Sewage treatment process for inhibiting calcification crystallization

Abstract

The invention relates to the technical field of sewage treatment power load regulation and control, and discloses a sewage treatment process for inhibiting calcification crystallization, which comprises the following steps: acquiring a working condition feature vector representing a sewage quality state; retrieving a regulation and control strategy of the controlled power load unit by using the stored Hash index structure; according to the strategy, the output frequency of a power supply source of the magnetic separation load unit is adjusted to generate a resonance magnetic field, and the output voltage of a driving power source of the nano bubble generation load unit is adjusted. A crystallization nucleation path is intervened through the synergistic effect of a physical energy field and interface adsorption, association mapping between the operation cost of the processing system and the actual crystallization risk is established, and the technical problems that the load is too heavy and the control response is lagged in online dynamic simulation calculation are solved.

Description

Technical Field

[0001] This invention relates to the field of power load regulation technology for wastewater treatment, and specifically to a wastewater treatment process for inhibiting calcification and crystallization. Background Technology

[0002] In current drainage systems for tunnels in limestone areas, groundwater carrying a large amount of calcium ions and residual chemical agents when seeping through the lining concrete is a common technical strategy. A common approach is to use electrostatic descaling equipment or chemical dosing devices with fixed power output. These actuators are driven by the power distribution system and operate at preset rated operating points to maintain a basic electrochemical reaction environment. However, in actual tunnel service environments, the outflow rate and chemical composition of groundwater exhibit dramatic nonlinear phase transition characteristics. The seepage path of groundwater and the dynamic changes in lining precipitates cause the water body to exhibit extremely high crystallization tendency and transient volatility. Fixed-parameter load control strategies show significant rigidity mismatch when dealing with such dynamic conditions. When the water quality is in a crystallization-sensitive period, the solidified power output cannot provide sufficient physical field strength or energy density to disrupt the formation conditions of crystal nuclei; while in non-sensitive periods, continuous power supply results in ineffective losses of the actuators.

[0003] To address these challenges, the industry has attempted to introduce threshold feedback control logic based on single-point water quality indicators. This logic adjusts the output current of the power supply system by monitoring changes in the concentration of specific ions. However, in practical applications, calcification crystallization is affected by... The synergistic influence of multiple parameters such as energy value, ion activity, and organic charge properties means that simply increasing the power load intensity or control frequency not only generates heat in the excitation coil and losses in high-frequency components, but also fails to accurately capture the inducing factors in the crystallization path. This improvement scheme based on hysteresis feedback is still logically an extension of open-loop regulation, and it is difficult to build an effective closed-loop power control under extreme operating conditions with multiple intertwined variables. For example, the utility model patent with authorization announcement number CN211871559U discloses an electrochemical descaling device that adds a filter mechanism and storage box to the drainage pipeline to physically intercept and prevent scale leakage. This type of design is a last-ditch remedy for the crystallization result, and the control strategy has static characteristics. It cannot achieve high-precision matching of execution instructions based on the transient fluctuations of fluid chemical composition. The lack of operating condition characteristic index leads to a structural mismatch between system energy output and actual crystallization risk. The power parameters of the actuator are difficult to align with the energy level requirements of crystallization kinetics. There is a lack of preventive intervention in the early stage of nucleation, making it difficult to block the nucleation path at the surface level.

[0004] Therefore, the technical problem to be solved by this invention is how to extract multi-dimensional working condition fingerprints and perform correlation retrieval to achieve millisecond-level generation of magnetic field frequency and energy density, thereby achieving dynamic hedging against the crystallization evolution path in the very early stage of crystal nucleus formation. Summary of the Invention

