Intelligent supervision method, system and device for sales and storage of secret-related wastes

Abstract

The invention provides an intelligent supervision method, system and device for sale and storage of secret-related waste, and the method comprises the steps: checking secret-related waste declaration data through a solid waste intelligent management system, verifying the corresponding secret-related waste, and generating verification data and waste types; according to the verification data and the type of the waste material, controlling a conveying device to convey the secret-related waste material to a sorting area for automatic sorting to obtain a physically crushed waste material and a non-physically crushed waste material; monitoring the waste volume of the physically crushed waste, controlling the conveying equipment to deliver the physically crushed waste to the corresponding crushing chamber, and driving the crushing equipment in the crushing chamber to perform crushing and destroying to obtain crushing process data; and the conveying equipment is controlled to transfer the non-physically crushed waste to a packaging area, harmless treatment equipment in the packaging area is driven to conduct harmless treatment, and harmless process data are obtained. According to the method, destroying resource configuration can be optimized, and the processing efficiency and safety are improved.

Description

Technical Field

[0001] This application relates to the technical field of classified waste disposal and storage, and in particular to intelligent monitoring methods, systems and devices for classified waste disposal and storage. Background Technology

[0002] With the accelerated pace of informatization, the quantity and diverse forms of classified waste have surged. Traditional, extensive management methods relying on manual registration are no longer sufficient to meet the requirements of modern confidentiality supervision for full-process traceability, refined control, and intelligent disposal. Existing technical solutions typically employ a segmented, independent processing model, with data fragmented across stages such as declaration verification, sorting and distribution, and destruction execution, lacking a unified collaborative mechanism. Verification is often limited to basic information registration, failing to establish a multi-dimensional physical characteristic verification system. This fragmented management model results in gaps in the regulatory chain, impacting disposal efficiency and failing to meet the compliance requirements for full lifecycle traceability of classified waste. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this application provides a method, system and device for intelligent supervision of the disposal and storage of classified waste, which can optimize the allocation of destruction resources, improve processing efficiency and security, and realize intelligent supervision of the entire life cycle of classified waste from declaration to destruction, thereby significantly improving supervision efficiency and compliance level.

[0004] This application provides a smart monitoring method for the disposal and storage of classified waste, including: The solid waste intelligent management system reviews the declaration data of classified waste materials, verifies the corresponding classified waste materials, and generates verification data and waste material types. Based on the verification data and the type of waste, the control conveying equipment transports the classified waste to the sorting area for automated sorting, resulting in physically crushed waste and non-physically crushed waste. Monitor the volume of the physically crushed waste, control the conveying equipment to deliver the physically crushed waste to the corresponding crushing chamber, and drive the crushing equipment in the crushing chamber to crush and destroy it, and obtain crushing process data; The conveying equipment is controlled to transfer the non-physically crushed waste to the packaging area, and the harmless treatment equipment in the packaging area is driven to perform harmless treatment, and the harmless treatment process data is obtained. The verification data, the crushing process data, and the harmless treatment process data are aggregated to generate an electronic file.

[0005] Preferably, the step of reviewing the declared data of classified waste through the solid waste intelligent management system, verifying the corresponding classified waste, and generating verification data and waste type information includes: The identity verification of the classified waste declaration data is performed, and the waste information, quantity and classification level are identified to obtain the declaration element data; Based on the declared data, the weight of the classified waste is collected by the intelligent weighing equipment of the solid waste intelligent management system to obtain the waste weight data. The surface features of the classified waste are extracted using the image acquisition equipment of the solid waste intelligent management system to generate waste appearance image data. The waste weight data and the waste appearance image data are verified to be consistent with the declared data, and the verification data and the waste type information are output.

[0006] Preferably, the step of controlling the conveying equipment to transport the classified waste to the sorting area for automated sorting based on the verification data and the waste type, to obtain physically crushed waste and non-physically crushed waste, includes: By jointly analyzing the verification data and the waste type, the physical properties and confidentiality level parameters of the waste are obtained; Based on the physical properties of the waste and the confidentiality level parameters, a sorting and conveying path is planned to generate sorting path information; Based on the sorting path information, the conveying equipment is controlled to transport the classified waste to the sorting area, and the classified waste is scanned from multiple angles based on the sorting area to obtain material characteristics and waste structure characteristics; Based on the material characteristics and the waste structure characteristics, the classified waste is classified and sorted to obtain the physically crushed waste and the non-physically crushed waste.

[0007] Preferably, the classification and sorting of the classified waste based on the material characteristics and the waste structure characteristics to obtain the physically crushed waste and the non-physically crushed waste includes: The waste type is determined by using preset classification rules to identify the material characteristics and the waste structure characteristics, and the category result is output. Based on the category results, the grasping path and operation mode of the sorting robot arm in the sorting area are planned, and sorting action instructions are generated; The sorting action command drives the sorting robotic arm to perform grabbing and transfer operations on the confidential waste, thereby obtaining the physically crushed waste and the non-physically crushed waste.

[0008] Preferably, the step of monitoring the volume of the physically crushed waste, controlling the conveying equipment to deliver the physically crushed waste to the corresponding crushing chamber, and driving the crushing equipment in the crushing chamber to crush and destroy it, and acquiring crushing process data, includes: The physical crushed waste is identified by size to obtain the waste volume; Based on the volume of the waste, the conveying equipment is controlled to deliver the physically crushed waste to the corresponding crushing chamber, generating crushing chamber allocation information; The solid waste intelligent management system controls the conveying path of the physically crushed waste based on the crushing chamber allocation information, and generates conveying information. The crushing equipment is controlled to crush the physical waste material according to the crushing chamber allocation information and the conveying information, and the crushing process data is collected simultaneously.

[0009] Preferably, the step of controlling the crushing equipment to crush the physically crushed waste material based on the crushing chamber allocation information and the conveying information, and simultaneously collecting the crushing process data, includes: Based on the volume of the waste, the physically crushed waste is divided into soft waste and hard waste; Based on the crushing chamber allocation information and the conveying information, the soft waste is conveyed to the first crushing chamber of the crushing chamber, and the crushing equipment is driven to perform soft crushing, and data on the destruction process of the first chamber is collected; Based on the crushing chamber allocation information and the conveying information, the hard waste material is conveyed to the second crushing chamber of the crushing chamber, and the crushing equipment is driven to crush the hard material, and data on the destruction process in the second chamber is collected; The crushing process data of the first chamber destruction process and the second chamber destruction process data are screened and integrated for crushing process characteristics, and the crushing process data is output.

[0010] Preferably, the control of the conveying equipment to transfer the non-physically crushed waste to the packaging area, and the driving of the harmless treatment equipment in the packaging area to perform harmless treatment, and to obtain harmless treatment process data, includes: The conveying equipment is controlled to transfer the non-physically crushed waste from the sorting area to the packaging area, and the size and material data of the non-physically crushed waste are collected during the transfer process; Based on the size and material data, the non-physically crushed waste is configured for harmless packaging, resulting in packaging configuration parameters and a harmless treatment scheme. Based on the packaging configuration parameters, the automated packaging equipment in the packaging area is controlled to perform a sealing packaging operation on the non-physically crushed waste material and output a sealed package. The device is driven to perform harmless treatment on the sealed packaging according to the harmless treatment plan, and the treatment process is monitored in real time to generate harmless treatment process data.

[0011] Preferably, the step of aggregating the verification data, the crushing process data, and the harmless treatment process data to generate an electronic archive includes: The solid waste intelligent management system performs correlation analysis and integrity verification on the verification data, the crushing process data, and the harmless treatment process data to obtain the correlation mapping relationship. Based on the aforementioned association mapping relationship, the verification data, the crushing process data, and the harmless disposal process dataset are fused from multiple sources and aligned with timestamps to obtain destruction process information; Based on the destruction process information, blockchain notarization and index archiving identifiers are added to generate archive data packages; The data package is structured and securely encapsulated to output the electronic archive.

[0012] This invention also provides a smart monitoring system for the disposal and storage of classified waste, applied to any of the smart monitoring methods for the disposal and storage of classified waste described above, comprising: The analysis module is used to review the declaration data of classified waste through the solid waste intelligent management system, verify the corresponding classified waste, and generate verification data and waste type. The processing module is used to control the conveying equipment to transport the classified waste to the sorting area for automated sorting based on the verification data and the waste type, so as to obtain physically crushed waste and non-physically crushed waste. The association module is used to monitor the volume of the physically crushed waste, control the conveying equipment to deliver the physically crushed waste to the corresponding crushing chamber, drive the crushing equipment in the crushing chamber to crush and destroy it, and obtain crushing process data. The module is used to control the conveying equipment to transfer the non-physical crushed waste to the packaging area, and drive the harmless treatment equipment in the packaging area to perform harmless treatment and obtain harmless treatment process data. An execution module is used to aggregate the verification data, the crushing process data, and the harmless treatment process data to generate an electronic file.

[0013] This application also provides a smart monitoring device for the disposal and storage of classified waste, including: Memory, used to store programs; A processor is used to execute the program to implement each step of the intelligent monitoring method for the disposal and storage of classified waste as described in any of the above-mentioned methods.

