Platinum Waste AI Sorting and Recycling Management System

Abstract

This invention relates to the field of precious metal recycling technology and discloses an AI-based sorting and recycling management system for platinum waste. The system's waste pretreatment unit receives platinum-containing waste and physically crushes it, outputting a standardized waste particle stream. The AI ​​sorting unit, connected to the waste pretreatment unit, acquires the spectral characteristic data of the waste particles, identifies the distribution pattern of platinum element through a deep learning model, and outputs a coordinate set of platinum-containing particles. The recycling unit receives the coordinate set of platinum-containing particles, controls a robotic arm to grasp target particles for high-temperature smelting, separates platinum metal from other impurities, and outputs molten platinum metal. The recycling verification unit monitors the purity parameters of the molten platinum metal; when the purity does not meet the standard, it triggers an impurity separation command, which is fed back to the recycling unit to re-execute the smelting operation. Through the coordinated operation of each unit, this system achieves intelligent management of the entire process of platinum waste, from pretreatment and precise sorting to recycling and quality verification.

Description

Technical Field

[0001] This invention relates to the field of precious metal recycling technology, specifically to an AI sorting and recycling management system for platinum waste. Background Technology

[0002] Platinum, a rare precious metal, plays an irreplaceable role in numerous fields such as catalytic conversion, electronic information, jewelry manufacturing, and new energy due to its unique physicochemical properties. However, the natural reserves of platinum are extremely limited, and the mining process involves high costs and a severe environmental burden, posing a significant challenge to the sustainable supply of platinum. Against this backdrop, the efficient recovery and recycling of platinum from platinum-containing waste has gradually become an important way to alleviate resource shortages.

[0003] The recycling and processing of platinum waste mainly relies on traditional physical sorting and chemical extraction methods. In the physical sorting stage, manual sorting or simple mechanical screening is often used, which is not only inefficient but also has limited accuracy in identifying platinum-containing particles, easily leading to the omission or misjudgment of target particles. Manual sorting is greatly affected by factors such as operator experience, physical strength, and attention, making it difficult to guarantee the stability of sorting quality after prolonged operation. Mechanical screening can only separate particles based on simple physical characteristics such as particle size and density, and cannot accurately identify the distribution of platinum composition within the particles. This results in a large number of particles with low platinum content but still of recycling value being discarded as waste.

[0004] In subsequent recycling processes, traditional methods typically involve directly smelting or chemically dissolving the mixed waste at high temperatures. This "one-size-fits-all" approach increases the difficulty of separating platinum from other impurities. Because it's impossible to specifically treat platinum-containing particles, the smelting process requires more energy to break down impurity inclusions, and the excessive use of chemical reagents exacerbates environmental pollution. Furthermore, existing recycling processes lack effective purity monitoring and feedback mechanisms. Purity testing is often only conducted on the final product after the entire process is complete. If purity is found to be substandard, the entire batch of waste must be reprocessed, increasing time and resource consumption, and potentially leading to increased platinum loss due to repeated processing.

[0005] With the increasing diversification of platinum-containing waste sources, the composition of waste is becoming more complex, and the particle morphology is also becoming more irregular. The shortcomings of traditional recycling technologies in terms of processing efficiency, resource utilization, and environmental performance are becoming increasingly prominent. How to achieve accurate identification, efficient sorting, and high-quality regeneration of platinum waste has become a key issue that urgently needs to be addressed in the field of precious metal recycling. Summary of the Invention

[0006] The purpose of this invention is to provide an AI sorting and recycling management system for platinum waste to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides an AI sorting and recycling management system for platinum waste, the system comprising:

[0008] The waste pretreatment unit receives platinum-containing waste and performs physical crushing, outputting a waste particle stream with standardized particle size.

[0009] The AI ​​sorting unit is connected to the waste pretreatment unit, acquires the spectral feature data of the waste particles, identifies the distribution pattern of platinum elements through a deep learning model, and outputs a set of coordinates of platinum-containing particles.

[0010] The recycling unit receives the coordinate set of platinum-containing particles sent by the AI ​​sorting unit, controls the robotic arm to grab the target particles for high-temperature melting, separates platinum metal from other impurities, and outputs molten platinum metal.

[0011] The regeneration verification unit monitors the purity parameters of the platinum molten metal. When the purity does not meet the standard, it triggers an impurity separation command and feeds the separation command back to the recycling unit to re-execute the smelting operation.