[0005] This invention proposes a wastewater treatment process for inhibiting calcification and crystallization, comprising the following steps: Step S101: Obtain the working condition feature vector characterizing the crystallization kinetic state in the wastewater. The working condition feature vector includes: calcium ion activity, solubility product deviation defined by the product of calcium ion activity and carbonate ion activity relative to the solubility product constant of calcite, and organic matter charge characterized by the concentration of residual reagents in the wastewater. Step S102: Input the operating condition feature vector into the processor and use the preset operating condition hash index structure to perform feature space matching to obtain the load control strategy of the controlled power load unit. The operating condition hash index structure is pre-established based on the mapping relationship between each feature parameter and the saturation critical point in the historical operating condition data. The load control strategy includes: the output frequency command for the magnetic separation load unit and the output voltage command for the nanobubble generating load unit. Step S103 involves adjusting the operating parameters of the power load unit in the wastewater treatment system according to the load control strategy. Specifically, this includes: adjusting the output frequency of the drive power supply of the magnetic separation load unit to generate a resonant magnetic field with a magnetic induction intensity fluctuating between 0.5T and 1.2T and a fluctuation frequency matching the resonant frequency of calcium ions, thereby changing the hydration energy of calcium ions and reducing their crystallization activity; simultaneously adjusting the output voltage of the drive power supply of the nanobubble generating load unit to inject nanobubbles with a particle size distribution in the range of 50nm to 200nm into the wastewater, degrading the residual coagulant components in the wastewater, and interfering with the nucleation path of crystallization nuclei through the superposition of physical energy field and physicochemical interface adsorption.

[0006] Preferably, before obtaining the operating condition feature vector in step S102, the method further includes using the processor to read the analog-to-digital conversion values ​​output by the monitoring sensor, extracting feature parameters representing calcium ion concentration, temperature, and pH value in the current water body, and encapsulating the feature parameters into a multi-dimensional feature vector; feature space matching is achieved by mapping the operating condition feature vector to the hash space and calculating the Hamming distance.

[0007] Preferably, the calculation rule for the solubility product deviation S in step S101 is as follows: obtain the activity of calcium ions in wastewater. Activity with carbonate ions The solubility product deviation S is determined using the following formula: ,in, Let be the solubility product constant of calcite.

[0008] Preferably, in step S103, when adjusting the output frequency of the drive power supply for the magnetic separation load unit, the adjustment step size of the output frequency is set based on the time change rate of the solubility product deviation S, and when the time change rate exceeds a preset threshold, the current switching frequency of the drive power supply is increased.

[0009] Preferably, in step S103, when adjusting the output voltage of the driving power supply of the nanobubble generating load unit, the set value of the output voltage is non-linearly mapped to the organic charge in the operating condition feature vector, and the unit volume injection density of the nanobubbles is controlled by adjusting the duty cycle of the driving power supply.

[0010] Preferably, the operating condition hash index structure in step S102 is established through the following steps: collecting historical water quality data, calculating the chemical equilibrium saturation corresponding to each set of historical water quality data, calibrating the power load operating parameters that keep the water quality in an unsaturated state, and storing the historical water quality data as an index key in the database.

[0011] Preferably, before obtaining the operating condition feature vector in step S101, the method further includes collecting particulate matter concentration data of wastewater and calculating the calcium carbonate deposition rate based on the slope of the time change of particulate matter concentration.

[0012] Preferably, the method further includes: determining whether the calcium carbonate deposition rate exceeds a preset crystallization sensitive period threshold, and triggering feature space matching in step S102 and switching the drive power supply to high power output mode when the calcium carbonate deposition rate exceeds the crystallization sensitive period threshold.

[0013] Preferably, adjusting the operating parameters in step S103 further includes: real-time monitoring of the solubility product deviation value; when the solubility product deviation continues to be lower than the preset balance threshold, reducing the excitation current duty cycle of the magnetic separation load unit drive power supply to enter a low-power operation state.

[0014] Preferably, the specific process of generating nanobubbles in step S103 is as follows: introducing compressed air and cooperating with high-speed shearing to form nanobubbles, and using the surface tension of nanobubbles to destroy the molecular chain structure of organic polymer components in wastewater.

[0015] The beneficial effects of this invention are: 1. In wastewater treatment to suppress calcification and crystallization, a real-time hash fingerprint is calculated based on the water quality characteristic parameters obtained by monitoring sensors. Combined with a preset crystallization kinetic characteristic index library, a local sensitive hash retrieval is performed to establish a correlation mapping between the current operating conditions and the historical optimal control parameters. By extracting key features such as solubility product deviation and organic matter charge, the crystallization evolution path under transient fluctuations of tunnel water inflow is identified in real time. This enables the control unit to output precise hedging commands for the current physicochemical state, effectively solving the response lag problem of traditional single-factor threshold feedback control in a multivariable coupled environment.

