A method for purifying and reusing foundation pit dewatering with water quality identification function

Abstract

This invention discloses a method for purifying and reusing foundation pit dewatering with water quality identification capabilities, belonging to the field of water treatment technology. The method includes: acquiring real-time multi-dimensional features of foundation pit dewatering; inputting these features into a dynamic hierarchical model to generate hierarchical decision instructions and dynamic diversion ratios; controlling a diversion execution mechanism to guide the foundation pit dewatering into a corresponding adaptive purification path for processing, obtaining purified effluent; monitoring the status of the purified effluent and the reuse tank and generating feedback signals to dynamically adjust the model parameters; and optimizing the scheduling and reuse of the purified effluent based on a defined water demand feature spectrum of end-user requirements. This invention achieves efficient adaptive purification and precise scheduling and reuse of foundation pit dewatering by combining multi-dimensional feature perception, intelligent hierarchical decision-making, and closed-loop feedback control.

Description

Technical Field

[0001] This invention relates to the field of water treatment technology, and in particular to a method for purifying and reusing groundwater from foundation pits with water quality identification capabilities. Background Technology

[0002] Dewatering of foundation pits is a necessary drainage measure taken during construction to ensure a dry working environment in the pit. The discharged groundwater is substantial in volume, but it typically contains high concentrations of suspended solids and sediment, and its chemical properties, such as pH, may fluctuate. Direct discharge could negatively impact the environment. Therefore, on-site purification and reuse of foundation pit dewatering water, such as for vehicle washing, road dust suppression, and green space irrigation, is a crucial approach to achieving green construction and sustainable water resource utilization.

[0003] In existing technologies, the treatment of dewatering in foundation pits typically employs a fixed process flow. A typical approach involves collecting the dewatering water and then purifying it sequentially through physical treatment units such as sedimentation tanks and sand filters. The purified water is then stored in a clear water tank for on-site use. The operation and control of the entire system are relatively simple, usually involving manual start-up and shutdown, or simple automated control based on a single parameter such as liquid level or time, lacking real-time response capabilities to changes in water quality.

[0004] However, the water quality of foundation pit dewatering is highly dynamic and uncertain, influenced by various factors such as rainfall, geological conditions, and construction activities. Fixed treatment processes are ill-suited to such drastic water quality fluctuations. When water quality is good, a fixed treatment process leads to unnecessary energy consumption; conversely, when water quality suddenly deteriorates, the treatment effect may fail to meet standards. Secondly, traditional control methods have low levels of intelligence, failing to finely adjust the operating parameters of the treatment unit based on real-time water quality conditions, resulting in low treatment efficiency and high operating costs. Finally, in the reuse stage, existing technologies typically treat purified water as a homogeneous source, supplying it in a crude manner without differentiating its allocation according to the varying water quality requirements of different water use scenarios, leading to inefficient use of water resources. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method for purifying and reusing foundation pit dewatering with water quality identification capabilities. This method combines real-time multi-dimensional water quality characteristic perception, dynamic model intelligent hierarchical decision-making, and closed-loop feedback control to achieve efficient adaptive purification and optimized scheduling and reuse of foundation pit dewatering.

[0006] The above objectives can be achieved through the following approach: A method for purifying and reusing foundation pit dewatering with water quality identification capabilities includes: acquiring real-time multi-dimensional water quality characteristics of foundation pit dewatering; inputting the real-time multi-dimensional water quality characteristics into a preset dynamic grading model with dynamic parameter adjustment capabilities to generate a grading decision command and dynamic diversion ratio for the current water flow; controlling a preset diversion execution mechanism to guide the foundation pit dewatering into a corresponding adaptive purification path based on the grading decision command and the dynamic diversion ratio; using a purification unit corresponding to the adaptive purification path to perform adaptive parameter purification processing on the imported foundation pit dewatering to obtain purified effluent; monitoring the water quality status of the purified effluent and the status within the clean water reuse tank, generating a status feedback signal, and using the status feedback signal to dynamically adjust the dynamic grading model; acquiring water demand signals from water users, and optimizing the scheduling and reuse of the purified effluent based on a preset water demand characteristic spectrum of different water users' water quality requirements.

[0007] Optionally, the acquisition of real-time multidimensional water quality characteristics of foundation pit dewatering includes: acquiring a first set of water quality parameters of the foundation pit dewatering through a preset water quality sensor array; acquiring a sequence of flow images of the foundation pit dewatering through a preset micro optical observation unit; performing image feature analysis on the flow image sequence to extract a second set of water quality parameters characterizing the morphology and distribution of particulate matter; and fusing the first set of water quality parameters and the second set of water quality parameters to generate real-time multidimensional water quality characteristics.

[0008] Optionally, the generation of the current water flow classification decision command and dynamic diversion ratio includes: the dynamic classification model receiving the real-time multi-dimensional water quality characteristics and outputting a probability estimate of the current water flow belonging to each water quality level; calculating the dynamic diversion ratio for controlling the diversion execution mechanism based on the probability estimate and a preset strategy optimization function; determining the dominant water quality level according to the probability estimate and generating a classification decision command.

[0009] Optionally, the generation of the status feedback signal includes: collecting water quality data of the purified effluent from each of the purification units to generate a unit effluent water quality signal; collecting mixed water quality data and liquid level data of the clear water reuse tank to generate a tank status signal; and combining the unit effluent water quality signal and the tank status signal to generate the status feedback signal.

[0010] Optionally, the step of dynamically adjusting the dynamic grading model using the state feedback signal includes: analyzing the effluent water quality signal of the unit in the state feedback signal to obtain the actual treatment efficiency of each purification unit; adjusting the level threshold parameters associated with the corresponding purification path in the dynamic grading model in reverse according to the deviation between the actual treatment efficiency and the efficiency target interval that is predefined as the ideal working effect range; analyzing the pool state signal in the state feedback signal and, in conjunction with a predefined water demand prediction function for predicting future water load, adjusting the parameters related to the direct reuse and diversion ratio in the dynamic grading model.

[0011] Optionally, obtaining purified effluent includes: when the graded decision instruction indicates a first purification path, directly introducing the pit dewatering into the clean water reuse tank as purified effluent; when the graded decision instruction indicates a second purification path, introducing the pit dewatering into a conventional physical purification unit; and dynamically adjusting the operating parameters of the separation components in the conventional physical purification unit based on the flow rate data of the pit dewatering flowing into the conventional physical purification unit and the turbidity data in the real-time multidimensional water quality characteristics, performing primary purification treatment to obtain the first purified effluent as the purified effluent.

[0012] Optionally, the method further includes: when the graded decision instruction indicates a third purification path, introducing the pit dewatering into a deep composite purification unit; adjusting the acid-base neutralization dosage in the deep composite purification unit based on the pH data in the real-time multidimensional water quality characteristics; selecting a target coagulant type from a coagulant decision library that maps particulate morphology to agent type and dynamically adjusting its dosage based on the particulate morphology characteristics represented by the second water quality parameter set in the real-time multidimensional water quality characteristics, and performing deep purification treatment to obtain the second purified effluent as the purified effluent.

[0013] Optionally, the optimized scheduling and reuse of the purified effluent includes: matching the water quality data in the clean water reuse tank with the water demand characteristic spectrum to determine the available water source blocks that meet each of the water demand signals; calculating and generating an optimal water supply scheduling instruction based on the priority of the water demand signals, water consumption, and the water quality level of the available water source blocks; and executing the optimal water supply scheduling instruction to control the water supply system to transport the water in the clean water reuse tank to the corresponding water-using terminals.

