Digital system and method for full life cycle environmental management of electroplating park

Abstract

The present application relates to the technical field of intelligent management, in particular to a full life cycle environmental management digital system and method for an electroplating park. The system comprises: a composite sensor layout unit that collects the electrochemical impedance spectrum, flow state disturbance signal, trivalent iron ion concentration, oxidation-reduction potential, pH value and temperature of the pipe wall; a deposit identification unit that identifies the initial thickness and composition of the deposit through pattern matching and corrects the fused thickness by taking into account the influence of roughness; an iron ion correction unit that calls a catalytic deposition correction model to generate a correction factor to adjust the fused thickness and output the corrected thickness; a state assessment and early warning unit that generates a confidence score and effectiveness indicator based on multi-parameter fusion and triggers multi-level early warning; and a retired base number generation unit that calculates the total amount of deposit, determines the hazardous waste code and generates a disposal plan. The system realizes accurate identification of deposit thickness and composition, ensuring the scientificity and compliance of pipeline full life cycle management.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology, and more specifically, to a digital system and method for the full lifecycle environmental management of electroplating industrial parks. Background Technology

[0002] Electroplating industrial parks generate large amounts of wastewater containing heavy metals (such as nickel, chromium, and copper) during production. This wastewater is collected through pipelines within the park and transported to a centralized wastewater treatment plant. Over long-term operation, heavy metal ions, suspended particles, and microbial metabolic products in the wastewater gradually deposit and scale on the inner walls of the pipelines, forming a complex sediment layer. This sediment not only reduces pipeline transport efficiency and increases pumping energy consumption, but more seriously, it can cause pipeline blockages, localized corrosion and perforation, and even lead to leaks of heavy metal-containing wastewater, resulting in severe soil and groundwater pollution incidents.

[0003] Currently, the management of sediment deposits in pipelines of electroplating industrial parks mainly relies on the following methods:

[0004] One method is to regularly open the pipe cover for inspection or use a pipe endoscope for random inspection. This method is cumbersome, time-consuming and labor-intensive, and cannot reflect the real-time status of the pipe. Often, by the time serious deposits are discovered, the best time for treatment has been missed.

[0005] Secondly, a single physical quantity sensor (such as a pressure sensor or an ultrasonic thickness gauge) is installed at key nodes of the pipeline to indirectly infer the deposition situation by monitoring pressure loss or changes in pipe wall thickness. However, this method is easily affected by factors such as flow disturbance and temperature changes, has low measurement accuracy, and cannot identify the specific composition of the deposits.

[0006] Third, the sediments are qualitatively or semi-quantitatively analyzed using electrochemical impedance spectroscopy or conventional mathematical models. However, existing models generally ignore the catalytic acceleration effect of ferric hydroxide colloids generated by the hydrolysis of ferric ions in electroplating wastewater on heavy metal deposition, and also do not consider the interference of microbial film adhesion on sensor signals, resulting in a large deviation between the predicted results and the actual results.

[0007] Therefore, there is an urgent need to provide a digital system and methodology for the full lifecycle environmental management of electroplating industrial parks. Summary of the Invention

[0008] The purpose of this invention is to provide a digital system and method for the full lifecycle environmental management of electroplating industrial parks, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, on the one hand, the present invention aims to provide a digital system for the full lifecycle environmental management of electroplating industrial parks, including:

[0010] A composite sensor deployment unit is used to install sensor components at key nodes of the pipeline to collect electrochemical impedance spectroscopy, flow disturbance signals, ferric ion concentration, redox potential, pH value and temperature of the pipe wall.

[0011] The sediment identification unit is used to identify the initial thickness and composition of the sediment by performing pattern matching between the electrochemical impedance spectroscopy and a pre-built standard database, and to extract the roughness level from the pressure pulsation component in the flow disturbance signal through a pre-trained convolutional neural network to correct the initial thickness and obtain the fused thickness.

[0012] The iron ion correction unit is used to receive the concentration of ferric ions, the pH value and the temperature from the composite sensor deployment unit, call the pre-stored catalytic deposition correction model to generate a correction factor, adjust the fusion thickness with the correction factor, and output the corrected thickness.

[0013] The status assessment and early warning unit is used to generate a confidence score and measurement validity indication by multi-parameter fusion based on the fluctuation of the pH value, the redox potential, and the fitting residual of the electrochemical impedance spectrum, and to trigger multi-level early warnings based on the changing trend of the correction thickness and the correction factor.

[0014] The decommissioning baseline generation unit is used to generate a historical data sequence accumulated over time based on the correction thickness, composition, ferric ion concentration and measurement validity indication, and to calculate the total amount of pipeline sediment, determine the hazardous waste code and generate a disposal plan identifier through volume integration and composition classification algorithms.

[0015] As a further improvement to this technical solution, in the sediment identification unit, the pre-built standard database contains impedance spectral features under different sediment types and thicknesses; the pattern matching is performed by a support vector machine classifier after dimensionality reduction using principal component analysis.

[0016] The specific method for extracting roughness level from the pressure pulsation component in the flow disturbance signal using a pre-trained convolutional neural network to correct the initial thickness is as follows: the time-frequency map of the pressure pulsation component is used as the input of the convolutional neural network, the pipe wall roughness level is output, and the correction coefficient is calculated according to a preset linear mapping relationship. Based on the correction coefficient, the initial thickness is optimized to obtain the fused thickness.

