulic gradient of water flow; for unsaturated soil, factors such as soil water characteristic curve and permeability function need to be considered. The finite element method is used to solve the seepage equation. The finite element method discretizes the continuous seepage field into a finite number of units, and by assuming an approximate solution in each unit, the partial differential equation is transformed into an algebraic equation system. Then, by solving this algebraic equation system, the water pressure and flow velocity, etc. parameters of seepage field in each unit are obtained. In the solving process, the nonlinear characteristics of soil, the coupling effect of seepage field and stress field, etc. factors need to be considered to improve the accuracy of simulation results. Through the finite element method, the dynamic change data of water pressure and flow velocity, etc. parameters of seepage field at different positions in the foundation pit are obtained. These data reflect the spatio-temporal variation characteristics of seepage field during the foundation pit dewatering process, providing important basis for subsequent soil parameter inversion and dewatering treatment decision-making. For example, through simulation, the water pressure variation curves of the central and peripheral regions of the foundation pit, as well as the flow velocity distribution at different depths can be obtained. During the foundation pit dewatering process, real-time monitoring of water level change, pumping rate, and recharge flow, etc. data is carried out, and these data are fed back to the three-dimensional seepage field dynamic simulation engine. These monitoring data can reflect the actual operation state of the dewatering treatment system, providing basis for dynamically adjusting the seepage field simulation. For example, through water level sensors, real-time water level change data in the foundation pit is obtained, and through flow meters, pumping and recharge flow data is monitored. Through data fusion, the dynamic change data of seepage field is updated and adjusted in real time. Through data fusion, the accuracy and reliability of seepage field simulation can be improved, making it more consistent with the actual situation.For example, based on monitored water level change data, the boundary and initial conditions in the finite element model are adjusted to reflect the impact of water level changes on the seepage field during dewatering. Based on the updated dynamic seepage field data, soil parameters are recalculated and dewatering treatment decisions are made. Real-time updates and adjustments enable simulation results to more accurately reflect the actual situation during foundation pit dewatering, providing support for optimizing dewatering treatment schemes. For instance, based on the adjusted seepage field data, parameters such as soil permeability and porosity are recalculated, providing a basis for the dynamic adjustment of pump unit operating parameters.

[0072] Dividing the foundation pit area into multiple finite element units allows for a more refined simulation of the seepage field at different locations within the pit. This division method takes into account the heterogeneity of the soil within the pit, improving the accuracy of the simulation results. Based on geological survey data and various monitoring data, the soil type, permeability coefficient, and porosity of each finite element unit are determined. These parameters are crucial for seepage field simulation, and accurate parameters enhance the reliability of the simulation results. The seepage equation is solved using the finite element method to obtain dynamic variation data of seepage field parameters such as water pressure and flow velocity at different locations within the foundation pit. This data reflects the temporal and spatial variations of the seepage field, providing a foundation for subsequent analysis and processing. Based on changes in water level, pumping rate, and recharge flow rate during the foundation pit dewatering process, the dynamic data is updated and adjusted in real time. This real-time update mechanism ensures that the simulation results remain consistent with actual conditions, improving the timeliness and practicality of the simulation. The dynamic seepage field data obtained through the above process provides a rich data foundation for machine learning-based soil parameter inversion algorithms. These data help the algorithm more accurately construct the mapping relationship between inversion parameters and displacement, improving the accuracy of soil parameter inversion. Accurate dynamic change data of the seepage field can improve the accuracy of soil parameter inversion, thus providing more accurate soil characteristic information for foundation pit dewatering treatment. This is of great significance for formulating reasonable dewatering plans, optimizing pump unit operating parameters, and controlling recharge pressure and water quality.

[0073] Optionally, the soil parameters retrieved by constructing a mapping relationship between inversion parameters and displacement include:

[0074] Based on the dynamic change data of the seepage field, a mapping relationship model between inversion parameters and displacement is established. The mapping relationship model is used to describe the nonlinear relationship between soil parameters and displacement.