[0014] The technical solution provided in this application may include the following beneficial effects: The solid waste intelligent management system verifies classified waste declaration data from multiple dimensions and verifies the characteristics of physical waste, significantly improving the accuracy and reliability of the verification process. Automated sorting based on verification data and waste type enables precise classification of crushed and non-crushed waste, effectively avoiding the risks of errors and omissions associated with manual sorting. By identifying the scale characteristics of crushed waste and matching the processing capacity of corresponding crushing chambers, the system optimizes the allocation of destruction resources, improving processing efficiency and safety. A dedicated harmless treatment process is used for non-crushed waste, ensuring that all types of waste are disposed of in compliance with regulations. By aggregating all operational data to generate electronic archives, a complete traceability chain is established, enabling intelligent supervision of classified waste throughout its entire lifecycle from declaration to destruction, significantly improving regulatory efficiency and compliance.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0016] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0017] Figure 1 A flowchart of a smart monitoring method for the disposal and storage of classified waste is provided for this application; Figure 2 A structural diagram of a smart monitoring system for the disposal and storage of classified waste materials is provided for this application; Figure 3 This application provides a structural diagram of a smart monitoring device for the disposal and storage of classified waste. Detailed Implementation

[0018] Preferred embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0019] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0020] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0021] Reference Figure 1 As shown, this application provides a smart monitoring method for the disposal and storage of classified waste, including: Step S1: Review the declaration data of classified waste materials through the solid waste intelligent management system, verify the corresponding classified waste materials, and generate verification data and waste material type; Step S2: Based on the verification data and the type of waste, control the conveying equipment to transport the classified waste to the sorting area for automated sorting to obtain physically crushed waste and non-physically crushed waste; Step S3: Monitor the volume of the physically crushed waste, control the conveying equipment to deliver the physically crushed waste to the corresponding crushing chamber, and drive the crushing equipment in the crushing chamber to crush and destroy it, and obtain crushing process data; Step S4: Control the conveying equipment to transfer the non-physically crushed waste to the packaging area, and drive the harmless treatment equipment in the packaging area to perform harmless treatment, and obtain harmless treatment process data; Step S5: Aggregate verification data, crushing process data and harmless treatment process data to generate electronic files.

[0022] Based on the steps described above, the detailed process is as follows: Step S1: The solid waste intelligent management system receives electronic declaration data of classified waste submitted by waste-related entities. This data includes key information such as waste type, quantity, classification level, and physical characteristics. The system performs structured parsing and integrity verification on the declaration data, extracts core parameters, and establishes a standardized data format.

[0023] The physical verification process is initiated, with smart weighing equipment connected via the Internet of Things automatically collecting the actual weight data of the waste, and simultaneously triggering an image acquisition device to obtain surface feature information of the waste. The system performs multi-dimensional comparative analysis between the declared data and the physical verification data, including weight deviation detection, appearance feature matching, and confidentiality level verification.

[0024] The verification process employs a combination of rule-based and feature-matching algorithms to comprehensively assess the authenticity and compliance of the waste materials. Ultimately, a verification data package is generated, containing the verification results, physical parameters of the waste materials, and safety level identifiers. Simultaneously, the confirmed waste type classification results are output. The verification data established in this step provides accurate input parameters for subsequent sorting processes, ensuring the accuracy and reliability of subsequent steps.

[0025] Step S2: Based on verification data and waste type information, an automated sorting process is initiated. The physical characteristic parameters and security level identifiers in the verification data are analyzed, and a sorting decision plan is generated according to a pre-set sorting rule library. The sorting rule library includes multi-dimensional judgment criteria such as material hardness thresholds, size ranges, and confidentiality level requirements. The control conveyor equipment dynamically plans the conveying path according to the sorting plan, accurately delivering confidential waste to designated workstations in the sorting area. The sorting area is equipped with a multi-sensor fusion detection device to collect and verify the real-time characteristics of the waste, ensuring the accuracy of the sorting decisions.

[0026] Based on the waste characteristic analysis, the system drives the sorting equipment to perform sorting operations, dividing the waste into two categories: physically shredderable waste and non-shredderable waste. Physically shredderable waste mainly includes hard materials, precision components, and other materials requiring physical shredding, while non-physically shredderable waste covers soft materials, chemicals, and other materials requiring special handling. After sorting, the system generates sorting log data including sorting time, category quantity, and processing path. The sorting results achieved in this step provide clear input for subsequent differentiated processing, ensuring that different types of waste receive the most suitable treatment.

[0027] Step S3: Based on the physical waste classification results output from the sorting process, the scale identification and crushing process is initiated. High-precision weight sensors and a 3D volumetric scanning device collect the physical scale parameters of the waste, including key indicators such as total mass, individual particle size distribution, and stacking density. The monitoring data is transmitted in real time to the central processing unit, where it is matched and analyzed against preset crushing chamber processing capacity parameters to generate the optimal allocation plan.

[0028] The control and conveying equipment transports waste materials to dedicated crushing chambers according to the allocation plan: Crushing Chamber 1 is equipped with a pulverizing device to process soft materials, while Crushing Chamber 2 is equipped with a high-pressure pulverizer to process hard materials. During the conveying process, RFID tracking technology and a visual positioning system are used to monitor the material flow in real time, ensuring the accuracy and traceability of the conveying path. A safety verification process is performed before driving the crushing chamber equipment, including operation authorization authentication, equipment status detection, and environmental safety assessment.

[0029] The crushing process integrates a multi-sensor monitoring network to collect real-time equipment operating parameters (spindle speed, hydraulic pressure, cutter temperature), energy consumption data, and processing status information. The generated crushing process data includes processing timestamps, equipment operating parameters, energy consumption statistics, and processing effect verification results, forming a complete process record archive.

[0030] Step S4: The control and conveying equipment transfers the sorted non-physically crushed waste to designated workstations in the packaging area. During the transfer, multispectral sensors collect real-time physicochemical properties of the medium, including chemical composition, physical state, and environmental risk indicators. The drive system automatically selects the appropriate treatment process based on the property analysis results: high-temperature melting is suitable for organic materials, chemical neutralization is for corrosive waste, and physical encapsulation is used for stabilizing substances.

[0031] The harmless treatment equipment dynamically adjusts process parameters based on the type of waste, including temperature control curves, pressure adjustment ranges, reaction time settings, and encapsulation strength requirements. Throughout the treatment process, environmental monitoring is implemented, using a sensor array to collect environmental parameters such as volatile organic compound concentration, dust dispersion level, noise intensity, and temperature and humidity changes. Equipment operating status, energy consumption data, and treatment progress are recorded simultaneously, forming a complete treatment log.

[0032] The generated harmless treatment process data includes process parameter records, environmental monitoring results, treatment effect verification data, and compliance assessment reports. All data is digitally signed and timestamped to ensure its authenticity and auditability. This step implements a harmless treatment system that fully guarantees the compliant disposal of non-crushable waste and avoids the risk of secondary pollution.

[0033] Step S5: A multi-source data acquisition channel was established, and verification data, crushing process data, and harmless treatment process data were standardized and preprocessed. Data preprocessing included key operations such as format unification, timestamp alignment, and missing value compensation. A distributed data processing architecture was adopted to perform correlation analysis on multi-source heterogeneous data, establishing cross-stage data mapping relationships and audit trails.

[0034] By using blockchain technology to record key operational nodes, the system ensures the immutability and traceability of data. During the generation of electronic archives, the system automatically adds security elements such as metadata identifiers, archival indexes, and digital signatures to form a structured archive package that meets the required standards.

[0035] The archive contains complete records of the disposal process, effectiveness verification data, and compliance documentation, supporting multi-dimensional retrieval and audit verification.

[0036] The output electronic records employ encrypted storage and a tiered authorization mechanism to ensure the security and confidentiality of the data. This electronic record system provides a complete, traceable chain of evidence for the disposal of classified waste, meeting the compliance requirements of regulatory authorities for the management of the disposal and storage of classified waste. This application provides a smart supervision method for the disposal and storage of classified waste. Through a solid waste intelligent management system, it verifies classified waste declaration data from multiple dimensions and checks the characteristics of the physical waste, significantly improving the accuracy and reliability of the verification process. Automated sorting based on verification data and waste type enables precise classification of crushed and non-crushed waste, effectively avoiding the risks of errors and omissions associated with manual sorting. By identifying the scale characteristics of crushed waste and matching the processing capacity of the corresponding crushing chamber, it optimizes the allocation of destruction resources, improving processing efficiency and safety. A dedicated harmless treatment process is used for non-crushed waste, ensuring that all types of waste are disposed of in compliance with regulations. By aggregating all operational data to generate electronic archives, a complete traceability chain is established, realizing smart supervision of classified waste throughout its entire lifecycle from declaration to destruction, significantly improving supervision efficiency and compliance levels.

[0037] In one embodiment, the solid waste intelligent management system reviews the declared data of classified waste, verifies the corresponding classified waste, and generates verification data and waste type information, including: The solid waste intelligent management system receives electronic declaration data packets submitted by waste-related entities through an encrypted transmission channel. The data packets are digitally signed using an asymmetric encryption algorithm to ensure the authenticity and integrity of the data source. After successful verification, the system deconstructs the data packets to extract key fields such as waste description information, quantity statistics, and confidentiality level identifiers.

[0038] Natural language processing (NLP) technology is used to semantically analyze the waste description text, identifying characteristic parameters such as waste material type, physical form, and special processing requirements. Quantitative statistics undergo a dual verification mechanism to ensure data consistency with subsequent physical verification. Classification and security level identifiers are categorized and mapped according to national security standards, converting them into internal system security processing level parameters.

[0039] All parsed data fields are converted into a structured data set in a unified format through standardized transformation rules. This data set contains core elements such as unique waste identification codes, classification codes, quantity approval values, and safety level parameters.

