Foundation pit dewatering treatment method and system, electronic equipment and storage medium

Abstract

The invention discloses a foundation pit dewatering treatment method and system, electronic equipment and a storage medium, and relates to the field of foundation pit dewatering treatment. The method comprises the following steps: acquiring various data from sensors mounted at preset positions around a foundation pit and inside the foundation pit, integrating the various data with meteorological data and geological survey data, and establishing a space-time database; 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 according to the three-dimensional seepage field dynamic simulation engine, using a soil parameter inversion algorithm of machine learning to invert soil parameters; according to the first water level change, a variable frequency pump group cooperative control algorithm is used for cooperative control over the pump set, and pump set operation parameters are dynamically adjusted for drainage; and controlling the recharge pressure and the water quality according to the second water level change of the foundation pit after drainage and the soil parameters. According to the technical scheme, efficient and intelligent treatment of foundation pit dewatering is achieved.

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, system, electronic equipment and storage medium. Background Art

[0002] In modern construction projects, with the accelerated advancement of urbanization, the height of buildings continues to increase, and the depth of foundation pit excavation also increases, which brings unprecedented challenges to groundwater control.

[0003] Currently, widely used methods for excavation dewatering primarily include regular manual inspections, automated pumps with fixed thresholds, and simple remote monitoring platforms. While these methods can achieve dewatering to a certain extent, they suffer from significant deficiencies in accuracy, real-time performance, and adaptability. Existing excavation dewatering systems lack intelligent analytical capabilities, making it difficult to accurately predict and respond to sudden groundwater level fluctuations. This can lead to over-extraction of water resources or waterlogging, impacting construction progress and safety while also increasing maintenance costs.

[0004] Therefore, how to achieve efficient and intelligent treatment of foundation pit dewatering while ensuring safety has become an urgent problem to be solved. Summary of the Invention

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

[0006] In a first aspect of the present application, a method for treating foundation pit dewatering is provided, which is applied to a foundation pit dewatering treatment platform. The method comprises: Acquire various data from sensors installed at preset locations around and within the foundation pit, including water level data, pore water pressure data, and deformation data, and integrate these data 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 in the future, and soil parameters are inverted using a machine learning soil parameter inversion algorithm based on a three-dimensional seepage field dynamic simulation engine; Based on the first water level change, the pump group is collaboratively controlled using a variable frequency pump group collaborative control algorithm to dynamically adjust the pump group operating parameters to drain water; The recharge pressure and water quality are controlled according to the second water level change of the foundation pit after drainage and the soil parameters.

[0007] Optionally, the soil parameters are inverted using a machine learning soil parameter inversion algorithm based on a three-dimensional seepage field dynamic simulation engine, including: Using a three-dimensional seepage field dynamic simulation engine and the finite element method, the seepage field in the foundation pit is numerically simulated to obtain dynamic change data of the seepage field; The soil parameter inversion algorithm based on machine learning is used to analyze and process the dynamic change data of the seepage field; By constructing a mapping relationship between the inversion parameters and the displacement, the soil parameters of the soil body are inverted, and the soil parameters include permeability and porosity.

[0008] Optionally, the three-dimensional seepage field dynamic simulation engine is used to perform numerical simulation of the seepage field in the foundation pit based on the finite element method to obtain dynamic change data of the seepage field, including: Dividing the foundation pit area into a plurality of finite element units, and determining the soil type, permeability coefficient, and porosity of each finite element unit based on the geological survey data and the various data; Establishing a seepage equation of the seepage field according to the soil type, permeability coefficient, and porosity of each finite element unit, solving the seepage equation by a finite element method, and obtaining dynamic change data of the foundation pit, wherein the dynamic change data includes water pressure and flow velocity seepage field parameters at different positions; The dynamically changing data is updated and adjusted in real time according to the water level changes, pumping rate and recharge flow rate during the foundation pit dewatering process.

[0009] Optionally, inverting soil parameters of the soil by constructing a mapping relationship between inversion parameters and displacements includes: Based on the dynamic change data of the seepage field, a mapping relationship model between inversion parameters and displacement is established, wherein the mapping relationship model is used to describe the nonlinear relationship between soil parameters and displacement; The mapping relationship model is used to convert the dynamic change data of the seepage field into predicted values of soil parameters.

[0010] Optionally, the step of using a variable frequency pump group collaborative control algorithm to collaboratively control the pump group according to the first water level change and dynamically adjusting the pump group operating parameters to perform drainage includes: determining the amount of water that needs to be discharged from the foundation pit according to the first water level change; Using a variable frequency pump group collaborative control algorithm to calculate the operating parameters of each pump group according to the amount of water to be discharged, the operating parameters include speed, flow rate and power; According to the calculated operating parameters, the operating status of each pump group is dynamically adjusted to achieve coordinated operation of the pump groups.

[0011] Optionally, controlling the recharge pressure and water quality according to the second water level change of the foundation pit after drainage and the soil parameters includes: Based on the gradient distribution characteristics of the second water level change and in combination with the permeability coefficient in the soil parameters, a three-dimensional dynamic recharge model is established; The three-dimensional dynamic recharge model is input into a multi-objective optimization algorithm, and the zone pressure threshold and flow ratio of each recharge well are calculated with groundwater level equilibrium as a constraint condition; The pressure output of each recharging pump group is dynamically adjusted through a fuzzy PID controller according to the zone pressure threshold of each recharging well.

[0012] Optionally, the controlling of the recharge pressure and water quality according to the second water level change of the foundation pit after drainage and the soil parameters further includes: In the recharge pipeline, a multispectral water quality sensor array is used to collect turbidity, conductivity and suspended solids concentration indicators; When any indicator exceeds the allowable threshold of the corresponding stratum in the geological survey data, the suspended solids concentration is reduced by adjusting the backwash frequency of the recharge well filter, and when the conductivity exceeds the standard for a preset time, the ion exchange device is activated to improve the water quality; The processed real-time water quality data is fed back to the three-dimensional dynamic recharge model for parameter compensation.