[0075] Using the mapping model, the dynamic change data of the seepage field is converted into predicted values ​​of soil parameters.

[0076] A three-dimensional seepage field dynamic simulation engine was used to numerically simulate the seepage field within the foundation pit using the finite element method, obtaining dynamic change data of the seepage field. This data includes seepage field parameters such as water pressure and flow velocity at different locations. Deformation data of the soil surrounding the foundation pit was acquired through sensors, reflecting the displacement of the soil under the influence of the seepage field. A complex nonlinear relationship exists between the dynamic change data of the seepage field and the soil parameters. This nonlinear relationship can be described by establishing a mapping model between inversion parameters and displacement. Machine learning algorithms, such as particle swarm optimization combined with multi-output least squares support vector regression (PSO-MLSSVM) or backpropagation neural networks, were used to construct the mapping model. These algorithms can learn and discover patterns from large amounts of data, predict the behavior of the soil under different stress conditions, and thus develop a soil constitutive model. The dynamic change data of the seepage field and displacement data were preprocessed, including data cleaning and normalization, to ensure data quality and consistency. The preprocessed data was then input into the mapping model for model training. Through training, the model learns the mapping relationship between the dynamic change data of the seepage field and the soil parameters. Using a trained mapping model, the dynamic changes in the seepage field are converted into predicted values ​​of soil parameters, including permeability coefficient and porosity. The accuracy and reliability of the model are verified by comparing the predicted values ​​with the actual measured values. If the predicted values ​​differ significantly from the actual values, the model needs to be adjusted and optimized.

[0077] Based on the dynamic changes in the seepage field, a mapping model between inversion parameters and displacement is established. This model can describe the nonlinear relationship between soil parameters and displacement, providing a more accurate mathematical basis for soil parameter inversion. Using this mapping model, the dynamic changes in the seepage field are converted into predicted values ​​of soil parameters. This method can more accurately reflect the relationship between soil parameters and the seepage field, improving the accuracy and reliability of the inversion results. The inverted soil parameters, such as permeability coefficient and porosity, can provide accurate soil characteristic information for foundation pit dewatering. These parameters are crucial for formulating reasonable dewatering schemes, optimizing pump operating parameters, and controlling recharge pressure and water quality. Accurate soil parameters enable the foundation pit dewatering platform to make more scientific decisions. For example, during drainage, the operating parameters of the pump set can be reasonably adjusted based on soil parameters and water level changes to achieve efficient drainage; during recharge, the recharge pressure and water quality can be controlled based on soil parameters and water level changes to maintain hydraulic balance within the foundation pit. Accurate soil parameter inversion allows for a better understanding of the seepage field within the foundation pit, thereby optimizing the dewatering process. For example, adjusting pump operating parameters can improve drainage efficiency and save energy; simultaneously, controlling recharge pressure and water quality can prevent problems such as ground subsidence or tilting of surrounding buildings caused by improper recharge. Accurate soil parameters enhance the effectiveness of foundation pit dewatering. For instance, during recharge, controlling recharge pressure and water quality helps maintain hydraulic balance within the foundation pit, preventing engineering risks caused by excessive water level fluctuations and ensuring the safety and stability of the dewatering process.

[0078] Optionally, the step of using a variable frequency pump group collaborative control algorithm to collaboratively control the pump group based on the first water level change, and dynamically adjusting the pump group operating parameters for drainage, includes:

[0079] Based on the first water level change, determine the amount of water that needs to be discharged from the foundation pit;

[0080] Using a variable frequency pump group collaborative control algorithm, the operating parameters of each pump group are calculated based on the required water discharge volume. These operating parameters include speed, flow rate, and power.

[0081] Based on the calculated operating parameters, the operating status of each pump group is dynamically adjusted to achieve coordinated operation of the pump groups.