[0017] As a further improvement to this technical solution, in the iron ion correction unit, the expression of the pre-stored catalytic deposition correction model is:

[0018]

[0019] in, As a correction factor; The deposition rate constant is denoted by . To measure the concentration of ferric ions; It is the apparent activation energy; It is the ideal gas constant; Absolute temperature; The effect of pH;

[0020] The pH influence function The maximum value is taken within the first predetermined pH range, the first constant is taken within the second predetermined pH range, and the second constant is taken within the third predetermined pH range, with the first constant being less than the second constant.

[0021] As a further improvement to this technical solution, the status assessment and early warning unit includes a microbial detection module, a full life cycle cumulative model module, and a risk early warning and linkage response module;

[0022] The microbial detection module is used to generate a confidence score by fusing multiple parameters based on the change in pH value, the redox potential and the fitting residual of the electrochemical impedance spectrum, and output a measurement validity indication based on the comparison result of the confidence score and the predetermined confidence threshold.

[0023] The full life cycle cumulative model module is used to create and store archive data for each section of the pipeline, and predict the thickness trend and the probability of detachment risk within a predetermined time period based on the historical sequence of the corrected thickness.

[0024] The risk warning and linkage response module is used to trigger multi-level warnings based on the correction thickness, the correction factor and the probability of detachment; wherein the multi-level warning includes at least three warning levels, each warning level corresponding to its own independent correction thickness threshold, thickness growth rate threshold and detachment risk probability threshold.

[0025] As a further improvement to this technical solution, in the microbial detection module, a high risk of microbial film interference is determined when at least one of the following conditions is met, and the measurement validity indicator is set to a distorted state:

[0026] The pH value fluctuates within a predetermined time window and exceeds a predetermined fluctuation threshold; the redox potential remains below a predetermined potential threshold for a period of time that exceeds a predetermined duration threshold; and the fitting residual of the electrochemical impedance spectroscopy is greater than a predetermined residual threshold.

[0027] If none of the above conditions are met, then calculate the confidence score. :

[0028]

[0029] In the formula, The pH fluctuation range within a predetermined time window; for Weighting coefficients; The duration for which the redox potential remains below a predetermined potential threshold. for Weighting coefficients; This represents the fitting residual of the electrochemical impedance spectroscopy. for Weighting coefficients;

[0030] When the confidence score When the measurement validity indicator is greater than or equal to the predetermined confidence threshold, the measurement validity indicator is set to a distorted state; otherwise, it is set to a valid state. When the measurement validity indicator is in a distorted state, a sterilization treatment command or an endoscopic examination task is generated simultaneously.

[0031] As a further improvement to this technical solution, the archive data includes pipeline attribute parameters entered from the construction phase, as well as the calibration thickness, confidence score, ferric ion concentration, pH value, temperature, redox potential, and total heavy metal concentration in the water quality recorded daily during operation.

[0032] As a further improvement to this technical solution, the specific steps in the full life cycle cumulative model module for predicting the thickness trend and the probability of detachment risk within a predetermined time period are as follows:

[0033] Obtain the historical sequence of the corrected thickness as observation data;

[0034] Initialize the model parameters of the double exponential growth model, which is specifically as follows:

[0035]

[0036] In the formula, To predict thickness, For time, This is the magnitude coefficient of the first exponential term; This is the growth rate coefficient of the first exponential term; This is the amplitude coefficient of the second exponential term; This is the growth rate coefficient of the second exponential term;

[0037] Using the extended Kalman filter, the model parameters are recursively updated online with the observed data to obtain the optimal parameter values ​​at the current time.

[0038] Substitute the optimal parameter values ​​into the double exponential growth model, extrapolate to calculate the predicted thickness at each time point within a predetermined time period, and generate a thickness trend curve.

[0039] The probability of detachment risk is calculated based on the degree of closeness between the predicted thickness and the preset detachment trigger thickness threshold.

[0040] As a further improvement to this technical solution, the multi-level early warning system in the risk warning and coordinated response module is specifically as follows:

[0041] When the corrected thickness reaches the first preset thickness threshold, or when it is predicted to reach the second preset thickness threshold within a predetermined prediction period, a first-level warning is triggered.

[0042] When the correction thickness reaches the third preset thickness threshold, or the weekly growth rate of the correction thickness reaches the predetermined growth rate threshold, or the correction factor is greater than the predetermined factor threshold, a second-level warning is triggered.

[0043] When the corrected thickness reaches the fourth preset thickness threshold, or when the probability of detachment risk is greater than the predetermined probability threshold, a third-level warning is triggered, and in the third-level warning state, the backup pipe section is switched, the risk pipe section is identified, and a warning command is sent to the wastewater treatment plant's central control system.

[0044] As a further improvement to this technical solution, the disposal scheme identifier in the decommissioning baseline generation unit includes at least one of the following: identification for recycling and treatment at a smelter, identification for safe landfill treatment, identification for acid leaching and recycling treatment, identification for solidification and landfill treatment, and identification for incineration followed by landfill treatment.

