Intelligent classification treatment method for hazardous wastes based on multi-parameter fusion

Abstract

The invention relates to the technical field of hazardous waste treatment, and discloses a hazardous waste intelligent classification treatment method based on multi-parameter fusion, comprising the following steps: collecting current multi-modal data of hazardous waste, and loading historical multi-modal data; the multi-modal data comprises chemical component data, 400-2500nm wave band images, a molecular vibration spectrum and a combustion heat value; in combination with the current multi-modal data and the historical multi-modal data, calling the classification model to carry out cross-modal feature fusion, and extracting a maximum correlation coefficient feature pair; establishing a linear programming model based on the maximum correlation coefficient feature pair, wherein the linear programming model is used for calculating the predicted emission amount of dioxin and optimizing the recovery rate of valuable metal; the actual emission amount of dioxin is monitored in real time, and if the deviation between the actual emission amount of dioxin and the predicted emission amount of dioxin is larger than a specified threshold value, classification calibration is triggered. According to the technical scheme, misjudgment caused by single information in a traditional method can be effectively avoided, and the recognition accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of hazardous waste treatment technology, and more specifically, to a method for intelligent classification and treatment of hazardous waste based on multi-parameter fusion. Background Technology

[0002] Currently, the amount of hazardous waste generated in my country is increasing year by year, while the management system is still imperfect, with blind spots in regulatory coverage, and illegal dumping and improper disposal incidents still occur from time to time. Traditional technical methods for the classification and treatment of hazardous waste face many pressing problems that need to be solved. To curb these irregularities, regulatory authorities have clearly stated the urgent requirement to establish a fully traceable and precise management system, necessitating efficient control from generation and transportation to disposal.

[0003] Currently, the industry generally relies on manual experience and single detection technologies. For example, patent CN115722460B only uses XRF to analyze heavy metal components. This method has serious data silo problems and is difficult to fully capture the complex characteristics of hazardous waste. According to the NIST SRM8692 standard sample test verification, the single detection method leads to a high misjudgment rate of mixed waste. In practical applications, copper-containing sludge has been misjudged as organic waste liquid, resulting in large fluctuations in calorific value after mixing, which seriously affects the stability and safety of the treatment process. Summary of the Invention

[0004] To address the aforementioned problems, this application provides a method for intelligent classification and treatment of hazardous waste based on multi-parameter fusion, comprising the following steps: Collect current multimodal data of hazardous waste and load historical multimodal data; the multimodal data includes: chemical composition data, images in the 400-2500nm band, molecular vibrational spectra and calorific value; By combining current and historical multimodal data, a classification model is invoked to perform cross-modal feature fusion and extract feature pairs with the highest correlation coefficient. A linear programming model was established based on the maximum correlation coefficient characteristic. The linear programming model was used to calculate the predicted dioxin emissions and optimize the recovery rate of valuable metals. Real-time monitoring of actual dioxin emissions; if the deviation between actual and predicted dioxin emissions exceeds a specified threshold, classification calibration is triggered.

[0005] The chemical composition data was acquired using GC-MS; the 400-2500 nm band images were acquired using a hyperspectral camera; the molecular vibrational spectra were acquired using a near-infrared spectrometer; and the calorific value was acquired using a calorific value sensor. Frequency domain decomposition was performed on the 400-2500 nm band images; the low-frequency components characterized matrix homogeneity, while the high-frequency components characterized the aggregation of microscale pollutants. Images in the same band can reflect the spectral characteristics of the waste.

[0006] Furthermore, performing the cross-modal feature fusion includes the following steps: Determine if concept drift has occurred. If concept drift has occurred, update the classification model using an incremental random forest model; retain important nodes and reconstruct the split threshold. Perform canonical correlation analysis on different types of data to extract the feature pairs with the highest correlation coefficient; The maximum correlation coefficient feature is used to reduce the dimensionality of the input sparse autoencoder to 50 dimensions.

[0007] The objective function of canonical correlation analysis is , The constraints are ;in, For the output control quantity of the PID controller, ΣXY represents the linear combination coefficients, and ΣXY represents the covariance matrix of chemical composition and spectral data.

[0008] Furthermore, the objective function of the linear programming model is: Max(0.6 × calorific value stability + 0.3 × valuable metal recovery rate - 0.1 × dioxin predicted emissions). The constraints of the linear programming model are: calorific value fluctuation ≤ ±5%; Cl⁻≤1.5wt%, S≤0.8wt%, and compliance with the incompatible waste segregation rule.