[0082] Based on the predicted first water level change, combined with the geometry of the foundation pit and the rate of water level change, the required water volume to be discharged from the pit is calculated. This step ensures the accuracy and rationality of the drainage volume, avoiding over-drainage or under-drainage. The variable frequency pump group collaborative control algorithm is a pump group control algorithm based on variable frequency speed regulation technology, capable of dynamically adjusting the operating parameters of the pump group according to actual needs. By changing the pump speed to adjust the pump flow rate and head, it has advantages such as good energy saving and stable operation. Based on the required water volume to be discharged, the variable frequency pump group collaborative control algorithm calculates the operating parameters of each pump group, including speed, flow rate, and power. The calculation of these parameters ensures the efficient operation of the pump group during the drainage process while avoiding energy waste. Based on the calculated operating parameters, the operating status of each pump group is adjusted in real time. Speed ​​control of the pump group motors is achieved through frequency converters, realizing stepless speed regulation of the pump groups and ensuring efficient operation during the drainage process. The variable frequency pump group collaborative control algorithm enables collaborative work between pump groups. Each pump group dynamically adjusts its operating parameters according to actual needs, ensuring the efficiency and stability of the drainage process. This collaborative working method not only improves drainage efficiency but also extends the service life of the pump set.

[0083] Based on the predicted changes in the initial water level, the required water volume to be discharged from the foundation pit can be accurately determined. This precise water volume calculation avoids over- or under-drainage, improving drainage efficiency and effectiveness. Using a variable frequency pump group collaborative control algorithm, the operating parameters of each pump group, including speed, flow rate, and power, are dynamically calculated according to the required water volume. This dynamic adjustment ensures efficient operation of the pump groups during drainage, further improving drainage efficiency. Variable frequency speed control technology adjusts the pump speed according to actual needs, avoiding energy waste associated with traditional fixed-speed pumps during drainage. Variable frequency speed control allows the motor to operate at its optimal speed, reducing energy loss and improving efficiency. Adopting variable frequency speed control technology can reduce motor energy consumption by more than 30%, resulting in significant energy savings. Dynamically adjusting the pump group's operating parameters avoids prolonged operation under high loads, reducing equipment wear and failure rates. Variable frequency speed control technology enables soft start and soft stop of the pumps, reducing shocks during start-up and shutdown and extending equipment lifespan. The variable frequency pump group collaborative control algorithm can monitor and adjust the pump group's operating status in real time, ensuring the stability and reliability of the drainage process. This collaborative working method effectively avoids drainage problems caused by the failure of a single pump unit, improving the stability of the entire drainage system. Through a variable frequency pump group collaborative control algorithm, coordinated operation between pump units is achieved. Each pump unit dynamically adjusts its operating parameters according to actual needs, ensuring the efficiency and stability of the drainage process. This collaborative working method effectively avoids drainage problems caused by the failure of a single pump unit, improving the stability of the entire drainage system. The variable frequency pump group collaborative control algorithm can monitor and adjust the operating status of the pump units in real time, ensuring the stability and reliability of the drainage process. This real-time monitoring and adjustment mechanism can effectively cope with various emergencies during the drainage process, further improving the system's stability.

[0084] Optionally, controlling the recharge pressure and water quality based on the second water level change in the foundation pit after drainage and the soil parameters includes:

[0085] Based on the gradient distribution characteristics of the second water level change, a three-dimensional dynamic recharge model is established in combination with the permeability coefficient in the soil parameters.

[0086] The three-dimensional dynamic recharge model is input into a multi-objective optimization algorithm, and the pressure threshold and flow ratio of each recharge well are calculated under the constraint of groundwater level balance.

[0087] The pressure output of each reinjection pump group is dynamically adjusted by a fuzzy PID controller based on the zone pressure threshold of each reinjection well.