[0014] Optionally, the method further includes: intercepting large debris in the foundation pit dewatering using a load sensing preprocessing device and acquiring an auxiliary load signal characterizing the physical load of the incoming water; using the auxiliary load signal as an auxiliary input parameter of the dynamic grading model; and using the auxiliary load signal to predict the water quality change trend of the foundation pit dewatering and adjust its internal weight parameters in advance.

[0015] Based on the same inventive concept, the present invention also provides a foundation pit dewatering purification and reuse system with water quality identification function, the system comprising: The multi-dimensional feature perception module is used to acquire real-time multi-dimensional water quality characteristics of the foundation pit dewatering. The intelligent classification decision module is used to input the real-time multidimensional water quality characteristics into a preset dynamic classification model with dynamic parameter adjustment function, and generate the classification decision command and dynamic diversion ratio of the current water flow. The diversion execution module is used to control the preset diversion execution mechanism to guide the pit dewatering into the corresponding adaptive purification path according to the hierarchical decision instruction and the dynamic diversion ratio. An adaptive purification module is used to perform adaptive parameter purification on the imported pit dewatering using a purification unit corresponding to the adaptive purification path, so as to obtain purified effluent. The status feedback monitoring module is used to monitor the water quality status of the purified effluent and the status of the clear water reuse tank, generate status feedback signals, and use the status feedback signals to dynamically adjust the dynamic grading model. The optimized scheduling and reuse module is used to acquire water demand signals from water terminals and optimize the scheduling and reuse of the purified water based on a preset definition of water demand characteristic spectrum of different water terminals for water quality requirements.

[0016] Compared with the prior art, the present invention has the following advantages: 1. Accurate identification and intelligent grading of dewatering water quality in foundation pits were achieved. By integrating traditional water quality sensor data with microscopic particle features extracted from optical observation images, a real-time multidimensional water quality characteristic was constructed, and a dynamic grading model was used for in-depth analysis. This method overcomes the limitations of traditional methods that rely on a single or a few macroscopic indicators for rough judgment, and can more comprehensively and accurately capture the instantaneous changes and intrinsic characteristics of water quality, providing a highly reliable decision-making basis for subsequent differentiated treatment.

[0017] 2. The system achieves full-process self-adaptation and efficient operation of the purification process. Based on accurate water quality classification results, the system can dynamically select the optimal purification path and adjust the operating parameters of the core treatment unit in real time. Whether it's the intensity of conventional physical purification or the type and dosage of reagents for deep chemical purification, it can achieve precise matching with the influent water quality, thereby avoiding waste of energy and chemicals and ensuring stable treatment results at low cost under various water quality conditions.

[0018] 3. A closed-loop control system with self-learning and optimization capabilities was constructed. By monitoring the effluent performance of each purification unit and the overall status of the clean water reuse tank in real time, the system can generate status feedback signals and use these signals to dynamically adjust the key parameters of the front-end decision-making model. This feedback mechanism enables the system to continuously learn and adapt to the degradation of equipment performance or the long-term drift of raw water characteristics, ensuring the long-term stability and efficiency of the entire purification and reuse system.

[0019] 4. The system achieves optimized scheduling and value enhancement of purified water resources. It not only produces qualified purified water but also, based on the differentiated water quality requirements of various water users and combined with the real-time water quality and quantity data from the clear water tank, formulates and executes the optimal water supply scheduling plan through optimization algorithms. This refined management model of quality-based water supply achieves the rational allocation of purified water resources, improving the overall utilization efficiency and economic value of recycled water.

[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 schematic flowchart of a method for purifying and reusing groundwater from a foundation pit with water quality identification function, according to an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of a real-time multidimensional water quality feature space model of a foundation pit dewatering purification and reuse method with water quality identification function according to an embodiment of the present invention.

[0024] Figure 3A schematic diagram of the adaptive parameter adjustment surface model of the physical separation component of a foundation pit dewatering purification and reuse method with water quality identification function according to an embodiment of the present invention.

[0025] Figure 4 This is a schematic diagram of a foundation pit dewatering purification and reuse system with water quality identification function according to an embodiment of the present invention. Detailed Implementation

[0026] 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.

[0027] Reference Figure 1 One embodiment of the present invention proposes a method for purifying and reusing foundation pit dewatering with water quality identification function. It adopts a combination of real-time multi-dimensional water quality characteristic perception, dynamic model intelligent hierarchical decision-making and closed-loop feedback control to achieve efficient adaptive purification and optimized scheduling and reuse of foundation pit dewatering.

[0028] The method described in this embodiment specifically includes: Obtain real-time multidimensional water quality characteristics of foundation pit dewatering; The real-time multidimensional water quality characteristics are input into a preset dynamic classification model with dynamic parameter adjustment function to generate the current water flow classification decision command and dynamic diversion ratio. Based on the hierarchical decision-making instructions and the dynamic diversion ratio, the preset diversion execution mechanism is controlled to guide the pit dewatering into the corresponding adaptive purification path. The imported pit dewatering water is subjected to adaptive parameter purification processing by the purification unit corresponding to the adaptive purification path to obtain purified effluent. The water quality status of the purified effluent and the state of the clear water reuse tank are monitored, a status feedback signal is generated, and the dynamic grading model is dynamically adjusted using the status feedback signal. The system acquires water demand signals from water-using terminals and optimizes the scheduling and reuse of purified water based on a predefined water demand characteristic spectrum of different water-using terminals for water quality requirements.

[0029] Specifically, firstly, multi-dimensional feature perception methods are used to comprehensively and in real-time grasp the dynamic changes in raw water quality. Then, a dynamic hierarchical model with self-adjusting parameters is used to deeply analyze these water quality characteristics, replacing traditional fixed threshold judgments and generating composite decision commands that include dominant path selection and fine-grained flow allocation. This command drives the subsequent purification system to perform differentiated and adaptive purification treatment. More importantly, a dual feedback mechanism is introduced. On the one hand, by monitoring the actual treatment effect of the purification unit and the storage status of the clear water tank, the front-end hierarchical decision model is adjusted in reverse, achieving self-correction and optimization of treatment efficiency. On the other hand, by sensing the actual water demand at the terminal, the purified water resources are optimized and scheduled according to quality and demand, realizing intelligent management of the entire chain from water purification to maximizing the value of water resources. This improves the accuracy and precision of water quality identification, enabling dynamic responses to drastic fluctuations in the water quality of foundation pit dewatering, laying a solid foundation for subsequent precise treatment. Secondly, the system achieves the matching of the most suitable treatment path and parameters based on the real-time water quality level during the purification process, effectively avoiding over-treatment of clean water and under-treatment of polluted water, reducing energy and chemical consumption, and improving the overall operational economy. Furthermore, the closed-loop feedback and self-adjustment mechanism endows the system with strong environmental adaptability and robustness, enabling it to continuously optimize its performance during long-term operation and ensuring stable compliance of effluent quality. Finally, optimized scheduling and reuse based on water demand characteristics realizes the differentiated and graded utilization of purified water, maximizing the utilization efficiency and value of reclaimed water resources, and providing an innovative technical solution for sustainable water resource management at construction sites.

[0030] Optionally, the acquisition of real-time multidimensional water quality characteristics of foundation pit dewatering includes: The first set of water quality parameters for the dewatering in the foundation pit is collected by a preset water quality sensor array; The flow image sequence of the groundwater in the foundation pit was obtained by using a preset miniature optical observation unit; Image feature analysis was performed on the flow image sequence to extract a second set of water quality parameters characterizing the morphology and distribution of particulate matter; By integrating the first water quality parameter set and the second water quality parameter set, real-time multidimensional water quality features are generated.