[0040] After the declaration data is established, the system automatically generates a data integrity verification report, recording the marking and processing logs of abnormal data during the parsing process. This data standardization process provides an accurate comparison benchmark for subsequent physical verification, ensuring a reliable data foundation for verifying the consistency between the declared data and the actual waste.

[0041] Based on the quantity and type of waste information included in the declaration data, the physical weight verification process is initiated. The intelligent weighing equipment automatically selects the appropriate weighing mode and accuracy level according to the type of waste. For precision electronic waste, a milligram-level high-precision weighing mode is used, while for bulk materials, a kilogram-level industrial weighing mode is activated.

[0042] The weighing process takes place in a controlled environment. The equipment automatically performs zero-point calibration and environmental compensation to eliminate the influence of external factors such as temperature, humidity, and vibration on the weighing results. After the waste material is placed on the weighing platform, the equipment uses a multi-segment weighing algorithm to collect stable weight values, automatically records the weight fluctuation curve, and calculates the final approved weight.

[0043] Weighing data is transmitted to the central processing unit in real time for deviation analysis compared with the declared weight in the declaration data. When the weight deviation exceeds a preset threshold, the system automatically triggers a review mechanism, requiring re-weighing or initiating a manual intervention process. The approved weight data is appended with a timestamp, equipment identification, and operator information, generating a waste weight data package with complete traceability information.

[0044] This data package establishes a mapping relationship with the previously generated declaration element data, forming the first cross-validation result between the declaration data and the physical verification data. The completion of weight data collection marks the initial implementation of the physical verification process, providing an important physical parameter basis for subsequent visual feature extraction.

[0045] A multispectral imaging system is used to collect comprehensive surface features of classified waste. The image acquisition equipment employs a circular array of multi-angle industrial cameras, coupled with a highly uniform illumination system, to ensure consistent image quality under varying lighting conditions. During acquisition, the equipment automatically adjusts the focal length and exposure parameters to optimize imaging effects for different waste materials.

[0046] For highly reflective metal scrap, a polarized light filtering mode is used to eliminate specular reflection interference; for transparent or semi-transparent scrap, backlight transmission imaging technology is used to obtain internal structural information. The image processing unit preprocesses the acquired raw images, including noise suppression, contrast enhancement, and geometric correction. Various visual features are extracted from the preprocessed images, including surface texture features, edge contour features, color distribution features, and defect detection features. Texture feature analysis employs a combination of local binary mode and gray-level co-occurrence matrix to quantify surface roughness and pattern regularity. Edge detection accurately identifies the geometric features of the scrap contour.

[0047] Color features are quantified using histogram statistics in the HSV color space. All extracted feature vectors are normalized and then mapped to waste weight data to form a multimodal feature dataset. This dataset contains 128-dimensional visual feature vectors and their confidence indices, providing comprehensive visual feature data for subsequent consistency verification. The quality assessment report generated during feature extraction records imaging parameters, feature extraction success rate, and anomaly markers, ensuring the reliability and traceability of the waste appearance image data.

[0048] Comprehensive analysis and verification of multi-source data are conducted. A multi-dimensional comparison index system is established, including core parameters such as weight deviation threshold, visual feature similarity threshold, and composite verification rules. Weight data consistency verification adopts a relative deviation algorithm to calculate the percentage deviation between the measured weight and the declared weight. A level one warning is triggered when the deviation exceeds ±5%.

[0049] Visual feature verification is achieved through similarity calculation in the feature vector space, employing a comprehensive evaluation method combining cosine similarity and Euclidean distance. The system performs matching degree analysis between the extracted visual feature vectors and the waste features described in the declaration data, generating a similarity scoring matrix. For key security indicators, such as the integrity of classified markings and the degree of waste damage, independent verification rules and weighting coefficients are set. Multi-source data fusion verification adopts the DS evidence theory framework, synthesizing the weight verification results and visual verification results to calculate the final credibility probability distribution.

[0050] When the credibility of the synthesized data falls below a preset threshold, the system automatically initiates a manual review process and records verification anomalies. For verified waste materials, the waste type is determined according to the national standard classification system based on the feature matching results, and complete verification data including verification status, verification score, and type code is output. The final output verification data package uses a structured storage format and includes three main parts: original declaration data, verification process data, and verification result data.

[0051] The data packets are secured with digital signatures and timestamped with operator identifiers to create an auditable verification record. The generation of this verification data marks the completion of the declaration verification phase, providing authoritative data input for subsequent sorting processes. This embodiment achieves multi-dimensional and precise acquisition of the physical characteristics of classified waste through the collaborative operation of intelligent weighing equipment and a multispectral imaging system, significantly improving the accuracy and reliability of verification data. Deep neural networks are used for visual feature extraction, effectively identifying surface texture, edge contours, and defect features of the waste, overcoming the subjective biases of traditional manual verification. Through a multi-source data fusion verification mechanism, consistency analysis is performed between weight data and waste appearance image data, establishing objective verification judgment standards. The entire verification process forms a complete digital traceability chain, ensuring the auditability of classified waste from declaration to verification.

[0052] In one embodiment, based on verification data and waste type, a control conveyor system transports classified waste to a sorting area for automated sorting, resulting in physically crushed waste and non-physically crushed waste, including: The system receives verification data packets and waste type identifiers generated during the preliminary verification process. The verification data packets contain approved waste weight data, visual feature vectors, and verification result identifiers, while the waste type identifiers adopt the national standard classification and coding system.

[0053] Multi-source data undergoes format standardization, converting weight data into standard mass units and normalizing the dimensions of visual feature vectors to ensure the data operates within the same measurement system. The parsing process employs a rule-based feature extraction method to extract key physical properties such as waste density parameters, surface hardness index, and structural integrity indices from the verification data. The classification level parameters are determined through a multi-layered verification mechanism, comparing the initial classification level identifier in the application data with the integrity score of the classification identifier identified in visual feature analysis, and finally generating the processing security level based on the confidentiality specification mapping table.

[0054] All parsed parameters undergo confidence-weighted calculations to generate a physical attribute dataset containing parameter values ​​and their confidence intervals. This dataset uses a structured storage format and includes core fields such as unique waste identifiers, physical attribute matrices, and security level codes. After parsing, the system automatically generates a data quality report, recording abnormal data processing logs and parameter correction records during the parsing process.

[0055] Based on the waste physical property dataset and confidentiality level parameters, a sorting and conveying path planning process is initiated. Path planning requires first establishing a sorting rule knowledge base, which includes multi-dimensional constraints such as material processing rules, security level specifications, and equipment capability parameters. The planning algorithm determines the initial sorting direction judgment logic based on waste density and surface hardness parameters; combined with confidentiality level parameters, it overlays security processing path constraints.

[0056] For highly classified waste, dedicated conveying channels and enhanced safety monitoring are automatically activated. Path optimization employs a dynamic weight allocation model, comprehensively considering multiple objectives such as conveying efficiency, safety risks, and equipment load balancing. During the planning process, equipment status monitoring data is invoked in real time to avoid conveying units under maintenance or at full load, ensuring the real-time feasibility of the path.

[0057] The generated sorting path information includes a detailed instruction set such as conveyor channel number, steering mechanism control sequence, speed adjustment parameters, and safety monitoring level. Each path instruction is appended with a timestamp and priority identifier, forming a complete conveying control scheme. After the path information is generated, the system automatically performs conflict detection and redundancy verification to ensure that there are no spatiotemporal conflicts between the conveying paths of different waste materials.

[0058] The output sorting path information packet is transmitted to the conveyor equipment in an encrypted data format, while simultaneously generating a path execution plan and emergency response plan. This step achieves precise matching between waste characteristics and processing resources in the sorting path planning, providing reliable operational guidance for subsequent automated sorting execution.

[0059] After receiving the sorting path instruction package, the conveyor equipment analyzes the operational instructions contained therein, including the conveyor channel control sequence, speed adjustment parameters, and safety monitoring level. The control servo drive mechanism precisely controls the start-stop rhythm and running speed of the conveyor belt according to the instructions, while the steering mechanism performs diversion operations based on the path plan, accurately guiding waste materials to the target sorting station.

[0060] During the conveying process, the safety monitoring module detects the location and status of the waste in real time, tracking its movement trajectory through a photoelectric sensor array to ensure that the conveying path is completely consistent with the planned instructions. When the waste arrives at the designated workstation in the sorting area, the multi-angle scanning module immediately initiates the feature acquisition program. The scanning module uses a circular array of industrial cameras and multispectral imaging units to simultaneously acquire image data of the waste surface from different perspectives. Each imaging unit is equipped with an independent light source control system that automatically adjusts the lighting intensity and angle according to the material characteristics of the waste, eliminating reflections and shadows.

[0061] During the scanning process, the system acquires multispectral image data in the visible, infrared, and ultraviolet bands to obtain comprehensive information on the surface and internal structure of the waste material. The image processing unit fuses the acquired multi-source image data and uses feature enhancement algorithms to extract the texture features, color features, and spectral reflectance characteristics of the material.

[0062] The geometric dimensions, volume parameters, and structural morphology features of the waste were calculated using 3D reconstruction technology to establish a complete 3D digital model of the waste. The feature extraction process employed a multi-layer convolutional neural network architecture, abstracting discriminative feature representations layer by layer from the original image data. The final generated material feature dataset includes texture parameters, spectral feature vectors, and material composition inference results, while the waste structural feature dataset includes geometric dimensions, structural integrity indicators, and defect detection results.