[0088] Real-time monitoring of water level changes within the foundation pit using sensors yields data on the second water level change after drainage. This data reflects the dynamic changes in the water level within the pit. Based on this second water level change data, the gradient distribution characteristics of the water level within the pit are calculated. These gradient distribution characteristics describe the rate and direction of water level change in space, providing a foundation for subsequent recharge model establishment. The foundation pit area is divided into multiple finite element units (FEMs). Based on geological survey data and various monitoring data, the soil type, permeability coefficient, and porosity of each FEM are determined. Combining the gradient distribution characteristics of the second water level change and the permeability coefficient in the soil parameters, a three-dimensional dynamic recharge model is established. This model can simulate water flow and water level changes during the recharge process, providing a basis for subsequent recharge pressure and flow control. A suitable multi-objective optimization algorithm, such as Particle Swarm Optimization (PSO) or Genetic Algorithm (GA), is selected to optimize the operating parameters of the recharge wells, using groundwater level equilibrium as a constraint. Optimization objectives include balanced groundwater level distribution, recharge well operating efficiency, and minimizing energy consumption. The zonal pressure threshold and flow ratio of each reinjection well were calculated using an optimization algorithm. The output data of the three-dimensional dynamic reinjection model, including seepage field parameters such as water pressure and flow velocity at different locations within the foundation pit, were used as input to the multi-objective optimization algorithm. The zonal pressure threshold refers to the upper and lower pressure limits of each reinjection well in different areas, and the flow ratio refers to the proportion of flow distribution among the reinjection wells. A fuzzy PID controller was designed to dynamically adjust the pressure output of each reinjection pump group based on the zonal pressure threshold of each reinjection well. The fuzzy PID controller can automatically adjust the operating parameters of the pump group based on the deviation between the actual reinjection pressure and the zonal pressure threshold. Through the fuzzy PID controller, the pressure output of the reinjection pump group is monitored in real time, and the operating parameters of the pump group are dynamically adjusted based on the deviation between the actual reinjection pressure and the zonal pressure threshold to ensure that the reinjection pressure curve tracks the zonal pressure threshold.

[0089] A three-dimensional dynamic recharge model is established based on the gradient distribution characteristics of the second water level change and the permeability coefficient in the soil parameters. This model can accurately describe the hydraulic conduction characteristics during the recharge process, providing a scientific basis for the control of recharge pressure and flow rate. By considering the spatial gradient of water level change and the permeability characteristics of the soil, the model can more accurately predict the flow of recharge water around the foundation pit, thereby achieving precise recharge control. The three-dimensional dynamic recharge model is input into a multi-objective optimization algorithm, with groundwater level equilibrium as a constraint, to calculate the zoned pressure threshold and flow rate ratio of each recharge well. The multi-objective optimization algorithm can comprehensively consider multiple optimization objectives, such as groundwater level equilibrium, recharge efficiency, and energy consumption, to find the optimal recharge scheme. Through the optimization algorithm, it can be ensured that the recharge pressure and flow rate of each recharge well achieve the best recharge effect while satisfying groundwater level equilibrium. According to the zoned pressure threshold of each recharge well, the pressure output of each recharge pump group is dynamically adjusted by a fuzzy PID controller. The fuzzy PID controller combines the advantages of fuzzy logic and PID control, adaptively adjusting control parameters based on the deviation between actual pressure and a threshold, achieving precise control of the reinjection pressure. This control method can quickly respond to water level changes, ensuring the efficiency and stability of the reinjection process. Through a multi-objective optimization algorithm, using groundwater level equilibrium as a constraint, it ensures that the reinjection process does not lead to excessive fluctuations in the groundwater level. This helps maintain the stability of the groundwater level around the foundation pit, reducing the impact on the surrounding environment, such as ground subsidence and building tilting.

[0090] 7. Optionally, the step of controlling the recharge pressure and water quality based on the second water level change in the foundation pit after drainage and the soil parameters further includes:

[0091] Turbidity, conductivity, and suspended solids concentration were collected in the reinjection pipeline using a multispectral water quality sensor array.

[0092] When any indicator exceeds the allowable threshold of the corresponding stratum in the geological exploration data, the concentration of suspended solids is reduced by adjusting the backwashing frequency of the reinjection well filter, and when the conductivity exceeds the standard for a preset time, the ion exchange device is started to modify the water quality.