[0045] On the other hand, the present invention provides a digital method for full life-cycle environmental management of electroplating industrial parks, which is used in any of the above-mentioned digital system for full life-cycle environmental management of electroplating industrial parks, and includes the following steps:

[0046] S1. Install sensor components at key nodes of the pipeline to collect electrochemical impedance spectroscopy, flow disturbance signals, ferric ion concentration, redox potential, pH value and temperature of the pipe wall;

[0047] S2. The electrochemical impedance spectroscopy is pattern matched with a pre-built standard database to identify the initial thickness and composition of the sediment; and the roughness level is extracted from the pressure pulsation component in the flow disturbance signal through a pre-trained convolutional neural network to correct the initial thickness and obtain the fused thickness.

[0048] S3. Based on the concentration of ferric ions, the pH value, and the temperature, a pre-stored catalytic deposition correction model is invoked to generate a correction factor, and the fusion thickness is adjusted using the correction factor to output the corrected thickness;

[0049] S4. Based on the fluctuation of the pH value, the redox potential, and the fitting residual of the electrochemical impedance spectroscopy, a confidence score and measurement validity indication are generated through multi-parameter fusion, and multi-level early warnings are triggered according to the changing trend of the correction thickness and the correction factor.

[0050] S5. Based on the correction thickness, the composition, the concentration of ferric ions, and the measurement validity indication, a historical data sequence accumulated over time is generated, and the total amount of pipeline sediment is calculated, the hazardous waste code is determined, and a disposal plan identifier is generated through volume integration and composition classification algorithms.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] 1. In the digital system and method for full life cycle environmental management of electroplating parks, the initial thickness and composition of the deposit are accurately identified by integrating electrochemical impedance spectroscopy mode matching and convolutional neural network correction of flow disturbance signals. Furthermore, by introducing a catalytic deposition correction model to quantify the accelerating effect of ferric ions, the accuracy of the correction thickness measurement is significantly improved, overcoming the shortcomings of traditional single sensor measurements that are susceptible to roughness and iron ion interference.

[0053] 2. In the digital system and method for the full life cycle environmental management of electroplating industrial parks, by constructing a confidence scoring mechanism based on multi-parameter fusion and a full life cycle cumulative model, the system realizes the automatic identification and data removal of abnormal measurement states such as microbial film interference. In addition, by combining the double exponential growth model and extended Kalman filter, the system dynamically predicts the thickness trend and assesses the probability of shedding risk, providing a reliable data foundation for graded early warning and decommissioning baseline calculation, and ensuring the scientific and compliant nature of pipeline full life cycle management. Attached Figure Description

[0054] Figure 1 This is an overall flowchart of the present invention;

[0055] Figure 2 This is a flowchart illustrating the overall method of the present invention. Detailed Implementation

[0056] 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, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1: Please refer to Figure 1As shown, a digital environmental management system for the entire lifecycle of an electroplating industrial park is provided, consisting of five core units connected sequentially: a composite sensor deployment unit, a sediment identification unit, an iron ion correction unit, a status assessment and early warning unit, and a decommissioning baseline generation unit. The composite sensor deployment unit collects raw sensor data and transmits it to the sediment identification unit and the status assessment and early warning unit respectively. The sediment identification unit processes the data and outputs the fusion thickness to the iron ion correction unit. The iron ion correction unit corrects the fusion thickness based on the sensor data and outputs the corrected thickness to the status assessment and early warning unit and the decommissioning baseline generation unit. The status assessment and early warning unit generates confidence scores, measurement validity indicators, and multi-level early warnings based on the corrected thickness and sensor data, and provides relevant historical data and early warning results to the decommissioning baseline generation unit. At the end of the entire lifecycle, the decommissioning baseline generation unit calculates the hazardous waste baseline based on the accumulated data and outputs a disposal plan. The following detailed description of the above units is provided with reference to specific embodiments.

[0058] To collect all the raw physical quantities required for subsequent algorithms in one go and avoid data silos from multiple independent systems, a composite sensor deployment unit is set up. This unit installs sensor components at key nodes of the pipeline to collect electrochemical impedance spectroscopy of the pipe wall, flow disturbance signals, iron ion concentration, redox potential, pH value, and temperature. The sensor components include: a ring four-electrode electrochemical impedance spectroscopy probe, a piezoresistive pressure sensor, a shear stress hot film probe, an iron ion concentration sensor, a redox potential sensor, a pH sensor, and a temperature sensor.

[0059] Specifically, composite sensors (one set at each) will be installed at the following key nodes in the wastewater transmission pipeline network of the electroplating industrial park:

[0060] Collection wells after the main wastewater discharge outlets of various electroplating enterprises;

[0061] The starting point, midpoint, and end point of the main pipeline for different materials (chromium, nickel, copper, composite, etc.);

[0062] Both ends and the middle of long straight pipe sections (>200m);

[0063] Pipeline elevation change points (climbing section, descending section);

[0064] Future decommissioning and dismantling segment points (with reserved monitoring interfaces).

[0065] To convert the raw electrochemical impedance spectroscopy and pressure pulsation into physically interpretable sediment thickness and composition, a sediment identification unit is set up. This unit identifies the thickness and composition of sediments by performing pattern matching between the electrochemical impedance spectroscopy and a pre-built standard database, and extracts the roughness level from the pressure pulsation component in the flow disturbance signal through a pre-trained convolutional neural network to correct the thickness and output the fused thickness.