[0063] All feature data are accompanied by the acquisition time, device identification, and quality assessment indicators, forming a complete feature description document. This document is sent to the sorting decision unit via a secure transmission protocol, providing detailed feature basis for subsequent classification and sorting. The raw image data generated during feature acquisition and the processing log are archived and stored synchronously to ensure the traceability and verifiability of the process.

[0064] Based on a dataset of material characteristics and waste structural features, an automated classification and sorting operation for classified waste is performed. First, a pre-trained feature classification model is loaded. This model, trained on a large amount of sample data, can accurately identify the feature patterns and structural characteristics of different materials. The model performs pattern matching on the input material feature vectors, calculates similarity scores with various standard materials, and generates a material type determination result. Simultaneously, a structural feature analysis unit comprehensively evaluates the geometric parameters and integrity indicators of the waste to determine its physical state and processing difficulty.

[0065] The classification rule base sets sorting thresholds for different material combinations and structural states based on national confidentiality standards and processing specifications. A multi-level judgment logic is employed, initially classifying materials by type and then refining the classification based on structural characteristics. For metallic waste, the focus is on hardness parameters and structural integrity; for plastic waste, the emphasis is on chemical composition and temperature resistance; and for paper waste, the main evaluation criteria are fiber structure and coating characteristics.

[0066] After receiving the classification decision results, the sorting equipment activates the corresponding sorting mechanism. The pneumatic sorting device automatically adjusts the spray pressure and angle according to the waste type, while the robotic arm sorting unit selects an appropriate gripping method based on the waste size and weight parameters. During the sorting process, a real-time quality detection system performs secondary verification of the sorting results, using high-speed vision sensors to capture the movement trajectory of the waste to ensure sorting accuracy.

[0067] When a sorting deviation is detected, a compensation mechanism is automatically triggered to adjust sorting parameters or start a repeat sorting process. The output sorting results include two categories: physically crushed waste and non-physically crushed waste. Physically crushed waste mainly includes materials that require physical crushing, such as metal components and plastic products, while non-physically crushed waste covers materials that require special handling, such as chemicals and electronic components.

[0068] Each category generates a detailed sorting list, recording information such as the quantity of waste, classification criteria, and processing recommendations. After sorting is completed, the system automatically updates the inventory status, generates a sorting operation report, and records key indicators such as classification accuracy, processing efficiency, and abnormal events.

[0069] This embodiment combines a multi-angle scanning system with multispectral imaging technology to achieve comprehensive feature acquisition of the surface texture and internal structure of classified waste, significantly improving the accuracy and reliability of material identification. The collaborative operation of an automated sorting mechanism and an intelligent decision engine enables automatic classification based on the physical properties and structural characteristics of the waste, effectively avoiding subjective errors and efficiency bottlenecks inherent in manual sorting. The dual safeguards of a sorting rule base and a real-time quality inspection system ensure the safety and compliance of the highly classified waste processing process, while also achieving precise separation of crushed and non-crushed waste.

[0070] In one embodiment, classified waste is sorted and classified based on material characteristics and waste structure characteristics to obtain physically crushed waste and non-physically crushed waste, including: The material feature dataset includes quantified texture parameters, spectral feature vectors, and material composition inference indicators, while the waste structure feature dataset includes geometric dimension measurements, structural integrity scores, and defect detection results. The pre-defined classification rule base is established based on national confidentiality standards and industry processing specifications, including multi-dimensional judgment logic such as material type discrimination rules, structural state assessment rules, and safety processing level mapping rules. The input feature data undergoes standardized preprocessing, converting feature parameters of different dimensions into evaluation indicators with unified metric standards.

[0071] Material type identification employs a feature similarity-based matching algorithm, comparing measured material features with standard material templates in a rule base to calculate feature matching degree and generate a material type confidence score. The structural condition assessment module comprehensively analyzes geometric dimensional deviations, structural integrity indicators, and defect distribution characteristics, outputting a physical condition level assessment result for the waste. The safety treatment level mapping unit determines the final treatment category based on material type and structural condition, referring to confidentiality requirements.

[0072] All judgment results undergo multi-rule collaborative verification and conflict resolution mechanisms to ensure the accuracy and consistency of the classification results. The final output classification results include decision-making elements such as waste type identification, processing priority score, and recommended processing method, forming a complete basis for sorting decisions. The waste type determination completed in this step provides an authoritative classification standard for subsequent sorting operations, ensuring the standardization and reliability of sorting processes.

[0073] The system analyzes the waste type identifier, physical characteristic parameters, and processing priority information contained in the classification results. Combining this with the dynamic characteristics of the robotic arm in the sorting area and the constraints of the workspace, it generates an optimal grasping path. The planning algorithm employs a hierarchical decision-making architecture: the top-level planning determines the macroscopic movement trajectory of the robotic arm, while the bottom-level planning refines the precise operation sequence of the end effector. For different types of waste materials, the planning system automatically adjusts the grasping strategy: metallic waste is grasped using magnetic adsorption or mechanical clamping, while fragile waste is grasped using a flexible grasping and pressure control mode.

[0074] During path optimization, environmental perception data is integrated in real time. Visual sensors are used to acquire the real-time location information of the waste material, dynamically correcting the path planning to compensate for positioning errors. The operation mode planning module formulates specific gripping parameters based on the structural characteristics of the waste material, including key operational indicators such as clamping force, contact point location, and conveying acceleration. For waste material with poor structural integrity, auxiliary support strategies and vibration reduction measures are automatically activated to ensure the safety and reliability of the conveying process.

[0075] The generated sorting action instructions include a complete motion control sequence, force control parameter settings, and safety monitoring strategies, forming directly executable robotic arm control code. The instruction set is transmitted to the robotic arm controller via a real-time communication protocol, while simultaneously generating contingency plans and exception handling procedures to ensure the robustness of the sorting operation. This step of sorting action planning achieves a precise conversion of classification results into specific operations, providing reliable technical support for subsequent automated sorting execution.

[0076] The physical scale parameters of the waste are collected through intelligent sensing devices, including key indicators such as total weight, individual unit size, and stacking density. The scale identification process employs multi-dimensional measurement technology, combining laser ranging and stereoscopic vision imaging, to accurately acquire the geometric features and spatial distribution of the waste. Based on the waste volume data and the crushing chamber's processing capacity matching model, an optimal allocation scheme is generated, and the waste is transported to the corresponding crushing and processing units.

[0077] The crushing chamber is equipped with differentiated processing equipment based on the characteristics of the waste: high-pressure crushing devices are used for hard metal waste, dedicated crushing equipment is used for precision electronic components, and shearing devices are used for soft materials. Real-time positioning and status monitoring technology is employed during the conveying process, using a combination of RFID identification and visual tracking to ensure the accuracy and traceability of the material conveying path. A safety verification process is performed before crushing operations begin, including operation authorization authentication, equipment status detection, and environmental safety assessment. Electronic lock mechanisms and interlocking devices ensure the safety and controllability of the processing process.

[0078] A multi-parameter monitoring system is integrated into the crushing process to collect data on equipment operating status, energy consumption indicators, and processing effects in real time, forming a complete process record. The final crushing process data includes processing timestamps, equipment operating parameters, energy consumption statistics, and processing effect verification results, providing detailed process evidence for subsequent data aggregation.

[0079] The physicochemical properties of uncrushable waste are analyzed, and key parameters such as chemical composition, physical state, and environmental risk indicators are obtained through multi-sensor fusion detection technology. Based on the property analysis results, the most suitable harmless treatment scheme is determined, including differentiated process paths such as high-temperature melting, chemical neutralization, and physical solidification. Controlled conveyor equipment transfers the waste to designated workstations in the packaging area, where automated packaging equipment completes the sealing and packaging operation. The packaging process uses environmentally friendly materials and airtight sealing technology to ensure that there is no risk of environmental pollution during the treatment process.

[0080] The harmless treatment equipment automatically adjusts process parameters based on the type of waste, including key indicators such as temperature control curves, pressure adjustment ranges, treatment time settings, and chemical reaction conditions. During treatment, an integrated environmental monitoring sensor network collects real-time environmental parameters such as volatile organic compound emission concentrations, dust diffusion levels, noise intensity, and temperature and humidity changes. The system simultaneously records equipment operating status, energy consumption data, and treatment progress, forming a complete treatment log. For chemical treatment processes, the system additionally collects process parameters such as pH value, concentration gradient, and reaction efficiency; for physical treatment processes, it focuses on recording encapsulation integrity, isolation effect, and stability indicators.

[0081] The generated harmless treatment process data includes processing parameters, environmental monitoring results, treatment effect verification data, and compliance assessment reports, ensuring that the treatment process fully complies with environmental regulatory requirements and safety standards. This step achieves a harmless treatment system that fully guarantees the compliant disposal of non-crushable waste, avoids the risk of secondary pollution, and realizes the coordinated development of resource recovery and environmental protection.

[0082] This embodiment employs intelligent sensing and multi-dimensional measurement technology to accurately identify the scale of physically crushed waste, automatically matching the most suitable crushing unit based on the waste characteristics, significantly improving the targeting and efficiency of the disposal process. Multi-sensor fusion detection technology is used to analyze the physicochemical properties of non-crushable waste, enabling precise formulation of harmless treatment plans and automatic optimization of process parameters, ensuring the environmental friendliness of the treatment process. By integrating a real-time monitoring and data acquisition system, the entire process from waste identification to final disposal is fully recorded, including operational parameters and environmental indicators, generating detailed processing data archives.