[0012] Preferably, the waste pretreatment unit includes:

[0013] The vibrating screening module performs multi-stage screening of the crushed waste, selects particles that meet the particle size threshold, and outputs them to the spectral acquisition channel.

[0014] The spectral excitation module emits multi-band excitation light sources during the particle screening and transport process, collects the reflection spectrum curves and generates a three-dimensional feature matrix.

[0015] The transmission control module dynamically adjusts the transmission band rate according to the acquisition progress of the spectral excitation module to ensure that the particle spacing meets the spectral acquisition requirements.

[0016] Preferably, the AI ​​sorting unit includes:

[0017] The feature extraction engine analyzes the platinum element feature peaks in the three-dimensional feature matrix and calculates the difference between the feature peak intensity and the background noise.

[0018] The dynamic modeling engine trains a convolutional neural network based on historical waste samples to match the mapping relationship between feature peak difference sequences and platinum element concentration in real time.

[0019] The coordinate positioning engine marks the spatial coordinates of high-concentration platinum particles based on the matching results, generating a coordinate data packet containing timestamps.

[0020] Preferably, the recycling unit includes:

[0021] The smelting control core receives the coordinate data packet and calculates the robotic arm's motion trajectory and the smelting furnace's start-up and shutdown sequence.

[0022] The plasma melting module performs gradient heating on the target particles in an inert gas environment and sets a multi-temperature melting curve based on the melting point of platinum metal.

[0023] The impurity separation module applies an electromagnetic field in the molten state to separate the platinum liquid from the impurity precipitate and monitors the position of the separation interface in real time.

[0024] Preferably, the regeneration verification unit comprises:

[0025] A spectral probe array is used to collect atomic emission spectra of molten platinum metal and identify characteristic wavelengths of non-platinum elements.

[0026] The purity analysis module calculates the ratio of the characteristic wavelength intensity of the target element to the main peak intensity of platinum.

[0027] The instruction generation module generates a set of separate instructions containing impurity type identifiers when the scaling factor exceeds a preset threshold.

[0028] Preferably, the system further includes a hierarchical storage unit:

[0029] The metal composition library receives the solidification parameters of the platinum molten metal output from the recycling unit and divides the storage areas according to the purity level.

[0030] The data traceability module records the temperature curve, impurity separation efficiency, and final purity test results for each smelting process;

[0031] The anomaly marking engine adds a quality correction tag to the corresponding storage area when it receives the separation instruction from the regeneration verification unit.

[0032] Preferably, the system includes a main control unit:

[0033] The instruction scheduling center receives the coordinate data packet from the AI ​​sorting unit and the separation instruction set from the regeneration verification unit, and allocates task priorities.

[0034] The equipment coordination module synchronously controls the conveyor belt speed, robotic arm movements, and smelting furnace power parameters according to task priorities.

[0035] The quality tracking module associates and stores the sorting efficiency and smelting purity data of each batch of waste materials into the data traceability module.

[0036] Preferably, the main control unit is connected to a remote monitoring terminal:

[0037] The real-time data channel transmits the spectral characteristic data, melting curve parameters, and purity detection results to the monitoring terminal.

[0038] The parameter correction interface receives model training parameters and smelting process parameters sent by the monitoring terminal.

[0039] The alarm triggering module sends an equipment maintenance request to the monitoring terminal when the number of impurity separations exceeds the threshold or the purity parameter is abnormal.

[0040] Preferably, the deep learning model includes a dynamic optimization mechanism, specifically:

[0041] Based on the sorting error of each batch of waste, the weights of the convolutional neural network kernels are adjusted to enhance the spectral feature learning intensity for incorrectly identified particle samples.

[0042] Preferably, the system further includes a renewable resource optimization module, which specifically operates as follows:

[0043] Cumulative data on total platinum metal output and energy consumption for each batch;

[0044] Based on the total platinum metal output and energy consumption data of each batch, the platinum recovery rate and energy consumption ratio per unit of waste were calculated.

[0045] A melting temperature curve adjustment scheme is generated based on the platinum recovery rate and energy consumption ratio.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] This platinum waste AI sorting and recycling management system provides a more efficient and precise solution for platinum waste recycling through the collaborative operation of its various units. The waste pretreatment unit physically crushes the platinum-containing waste, transforming the originally varied waste into a standardized particle stream. This standardization process provides a unified basis for subsequent particle detection and sorting, avoiding identification bias caused by excessive differences in particle size. It ensures that every waste particle can be analyzed under the same detection standards, creating favorable conditions for accurate sorting.

