A pile foundation mud construction silt dewatering parameter self-adaptive method and system

Abstract

The application discloses a pile foundation mud construction sludge dewatering parameter self-adaptive method and system, belongs to the technical field of building foundation engineering and foundation treatment, and comprises collecting initial physical characteristic data of pile foundation mud, extracting initial water content and internal structure impedance characteristic; collecting current operation condition data of a dewatering execution mechanism in real time, and constructing a multi-dimensional operation state matrix; based on the initial water content, the internal structure impedance characteristic and the multi-dimensional operation state matrix, a dewatering parameter dynamic optimization model is established and executed to generate target dewatering regulation and control instructions; the instructions are issued to a dewatering control programmable logic controller to drive the execution mechanism to perform sludge solid-liquid separation operation. The method of extracting initial characteristics of mud and real-time working condition data to construct a dynamic optimization model can realize self-adaptive and accurate regulation and control of dewatering parameters in the solid-liquid separation operation, and improve dewatering energy efficiency and operation safety.

Description

Technical Field

[0001] This invention relates to the field of building foundation engineering and foundation treatment technology, and in particular to an adaptive method and system for sludge dewatering parameters in pile foundation slurry construction. Background Technology

[0002] During pile foundation construction in building engineering and geological exploration, a large amount of waste mud with high water content is inevitably generated. This mud contains a large number of extremely fine suspended particles and clay minerals, with a complex internal structure and extremely strong water binding force, resulting in an extremely slow natural settlement rate. If this waste mud is not efficiently separated into solid and liquid and dehydrated and consolidated, it will not only occupy a large area of ​​construction site, but also cause serious mud and water pollution to the surrounding ecological environment, thus restricting the overall progress of foundation treatment and foundation engineering construction.

[0003] Currently, for the dewatering treatment of pile foundation sludge, construction sites commonly use traditional mechanical filter pressing, natural drying, or simple chemical precipitation dewatering with the addition of flocculants. In these conventional treatment processes, operators usually set the operating power, dewatering time, and electromechanical control parameters of the filter press based on subjective experience or fixed equipment operating parameters, or use constant current electroosmotic dewatering equipment to forcibly remove moisture from the sludge.

[0004] However, traditional mud dewatering methods cannot adapt to the dynamic changes in the physical properties of mud under different formation conditions. Fixed-parameter dewatering equipment often suffers from incomplete dewatering, localized overheating of electrode plates, and even mud cake coking when dealing with muds with significant differences in water content, viscosity, and internal impedance. Due to the lack of multi-dimensional monitoring of real-time mud conditions and adaptive dynamic adjustment mechanisms for parameters, traditional methods not only have high dewatering energy consumption and low consolidation efficiency, but also easily cause equipment wear and energy waste, failing to meet the stringent requirements of modern green and efficient foundation engineering construction.

[0005] In related technologies, Chinese invention patent CN113443812B discloses a method for rapidly adjusting the flocculation conditions and dewatering process of cyanobacterial sludge. The method includes: under the same moisture content of the cyanobacterial sludge and with different amounts of flocculant added, analyzing and measuring data on the specific resistance of the filter cake (defined, experimental, and production). Through fitting analysis of the experimental data and a mathematical model, a mathematical model is established between the specific resistance of the filter cake and the experimental filter cake under different amounts of flocculant added. Based on the mathematical model, the required amount of flocculant is calculated, and the flocculant is added to the cyanobacterial sludge, stirred, and then pumped into a plate and frame filter press for dewatering. Summary of the Invention

[0006] To address the aforementioned issues, this invention provides an adaptive method and system for sludge dewatering parameters in pile foundation slurry construction. By extracting the initial moisture content and internal structural impedance characteristics to construct a dynamic optimization model for dewatering parameters, it is possible to achieve adaptive and precise control of parameters such as DC bias voltage and ultrasonic duty cycle during sludge solid-liquid separation operations, thereby improving the energy efficiency and operational safety of sludge dewatering.

[0007] The above objectives can be achieved through the following approach: An adaptive method for sludge dewatering parameters in pile foundation slurry construction includes: collecting initial physical characteristic data of the pile foundation slurry; extracting initial moisture content and internal structural impedance characteristics based on the initial physical characteristic data; collecting real-time operating condition data of the dewatering actuator; constructing a multi-dimensional operation state matrix based on the current operating condition data; establishing a dynamic optimization model for dewatering parameters based on the initial moisture content, the internal structural impedance characteristics, and the multi-dimensional operation state matrix; executing the dynamic optimization model to generate a target dewatering control command; and sending the target dewatering control command to a dewatering control programmable logic controller (PLC), which then drives the dewatering actuator to perform sludge solid-liquid separation operations.

[0008] Optionally, the acquisition of initial physical characteristic data of the pile foundation mud, and the extraction of initial moisture content and internal structural impedance characteristics based on the initial physical characteristic data, includes: controlling a microwave moisture detection probe to emit detection microwaves into the pile foundation mud to obtain microwave reflection loss characteristics; extracting a preset moisture content mapping dictionary to match and search the microwave reflection loss characteristics to generate the initial moisture content; triggering an ultrasonic detection component to inject high-frequency sound waves into the pile foundation mud and reading the transmitted sound wave energy signal; calculating the energy difference gradient between the high-frequency sound wave and the transmitted sound wave energy signal to generate acoustic impedance attenuation characteristics; and using the acoustic impedance attenuation characteristics as the internal structural impedance characteristics.

[0009] Optionally, the real-time acquisition of the current operating condition data of the dehydration actuator and the construction of a multi-dimensional operation state matrix based on the current operating condition data include: real-time extraction of the inter-electrode voltage gradient features fed back by the electroosmotic anode and electroosmotic cathode plates deployed inside the dehydration actuator, extraction of the standing wave resonant frequency features fed back by the piezoelectric transducer, and extraction of the polar zone drainage velocity features fed back by the drainage flow sensor; time-domain alignment processing of the inter-electrode voltage gradient features, the standing wave resonant frequency features, and the polar zone drainage velocity features according to the acquisition time sequence to generate a multi-physics operating condition vector; extraction of multiple multi-physics operating condition vectors within a continuous time sliding window, and splicing of the multiple multi-physics operating condition vectors using spatial tensors to construct the multi-dimensional operation state matrix.

[0010] Optionally, establishing a dynamic optimization model for dewatering parameters based on the initial moisture content, the internal structural impedance characteristics, and the multi-dimensional operation state matrix includes: calling a historical mud dewatering database, extracting historical moisture content, historical structural impedance characteristics, historical operation state matrix, and corresponding calibrated dewatering electrical parameter labels from the historical mud dewatering database; inputting the historical moisture content, historical structural impedance characteristics, and historical operation state matrix into an initial deep learning network for feature nonlinear mapping to generate estimated dewatering electrical parameters; extracting a cost function calculation module to calculate the loss value between the estimated dewatering electrical parameters and the calibrated dewatering electrical parameter labels to generate a dewatering energy efficiency error; and triggering a backpropagation algorithm based on the dewatering energy efficiency error to update the node weights of the initial deep learning network to generate the dynamic optimization model for dewatering parameters.

[0011] Optionally, the step of inputting the historical water content, the historical structural impedance features, and the historical operational state matrix into an initial deep learning network for feature nonlinear mapping to generate estimated dehydration electrical parameters includes: activating the feature encoding layer of the initial deep learning network to perform numerical normalization on the historical water content and the historical structural impedance features to generate an initial state representation vector; starting a multi-scale convolutional kernel to scan the local receptive field of the historical operational state matrix to extract hidden field coupling feature maps; using a pooling layer to compress the spatial dimension of the hidden field coupling feature maps; performing a fully connected fusion of the dimensionality-reduced hidden field coupling feature maps and the initial state representation vector to construct a comprehensive state vector; and inputting the comprehensive state vector into a prediction mapping layer to generate the estimated dehydration electrical parameters.