[0009] The predicted dioxin emissions are calculated using a temporal convolutional network. The input parameters for the calculation are the incineration temperature T, chlorine content (Cl%), and catalyst metal content (Cu%), expressed as follows: ; The recovery rate of valuable metals is optimized by using a gradient boosting decision tree. The input features include the initial concentrations of Au, Ag, and Pd in ​​the waste and the pH value.

[0010] Furthermore, classification calibration refers to adjusting the control of the incinerator through a PID controller to adjust dioxin emissions and then re-analyzing from the cross-modal feature fusion step. The control of the incinerator by the PID controller is expressed as follows: ; Where e(t) represents the actual and predicted dioxin emissions, and Kp, Ki, and Kd are the control coefficients.

[0011] This invention combines multimodal cycling to comprehensively characterize the complex physicochemical properties of waste, effectively avoiding misjudgments caused by limited information in traditional methods and significantly improving identification accuracy. During the detection process, a dynamic concept drift detection mechanism is introduced to ensure the long-term stability and adaptive operation of the classification model; while retaining key information, computational complexity is reduced, accelerating the matching decision-making speed. Finally, by constructing a linear programming model with stable calorific value, high metal recovery rate, and low dioxin emissions as objectives, and combining TCN and gradient boosting trees for accurate prediction and optimization, efficient, intelligent, and resource-based hazardous waste treatment is achieved while meeting stringent environmental regulatory requirements, resulting in a dual improvement in environmental and economic benefits. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating the steps of an intelligent classification and treatment method for hazardous waste provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the implementation process of the intelligent classification and treatment method for hazardous waste provided in an embodiment of the present invention. Detailed Implementation

[0013] This invention provides an intelligent classification and treatment method for hazardous waste based on multi-parameter fusion. It integrates multiple sources of equipment such as GC-MS, hyperspectral camera, and near-infrared spectrometer to simultaneously collect multimodal data such as chemical composition, images, spectra, and calorific value of hazardous waste. It comprehensively characterizes the physicochemical properties of hazardous waste by combining frequency domain decomposition and texture analysis. Cross-modal feature fusion and dimensionality reduction are achieved through canonical correlation analysis (CCA) and sparse autoencoder. A dynamic concept drift detection mechanism based on KL divergence is introduced to trigger incremental random forest model updates and optimize node splitting with Gini exponent. Finally, a multi-objective linear programming model is constructed, which combines TCN to predict dioxin emissions and gradient boosting tree to optimize metal recovery rate, thereby improving classification accuracy and resource utilization efficiency while ensuring environmental compliance.

[0014] The specific implementation of the present invention will now be described in detail with reference to the accompanying drawings.

[0015] Figure 1 The basic steps of an intelligent classification and treatment method for hazardous waste are provided, including: Step S100: Collect current multimodal data of hazardous waste and load historical multimodal data; In this step, multiple devices are used to acquire multimodal data, including chemical composition data, images in specified bands, molecular vibrational spectra, and calorific value. Multimodal data acquisition, such as Figure 2 The data acquisition section is shown below, including: Obtaining chemical composition data of hazardous waste through GC-MS helps to understand the specific chemical composition of the waste; Images in the 400-2500 nm band were acquired using a hyperspectral camera. Images in different bands can reflect the spectral characteristics of waste. Frequency domain decomposition was performed on the 400-2500 nm band images. The low-frequency component (0-0.1 Hz) characterizes the matrix homogeneity, while the high-frequency component (>10 Hz) characterizes the microscale pollutant aggregation. This is very helpful for analyzing the physical structure of waste and the distribution of pollutants. Near-infrared spectroscopy can be used to obtain molecular vibrational spectra, which can be used to analyze molecular structure and chemical bond information. The combustion calorific value is obtained by using a calorific value sensor, which provides important parameters for subsequent compatibility optimization.

[0016] Because the data collected by different devices are complementary, combining these data can provide a more comprehensive understanding of the characteristics of hazardous waste. For example, chemical composition data can identify the types of hazardous substances in the waste, while hyperspectral images and molecular vibrational spectra can further analyze the properties of the waste at the physical and chemical structural level.