[0093] The processed real-time water quality data is fed back to the three-dimensional dynamic reinjection model for parameter compensation.

[0094] Deploying a multispectral water quality sensor array in the reinjection pipeline enables real-time acquisition of water quality indicators such as turbidity, conductivity, and suspended solids concentration. These sensors, based on spectral technology, can simultaneously and accurately detect multiple water quality parameters, providing a more comprehensive water quality analysis. For example, Leisen Optics' iSpecWQ-UV / VIS multi-parameter spectral water quality measurement module can simultaneously detect multiple parameters in the water body online, such as turbidity and nitrate nitrogen. When the suspended solids concentration exceeds the allowable threshold, the backwashing frequency of the reinjection well filter is adjusted to reduce the concentration. Backwashing effectively removes suspended solids from the filter surface, restoring the filter's filtration performance. When the conductivity exceeds the standard for a preset duration, an ion exchange device is activated to modify the water quality. The ion exchange device exchanges ions with the water using ion exchange resin, removing inorganic salts and thus reducing conductivity. For example, a co-current regeneration ion exchanger is a common ion exchange device that can effectively remove various inorganic salts from the water. The treated real-time water quality data is fed back to the three-dimensional dynamic reinjection model for parameter compensation. Real-time monitoring and feedback ensure that the recharge water quality meets requirements, preventing adverse impacts on the surrounding environment of the foundation pit. For example, the Memosens Wave CAS80E full-spectrum sensor can output the measurement results of relevant analytical parameters in real time, supporting the analysis of multiple standard parameters for water and wastewater monitoring. Based on real-time water quality data, parameter compensation is performed on the three-dimensional dynamic recharge model to ensure the model's accuracy and reliability. This helps optimize the recharge process and improve recharge efficiency and effectiveness.

[0095] In the reinjection pipeline, a multispectral water quality sensor array collects turbidity, conductivity, and suspended solids concentration indicators. The multispectral water quality sensor can simultaneously and accurately detect multiple water quality parameters, providing a more comprehensive analysis of the water quality. This real-time monitoring method can promptly identify water quality problems, providing data support for subsequent control measures. Based on the zoned pressure thresholds of each reinjection well, a fuzzy PID controller dynamically adjusts the pressure output of each reinjection pump group. The fuzzy PID controller combines the advantages of fuzzy logic and PID control, adaptively adjusting control parameters according to the deviation between the actual pressure and the threshold, achieving precise control of the reinjection pressure. This control method can quickly respond to water level changes, ensuring the efficiency and stability of the reinjection process. When the suspended solids concentration exceeds the allowable threshold, the backwashing frequency of the reinjection well filter is adjusted to reduce the suspended solids concentration. This treatment method can effectively remove suspended solids from the reinjection water, reduce clogging of the reinjection wells, and improve reinjection efficiency. When the conductivity exceeds the standard for a preset time, an ion exchange device is activated to modify the water quality. The ion exchange device can effectively remove ions from the water, reduce conductivity, and improve water quality. This treatment method ensures that the reinjection water quality meets requirements, reducing the impact on the surrounding environment of the foundation pit. Real-time water quality data after treatment is fed back to a three-dimensional dynamic reinjection model for parameter compensation. This feedback mechanism updates the water quality parameters in the model in a timely manner, improving the model's accuracy and reliability. Through parameter compensation, the model can better predict the hydraulic conduction characteristics during the reinjection process, providing a more scientific basis for controlling reinjection pressure and flow. The use of a multispectral water quality sensor array and ion exchange device reduces reliance on chemical reagents. The multispectral water quality sensor can monitor water quality without adding chemical reagents, and the ion exchange device does not require chemical reagents for water modification. This helps reduce secondary pollution and protect the environment.