[0012] Optionally, the step of executing the dynamic optimization model for dehydration parameters to generate the target dehydration control command includes: inputting the initial moisture content, the internal structural impedance characteristics, and the multi-dimensional operating state matrix at the current moment into the dynamic optimization model for dehydration parameters for forward reasoning, and outputting the target DC bias voltage characteristics and the target ultrasonic duty cycle characteristics; and encapsulating the target DC bias voltage characteristics and the target ultrasonic duty cycle characteristics according to a preset industrial control bus communication protocol specification to construct the target dehydration control command.

[0013] Optionally, the method further includes: collecting real-time temperature distribution characteristics and real-time consolidation resistivity characteristics of the slurry inside the dewatering actuator; determining whether the real-time temperature distribution characteristics exceed the upper limit of the safe temperature threshold or whether the real-time consolidation resistivity characteristics reach the electroosmotic shutdown hardening threshold; if it is determined that the real-time temperature distribution characteristics exceed the upper limit of the safe temperature threshold or the real-time consolidation resistivity characteristics reach the electroosmotic shutdown hardening threshold, generating an anti-coking intervention command and sending it to the dewatering control programmable logic controller.

[0014] Optionally, the step of generating the anti-coking intervention command and sending it to the dehydration control programmable logic controller includes: executing an over-limit judgment logic command to extract the temperature overflow difference of the real-time temperature distribution characteristics exceeding the upper limit of the safe temperature threshold; querying a preset safety protection action mapping table based on the temperature overflow difference and the real-time consolidation resistivity characteristics, and matching and generating a power supply duty cycle reduction command or an electrode reverse pulse cleaning command; combining the power supply duty cycle reduction command and the electrode reverse pulse cleaning command to construct the anti-coking intervention command, and sending the anti-coking intervention command to the dehydration control programmable logic controller based on the underlying hardware interface.

[0015] Optionally, the method further includes: receiving a batch dewatering completion interruption signal from the dewatering control programmable logic controller; extracting the initial moisture content, internal structural impedance characteristics, and multi-dimensional operating state matrix of the batch based on the batch dewatering completion interruption signal; and reading the final shear strength characteristics of the dewatered cake detected and fed back by the physical shear force sensor; concatenating and encapsulating the initial moisture content, internal structural impedance characteristics, multi-dimensional operating state matrix, and final shear strength characteristics of the dewatered cake to construct a dewatering working condition experience sample; transmitting the dewatering working condition experience sample to the parameter update database; and triggering an incremental learning mechanism to fine-tune the hyperparameters of the dewatering parameter dynamic optimization model using the dewatering working condition experience sample.

[0016] Based on the same inventive concept, the present invention also provides an adaptive system for sludge dewatering parameters in pile foundation slurry construction, the system comprising: The initial feature extraction module is used to collect the initial physical feature data of the pile foundation mud, and extract the initial water content and internal structural impedance features based on the initial physical feature data; The operating condition matrix construction module is used to collect the current operating condition data of the dehydration actuator in real time and construct a multi-dimensional operating status matrix based on the current operating condition data. The optimization model establishment module is used to establish a dynamic optimization model for dehydration parameters based on the initial moisture content, the internal structural impedance characteristics, and the multidimensional operating state matrix. The control command generation module is used to execute the dynamic optimization model of the dehydration parameters and generate the target dehydration control command; The dewatering execution drive module is used to send the target dewatering control command to the dewatering control programmable logic controller, and drive the dewatering actuator to perform sludge solid-liquid separation operation based on the dewatering control programmable logic controller.

[0017] Compared with the prior art, the present invention has the following advantages: 1. High-precision sensing of mud physical properties and dynamic characterization of multi-dimensional operating conditions were achieved. Microwave moisture detection and ultrasonic detection technologies were used to accurately extract the initial water content and internal acoustic impedance attenuation characteristics of the sludge, and a multi-dimensional operating state matrix was constructed by combining real-time feedback data from electroosmotic plates and piezoelectric transducers. This design overcomes the problem of distorted operating condition assessment caused by single parameter monitoring in traditional dewatering processes, laying a solid data foundation for subsequent dewatering parameter control.

[0018] 2. Improved parameter optimization efficiency and dewatering energy efficiency in the sludge solid-liquid separation process. A dynamic parameter optimization model was constructed by using a deep learning network to perform feature nonlinear mapping and autonomous learning on massive historical sludge dewatering data. This model can forward infer the optimal DC bias voltage and ultrasonic duty cycle based on the real-time state of the sludge, achieving automated and adaptive control of the dewatering operation, reducing dewatering energy consumption, and improving the final dried cake quality.

[0019] 3. By monitoring the temperature distribution and consolidation resistivity inside the dewatering actuator in real time, anti-coking intervention commands can be automatically generated when facing overheating or hardening thresholds, effectively avoiding equipment damage and electrode plate failure. Simultaneously, after each batch of dewatering is completed, new samples can be used to fine-tune the hyperparameters of the optimization model, allowing the model to continuously adapt to changes in mud characteristics under different geological conditions, ensuring the long-term operational effectiveness of the equipment.

[0020] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating an adaptive method for sludge dewatering parameters in pile foundation slurry construction according to an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of the structure of an adaptive system for sludge dewatering parameters in pile foundation slurry construction according to an embodiment of the present invention.

[0024] Figure 3This is a schematic diagram illustrating the mapping relationship between microwave reflection loss and initial moisture content of an adaptive method and system for dewatering sludge during pile foundation slurry construction, according to an embodiment of the present invention.

[0025] Figure 4 This is a schematic diagram of the spatial attenuation model curve of the internal acoustic energy of a pile foundation slurry construction sludge dewatering parameter adaptive method and system according to an embodiment of the present invention.

[0026] Figure 5 This is a thermodynamic diagram illustrating the time-series evolution of a multidimensional operational state matrix of an adaptive method and system for sludge dewatering parameters in pile foundation slurry construction, according to an embodiment of the present invention.

[0027] Figure 6 This is a schematic diagram of the training error convergence curve of the dynamic optimization model for dewatering parameters of a pile foundation slurry construction silt dewatering parameter adaptive method and system according to an embodiment of the present invention.

[0028] Figure 7 This is a schematic diagram of an adaptive method and system for sludge dewatering parameters in pile foundation slurry construction, and a dual safety threshold exceeding intervention model for the later stage of slurry dewatering, according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Reference Figure 1 One embodiment of the present invention proposes an adaptive method for sludge dewatering parameters in pile foundation slurry construction. By extracting the initial water content and internal structural impedance characteristics to construct a dynamic optimization model for dewatering parameters, it is possible to achieve adaptive and precise control of parameters such as DC bias voltage and ultrasonic duty cycle during sludge solid-liquid separation, thereby improving the energy efficiency and operational safety of sludge dewatering.

[0031] The method described in this embodiment specifically includes: Collect initial physical characteristic data of pile foundation mud, and extract initial water content and internal structural impedance characteristics based on the initial physical characteristic data; Real-time acquisition of current operating condition data of the dehydration actuator, and construction of a multi-dimensional operation status matrix based on the current operating condition data; Based on the initial moisture content, the internal structural impedance characteristics, and the multidimensional operating state matrix, a dynamic optimization model for dehydration parameters is established. The dynamic optimization model for the dehydration parameters is executed to generate the target dehydration control command. The target dehydration control command is sent to the dehydration control programmable logic controller, and the dehydration actuator is driven to perform sludge solid-liquid separation operation based on the dehydration control programmable logic controller.

[0032] Specifically, the initial physical characteristics of the pile foundation slurry are collected to extract its initial moisture content and internal structural impedance characteristics. Real-time operating data of the dewatering actuator is collected to construct a multi-dimensional operational state matrix. Based on the initial characteristics and operational state matrix, a dynamic optimization model for dewatering parameters is established and executed to generate target dewatering control commands. These commands are then sent to the dewatering control programmable logic controller (PLC) to drive the dewatering actuator to complete the solid-liquid separation of the sludge. By comprehensively analyzing the initial moisture content, internal structural impedance characteristics, and real-time multi-dimensional operational states of the slurry, the limitations of fixed parameters in traditional processes are overcome. Dynamic optimization and adaptive adjustment of dewatering parameters are achieved, enabling precise matching of optimal treatment parameters for slurry in different states and sending these parameters to the control system. This results in a high degree of automation and precise control of the sludge solid-liquid separation operation, effectively improving the overall efficiency and reliability of slurry dewatering treatment.