[0017] In this step, in addition to the already defined data, hyperspectral images can be processed using techniques such as texture analysis and spatial distribution clustering. Texture analysis can further quantify the surface roughness and particle distribution of waste, helping to determine the physical state of the waste; spatial distribution clustering can classify regions with similar spectral characteristics in the image, visually presenting the distribution range and proportion of different components in the waste, providing a more detailed basis for accurate classification.

[0018] This invention provides specific embodiments for the disposal of medical waste (containing chlorinated plastics) and the classification and treatment of hazardous waste. The medical waste in the embodiments includes PVC infusion tubes (Cl content 56wt%), silver-containing X-ray films (Ag content 0.3wt%), and antibiotic residues (calorific value 15MJ / kg). Due to the mixture of various wastes with different properties, the processing complexity is increased.

[0019] In this step, GC-MS was used to detect dichloroethane (m / z=99) in the waste, clarifying its chemical composition. A hyperspectral camera acquired images in the 400-2500 nm band; frequency domain decomposition revealed a decrease in reflectance at 1450 nm, identifying a Cl accumulation region and reflecting the pollutant distribution characteristics in the waste's physical structure. Near-infrared spectroscopy was used to obtain molecular vibrational spectra, reflecting the overtone and combination frequency vibrations of hydrogen-containing groups (such as CH, OH, NH, etc.) in molecules. In medical waste treatment, analysis of the acquired molecular vibrational spectra can help determine the molecular structural integrity of PVC infusion tubing and the functional group characteristics of organic matter in antibiotic residues. For example, changes in the position and intensity of the vibrational absorption peaks of the CH bonds in PVC can reflect whether degradation has occurred or the presence of impurities; the characteristic peaks of the OH groups in antibiotic residues can help determine the composition of alcohols, phenols, or carboxylic acids, thus providing a more comprehensive understanding of the chemical properties of the waste and offering more multi-dimensional data support for subsequent compatibility optimization and treatment process selection; the measured calorific value of the calorific value sensor is 18.5 MJ / kg, providing key data for compatibility optimization. Data collected by multiple devices complement each other, comprehensively presenting the characteristics of the waste.

[0020] Step S110: Combining current multimodal data and historical multimodal data, call the classification model to perform cross-modal feature fusion, and extract the feature with the maximum correlation coefficient to reduce the dimensionality of the input sparse autoencoder; The specific implementation process of this step is as follows: Figure 2 The feature extraction part includes the following steps: 1) First, determine whether concept drift has occurred; Concept drift refers to the phenomenon where the distribution of data changes over time. In hazardous waste management, due to the diversity and uncertainty of waste sources, the data distribution may change. Dynamic concept drift detection can promptly identify changes in data distribution, providing a basis for subsequent model updates.

[0021] The method for determining whether concept drift has occurred is as follows: The KL divergence between the current multimodal data and the historical multimodal data is calculated using a sliding window. If the KL divergence is greater than the divergence threshold (e.g., set to 0.25), concept drift is determined to have occurred. The KL divergence is calculated using Gaussian kernel density estimation, and the formula is as follows: Where P(x) is the distribution of the new window data and Q(x) is the distribution of the historical data.

[0022] If concept drift occurs, the classification model is updated using an incremental random forest model; important nodes are retained and the split threshold is reconstructed.

[0023] Specifically, the node splitting criterion of incremental random forest adopts dynamic weighting of Gini exponent, as shown in the formula; ,in, As an additional coefficient, and .

[0024] Incremental random forests can update the model without retraining the entire model, improving update efficiency. Retaining important nodes and reconstructing the split threshold can adapt to new data distributions while maintaining model stability.

[0025] In the case of medical waste disposal, using 200 samples as a sliding window, Gaussian kernel density estimation was employed to calculate the KL divergence between the new and historical data. The result showed KL = 0.31 > 0.25, indicating concept drift and timely detection of changes in data distribution. After detecting concept drift, an incremental random forest was used to update the classification model. A node splitting criterion with dynamic weighting using the Gini index (α = 0.7) was applied to retain important nodes and reconstruct the splitting threshold. The updated classification accuracy improved to 92.7%, allowing the model to adapt to the new data distribution.

[0026] In the process of dynamic concept drift detection and classification model update, we can further analyze the changes in the contribution of different features to the classification results before and after the model update, and understand which data features play a key role when waste characteristics change, so as to optimize data collection and model training strategies in a targeted manner.