[0016] 2. By coordinating the excitation coil frequency of the metal ion magnetic separation device with the injection pressure of the nanobubble generator, a deep synergy is achieved between the physical adsorption process and the physicochemical degradation process. The magnetic field generated by the excitation current fluctuates in the range of 0.5T to 1.2T, and its ion activity is reduced by matching the resonant frequency with that of calcium ions. At the same time, the injected nanobubbles with a particle size distribution of 50nm to 200nm utilize their surface energy to degrade the residual quick-coagulants and water-reducing agents and other high-molecular organic matter in the wastewater. This composite mechanism simultaneously blocks the two crystallization pathways of ion nucleation and organic induction at the surface level, thereby achieving structural intervention against the risk of sludge blockage at the very early stage of crystallization nucleus formation.

[0017] 3. The control unit dynamically adjusts the operating conditions of the external load based on the optimal strategy obtained through retrieval. Since the adjustment action is entirely based on the precise matching of real-time water quality characteristics and historical successful strategies, rather than maintaining high-energy-consuming operation throughout the day, it achieves linear decoupling between the operating cost of the treatment system and the actual crystallization risk. Hash indexing technology is used to replace complex online dynamic simulation calculations, reducing the computational load of the processor and ensuring that the output frequency of control commands can cover the transient fluctuations of groundwater components. While ensuring that the water quality is maintained in an unsaturated state, it effectively eliminates the risk of drainage system corrosion and secondary pollution hazards caused by conventional chemical cleaning schemes. Attached Figure Description

[0018] 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the power load control process retrieved by the hash index of this invention; Figure 2 This is the logic diagram for the decomposition of operating condition feature vectors and the collaborative control of dual load units in this invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] A wastewater treatment process for inhibiting calcification crystallization includes the following steps: Step S101: Obtain the working condition feature vector characterizing the crystallization kinetic state in the wastewater. The working condition feature vector includes: calcium ion activity, solubility product deviation defined by the product of calcium ion activity and carbonate ion activity relative to the solubility product constant of calcite, and organic matter charge characterized by the concentration of residual reagents in the wastewater. Step S102: Input the operating condition feature vector into the processor and use the preset operating condition hash index structure to perform feature space matching to obtain the load control strategy of the controlled power load unit. The operating condition hash index structure is pre-established based on the mapping relationship between each feature parameter and the saturation critical point in the historical operating condition data. The load control strategy includes: the output frequency command for the magnetic separation load unit and the output voltage command for the nanobubble generating load unit. Step S103 involves adjusting the operating parameters of the power load unit in the wastewater treatment system according to the load control strategy. Specifically, this includes: adjusting the output frequency of the drive power supply of the magnetic separation load unit to generate a resonant magnetic field with a magnetic induction intensity fluctuating between 0.5T and 1.2T and a fluctuation frequency matching the resonant frequency of calcium ions, thereby changing the hydration energy of calcium ions and reducing their crystallization activity; simultaneously adjusting the output voltage of the drive power supply of the nanobubble generating load unit to inject nanobubbles with a particle size distribution in the range of 50nm to 200nm into the wastewater, degrading the residual coagulant components in the wastewater, and interfering with the nucleation path of crystallization nuclei through the superposition of physical energy field and physicochemical interface adsorption.

[0022] Preferably, before obtaining the operating condition feature vector in step S102, the method further includes using the processor to read the analog-to-digital conversion values ​​output by the monitoring sensor, extracting feature parameters representing calcium ion concentration, temperature, and pH value in the current water body, and encapsulating the feature parameters into a multi-dimensional feature vector; feature space matching is achieved by mapping the operating condition feature vector to the hash space and calculating the Hamming distance.

[0023] Preferably, the calculation rule for the solubility product deviation S in step S101 is as follows: obtain the activity of calcium ions in wastewater. Activity with carbonate ions The solubility product deviation S is determined using the following formula: ,in, Let be the solubility product constant of calcite.

[0024] Preferably, in step S103, when adjusting the output frequency of the drive power supply for the magnetic separation load unit, the adjustment step size of the output frequency is set based on the time change rate of the solubility product deviation S, and when the time change rate exceeds a preset threshold, the current switching frequency of the drive power supply is increased.

[0025] Preferably, in step S103, when adjusting the output voltage of the driving power supply of the nanobubble generating load unit, the set value of the output voltage is non-linearly mapped to the organic charge in the operating condition feature vector, and the unit volume injection density of the nanobubbles is controlled by adjusting the duty cycle of the driving power supply.