[0066] In the sediment identification unit, the pre-built standard database contains impedance spectral characteristics for different sediment types and thicknesses;

[0067] Pattern matching is performed by a support vector machine classifier after dimensionality reduction using principal component analysis.

[0068] The specific method for extracting roughness levels from the pressure pulsation component in the flow disturbance signal using a pre-trained convolutional neural network to correct the thickness is as follows:

[0069] The time-frequency map of the pressure pulsation component is used as the input of the convolutional neural network to output the pipe wall roughness level. Then, the correction coefficient is calculated according to the preset linear mapping relationship, and the initial thickness is optimized based on the correction coefficient to obtain the fused thickness.

[0070] The specific implementation method is as follows:

[0071] The pre-built standard database was constructed based on a laboratory-simulated pipeline system. Under controlled temperature, pH, flow rate, and heavy metal ion concentration, the system operated for 3 to 12 months, naturally growing deposit samples with thicknesses ranging from 0 to 15 mm and compositions including nickel scale, chromium scale, copper scale, mixed scale, or microbial mineralized scale. Broadband electrochemical impedance spectroscopy (EIS) was collected for each sample from 0.1 Hz to 100 kHz, and impedance modes were recorded. With phase angle This forms a standardized, tagged database.

[0072] After inputting the measured impedance spectrum, first convert the real part... With the imaginary part The original 120-dimensional feature vector is concatenated and then reduced to the first 15 principal components (cumulative variance contribution rate ≥ 95%) through principal component analysis (PCA) to obtain the dimensionality-reduced feature vector. Subsequently, a one-to-one support vector machine (SVM) multi-classifier was used, with the kernel function being the radial basis function kernel. ,in , Given two dimensionality-reduced feature vectors, As kernel parameters, output sediment thickness range and component type probability ( (For category index), the initial thickness is the midpoint of the probability-weighted interval:

[0073]

[0074] in This represents the initial thickness identified by impedance spectroscopy.

[0075] To correct for the influence of roughness on EIS measurements, pressure pulsation signals were... (Sampling rate ≥ 100Hz) After bandpass filtering at 0.5~50Hz, a short-time Fourier transform is performed to obtain the time-frequency diagram. In the formula, It is a Hanning window, 256 points long, with an overlap rate of 75%. For time, For frequency, It is the integral variable.

[0076] The time-frequency image was normalized to 224×224 pixels and then input into a pre-trained ResNet-18 convolutional neural network. The output layer was a 5-node softmax layer, corresponding to roughness levels. ∈{1,2,3,4,5} (1 for smooth, 5 for severely coarse). Based on a preset linear mapping. Calculate the correction factor Final fusion thickness .

[0077] Because the ferric hydroxide colloid generated by the hydrolysis of ferric ions in electroplating wastewater has a catalytic acceleration effect on heavy metal deposition, an iron ion correction unit is set up. By receiving the iron ion concentration, pH value and temperature from the composite sensor deployment unit, the unit calls the pre-stored catalytic deposition correction model to generate a correction factor, and adjusts the fusion thickness with the correction factor to output the correction thickness.

[0078] In the iron ion correction unit, the expression for the pre-stored catalytic deposition correction model is:

[0079]

[0080] in, This is a correction factor used to adjust the fusion thickness. Make magnification adjustments; This is the deposition rate constant, which needs to be calibrated through batch experiments in the laboratory. The typical value range is 0.15 to 0.45. To accurately measure the concentration of ferric ions, the measurement range covers 0.1–200 mg / L; The apparent activation energy reflects the temperature sensitivity of the iron ion hydrolysis and coprecipitation process, and is taken as 25–35. It is the ideal gas constant; The absolute temperature is collected in real time by a temperature sensor. This is a pH effect function used to describe the changes in the degree of iron ion hydrolysis under different acid and alkaline conditions;

[0081] Then correct the thickness for: .

[0082] pH effect function The pH influence function takes its maximum value within a first predetermined pH range, a first constant within a second predetermined pH range, and a second constant within a third predetermined pH range, with the first constant being less than the second constant. Specifically, the pH influence function... The segmented values ​​are determined based on the chemical equilibrium of ferric ion hydrolysis. When the pH value is within the first predetermined pH range of 3 to 6, ferric ions hydrolyze to form... Colloidal reactions are the most complete, with the strongest flocculation and co-precipitation effects, therefore The maximum value is set to 1.0; when the pH value is less than the second predetermined pH range of 3, the strongly acidic environment inhibits the hydrolysis of iron ions, and very little colloid formation occurs. Take 0.3; when the pH value is greater than the third predetermined pH range of 6, iron ions tend to form stable dissolved complexes or precipitate excessive aging, thus reducing their catalytic effect on deposition. The value is set to 0.6. The above three intervals and their corresponding constant values ​​are obtained through statistical optimization based on test data from a large number of actual wastewater samples from electroplating industrial parks, and have strong universality.