[0027] 2) Perform cross-modal feature fusion: Cross-modal feature fusion can perform canonical correlation analysis on different types of data to extract more representative feature pairs with the highest correlation coefficients. Dimensionality reduction is performed during the fusion process to reduce the dimensionality of the data, reduce computational complexity, and avoid overfitting problems. Canonical correlation analysis is performed on chemical composition data and hyperspectral images.

[0028] In this step, the feature pairs with the largest correlation coefficient are extracted and input into a sparse autoencoder to reduce the dimensionality to 50 dimensions. The objective function of canonical correlation analysis is: , The constraints are ;in, For the output control quantity of the PID controller, ΣXY represents the linear combination coefficients, and ΣXY represents the covariance matrix of chemical composition and spectral data.

[0029] In the case of medical waste treatment, canonical correlation analysis (CCA) was performed on the chemical composition data and hyperspectral image data obtained by GC-MS. Based on the objective function and constraints, the feature pairs with the largest correlation coefficient were extracted and then input into a sparse autoencoder to reduce the dimensionality to 50 dimensions. The features of different modal data were integrated to reduce the computational complexity.

[0030] Step S120: Establish a linear programming model, which is used to calculate the predicted dioxin emissions and optimize the recovery rate of valuable metals; In this invention, a multi-objective matching optimization is achieved through a linear programming model, which can realize the optimal solution for multiple objectives under certain constraints. Specifically, the multiple objectives include: improving calorific value stability to ensure efficient incineration; improving the recovery rate of valuable metals to achieve resource recycling; and reducing predicted dioxin emissions to reduce environmental pollution.

[0031] The objective function of the linear programming model is: Max(0.6 × calorific value stability + 0.3 × valuable metal recovery rate - 0.1 × dioxin predicted emissions); The constraints of the linear programming model are: calorific value fluctuation ≤ ±5%; Cl⁻≤1.5wt%, S≤0.8wt%, and compliance with the incompatible waste segregation rule.

[0032] The predicted dioxin emissions are calculated using a temporal convolutional network. The input parameters for this calculation are incineration temperature T, chlorine content (Cl%), and catalyst metal content (Cu%), expressed as follows: ; The recovery rate of valuable metals is optimized by using a gradient boosting decision tree. The input features include the initial concentrations of Au, Ag, and Pd in ​​the waste and the pH value.

[0033] In the medical waste treatment case, the objective function of the linear programming model established in this step is Max(0.6 × calorific value stability + 0.3 × valuable metal recovery rate - 0.1 × predicted dioxin emissions), with constraints including calorific value fluctuation ≤ ±5%, Cl⁻ ≤ 1.5wt%, S ≤ 0.8wt%, and incompatible waste segregation rules. The predicted dioxin emissions are calculated using a temporal convolutional network (TCN) with input incineration temperature, chlorine content, and catalyst metal content. The valuable metal recovery rate is optimized using a gradient boosting decision tree (GBDT) based on the initial concentrations of Au, Ag, and Pd in ​​the waste and the pH value. The final output shows a mixed copper-containing circuit board ratio of 10%, a predicted dioxin emission of 10.3 ng-TEQ / m³, and an expected Ag recovery rate of 85%.

[0034] Step S130: Monitor the actual dioxin emissions in real time. If the deviation between the actual dioxin emissions and the predicted dioxin emissions is greater than a specified threshold, trigger classification calibration.

[0035] During the incineration process, dioxin emissions are monitored in real time using a flue gas analyzer. If the actual dioxin emissions deviate from the predicted dioxin emissions by more than 15%, a classification calibration is performed. Classification calibration refers to adjusting the control of the incinerator through a PID controller to adjust dioxin emissions and restarting the analysis from step S110.

[0036] The control of the incinerator by the PID controller is expressed as follows: ; Where e(t) represents the actual and predicted dioxin emissions, and Kp, Ki, and Kd are control coefficients, which can be set to Kp=0.8, Ki=0.2, and Kd=0.1 in this invention.

[0037] This step enables real-time feedback calibration, allowing adjustments to the entire treatment process based on actual monitoring results to ensure dioxin emissions remain within a controllable range. The PID controller adjusts the incineration temperature and time based on the magnitude, integral, and derivative of the deviation, resulting in good control performance.