[0083] In one embodiment, the volume of physically crushed waste is monitored, the conveying equipment is controlled to deliver the physically crushed waste to the corresponding crushing chamber, and the crushing equipment in the crushing chamber is driven to crush and destroy it, acquiring crushing process data, including: A non-contact laser measuring device is used to scan the surface of physically crushed waste, and the external dimensions of the waste are obtained through optical reflection characteristic analysis. Structural characteristic monitoring is achieved through a 3D imaging unit, which collects the external contour and internal structural feature parameters of the waste. Scale monitoring relies on a dynamic weighing platform and a volume measuring instrument working synchronously to acquire waste mass parameters and volume distribution data in real time. The monitoring data is processed by an environmental compensation algorithm to eliminate interference from external factors such as temperature and humidity.

[0084] The data analysis unit performs multi-source data fusion processing to generate a waste volume dataset containing size specifications, mass distribution, and stacking characteristics. This dataset is stored in a standardized format and includes monitoring timestamps and equipment identifiers, forming a traceable scale monitoring record. Upon completion of monitoring, a quality inspection report is generated, recording any anomalies encountered during data collection and the corresponding handling measures, ensuring the accuracy and reliability of the scale data.

[0085] Based on the physical parameter characteristics of the waste volume dataset, the crushing chamber allocation decision process is initiated. The allocation system loads the crushing chamber processing capacity configuration file, which defines the processing parameter range for each crushing chamber, including constraints such as maximum size capacity, mass tolerance threshold, and processing efficiency indicators. The matching algorithm adopts a multi-objective optimization principle, performing matching analysis between waste size parameters and crushing chamber inlet specifications, adapting mass data to crushing chamber load-bearing capacity, and correlating structural features with crushing chamber processing technology.

[0086] The evaluation process incorporates a dynamic weighting adjustment mechanism, prioritizing the allocation of oversized waste to the wide-mouth crushing chamber and assigning high-density waste to the heavy-duty processing unit, ensuring both transport safety and processing efficiency. Once the allocation decision is generated, an allocation information package is created, containing the target crushing chamber number, transport priority, and any special processing requirements.

[0087] This information is transmitted to the conveying control unit via a secure transmission protocol, simultaneously updating the crushing chamber status monitoring log to indicate the equipment's operating status and estimated processing time. The allocation results undergo conflict detection and optimization verification to avoid resource allocation conflicts or unbalanced loads. The final output crushing chamber allocation information includes waste material identification codes, crushing chamber technical parameters, and processing priority identifiers, providing a basis for subsequent conveying control and crushing operations.

[0088] Based on the crushing chamber allocation information generated in the previous step, the conveying path control process is initiated. The crushing chamber allocation information includes key parameters such as the target crushing chamber number, processing priority, and special operation instructions, providing a decision-making basis for conveying path planning. The conveying path control module first parses the crushing chamber location data and processing requirements from the allocation information, and dynamically calculates the optimal conveying route by combining the plant layout map and equipment status logs. The path planning algorithm adopts a multi-objective optimization principle, comprehensively considering factors such as path length, equipment load balancing, and conveying time efficiency to generate a preliminary path plan.

[0089] For high-priority or special waste materials, path planning incorporates weighted coefficients to ensure priority transport of critical materials and selection of the safest routes. Once the transport path is determined, control commands are issued to the conveyor equipment execution layer. The conveyor equipment includes units such as conveyor belt systems, automated guided vehicles (AGVs), and robotic arms, which adjust their operating parameters according to the path commands. The conveyor belt speed is dynamically adjusted based on the characteristics of the waste material: for fragile or high-density waste materials, a low-speed, stable mode is used to reduce vibration and impact; for standard waste materials, an economical speed mode is activated to improve efficiency.

[0090] The automated guided vehicle (AGV) path is updated in real time via wireless communication, avoiding obstacles and maintenance areas to ensure continuous transport. The robotic arm operating unit automatically adjusts its gripping force and placement accuracy based on the size and weight of the waste material to prevent damage. A real-time monitoring mechanism is integrated into the transport process, collecting transport status data through an IoT sensor network. Position sensors track the movement of the waste material on the conveyor line in real time, speed sensors monitor the conveyor belt speed, and pressure sensors detect load changes.

[0091] All sensor data converges to the central processing unit for anomaly detection and dynamic adjustment. When path deviation or equipment malfunction is detected, the system automatically triggers corrective procedures, such as rerouting or suspending transport, to ensure transport safety. The generated transport information includes a complete path execution record, equipment operating parameters, and anomaly handling logs. The path execution record details the start and end points, transit nodes, and timestamps; equipment operating parameters include speed settings, energy consumption data, and maintenance status; the anomaly handling log records all interruption events and corrective actions.

[0092] The conveyed information is stored in a structured data format, with digital signatures and timestamps added to ensure authenticity and traceability. This information is synchronized to the crushing chamber control system in real time, providing preparation time and operating context for subsequent crushing processes.

[0093] Based on the crushing chamber allocation and conveying information, the crushing and data acquisition process is initiated. The solid waste intelligent management system receives the crushing chamber configuration parameters from the allocation information and the material status data from the conveying information, and performs equipment pre-processing on the crushing equipment within the crushing chamber. Pre-processing includes self-inspection of the crushing equipment, environmental safety assessment, and verification of operating permissions to ensure the equipment is in a ready state.

[0094] The self-inspection process includes tool wear detection, hydraulic system pressure calibration, and motor load testing; any abnormality triggers a maintenance alarm. Environmental safety assessments are conducted using gas sensors, temperature detectors, and a fire monitoring unit to confirm that the processing environment meets safety standards.

[0095] The crushing equipment dynamically adjusts its operating mode based on the characteristics of the waste material. The first crushing chamber is equipped with a grinding device for soft materials, featuring high-speed rotating blades and a flexible feeding system to ensure uniform material crushing. The second crushing chamber handles hard materials, utilizing a high-pressure pulverizer and a wafer crushing unit, adjusting pressure and impact frequency to accommodate waste materials of varying hardness. Equipment parameter settings are based on the material hardness index and structural characteristics in the allocated information, as well as the material size and weight data in the conveying information, achieving precise control. After crushing begins, real-time process monitoring and data acquisition are implemented.

[0096] A multi-sensor network operates synchronously: vibration sensors monitor equipment operational stability, temperature sensors track cutter thermal changes, power sensors record energy consumption curves, and vision sensors capture crushing effects and material particle size distribution. Data acquisition frequency is adjusted according to the processing stage: high-frequency sampling in the initial stage to capture startup characteristics, and regular sampling to record continuous parameters in the stable stage.

[0097] All collected data are appended with device identifiers and timestamps to form a time-series dataset. Simultaneously collected crushing process data includes processing parameters, equipment operating indicators, and environmental monitoring results. Processing parameters record crushing time, throughput, energy consumption, and efficiency indicators; equipment operating indicators include spindle speed, hydraulic pressure, tool temperature, and maintenance status; environmental monitoring results cover dust concentration, noise levels, and temperature and humidity changes. The data undergoes standardization and redundancy verification to ensure completeness and accuracy.

[0098] The generated data packets from the shredding process are stored in an encrypted format, comprising a raw data layer, a feature extraction layer, and an audit report layer, supporting multi-level querying and analysis. The data packets are uploaded to a central database via a secure transmission protocol, forming an immutable chain of evidence for destruction and providing reliable input for the generation of electronic records.

[0099] This embodiment utilizes multi-dimensional physical characteristic analysis technology to accurately identify physically crushed waste. It can automatically match the most suitable crushing unit based on material hardness, structural characteristics, and scale parameters, significantly improving the targeting and resource utilization efficiency of the disposal process. An intelligent allocation algorithm precisely matches waste characteristics with the crushing chamber's processing capacity, optimizing equipment resource allocation and avoiding problems of insufficient processing capacity or idle resources. Real-time conveyor path control and status monitoring ensure the safety and traceability of waste during transportation, reducing operational risks.

[0100] In one embodiment, the crushing equipment is controlled to crush physical waste materials based on crushing chamber allocation information and conveying information, and the crushing process data is collected simultaneously, including: The waste volume data includes key indicators such as mass, volume, and stacking density, which originate from the initial waste size identification stage. The sorting process employs a classification algorithm based on a size threshold: first, a mass-to-volume ratio threshold is set to initially classify low-density waste as soft waste and high-density waste as hard waste; second, the stacking density parameter is introduced for secondary verification, and for boundary cases, a comprehensive judgment is made in conjunction with the size distribution data.

[0101] Structural characteristic parameters (such as porosity and toughness) serve as auxiliary references to ensure the accuracy of the classification. The classification algorithm executes a multi-level decision-making process: the first level performs coarse classification based on the mass-to-volume ratio, the second level calibrates using stacking density, and the third level applies structural features for final confirmation. All classification results are assigned a confidence score, and a manual review mechanism is triggered when the score falls below a preset standard. The final output includes two categories: soft waste and hard waste, each containing a summary of scale parameters and a description of the classification criteria. The classification results achieved in this step provide accurate input for subsequent differentiated processing, ensuring that each type of waste receives the most suitable treatment.