[0083] The status assessment and early warning unit is used to generate confidence scores and measurement validity indicators based on pH fluctuations, redox potentials, fitting residuals of electrochemical impedance spectroscopy, and historical sequences of correction thickness. It also triggers multi-level early warnings based on the changing trend of correction thickness and correction factors.

[0084] The status assessment and early warning unit includes a microbial detection module, a full life cycle cumulative model module, and a risk early warning and coordinated response module;

[0085] The microbial detection module is used to generate a confidence score based on the fitting residuals of pH value change, redox potential and electrochemical impedance spectroscopy, and outputs a measurement validity indication based on the comparison result of the confidence score and the predetermined confidence threshold.

[0086] In the microbial detection module, a high risk of microbial film interference is determined when at least one of the following conditions is met, and the measurement validity indicator is set to a distorted state:

[0087] The pH value fluctuates within a predetermined time window and exceeds a predetermined fluctuation threshold; the redox potential remains below a predetermined potential threshold for a period of time that exceeds a predetermined duration threshold; the fitting residual of the electrochemical impedance spectroscopy is greater than a predetermined residual threshold.

[0088] If none of the above conditions are met, then calculate the confidence score. :

[0089]

[0090] In the formula, The pH fluctuation range within a predetermined time window; for Weighting coefficients; The duration for which the redox potential remains below a predetermined potential threshold. for Weighting coefficients; This represents the fitting residual of the electrochemical impedance spectroscopy. for Weighting coefficients;

[0091] When confidence score When the measurement validity indicator is greater than or equal to the predetermined confidence threshold, the measurement validity indicator is set to a distorted state; otherwise, it is set to a valid state. When the measurement validity indicator is in a distorted state, a sterilization treatment command or an endoscopic examination task is generated simultaneously.

[0092] The full life cycle cumulative model module is used to create and store archive data for each section of pipe, and predict the thickness trend and the probability of detachment risk within a predetermined time period based on the historical sequence of corrected thickness.

[0093] The archive data includes pipeline attribute parameters entered during the construction phase, as well as daily records during operation of calibration thickness, confidence score, iron ion concentration, pH value, temperature, redox potential, and total concentration of heavy metals in the water.

[0094] In the full lifecycle cumulative model module, the specific steps for predicting the thickness trend and the probability of detachment risk within a predetermined time period are as follows:

[0095] First, obtain the correction thickness observation sequence of the current pipeline segment over a past period. ;

[0096] Initialize the model parameters of the double exponential growth model, which is specifically as follows:

[0097]

[0098] In the formula, To predict thickness, For time, , is the amplitude coefficient of the first exponential term, in millimeters (mm), characterizing the growth contribution of the sediment during the initial rapid nucleation stage; This is the growth rate coefficient of the first exponential term, expressed in daily figures. This controls the growth rate of sediments in the initial stage; , which is the amplitude coefficient of the second exponential term, in millimeters (mm), characterizing the contribution of the later stable growth stage of sediments; This is the growth rate coefficient of the second exponential term, expressed in daily figures. This controls the growth rate of sediments in the later stages;

[0099] Using the double exponential growth model as the state transition model, the model parameters... , , , As the state variable to be estimated, the extended Kalman filter recursive formula is used to update the estimation of model parameters at each sampling time using the new corrected thickness observation.

[0100] The time update equation of the extended Kalman filter is based on the assumption of constant parameters (i.e.) The state prediction is performed; the measurement update equation calculates the observation residual (i.e., the difference between the measured corrected thickness and the current model predicted thickness) and adjusts the parameter estimate based on the Kalman gain.

[0101] After multiple iterations, the filter converges to a stable optimal parameter estimate. Finally, the optimal parameters are substituted into the double exponential model to extrapolate and calculate the thickness curve within a predetermined time period. Based on the closeness between the predicted thickness and the preset detachment trigger thickness threshold, the probability of detachment risk is assessed, as follows:

[0102] Set a thickness threshold to trigger shedding. (For example, 0.08 times the pipe diameter can be preset based on pipe material and operating history). Using a double exponential growth model with updated parameters, the future predetermined prediction period is extrapolated. Predicted thickness curve within 30 days (e.g.) It is determined that the predicted thickness first reaches its maximum within this time window. time .

[0103] if If the thickness is consistently below the threshold throughout the entire cycle, then the probability of detachment is not present. ;

[0104] if (Currently met), then ;

[0105] Otherwise, define the remaining time margin. Using the logistic function to... Mapped to probability:

[0106]

[0107] in Risk sensitivity coefficient (typical value) ), The half-life (typically 10 days) indicates when... The probability of favorable timing is 0.5. This function... The probability increases smoothly as the threshold decreases, avoiding threshold jumps.

[0108] This full lifecycle cumulative model module transforms deterministic thickness threshold judgments into continuous probabilistic assessments, taking into account the time dimension. It can anticipate the gradual increase in risk, avoiding the delays or false alarms caused by simple "either / or" alarms. Meanwhile, parameters... and The system can be adjusted online based on actual operating data, giving it self-adaptive capabilities.