[0033] Optionally, the initial physical characteristic data of the pile foundation mud is collected, and the initial water content and internal structural impedance characteristics are extracted based on the initial physical characteristic data, including: The microwave moisture detection probe is controlled to emit detection microwaves into the pile foundation mud to obtain microwave reflection loss characteristics. A preset moisture content mapping dictionary is extracted and matched with the microwave reflection loss characteristics to generate the initial moisture content. The ultrasonic detection component is triggered to inject high-frequency sound waves into the pile foundation mud and read the transmitted sound wave energy signal. The energy difference gradient between the high-frequency sound wave and the transmitted sound wave energy signal is calculated to generate an acoustic impedance attenuation feature, which is then used as the impedance feature of the internal structure.

[0034] Specifically, a microwave moisture detection probe is controlled to emit probe microwaves into the pile foundation mud to obtain microwave reflection loss characteristics. A pre-set moisture content mapping dictionary is extracted, and the extracted microwave reflection loss characteristics are matched and searched to generate the initial moisture content. Subsequently, an ultrasonic detection component is triggered to inject high-frequency sound waves into the pile foundation mud, and the transmitted sound wave energy signal after penetrating the mud is read at the receiving end. The energy difference gradient between the high-frequency sound wave and the transmitted sound wave energy signal is calculated to generate acoustic impedance attenuation characteristics, which are then directly used as internal structural impedance characteristics for subsequent data fusion. The specific formula for generating the acoustic impedance attenuation characteristics is as follows: ; A represents the acoustic impedance attenuation characteristic. This represents the initial energy of the injected high-frequency sound waves. This represents the transmitted sound wave energy signal read, and d represents the mud penetration path length between the transmitter and receiver of the ultrasonic detection component. For example... Figure 3 The diagram illustrates the linear interpolation relationship between microwave reflection loss characteristics in the preset moisture content mapping dictionary and the initial moisture content. The curve clearly shows the interpolation addressing process in the calculation example where a microwave loss of 35 watts precisely corresponds to a moisture content of 75%. Figure 4 The diagram illustrates the nonlinear physical trend of acoustic energy attenuation with increasing penetration distance when high-frequency sound waves propagate within a mud medium. It visually reflects the acoustic impedance characteristic extraction process where an initial energy of 100 joules attenuates to 40 joules after traversing a 0.5-meter path. The microwave moisture detection probe is a sensing device that measures the moisture content of a substance by utilizing the difference in loss characteristics of electromagnetic waves propagating in media with different dielectric constants. The detection microwave is an electromagnetic wave with a predetermined high-frequency band and directional radiation properties. The microwave reflection loss characteristic is the numerical difference between the transmitted microwave signal power and the power captured by the receiving module after reflection from the mud surface. The moisture content mapping dictionary is a benchmark data table comparing microwave power loss values ​​pre-entered into the controller's storage unit with the moisture content of the mud entity. The initial moisture content is the percentage of water mass within the mud at the current detection moment relative to the total mass of the mud. The ultrasonic detection component is acoustic detection hardware comprising a piezoelectric transmitter and a piezoelectric receiver. High-frequency sound waves are mechanical waves with frequencies exceeding conventional acoustic bands and strong penetrating properties in high-density fluids. The transmitted acoustic wave energy signal is the energy scale value of the acoustic beam after passing through the mud medium and reaching the receiving end, where it is converted into an electrical signal. The energy difference gradient is the spatial rate of change of the difference between the input and output acoustic wave energy over a unit propagation distance. The acoustic impedance attenuation characteristic is a physical index that quantitatively reflects the strength of the absorption and scattering of sound waves by the complex internal structure of the mud. The internal structural impedance characteristic is a comprehensive physical parameter characterizing the density of the solid particle arrangement and the water connectivity obstruction characteristics within the pores of the mud.

[0035] For example, a microwave moisture detection probe emits 2.45 GHz microwaves into the pile foundation mud. The probe's original transmission power is recorded as 50 watts, and the interface reflection power measured by the probe's receiving module is 15 watts. Subtracting the two directly yields a microwave reflection loss characteristic value of 35 watts. A pre-set moisture content mapping dictionary is extracted for data matching. The dataset of the moisture content mapping dictionary is pre-calibrated in the laboratory according to the standard drying and weighing method. The dictionary's internal rules specify that 30 watts of loss corresponds to 70% moisture content, and 40 watts of loss corresponds to 80% moisture content. A linear interpolation algorithm is automatically invoked to perform calculations. Based on the proportional relationship of the loss value within the linear interval, the interpolation result is obtained, accurately generating an initial moisture content value of 75%. After completing the moisture content extraction task, the processor immediately triggers the ultrasonic detection component to continuously inject high-frequency sound waves at a frequency of 50 kHz into the mud. The sensor measures the initial energy of the high-frequency sound waves. The energy level is 100 joules. The length L of the mud penetration path between the probe's transmitter and receiver is precisely set to 0.5 meters. The transmitted sound wave energy signal extracted by the receiver... The value is 40 joules. The system substitutes all the extracted real-time data into the energy difference gradient formula to perform the calculation. The specific calculation process is as follows: The calculated value is 120, with the physical unit being joules per meter. This value of 120 joules per meter is directly generated as an acoustic impedance attenuation characteristic and seamlessly used as an internal structural impedance characteristic for register assignment. This quantitatively characterizes the current particle viscosity and agglomeration state of the mud, thus providing a highly reliable multidimensional basic data source for the adaptive control of the subsequent dewatering model.

[0036] Optionally, the real-time acquisition of the current operating condition data of the dehydration actuator, and the construction of a multi-dimensional operation status matrix based on the current operating condition data, includes: The inter-electrode voltage gradient characteristics fed back by the electroosmotic anode plate and electroosmotic cathode plate deployed inside the dehydration actuator are extracted in real time; the standing wave resonant frequency characteristics fed back by the piezoelectric transducer are extracted; and the polar zone drainage flow velocity characteristics fed back by the drainage flow sensor are extracted. The inter-electrode voltage gradient characteristics, the standing wave resonant frequency characteristics, and the polar region drainage velocity characteristics are time-domain aligned according to the acquisition time series to generate a multi-physics field operating condition vector. Multiple multiphysics field condition vectors within a continuous time sliding window are extracted, and the multiple multiphysics field condition vectors are spliced ​​together using spatial tensors to construct the multidimensional operation state matrix.

[0037] Specifically, the inter-electrode voltage gradient features fed back from the electroosmotic anode and cathode plates deployed inside the dehydration actuator are extracted in real time; the standing wave resonant frequency features fed back from the piezoelectric transducer are extracted; and the drainage velocity features in the electrode zone fed back from the drainage flow sensor are extracted. The inter-electrode voltage gradient features, standing wave resonant frequency features, and electrode zone drainage velocity features are time-domain aligned according to the acquisition time series to generate a multi-physics operating condition vector. Multiple multi-physics operating condition vectors within a continuous time sliding window are extracted, and these vectors are spliced ​​using a spatial tensor to construct a multi-dimensional operating state matrix. To accurately characterize the physical relationship between the electroosmotic driving force and the drainage effect and to generate supplementary features in the operating condition vector, a state coupling feature quantity formula is introduced: ; This represents the state coupling feature quantity at the current moment. This represents the characteristics of the inter-electrode voltage gradient. Indicates the resonant frequency characteristics of the standing wave. This indicates the characteristics of polar drainage velocity. This represents a preset tolerance coefficient to prevent the denominator from being zero. Combining this supplementary feature, the formula for concatenating the multi-dimensional job state matrix is ​​as follows: ; M represents the multidimensional job state matrix, and n represents the total number of time steps contained in the continuous-time sliding window. This represents a comprehensive column vector containing all monitored features. For example... Figure 5 The diagram, presented in the form of a normalized thermodynamic matrix, visually illustrates the multidimensional operational state evolution trajectory of inter-electrode voltage gradient, standing wave resonant frequency, polar zone drainage velocity, and state coupling characteristics within a sliding window of five consecutive time steps, under spatial tensor splicing. The dewatering actuator is the physical device entity that performs forced separation of water from sludge. The electroosmotic anode and cathode plates are conductive electrode assemblies that apply a directional DC electric field to the sludge. The inter-electrode voltage gradient characteristic is the ratio of the voltage difference between the plates to the distance between them. The piezoelectric transducer is a device that converts electrical energy into high-frequency mechanical vibration. The standing wave resonant frequency characteristic is the interference frequency value of the stable standing wave formed by the sound wave within the dewatering chamber. The drainage flow sensor is a measuring instrument that detects the volumetric flow rate of the discharged liquid in real time. The polar zone drainage velocity characteristic is the volumetric velocity of the liquid phase flowing through the measured cross-section. Time-domain alignment processing is a data normalization algorithm that synchronizes sensor data from different sampling frequencies to the same time reference. The multi-physics operating condition vector is a one-dimensional data array that aggregates electric field, sound field, and flow field parameters at the same moment. A continuous-time sliding window is a fixed-length observation interval that moves along a time series at fixed steps. Spatial tensor splicing is an algebraic operation that combines multiple low-dimensional data along a new dimension into a high-dimensional matrix. A multidimensional operational state matrix is ​​a two-dimensional data table that records the evolution of multiphysics parameters over time.