[0013] In a second aspect of the present application, a foundation pit dewatering processing system is provided, comprising a data module, a parameter module, a control module, and a recharge module, wherein: a data module configured to acquire various data from sensors installed at preset locations around and inside the foundation pit, including water level data, pore water pressure data, and deformation data, and integrate the various data with meteorological data and geological survey data to establish a spatiotemporal database; a parameter module configured to predict the first water level change within a preset future time period using a preset LSTM neural network model based on the data in the spatiotemporal database, and to invert soil parameters using a soil parameter inversion algorithm based on machine learning based on a three-dimensional seepage field dynamic simulation engine; a control module configured to collaboratively control the pump group using a variable frequency pump group collaborative control algorithm according to the first water level change, and dynamically adjust the operating parameters of the pump group to perform drainage; The recharge module is configured to control the recharge pressure and water quality according to the second water level change of the foundation pit after drainage and the soil parameters.

[0014] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0015] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Using an LSTM neural network model to predict the first water level change within a preset time in the future can effectively process time series data, capture long-term dependencies in water level changes, and improve prediction accuracy. Through its internal gating mechanisms, such as the forget gate, input gate, and output gate, the LSTM model can adaptively adjust the flow of information, thereby better processing the complex patterns and dynamic changes in water level data. 2. Combined with the three-dimensional seepage field dynamic simulation engine, it can more accurately simulate the dynamic changes of the seepage field in the foundation pit, providing more precise data support for the inversion of soil parameters. Through the finite element method, numerical simulation of the seepage field in the foundation pit can obtain more detailed seepage field parameters, such as water pressure and flow rate at different locations, thereby improving the accuracy of soil parameter inversion; 3. Based on the predicted first water level change, the variable frequency pump group collaborative control algorithm is used to coordinate the pump group and dynamically adjust the pump group operating parameters to drain water. This can achieve efficient coordinated operation of the pump group and improve drainage efficiency. By real-time monitoring of water level changes in the foundation pit and dynamic changes in the seepage field data, the operating parameters of the pump group, such as speed, flow rate, and power, are dynamically adjusted to achieve coordinated operation of the pump group and improve drainage efficiency. 4. Controlling the recharge pressure and water quality based on the second water level change and soil parameters of the foundation pit after drainage 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, combined with 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 zone pressure threshold, thereby achieving precise control of the recharge process; 5. Deploy a multispectral water quality sensor array in the recharge pipeline to collect turbidity, conductivity, and suspended solids concentration indicators in real time, enabling timely monitoring of recharge water quality changes and providing real-time data support for water quality control. When water quality indicators exceed the permitted threshold, a two-stage treatment mechanism is triggered to modify the water quality by adjusting the backwash frequency of the recharge well filter or activating the ion exchange device to ensure that the recharge water quality meets the requirements; 6. A recharge efficiency assessment matrix has been established, encompassing indicators such as the formation water absorption attenuation coefficient, chemical compatibility index, and hydraulic fracturing risk value. This matrix enables real-time assessment of the recharge system's effectiveness, enabling timely identification of potential issues and the implementation of appropriate measures. When the attenuation coefficient exceeds the warning value, a well rotation plan is automatically generated, maintaining system performance by switching active recharge well combinations. When the risk value reaches a critical level, a three-dimensional seepage field dynamic simulation engine is used to verify the hydraulic gradient, and a viscoelastic constitutive model is introduced to modify the upper limit of the recharge pressure, thereby enhancing the system's adaptability and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the method for treating foundation pit dewatering disclosed in the embodiment of the present application; Figure 2 It is a module schematic diagram of the foundation pit dewatering treatment system disclosed in the embodiment of the present application; Figure 3 This is a structural diagram of an electronic device disclosed in an embodiment of the present application.

[0018] Explanation of reference numerals: 201, data module; 202, parameter module; 203, control module; 204, recharging module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0019] In order 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0020] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0021] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0022] This embodiment discloses a method for processing foundation pit dewatering, which is applied to a foundation pit dewatering processing platform. Figure 1 This is a flow chart of the method for treating foundation pit dewatering disclosed in the embodiment of the present application, such as Figure 1 As shown, the method includes the following steps: S101. Acquire various data from sensors installed at preset locations around and within the foundation pit, including water level data, pore water pressure data, and deformation data, and integrate the various data with meteorological data and geological survey data to establish a spatiotemporal database. S102: using a preset LSTM neural network model to predict a first water level change within a preset future time based on the data in the spatiotemporal database, and using a machine-learning soil parameter inversion algorithm to invert soil parameters based on a three-dimensional seepage field dynamic simulation engine; S103: Based on the first water level change, using a variable frequency pump group collaborative control algorithm to collaboratively control the pump group and dynamically adjust the pump group operating parameters to drain water; S104. Controlling the recharge pressure and water quality according to the second water level change of the foundation pit after drainage and the soil parameters.