[0048] The AI ​​sorting unit leverages spectral feature data acquisition and deep learning model analysis to overcome the limitations of traditional sorting methods that rely on human experience or simple physical properties. Spectral feature data reflects the internal composition of particles, while deep learning models, through learning from large amounts of data, can accurately capture the distribution patterns of platinum, thereby accurately identifying platinum-containing particles and outputting a coordinate set. This data- and algorithm-based sorting method eliminates the subjectivity and fatigue of manual operation, maintaining stable identification accuracy in continuous operation, effectively reducing missed or false detections of platinum-containing particles, and allowing for more comprehensive identification of particles with recycling value.

[0049] After receiving the coordinate set of platinum-containing particles, the recycling unit uses a robotic arm to grasp the target particles for high-temperature melting, achieving targeted processing of platinum-containing particles. Compared to the overall melting of mixed waste in traditional processes, this precise grasping method reduces the mixing of non-target particles, lowers the difficulty of impurity separation during melting, makes the separation of platinum from other impurity components more targeted, reduces the ineffective consumption of energy and reagents, and makes the melting process more efficient.

[0050] The regeneration verification unit monitors the purity parameters of the molten platinum in real time, forming a closed-loop quality control system. When the purity fails to meet the standard, an impurity separation command is promptly triggered and fed back to the recycling unit to re-execute the smelting operation. This dynamic adjustment mechanism avoids the batch rework problems caused by final failure to meet standards in traditional processes. By promptly identifying and correcting purity issues during the regeneration process, the problem of incomplete impurity separation can be resolved at an early stage, reducing platinum loss during repeated processing and shortening the overall regeneration cycle time, making quality control in the regeneration process more proactive and effective.

[0051] The synergistic effect of each unit enables the entire system to form a coherent and precise processing chain from waste input to platinum recycling output, improving the level of intelligence in platinum waste recycling, reducing resource waste and environmental burden, and making the recycling of platinum resources more efficient and sustainable. Attached Figure Description

[0052] Figure 1 This is a timing diagram of the platinum waste AI sorting and recycling management system described in this invention;

[0053] Figure 2 A flowchart illustrating the operation of the waste pretreatment unit;

[0054] Figure 3 A flowchart illustrating the operation of the regeneration verification unit;

[0055] Figure 4 The flowchart shows the operation of the main control unit. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Please see Figure 1The present invention provides an AI sorting and recycling management system for platinum waste, the system comprising: a waste pretreatment unit, an AI sorting unit, a recycling unit, and a recycling verification unit.

[0058] The waste pretreatment unit receives platinum-containing waste and transforms it into a particle stream with standardized particle size through physical crushing. The AI ​​sorting unit, connected to the waste pretreatment unit, acquires the spectral characteristic data of the waste particles and identifies the distribution pattern of platinum element through a deep learning model, outputting a coordinate set of platinum-containing particles. The recycling unit receives the coordinate set of platinum-containing particles, controls a robotic arm to grasp the target particles for high-temperature smelting, separates platinum metal from other impurities, and outputs molten platinum metal. The regeneration verification unit monitors the purity parameters of the molten platinum metal; if the purity does not meet the standard, an impurity separation command is triggered and fed back to the recycling unit to re-execute the smelting operation.

[0059] Example 1: See Figure 2 The waste pretreatment unit uses a vibrating screening module to perform multi-stage screening of the particle stream after physical crushing of platinum-containing waste. The crushed waste particles enter the vibrating screening module, which employs a multi-layer screen structure with screen apertures set according to the target particle size threshold. Under vibration, the particles move along the screen surface; oversized particles are intercepted by the upper screen and returned to the crushing stage for reprocessing, while particles meeting the requirements pass through the screen and enter the lower conveyor belt. During screening, the vibration frequency and amplitude can be dynamically adjusted to adapt to changes in the physical properties of different batches of waste. The screened particle stream enters the spectral acquisition channel, and the conveyor belt surface is coated with an anti-reflective coating to reduce background light interference.