[0038] For example, the signals from each sensor are synchronized in real time, and a preset tolerance coefficient is set. The default value is 0.001 to ensure the stability of mathematical calculations. At a certain sampling time t, the electrode spacing of the dehydration actuator is set to 0.5 meters, and the measured voltage between the electrodes is 25 volts. The characteristic voltage gradient between the electrodes is then calculated. 50 volts per meter; piezoelectric transducer feedback standing wave resonant frequency characteristics The frequency is 20,000 Hz; the discharge flow sensor measures the characteristics of the discharge velocity in the polar region. The flow rate is 4.999 liters per minute. Substituting this into the formula for the state-coupled characteristic quantity, the calculation process is as follows: The state coupling characteristic was obtained. The value is 200,000. The system will arrange the extracted features in sequence to generate a multiphysics condition vector. The total number of time steps n in the continuous-time sliding window is set to a default value of 5. The multiphysics condition vectors generated at the most recent 5 consecutive time points are extracted, and a spatial tensor concatenation operation is performed to construct a multidimensional operational state matrix M. This involves combining these 5 independent column vectors side-by-side into a matrix of size M. The digital matrix thus completely and quantitatively preserves the evolution trajectory of the dehydration electromechanical operating conditions within that time window.

[0039] Optionally, establishing a dynamic optimization model for dehydration parameters based on the initial moisture content, the internal structural impedance characteristics, and the multidimensional operating state matrix includes: Call the mud dewatering history database and extract the historical moisture content, historical structural impedance characteristics, historical operation status matrix and corresponding calibration dewatering electrical parameter labels from the mud dewatering history database; The historical moisture content, the historical structural impedance characteristics, and the historical operational state matrix are input into the initial deep learning network for feature nonlinear mapping to generate estimated dehydration electrical parameters. The cost function extraction module calculates the loss value between the estimated dehydration electrical parameters and the calibrated dehydration electrical parameter labels to generate a dehydration energy efficiency error. Based on the dehydration energy efficiency error, the backpropagation algorithm is triggered to update the node weights of the initial deep learning network, thereby generating a dynamic optimization model for the dehydration parameters.

[0040] Specifically, the historical database of mud dewatering is accessed to extract historical moisture content, historical structural impedance features, historical operational state matrices, and corresponding calibrated dewatering electrical parameter labels. These historical moisture content, structural impedance features, and operational state matrices are then input into the initial deep learning network for nonlinear feature mapping to generate estimated dewatering electrical parameters. The cost function calculation module calculates the loss value between the estimated and calibrated dewatering electrical parameters to generate the dewatering energy efficiency error. Based on this error, a backpropagation algorithm is triggered to update the node weights of the initial deep learning network, generating a dynamic optimization model for the dewatering parameters. Considering data fitting requirements, the following specific calculation formula for quantifying the dewatering energy efficiency error is introduced: ; This represents the dehydration efficiency error, where N represents the batch sample size. This represents the estimated dehydration electrical parameters for the i-th sample. This represents the label for the calibrated dehydration electrical parameters of the i-th sample. This represents the regularization weight coefficient. This represents the node weights of the initial deep learning network. For example... Figure 6The diagram illustrates the autonomous optimization and evolution process of the initial deep learning network after accessing the historical dewatering dataset. The dewatering efficiency error rapidly decreased from an initial 16.1 and smoothly converged to a minimum range as the number of backpropagation algorithm iterations increased. The historical mud dewatering database is a pre-established and stored set of structured information containing multi-dimensional monitoring data and corresponding optimal control strategies for the entire lifecycle of past pile foundation mud dewatering operations. Historical moisture content, historical structural impedance characteristics, and the historical operation state matrix represent the initial moisture content of the mud, acoustic attenuation resistance assessment data, and a comprehensive record of multi-physics operating conditions in past operation records, respectively. The calibrated dewatering electrical parameter labels are reference values ​​of actual electrical parameters in the historical database that have been manually verified or confirmed by long-term system optimization to achieve the best dewatering effect. The initial deep learning network is a primitive artificial neural network architecture containing multiple layers of neurons that has not yet undergone targeted data fine-tuning training. Feature nonlinear mapping is a mathematical transformation process that uses activation functions to project input layer data into a high-dimensional hidden space to extract complex nonlinear correlations. The estimated dehydration electrical parameters are the predicted values ​​of DC bias voltage and ultrasonic duty cycle calculated and derived by the network model based on the current input features. The cost function calculation module is a software logic unit that performs a quantitative evaluation of the difference between the predicted and actual values. The dehydration energy efficiency error is a specific numerical loss measure of the deviation between the model's current predicted dehydration parameters and the known optimal parameters. The backpropagation algorithm is a method that uses the chain rule of calculus to propagate the output layer error layer by layer to calculate the gradient of each layer. The node weights are adjustable multiplicative parameters that connect different neurons within the neural network and determine the signal transmission strength. The dynamic optimization model for dehydration parameters is an intelligent analysis program that can adaptively output the optimal dehydration control strategy after iterative convergence through training on massive amounts of historical data.

[0041] For example, a batch of historical records with an initial water content of 75% and an acoustic impedance attenuation characteristic of 120 joules per meter are extracted from the mud dewatering history database, and the corresponding calibrated dewatering electrical parameter labels under this characteristic state are extracted simultaneously. The value is 50 volts. The historical job status matrix, including this batch of data, is input into the initial deep learning network to perform a nonlinear mapping of features. The network's forward inference generates the estimated dehydration electrical parameters. The value is 46 volts. To evaluate network accuracy and trigger the learning mechanism, the default value for the batch size N is set to 1, and a regularization weight coefficient is set to prevent overfitting. The default value is 0.01. Let the sum of squared weights of the key nodes in the current network be set. The value is 10. The cost function calculation module is called to substitute the above value into the dehydration energy efficiency error formula for calculation. The specific calculation process is as follows: The dehydration energy efficiency error was calculated. The value is 16.1. Based on this error value of 16.1, the backpropagation algorithm is triggered to calculate the gradient and update the node weights along the network topology. After multiple iterations, the dehydration energy efficiency error decreases and stabilizes in the minimum range. This completes the model evolution and generates a dynamic optimization model for dehydration parameters with high-precision adaptive decision-making capabilities, which can be used for the accurate issuance of subsequent target control commands.

[0042] Optionally, the step of inputting the historical moisture content, the historical structural impedance characteristics, and the historical operating state matrix into the initial deep learning network for feature nonlinear mapping to generate estimated dehydration electrical parameters includes: The feature encoding layer of the initial deep learning network is activated to perform numerical normalization on the historical water content and the historical structural impedance features, thereby generating an initial state representation vector. A multi-scale convolutional kernel is activated to perform a local receptive field scan on the historical job state matrix to extract hidden field coupling feature maps. The spatial dimension of the hidden field coupling feature map is compressed using a pooling layer. The dimension-reduced hidden field coupling feature map is then fully connected and fused with the initial state representation vector to construct a comprehensive state vector. The comprehensive state vector is then input into the prediction mapping layer to generate the estimated dehydration electrical parameters.