[0023] Sensors installed at pre-set locations around and within the foundation pit collect various data, including water level data, pore water pressure data, and deformation data. These sensors monitor water level and pore water pressure changes within the foundation pit in real time, as well as deformation of the surrounding soil, providing essential data for subsequent analysis. This sensor data is integrated with meteorological and geological survey data to create a spatiotemporal database. Meteorological data includes rainfall, evaporation, and air temperature, while geological survey data includes soil structure, soil type, and permeability coefficient. This integration of data provides a comprehensive understanding of the hydrogeological conditions and environmental factors of the foundation pit, providing rich data support for subsequent water level prediction and soil parameter inversion. Based on the data in the spatiotemporal database, a pre-set LSTM (Long Short-Term Memory) neural network model is used to predict the first water level change within a pre-set timeframe. The LSTM neural network model effectively processes time series data, capturing long-term dependencies in water level changes and enabling accurate future water level predictions. Based on a three-dimensional seepage field dynamic simulation engine, a machine learning-based soil parameter inversion algorithm is used to invert soil parameters. The three-dimensional dynamic seepage field simulation engine uses the finite element method to numerically simulate the seepage field within the foundation pit, generating dynamic seepage field data. A machine-learning-based soil parameter inversion algorithm analyzes and processes this dynamic seepage field data, constructing a mapping relationship between inversion parameters and displacement, and inverting soil parameters such as permeability and porosity. Based on the predicted first water level change, a variable frequency pump group coordinated control algorithm is used to coordinate the pump groups. This algorithm calculates the operating parameters of each pump group, including speed, flow rate, and power, based on the required water discharge volume. By dynamically adjusting the operating status of each pump group, coordinated operation is achieved, improving drainage efficiency and saving energy. Based on the coordinated control of the pump groups, the operating parameters of the pump groups are dynamically adjusted to ensure drainage. By monitoring drainage results in real time and adjusting pump group operating parameters based on actual drainage conditions, the efficiency and stability of the drainage process are ensured. The recharge pressure is controlled based on the second water level change and soil parameters of the foundation pit after drainage. By establishing a three-dimensional dynamic recharge model, combining the gradient distribution characteristics of the second water level and the permeability coefficient of soil parameters, a multi-objective optimization algorithm was input. Using groundwater level equilibrium as a constraint, the zoned pressure thresholds and flow rate ratios for each recharge well were calculated. A fuzzy PID (Proportional-Integral-Derivative) controller dynamically adjusts the pressure output of the recharge pump group, ensuring that the actual recharge pressure curve tracks the zoned pressure thresholds, ensuring the rationality and safety of the recharge process. A multispectral water quality sensor array was deployed in the recharge pipeline to collect real-time turbidity, conductivity, and suspended solids concentration indicators. If any indicator exceeds the permissible threshold for the corresponding formation as determined by geological survey data, a two-stage processing mechanism is triggered.The primary treatment reduces suspended solids concentration by adjusting the backwash frequency of the recharge well filter. If conductivity consistently exceeds the standard in the secondary treatment, the ion exchange device is activated to improve water quality. Real-time water quality data after treatment is fed back into the 3D dynamic recharge model for parameter compensation, ensuring that recharge water quality meets requirements.

[0024] Optionally, the soil parameters are inverted using a machine learning soil parameter inversion algorithm based on a three-dimensional seepage field dynamic simulation engine, including: Using a three-dimensional seepage field dynamic simulation engine and the finite element method, the seepage field in the foundation pit is numerically simulated to obtain dynamic change data of the seepage field; The soil parameter inversion algorithm based on machine learning is used to analyze and process the dynamic change data of the seepage field; By constructing a mapping relationship between the inversion parameters and the displacement, the soil parameters of the soil body are inverted, and the soil parameters include permeability and porosity.

[0025] The three-dimensional dynamic seepage field simulation engine numerically simulates the seepage field within a foundation pit using the finite element method. The finite element method divides the foundation pit area into multiple finite element cells. Parameters such as soil type, permeability coefficient, and porosity for each cell are determined based on geological survey data and sensor data. By solving the seepage equation, dynamic data on seepage field parameters such as water pressure and flow velocity at different locations within the foundation pit are obtained. This data reflects the changes in hydraulic gradients and flow paths within the foundation pit, providing basic data for subsequent soil parameter inversion. A machine-learning soil parameter inversion algorithm analyzes and processes this dynamic seepage field data. This data includes the temporal and spatial variations of seepage field parameters such as water pressure and flow velocity. The algorithm learns the characteristics and patterns of this data to extract characteristic information related to soil parameters. For example, by analyzing the changing trends of water pressure and the distribution of flow velocity, a preliminary estimate of the soil's permeability characteristics and pore structure can be made. By constructing a mapping model between inversion parameters and displacement, dynamic seepage field data is converted into predicted soil parameter values. Mapping models describe the nonlinear relationship between soil parameters and displacement. For example, by establishing a mapping model between inverse parameters and displacement based on dynamic seepage field data, the soil's permeability and porosity can be predicted. The permeability reflects the soil's ability to penetrate water, while the porosity reflects the proportion of pores in the soil. These parameters are important for foundation pit dewatering and soil stability analysis.

[0026] Using a three-dimensional dynamic seepage field simulation engine and the finite element method, numerical simulations of the seepage field within the foundation pit are performed, generating dynamic seepage field data. This data, including seepage field parameters such as water pressure and flow velocity, provides rich information for soil parameter inversion. Machine learning-based soil parameter inversion algorithms, such as the particle swarm optimization algorithm combined with multi-output least squares support vector regression (PSO-MLSSVR) or a BP neural network, are used to analyze and process the dynamic seepage field data. Machine learning algorithms can automatically learn patterns and regularities in the data, improving the accuracy and reliability of the inversion. By constructing a mapping relationship between inversion parameters and displacement, the dynamic seepage field data is linked to soil parameters. This mapping relationship more accurately reflects the relationship between soil parameters and the seepage field, further improving the accuracy of the inversion. Accurate soil parameters support more informed decision-making by the foundation pit dewatering treatment platform. For example, during drainage, the pump operating parameters can be adjusted based on soil parameters and water level fluctuations to achieve efficient drainage. During recharge, the recharge pressure and water quality can be controlled based on soil parameters and water level fluctuations to maintain hydraulic balance within the foundation pit. Accurate soil parameter inversion enables a better understanding of the seepage field within the foundation pit, thereby optimizing the drainage process. For example, rationally adjusting the pump operating parameters can improve drainage efficiency and save energy. Furthermore, controlling recharge pressure and water quality can prevent problems such as ground subsidence or tilting of surrounding buildings caused by improper recharge.