[0102] Based on the soft waste classification results obtained in the previous step, the targeted processing flow of soft waste is initiated strictly according to the equipment configuration instructions in the crushing chamber allocation information and the path parameters in the conveying information. The crushing chamber allocation information specifies crushing chamber one as the processing target and includes equipment operation requirements (such as cutter speed range and feed rate); the conveying information provides the real-time location status of the waste and details of the conveying path.

[0103] Based on the conveying information, the conveying equipment is controlled, and the optimal conveying route is dynamically planned to avoid maintenance areas and high-load channels, ensuring that soft waste is smoothly transferred to the feed inlet of the crushing chamber via a dedicated conveyor line. Real-time monitoring is integrated during the conveying process, and shock absorption devices and speed control strategies are used to maintain material integrity, while sensors track the waste status.

[0104] The crushing chamber equipment performs a soft grinding operation: the grinding equipment automatically adjusts its working parameters according to the allocation information, setting appropriate cutter speed and feed rate to avoid over-grinding and dust generation. During the process, a multi-parameter monitoring network collects equipment operating data in real time, including motor load, cutter temperature, vibration frequency, and energy consumption curves; environmental sensors simultaneously monitor dust concentration, noise level, and temperature and humidity changes.

[0105] The collected data from the single-chamber disposal process includes equipment operating logs, energy consumption statistics, processing efficiency indicators, and environmental monitoring reports. All data is timestamped and identified by the equipment, forming a complete process record. Data packets are uploaded to the central database in real time via a secure transmission protocol, providing the original basis for subsequent data integration. This step ensures the high efficiency and safety of soft waste treatment, fully complying with the soft waste treatment requirements of claim 6.

[0106] Based on the classification results of hard waste and the allocation instructions of the crushing chamber, a specialized processing flow for hard waste is initiated. The conveying control system analyzes the equipment parameters and processing requirements of the second crushing chamber from the allocation information, plans a dedicated conveying path using a reinforced conveying device, and equips it with shock-absorbing supports and anti-skid design to ensure the stability of high-density waste conveying.

[0107] Hard waste is transferred to the feed inlet of the second crushing chamber via a heavy-duty conveyor line. A dual monitoring mechanism is implemented during the conveying process: a laser rangefinder monitors the waste displacement in real time, and a pressure sensor detects changes in the conveyor belt load to prevent overloading. The pre-treatment system of the second crushing chamber adopts a stepped feeding design, using a combination of a vibrating feeder and a magnetic separator to achieve preliminary sorting and uniform feeding of the metal waste.

[0108] The hard material crushing equipment dynamically adjusts its operating parameters based on the characteristics of the waste: for high-hardness metal waste, a hydraulically driven crushing mode is used, with the pressure value automatically adjusted according to the material hardness; for composite material waste, a multi-stage crushing process is employed, first coarse crushing and then fine crushing. During equipment operation, a multi-dimensional monitoring system continuously collects mechanical parameters such as spindle speed, torque output, hydraulic pressure, and tool wear status. The environmental monitoring system pays special attention to safety hazards during hard material processing, using a spark detector to monitor temperature changes within the crushing chamber in real time, a dust sensor to detect particulate matter concentration, and a noise monitor to record the operating sound level.

[0109] All operational data is stored in a time-series format, comprising three main categories: equipment status data, energy consumption data, and environmental monitoring data. The generated two-chamber destruction process data is uploaded to a central database via industrial Ethernet, with the data packet including equipment calibration certificates and verification codes to ensure data integrity and reliability.

[0110] Comprehensive processing and in-depth analysis were performed on the data from the first and second chamber destruction processes. The raw operational data underwent quality verification, and invalid data segments caused by sensor malfunctions or transmission interruptions were removed, while retaining complete and valid data sequences.

[0111] The screening process employs a sliding verification mechanism based on a time window to perform triple verification of data continuity, numerical range, and trend. The feature integration module extracts key feature indicators from massive operational data: processing efficiency coefficients, energy consumption indicators, and equipment health status scores from equipment operation data; and dust emission rates, noise peak values, and temperature stability parameters from environmental monitoring data. Feature extraction utilizes a multi-resolution analysis method, preserving both macroscopic operational trends and capturing microscopic operational characteristics.

[0112] Data fusion processing establishes a correlation model between operational data and processing results, and machine learning algorithms identify the mapping relationship between operating parameters and crushing quality. The integration process pays special attention to the synergistic analysis of the two types of crushing chambers, comparing the energy consumption differences and efficiency characteristics of soft and hard waste treatment, and generating a comparative analysis report.

[0113] The output crushing process data adopts a layered storage structure: the raw data layer retains the complete operational data sequence; the feature layer stores and extracts key indicators; and the application layer generates a standardized destruction report. The report includes total processing statistics, energy efficiency assessment, equipment utilization analysis, and environmental compliance certification. All data is digitally signed to ensure immutability, forming a legally valid destruction certificate.

[0114] This embodiment separates physically crushed waste into soft and hard waste based on its material properties. It automatically matches the most suitable crushing method according to the physical properties of the waste, thereby achieving precise processing of waste with different characteristics and significantly improving disposal efficiency and processing quality. By separately conveying soft and hard waste to a dedicated crushing chamber for differentiated crushing operations, it ensures that each type of waste receives the most suitable processing parameters, overcoming the equipment wear or incomplete processing problems that may result from traditional single-processing methods.

[0115] In one embodiment, a control conveying device transfers non-physically crushed waste to a packaging area, and drives a harmless treatment device in the packaging area to perform harmless treatment, acquiring harmless treatment process data, including: The control and conveying equipment initiates the transfer process for non-physically crushed waste, transporting it from the sorting area to the packaging area. The conveying equipment employs an intelligent conveying system, integrating a multi-sensor array to simultaneously perform data acquisition tasks during the transfer process. Dimensional data acquisition is achieved through a laser scanning unit: laser emitters are positioned along the conveying path to perform non-contact scanning of the waste, acquiring its length, width, and height in real time using the principle of triangulation; the scanned data is reconstructed using point cloud processing algorithms to generate a 3D model and calculate volume parameters. For irregularly shaped waste, multi-view scanning technology is employed to ensure measurement integrity.

[0116] Material data acquisition is achieved through the collaborative work of a multispectral imager and a near-infrared spectrometer: the multispectral imager captures the surface texture, color, and gloss characteristics of the waste material to generate high-resolution image data; the near-infrared spectrometer performs a penetrating scan of the waste material to obtain internal composition information, and identifies the material type (such as plastic, metal, glass, or composite material) through a spectral matching algorithm.

[0117] All acquired data is transmitted to the central processing unit in real time for filtering, calibration, and outlier removal to eliminate measurement errors. The final output includes standardized dimensional and material characteristic datasets, with timestamps and equipment identifiers added to create a traceable record of the transport process. This step ensures that transport and data acquisition are synchronized, guaranteeing the real-time nature and accuracy of the data.

[0118] Based on the previously collected size and material data, the harmless packaging configuration process is initiated. The configuration engine loads the packaging rule base, which is based on industry standards and environmental regulations and includes guidelines for packaging material selection, sealing strength requirements, and processing parameters. Size data is used to determine the packaging container specifications: through volume calculation and shape matching algorithms, the optimal packaging size is selected to avoid over-packaging or space waste; for waste materials with special shapes, an adaptive container design is adopted to ensure tight sealing.

[0119] Material data is used to assess environmental risk levels: the corrosivity, toxicity, and flammability of waste are analyzed using a risk matrix model to determine the required packaging material type (such as leak-proof containers, impact-resistant foam, or chemically resistant sealing films). Packaging configuration parameters are generated using a multi-objective optimization algorithm to balance packaging cost, safety, and environmental friendliness: parameters include material thickness, sealing pressure, cushioning layer density, and labeling information; for high-risk materials, automatic enhancements are implemented, such as adding double seals or special barrier layers.

[0120] The harmless treatment plan is tailored to the material characteristics, involving methods such as high-temperature incineration, chemical neutralization, biodegradation, or physical solidification. Equipment compatibility, energy efficiency, and emission standards are considered during plan development to ensure compliance. The configuration results are validated through digital twin simulation, testing the packaging solution's compressive strength, sealing integrity, and environmental adaptability during the treatment process.

[0121] The output packaging configuration parameters and harmless treatment plan form a detailed instruction set, including a material list, operating procedures, and safety measures, providing precise guidance for subsequent packaging and treatment. This intelligent configuration ensures that non-crushable waste receives safe, efficient, and environmentally friendly treatment.

[0122] Based on the packaging configuration parameters, the automated packaging process is initiated. These parameters include key instructions such as packaging material specifications, sealing strength requirements, and cushioning design indicators. These parameters are derived from a comprehensive analysis of waste material size and material data. According to the parameter instructions, the automated packaging equipment selects suitable packaging materials from the material library, such as leak-proof containers, impact-resistant foam, or chemically resistant sealing films.

[0123] The material selection process adheres to environmental standards, prioritizing the use of biodegradable or recyclable materials to minimize environmental impact. The equipment then adjusts the operating mode of the packaging robotic arm, setting the gripping force and placement accuracy for waste materials of different sizes to ensure the waste is not damaged during packaging. The packaging operation employs a multi-station collaborative approach: the main station loads the waste into containers, while the auxiliary station adds fillers and performs sealing.

[0124] For irregularly shaped waste materials, the equipment uses 3D scanning to adjust the packaging posture in real time, avoiding gaps or excessive compression. The sealing process employs heat sealing, pressing, or adhesive bonding technologies, controlling temperature, pressure, and time variables based on seal strength parameters to ensure packaging integrity. A quality inspection unit is integrated into the process, using visual sensors to check packaging sealing, label accuracy, and appearance compliance; any abnormalities trigger automatic correction or alarm mechanisms.