[0043] Specifically, the feature encoding layer of the initial deep learning network is activated to numerically normalize the historical water content and historical structural impedance features, generating an initial state representation vector. Multi-scale convolutional kernels are then used to scan the local receptive field of the historical operational state matrix, extracting hidden field coupling feature maps. Pooling layers are used to compress the spatial dimension of the hidden field coupling feature maps. The dimensionality-reduced hidden field coupling feature maps are then fused with the initial state representation vector through a fully connected layer to construct a comprehensive state vector. This comprehensive state vector is input into the prediction mapping layer to generate estimated dehydration electrical parameters. To eliminate gradient divergence caused by data with different dimensions, the specific formula for numerical normalization is introduced as follows: ; The normalized eigenvalues ​​are represented by X, which represents the historical water content or historical structural impedance characteristics of the original input. This represents the minimum boundary of the corresponding feature in the mud history dataset. This represents the maximum boundary of the corresponding feature in the mud history dataset. To achieve cross-modal integration of features of different dimensions, a fully connected fusion formula for constructing the comprehensive state vector is introduced as follows: ; This represents the feature components of the generated integrated state vector. This represents the principal component scalar extracted from the initial state representation vector. This represents the principal component scalar extracted from the dimensionality-reduced hidden field coupled feature map. and The values ​​represent the corresponding mapping weights of the fusion layer, and B represents the bias term constant of the fusion network node. The feature encoding layer is the initial processing module in the neural network used to transform heterogeneous input physical quantities into a unified format hidden layer representation. Numerical normalization is a linear mathematical mapping operation that losslessly scales physical features of different dimensions and orders of magnitude to a specific dimensionless interval. The initial state representation vector is a low-dimensional mathematical coordinate representation of the inherent physical properties of mud in the feature space after scale unification. The multi-scale convolutional kernel is a group of two-dimensional filtering matrices containing various receptive field sizes to capture multi-granular spatiotemporal correlation features. Local receptive field scanning is the operation of sliding the convolutional filter on the input matrix and performing local dot product operations to extract regional texture features. The hidden field coupled feature map is a high-dimensional tensor reflecting the deep features of implicit correlations between multiple physical fields, generated after convolution of the working state matrix. The pooling layer is a structural unit that reduces the number of feature map nodes while retaining significant information to prevent network overfitting through downsampling operations. Spatial dimension is the dimension representing the feature width or time series span in the tensor data. Fully connected fusion is a process of integrating local features from multiple different sources through matrix flattening and global information interaction via a fully connected network. The comprehensive state vector is the final multidimensional representation column vector aggregating the static initial properties of the mud and the global features of dynamic electromechanical operating conditions. The prediction mapping layer is a terminal network neuron module that receives the comprehensive feature input and outputs the predicted values ​​of the final control parameters through a nonlinear activation function.

[0044] For example, the original input feature X value of historical moisture content is obtained as 75%, and the historical structural impedance feature X value is obtained as 120 joules per meter. To accelerate network convergence, a maximum moisture content value is set based on the statistical distribution boundary of the historical dehydration dataset. 95% and the minimum value It is 35%; similarly, the maximum value of the acoustic impedance characteristic is set. 220 joules per meter and a minimum value The value is 20 joules per meter. The system substitutes the moisture content data into the numerical normalization formula to perform the calculation. The specific calculation process is as follows: The calculated result is 0.667; substituting the impedance data into the same formula, the specific calculation process is as follows: The calculated result is 0.50. These two numerical values ​​are encoded to generate an initial state representation vector, and their average value is extracted to generate the principal component scalar. The value is 0.58. Meanwhile, the aforementioned 4x5 historical job state matrix, after being scanned by the local receptive field of multi-scale convolutional kernels, generates a hidden field coupling feature map. Subsequently, the pooling layer uses a maximum downsampling mechanism to compress its spatial dimension, extracting the principal component scalar of the dimensionality-reduced hidden field coupling feature map. The value is 0.80. Then, a fully connected fusion operation is performed, setting the fusion mapping weights according to the network's pre-training architecture. The default value is 10, which is the mapping weight. The default value is 20, and the default value of the bias term constant B is set to 5. Substitute the data into the formula and perform the calculation. The specific calculation process is as follows: The characteristic components of the comprehensive state vector are calculated. The value is 26.8. Finally, this comprehensive feature component is input into the prediction mapping layer. The prediction mapping layer outputs the final estimated dehydration electrical parameters based on the activation function, which are then connected to the aforementioned 46-volt prediction value to complete the subsequent error calculation.

[0045] Optionally, the step of executing the dynamic optimization model for the dehydration parameters and generating the target dehydration control command includes: The initial moisture content, internal structural impedance characteristics, and multidimensional operating state matrix at the current moment are input into the dynamic optimization model for dehydration parameters for forward reasoning, and the target DC bias voltage characteristics and target ultrasonic duty cycle characteristics are output. The target DC bias voltage characteristics and the target ultrasonic duty cycle characteristics are encoded and encapsulated according to the preset industrial control bus communication protocol specifications to construct the target dehydration control command.

[0046] Specifically, the initial moisture content, internal structural impedance characteristics, and multi-dimensional operating state matrix at the current moment are input into the dynamic optimization model for dehydration parameters for forward inference, outputting the target DC bias voltage characteristics and the target ultrasonic duty cycle characteristics. The target DC bias voltage characteristics and the target ultrasonic duty cycle characteristics are then encoded and encapsulated according to a preset industrial control bus communication protocol specification to construct the target dehydration control command. To achieve accurate mapping and conversion from physical control parameters to digital communication messages, the following encoded digital conversion formula is introduced for calculating the message load of the underlying drive unit: ; This indicates the value of the encoded numeric register encapsulated into the message. This indicates the characteristics of the target DC bias voltage. This indicates the maximum output voltage range of the power supply hardware for the dehydration actuator; K represents the bit width and resolution of the underlying digital-to-analog converter. This represents the round-down operator. Forward inference refers to the execution stage where the trained neural network model receives real-time input data and directly derives the prediction result through layers of feedforward calculations. The target DC bias voltage characteristic is the voltage control parameter derived from the optimization model, applied across the electroosmotic electrode to provide the optimal driving force for the dehydration electric field. The target ultrasonic duty cycle characteristic is the optimal ratio of high-frequency sound wave emission time of the piezoelectric transducer within one pulse working cycle, determined by the model calculation. The industrial control bus communication protocol specification is a standardized digital communication rule defining the baud rate and frame structure for data transmission between the field controller and the underlying actuator. Message encoding and encapsulation is the software processing procedure that converts the physical control parameters of the business logic layer into a hexadecimal byte stream conforming to the communication protocol frame format requirements. The target dehydration control instruction is the final downlink control data packet containing encapsulated voltage and duty cycle control words and possessing an executable checksum. The encoded digital register value is the integer drive code directly recognized by the controller's underlying hardware digital-to-analog converter module. The maximum output voltage range is the limit supply voltage boundary value supported by the power supply module hardware of the dehydration actuator. The resolution bit width of a digital-to-analog converter is determined by the number of binary bits that determine the voltage regulation accuracy and discrete control step size.