[0027] Optionally, the three-dimensional seepage field dynamic simulation engine is used to perform numerical simulation of the seepage field in the foundation pit based on the finite element method to obtain dynamic change data of the seepage field, including: Dividing the foundation pit area into a plurality of finite element units, and determining the soil type, permeability coefficient, and porosity of each finite element unit based on the geological survey data and the various data; Establishing a seepage equation of the seepage field according to the soil type, permeability coefficient, and porosity of each finite element unit, solving the seepage equation by a finite element method, and obtaining dynamic change data of the foundation pit, wherein the dynamic change data includes water pressure and flow velocity seepage field parameters at different positions; The dynamically changing data is updated and adjusted in real time according to the water level changes, pumping rate and recharge flow rate during the foundation pit dewatering process.

[0028] Based on geological survey data and various monitoring data, a detailed analysis of the soil type in the excavation area is conducted. Different soil types have different physical and mechanical properties, such as permeability and porosity, which significantly influence the distribution and variability of the seepage field. For example, sand typically has a higher permeability, while clay has a relatively lower permeability. Based on the analysis of these soil properties, the excavation area is divided into multiple finite element cells. The size and shape of each finite element can be adjusted based on the actual situation to ensure simulation accuracy and computational efficiency. The division process takes into account factors such as the heterogeneous and anisotropic properties of the soil, as well as the geological boundary conditions surrounding the excavation. For each finite element, parameters such as soil type, permeability, and porosity are determined based on geological survey data and various monitoring data. These parameters are the foundation of the numerical simulation of the seepage field, and accurate values enhance the reliability of the simulation results. For example, drilling and sampling analysis can provide permeability and porosity data at different depths and locations. The seepage equation for the seepage field is established based on parameters such as soil type, permeability, and porosity for each finite element. The seepage equation is typically based on Darcy's law and the continuity equation, describing the movement of water in soil. For example, for saturated soil, Darcy's law can be used to describe the relationship between the water infiltration velocity and the hydraulic gradient; for unsaturated soil, factors such as the soil moisture characteristic curve and the permeability function need to be considered. The seepage equation is solved using the finite element method. The finite element method discretizes the continuous seepage field into a finite number of elements. By assuming an approximate solution within each element, the partial differential equation is transformed into a system of algebraic equations. This system of algebraic equations is then solved to obtain seepage field parameters such as water pressure and flow velocity within each element. During the solution process, factors such as the nonlinear characteristics of the soil and the coupling effects between the seepage field and the stress field need to be considered to improve the accuracy of the simulation results. Solving the seepage equation using the finite element method yields dynamic data on seepage field parameters such as water pressure and flow velocity at different locations within the foundation pit. These data reflect the spatiotemporal variations of the seepage field during the pit dewatering process, providing an important basis for subsequent soil parameter inversion and dewatering treatment decisions. For example, the simulation yields water pressure curves in the central and peripheral areas of the pit, as well as flow velocity distributions at different depths. During the pit dewatering process, data such as water level changes, pumping rates, and recharge rates are monitored in real time and fed into the 3D seepage field dynamic simulation engine. This monitoring data reflects the actual operating status of the dewatering treatment system and provides a basis for dynamic adjustments to the seepage field simulation. For example, water level sensors capture real-time water level changes within the pit, while flow meters monitor pumping and recharge rates. This monitoring data is fused with finite element simulation data to update and adjust the dynamic seepage field data in real time. This data fusion improves the accuracy and reliability of the seepage field simulation, making it more realistic.For example, based on monitored water level 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 parameter inversion and dewatering treatment decisions are re-performed. Through real-time updates and adjustments, the simulation results can more accurately reflect the actual conditions during foundation pit dewatering, providing support for optimizing dewatering treatment plans. For example, based on the adjusted seepage field data, soil parameters such as permeability and porosity are recalculated, providing a basis for dynamic adjustment of pump unit operating parameters.

[0029] Dividing the excavation area into multiple finite element units enables more detailed simulation of the seepage field at different locations within the excavation. This division takes into account the heterogeneity of the soil within the excavation, 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 are determined. These parameters are crucial for seepage field simulation, and accurate parameters enhance the reliability of the simulation results. Solving the seepage equation using the finite element method yields dynamic data on seepage field parameters, such as water pressure and flow velocity, at different locations within the excavation. This data reflects the temporal and spatial variations of the seepage field, providing a foundation for subsequent analysis and processing. The dynamic data is updated and adjusted in real time based on water level changes, pumping rates, and recharge flows during the excavation dewatering process. This real-time update mechanism ensures that the simulation results are consistent with the actual situation, improving the timeliness and practicality of the simulation. The dynamic seepage field data obtained through this process provides a rich data foundation for the soil parameter inversion algorithm used in machine learning. This data helps the algorithm more accurately map the inversion parameters to displacements, improving the accuracy of soil parameter inversion. Accurate data on the dynamic changes in the seepage field can improve the accuracy of soil parameter inversion, providing more accurate soil property information for foundation pit dewatering. This is crucial for developing appropriate dewatering plans, optimizing pumping unit operating parameters, and controlling recharge pressure and water quality.

[0030] Optionally, inverting soil parameters of the soil by constructing a mapping relationship between inversion parameters and displacements includes: Based on the dynamic change data of the seepage field, a mapping relationship model between inversion parameters and displacement is established, wherein the mapping relationship model is used to describe the nonlinear relationship between soil parameters and displacement; The mapping relationship model is used to convert the dynamic change data of the seepage field into predicted values of soil parameters.