[0060] The spectral excitation module emits a multi-band excitation source during particle transport, covering the ultraviolet to near-infrared wavelength range. The excitation source illuminates the particles on the transport belt at a specific angle, and the reflected light signal is collected by a high-sensitivity spectral sensor. An array of sensors is arranged along the transport belt, with each sensor corresponding to a specific wavelength band, ensuring the continuity and integrity of the spectral data. As the particles pass through the excitation region, the reflectance spectrum curve is recorded in real time and converted into a three-dimensional feature matrix. This matrix contains wavelength, reflection intensity, and particle position information for subsequent platinum element identification. During spectral acquisition, the light source intensity and sensor sensitivity are automatically adjusted according to the particle surface characteristics to avoid signal saturation or excessively low signal-to-noise ratios.

[0061] The transmission control module dynamically adjusts the conveyor belt speed based on the acquisition progress of the spectral excitation module. The spectral sensor provides real-time feedback on particle arrival time and spectral acquisition status, and the control module adjusts the conveyor belt motor speed using a closed-loop adjustment algorithm. When the particle spacing is too small, the conveyor belt decelerates to extend the spectral acquisition time; when the particle spacing is too large, the conveyor belt accelerates to improve processing efficiency. A position encoder is installed on the conveyor belt surface to accurately record the coordinate information of each particle, ensuring a strict correspondence between the spectral data and the particle position. During conveyor belt speed adjustment, acceleration and deceleration are controlled within a reasonable range to prevent particle position shift due to inertial slippage.

[0062] The feature extraction engine of the AI ​​sorting unit analyzes the three-dimensional feature matrix to identify the characteristic peaks of platinum. Peak identification is based on a predefined platinum spectral database, determining the probability of platinum presence by matching specific wavelength positions and intensity distributions in the reflectance spectrum. Background noise is suppressed using a sliding window filtering algorithm, and the difference between the characteristic peak intensity and the noise serves as a preliminary indicator of platinum concentration. The feature extraction engine outputs a set of difference sequences reflecting the signal intensity of platinum at different wavelengths.

[0063] The dynamic modeling engine analyzes the difference sequence using a convolutional neural network trained on historical waste samples. The neural network's input layer receives the difference sequence, extracts high-order features through multiple convolutional and pooling operations, and the output layer maps to platinum concentration values. During training, the network weights are optimized using a backpropagation algorithm, and the loss function comprehensively considers both recognition accuracy and generalization ability. During online operation, the dynamic modeling engine matches the mapping relationship between the current particle's feature peak difference sequence and historical data in real time, outputting an estimated platinum concentration. The network weights are updated periodically to adapt to potential changes in the waste composition.

[0064] The coordinate positioning engine marks the spatial coordinates of high-concentration platinum particles based on the output of the dynamic modeling engine. The coordinate data packet contains the particle's two-dimensional position on the conveyor belt, a timestamp, and an estimated platinum concentration. The coordinate data is transmitted to the recycling unit via an industrial communication protocol, using a standardized data packet format to ensure compatibility between different subsystems. The timestamp is generated synchronously by the system's master clock, with accuracy meeting the timing requirements of the robotic arm's gripping and melting control. The coordinate positioning engine also records the position information of low-concentration particles for subsequent sorting efficiency analysis and model optimization.

[0065] Data interaction between the waste pretreatment unit and the AI ​​sorting unit is achieved via a high-speed industrial bus. The transmission delay of spectral feature matrices and coordinate data packets is controlled within milliseconds, meeting real-time processing requirements. The system continuously monitors the status of each module during operation, triggering automatic alarms or shutdown protection in case of abnormalities. The hardware equipment in the pretreatment and sorting stages adopts a modular design, facilitating maintenance and upgrades. Key components such as spectral sensors and conveyor belt motors have redundant configurations, improving system reliability.

[0066] The training data for the deep learning model comes from actual waste processing records, covering samples with different sources, compositions, and particle size distributions. Data augmentation techniques are employed during training to simulate noise and interference during spectral acquisition, improving model robustness. Model performance is evaluated through cross-validation to avoid overfitting or underfitting. During online runtime, the model's inference results are compared with actual sorting performance; if the error exceeds a threshold, a model retraining process is triggered. The computational tasks for feature extraction and dynamic modeling are performed by dedicated hardware accelerators to ensure processing speed meets production line cycle time requirements.

[0067] The vibrating screen module's screen is replaced periodically, and wear detection is achieved based on image analysis and vibration signal monitoring. The lifespan of the light source in the spectral excitation module is predicted using time accumulation and output power monitoring, prompting maintenance when the lifespan is nearing its limit. The adjustment algorithm parameters of the transmission control module are optimized based on actual operating data to balance processing efficiency and sorting accuracy. The AI ​​sorting unit's software system supports online updates, and new functions or optimized algorithms can be imported through remote deployment. The entire implementation of Example 1 emphasizes the collaborative work between modules, achieving stable waste sorting results through refined parameter control and real-time feedback mechanisms.