[0031] Specifically, a hardware combination including turbidity electrodes and pH probes is installed at equal intervals on the inner wall of the main inlet pipe of the foundation pit pump. This allows for continuous electrical signal acquisition of the flowing water at a fixed frequency, which is then converted into digital signals. The preset water quality sensor array is a real-time in-situ monitoring device integrating multiple electrochemical and optical sensing elements. The first water quality parameter set is a continuous set of values ​​reflecting the macroscopic physicochemical properties of the water. Next, a preset micro-optical observation unit acquires a sequence of flow images of the foundation pit precipitation, and image feature analysis is performed on the flow image sequence to extract a second set of water quality parameters characterizing the morphology and distribution of particulate matter. A high-speed industrial camera and a stroboscopic light source are installed outside the transparent observation section of the pumping pipeline to continuously capture cross-sectional images of the water flow at a high frame rate. Then, an edge detection algorithm is used to identify the contours of suspended particles in the images, calculate the area and perimeter of each particle, and thus obtain morphological feature values. The edge detection algorithm is a computer vision processing method that locates the boundary contours of particles by calculating the extreme points of the first derivative in the image's grayscale space. The particle roundness formula required for this process is as follows: ; In the formula, C is a dimensionless quantity for particle roundness, A is the identified two-dimensional projected area of ​​the particle in square micrometers, and P is the perimeter of the particle's projected outline in micrometers. Pi is a constant. The preset micro-optical observation unit is a fluid vision capture hardware comprising a high-frequency imaging device and a supplementary lighting module. The flow image sequence is a visual record of two-dimensional water slices arranged continuously by timestamps. The second water quality parameter set is a data combination that quantitatively describes the microscopic geometric characteristics of suspended solids in the water. Subsequently, the first and second water quality parameter sets are fused to generate real-time multidimensional water quality features. The collected physicochemical values ​​and microscopic morphological feature values ​​are then normalized by range and concatenated to construct a feature vector containing both macroscopic and microscopic dimensions, serving as the input for subsequent models. The range normalization formula is as follows: ; in the formula X represents the dimensionless value of the standardized feature, where X is the original value of the feature currently acquired, and its dimension depends on the specific parameters. This is the historical lowest threshold value for this parameter that has been stored in advance. This is the highest historical threshold value for this parameter, which is stored in advance. The real-time multidimensional water quality characteristics are represented by a high-dimensional mathematical vector that integrates macroscopic physicochemical properties and microscopic particle morphology. For example... Figure 2 The image shows the fused multidimensional feature radar distribution model. This model maps heterogeneous data such as standardized turbidity, particle roundness, pH value, physical load signal, and water level into the same feature space. By observing the changes in the shape and area of ​​the radar polygons, the system can capture the complex pollution state of the foundation pit dewatering in real time, providing high-dimensional raw data input for subsequent intelligent classification.

[0032] For example, during the continuous pumping phase of a deep foundation pit dewatering project, a multi-dimensional feature acquisition process is initiated. A pre-set water quality sensor array first acquires data at a frequency of once per second, showing a turbidity value of 150 NTU and a pH of 7.5. This data combination constitutes the first set of water quality parameters, where NTU stands for scattering turbidity unit. Simultaneously, a pre-set micro-optical observation unit captures a sequence of flow images of the water flow within the pipeline at a rate of 60 frames per second. A typical suspended particle is extracted from the first frame image, and pixel statistics are used to calculate the particle's two-dimensional projected area as 50 square micrometers and its projected perimeter as 30 micrometers. These two values ​​are then substituted into the particle roundness formula to calculate the roundness. The roundness threshold was set to 0.7. This threshold is based on the fact that numerous previous geological surveys showed that the roundness of irregular hard sand particles in the foundation pit sediment is usually below 0.7, while the roundness of clay-like flocculent suspended matter is usually greater than or equal to 0.7. Therefore, the currently calculated value of 0.697 indicates that the main suspended matter in this water body is hard sand particles, and this roundness value constitutes the second set of water quality parameters. Subsequently, a fusion operation was performed, substituting the turbidity value of 150 NTU into the standardization formula. The lowest historical turbidity threshold was set to 0, and the highest threshold was set to 500. This extreme range is based on the upper and lower limits of fluctuations in the historical groundwater quality monitoring records of this project, and the standardized turbidity was calculated. Similarly, roundness is standardized. The standardized turbidity value is concatenated with the roundness value to generate a real-time multidimensional water quality feature vector containing these two dimensions, which is then output to the classification model.

[0033] Optionally, the generation of the hierarchical decision command and dynamic diversion ratio for the current water flow includes: The dynamic grading model receives the real-time multidimensional water quality features and outputs the probability estimate of the current water flow belonging to each water quality level. Based on the probability estimate and the preset strategy optimization function, the dynamic diversion ratio used to control the diversion execution mechanism is calculated; Based on the probability estimate, the dominant water quality level is determined, and a graded decision instruction is generated.

[0034] Specifically, the input multidimensional feature vector is multiplied by a preset weight matrix, and a bias term is added to obtain the raw assessment scores for each water quality level. Then, a normalized exponential function is used to map these discrete scores into a probability distribution that sums to one. The formula for the normalized exponential function is as follows: ; in the formula Let i be a dimensionless quantity representing the probability estimate of the i-th water quality level. Let be the dimensionless original assessment score for the i-th water quality level, K be the total number of water quality levels (system-preset as a constant), and e be the base of the natural logarithm (constant). Based on probability estimates and a preset strategy optimization function, the dynamic diversion ratio used to control the diversion actuator is calculated. This calculation process sets a confidence threshold. When the highest probability estimate is greater than or equal to this confidence threshold, all water flow is allocated to the purification path corresponding to the highest probability. When the highest probability estimate is less than this confidence threshold, a boundary optimization strategy is triggered, extracting the top two probability estimates and normalizing them as the flow allocation ratio for the corresponding two purification paths. The classification label corresponding to the item with the largest value in the probability distribution is extracted as the dominant water quality level and encoded into a specific format electronic command, which is then sent to the underlying control bus. The dynamic grading model is a machine learning classification algorithm structure that can automatically update the internal network weights based on real-time feedback to adapt to water quality fluctuations; the probability estimate is the mathematical likelihood of the current water body belonging to different pre-defined water quality levels, usually expressed as a value between zero and one; the preset strategy optimization function is a mathematical mapping rule that transforms the classification probability into a physical flow allocation ratio, the core purpose of which is to minimize the load on the deep treatment unit while ensuring that the treatment meets the standards; the dynamic diversion ratio is a control parameter that instructs the diversion execution mechanism to introduce the total water flow into different treatment paths according to a specific volume percentage; the dominant water quality level is the category with the largest value in the probability estimate distribution, representing the most important pollution characteristic of the current water flow; the grading decision instruction is an electronic control signal that contains the dominant water quality level information and is used to trigger the corresponding adaptive purification path start sequence.