[0038] Applied to the medical waste treatment process, the flue gas analyzer monitors dioxin emissions in real time during incineration. The measured value was 12 ng-TEQ / m³, deviating from the predicted value by 16.5% (>15%), triggering data feedback to the dynamic concept drift detection step. A PID controller (Kp=0.8, Ki=0.2, Kd=0.1) adjusts the control quantity based on the deviation between the measured and predicted dioxin values, achieving dynamic adjustment of the treatment process. The final treatment results are satisfactory, with calorific value fluctuations controlled within ±4.2%. Dioxin emissions, measured by EPA Method 23, were 0.8 ng-TEQ / m³, and the Ag recovery rate reached 85% as verified by ICP-MS, achieving multi-objective optimization effects.

[0039] This invention also provides a method for hazardous material classification and treatment of electronic waste (rich in precious metals): Electronic waste is primarily composed of electronic waste rich in precious metals, and the focus of its treatment is on precious metal recovery. The treatment process is as follows: 1) Synchronous acquisition of multimodal data: GC-MS acquires chemical composition data: In addition to detecting conventional organic pollutants, GC-MS can also be used to analyze flux residues on the surface of electronic components and additives in plastic casings. These chemical components not only affect the physicochemical properties of waste, but may also react chemically with the target metal during precious metal recycling, interfering with the recycling process.

[0040] By accurately analyzing its composition and content using GC-MS, potential side reactions during the processing can be predicted in advance, allowing for optimization of the formulation, such as adjusting the amount of oxidant or reducing agent added, to ensure that the recovery rate of precious metals is not affected.

[0041] Images in the 400-2500nm band were acquired using a hyperspectral camera, and the physical structure of the waste was analyzed by frequency domain decomposition. Near-infrared spectroscopy acquires molecular vibrational spectra. Polymer materials in electronic waste (such as circuit board substrates and insulating layers) exhibit unique vibrational spectral characteristics under near-infrared spectroscopy. By analyzing these spectra, different types of polymer materials can be identified, and their thermal and chemical stability can be determined. For example, if an absorption peak is detected at a specific wavelength for a certain type of polymer material, it indicates poor thermal stability. In subsequent heat treatment processes, temperature and time need to be strictly controlled to prevent material decomposition and the generation of harmful gases, while also preventing adverse effects on precious metal recovery, such as the formation of a coating layer that hinders metal leaching. Calorific value sensors acquire combustion heat values, providing fundamental data for subsequent processing. 2) Dynamic concept drift detection, classification model update, and cross-modal feature fusion: The steps are the same as those for medical waste treatment. The model is updated in a timely manner when the data changes, and features from different modalities are fused. Multi-objective matching optimization: A linear programming model is constructed using Python and the SciPy library, with objective function coefficients... c=[-0.6,-0.3,0.1] The Cl / S / taboo constraints are A_ub=[[0.05,0,0],[0,0,0.03,0],[1,1,0]], with an upper limit of b_ub=[1.5,0.8,1]. The optimal ratio is [0.35,0.28,0.37] obtained by solving using the linprog function. Dioxin predicted emissions are calculated using TCN, and valuable metal recovery rates are optimized using GBDT based on the initial concentrations of Au, Ag, and Pd in ​​the waste and the pH value. 3) Real-time feedback calibration: Consistent with the medical waste treatment process, it monitors dioxin emissions in real time and adjusts the incinerator's treatment process through a PID controller based on deviations. After processing, the Pd recovery rate reached 91% (the original waste material contained 0.2wt% Pd), the calorific value fluctuation was controlled within ±3.8%, and the calculation time for each batch was only 38 seconds, achieving efficient and accurate precious metal enrichment processing while meeting the requirements of multi-objective optimization.

[0042] In multi-objective optimization, in addition to considering predicted dioxin emissions and valuable metal recovery rates, the evaluation of the treatment effect on hazardous substances (such as heavy metals like lead and mercury) in electronic waste can also be incorporated. By establishing corresponding predictive models and incorporating them into the objective function or constraints of linear programming, it can be ensured that while enriching precious metals, the emissions of other pollutants are effectively controlled. In the real-time feedback calibration stage, in addition to monitoring dioxin emissions, real-time monitoring of other key gases (such as sulfur dioxide and nitrogen oxides) and heavy metal vapors can be added to gain a more comprehensive understanding of the environmental impact during the treatment process, allowing for timely adjustments to process parameters and achieving green and environmentally friendly treatment.