[0109] Furthermore, in this embodiment, the predicted thickness trend and detachment risk probability within a predetermined time period include a first prediction period and a second prediction period; the first prediction period is set to 30 days, and the second prediction period is set to 180 days. The 30-day short-term prediction mainly targets risk warnings during the operational period: when the predicted corrected thickness will reach 0.05 times the pipe diameter within the next 30 days, a yellow warning is triggered, and an endoscopic inspection is recommended; when the predicted thickness reaches 0.08 times the pipe diameter or the detachment risk probability exceeds 0.7, a red warning is triggered, and the system automatically switches to a backup pipe section and notifies the wastewater treatment plant. The 180-day long-term prediction serves annual maintenance planning and decommissioning prediction: the operations department can formulate a cleaning plan and budget for the next six months based on the prediction results. Two years before decommissioning, the total amount and composition of sediment in each pipe section can be estimated through long-term trends, allowing for advance processing of hazardous waste disposal qualifications and transfer manifests, avoiding delays in acceptance or penalties due to missing data during decommissioning.

[0110] In this embodiment, the risk warning and coordinated response module adjusts the thickness accordingly. (Unit: mm), Correction factor And the probability of shedding risk from the full lifecycle cumulative model module. (Value range 0-1) Triggers a Level 3 warning.

[0111] Each threshold is pre-calibrated and linked to the pipe diameter: First preset thickness threshold (8mm), second preset thickness threshold (10mm), third preset thickness threshold (12mm), fourth preset thickness threshold (16mm); Predicted growth rate threshold (Weekly growth rate is the slope of the past 7-day linear regression divided by the current thickness); Predetermined factor threshold Predetermined probability threshold The above values ​​can be scaled and adjusted according to different pipe diameters, materials, and wastewater characteristics, and do not constitute a limitation of the present invention. The specific warning logic is as follows:

[0112] When satisfied , or predict through the full - life - cycle cumulative model module that the corrected thickness will reach within 30 days , triggering a first - level warning. After triggering, the system displays a yellow warning icon on the interface of the park environmental management platform, prompting to arrange endoscopic inspection or low - intensity pigging within this week.

[0113] When it meets , or the weekly growth rate of the corrected thickness (i.e., ≥5% / day), or the correction factor (i.e., ≥1.8), a second - level warning is triggered. After triggering, the system pushes a work order to the operation and maintenance person in charge, requiring to formulate a pigging plan within 72 hours and complete it within two weeks.

[0114] When it meets , or the probability of shedding risk (i.e., ≥0.7), a third - level warning is triggered. The third - level warning is the highest level, and the system immediately executes the following linkage disposal actions:

[0115] If there is a spare pipe section, the valve is automatically switched; the risk pipe section is highlighted and flashes an alarm on the GIS map; a warning instruction is sent to the central control of the wastewater treatment plant, requiring to increase the frequency of water quality heavy - metal detection to once every 15 minutes, and pre - increase 20% coagulant and 15% heavy - metal scavenger; at the same time, an emergency disposal work order is generated and the park environmental protection person in charge is notified by text message.

[0116] In the above three - level warning logic, the design of gradually increasing thickness thresholds ensures the gradualness of warnings: the yellow warning is used for early reminder, the orange warning requires planned intervention, and the red warning triggers an emergency linkage. The addition of the weekly growth rate condition and the correction factor condition enables the system to respond in a timely manner to measurement deviations caused by accelerated sedimentation in the short term (such as iron - ion impact) or microbial - film interference, avoiding warning lag that may be caused by relying solely on absolute thickness thresholds.

[0117] Furthermore, a decommissioning base number generation unit is provided, which is used to generate a historical data sequence formed by time - accumulation based on the corrected thickness, composition, iron - ion concentration, and measurement validity indication, and calculate the total amount of pipeline sediment, determine the hazardous waste code, and generate a disposal plan identifier through volume integration and composition classification algorithms.

[0118] In the decommissioning base number generation unit, the specific steps of calculating the total amount of pipeline sediment, determining the hazardous waste code, and generating a disposal plan identifier through volume integration and composition classification algorithms are as follows:

[0119] First, according to the measurement validity indication, the data points corresponding to the judged distortion state are剔除 (removed), ensuring the effectiveness of the calculation basis.

[0120] The pipe is discretized into micro-segments per meter. The volume of sediment in each micro-segment is calculated as: Corrected thickness × Pipe inner wall perimeter × 1m. The total volume is obtained by summing these segments. Based on the time-weighted proportions of historical component classification, the average density was read from a pre-stored density table (nickel scale 1.8 t / m³, chromium scale 1.5 t / m³, mixed scale weighted at 1.2–2.0 t / m³). Then the total mass .

[0121] Based on the statistical distribution of component classification results for different time periods in historical data, the main hazardous components and their mass fractions are determined.

[0122] Based on the main hazardous components, iron content, and iron colloidal encapsulation characteristics (long-term high iron ion concentration and microbial film interference frequency >30% indicate encapsulation), output at least one of the following indicators:

[0123] Iron ion concentration (mass fraction) > 30%: Indicates the item is to be sent to a smelter for recycling and processing.

[0124] The presence of heavy metals encapsulated in ferrous colloids indicates a need for direct and safe landfill disposal.

[0125] Composition is nickel / copper based and iron content ≤30%: Acid dissolution and recovery treatment label;

[0126] The composition is chromium-based: solidified landfill treatment marking;

[0127] Composition is mixed: Incineration followed by landfill disposal label.