[0047] For example, the initial water content of 75% at the current moment, the internal structural impedance characteristics of 120 joules per meter, and the aforementioned extracted and constructed multidimensional operational state matrix are all input into the dynamic optimization model for dewatering parameters, which has already undergone backpropagation weight updates. The model network performs forward inference operations through matrix multiplication of the weights of each hidden layer node and nonlinear activation mapping, and the output directly outputs the optimal target DC bias voltage characteristics for the current particle aggregation state inside the mud. The value is 46 volts, and the synchronous output target ultrasonic duty cycle characteristic value is 60%. To send the decision parameters to the dehydration control programmable logic controller, the analog physical quantities are converted into digital communication codes. Based on the equipment manual of the underlying dehydration actuator power supply hardware, the maximum output voltage range of the system hardware is set. The default value is 100 volts, and the default resolution bit width K of the digital-to-analog converter on the hardware motherboard is set to twelve bits. The system substitutes the extracted and confirmed parameters into the encoding-to-digital conversion formula to perform the calculation. The specific calculation process is as follows: That is, calculate 0.46 multiplied by 4095 and round down to obtain the value of the encoded digital register. The value is 1883. The system then converts the integer value 1883 into the corresponding hexadecimal machine control word according to the standard industrial control bus communication protocol specification. Combined with the hexadecimal machine code corresponding to a 60% duty cycle, a start device address header and a cyclic redundancy check tail code are added to both ends of the data segment to complete the entire message encoding and encapsulation process. Finally, a target dehydration control instruction with a complete communication structure is generated for the system's underlying serial bus to issue instructions quickly and without errors.

[0048] Optionally, the method further includes: The real-time temperature distribution characteristics and real-time consolidation resistivity characteristics of the mud inside the dewatering actuator were collected. Determine whether the real-time temperature distribution characteristics exceed the upper limit of the safe temperature threshold or whether the real-time consolidation resistivity characteristics reach the electroosmotic shutdown hardening threshold. If the real-time temperature distribution characteristic is determined to exceed the upper limit of the safe temperature threshold or the real-time consolidation resistivity characteristic reaches the electroosmosis shutdown hardening threshold, an anti-coking intervention command is generated and sent to the dehydration control programmable logic controller.

[0049] Specifically, the system collects real-time temperature distribution and solidification resistivity characteristics of the slurry inside the dewatering actuator; it determines whether the real-time temperature distribution exceeds the upper limit of the safe temperature threshold or whether the real-time solidification resistivity reaches the electroosmotic shutdown hardening threshold; if the real-time temperature distribution exceeds the upper limit of the safe temperature threshold or the real-time solidification resistivity reaches the electroosmotic shutdown hardening threshold, an anti-coking intervention command is generated and sent to the dewatering control programmable logic controller. To achieve quantitative monitoring of the electrical properties during the slurry solidification process, a specific calculation formula for calculating the real-time solidification resistivity characteristics is introduced as follows: ; This represents the real-time solidification resistivity characteristics. This indicates the real-time applied voltage across the two ends of the dehydration actuator. This indicates the real-time operating current in the circuit. This indicates the contact cross-sectional area between the electrode plate and the mud. This indicates the dynamic spacing between the electrodes. The real-time temperature distribution characteristic is temperature data characterizing the thermodynamic state of different regions, collected in real-time by thermal sensors deployed at multiple points within the mud. The real-time consolidation resistivity characteristic is a physical parameter characterizing the significant increase in resistivity caused by internal moisture loss and ion conduction channel disruption during the dehydration and hardening process of the mud. The upper limit of the safe temperature threshold is a critical value predetermined by the system based on the heat resistance level of the insulation material and the boiling characteristics of the mud, representing the extreme high impedance value that prevents physical coking of the mud or thermal damage to the electrode plates. The electroosmosis shutdown hardening threshold is an experimentally calibrated reference value representing an extremely high impedance where continued energization would result in unnecessary energy consumption and polarization heating, indicating that the mud has reached its optimal dehydration state. The anti-coking intervention command is a comprehensive safety control data package integrating multiple protective measures such as cooling, power reduction, and electrode cleaning. The real-time applied voltage is the potential difference applied between the positive and negative electrodes of the electroosmosis plates during dehydration operations to drive moisture migration. The real-time operating current is the charge-directed flow rate that forms a complete closed loop through the mud medium to maintain the electrochemical reaction. The contact cross-sectional area is the effective working plane area where the conductive surface of the electrode is actually embedded and maintains close physical contact with the mud. The dynamic spacing is the physical linear span between the two opposing electroosmotic plates that continuously decreases as the mud volume compresses.

[0050] For example, the device status is continuously read via an internally deployed sensor network. This includes the real-time applied voltage across the dehydration actuator. Currently operating stably at 46 volts, consistent with the predicted output characteristics, the real-time operating current in the circuit is being monitored. The value is 5 amperes. The equipment measures the contact cross-sectional area between the electrode plate and the mud. The area is fixed at 2 square meters, and the ranging module provides feedback on the dynamic distance between the plates. It has been compressed to 0.4 meters with the water drained. The formula for calculating the real-time consolidation resistivity characteristics is extracted; the specific calculation process is as follows: The real-time consolidation resistivity characteristics were calculated. The value is 46 ohm-meters. Simultaneously, the highest value of the real-time temperature distribution characteristic fed back by the thermal sensor is 75 degrees Celsius. According to the standard physical property engineering manual, the default upper limit of the safe temperature threshold is set to 85 degrees Celsius, and based on the fitting results of multiple pile foundation mud dewatering experiments, the default value of the electroosmotic shutdown hardening threshold is set to 40 ohm-meters. Executing the over-limit judgment logic operation, it is found that the real-time temperature of 75 degrees Celsius does not exceed the upper limit of the safe temperature threshold of 85 degrees Celsius, but the real-time consolidation resistivity characteristic of 46 ohm-meters has exceeded the electroosmotic shutdown hardening threshold of 40 ohm-meters. Meeting one of the two independent conditions triggers the protection mechanism. Determining that the shutdown intervention conditions are met, an anti-coking intervention command containing a duty cycle reset code is immediately generated and sent directly to the dewatering control programmable logic controller via the field communication bus, forcibly driving the equipment to cut off the electroosmotic power supply, thereby preventing internal breakdown or coking of the mud due to excessive dewatering.

[0051] Optionally, the step of generating the anti-coking intervention command and sending it to the dehydration control programmable logic controller includes: The logic instruction for judging the limit is executed to extract the temperature overflow difference where the real-time temperature distribution feature exceeds the upper limit of the safe temperature threshold. Based on the temperature overflow difference and the real-time consolidation resistivity characteristics, query the preset safety protection action mapping table, and generate a power supply duty cycle reduction command or an electrode reverse pulse cleaning command. The anti-coking intervention command is constructed by combining the power supply duty cycle reduction command with the electrode reverse pulse cleaning command, and the anti-coking intervention command is sent to the dehydration control programmable logic controller based on the underlying hardware interface.

[0052] Specifically, the execution of the over-limit judgment logic instruction extracts the temperature overflow difference exceeding the upper limit of the safe temperature threshold based on the real-time temperature distribution characteristics; based on the temperature overflow difference and the real-time consolidation resistivity characteristics, it queries a preset safety protection action mapping table to generate either a power supply duty cycle reduction instruction or an electrode reverse pulse cleaning instruction; it combines the power supply duty cycle reduction instruction and the electrode reverse pulse cleaning instruction to construct an anti-coking intervention instruction, which is then sent to the dehydration control programmable logic controller based on the underlying hardware interface. To achieve the quantitative conversion of multi-dimensional abnormal characteristics into specific intervention actions, a mathematical extraction formula for calculating the query index value of the safety protection action mapping table is introduced as follows:

[0053] This represents the temperature overflow difference. This represents the highest temperature value extracted from the real-time temperature distribution features. Indicates the upper limit of the safe temperature threshold. This represents the query index value of the security protection action mapping table. This represents the preset temperature weighting adjustment constant. This represents the preset impedance weighting adjustment constant. This represents the real-time solidification resistivity characteristics. This represents the floor function operator. For example... Figure 7The diagram uses dual Y-axis recordings to document the real-time temperature and consolidation resistivity of the sludge during the later stages of dewatering, clearly marking the 85°C safe temperature threshold and the 40 ohm-meter electroosmotic hardening threshold. It also demonstrates the critical state triggering the anti-coking intervention command when the resistivity climbs to 46 ohm-meters. The limit-crossing logic command is the underlying machine code within the controller that performs a numerical comparison and triggers a safety condition branch. The temperature overflow difference is the arithmetic difference between the currently measured highest temperature and the preset safe temperature threshold. The safety protection action mapping table is a two-dimensional relational data structure pre-stored in memory, containing different combinations of abnormal states and their corresponding optimal intervention strategy rules. The power supply duty cycle reduction command is a control message that forces the power module of the dewatering actuator to reduce the proportion of energization time within a single pulse cycle. The electrode reverse pulse cleaning command is a drive signal that instantly reverses the output polarity of the drive power supply to electrochemically peel off the high-resistance passivation film adhering to the electrode plate surface. The underlying hardware interface is the communication port hardware circuit for electrical connection and physical signal conversion between the main control chip and external actuators. The programmable logic controller (PLC) for dehydration control is an industrial computing device that directly receives control commands on-site and precisely controls contactors and solid-state relays according to timing logic.