[0096] This embodiment also discloses a system for treating foundation pit dewatering. Figure 2 This is a schematic diagram of the modules of the foundation pit dewatering treatment system disclosed in the embodiments of this application, as shown below. Figure 2 As shown, the system includes a data module 201, a parameter module 202, a control module 203, and a recharge module 204, wherein:

[0097] Data module 201 is configured to acquire various types of data from sensors installed at preset locations around and inside the foundation pit. These various types of data include water level data, pore water pressure data, and deformation data. The data is then integrated with meteorological data and geological survey data to establish a spatiotemporal database.

[0098] The parameter module 202 is configured to predict the first water level change within a preset time period based on the data in the spatiotemporal database using a preset LSTM neural network model, and to invert the soil parameters using a machine learning soil parameter inversion algorithm based on the three-dimensional seepage field dynamic simulation engine.

[0099] Control module 203 is configured to use a variable frequency pump group collaborative control algorithm to coordinately control the pump group according to the first water level change, and dynamically adjust the pump group operating parameters to drain water.

[0100] The recharge module 204 is configured to control the recharge pressure and water quality based on the second water level change of the foundation pit after drainage and the soil parameters.

[0101] Optionally, the parameter module 202 is configured to:

[0102] Using a three-dimensional seepage field dynamic simulation engine, the seepage field in the foundation pit is numerically simulated based on the finite element method to obtain dynamic change data of the seepage field.

[0103] The dynamic change data of the seepage field are analyzed and processed using a soil parameter inversion algorithm based on machine learning.

[0104] By constructing a mapping relationship between inversion parameters and displacement, the soil parameters, including permeability coefficient and porosity, are inverted.

[0105] Optionally, the parameter module 202 is configured to:

[0106] The foundation pit area is divided into multiple finite element units. Based on the geological survey data and the various types of data, the soil type, permeability coefficient and porosity of each finite element unit are determined.

[0107] The seepage equation of the seepage field is established based on the soil type, permeability coefficient and porosity of each finite element element. The seepage equation is solved by the finite element method to obtain the dynamic change data of the foundation pit. The dynamic change data includes the seepage field parameters of water pressure and flow velocity at different locations.

[0108] The dynamic change data is updated and adjusted in real time based on the water level changes, pumping rate, and recharge flow rate during the foundation pit dewatering process.

[0109] Optionally, the parameter module 202 is configured to:

[0110] Based on the dynamic change data of the seepage field, a mapping relationship model between inversion parameters and displacement is established. The mapping relationship model is used to describe the nonlinear relationship between soil parameters and displacement.

[0111] Using the mapping model, the dynamic change data of the seepage field is converted into predicted values ​​of soil parameters.

[0112] Optionally, the control module 203 is configured to:

[0113] Based on the first water level change, determine the amount of water that needs to be discharged from the foundation pit;

[0114] Using a variable frequency pump group collaborative control algorithm, the operating parameters of each pump group are calculated based on the required water discharge volume. These operating parameters include speed, flow rate, and power.

[0115] Based on the calculated operating parameters, the operating status of each pump group is dynamically adjusted to achieve coordinated operation of the pump groups.

[0116] Optionally, the recharge module 204 is configured to:

[0117] Based on the gradient distribution characteristics of the second water level change, a three-dimensional dynamic recharge model is established in combination with the permeability coefficient in the soil parameters.

[0118] The three-dimensional dynamic recharge model is input into a multi-objective optimization algorithm, and the pressure threshold and flow ratio of each recharge well are calculated under the constraint of groundwater level balance.

[0119] The pressure output of each reinjection pump group is dynamically adjusted by a fuzzy PID controller based on the zone pressure threshold of each reinjection well.

[0120] Optionally, the recharge module 204 is further configured to:

[0121] Turbidity, conductivity, and suspended solids concentration were collected in the reinjection pipeline using a multispectral water quality sensor array.

[0122] When any indicator exceeds the allowable threshold of the corresponding stratum in the geological exploration data, the concentration of suspended solids is reduced by adjusting the backwashing frequency of the reinjection well filter, and when the conductivity exceeds the standard for a preset time, the ion exchange device is started to modify the water quality.