[0031] Using a three-dimensional dynamic seepage field simulation engine and the finite element method, the seepage field within the foundation pit is numerically simulated, generating dynamic seepage field data. This data includes seepage field parameters such as water pressure and flow velocity at different locations. Sensors are used to obtain deformation data of the soil surrounding the foundation pit, reflecting the displacement of the soil under the influence of the seepage field. A complex nonlinear relationship exists between the dynamic seepage field data and soil parameters. This nonlinear relationship can be described by establishing a mapping model between inversion parameters and displacement. Machine learning algorithms, such as the particle swarm optimization algorithm combined with a multi-output least squares support vector regression machine (PSO-MLSSVM) or a BP neural network, are used to construct the mapping model. These algorithms can learn and discover patterns from large amounts of data, predict soil behavior under different stress conditions, and ultimately develop a soil constitutive model. The dynamic seepage field data and displacement data are preprocessed, including data cleaning and normalization, to ensure data quality and consistency. The preprocessed data is then input into the mapping model for model training. Through training, the model learns the mapping relationship between the dynamic seepage field data and soil parameters. Using the trained mapping model, the dynamic changes in the seepage field are converted into predicted values for soil parameters, such as permeability 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.

[0032] Based on the dynamic seepage field data, a mapping model between inversion parameters and displacements was established. This model describes the nonlinear relationship between soil parameters and displacements, providing a more accurate mathematical basis for the inversion of soil parameters. Using this mapping model, the dynamic seepage field data is converted into predicted values of soil parameters. This method more accurately reflects 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 and porosity, provide accurate soil property information for foundation pit dewatering. These parameters are crucial for developing appropriate dewatering plans, optimizing pump operating parameters, and controlling recharge pressure and water quality. Accurate soil parameters enable the foundation pit dewatering platform to make more informed decisions. For example, during the drainage process, pump operating parameters can be adjusted based on soil parameter and water level fluctuations to achieve efficient drainage. During the recharge process, recharge pressure and water quality can be controlled based on soil parameter and water level fluctuations to maintain hydraulic balance within the foundation pit. Accurate soil parameter inversion enables a better understanding of the seepage field within the foundation pit, thereby optimizing the dewatering process. For example, rationally adjusting the operating parameters of the pump unit can improve drainage efficiency and save energy. Furthermore, 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. Accurate soil parameters can improve the effectiveness of foundation pit dewatering treatment. For example, during the recharge process, controlling the recharge pressure and water quality can better maintain hydraulic balance within the foundation pit, prevent engineering risks caused by excessive water level fluctuations, and ensure the safety and stability of the foundation pit dewatering treatment.

[0033] Optionally, the step of using a variable frequency pump group collaborative control algorithm to collaboratively control the pump group according to the first water level change and dynamically adjusting the pump group operating parameters to perform drainage includes: determining the amount of water that needs to be discharged from the foundation pit according to the first water level change; Using a variable frequency pump group collaborative control algorithm to calculate the operating parameters of each pump group according to the amount of water to be discharged, the operating parameters include speed, flow rate and power; According to the calculated operating parameters, the operating status of each pump group is dynamically adjusted to achieve coordinated operation of the pump groups.

[0034] The amount of water required to be discharged from the foundation pit is calculated based on the predicted first water level change, the pit geometry, and the rate of water level change. This step ensures the accuracy and rationality of the discharge volume and avoids over- or under-drainage. The variable frequency pump group coordinated control algorithm, based on variable frequency speed regulation technology, dynamically adjusts the operating parameters of the pump groups based on actual demand. By adjusting the pump speed to adjust the flow rate and head, it offers advantages such as energy savings and stable operation. Based on the required discharge volume, the variable frequency pump group coordinated control algorithm calculates the operating parameters of each pump group, including speed, flow rate, and power. These calculations ensure efficient operation of the pump groups during the drainage process while avoiding energy waste. Based on these calculated operating parameters, the operating status of each pump group is adjusted in real time. The variable frequency drive (VFD) controls the speed of the pump group motors, achieving stepless speed regulation and ensuring efficient operation during the drainage process. The variable frequency pump group coordinated control algorithm enables coordinated operation between pump groups. Each pump group dynamically adjusts its operating parameters based on actual demand, ensuring efficient and stable drainage. This collaborative working mode not only improves drainage efficiency, but also extends the service life of the pump unit.

[0035] Based on the predicted first water level change, the amount of water required to be discharged from the foundation pit can be accurately determined. This precise water volume calculation avoids over-drainage or under-drainage, improving drainage efficiency and effectiveness. Using a variable frequency pump group coordinated control algorithm, the operating parameters of each pump group, including speed, flow rate, and power, are dynamically calculated based on the required water volume. This dynamic adjustment ensures efficient operation of the pump group during the drainage process, further improving drainage efficiency. Variable frequency speed regulation technology adjusts the pump speed according to actual demand, avoiding the energy waste associated with traditional fixed-speed pumps. Variable frequency speed regulation enables the motor to operate at the optimal speed, reducing energy loss and improving efficiency. Using variable frequency speed regulation can reduce motor energy consumption by over 30%, resulting in significant energy savings. Dynamic adjustment of the pump group's operating parameters avoids prolonged operation under high load, reducing equipment wear and failure. Variable frequency speed regulation enables soft starting and stopping of the pumps, minimizing shock during startup and shutdown, and extending equipment life. The variable frequency pump group coordinated control algorithm monitors and adjusts the operating status of the pump group 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 group, improving the stability of the entire drainage system. The variable frequency pump group collaborative control algorithm enables collaborative operation between pump groups. Each pump group dynamically adjusts its operating parameters based on actual needs to ensure the efficiency and stability of the drainage process. This collaborative working method effectively avoids drainage problems caused by the failure of a single pump group, improving the stability of the entire drainage system. The variable frequency pump group collaborative control algorithm monitors and adjusts the operating status of the pump groups in real time, ensuring the stability and reliability of the drainage process. This real-time monitoring and adjustment mechanism effectively responds to various emergencies during the drainage process, further improving system stability.