[0128] Finally, the thickness curve data, estimated total mass of sediment, composition range data, hazardous components, and the identification of the disposal plan for each section of the pipeline are summarized to generate a hazardous waste baseline data record.

[0129] Example 2: A digital method for full lifecycle environmental management of electroplating industrial parks is provided for use in any of the above-mentioned digital systems for full lifecycle environmental management of electroplating industrial parks, including the following steps:

[0130] S1. Install sensor components at key nodes of the pipeline to collect electrochemical impedance spectroscopy, flow disturbance signals, ferric ion concentration, redox potential, pH value and temperature of the pipe wall;

[0131] S2. The electrochemical impedance spectroscopy is pattern matched with a pre-built standard database to identify the initial thickness and composition of the sediment; and the roughness level is extracted from the pressure pulsation component in the flow disturbance signal through a pre-trained convolutional neural network to correct the initial thickness and obtain the fused thickness.

[0132] S3. Based on the concentration of ferric ions, pH value and temperature, call the pre-stored catalytic deposition correction model to generate a correction factor, and adjust the fusion thickness with the correction factor to output the corrected thickness;

[0133] S4. Based on the pH fluctuation, redox potential, and fitting residual of electrochemical impedance spectroscopy, a confidence score and measurement validity indication are generated through multi-parameter fusion, and multi-level early warnings are triggered according to the change trend of correction thickness and correction factor.

[0134] S5. Based on the calibration thickness, composition, ferric ion concentration and measurement validity indication, a historical data sequence accumulated over time is generated. The total amount of pipeline sediment is calculated, hazardous waste codes are determined, and disposal scheme identifiers are generated through volume integration and composition classification algorithms.

[0135] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A digital system for full life cycle environmental management of electroplating parks, characterized by, include: A composite sensor deployment unit is used to install sensor components at key nodes of the pipeline to collect electrochemical impedance spectroscopy, flow disturbance signals, ferric ion concentration, redox potential, pH value and temperature of the pipe wall. The sediment identification unit is used to identify the initial thickness and composition of the sediment by performing pattern matching between the electrochemical impedance spectroscopy and a pre-built standard database, and to extract the roughness level from the pressure pulsation component in the flow disturbance signal through a pre-trained convolutional neural network to correct the initial thickness and obtain the fused thickness. The iron ion correction unit is used to receive the concentration of ferric ions, the pH value and the temperature from the composite sensor deployment unit, call the pre-stored catalytic deposition correction model to generate a correction factor, adjust the fusion thickness with the correction factor, and output the corrected thickness. The status assessment and early warning unit is used to generate a confidence score and measurement validity indication by multi-parameter fusion based on the fluctuation of the pH value, the redox potential, and the fitting residual of the electrochemical impedance spectrum, and to trigger multi-level early warnings based on the changing trend of the correction thickness and the correction factor. The decommissioning baseline generation unit is used to generate a historical data sequence accumulated over time based on the correction thickness, composition, ferric ion concentration and measurement validity indication, and to calculate the total amount of pipeline sediment, determine the hazardous waste code and generate a disposal plan identifier through volume integration and composition classification algorithms.

2. The electroplating park full life cycle environmental management digital system according to claim 1, characterized in that: In the sediment identification unit, the pre-built standard database contains impedance spectral features for different sediment types and thicknesses; the pattern matching is performed by a support vector machine classifier after dimensionality reduction using principal component analysis. The specific method for extracting roughness level from the pressure pulsation component in the flow disturbance signal using a pre-trained convolutional neural network to correct the initial thickness is as follows: the time-frequency map of the pressure pulsation component is used as the input of the convolutional neural network, the pipe wall roughness level is output, and the correction coefficient is calculated according to a preset linear mapping relationship. Based on the correction coefficient, the initial thickness is optimized to obtain the fused thickness.

3. The electroplating park full life cycle environmental management digital system according to claim 2, characterized in that: In the iron ion correction unit, the expression of the pre-stored catalytic deposition correction model is: wherein, is a correction factor; is a deposition rate constant; is the measured concentration of trivalent iron ions; is the apparent activation energy; is the ideal gas constant; is the absolute temperature; is the pH influence function; The pH influence function The maximum value is taken within the first predetermined pH range, the first constant is taken within the second predetermined pH range, and the second constant is taken within the third predetermined pH range, with the first constant being less than the second constant.

4. The digital system for full lifecycle environmental management of electroplating industrial parks according to claim 3, characterized in that: The status assessment and early warning unit includes a microbial detection module, a full life cycle cumulative model module, and a risk early warning and coordinated response module. The microbial detection module is used to generate a confidence score by fusing multiple parameters based on the change in pH value, the redox potential and the fitting residual of the electrochemical impedance spectrum, and output a measurement validity indication based on the comparison result of the confidence score and the predetermined confidence threshold. The full life cycle cumulative model module is used to create and store archive data for each section of the pipeline, and predict the thickness trend and the probability of detachment risk within a predetermined time period based on the historical sequence of the corrected thickness. The risk warning and linkage response module is used to trigger multi-level warnings based on the correction thickness, the correction factor and the probability of detachment; wherein the multi-level warning includes at least three warning levels, each warning level corresponding to its own independent correction thickness threshold, thickness growth rate threshold and detachment risk probability threshold.