[0026] Preferably, the operating condition hash index structure in step S102 is established through the following steps: collecting historical water quality data, calculating the chemical equilibrium saturation corresponding to each set of historical water quality data, calibrating the power load operating parameters that keep the water quality in an unsaturated state, and storing the historical water quality data as an index key in the database.

[0027] Preferably, before obtaining the operating condition feature vector in step S101, the method further includes collecting particulate matter concentration data of wastewater and calculating the calcium carbonate deposition rate based on the slope of the time change of particulate matter concentration.

[0028] Preferably, the method further includes: determining whether the calcium carbonate deposition rate exceeds a preset crystallization sensitive period threshold, and triggering feature space matching in step S102 and switching the drive power supply to high power output mode when the calcium carbonate deposition rate exceeds the crystallization sensitive period threshold.

[0029] Preferably, adjusting the operating parameters in step S103 further includes: real-time monitoring of the solubility product deviation value; when the solubility product deviation continues to be lower than the preset balance threshold, reducing the excitation current duty cycle of the magnetic separation load unit drive power supply to enter a low-power operation state.

[0030] Preferably, the specific process of generating nanobubbles in step S103 is as follows: introducing compressed air and cooperating with high-speed shearing to form nanobubbles, and using the surface tension of nanobubbles to destroy the molecular chain structure of organic polymer components in wastewater.

[0031] Example 1: In a specific limestone tunnel drainage engineering application scenario, groundwater infiltrates through the lining concrete, carrying a large amount of calcium ions and residual organic agents such as quick-setting agents. This causes the sewage in the drainage system to exhibit an extremely high tendency to crystallize and the water quality to show drastic nonlinear fluctuations. This rheological environment makes traditional fixed-parameter control strategies unable to provide sufficient physical field strength when the water quality is in the crystallization-sensitive period, while generating unnecessary power loss and actuator heating during non-sensitive periods. The system uses a bus to read the analog-to-digital conversion values ​​output by the monitoring sensors in real time, extracting the current calcium ion concentration, temperature, and other parameters in the water. The characteristic parameters of the value are used to calculate the activity of calcium ions in wastewater. Activity with carbonate ions This allows us to determine the solubility product deviation S; the specific calculation formula is as follows: Where S is the solubility product deviation, This refers to the activity of calcium ions. This represents the activity of carbonate ions. The solubility product constant of calcite is given. The processor encapsulates the calcium ion activity, solubility product deviation S, and organic matter charge characterized by the concentration of residual reagents in wastewater into a multi-dimensional operating condition feature vector, and maps it to a preset operating condition hash index structure. By calculating the Hamming distance between the current feature vector and the historical best control strategy index key, the optimal load control strategy for the current transient water quality is retrieved, which includes the output frequency command for the magnetic separation load unit and the output voltage command for the nanobubble generating load unit.

[0032] After receiving the load regulation strategy, the control unit adjusts the output frequency of the magnetic separation load unit drive power supply to drive the excitation coil to generate a magnetic induction intensity fluctuating between 0.5T and 1.2T, with the fluctuation frequency matching the resonant magnetic field of the calcium ion resonance frequency. The calibration process for this calcium ion resonance frequency is as follows: During the system power-on self-test phase, the processor drive power supply performs a step-by-step frequency sweep, with the sweep range set between 50Hz and 3000Hz and the frequency step increment being 10Hz. Simultaneously, a high-sensitivity current transformer is used to monitor the impedance feedback of the excitation circuit. When a step decrease in the circuit impedance amplitude is detected and the corresponding phase angle shift reaches its maximum value, the processor automatically locks this frequency point as the calcium ion resonance frequency in the current fluid environment. The characteristic resonance frequency is set as the reference carrier frequency for the control command. By changing the hydration energy of calcium ions, their crystallization activity is reduced. Simultaneously, the control unit synchronously adjusts the output voltage of the driving power supply of the nanobubble generating load unit. By adjusting the duty cycle of the driving power supply, the injection density per unit volume of nanobubbles with a particle size distribution in the range of 50nm to 200nm is controlled. The surface tension of the nanobubbles is used to destroy the molecular chain structure of organic macromolecules in the wastewater, thereby achieving the physicochemical degradation of the quick-coagulant components. This superposition of physical energy field and physicochemical interface adsorption interferes with the nucleation path of crystallization nuclei, achieving linear decoupling between the operating cost of the treatment system and the actual crystallization risk, while ensuring the wastewater... The value adjustment range is maintained between 6.8 and 7.2, and the water quality is in an unsaturated state, which eliminates the corrosion and secondary pollution of the drainage system that may be caused by conventional chemical cleaning solutions.