[0035] For example, during the operation of a foundation pit dewatering system at a construction site, the acquired real-time multidimensional water quality feature vector includes two dimensions: a standardized turbidity value of 0.3 and a standardized particle sphericity value of 0.7. Three water quality levels are defined: Level A representing direct reuse, Level B representing conventional physical purification, and Level C representing deep composite purification. The dynamic grading model internally predefines fixed weight parameters and bias terms for these three levels. This parameter combination is based on the statistical correlation between various indicators and the required treatment intensity in local geological historical data. The weight vector for Level A is -2 and zero with a bias of 1; the weight vector for Level B is 2 and -2 with a bias of 0; and the weight vector for Level C is 2 and 2 with a bias of -1. First, calculate the raw assessment score for each level. The score for level A is -2 multiplied by 0.3, plus 0 multiplied by 0.7, plus 1, which equals 0.4. The score for level B is 2 multiplied by 0.3, minus 2 multiplied by 0.7, plus 0, which equals -0.8. The score for level C is 2 multiplied by 0.3, plus 2 multiplied by 0.7, minus 1, which equals 1.0. The formula after substituting the specific data into the normalized exponential function (SoftmaxFunction) is as follows: ; ; Based on the calculation results, the maximum value in the system is 0.58, corresponding to grade C. Therefore, grade C is determined as the dominant water quality grade, and a graded decision instruction pointing to the deep composite purification path is generated. Subsequently, the dynamic diversion ratio is calculated according to the preset strategy optimization function. The preset confidence threshold is 0.8, which is based on the lower limit of safety redundancy required by the system to ensure that single-pipeline treatment meets standards. Since the highest probability of 0.58 being less than 0.8 triggers the boundary optimization strategy, the probabilities of the top two values ​​are extracted: 0.58 for grade C and 0.32 for grade A. The sum of these two values ​​is 0.90, thus the diversion ratio for grade C is approximately 64.4% (0.58 divided by 0.90), and the diversion ratio for grade A is approximately 35.6% (0.32 divided by 0.90). Finally, the dynamic diversion ratio control actuator directs 64.4% of the influent into the deep composite purification unit and 35.6% into the clean water reuse tank.

[0036] Optionally, the generation of the state feedback signal includes: Collect water quality data of the purified water from each of the purification units and generate unit effluent water quality signals; Collect mixed water quality data and liquid level data of the clear water reuse tank to generate a status signal inside the tank; The effluent water quality signal from the unit is combined with the state signal inside the pool to generate the state feedback signal.

[0037] Specifically, an online water quality monitoring probe is installed at the end of the effluent discharge pipe to continuously read the turbidity and pH values ​​of the effluent water at a set sampling period, and these low-level analog data are converted into digital signal packets under a standard communication protocol. Mixed water quality data and liquid level data of the clear water reuse tank are collected to generate a tank status signal. A multi-parameter water quality analyzer is suspended at the center of the clear water reuse tank to obtain the overall water quality parameters after thorough mixing. Simultaneously, an ultrasonic level gauge is installed at the top of the tank to detect the distance to the water surface to calculate the actual water depth. The measured water quality and depth data are then synchronously encoded. The unit effluent water quality signal and the tank status signal are combined to generate the status feedback signal. After receiving the above multi-source signals, the central control board, according to preset timestamp alignment rules and data structure templates, concatenates the data from each independent node into a long sequence array containing multiple dimensions, which serves as unified status feedback data transmitted to the decision-making level. The purified effluent water quality data is a set of numerical values ​​reflecting various physicochemical indicators of the effluent water after specific physical or chemical treatment processes; the unit effluent water quality signal is a standardized digital electrical signal carrying quantitative evaluation information of the purification effect of each independent treatment unit; the mixed water quality data is the overall water quality characteristic index formed by the physical mixing of water bodies purified through different paths after they are collected in the terminal water tank; the liquid level height data is a real-time geometric depth measurement value characterizing the water storage volume and available storage space in the terminal water tank; the tank status signal is a composite electronic data packet that comprehensively reflects the current water quality qualification level and water reserve status of the terminal water storage facility; and the status feedback signal is a global monitoring information flow that integrates the efficiency of each front-end treatment node and the overall water storage status at the end to drive the closed-loop self-correction of the decision-making model.

[0038] For example, in the second hour of operation of a foundation pit dewatering and purification system, the process for generating a status feedback signal is executed. A probe installed on the outlet pipe of the conventional physical purification unit collects the current purified water quality data, i.e., turbidity of 12 NTU. A probe installed on the outlet pipe of the deep composite purification unit collects the turbidity of 4 NTU and the pH of 7.1. These values ​​are packaged to generate the unit's effluent water quality signal. Simultaneously, a measuring instrument installed in the clear water reuse tank collects the mixed water quality data, i.e., overall turbidity of 8 NTU, and the ultrasonic level gauge at the top detects the current liquid level height as 3.6 meters. The maximum physical design depth of the clear water reuse tank is set to 4.5 meters, based on the actual civil engineering dimensions of the on-site cast-in-place concrete storage tank. The current safety margin of the clear water reuse tank is calculated by subtracting the current water depth (0.9 meters) from the maximum water depth, thus obtaining a volume occupancy rate of 3.6 divided by 4.5, which equals 80%. The high liquid level warning threshold was set at 85%, based on the safety buffer space requirement in building water supply and drainage design codes to prevent overflow caused by sudden water ingress such as rainstorms. Subsequently, the unit turbidity of 12 NTU and 4 NTU, the mixed turbidity of 8 NTU in the pool, and the volume occupancy rate of 80% were concatenated into a continuous hexadecimal data stream according to a preset data frame format, generating the final status feedback signal and sending it to the dynamic grading model.

[0039] Optionally, the step of dynamically adjusting the dynamic hierarchical model using the state feedback signal includes: By analyzing the effluent water quality signal of the unit in the status feedback signal, the actual treatment efficiency of each purification unit can be obtained. Based on the deviation between the actual processing efficiency and the preset target range of ideal working effect, the level threshold parameters associated with the corresponding purification path in the dynamic grading model are adjusted in reverse. The pool state signal in the state feedback signal is analyzed, and combined with a preset water demand prediction function for predicting future water load, the parameters related to direct reuse and diversion ratio in the dynamic grading model are adjusted.

[0040] Specifically, the system receives and analyzes the effluent water quality signals from the status feedback signals. By comparing the pollutant concentrations of the influent and effluent, the removal rate is calculated to obtain the actual treatment efficiency of each purification unit. Subsequently, based on the deviation between the actual treatment efficiency and the preset target efficiency range (defined as the ideal working effect range), the level threshold parameters associated with the corresponding purification path in the dynamic grading model are adjusted using an error backpropagation algorithm. Simultaneously, the system analyzes the pool status signals from the status feedback signals to extract the liquid level and mixed water quality indicators. Combined with a preset water demand prediction function used to predict future water load, the expected water demand is calculated. Based on the comparison between the remaining liquid level and the expected water demand, the parameters related to direct reuse and diversion ratios in the dynamic grading model are adjusted. The actual treatment efficiency is a quantitative indicator that characterizes the ability of each purification unit to remove pollutants from the foundation pit dewatering or the degree of water quality improvement; the efficiency target range is the upper and lower limits of the pollutant removal rate or effluent quality qualification value that the representative purification unit should achieve under the best working conditions, as preset by the system; the grade threshold parameter is the mathematical weight or critical discrimination value used in the dynamic grading model to define the boundary of different water quality grade classifications; the water demand prediction function is a mathematical model that combines historical water consumption curves and current construction progress to predict the water consumption trend of each water terminal in a specific future time period.