[0123] The processed real-time water quality data is fed back to the three-dimensional dynamic reinjection model for parameter compensation.

[0124] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0125] This embodiment also discloses an electronic device, as shown in the reference. Figure 3 The electronic device may include: at least one processor 301, at least one communication bus 302, user interface 303, network interface 304, and at least one memory 305.

[0126] The communication bus 302 is used to enable communication between these components.

[0127] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0128] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0129] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0130] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for handling foundation pit dewatering.

[0131] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call the application program stored in the memory 305 for the processing method of foundation pit dewatering. When executed by one or more processors 301, the electronic device executes one or more methods as described in the above embodiments.

[0132] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0133] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0137] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory 305 includes various media capable of storing program code, such as a USB flash drive, external hard drive, magnetic disk, or optical disk.

[0138] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the disclosure in this specification. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for treating foundation pit dewatering, characterized in that, The method, applied to a foundation pit dewatering treatment platform, includes: Various types of data are acquired from sensors installed at preset locations around and inside the foundation pit. These data include water level data, pore water pressure data, and deformation data. The data are then integrated with meteorological data and geological survey data to establish a spatiotemporal database. Based on the data in the spatiotemporal database, a preset LSTM neural network model is used to predict the first water level change within a preset time period in the future, and the soil parameters are inverted using a machine learning soil parameter inversion algorithm based on the three-dimensional seepage field dynamic simulation engine. Based on the first water level change, the pump group is controlled collaboratively using a variable frequency pump group collaborative control algorithm to dynamically adjust the pump group operating parameters for drainage. The recharge pressure and water quality are controlled based on the second water level change in the foundation pit after drainage and the soil parameters. The process of retrieving soil parameters using a machine learning-based soil parameter inversion algorithm based on a three-dimensional seepage field dynamic simulation engine includes: Using a three-dimensional seepage field dynamic simulation engine, the seepage field in the foundation pit is numerically simulated based on the finite element method to obtain dynamic change data of the seepage field. The dynamic change data of the seepage field are analyzed and processed using a soil parameter inversion algorithm based on machine learning. By constructing a mapping relationship between inversion parameters and displacement, soil parameters, including permeability coefficient and porosity, are retrieved. The step of using a variable frequency pump group collaborative control algorithm to collaboratively control the pump group based on the first water level change, and dynamically adjusting the pump group operating parameters for drainage, includes: Based on the first water level change, determine the amount of water that needs to be discharged from the foundation pit; Using a variable frequency pump group collaborative control algorithm, the operating parameters of each pump group are calculated based on the required water discharge volume. These operating parameters include speed, flow rate, and power. Based on the calculated operating parameters, the operating status of each pump unit is dynamically adjusted to achieve coordinated operation of the pump units. The control of recharge pressure and water quality based on the second water level change in the foundation pit after drainage and the soil parameters includes: Based on the gradient distribution characteristics of the second water level change, a three-dimensional dynamic recharge model is established in combination with the permeability coefficient in the soil parameters. The three-dimensional dynamic recharge model is input into a multi-objective optimization algorithm, and the pressure threshold and flow ratio of each recharge well are calculated under the constraint of groundwater level balance. The pressure output of each reinjection pump group is dynamically adjusted by a fuzzy PID controller based on the zone pressure threshold of each reinjection well.

2. The method for treating foundation pit dewatering according to claim 1, characterized in that, The method utilizes a three-dimensional seepage field dynamic simulation engine to numerically simulate the seepage field within the foundation pit based on the finite element method, obtaining dynamic change data of the seepage field, including: The foundation pit area is divided into multiple finite element units. Based on the geological survey data and the various types of data, the soil type, permeability coefficient and porosity of each finite element unit are determined. The seepage equation of the seepage field is established based on the soil type, permeability coefficient and porosity of each finite element element. The seepage equation is solved by the finite element method to obtain the dynamic change data of the foundation pit. The dynamic change data includes the seepage field parameters of water pressure and flow velocity at different locations. The dynamic change data is updated and adjusted in real time based on the water level changes, pumping rate, and recharge flow rate during the foundation pit dewatering process.