[0043] Regarding the case of medical waste disposal, this invention also provides comparative examples of each stage of the treatment process: Comparative Case 1: Dynamic concept drift detection function disabled; Although Comparative Case 1 also adopted the same data acquisition, cross-modal feature fusion, multi-target compatibility optimization, and real-time feedback calibration steps, it could not detect and respond to changes in data distribution. After 30 days of continuous processing, due to the lack of updating solvent escape trends, the classification accuracy decreased from 93.5% in Example 1 to 76.2%; the failure to capture changes in Cl content in a timely manner led to a significant increase in dioxin prediction bias from 12% to 41%, resulting in a significant reduction in processing effectiveness and highlighting the importance of dynamic concept drift detection.

[0044] Comparative Case 2: Simple Feature Concatenation Replaces Cross-Modal Feature Fusion; Although the comparative case also adopted steps such as data acquisition, dynamic concept drift detection, classification model update, multi-objective matching optimization, and real-time feedback calibration, the processing methods for features were different; the final F1 score for mixed waste identification decreased from 0.92 in Example 2 to 0.73, because simple feature concatenation caused feature space conflicts, reducing the accuracy of identification; the matching calculation time increased from 45 seconds to 152 seconds, because without dimensionality reduction processing, the feature dimension increased from 200 dimensions to 50 dimensions, causing the curse of dimensionality and seriously affecting processing efficiency, demonstrating the advantages of using the CCA method for cross-modal feature fusion.

[0045] Therefore, the processing method provided by this invention utilizes multiple devices such as GC-MS, hyperspectral cameras, and near-infrared spectrometers to simultaneously collect data, comprehensively acquiring the chemical composition, physical structure, molecular vibration, and other characteristics of medical waste, providing rich information for accurate classification and compatibility optimization. The multimodal data acquisition and fusion method effectively addresses different types of hazardous waste, avoiding misjudgments caused by a single data source and improving overall processing accuracy. Simultaneously, the dynamic concept drift detection and classification model update mechanism plays a crucial role. When a change in data distribution is detected (KL=0.31>0.25), the classification model is updated promptly, improving classification accuracy to 92.7%. Comparing Case 1, where drift detection was disabled, the classification accuracy and dioxin prediction accuracy decreased significantly, fully demonstrating that this mechanism can effectively address dynamic changes in hazardous waste composition and ensure the long-term stable operation of the treatment system.

[0046] The processing method provided in this invention employs canonical correlation analysis (CCA) and a sparse autoencoder for cross-modal feature fusion. This reduces data dimensionality and integrates features from different modalities in both medical waste and electronic waste treatment cases. Compared to the simple feature concatenation in Comparative Example 2, it significantly improves the accuracy and efficiency of mixed waste identification. Multi-objective matching optimization, combined with algorithms such as linear programming, temporal convolutional networks (TCN), and gradient boosting decision trees (GBDT), achieves a balance between calorific value stability, silver recovery, and dioxin emission reduction in the medical waste treatment case, and efficiently completes precious metal enrichment in the electronic waste treatment case. This demonstrates that the method can simultaneously address multiple objectives such as environmental protection and resource recycling.

[0047] Furthermore, in the medical waste treatment case, dioxin emissions were monitored in real time using a flue gas analyzer. When the measured value deviated from the predicted value by more than 15%, data feedback and PID controller adjustments were triggered, ultimately resulting in dioxin emissions of only 0.8 ng-TEQ / m³. A similar real-time feedback calibration mechanism was used in the electronic waste treatment case to ensure the environmental friendliness of the treatment process and prevent pollutant emissions from exceeding standards.

[0048] This invention utilizes simultaneous multimodal data acquisition (including chemical composition, hyperspectral images, near-infrared spectra, and calorific value), combined with frequency domain decomposition and texture analysis of hyperspectral images, to comprehensively characterize the complex physicochemical properties of waste. This effectively avoids misjudgments caused by limited information in traditional methods, significantly improving identification accuracy. A dynamic concept drift detection mechanism is introduced, using a sliding window to calculate KL divergence. When significant changes occur in data distribution (KL > 0.25), an incremental random forest model update is automatically triggered, and the node splitting threshold is dynamically adjusted using the Gini index to ensure long-term model stability and adaptive operation. Canonical correlation analysis (CCA) is employed to fuse chemical and image features, and the dimensionality is reduced to 50 dimensions using a sparse autoencoder. This reduces computational complexity while preserving key information, accelerating the matching decision-making process. Finally, by constructing a linear programming model with stable calorific value, high metal recovery rate, and low dioxin emissions as objectives, and combining TCN and gradient boosting trees for accurate prediction and optimization, this invention achieves efficient, intelligent, and resource-based hazardous waste treatment while meeting stringent environmental regulations, resulting in a dual improvement in environmental and economic benefits.