[0033] The wastewater treatment system determines its intervention depth by real-time monitoring of the solubility product deviation S. When the solubility product deviation S remains below a preset equilibrium threshold, the system automatically reduces the output power of the power load unit to enter an energy-saving inspection mode. If the solubility product deviation S shows an upward trend accompanied by a surge in organic matter charge Q, the processor will reset the optimal strategy point in the hash space and execute a step response of the power load through the control unit, thereby maintaining the dynamic equilibrium of the drainage channel. The quantification result of the sedimentation rate warning index R is directly fed back to the processor's scheduling logic, and the calculation method follows the principle of homogeneity of physical dimensions. In the formula, R is the early warning index of deposition rate. The concentration of the effluent. Let T be the influent concentration, T be the sampling period, and A be the effective deposition area. The sampling period parameter in the formula is dynamically compensated by the transient influent flow rate fed back by the flow meter. The calculation logic is as follows: divide the 100L effective physical volume of the wastewater treatment reaction chamber by the current hourly influent flow rate in cubic meters. The quotient obtained is the hydraulic retention time, which is used as the sampling period in the formula. Through this logic, it is ensured that the difference between the influent and effluent concentrations is for the same batch of fluid that has undergone a sufficient reaction time in the physical field. This eliminates the structural interference caused by sudden changes in fluid flow rate on the accuracy of deposition rate calculation. This closed-loop control method based on feature space matching enables high-frequency alignment between the intensity of physical field intervention and the transient nucleation risk of wastewater.

[0034] Example 2: In a simulated verification scenario of tunnel drainage engineering in a limestone area, a circulating water test platform was used to test the process efficiency. The water quality monitoring unit of this test platform has a sampling frequency of 100Hz and a signal resolution of ±0.1%. The original water sample used in the test was taken from the on-site drainage pumping station. The mass concentration fluctuated between 450 mg / L and 550 mg / L initially. The values ​​were between 8.5 and 9.2, and the water contained residual quick-coagulant components at a mass concentration of 50 mg / L. The test environment was superimposed with a power frequency interference signal with a signal-to-noise ratio of 20 dB. To extract the true kinetic characteristics in a high-noise environment, the processor was configured with a sliding time buffer of 256 sampling points. A signal sampling frequency of 1000 Hz was used to discretize the original analog signal. The window sliding step size for each processing cycle was set to 128 sampling points to ensure a 50% overlap between adjacent windows. Within each window, the processor performed median filtering and arithmetic mean calculation on the sampling points to eliminate random voltage spikes caused by 50 Hz power frequency electromagnetic radiation, thereby ensuring that the data participating in the hash index retrieval has a high degree of physical representativeness. When setting the sampling period T, the main considerations were to balance the impact of water quality fluctuations on the real-time performance of data acquisition and the processor's computational load. transient rate of change Below When the sampling period T approaches the upper limit of the range of values, 60s, When the threshold is exceeded, in order to capture the characteristic jumps during the crystallization sensitive period, the sampling period T is adjusted to 10s, with an initial flow rate of Furthermore, under the condition of a water temperature of 20℃, the benchmark sampling period determined by the experiment was 15s.

[0035] During the performance verification process, four experimental groups were set up to observe the correlation between technical characteristics. Control group 1 used a static magnetic field with a magnetic induction intensity of 1.0T; control group 2 only activated the nanobubble generator with a particle size distribution around 100nm; control group 3 removed the operating condition hash index structure and adopted a fixed parameter control mode; and the experimental group implemented a complete power load control strategy. After 72 hours of operation, the deposition mass m per unit area on the inner wall of the pipeline was measured. The results showed that the crystallization inhibition rate of control group 1 was 42.5%, the crystallization inhibition rate of control group 2 was 38.2%, while the experimental group achieved a crystallization inhibition rate of 94.6% within a solubility product deviation S gradient of 1.5 to 2.0. The deposition mass m decreased from that of the original water sample. Down to Data shows that the resonant magnetic field generated by the magnetically separated load unit reduces calcium ion activity. By weakening the nucleation driving force, the nanobubbles generated by the nanobubble generating support unit degrade the organic components that serve as crystallization nuclei through surface adsorption. The synergistic effect of the two causes the inhibition efficiency to increase nonlinearly.