[0125] After packaging is completed, a sealed package is generated, with a unique identification code attached to each package, recording the packaging time, operator information, and quality inspection results. The package data is uploaded to a central database in real time, creating a traceable record. This automated packaging process not only improves processing efficiency but also ensures the safe sealing of waste materials before transfer and treatment, providing standardized input for subsequent harmless disposal.

[0126] Based on the sealed packaging and the harmless treatment plan, a specialized treatment process was initiated. The harmless treatment plan defined in detail the treatment method, process parameters, and environmental control requirements, such as the temperature profile for high-temperature incineration, the reagent ratio for chemical neutralization, or the time period for biodegradation. The treatment equipment verified the compatibility of the packaging label with the plan to ensure accurate treatment. The treatment unit parameters were automatically configured according to the plan's instructions, such as adjusting the incinerator temperature, setting the reactor stirring speed, or activating the biological treatment microbial community. The treatment process was strictly monitored in real time, with a sensor network collecting key indicators, including temperature, pressure, pH value, pollutant concentration, and equipment operating status.

[0127] For high-temperature treatments, infrared thermometers and gas analyzers track combustion efficiency and emissions; for chemical treatments, conductivity sensors and spectrometers monitor reaction progress and neutralization effectiveness; for biological treatments, humidity and oxygen sensors optimize the degradation environment. All data is recorded with high-frequency sampling, forming a continuous process log. After treatment, effectiveness verification is performed, and the thoroughness of the treatment is assessed through sampling analysis or non-destructive testing, such as detecting residue concentration, assessing degradation rate, or verifying curing strength.

[0128] The generated harmless treatment process data includes processing time, energy consumption statistics, environmental emission indicators, and verification results. The data package is structured and digitally signed to ensure authenticity and completeness. The final output supports auditing and compliance checks, fully reflecting the responsibility for the entire harmless treatment chain. The treatment system implemented in this step fully guarantees the compliant disposal of non-crushable waste, avoids the risk of secondary pollution, and provides a traceable chain of data evidence.

[0129] This embodiment utilizes physical property identification technology to perform precise size and material analysis on non-physically crushed waste, providing an accurate data foundation for subsequent packaging configuration. This ensures a high degree of matching between the treatment plan and the waste characteristics, improving the targeting and effectiveness of the treatment. Harmless packaging configuration parameters are formulated based on the waste size and material data, ensuring that the packaging materials and treatment processes are compatible with the environmental risk level of the waste. This overcomes the problems of incomplete treatment or secondary pollution caused by improper packaging in traditional methods. Automated packaging processes for sealing not only improve processing efficiency but also ensure the integrity and safety of the packaging, reducing the risk of human intervention and errors during operation.

[0130] In one embodiment, the aggregation of verification data, crushing process data, and harmless treatment process data to generate an electronic archive includes: The solid waste intelligent management system performs correlation analysis and integrity verification on verification data, crushing process data, and harmless treatment process data to obtain correlation mapping relationships. Verification data includes waste declaration information and physical verification results; crushing process data records crushing process parameters; and harmless treatment process data records harmless treatment details. The correlation analysis first establishes a data correlation model, matching and connecting data records from different sources using unique waste identifiers to form a correlation network based on time series and processing logic.

[0131] The analysis process employs a graph-based association mining algorithm to identify temporal relationships, causal connections, and logical dependencies between data nodes, constructing a complete processing chain mapping. The integrity verification module performs multi-dimensional integrity assessments on each data source, including data field completeness checks, numerical range reasonableness verification, and logical consistency checks. Redundant data comparison technology is used during verification to cross-validate the records of the same indicators in different data sources, ensuring data accuracy. For missing data or outliers, a data repair mechanism is activated, supplementing and improving the data through interpolation algorithms or correlation data derivation.

[0132] The generated association mapping includes a data node connection graph, an integrity assessment report, and anomaly handling records, providing a structural framework and quality assurance for subsequent data fusion. The association mapping established in this step ensures that subsequent data processing is based on accurate and complete data.

[0133] Based on the previously generated association mapping relationships, the multi-source data fusion processing flow is initiated. The data fusion engine first parses the node connection rules and data dependencies in the association mapping to determine the priority and weight allocation scheme for data fusion. A layered fusion strategy is adopted for multi-source data: the raw data layer retains the independent characteristics of each data source, the feature layer extracts key indicators for integration, and the application layer generates synthetic data oriented towards the destruction process. The timestamp alignment module standardizes the time stamps of heterogeneous data sources, uniformly converting them into a high-precision time series format.

[0134] During the alignment process, a dynamic time warping algorithm is employed to compensate for time synchronization errors between different acquisition devices, ensuring time consistency of events across data sources. For critical processing nodes, such as waste material warehousing, sorting operations, and destruction times, a time synchronization benchmark accurate to the millisecond level is established. The data fusion process performs dual processing: feature-level fusion integrates physical parameters, equipment status, and environmental indicators from different data sources to generate composite feature vectors; decision-level fusion, based on association rules and business logic, synthesizes process event records with business significance.

[0135] The output destruction process information is stored in a standardized time-series format, including complete processing event records, equipment operation logs, and environmental parameter change curves, forming a traceable overview of the destruction process. This process sequence provides a clear chronological and structurally standardized data foundation for subsequent evidence preservation and archiving.

[0136] Based on the destruction process information generated in the previous step, the blockchain evidence storage and archiving identifier addition process is initiated. The blockchain evidence storage engine first divides the destruction process information into time blocks, with each block containing processing event records, device operation data, and environmental parameters for a continuous time period. Distributed ledger technology is used to perform hash calculations on each data block to generate a digital fingerprint, and the hash value is broadcast to blockchain network nodes for verification and storage through a consensus algorithm.

[0137] The evidence preservation process employs a multi-copy redundant storage strategy to ensure data immutability and long-term traceability. A timestamp service adds authoritative time authentication to each block, forming a legally valid chain of time-based evidence. The index archiving identifier addition module establishes a multi-dimensional index system based on the business logic of the destruction process. First, a classification index is established by waste type to categorize processing records of different materials; a time-series index is established by time dimension, supporting fast retrieval by time range; and a process index is established by processing stage, recording key operation nodes at each stage. The index identifier adopts a hierarchical coding structure, including metadata such as file number, storage location, permission level, and retention period.

[0138] All index information is mapped to blockchain-based evidence data, forming a two-way traceable retrieval mechanism. The final generated archive data package adopts a hybrid storage structure: the original data layer retains complete destruction process information, the evidence layer stores blockchain hash values ​​and timestamps, and the index layer contains multi-dimensional retrieval identifiers. The data package is encrypted and then digitally signed to ensure integrity verification and identity authentication functions. This archive data package provides standardized input for the final electronic archive generation, meeting the requirements for long-term archiving and audit verification.

[0139] The system receives the archive data packets generated in the previous step and performs final structured processing and secure encapsulation. It parses the hybrid storage structure of the archive data packets, performs format standardization conversion on the original data layer, and unifies heterogeneous data into an open document format that meets long-term preservation requirements. During data reorganization, it establishes internal logical connections and reorganizes the data presentation order according to the "declaration-sorting-destruction" business process, enhancing data readability and understandability.

[0140] The metadata enhancement module adds technical and management metadata: technical metadata includes technical support information such as file format, encoding scheme, and creation environment; management metadata includes management attributes such as archive date, retention period, and access permissions. All metadata uses a standardized encoding system to ensure cross-platform and cross-system interoperability. The secure encapsulation process employs a multi-layered protection mechanism: firstly, structured data is encrypted using asymmetric encryption algorithms to ensure data confidentiality; digital signatures are added to provide identity authentication and integrity protection; and watermark information is embedded to implement copyright identification and anti-counterfeiting functions.

[0141] The packaging format uses an internationally standard container format, supporting data compression and multi-volume storage to meet the needs of storing different types of waste. The final output electronic archive contains complete data content, metadata descriptions, and security control mechanisms, forming a self-contained, self-interpreting, and self-protecting archival unit.

[0142] The electronic records are transmitted to a long-term preservation system via a secure transmission protocol, and an archiving acceptance report is generated simultaneously, recording key parameters and quality indicators of the archiving process. These electronic records meet the full lifecycle management needs of the supervision of classified waste disposal, possessing legal evidentiary value and long-term usability.

[0143] This embodiment utilizes multi-source data association analysis and integrity verification technology to establish an accurate mapping relationship between verification data, crushing process data, and harmless disposal process data, ensuring the integrity and consistency of the data chain and providing a reliable data foundation for subsequent processing. Based on the association mapping, multi-source data fusion and timestamp alignment generate a clear time-series destruction process information, effectively solving the data silo problem in traditional methods and achieving seamless data integration throughout the entire process. Blockchain notarization and index identification technology ensure the immutability and rapid retrieval capability of the destroyed data, enhancing its legal validity and auditing convenience. The use of structured processing and secure encapsulation technology to output electronic archives not only guarantees the long-term readability and security of the data but also enables self-verification of archived files, significantly improving the compliance and credibility of classified waste supervision.