[0041] For example, continuing the previous operating conditions, the status feedback signal is analyzed to obtain that the influent turbidity of the conventional physical purification unit is 150 NTU and the effluent turbidity is 12 NTU. The actual treatment efficiency of the unit is calculated as 92% by subtracting the effluent from the influent and then dividing by the influent. The preset efficiency target range for this conventional physical purification unit is retrieved as 80% to 85%, which is based on the calibrated turbidity removal upper limit of the combined process of the cyclone separator and conventional quartz sand filter under normal load. Since the actual efficiency exceeds the upper limit of the target range, the positive deviation is calculated as 92% - 85% = 7%. This positive deviation indicates that the current physical purification path has excess processing capacity, and accordingly, the level threshold parameter is adjusted. The base learning rate is set to 0.1, which is based on engineering experience to prevent system oscillation caused by excessively large single correction steps. Multiplying the deviation by the learning rate yields an adjustment of 0.007. Subsequently, the threshold weight for determining whether water flows into the conventional physical purification path in the dynamic grading model is lowered from the original preset value of 0.5 by 0.007 to 0.493, thus appropriately relaxing the entry of more water into the physical treatment path in the next decision-making cycle. Simultaneously, analyzing the pool's state signals reveals that the current volume occupancy rate has reached a high level of 80%. Substituting this into the water demand prediction function, which uses an algorithm of multiplying the base usage by a time variation coefficient, the current base water usage is read as 10 cubic meters per hour. Based on the built-in construction schedule, it is determined that the off-peak period of concentrated concrete curing is approaching, and a time variation coefficient of 0.8 is set, calculating the predicted water load for the next hour to be 8 cubic meters. Assessing the current high liquid level in the pool and the decreasing future water demand, a very high overflow risk is identified. The overflow risk adjustment coefficient is set to 0.2. This coefficient is directly applied to the parameters related to the diversion ratio. The original preset baseline flow ratio of 30% allocated to the direct reuse path is forcibly reduced by 30% × 0.2 = 6%, and the new upper limit of the direct reuse ratio is limited to 24%.

[0042] Optionally, obtaining purified effluent includes: When the hierarchical decision instruction indicates the first purification path, the dewatering water from the foundation pit is directly introduced into the clean water reuse tank as purified effluent. When the hierarchical decision instruction indicates the second purification path, the pit dewatering is directed into the conventional physical purification unit. Based on the flow rate data of the foundation pit dewatering flowing into the conventional physical purification unit and the turbidity data in the real-time multidimensional water quality characteristics, the operating parameters of the separation components in the conventional physical purification unit are dynamically adjusted to perform primary purification treatment and obtain the first purified effluent as the purified effluent.

[0043] Specifically, when the tiered decision instruction indicates the first purification path, an opening command is sent to the direct bypass valve connecting the pit pumping main pipe and the terminal reuse tank, allowing the raw water deemed high-quality to flow directly into the terminal storage tank without passing through any filtration container. When the tiered decision instruction indicates the second purification path, the direct bypass valve is closed and the main control valve leading to the multi-stage hydrocyclone or quartz sand filter is simultaneously opened, guiding the water flow containing a moderate suspended solids load into the physical interception treatment zone. Based on the flow rate data of the pit dewatering flowing into the conventional physical purification unit and the turbidity data in the real-time multi-dimensional water quality characteristics, the operating parameters of the separation components in the conventional physical purification unit are dynamically adjusted to perform primary purification treatment and obtain the first purified effluent as the purified water. Using the electromagnetic flowmeters arranged on the branch pipes and the turbidity values ​​retained at the front end for joint calculation, the calculated control electrical signal is directly applied to the frequency converter driver or drain solenoid valve of the filtration equipment, forcing the equipment to operate under the conditions best matched to the current hydraulic load and output the water after preliminary turbidity removal. The first purification path is a physical flow channel that determines the water quality meets the direct reuse standard without additional treatment; the second purification path is a fluid loop designed for water containing moderate concentrations of large suspended particles, incorporating basic filtration and sedimentation functions; the conventional physical purification unit is a collection of equipment that removes large-particle impurities from water using non-chemical means such as gravity sedimentation or mechanical centrifugal interception; the separation component is the core actuator within the conventional physical purification unit that directly performs solid-liquid separation, such as a cyclone centrifuge or an automatic backwash filter; primary purification treatment is a preliminary water quality improvement process that reduces the macroscopic turbidity of the water body through physical blocking and centrifugal separation without changing its chemical microscopic properties; the first purified effluent is a transitional or final state of water body that meets the specific mid-range water demand characteristics after treatment by the conventional physical purification unit. Figure 3 The diagram illustrates the adaptive control mechanism of separation components such as hydrocyclones in a conventional physical purification unit, presented in the form of a three-dimensional response surface. The surface describes the response relationship between the target centrifugal speed and the synchronous changes in influent flow rate and turbidity. Through this linear or nonlinear surface fitting, the optimal operating frequency can be dynamically locked based on the measured hydraulic load, achieving an optimal balance between purification effect and energy consumption.

[0044] For example, during the third cycle of continuous operation, the central control bus receives a tiered decision instruction for the current water flow, explicitly directing 30% of the water flow to the first purification path and 70% to the second purification path. The electric proportional control valve in the linkage network directly separates the 100 cubic meters per hour of pit dewatering into a 30 cubic meter per hour stream, which is then directed to the clean water reuse tank as untreated purified effluent. The remaining 70 cubic meters per hour is smoothly introduced into a cyclone centrifugal desander, a conventional physical purification unit. An electromagnetic flowmeter installed on the main inlet pipe measures the current flow rate into this unit as 70 cubic meters per hour, while real-time multi-dimensional water quality characteristics from the front-end sensor array show a turbidity of 120 NTU. The system incorporates a dynamic adjustment calculation formula for the separation component's operating parameters. This formula defines the target centrifugal speed of the separation component as equal to the base speed plus the flow rate adjustment coefficient multiplied by the flow rate data, plus the turbidity adjustment coefficient multiplied by the turbidity data. The target centrifugal speed in the formula is measured in revolutions per minute (RPM). The base speed is preset to 800 RPM, based on the minimum safe speed limit required for the motor to maintain an effective centrifugal force field without severe resonance. The flow rate adjustment coefficient is preset to +5, based on fluid dynamics tests showing that an increase of one cubic meter per hour in flow rate requires a compensation of five RPM to overcome the decrease in particle settling efficiency caused by shortened hydraulic residence time. The turbidity adjustment coefficient is preset to +2, based on empirical calibration that high-turbidity water contains more fine particles, necessitating enhanced centrifugal field gradients for forced separation. The complete calculation process from parameter substitution to final result is as follows: Upon receiving this value, the frequency converter immediately and smoothly increases the speed of the cyclone centrifugal desander from the standby state and locks it at 1,390 revolutions per minute to perform primary purification treatment. Finally, the treated large particles are discharged from the bottom sludge discharge valve, and the supernatant flows into the next stage collection pipeline as the first purified effluent.

[0045] Optionally, the method further includes: When the hierarchical decision instruction indicates the third purification path, the pit dewatering is directed into the deep composite purification unit. Based on the pH value data in the real-time multidimensional water quality characteristics, adjust the acid-base neutralization dosage in the deep composite purification unit; Based on the particulate matter morphology characteristics represented by the second set of water quality parameters in the real-time multidimensional water quality features, a target coagulant type is selected from a coagulant decision library that maps particulate matter morphology to agent type, and its dosage is dynamically adjusted to perform deep purification treatment to obtain the second purified effluent as the purified effluent.