[0036] To determine the boundary effects of process parameters, gradient sensitivity analysis was performed on magnetic induction intensity B and nanobubble injection density D. When the magnetic induction intensity B was set at 0.3T, the measured crystallization inhibition rate was only 56.4% because the energy field intensity could not effectively overcome the hydration energy barrier of calcium ions. When the magnetic induction intensity B was increased to 1.2T, the crystallization inhibition rate entered the saturation region around 95%. Further increasing the magnetic induction intensity B to 1.5T resulted in an increase of only 0.3% in the inhibition rate, accompanied by a 12.5% ​​decrease in the system energy efficiency ratio due to the heating of the excitation coil. Furthermore, verification of the nanobubble particle size showed that when the bubble size exceeded 250nm, the reduced Brownian motion caused the residence time in wastewater to shorten from 120s to 45s, leading to an inflection point in the organic matter degradation efficiency, dropping from above 90%. The measured values ​​were reduced to 62.8%. These measurements confirm that the system operates within the optimal window for magnetic induction intensity (0.5T to 1.2T) and bubble particle size (50nm to 200nm). In long-term stability testing, the system faced a disturbance where the suspended solids content in wastewater suddenly increased from 200mg / L to 800mg / L. Through real-time matching of the operating condition feature vector, compensation commands were retrieved and output from the hash space, dynamically increasing the output power of the controlled power load unit. This kept the fluctuation range of the solubility product deviation S within ±5.5%, demonstrating its adaptability to dynamic water quality. By comparing the output signals of the experimental group before and after electromagnetic noise injection, the correlation coefficient of the control parameters was measured to be no less than 0.985, indicating that the locally sensitive hash structure filtered out random disturbance signals and ensured the determinism of the control logic.

[0037] Example 3: In a tunnel drainage system maintenance scenario where groundwater salinity periodically increases due to geological structural changes, the original treatment process parameters exhibit control deviations due to drift in the crystallization kinetic environment. This manifests as an increase in the solubility product deviation (S) index at the end of the drainage pipe. The system executes an online calibration and multi-unit load collaborative reconstruction procedure targeting the operating condition hash index structure. The initial state used for calibration targets areas carrying accelerator residues and... For real-time wastewater with a mass concentration of 600 mg / L, the processor reads the baseline values ​​of the electrodes and temperature sensors at a standard water temperature of 25℃ via the bus interface and initiates the saturation critical point calibration process. A 5% sodium carbonate solution is uniformly dripped into the wastewater sample using a dosing pump. Simultaneously, a laser scattering particle size analyzer monitors the appearance time of particles larger than 0.5 μm and records the solubility product deviation S at this point. When a step increase in particle scattering intensity is observed, the instantaneous S value is defined as the saturation critical point. .

[0038] To improve the determinism of hash index matching, the processor performs a discretization mapping operation on the feature vector V, calcium ion activity. The solubility product deviation S and the organic charge Q are all converted into integer values ​​using a linear quantization operator. The quantization step is set according to the sensor resolution. Among them, the quantized value of calcium ion activity is... The calculation formula is as follows: ,in, These are the quantized feature components. To measure calcium ion activity, The preset lower limit for calcium ion activity measurement is Δa, which is the step length of the corresponding quantization interval and is set to 0.01 in this embodiment. The processor combines the quantized components into an integer vector and uses a consistent hashing algorithm to map it to the control strategy space in memory to obtain the corresponding pulse drive parameters. When the system faces a multi-load unit cluster deployment of a large drainage station, the control unit introduces a load weight factor W to perform collaborative management. The load weight factor W is calculated in real time based on the current temperature rise of the excitation coil of each unit and the cumulative operating hours of the nanobubble generator. The high-frequency control command is allocated to the load unit with a higher weight factor W index during operation. During the process, the processor maintains a sliding time window of 24 hours and periodically compares the deposition rate residuals before and after processing. If the residual exceeds the preset deviation tolerance of 8.5% for three consecutive windows, it is determined that a sudden change in geological conditions has occurred, triggering a hash table reconstruction program. The reconstruction program incrementally corrects the hash index structure by retrieving historical feature points in the buffer, so that the system can return to dynamic equilibrium without interrupting drainage. This repair procedure, through a defined mathematical mapping and experimental calibration process, ensures that the output of magnetic induction intensity in the range of 0.5T to 1.2T always matches the transient crystallization risk, maintaining the stability of the process framework in complex industrial environments.