[0068] Example 2: See Figure 3 The smelting control core of the recycling unit receives a coordinate data packet of platinum-containing particles from the AI ​​sorting unit. This data packet contains the spatial location and temporal sequence information of the target particles. After parsing the coordinate data, the smelting control core generates a 3D path plan for the robotic arm's motion trajectory. The path planning algorithm considers the geometric relationship between the range of motion of the robotic arm joints and the dynamic position of the conveyor belt, calculating the optimal gripping posture. Simultaneously, based on the pre-processing state of the smelting furnace, it generates a sequence of timing commands for furnace preheating, feed inlet opening, and smelting initiation. These timing commands are synchronized with the robotic arm's movements at the millisecond level, achieving coordinated control through a hardware clock signal.

[0069] The plasma melting module employs a ring electrode structure, purging an inert gas environment of argon into a sealed melting chamber. Inert gas purity is monitored using an online mass spectrometer, maintaining oxygen content below 50 ppm. After the target particles are introduced into the melting chamber via a robotic arm, a plasma arc forms between the electrodes, generating a high-temperature plasma flow that gradient-heats the particles. The heating process consists of four stages: a low-temperature zone (300-800℃) to remove volatile organic compounds; a medium-temperature zone (800-1500℃) to decompose metal compounds; a high-temperature zone (1500-1750℃) to liquefy the platinum component; and a homogenization zone (1750±20℃) to maintain the molten state. The duration of each temperature zone is automatically adjusted according to the particle mass, with a mass sensor providing real-time feedback on molten pool weight changes. Temperature control is achieved by adjusting the plasma current frequency (50-150kHz) and power density (5-15kW / cm²). 2 This is achieved by using a thermocouple array to monitor the temperature distribution at 12 points on the inner wall of the cavity.

[0070] The impurity separation module is activated in a high-temperature molten state, with two sets of orthogonal electromagnetic coils arranged around the periphery of the melting chamber. When the melt reaches a preset state, a steady-state axial magnetic field of 0.8-1.2 Tesla is applied, simultaneously superimposed with a pulsed tangential magnetic field of 0.3 Tesla amplitude. The magnetic field causes the platinum melt to swirl, and higher-density impurity components (such as iridium and ruthenium alloys) migrate towards the edge of the molten pool. An X-ray transmission device captures real-time images of the molten pool cross-section, and an image processing algorithm identifies the interface position between the platinum melt and the impurity layer. When the interface clarity reaches a set threshold, the bottom slag discharge valve opens, tilting the melting chamber 15 degrees to allow impurity deposits to be discharged along the guide channel. The slag discharge process continues until the X-ray detection interface displacement is less than 0.5 mm, after which the chamber resets and enters the purification stage.

[0071] The spectral probe array of the regeneration verification unit consists of eight fiber optic spectrometers, with the probe tips directly contacting the molten surface. The array acquires atomic emission spectra in the 400-780 nm band at a frequency of 20 Hz, with each spectrometer covering a 50 nm bandwidth. The probes are mounted at the end effector of a robotic arm, enabling 3D scanning of the molten pool surface with a scan point spacing of 5 mm. After preprocessing, the spectral data is used to extract characteristic wavelengths of non-platinum elements such as copper (521.8 nm), nickel (361.9 nm), and iron (371.9 nm) using a feature recognition algorithm. Noise suppression employs wavelet transform technology, with a decomposition scale of six levels and a high-frequency component threshold set to three times the signal-to-noise ratio.

[0072] The purity analysis module receives characteristic wavelength intensity data and first calculates the baseline intensity of the platinum main peak (265.9 nm). The characteristic wavelength intensity value of the target impurity element is divided by the platinum main peak intensity value to obtain a proportionality coefficient matrix. The proportionality coefficient calculation uses a moving average filter with a window size of 50 consecutive sampling points. Each impurity element has an independently set coefficient threshold, and the threshold range is dynamically adjusted according to the platinum metal industry standard (GB / T Gold-Platinum Alloy Composition Standard). The system establishes an impurity element association rule base; when an element is detected to exceed the standard, the monitoring frequency of other related elements is automatically adjusted.