3. The method for treating foundation pit dewatering according to claim 2, characterized in that, The soil parameters obtained by constructing a mapping relationship between inversion parameters and displacement include: Based on the dynamic change data of the seepage field, a mapping relationship model between inversion parameters and displacement is established. The mapping relationship model is used to describe the nonlinear relationship between soil parameters and displacement. Using the mapping model, the dynamic change data of the seepage field is converted into predicted values ​​of soil parameters.

4. The method for treating foundation pit dewatering according to claim 1, characterized in that, The method of controlling the recharge pressure and water quality based on the second water level change in the foundation pit after drainage and the soil parameters also includes: Turbidity, conductivity, and suspended solids concentration were collected in the reinjection pipeline using a multispectral water quality sensor array. When any indicator exceeds the allowable threshold of the corresponding stratum in the geological exploration data, the concentration of suspended solids is reduced by adjusting the backwashing frequency of the reinjection well filter, and when the conductivity exceeds the standard for a preset time, the ion exchange device is started to modify the water quality. The processed real-time water quality data is fed back to the three-dimensional dynamic reinjection model for parameter compensation.

5. A system for treating foundation pit dewatering, characterized in that, It includes a data module, a parameter module, a control module, and a recharge module, among which: The data module is configured to acquire various types of data from sensors installed at preset locations around and inside the foundation pit. These data include water level data, pore water pressure data, and deformation data. The data is then integrated with meteorological data and geological survey data to establish a spatiotemporal database. The parameter module is configured to predict the first water level change within a preset time period based on the data in the spatiotemporal database using a preset LSTM neural network model, and to invert the soil parameters using a machine learning soil parameter inversion algorithm based on the three-dimensional seepage field dynamic simulation engine. The control module is configured to use a variable frequency pump group collaborative control algorithm to coordinate the pump group according to the first water level change, and dynamically adjust the pump group operating parameters to drain water. The recharge module is configured to control the recharge pressure and water quality based on the second water level change in the foundation pit after drainage and the soil parameters. The process of retrieving soil parameters using a machine learning-based soil parameter inversion algorithm based on a three-dimensional seepage field dynamic simulation engine includes: Using a three-dimensional seepage field dynamic simulation engine, the seepage field in the foundation pit is numerically simulated based on the finite element method to obtain dynamic change data of the seepage field. The dynamic change data of the seepage field are analyzed and processed using a soil parameter inversion algorithm based on machine learning. By constructing a mapping relationship between inversion parameters and displacement, soil parameters, including permeability coefficient and porosity, are retrieved. The step of using a variable frequency pump group collaborative control algorithm to collaboratively control the pump group based on the first water level change, and dynamically adjusting the pump group operating parameters for drainage, includes: Based on the first water level change, determine the amount of water that needs to be discharged from the foundation pit; Using a variable frequency pump group collaborative control algorithm, the operating parameters of each pump group are calculated based on the required water discharge volume. These operating parameters include speed, flow rate, and power. Based on the calculated operating parameters, the operating status of each pump unit is dynamically adjusted to achieve coordinated operation of the pump units. The control of recharge pressure and water quality based on the second water level change in the foundation pit after drainage and the soil parameters includes: Based on the gradient distribution characteristics of the second water level change, a three-dimensional dynamic recharge model is established in combination with the permeability coefficient in the soil parameters. The three-dimensional dynamic recharge model is input into a multi-objective optimization algorithm, and the pressure threshold and flow ratio of each recharge well are calculated under the constraint of groundwater level balance. The pressure output of each reinjection pump group is dynamically adjusted by a fuzzy PID controller based on the zone pressure threshold of each reinjection well.

6. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. Both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-4.

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