[0039] Example 4: In the offline calibration scenario before the deployment of the wastewater treatment system, a basic mapping relationship of the operating condition hash index structure is established through controlled kinetic experiments, and calcium ion activity with different concentration gradients is selected. The test sample group of organic matter charge Q was adjusted from 1.0 to 3.0 in a simulated reaction tank with constant temperature stirring function. The magnetic field output frequency and nanobubble injection voltage command corresponding to reducing the calcium carbonate deposition rate R to the preset level under different water quality characteristic vector V conditions were recorded. The hash mapping value of each characteristic component was calculated and used as the index key. The optimal combination of control parameters obtained by actual measurement was stored in the corresponding address unit of the memory. By covering the water quality fluctuation conditions within the range, the correlation between physical characteristics and control logic within the working condition hash index structure was established.

[0040] When the system is connected to a drainage pumping station with specific geological characteristics, the control unit initiates a field baseline calibration procedure before operation, and reads the initial background potential deviation in the drainage pipeline through the sensor bus. The system performs a zeroing process, and the control unit drives the magnetic separation load unit to output a detection current with a frequency sweep between 50Hz and 500Hz. The processor identifies the ion background characteristics of the current water body based on the calculated calcium ion electrochemical response impedance frequency response curve and fine-tunes the weight allocation coefficient of the hash bucket. By injecting tracer signals into the sewage flow channel and monitoring the execution response time of the nanobubble generating load unit, the closed-loop delay Δt of the system control loop is calculated and determined. This parameter is injected into the compensation operator of the control algorithm to dynamically align the output timing of the load regulation strategy with the evolution stage of the crystallization nuclei in the pipeline.

[0041] Example 5: In the initial calibration scenario of a newly completed highway tunnel drainage station, the system is in bypass loop state by reading the initial water quality parameters and executing the linearization alignment procedure of the control mapping table. The control unit drives the nanobubble generating load unit to perform step frequency sweep within a duty cycle range of 10% to 90%, recording the rate of change of sewage turbidity ΔNTU under different injection powers to determine the gain coefficient γ of the current actuator. The system introduces standard water samples with known activity, and calculates the sensor gain correction operator based on the deviation between the collected standard activity value and the measured value. The calculation formula is as follows: ,in, For gain correction operator, The measured value of calcium ion activity in the standard water sample is shown. This is the original calcium ion activity value read by the sensor. This is the system bias constant; the processor will use the gain correction operator. The configuration sector stored in the memory completes the hardware background compensation for the measurement front end.

[0042] When the drainage system is in online operation, a drift compensation procedure based on dual-sensor redundancy verification is initiated to address logic failures caused by electrode aging. This procedure periodically compares... The sensor's measured value and the theoretical potential value derived inversely from the solubility product deviation S are compared with the residual ΔV between them, and the residual ΔV is compared with the preset drift threshold of 0.02V. If the measured residual ΔV exceeds the drift threshold for five consecutive sampling points, the processor determines that the sensor has entered the unsteady-state region, automatically calls the safety benchmark weight stored in the read-only memory, and increases the excitation current of the magnetic separation load unit to ensure that the physical strength of the actuator covers the maximum crystallization risk. The fault-oriented parameter fine-tuning logic reduces the system's dependence on the real-time accuracy of the monitoring components, and the sewage treatment system maintains the dynamic balance of the drainage pipeline under the condition of decreased monitoring component performance.