[0144] Reference Figure 2 As shown, this invention provides a smart monitoring system for the disposal and storage of classified waste, and a smart monitoring method for the disposal and storage of classified waste applied to any of the above-mentioned methods, comprising: The analysis module is used to review the declaration data of classified waste through the solid waste intelligent management system, verify the corresponding classified waste, and generate verification data and waste type. The processing module is used to control the conveying equipment to transport confidential waste to the sorting area for automated sorting based on the verification data and waste type, resulting in physically crushed waste and non-physically crushed waste. The association module is used to monitor the volume of physically crushed waste, control the conveying equipment to deliver the physically crushed waste to the corresponding crushing chamber, drive the crushing equipment in the crushing chamber to crush and destroy it, and obtain crushing process data. The module is used to control the conveying equipment to transfer non-physically crushed waste to the packaging area, drive the harmless treatment equipment in the packaging area to perform harmless treatment, and obtain harmless treatment process data. The execution module is used to aggregate verification data, crushing process data, and harmless treatment process data to generate electronic archives.

[0145] This application provides a smart supervision system for the disposal and storage of classified waste. Through a solid waste intelligent management system, it verifies classified waste declaration data from multiple dimensions and checks the characteristics of the physical waste, significantly improving the accuracy and reliability of the verification process. Based on the verification data and the automated sorting of waste types, it achieves accurate classification of crushed and non-crushed waste, effectively avoiding the risks of errors and omissions associated with manual sorting. By identifying the scale characteristics of crushed waste and matching the processing capacity of the corresponding crushing chamber, it optimizes the allocation of destruction resources, improving processing efficiency and safety. A specialized harmless treatment process is used for non-crushed waste, ensuring that all types of waste are disposed of in compliance with regulations. By aggregating all operational data to generate electronic archives, a complete traceability chain is established, realizing smart supervision of classified waste throughout its entire lifecycle from declaration to destruction, significantly improving supervision efficiency and compliance levels.

[0146] Reference Figure 3 As shown, the present invention also provides a smart monitoring device for the disposal and storage of classified waste, comprising: Memory, used to store programs; A processor is used to execute programs to implement the various steps of a smart monitoring method for the disposal and storage of classified waste, as described above.

[0147] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments concerning the apparatus in the above embodiments, and will not be elaborated further here.

[0148] The solution of this application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the descriptions of each embodiment have different emphases; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. Those skilled in the art should also understand that the actions and modules involved in the specification are not necessarily essential to this application. Furthermore, it is understood that the steps in the method of this application embodiment can be adjusted, combined, and deleted according to actual needs, and the modules in the device of this application embodiment can be combined, divided, and deleted according to actual needs.

[0149] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0150] Alternatively, this application may also be implemented as a non-transitory machine-readable storage waste (or computer-readable storage waste, or machine-readable storage waste) storing executable code (or computer program, or computer instruction code) thereon, which, when executed by a processor of an electronic device (or electronic device, server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.

[0151] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in connection with the present application can be implemented as electronic hardware, computer software, or a combination of both.

[0152] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems and methods according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0153] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for intelligent supervision of classified waste storage, characterized in that, The method comprises the following steps: auditing the confidential waste declaration data through the solid waste intelligent management system, verifying the corresponding confidential waste, generating verification data and waste type; According to the verification data and the waste type, control the conveying equipment to convey the confidential waste to the sorting area for automatic sorting, and obtain the physical broken waste and the non-physical broken waste; Monitoring the waste volume of the physical broken waste, controlling the conveying equipment to distribute the physical broken waste to the corresponding crushing chamber, and driving the crushing equipment in the crushing chamber to crush and destroy, obtaining the crushing process data; Control the conveying equipment to transfer the non-physical broken waste to the packing area, and drive the harmless treatment equipment in the packing area to carry out harmless treatment, and obtain the harmless process data; Aggregate the verification data, the crushing process data and the harmless process data to generate an electronic file.

2. The method according to claim 1, wherein, The method comprises the following steps: Identity verification is performed on the confidential waste declaration data, and waste information, quantity and confidential level are identified to obtain declaration element data; According to the declaration element data, the weight of the confidential waste is collected through the intelligent weighing equipment of the solid waste intelligent management system to obtain the weight data of the waste; Use the image acquisition device of the solid waste intelligent management system to extract the surface features of the confidential waste, and generate waste appearance image data; Consistency verification is performed on the waste weight data and the waste appearance image data and the declaration data, and the verification data and the waste type information are output.

3. The method of claim 1, wherein the method further comprises: The method comprises the following steps: Joint analysis is performed on the verification data and the waste type to obtain waste physical properties and confidential level parameters; According to the waste physical properties and the confidential level parameters, the sorting conveying path is planned, and the sorting path information is generated; According to the sorting path information, the conveying equipment is controlled to convey the confidential waste to the sorting area, and the confidential waste is scanned from multiple angles according to the sorting area to obtain material characteristics and waste structure characteristics; Based on the material characteristics and the waste structure characteristics, the confidential waste is classified and sorted to obtain the physical broken waste and the non-physical broken waste.

4. The method according to claim 3, wherein, The method comprises the following steps: Use the preset classification rule to determine the waste type of the material characteristics and the waste structure characteristics, and output the category result; Based on the category result, the grabbing path and operation mode of the sorting mechanical arm of the sorting area are planned, and the sorting action instruction is generated; The sorting mechanical arm is driven by the sorting action instruction to execute grabbing and transferring operations on the confidential waste, and the physical broken waste and the non-physical broken waste are obtained.

5. The method of claim 1, wherein the method further comprises: The monitoring of the waste volume of the physical crushing waste, the control of the conveying equipment to distribute the physical crushing waste to the corresponding crushing chamber, and the driving of the crushing equipment in the crushing chamber for crushing destruction to obtain crushing process data, includes: Waste size recognition is performed on the physical crushing waste to obtain the waste volume; According to the waste volume, the conveying equipment is controlled to distribute the physical crushing waste to the corresponding crushing chamber, and crushing chamber allocation information is generated; Through the solid waste intelligent management system, the physical crushing waste is controlled according to the crushing chamber allocation information to generate conveying information; According to the crushing chamber allocation information and the conveying information, the physical crushing waste is controlled for crushing treatment by the crushing equipment, and the crushing process data is synchronously collected.

6. The method according to claim 5, wherein, According to the crushing chamber allocation information and the conveying information, the physical crushing waste is controlled for crushing treatment by the crushing equipment, and the crushing process data is synchronously collected. According to the waste volume, the physical crushing waste is split into soft waste and hard waste; According to the crushing chamber allocation information and the conveying information, the soft waste is conveyed to the first crushing chamber of the crushing chamber, and the crushing equipment is driven for soft crushing, and first chamber destruction process data is collected; According to the crushing chamber allocation information and the conveying information, the hard waste is conveyed to the second crushing chamber of the crushing chamber, and the crushing equipment is driven for hard crushing, and second chamber destruction process data is collected; The first chamber destruction process data and the second chamber destruction process data are subjected to crushing process screening and feature integration, and the crushing process data is output.

7. The method of claim 1, wherein the method further comprises: The control of the conveying equipment to transfer the non-physical crushing waste to the packaging area, and the driving of the harmless treatment equipment in the packaging area for harmless treatment to obtain harmless process data, includes: The conveying equipment is controlled to transfer the non-physical crushing waste from the sorting area to the packaging area, and size data and material data of the non-physical crushing waste are collected during the transfer process; Based on the size data and the material data, the non-physical crushing waste is configured for harmless packaging to obtain packaging configuration parameters and a harmless treatment scheme According to the packaging configuration parameters, the automatic packaging equipment of the packaging area is controlled to perform sealing and packaging operations on the non-physical crushing waste, and a sealed package is output; The harmless treatment equipment is driven to perform harmless treatment on the sealed package according to the harmless treatment scheme, and the treatment process is monitored in real time to generate the harmless process data.

8. The method of claim 1, wherein the method further comprises: The aggregation of the verification data, the crushing process data, and the harmless process data to generate an electronic file, includes: Through the solid waste intelligent management system, the verification data, the crushing process data, and the harmless process data are subjected to correlation analysis and integrity verification to obtain a correlation mapping relationship; Based on the correlation mapping relationship, the verification data, the crushing process data, and the harmless process data set are subjected to multi-source fusion and timestamp alignment to obtain destruction process information; According to the destruction process information, a blockchain notarization and an index archive identification are added, and an archive data package is generated; The archive data package is structured and securely packaged, and the electronic archive is output.

9. A system for intelligent monitoring of classified waste storage, characterized in that, The method is applied to the method for intelligent supervision of classified waste storage according to any one of claims 1-8, comprising: An analysis module is configured to audit classified waste declaration data and verify corresponding classified waste through a solid waste intelligent management system, generate verification data and waste types; A processing module is configured to control a conveying device to convey the classified waste to a sorting area for automatic sorting according to the verification data and the waste types, and obtain physically crushed waste and non-physically crushed waste; An association module is configured to monitor the waste volume of the physically crushed waste, control the conveying device to distribute the physically crushed waste to a corresponding crushing chamber, and drive a crushing device in the crushing chamber to crush and destroy, and obtain crushing process data; A construction module is configured to control the conveying device to transfer the non-physically crushed waste to a packaging area, and drive a harmless treatment device in the packaging area to perform harmless treatment, and obtain harmless process data; An execution module is configured to aggregate the verification data, the crushing process data, and the harmless process data to generate an electronic archive.

10. A classified information waste storage intelligent supervision device, characterized in that, Comprise: A memory for storing a program; A processor for executing the program to realize each step of the method for intelligent supervision of classified waste storage according to any one of claims 1-8.

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