[0049] The above-disclosed embodiments are merely a few specific examples of the present invention. However, the present invention is not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A method for intelligent classification and treatment of hazardous waste based on multi-parameter fusion, characterized in that, Includes the following steps: Collect current multimodal data of hazardous waste and load historical multimodal data; the multimodal data includes: chemical composition data, images in the 400-2500nm band, molecular vibrational spectra and calorific value; By combining current and historical multimodal data, a classification model is invoked to perform cross-modal feature fusion and extract feature pairs with the highest correlation coefficient. A linear programming model is established based on the maximum correlation coefficient characteristic. The linear programming model is used to calculate the predicted dioxin emissions and optimize the recovery rate of valuable metals. Real-time monitoring of actual dioxin emissions; if the deviation between the actual dioxin emissions and the predicted dioxin emissions exceeds a specified threshold, classification calibration is triggered.

2. The intelligent classification and treatment method for hazardous waste based on multi-parameter fusion according to claim 1, characterized in that, The chemical composition data were acquired by GC-MS; the 400-2500 nm band images were acquired by a hyperspectral camera; the molecular vibrational spectra were acquired by a near-infrared spectrometer; and the calorific value was acquired by a calorific value sensor.

3. The intelligent classification and treatment method for hazardous waste based on multi-parameter fusion according to claim 1, characterized in that, Performing the cross-modal feature fusion includes the following steps: Determine if concept drift has occurred. If concept drift has occurred, update the classification model using an incremental random forest model; retain important nodes and reconstruct the split threshold. Perform canonical correlation analysis on different types of data to extract the feature pairs with the highest correlation coefficient; The maximum correlation coefficient feature is used to reduce the dimensionality of the input sparse autoencoder to 50 dimensions.

4. The intelligent classification and treatment method for hazardous waste based on multi-parameter fusion according to claim 3, characterized in that, The objective function of the canonical correlation analysis is: , The constraints are ;in, For the output control quantity of the PID controller, ΣXY represents the linear combination coefficients, and ΣXY represents the covariance matrix of chemical composition and spectral data.

5. The intelligent classification and treatment method for hazardous waste based on multi-parameter fusion according to claim 1, characterized in that, The objective function of the linear programming model is: Max(0.6 × calorific value stability + 0.3 × valuable metal recovery rate - 0.1 × dioxin predicted emissions); The constraints of the linear programming model are: calorific value fluctuation ≤ ±5%; Cl⁻≤1.5wt%, S≤0.8wt%, and compliance with the incompatible waste segregation rule.

6. The intelligent classification and treatment method for hazardous waste based on multi-parameter fusion according to claim 5, characterized in that, The calculation of predicted dioxin emissions is achieved through a temporal convolutional network. The input parameters for the calculation are incineration temperature T, chlorine content (Cl%), and catalyst metal content (Cu%), expressed as follows: ； The recovery rate of valuable metals is optimized by using a gradient boosting decision tree. The input features include the initial concentrations of Au, Ag, and Pd in ​​the waste and the pH value.

7. The intelligent classification and treatment method for hazardous waste based on multi-parameter fusion according to claim 1, characterized in that, The classification calibration refers to adjusting the control of the incinerator through a PID controller to adjust dioxin emissions and then re-analyzing from the cross-modal feature fusion step. The control of the incinerator by the PID controller is expressed as follows: ； Where e(t) represents the actual and predicted dioxin emissions, and Kp, Ki, and Kd are the control coefficients.

8. The intelligent classification and treatment method for hazardous waste based on multi-parameter fusion according to claim 2, characterized in that, Frequency domain decomposition was performed on the 400-2500nm band images. The low-frequency components characterize matrix homogeneity, while the high-frequency components characterize microscale pollutant aggregation. Images in the same band can reflect the spectral characteristics of the waste.

Images