[0043] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A wastewater treatment process for inhibiting calcification and crystallization, characterized in that, Includes the following steps: Step S101: Obtain the working condition feature vector characterizing the crystallization kinetic state in the wastewater. The working condition feature vector includes: calcium ion activity, solubility product deviation defined by the product of calcium ion activity and carbonate ion activity relative to the solubility product constant of calcite, and organic matter charge characterized by the concentration of residual reagents in the wastewater. Step S102: Input the operating condition feature vector into the processor and use the preset operating condition hash index structure to perform feature space matching to obtain the load control strategy of the controlled power load unit. The operating condition hash index structure is pre-established based on the mapping relationship between each feature parameter and the saturation critical point in the historical operating condition data. The load control strategy includes: the output frequency command for the magnetic separation load unit and the output voltage command for the nanobubble generating load unit. Step S103 involves adjusting the operating parameters of the power load unit in the wastewater treatment system according to the load control strategy. Specifically, this includes: adjusting the output frequency of the drive power supply of the magnetic separation load unit to generate a resonant magnetic field with a magnetic induction intensity fluctuating between 0.5T and 1.2T and a fluctuation frequency matching the resonant frequency of calcium ions, thereby changing the hydration energy of calcium ions and reducing their crystallization activity; simultaneously adjusting the output voltage of the drive power supply of the nanobubble generating load unit to inject nanobubbles with a particle size distribution in the range of 50nm to 200nm into the wastewater, degrading the residual coagulant components in the wastewater, and interfering with the nucleation path of crystallization nuclei through the superposition of physical energy field and physicochemical interface adsorption.

2. The wastewater treatment process for inhibiting calcification and crystallization according to claim 1, characterized in that, Before obtaining the operating condition feature vector in step S102, the process also includes using the processor to read the analog-to-digital conversion values ​​output by the monitoring sensor, extracting the feature parameters representing calcium ion concentration, temperature and pH value in the current water body, and encapsulating the feature parameters into a multi-dimensional feature vector. Feature space matching is achieved by mapping the working condition feature vectors to the hash space and calculating the Hamming distance.

3. The wastewater treatment process for inhibiting calcification and crystallization according to claim 1, characterized in that, The calculation rule for the solubility product deviation S in step S101 is as follows: obtain the activity of calcium ions in wastewater. Activity of carbonate ions ; The solubility product deviation S is determined using the following formula: ,in, Let be the solubility product constant of calcite.

4. A wastewater treatment process for inhibiting calcification and crystallization according to claim 1, characterized in that, In step S103, when adjusting the output frequency of the drive power supply of the magnetic separation load unit, the adjustment step size of the output frequency is set based on the time change rate of the solubility product deviation S, and when the time change rate exceeds the preset threshold, the current switching frequency of the drive power supply is increased.

5. A wastewater treatment process for inhibiting calcification and crystallization according to claim 1, characterized in that, In step S103, when adjusting the output voltage of the driving power supply of the nanobubble generating load unit, the set value of the output voltage is nonlinearly mapped to the organic charge in the operating condition characteristic vector. The unit volume injection density of the nanobubbles is controlled by adjusting the duty cycle of the driving power supply.

6. A wastewater treatment process for inhibiting calcification and crystallization according to claim 1, characterized in that, The operating condition hash index structure in step S102 is established through the following steps: collecting historical water quality data, calculating the chemical equilibrium saturation corresponding to each set of historical water quality data, calibrating the power load operating parameters that keep the water quality in an unsaturated state, and storing the historical water quality data as an index key in the database.

7. A wastewater treatment process for inhibiting calcification and crystallization according to claim 1, characterized in that, Before obtaining the operating condition feature vector in step S101, the process also includes collecting particulate matter concentration data of wastewater and calculating the calcium carbonate deposition rate based on the slope of the time change of particulate matter concentration.

8. A wastewater treatment process for inhibiting calcification crystallization according to claim 7, characterized in that, Also includes: Determine whether the calcium carbonate deposition rate exceeds the preset crystallization sensitive period threshold, and if the calcium carbonate deposition rate exceeds the crystallization sensitive period threshold, trigger feature space matching in step S102 and switch the drive power supply to high power output mode.

9. A wastewater treatment process for inhibiting calcification and crystallization according to claim 1, characterized in that, The adjustment of operating parameters in step S103 also includes: real-time monitoring of the solubility product deviation value; when the solubility product deviation continues to be lower than the preset balance threshold, reducing the excitation current duty cycle of the magnetic separation load unit drive power supply to enter a low power consumption operating state.

10. A wastewater treatment process for inhibiting calcification and crystallization according to claim 1, characterized in that, The specific process of generating nanobubbles in step S103 is as follows: pressurized air is introduced and combined with high-speed shearing to form nanobubbles, and the surface tension of the nanobubbles is used to destroy the molecular chain structure of organic polymer components in wastewater.

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