[0036] Optionally, controlling the recharge pressure and water quality according to the second water level change of the foundation pit after drainage and the soil parameters includes: Based on the gradient distribution characteristics of the second water level change and in combination with the permeability coefficient in the soil parameters, a three-dimensional dynamic recharge model is established; The three-dimensional dynamic recharge model is input into a multi-objective optimization algorithm, and the zone pressure threshold and flow ratio of each recharge well are calculated with groundwater level equilibrium as a constraint condition; The pressure output of each recharging pump group is dynamically adjusted through a fuzzy PID controller according to the zone pressure threshold of each recharging well.

[0037] Sensors monitor the water level changes within the foundation pit in real time, capturing secondary water level change data after drainage. This data reflects the dynamic changes in the water level within the foundation pit. Based on this secondary water level change data, the gradient distribution characteristics of the water level within the foundation pit are calculated. This gradient distribution characteristic describes the rate and direction of water level change in space, providing a foundation for the subsequent development of a recharge model. The foundation pit area is divided into multiple finite element cells. Based on geological survey data and various monitoring data, the soil type, permeability coefficient, and porosity of each finite element are determined. Combining the gradient distribution characteristics of the secondary water level change with the permeability coefficient within the soil parameters, a three-dimensional dynamic recharge model is developed. This model simulates 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 well, subject to groundwater level equilibrium as a constraint. Optimization objectives include achieving a balanced groundwater level distribution, operating efficiency of the recharge well, and minimizing energy consumption. The zoning pressure threshold and flow ratio of each recharge well are calculated through the optimization algorithm. The output data of the three-dimensional dynamic recharge model is used as the input of the multi-objective optimization algorithm, including seepage field parameters such as water pressure and flow velocity at different locations in the foundation pit. The zoning pressure threshold and flow ratio of each recharge well are calculated through the multi-objective optimization algorithm. The zoning pressure threshold refers to the upper and lower pressure limits of each recharge well in different areas, and the flow ratio refers to the flow distribution ratio between the recharge wells. A fuzzy PID controller is designed to dynamically adjust the pressure output of each recharge pump group according to the zoning pressure threshold of each recharge well. The fuzzy PID controller can automatically adjust the operating parameters of the pump group according to the deviation between the actual recharge pressure and the zoning pressure threshold. The fuzzy PID controller monitors the pressure output of the recharge pump group in real time, and dynamically adjusts the operating parameters of the pump group according to the deviation between the actual recharge pressure and the zoning pressure threshold to ensure that the recharge pressure curve tracks the zoning pressure threshold.

[0038] A three-dimensional dynamic recharge model was established based on the gradient distribution characteristics of the second water level change and the permeability coefficient of the soil parameters. This model accurately describes the hydraulic conductivity characteristics of the recharge process, providing a scientific basis for controlling recharge pressure and flow rate. By considering the spatial gradient of the 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 was input into a multi-objective optimization algorithm, and the zoned pressure thresholds and flow rate ratios for each recharge well were calculated, with groundwater level equilibrium as a constraint. The multi-objective optimization algorithm comprehensively considers multiple optimization objectives, such as groundwater level equilibrium, recharge efficiency, and energy consumption, to determine the optimal recharge solution. The optimization algorithm ensures that the recharge pressure and flow rate of each recharge well achieve the optimal recharge effect while maintaining groundwater level equilibrium. Based on the zoned pressure thresholds of each recharge well, a fuzzy PID controller dynamically adjusts the pressure output of each recharge pump group. The fuzzy PID controller combines the advantages of fuzzy logic and PID control. It can adaptively adjust control parameters based on the deviation between actual pressure and threshold values, achieving precise control of recharge pressure. This control method can quickly respond to water level changes, ensuring the efficiency and stability of the recharge process. A multi-objective optimization algorithm, using groundwater level equilibrium as a constraint, ensures that the recharge process does not cause excessive groundwater level fluctuations. This helps maintain a stable groundwater level around the foundation pit and minimizes environmental impacts such as ground subsidence and building tilt.

[0039] 7 Optionally, the controlling of the recharge pressure and water quality according to the second water level change of the foundation pit after drainage and the soil parameters further includes: In the recharge pipeline, a multispectral water quality sensor array is used to collect turbidity, conductivity and suspended solids concentration indicators; When any indicator exceeds the allowable threshold of the corresponding stratum in the geological survey data, the suspended solids concentration is reduced by adjusting the backwash frequency of the recharge well filter, and when the conductivity exceeds the standard for a preset time, the ion exchange device is activated to improve the water quality; The processed real-time water quality data is fed back to the three-dimensional dynamic recharge model for parameter compensation.

[0040] Deploying a multispectral water quality sensor array in the recharge pipeline enables real-time collection of water quality indicators such as turbidity, conductivity, and suspended solids concentration. Based on spectral technology, these sensors can simultaneously and accurately measure multiple water quality parameters, providing a more comprehensive water quality analysis. For example, the iSpecWQ-UV / VIS multi-parameter spectral water quality measurement module from Lyson Optics enables simultaneous online monitoring of multiple parameters, including turbidity and nitrate nitrogen. When suspended solids concentration exceeds the permitted threshold, the backwash frequency of the recharge well filter is adjusted to reduce the concentration. Backwashing effectively removes suspended solids from the filter surface and restores filter performance. When conductivity exceeds the permitted threshold for a preset period of time, an ion exchange device is activated to improve water quality. Ion exchange devices use ion exchange resins to exchange ions with water, removing inorganic salts and thus reducing conductivity. For example, a downstream regenerative ion exchanger is a common ion exchange device that effectively removes various inorganic salts from water. This processed, real-time water quality data is fed back into the three-dimensional dynamic recharge model for parameter compensation. Real-time monitoring and feedback ensure that recharge water quality meets requirements and avoids adverse impacts on the surrounding environment. For example, the Memosens Wave CAS80E full-spectrum sensor outputs real-time measurement results of relevant analytical parameters, supporting multiple standard parameter analyses for water and wastewater monitoring. Based on real-time water quality data, parameter compensation is applied to the three-dimensional dynamic recharge model to ensure its accuracy and reliability. This helps optimize the recharge process and improve its efficiency and effectiveness.