[0046] Specifically, when the tiered decision instruction indicates the third purification path, the dewatering from the foundation pit is directed into the deep composite purification unit. The electric diversion valve group of the underlying pipe network is switched to direct the water body determined to have the highest pollution level or the most complex composition into the terminal treatment tank, which includes chemical reactions and fine filtration. Based on the pH data in the real-time multi-dimensional water quality characteristics, the dosage of acid-base neutralization chemicals in the deep composite purification unit is adjusted. The pH values ​​synchronously transmitted from the front-end sensor array are extracted, compared with the standard neutral water quality range, and the required hydrogen ion or hydroxide ion compensation equivalent is calculated. Based on this, a precise pulse control signal is output to the variable frequency metering pump to inject the corresponding concentration of acid or alkali solution. Based on the particulate matter morphology characteristics represented by the second water quality parameter set in the real-time multi-dimensional water quality characteristics, a target coagulant type is selected from a coagulant decision library that maps particulate matter morphology to chemical types, and its dosage is dynamically adjusted to perform deep purification treatment, resulting in the second purified effluent. The morphological indicators such as particle outline and surface roughness extracted by the microscopic optics observation unit are analyzed and retrieved by feature matching in a pre-set expert system relational database to identify the most suitable chemical agents for settling such particles. At the same time, the optimal dosage concentration is calculated in conjunction with the influent flow rate, and the corresponding agent dosing actuator is driven to complete the mixing reaction, ultimately causing the suspended solids to flocculate and settle, and outputting clear, high-grade reclaimed water. The third purification path is a final fluid loop designed for water bodies with severe acid-base imbalance or containing a large amount of fine suspended matter that is difficult to settle physically. It includes chemical conditioning and deep solid-liquid separation. The deep composite purification unit is a heavily polluted water treatment facility that integrates an acid-base adjustment tank, a chemical coagulation sedimentation tank, and precision filtration components. The acid-base neutralization dosage is the chemical reagent injection rate that restores the acidic or alkaline foundation pit dewatering to a level suitable for subsequent chemical flocculation and final discharge or reuse standards. The coagulant decision library is a pre-established electronic data table that stores the optimal matching relationship between the morphological characteristics of various micro-particles and the physicochemical properties of different commercially available polymeric flocculants. The target coagulant type is the specific name or model of the chemical agent selected by the system after morphological characteristic matching for the current specific water quality to break down and crystallize. The second purified effluent is pure water that has reached the highest level of reuse standards after strict chemical conditioning and deep composite treatment.

[0047] For example, in response to the extraction of slurry water from a foundation pit after a sudden rainstorm, the tiered decision-making command directs the entire flow of 50 cubic meters per hour into the third purification path, leading to the deep composite purification unit. Analysis of real-time multi-dimensional water quality characteristics reveals a current pH value of 9.0, indicating strong alkalinity. The built-in acid-base neutralization dosage calculation formula is retrieved. This formula defines the acid dosage as the influent flow rate multiplied by the difference between the measured pH value and the target pH value, then multiplied by the acidity requirement coefficient. The target pH value is set at 7.0, and the acidity requirement coefficient is preset to 0.5 liters of standard concentration hydrochloric acid per cubic meter of water for every one-unit decrease in pH. This coefficient is based on the average consumption experience value calibrated from titration experiments of the groundwater carbonate buffer system in the construction site geological survey report. Substituting the data, the acid dosage is calculated as follows: Immediately control the hydrochloric acid metering pump to inject the agent into the equalization tank at a rate of 50 liters per hour. Simultaneously, analyze the second water quality parameter set and find that the roundness value of the suspended particles in the current water flow is 0.85. Access the coagulant decision library, whose preset mapping rules are based on the fact that highly rounded and loose particles are usually clay colloids that require agents with strong charge neutralization capabilities, while low-rounded and hard particles require agents with strong polymer bridging capabilities. The decision library rules stipulate that particles with a roundness greater than 0.7 are mapped to polyaluminum chloride as the target coagulant type. After locking the agent, the system retrieves the dynamic adjustment formula for the dosage, defining the target dosage as equal to the base dosage rate multiplied by the influent flow rate and then multiplied by the morphology correction weight. The base dosage rate is preset to 0.1 kg per cubic meter. The morphology correction weight formula is 1 plus the difference between the roundness and the judgment threshold of 0.7 multiplied by an amplification factor of 2.0. The amplification factor of 2.0 is based on the physicochemical law that the surface area of ​​colloidal particles increases exponentially with increasing roundness, and the required charge neutralization sites also increase exponentially. The morphology correction weight is calculated as follows: The target dosage was then calculated by multiplying the base dosage rate of 0.1 by the flow rate of 50 and then by the weight of 1.3, resulting in a final dosage of 6.5 kg / hour for polyaluminum chloride. The dry powder dosing machine was then activated at this rate to prepare the solution and inject it into the coagulation tank for deep purification treatment, ultimately producing clear, second-stage purified effluent.

[0048] Optionally, the optimized scheduling and reuse of the purified effluent includes: The water quality data in the water reuse tank is matched with the water demand characteristic spectrum to determine the available water source blocks that meet each of the water demand signals. Based on the priority of the water demand signal, the water consumption, and the water quality level of the available water source block, the optimal water supply scheduling instruction is calculated and generated. The optimal water supply scheduling command is executed to control the water supply system to transport the water in the clean water reuse tank to the corresponding water-using terminal.

[0049] Specifically, optimizing the scheduling and reuse of purified water involves matching the water quality data in the clean water reuse tank with the water demand characteristic spectrum to determine available water source blocks that meet various water demand signals. Real-time extraction of water quality data such as turbidity and pH from multi-parameter sensors in the clean water reuse tank is performed. This data is then compared one by one across a pre-configured water demand characteristic spectrum in the system database, covering the water quality tolerance limits of various construction machinery and operational processes on site. Water bodies whose water quality indicators are completely within the allowable range of specific water demand are selected and logically marked as available water source blocks for specific terminals. Subsequently, based on the priority of water demand signals, water consumption, and the water quality level of available water source blocks, the optimal water supply scheduling instruction is calculated and generated. The request messages submitted by each water terminal through the on-site interactive panel are read to obtain their water consumption and priority. A weighted allocation algorithm is used to prioritize the supply to high-priority terminals when the available water supply is insufficient to meet all demands. The multi-dimensional allocation results are encoded into a control word sequence containing the target node and start / stop time, i.e., the optimal water supply scheduling instruction. Finally, the optimal water supply scheduling command is executed to control the water supply system to deliver water from the clean water reuse tank to the corresponding water-using terminals. The scheduling command is parsed and converted into relay opening and closing actions in the electrical control cabinet, precisely starting the variable frequency water supply pumps and electric control valves of the corresponding pipelines, and pressurizing and delivering the purified water in the storage tank to the designated construction water points according to the predetermined flow rate and timing. The system includes: a continuous set of values ​​reflecting the current physical and chemical properties of the mixed water in the water reuse pool; a multi-dimensional data lookup table pre-set by the system, specifying the upper limits of tolerance for impurity concentration and pH of water sources for different types of construction site operations, such as concrete curing or road watering; a virtual water resource pool whose water quality indicators meet the requirements of a specific water terminal after logical comparison by the system; a priority of water demand signals based on the urgency of construction progress and the safety importance of the operation category, pre-dividing the order of water request processing; an optimal water supply scheduling instruction generated by the central control system after comprehensively weighing multiple demands and available resources, used to directly drive the bottom pumps and valves to perform precise physical allocation of water volume; and a water supply system consisting of a variable frequency constant pressure pump group, multiple branch pipe networks, and electric regulating valves, which are mechanical and electrical actuators used to achieve physical water transport and pressure maintenance.