[0054] For example, the highest real-time temperature value of the current internal mud is read. The set upper limit for the safe temperature threshold is 90 degrees Celsius. The default value is 85 degrees Celsius. The over-limit judgment logic instruction substitutes the two values ​​into the temperature overflow difference formula for calculation. The specific calculation process is as follows: The temperature overflow difference was calculated. The temperature is 5 degrees Celsius. Simultaneously, the current real-time consolidation resistivity characteristics are extracted. The value is 45 ohmmeters. A temperature weighting adjustment constant is set for table lookup matching. The default value is 2.0 indexes per degree Celsius, which sets the impedance weighting adjustment constant. The default value is 0.5 indexes per ohmmeter. Substitute the above data into the query index value calculation formula; the specific calculation process is as follows: That is, calculate the sum of 10 and 22.5 and round down to obtain the query index value. The index value is 32. Using this index value 32, a pre-set safety protection action mapping table is queried. The table's rules mandate that dual protection must be activated simultaneously when the index value is greater than 30. Based on this, a power supply duty cycle reduction command and an electrode reverse pulse cleaning command are successfully matched and generated. At the software layer, these two commands are logically combined and encapsulated into a data frame to construct a complete anti-coking intervention command. This command is then directly sent to the dewatering control programmable logic controller via the serial communication pin in the underlying hardware interface. This drives the field equipment to simultaneously reduce output power and execute electrode reverse depolarization, cutting off the physical conditions for mud coking.

[0055] Optionally, the method further includes: Receive the batch dewatering completion interrupt signal returned by the dewatering control programmable logic controller, extract the initial moisture content, internal structural impedance characteristics, and multi-dimensional operation state matrix of the batch based on the batch dewatering completion interrupt signal, and read the final shear strength characteristics of the dewatered mud cake detected and fed back by the physical shear force sensor. The initial moisture content, the internal structural impedance characteristics, the multidimensional operating state matrix, and the final shear strength characteristics of the dewatered cake are spliced ​​and encapsulated to construct an experience sample of dewatering conditions. The experience samples of the dehydration conditions are transmitted to the parameter update database, triggering an incremental learning mechanism to fine-tune the hyperparameters of the dynamic optimization model of the dehydration parameters using the experience samples of the dehydration conditions.

[0056] Specifically, the process receives a batch dewatering completion interruption signal. Based on this signal, it extracts the initial moisture content, internal structural impedance characteristics, and multi-dimensional operational state matrix of the batch. It also reads the final shear strength characteristics of the dewatered cake from the physical shear sensor. The initial moisture content, internal structural impedance characteristics, multi-dimensional operational state matrix, and final shear strength characteristics of the dewatered cake are then concatenated and encapsulated to construct a dewatering condition experience sample. This sample is then transmitted to the parameter update database, triggering an incremental learning mechanism to fine-tune the hyperparameters of the dynamic optimization model for dewatering parameters using the experience sample. To achieve adaptive iteration of model parameters based on the latest batch operation results, the following specific calculation formula for model hyperparameter fine-tuning and updating is introduced: ; This represents the updated model hyperparameters. This represents the model hyperparameters before the update. Let L represent the fine-tuning learning rate of the incremental learning mechanism, and let L represent the sample loss function constructed based on the final shear strength characteristics of the dehydrated mud cake. This represents the gradient partial derivative of the sample loss function with respect to the model hyperparameters before the update. The batch dewatering completion interruption signal is a hardware status identifier sent by the bottom-level controller to the main control system after the completion of a single solid-liquid separation operation, triggering subsequent data processing. The physical shear sensor is a mechanical measuring device installed at the sludge discharge port or inside the filter press chamber to directly measure the shear strength resistance of the solidified mud cake. The final shear strength characteristic of the dewatered mud cake is the maximum critical stress value that the mud, after consolidation at the end of the dewatering operation, can withstand external shear force without yielding. Data stitching and encapsulation is a software processing procedure that strictly aligns heterogeneous multidimensional data from different sensors and control links according to preset spatiotemporal dimensions and packages them into a single standard data frame. The dewatering operating condition experience sample is a standardized dataset unit containing the initial state of the mud, real-time operating conditions, and the closed-loop mapping relationship of the final dewatering effect. The parameter update database is a backend storage system specifically designed for persistently storing high-quality operating condition samples from each run of the dewatering unit for long-term iterative evolution of the model. Incremental learning is an algorithmic strategy that optimizes the weights of certain network layers in a deep learning model using only a small amount of newly acquired sample data without forgetting the existing knowledge system. Hyperparameter fine-tuning is a process of using the latest batch of actual job results as feedback signals to iteratively update the learning rate, regularization coefficient, or node weights in the network topology of the optimization model.

[0057] For example, after the dewatering actuator cuts off the power and completes the current round of solid-liquid separation, it receives a batch dewatering completion interruption signal from the programmable logic controller (PLC) for dewatering control. Based on this signal, it automatically retrieves the initial moisture content of the batch (75%), the internal structural impedance characteristic (120 joules per meter), and the aforementioned multi-dimensional operational state matrix recorded in the memory space. It reads the final shear strength characteristic value of the dewatered cake, detected and fed back by the physical shear force sensor installed at the discharge port, which is 45 kPa. It then concatenates and encapsulates the above characteristic data to construct a complete dewatering condition experience sample and securely transmits it to the parameter update database. To enable the model to continuously adapt to changes in specific geological conditions, an incremental learning mechanism is triggered to fine-tune the hyperparameters of the dynamic optimization model for dewatering parameters. It reads a key weight node of a hidden layer during forward prediction and sets the model hyperparameters before the update. The current value is 0.80, and the fine-tuning learning rate of the incremental learning mechanism is set according to the principle of minimizing empirical risk. The default value is 0.01. During backpropagation validation, the model calculates the partial derivative of the loss function gradient for that batch of samples by comparing the error between the predicted shear strength and the actual shear strength. The value is -5.0. The system substitutes the above values ​​into the model hyperparameter fine-tuning formula and performs the calculation. The specific calculation process is as follows: That is, adding 0.80 and 0.05 together to calculate the updated model hyperparameters. The value is 0.85. This updated value of 0.85 is overwritten to the corresponding neuron node in the network, completing a closed loop from a single mud dewatering operation to the evolution of the model's deep learning capabilities. This enables the equipment to have higher parameter control optimization accuracy when processing mud with the same structural impedance in subsequent operations.

[0058] Reference Figure 2 Based on the same inventive concept, the present invention also provides an adaptive system for sludge dewatering parameters in pile foundation slurry construction, the system comprising: The initial feature extraction module is used to collect the initial physical feature data of the pile foundation mud, and extract the initial water content and internal structural impedance features based on the initial physical feature data; The operating condition matrix construction module is used to collect the current operating condition data of the dehydration actuator in real time and construct a multi-dimensional operating status matrix based on the current operating condition data. The optimization model establishment module is used to establish a dynamic optimization model for dehydration parameters based on the initial moisture content, the internal structural impedance characteristics, and the multidimensional operating state matrix. The control command generation module is used to execute the dynamic optimization model of the dehydration parameters and generate the target dehydration control command; The dewatering execution drive module is used to send the target dewatering control command to the dewatering control programmable logic controller, and drive the dewatering actuator to perform sludge solid-liquid separation operation based on the dewatering control programmable logic controller.