[0041] In the recharge pipeline, a multispectral water quality sensor array collects turbidity, conductivity, and suspended solids concentration indicators. Multispectral water quality sensors can simultaneously and accurately detect multiple water quality parameters, providing a more comprehensive analysis of water quality. This real-time monitoring method can promptly identify water quality issues and provide data support for subsequent control measures. Based on the zoned pressure threshold of each recharge well, a fuzzy-PID controller dynamically adjusts the pressure output of each recharge pump group. Combining the advantages of fuzzy logic and PID control, the fuzzy-PID controller can adaptively adjust control parameters based on the deviation between actual pressure and the threshold, achieving precise control of recharge pressure. This control method can quickly respond to water level changes, ensuring the efficiency and stability of the recharge process. When the suspended solids concentration exceeds the allowable threshold, the backwash frequency of the recharge well filter is adjusted to reduce the suspended solids concentration. This treatment method effectively removes suspended solids from the recharge water, reduces clogging of the recharge well, and improves recharge efficiency. When the conductivity exceeds the standard for a preset period of time, the ion exchange device is activated to improve the water quality. The ion exchange device effectively removes ions from the water, reducing conductivity and improving water quality. This treatment method ensures that the quality of the reinjection water meets the requirements and reduces the impact on the environment surrounding the foundation pit. The processed real-time water quality data is fed back to the three-dimensional dynamic reinjection model for parameter compensation. This feedback mechanism can promptly update the water quality parameters in the model, improving the model's accuracy and reliability. Through parameter compensation, the model can better predict the hydraulic conductivity characteristics during the reinjection process, providing a more scientific basis for controlling reinjection pressure and flow. The use of multispectral water quality sensor arrays and ion exchange devices reduces dependence on chemical reagents. Multispectral water quality sensors can achieve water quality monitoring without the addition of chemical reagents, and ion exchange devices also eliminate the need for chemical reagents to modify water quality. This helps reduce secondary pollution and protect the environment.

[0042] This embodiment also discloses a foundation pit dewatering treatment system. Figure 2 This is a module diagram of the foundation pit dewatering treatment system disclosed in the embodiment of the present application, such as 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: The data module 201 is configured to acquire various data from sensors installed at preset locations around and inside the foundation pit, including water level data, pore water pressure data, and deformation data, and integrate the various data with meteorological data and geological survey data to establish a spatiotemporal database; A parameter module 202 is configured to predict a first water level change within a preset time in the future using a preset LSTM neural network model based on the data in the spatiotemporal database, and to invert soil parameters using a soil parameter inversion algorithm based on machine learning based on a three-dimensional seepage field dynamic simulation engine; A control module 203 is configured to collaboratively control the pump group using a variable frequency pump group collaborative control algorithm according to the first water level change, and dynamically adjust the operating parameters of the pump group to perform drainage; The recharge module 204 is configured to control the recharge pressure and water quality according to the second water level change of the foundation pit after drainage and the soil parameters.

[0043] Optionally, the parameter module 202 is configured to: Using a three-dimensional seepage field dynamic simulation engine and the finite element method, the seepage field in the foundation pit is numerically simulated to obtain dynamic change data of the seepage field; The soil parameter inversion algorithm based on machine learning is used to analyze and process the dynamic change data of the seepage field; By constructing a mapping relationship between the inversion parameters and the displacement, the soil parameters of the soil body are inverted, and the soil parameters include permeability and porosity.

[0044] Optionally, the parameter module 202 is configured to: Dividing the foundation pit area into a plurality of finite element units, and determining the soil type, permeability coefficient, and porosity of each finite element unit based on the geological survey data and the various data; Establishing a seepage equation of the seepage field according to the soil type, permeability coefficient, and porosity of each finite element unit, solving the seepage equation by a finite element method, and obtaining dynamic change data of the foundation pit, wherein the dynamic change data includes water pressure and flow velocity seepage field parameters at different positions; The dynamically changing data is updated and adjusted in real time according to the water level changes, pumping rate and recharge flow rate during the foundation pit dewatering process.

[0045] Optionally, the parameter module 202 is configured to: Based on the dynamic change data of the seepage field, a mapping relationship model between inversion parameters and displacement is established, wherein the mapping relationship model is used to describe the nonlinear relationship between soil parameters and displacement; The mapping relationship model is used to convert the dynamic change data of the seepage field into predicted values of soil parameters.

[0046] Optionally, the control module 203 is configured to: determining the amount of water that needs to be discharged from the foundation pit according to the first water level change; Using a variable frequency pump group collaborative control algorithm to calculate the operating parameters of each pump group according to the amount of water to be discharged, the operating parameters include speed, flow rate and power; According to the calculated operating parameters, the operating status of each pump group is dynamically adjusted to achieve coordinated operation of the pump groups.

[0047] Optionally, the recharging module 204 is configured to: Based on the gradient distribution characteristics of the second water level change and in combination with the permeability coefficient in the soil parameters, a three-dimensional dynamic recharge model is established; The three-dimensional dynamic recharge model is input into a multi-objective optimization algorithm, and the zone pressure threshold and flow ratio of each recharge well are calculated with groundwater level equilibrium as a constraint condition; The pressure output of each recharging pump group is dynamically adjusted through a fuzzy PID controller according to the zone pressure threshold of each recharging well.