[0050] For example, in the fourth phase of operation, water demand signals from two water terminals were acquired. The first signal came from the concrete mixing plant in the core area, with a water demand of 50 cubic meters per hour, and its priority was preset to the highest level, level one. This priority was based on the stringent requirements for the continuity of concrete pouring and structural safety stipulated in the National Construction Engineering Quality Management Regulations. The second signal came from the dust suppression water trucks on the periphery of the site, with a water demand of 30 cubic meters per hour, and its priority was preset to the lower level, level two. This priority was based on the engineering common sense that although dust control is an environmental requirement, short-term interruptions will not cause structural safety accidents. The water quality data extracted from the clean water reuse tank showed a turbidity of 8 NTU and a pH of 7.2. This data was matched with the water demand characteristic spectrum, which specifies that the upper limit of water quality tolerance for the concrete mixing plant is a turbidity not exceeding 10 NTU and a pH between 6.5 and 8.5, while the upper limit of water quality tolerance for the dust suppression water truck is a turbidity not exceeding 50 NTU and a pH between 6.0 and 9.0. The system determines that the current water quality in the pool meets both requirements, thus identifying the entire water body in the clean water reuse pool as the available water source block to satisfy each water demand signal. The total available water storage capacity of the clean water reuse pool is read as 60 cubic meters. Since the available water volume of 60 cubic meters is less than the total demand of 80 cubic meters from both ends, priority-based optimized scheduling calculation is triggered. The system's built-in weighted allocation algorithm sets the allocation weight of the first priority level to 100%, meaning it must be fully satisfied, and the allocation weight of the second priority level to the proportion of the remaining amount after the first priority level is satisfied. The calculated allocation volume for the concrete mixing plant is 50 cubic meters multiplied by 100%, which equals 50 cubic meters. The remaining water volume is then calculated to be 10 cubic meters. The system allocates all of the remaining 10 cubic meters to the dust suppression water truck, with the actual demand satisfaction rate of the dust suppression water truck being 10 divided by 30, which equals 33.3%. Based on this, an optimal water supply scheduling instruction is generated, which pumps 50 cubic meters to water supply branch one and 10 cubic meters to water supply branch two. The underlying control bus executes the optimal water supply scheduling command, controlling the No. 1 variable frequency pump of the water supply system to operate at the rated frequency to accurately deliver 50 cubic meters of water to the water storage tower of the concrete mixing plant, and simultaneously controlling the No. 2 variable frequency pump to operate at a low frequency to deliver 10 cubic meters of water to the water loading arm of the dust suppression sprinkler truck.

[0051] Optionally, the method further includes: Large debris in the foundation pit dewatering is intercepted by the load sensing preprocessing device, and an auxiliary load signal characterizing the physical load of the incoming water is obtained. The auxiliary load signal is used as an auxiliary input parameter for the dynamic grading model; The dynamic grading model uses the auxiliary load signal to predict the water quality change trend of the foundation pit dewatering and adjusts its internal weight parameters in advance.

[0052] Specifically, a load sensing preprocessing device intercepts large debris in the foundation pit dewatering and acquires an auxiliary load signal characterizing the physical load of the incoming water. A mechanical bar screen with an automatic cleaning scraper is installed at the front end of the main inlet pipe of the foundation pit pump, using physical bars to block branches, plastic bags, or large clumps of mud sucked up by the water flow. Simultaneously, a high-precision torque sensor is installed on the force-bearing support shaft of the mechanical bar screen to continuously measure the physical thrust borne by the bars when intercepting debris, and converts these mechanical deformation electrical signals into digital signals characterizing the intensity of macroscopic debris inflow. This auxiliary load signal is used as an auxiliary input parameter for the dynamic grading model. After receiving the torque digital signal, it undergoes low-pass filtering and amplitude normalization to construct an additional data channel, which is aligned and spliced ​​with the preceding water quality physicochemical parameters and microscopic particle morphology data, and fed into the input layer of the central decision model. The dynamic grading model uses the auxiliary load signal to predict the water quality change trend of the foundation pit dewatering and adjusts its internal weight parameters in advance. The decision model incorporates a time series prediction module to analyze the ramp-up slope of continuously input load signals. When a sharp increase in interception load is detected within a short period, the model determines that the influent water source is about to change from relatively clear to heavily turbid. It then proactively modifies the parameters of the corresponding high-pollution purification path through a feedforward control channel. The weight update formula required for this prediction and adjustment process is as follows: ; in the formula The adjusted internal weight parameters are dimensionless. The original preset internal weight parameters are dimensionless. To predict the dimensionless quantity of the sensitivity coefficient, The quantity representing the change in the auxiliary load signal within the observation time window is in Newtons. The time span of the observation window is measured in seconds. The auxiliary load signal is the rate of change of the load signal over time. The load sensing preprocessing device is a primary filter assembly integrating a physical and mechanical interception structure and force sensing elements to block large debris at the front end and simultaneously measure macroscopic hydraulic thrust. Large debris refers to macroscopic floating or suspended matter carried by the pit dewatering, which is much larger than conventional silt particles and easily causes subsequent pipe blockage or pump impeller jamming. The auxiliary load signal is a continuous electrical signal derived from the physical force state of the front-end interception device, used to characterize the instantaneous influx intensity of macroscopic solid matter in the current influent. The auxiliary input parameters are feedforward data variables added to the model in addition to the conventional water quality physicochemical and microscopic morphological characteristics to enhance its perception of extreme operating conditions. The water quality change trend is the time-domain trend of a rapid deterioration or improvement in water turbidity or pollutant concentration in the near future, calculated based on the dynamic fluctuation slope of the macroscopic physical load of the influent. The internal weighting parameters are product factors used in the dynamic grading model to determine the proportion of probability calculations for each grade.

[0053] For example, during the excavation of a large foundation pit down to a water-bearing sand layer, a sudden localized breach in the borehole wall caused a large amount of highly concentrated mud to be sucked into the pump. The torque sensor of the load-sensing preprocessing device installed at the front end began continuously collecting data. A set observation time window was established. The time window is five seconds, based on the average physical time it takes for water to flow from the front-end screen to the subsequent high-precision water quality sensor, ensuring the action is completed just before contaminated water arrives. At time zero, the sensor measures an initial thrust of 100N, which surges to 500N at the fifth second due to the impact of a large accumulation of mud. The change in the auxiliary load signal within the observation time window is calculated. The value is 100N. Therefore, the load change rate is calculated to be 400 divided by 5, which equals 80 Newtons per second. The original preset internal weight parameters of the deep composite purification path are retrieved. It is 0.5. The built-in predictive sensitivity coefficient. The preset value is 0.005. This coefficient is based on engineering experience calibration determined through multiple field mud impact simulation experiments, designed to both capture disastrous mud inrushes and shield against interference from conventional tree branches. The value is then substituted into the weight update formula for verification. Upon receiving new weight parameters, the dynamic grading model overwrites the current stable output based on lagging water quality data, triggering the activation command for the third purification path targeting severe water quality.