5. The digital system for full lifecycle environmental management of electroplating industrial parks according to claim 4, characterized in that: In the microbial detection module, a high risk of microbial film interference is determined when at least one of the following conditions is met, and the measurement validity indicator is set to a distorted state: The pH value fluctuates within a predetermined time window and exceeds a predetermined fluctuation threshold; the redox potential remains below a predetermined potential threshold for a period of time that exceeds a predetermined duration threshold; and the fitting residual of the electrochemical impedance spectroscopy is greater than a predetermined residual threshold. If none of the above conditions are met, then calculate the confidence score. : In the formula, The pH fluctuation range within a predetermined time window; for Weighting coefficients; The duration for which the redox potential remains below a predetermined potential threshold. for Weighting coefficients; This represents the fitting residual of the electrochemical impedance spectroscopy. for Weighting coefficients; When the confidence score When the measurement validity indicator is greater than or equal to the predetermined confidence threshold, the measurement validity indicator is set to a distorted state; otherwise, it is set to a valid state. When the measurement validity indicator is in a distorted state, a sterilization treatment command or an endoscopic examination task is generated simultaneously.

6. The digital system for full lifecycle environmental management of electroplating industrial parks according to claim 5, characterized in that: The archive data includes pipeline attribute parameters entered during the construction phase, as well as the calibration thickness, confidence score, ferric ion concentration, pH value, temperature, redox potential, and total heavy metal concentration in the water recorded daily during operation.

7. The digital system for full lifecycle environmental management of electroplating industrial parks according to claim 6, characterized in that: The specific steps in the full lifecycle cumulative model module for predicting the thickness trend and the probability of detachment risk within a predetermined time period are as follows: Obtain the historical sequence of the corrected thickness as observation data; Initialize the model parameters of the double exponential growth model, which is specifically as follows: In the formula, To predict thickness, For time, This is the magnitude coefficient of the first exponential term; This is the growth rate coefficient of the first exponential term; This is the amplitude coefficient of the second exponential term; This is the growth rate coefficient of the second exponential term; Using the extended Kalman filter, the model parameters are recursively updated online with the observed data to obtain the optimal parameter values ​​at the current time. Substitute the optimal parameter values ​​into the double exponential growth model, extrapolate to calculate the predicted thickness at each time point within a predetermined time period, and generate a thickness trend curve. The probability of detachment risk is calculated based on the degree of closeness between the predicted thickness and the preset detachment trigger thickness threshold.

8. The digital system for full lifecycle environmental management of electroplating industrial parks according to claim 7, characterized in that: In the aforementioned risk warning and coordinated response module, the multi-level warning system specifically includes: When the corrected thickness reaches the first preset thickness threshold, or when it is predicted to reach the second preset thickness threshold within a predetermined prediction period, a first-level warning is triggered. When the correction thickness reaches the third preset thickness threshold, or the weekly growth rate of the correction thickness reaches the predetermined growth rate threshold, or the correction factor is greater than the predetermined factor threshold, a second-level warning is triggered. When the corrected thickness reaches the fourth preset thickness threshold, or when the probability of detachment risk is greater than the predetermined probability threshold, a third-level warning is triggered, and in the third-level warning state, the backup pipe section is switched, the risk pipe section is identified, and a warning command is sent to the wastewater treatment plant's central control system.

9. The digital system for full lifecycle environmental management of electroplating industrial parks according to claim 8, characterized in that: In the decommissioning baseline generation unit, the disposal plan identifier includes at least one of the following: identification for recycling and treatment at a smelter, identification for safe landfill treatment, identification for acid leaching and recycling treatment, identification for solidification and landfill treatment, and identification for incineration followed by landfill treatment.

10. A digital method for full lifecycle environmental management of electroplating industrial parks, used in the digital system for full lifecycle environmental management of electroplating industrial parks as described in any one of claims 1-9, characterized in that: Includes the following steps: S1. Install sensor components at key nodes of the pipeline to collect electrochemical impedance spectroscopy, flow disturbance signals, ferric ion concentration, redox potential, pH value and temperature of the pipe wall; S2. The electrochemical impedance spectroscopy is pattern matched with a pre-built standard database to identify the initial thickness and composition of the sediment; and the roughness level is extracted from the pressure pulsation component in the flow disturbance signal through a pre-trained convolutional neural network to correct the initial thickness and obtain the fused thickness. S3. Based on the concentration of ferric ions, the pH value, and the temperature, a pre-stored catalytic deposition correction model is invoked to generate a correction factor, and the fusion thickness is adjusted using the correction factor to output the corrected thickness; S4. Based on the fluctuation of the pH value, the redox potential, and the fitting residual of the electrochemical impedance spectroscopy, a confidence score and measurement validity indication are generated through multi-parameter fusion, and multi-level early warnings are triggered according to the changing trend of the correction thickness and the correction factor. S5. Based on the correction thickness, the composition, the concentration of ferric ions, and the measurement validity indication, a historical data sequence accumulated over time is generated, and the total amount of pipeline sediment is calculated, the hazardous waste code is determined, and a disposal plan identifier is generated through volume integration and composition classification algorithms.

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