[0059] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0060] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. An adaptive method for dewatering parameters of sludge in pile foundation slurry construction, characterized in that, include: Collect initial physical characteristic data of pile foundation mud, and extract initial water content and internal structural impedance characteristics based on the initial physical characteristic data; Real-time acquisition of current operating condition data of the dehydration actuator, and construction of a multi-dimensional operation status matrix based on the current operating condition data; Based on the initial moisture content, the internal structural impedance characteristics, and the multidimensional operating state matrix, a dynamic optimization model for dewatering parameters is established. This process includes: accessing a historical mud dewatering database and extracting historical moisture content, historical structural impedance characteristics, historical operating state matrices, and corresponding calibrated dewatering electrical parameter labels; inputting the historical moisture content, historical structural impedance characteristics, and historical operating state matrix into an initial deep learning network for feature nonlinear mapping to generate estimated dewatering electrical parameters; extracting a cost function calculation module to calculate the loss value between the estimated dewatering electrical parameters and the calibrated dewatering electrical parameter labels to generate a dewatering energy efficiency error; and triggering a backpropagation algorithm based on the dewatering energy efficiency error to update the node weights of the initial deep learning network, thereby generating the dynamic optimization model for dewatering parameters. The dynamic optimization model for the dehydration parameters is executed to generate the target dehydration control command. The target dehydration control command is sent to the dehydration control programmable logic controller, and the dehydration actuator is driven to perform sludge solid-liquid separation operation based on the dehydration control programmable logic controller.

2. The adaptive method for dewatering parameters of sludge in pile foundation slurry construction according to claim 1, characterized in that, The initial physical characteristic data of the pile foundation mud are collected, and the initial water content and internal structural impedance characteristics are extracted based on the initial physical characteristic data, including: The microwave moisture detection probe is controlled to emit detection microwaves into the pile foundation mud to obtain microwave reflection loss characteristics. A preset moisture content mapping dictionary is extracted and matched with the microwave reflection loss characteristics to generate the initial moisture content. The ultrasonic detection component is triggered to inject high-frequency sound waves into the pile foundation mud and read the transmitted sound wave energy signal. The energy difference gradient between the high-frequency sound wave and the transmitted sound wave energy signal is calculated to generate an acoustic impedance attenuation feature, which is then used as the impedance feature of the internal structure.

3. The adaptive method for dewatering parameters of sludge in pile foundation slurry construction according to claim 1, characterized in that, The real-time acquisition of current operating condition data of the dehydration actuator, and the construction of a multi-dimensional operation status matrix based on the current operating condition data, includes: The inter-electrode voltage gradient characteristics fed back by the electroosmotic anode plate and electroosmotic cathode plate deployed inside the dehydration actuator are extracted in real time; the standing wave resonant frequency characteristics fed back by the piezoelectric transducer are extracted; and the polar zone drainage flow velocity characteristics fed back by the drainage flow sensor are extracted. The inter-electrode voltage gradient characteristics, the standing wave resonant frequency characteristics, and the polar region drainage velocity characteristics are time-domain aligned according to the acquisition time series to generate a multi-physics field operating condition vector. Multiple multiphysics field condition vectors within a continuous time sliding window are extracted, and the multiple multiphysics field condition vectors are spliced ​​together using spatial tensors to construct the multidimensional operation state matrix.

4. The adaptive method for dewatering parameters of sludge in pile foundation slurry construction according to claim 1, characterized in that, The step of inputting the historical moisture content, the historical structural impedance characteristics, and the historical operational state matrix into an initial deep learning network for feature nonlinear mapping to generate estimated dehydration electrical parameters includes: The feature encoding layer of the initial deep learning network is activated to perform numerical normalization on the historical water content and the historical structural impedance features, thereby generating an initial state representation vector. A multi-scale convolutional kernel is activated to perform a local receptive field scan on the historical job state matrix to extract hidden field coupling feature maps. The spatial dimension of the hidden field coupling feature map is compressed using a pooling layer. The dimension-reduced hidden field coupling feature map is then fully connected and fused with the initial state representation vector to construct a comprehensive state vector. The comprehensive state vector is then input into the prediction mapping layer to generate the estimated dehydration electrical parameters.

5. The adaptive method for dewatering parameters of sludge in pile foundation slurry construction according to claim 1, characterized in that, The step of executing the dynamic optimization model for dehydration parameters and generating the target dehydration control command includes: The initial moisture content, internal structural impedance characteristics, and multidimensional operating state matrix at the current moment are input into the dynamic optimization model for dehydration parameters for forward reasoning, and the target DC bias voltage characteristics and target ultrasonic duty cycle characteristics are output. The target DC bias voltage characteristics and the target ultrasonic duty cycle characteristics are encoded and encapsulated according to the preset industrial control bus communication protocol specifications to construct the target dehydration control command.

6. The adaptive method for dewatering parameters of sludge in pile foundation slurry construction according to claim 1, characterized in that, The method further includes: The real-time temperature distribution characteristics and real-time consolidation resistivity characteristics of the mud inside the dewatering actuator were collected. Determine whether the real-time temperature distribution characteristics exceed the upper limit of the safe temperature threshold or whether the real-time consolidation resistivity characteristics reach the electroosmosis shutdown hardening threshold. If the real-time temperature distribution characteristic is determined to exceed the upper limit of the safe temperature threshold or the real-time consolidation resistivity characteristic reaches the electroosmosis shutdown hardening threshold, an anti-coking intervention command is generated and sent to the dehydration control programmable logic controller.

7. The adaptive method for dewatering parameters of sludge in pile foundation slurry construction according to claim 6, characterized in that, The process of generating the anti-scorching intervention command and sending it to the dehydration control programmable logic controller includes: The logic instruction for judging the limit is executed to extract the temperature overflow difference where the real-time temperature distribution feature exceeds the upper limit of the safe temperature threshold. Based on the temperature overflow difference and the real-time consolidation resistivity characteristics, query the preset safety protection action mapping table, and generate a power supply duty cycle reduction command or an electrode reverse pulse cleaning command. The anti-coking intervention command is constructed by combining the power supply duty cycle reduction command with the electrode reverse pulse cleaning command, and the anti-coking intervention command is sent to the dehydration control programmable logic controller based on the underlying hardware interface.

8. The adaptive method for dewatering parameters of sludge in pile foundation slurry construction according to claim 1, characterized in that, The method further includes: Receive the batch dewatering completion interrupt signal returned by the dewatering control programmable logic controller, extract the initial moisture content, internal structural impedance characteristics, and multi-dimensional operation state matrix of the batch based on the batch dewatering completion interrupt signal, and read the final shear strength characteristics of the dewatered mud cake detected and fed back by the physical shear force sensor. The initial moisture content, the internal structural impedance characteristics, the multidimensional operating state matrix, and the final shear strength characteristics of the dewatered cake are spliced ​​and encapsulated to construct an experience sample of dewatering conditions. The experience samples of the dehydration conditions are transmitted to the parameter update database, triggering an incremental learning mechanism to fine-tune the hyperparameters of the dynamic optimization model of the dehydration parameters using the experience samples of the dehydration conditions.

9. An adaptive system for sludge dewatering parameters in pile foundation slurry construction, applied to an adaptive method for sludge dewatering parameters in pile foundation slurry construction as described in any one of claims 1-8, characterized in that, The system includes: The initial feature extraction module is used to collect the initial physical feature data of the pile foundation mud, and extract the initial water content and internal structural impedance features based on the initial physical feature data; The operating condition matrix construction module is used to collect the current operating condition data of the dehydration actuator in real time and construct a multi-dimensional operating status matrix based on the current operating condition data. The optimization model establishment module is used to establish a dynamic optimization model for dehydration parameters based on the initial moisture content, the internal structural impedance characteristics, and the multidimensional operating state matrix. The control instruction generation module is used to execute the dynamic optimization model of the dehydration parameters and generate the target dehydration control instruction; The dewatering execution drive module is used to send the target dewatering control command to the dewatering control programmable logic controller, and drive the dewatering actuator to perform sludge solid-liquid separation operation based on the dewatering control programmable logic controller.

Images