[0054] Reference Figure 4 Based on the same inventive concept, the present invention also provides a method system for purifying and reusing foundation pit dewatering with water quality identification function, the system comprising: The multi-dimensional feature perception module is used to acquire real-time multi-dimensional water quality characteristics of the foundation pit dewatering. The intelligent classification decision module is used to input the real-time multidimensional water quality characteristics into a preset dynamic classification model with dynamic parameter adjustment function, and generate the classification decision command and dynamic diversion ratio of the current water flow. The diversion execution module is used to control the preset diversion execution mechanism to guide the pit dewatering into the corresponding adaptive purification path according to the hierarchical decision instruction and the dynamic diversion ratio. An adaptive purification module is used to perform adaptive parameter purification on the imported pit dewatering using a purification unit corresponding to the adaptive purification path, so as to obtain purified effluent. The status feedback monitoring module is used to monitor the water quality status of the purified effluent and the status of the clear water reuse tank, generate status feedback signals, and use the status feedback signals to dynamically adjust the dynamic grading model. The optimized scheduling and reuse module is used to acquire water demand signals from water terminals and optimize the scheduling and reuse of the purified water based on a preset definition of water demand characteristic spectrum of different water terminals for water quality requirements.

[0055] 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.

[0056] 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. A method for purifying and reusing foundation pit dewatering with water quality identification function, characterized in that: Obtain real-time multidimensional water quality characteristics of foundation pit dewatering; The real-time multidimensional water quality characteristics are input into a preset dynamic classification model with dynamic parameter adjustment function to generate the current water flow classification decision command and dynamic diversion ratio. Based on the hierarchical decision-making instructions and the dynamic diversion ratio, the preset diversion execution mechanism is controlled to guide the pit dewatering into the corresponding adaptive purification path. The imported pit dewatering water is subjected to adaptive parameter purification processing by the purification unit corresponding to the adaptive purification path to obtain purified effluent. The water quality status of the purified effluent and the state of the clear water reuse tank are monitored, a status feedback signal is generated, and the dynamic grading model is dynamically adjusted using the status feedback signal. The system acquires water demand signals from water-using terminals and optimizes the scheduling and reuse of purified water based on a predefined water demand characteristic spectrum of different water-using terminals for water quality requirements.

2. The method for purifying and reusing foundation pit dewatering with water quality identification function according to claim 1, characterized in that, The real-time multidimensional water quality characteristics of the foundation pit dewatering obtained include: The first set of water quality parameters for the dewatering in the foundation pit is collected by a preset water quality sensor array; The flow image sequence of the groundwater in the foundation pit was obtained by using a preset miniature optical observation unit; Image feature analysis was performed on the flow image sequence to extract a second set of water quality parameters characterizing the morphology and distribution of particulate matter; By integrating the first water quality parameter set and the second water quality parameter set, real-time multidimensional water quality features are generated.

3. The method for purifying and reusing foundation pit dewatering with water quality identification function according to claim 1, characterized in that, The hierarchical decision-making instructions and dynamic diversion ratios for generating the current water flow include: The dynamic grading model receives the real-time multidimensional water quality features and outputs the probability estimate of the current water flow belonging to each water quality level. Based on the probability estimate and the preset strategy optimization function, the dynamic diversion ratio used to control the diversion execution mechanism is calculated; Based on the probability estimate, the dominant water quality level is determined, and a graded decision instruction is generated.

4. The method for purifying and reusing foundation pit dewatering with water quality identification function according to claim 1, characterized in that, The generated state feedback signal includes: Collect water quality data of the purified water from each of the purification units and generate unit effluent water quality signals; Collect mixed water quality data and liquid level data of the clear water reuse tank to generate a status signal inside the tank; The effluent water quality signal from the unit is combined with the state signal inside the pool to generate the state feedback signal.

5. A method for purifying and reusing foundation pit dewatering with water quality identification function according to claim 4, characterized in that, The step of dynamically adjusting the dynamic hierarchical model using the state feedback signal includes: By analyzing the effluent water quality signal of the unit in the status feedback signal, the actual treatment efficiency of each purification unit can be obtained. Based on the deviation between the actual processing efficiency and the preset target range of ideal working effect, the level threshold parameters associated with the corresponding purification path in the dynamic grading model are adjusted in reverse. The pool state signal in the state feedback signal is analyzed, and combined with a preset water demand prediction function for predicting future water load, the parameters related to direct reuse and diversion ratio in the dynamic grading model are adjusted.

6. A method for purifying and reusing foundation pit dewatering with water quality identification function according to claim 1, characterized in that, The purified effluent includes: When the hierarchical decision instruction indicates the first purification path, the dewatering water from the foundation pit is directly introduced into the clean water reuse tank as purified effluent. When the hierarchical decision instruction indicates the second purification path, the pit dewatering is directed into the conventional physical purification unit. Based on the flow rate data of the foundation pit dewatering flowing into the conventional physical purification unit and the turbidity data in the real-time multidimensional water quality characteristics, the operating parameters of the separation components in the conventional physical purification unit are dynamically adjusted to perform primary purification treatment and obtain the first purified effluent as the purified effluent.

7. A method for purifying and reusing foundation pit dewatering with water quality identification function according to claim 6, characterized in that, The method further includes: When the hierarchical decision instruction indicates the third purification path, the pit dewatering is directed into the deep composite purification unit. Based on the pH value data in the real-time multidimensional water quality characteristics, adjust the acid-base neutralization dosage in the deep composite purification unit; Based on the particulate matter morphology characteristics represented by the second set of water quality parameters in the real-time multidimensional water quality features, a target coagulant type is selected from a coagulant decision library that maps particulate matter morphology to agent type, and its dosage is dynamically adjusted to perform deep purification treatment to obtain the second purified effluent as the purified effluent.

8. A method for purifying and reusing foundation pit dewatering with water quality identification function according to claim 1, characterized in that, The optimized scheduling and reuse of the purified effluent includes: The water quality data in the water reuse tank is matched with the water demand characteristic spectrum to determine the available water source blocks that meet each of the water demand signals. Based on the priority of the water demand signal, the water consumption, and the water quality level of the available water source block, the optimal water supply scheduling instruction is calculated and generated. The optimal water supply scheduling command is executed to control the water supply system to transport the water in the clean water reuse tank to the corresponding water-using terminal.

9. A method for purifying and reusing foundation pit dewatering with water quality identification function according to claim 1, characterized in that, The method further includes: Large debris in the foundation pit dewatering is intercepted by the load sensing preprocessing device, and an auxiliary load signal characterizing the physical load of the incoming water is obtained. The auxiliary load signal is used as an auxiliary input parameter for the dynamic grading model; The dynamic grading model uses the auxiliary load signal to predict the water quality change trend of the foundation pit dewatering and adjusts its internal weight parameters in advance.

10. A foundation pit dewatering purification and reuse system with water quality identification function, applied to the foundation pit dewatering purification and reuse method with water quality identification function as described in any one of claims 1-9, characterized in that, The system includes: The multi-dimensional feature perception module is used to acquire real-time multi-dimensional water quality characteristics of the foundation pit dewatering. The intelligent classification decision module is used to input the real-time multidimensional water quality characteristics into a preset dynamic classification model with dynamic parameter adjustment function, and generate the classification decision command and dynamic diversion ratio of the current water flow. The diversion execution module is used to control the preset diversion execution mechanism to guide the pit dewatering into the corresponding adaptive purification path according to the hierarchical decision instruction and the dynamic diversion ratio. An adaptive purification module is used to perform adaptive parameter purification on the imported pit dewatering using a purification unit corresponding to the adaptive purification path, so as to obtain purified effluent. The status feedback monitoring module is used to monitor the water quality status of the purified effluent and the status of the clear water reuse tank, generate status feedback signals, and use the status feedback signals to dynamically adjust the dynamic grading model. The optimized scheduling and reuse module is used to acquire water demand signals from water terminals and optimize the scheduling and reuse of the purified water based on a preset definition of water demand characteristic spectrum of different water terminals for water quality requirements.

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