[0048] Optionally, the recharging module 204 is further configured to: In the recharge pipeline, a multispectral water quality sensor array is used to collect turbidity, conductivity and suspended solids concentration indicators; When any indicator exceeds the allowable threshold of the corresponding stratum in the geological survey data, the suspended solids concentration is reduced by adjusting the backwash frequency of the recharge well filter, and when the conductivity exceeds the standard for a preset time, the ion exchange device is activated to improve the water quality; The processed real-time water quality data is fed back to the three-dimensional dynamic recharge model for parameter compensation.

[0049] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual 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 device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0050] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0051] The communication bus 302 is used to implement the connection and communication between these components.

[0052] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0053] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0054] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0055] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, 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 a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a method for processing foundation pit dewatering.

[0056] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call the application program for the processing method of foundation pit dewatering stored in the memory 305. When executed by one or more processors 301, the electronic device executes one or more methods as in the above embodiments.

[0057] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0058] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0059] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0060] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0061] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0062] 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 memory. Based on this understanding, the technical solution of this application, or the portion 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory 305 includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a magnetic disk, or an optical disk.

[0063] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for treating foundation pit dewatering, characterized in that: Applied to a foundation pit dewatering treatment platform, the method includes: Acquire various data from sensors installed at preset locations around and within the foundation pit, including water level data, pore water pressure data, and deformation data, and integrate these data 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 in the future, and soil parameters are inverted using a machine learning soil parameter inversion algorithm based on a three-dimensional seepage field dynamic simulation engine; Based on the first water level change, the pump group is collaboratively controlled using a variable frequency pump group collaborative control algorithm to dynamically adjust the pump group operating parameters to drain water; The recharge pressure and water quality are controlled according to the second water level change of the foundation pit after drainage and the soil parameters.

2. The method for treating foundation pit dewatering according to claim 1, characterized in that: The soil parameters inversion algorithm based on the three-dimensional seepage field dynamic simulation engine and machine learning are inverted, including: Using a three-dimensional seepage field dynamic simulation engine and the finite element method, the seepage field in the foundation pit is numerically simulated to obtain dynamic change data of the seepage field; The soil parameter inversion algorithm based on machine learning is used to analyze and process the dynamic change data of the seepage field; By constructing a mapping relationship between the inversion parameters and the displacement, the soil parameters of the soil body are inverted, and the soil parameters include permeability and porosity.

3. The method for treating foundation pit dewatering according to claim 2, characterized in that: The three-dimensional seepage field dynamic simulation engine is used to perform numerical simulation of the seepage field in the foundation pit based on the finite element method to obtain dynamic change data of the seepage field, including: Dividing the foundation pit area into a plurality of finite element units, and determining the soil type, permeability coefficient, and porosity of each finite element unit based on the geological survey data and the various data; Establishing a seepage equation of the seepage field according to the soil type, permeability coefficient, and porosity of each finite element unit, solving the seepage equation by a finite element method, and obtaining dynamic change data of the foundation pit, wherein the dynamic change data includes water pressure and flow velocity seepage field parameters at different positions; The dynamically changing data is updated and adjusted in real time according to the water level changes, pumping rate and recharge flow rate during the foundation pit dewatering process.

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

5. The method for treating foundation pit dewatering according to claim 1, characterized in that: The method of using a variable frequency pump group collaborative control algorithm to collaboratively control the pump group according to the first water level change and dynamically adjusting the pump group operating parameters to perform drainage includes: determining the amount of water that needs to be discharged from the foundation pit according to the first water level change; Using a variable frequency pump group collaborative control algorithm to calculate the operating parameters of each pump group according to the amount of water to be discharged, the operating parameters include speed, flow rate and power; According to the calculated operating parameters, the operating status of each pump group is dynamically adjusted to achieve coordinated operation of the pump groups.

6. The method for treating foundation pit dewatering according to claim 1, characterized in that: The controlling of the recharge pressure and water quality according to the second water level change of the foundation pit after drainage and the soil parameters includes: Based on the gradient distribution characteristics of the second water level change and in combination with the permeability coefficient in the soil parameters, a three-dimensional dynamic recharge model is established; The three-dimensional dynamic recharge model is input into a multi-objective optimization algorithm, and the zone pressure threshold and flow ratio of each recharge well are calculated with groundwater level equilibrium as a constraint condition; The pressure output of each recharging pump group is dynamically adjusted through a fuzzy PID controller according to the zone pressure threshold of each recharging well.

7. The method for treating foundation pit dewatering according to claim 6, characterized in that: The controlling of the recharge pressure and water quality according to the second water level change of the foundation pit after drainage and the soil parameters further comprises: In the recharge pipeline, a multispectral water quality sensor array is used to collect turbidity, conductivity and suspended solids concentration indicators; When any indicator exceeds the allowable threshold of the corresponding stratum in the geological survey data, the suspended solids concentration is reduced by adjusting the backwash frequency of the recharge well filter, and when the conductivity exceeds the standard for a preset time, the ion exchange device is activated to improve the water quality; The processed real-time water quality data is fed back to the three-dimensional dynamic recharge model for parameter compensation.

8. A foundation pit dewatering treatment system, characterized in that: It includes data module, parameter module, control module and recharge module, among which: a data module configured to acquire various data from sensors installed at preset locations around and inside the foundation pit, including water level data, pore water pressure data, and deformation data, and integrate the various data with meteorological data and geological survey data to establish a spatiotemporal database; a parameter module configured to predict the first water level change within a preset future time period using a preset LSTM neural network model based on the data in the spatiotemporal database, and to invert soil parameters using a soil parameter inversion algorithm based on machine learning based on a three-dimensional seepage field dynamic simulation engine; a control module configured to collaboratively control the pump group using a variable frequency pump group collaborative control algorithm according to the first water level change, and dynamically adjust the operating parameters of the pump group to perform drainage; The recharge module is configured to control the recharge pressure and water quality according to the second water level change of the foundation pit after drainage and the soil parameters.

9. An electronic device, characterized in that: The electronic device comprises a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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