[0073] The instruction generation module marks abnormal events when the proportional coefficient exceeds a threshold. Each event records the category of the exceeding element, the degree of exceeding (coefficient ratio), and its spatial location. When the cumulative number of exceeding events of the same impurity reaches 3 in a single melting process, or the total number of different impurity events reaches 5, a structured separation instruction set is generated. The instruction set contains four fields: impurity type code (e.g., Cu01, Ni02), separation method identifier (magnetic separation / chemical separation), processing intensity parameters (current value / reagent concentration), and required processing time. The instruction set is transmitted to the melting control core of the recycling unit via industrial Ethernet, triggering the secondary processing flow of the impurity separation module. During the secondary processing, the melting furnace is kept at a constant temperature, and the impurity separation module switches its operating mode according to the instruction type: in magnetic separation mode, the pulse magnetic field frequency is increased to 15Hz; in chemical separation mode, borax flux is injected and the temperature is lowered to 1200℃.

[0074] The annular electrode of the plasma melting module employs a copper alloy water-cooled structure, with the cooling water flow dynamically adjusted according to the electrode temperature. Temperature sensors are embedded at three depths within the electrode, and temperature gradient data is used to calculate the electrode wear rate. A protective power reduction operation is automatically triggered when the electrode surface temperature exceeds 650°C. Four strain sensors are installed in the zirconia lining of the melting chamber to monitor thermal expansion stress in real time; stress data is used to optimize the heating rate curve.

[0075] The regeneration verification unit is equipped with an anti-contamination mechanism, and the spectral probe automatically retracts into the cleaning chamber after each contact with the molten liquid. The cleaning chamber contains an ultrasonic cleaning tank and a nitrogen purging device, with a fixed cleaning cycle of three testing tasks. Spectrometer wavelength calibration uses a built-in mercury lamp as a standard light source, and the calibration process is automatically executed at midnight daily. The abnormal data recording mechanism includes six anomaly types, such as marking outlier spectral data values ​​and marking interference from molten pool surface fluctuations. Marked data is not used in purity calculations but is stored in the historical database.

[0076] The smelting control core features redundant control capabilities; in the event of a main controller failure, the backup controller takes over control within 300 milliseconds. All process parameter changes are version-logged, including records of operator-manual modifications. Smelting cycle data is packaged and stored every 5 seconds, with each data package containing 27 types of parameters, including temperature field distribution diagrams, current and voltage waveforms, and spectral characteristic peaks. The water cooling system of the electromagnetic field generator is equipped with triple interlock protection based on flow rate, temperature, and pressure; any abnormality in any parameter immediately cuts off the magnetic field power supply.

[0077] During implementation, the system establishes a smelting quality traceability chain. Each smelting batch is assigned a unique code, linking it to the original waste batch information, sorting process data, smelting parameters, and final purification results. The traceability chain supports forward queries to the original spectral characteristics of individual platinum-containing particles and backward queries to the factory inspection report of the recycled platinum ingots. The user interface displays a real-time 3D temperature cloud map of the smelting chamber, a heat map of impurity distribution, and a vector diagram of platinum melt flow, with the visualized data updating every 1 second.

[0078] Example 3: See Figure 4 The graded storage unit's metal composition library receives platinum molten metal solidification parameters from the recycling unit. These parameters include the molten metal cooling rate, solidification temperature, and final morphology. Based on purity analysis results, the system allocates metal blocks to different storage areas. A three-tiered purity system is used: high purity (≥99.95%), medium purity (99.90%-99.94%), and standard purity (99.85%-99.89%). Each storage area corresponds to a dedicated silo, whose environmental parameters are monitored by temperature and humidity sensors, with the temperature controlled at 20±2℃ and relative humidity below 30%. After the platinum metal blocks enter the silo, their physical properties (such as density and hardness) are measured using a non-contact detector and correlated to the corresponding area. The high-purity area is sealed with inert gas, maintaining an argon concentration above 95%; the medium-purity and standard purity areas are protected with nitrogen. The unique identifier of the metal block is generated by a laser marking system, which includes the batch number, melting point value and solidification time stamp, with the marking depth controlled between 50-100 micrometers.

[0079] The data traceability module records key parameters for each smelting process. Internally, the module contains a time-series database that stores smelting curves, impurity separation efficiency, and final purity test results along a timeline. The smelting curve includes temperature distribution data for the gradient heating phase, with data points acquired at a frequency of 5Hz. Impurity separation efficiency is calculated using the impurity mass ratio before and after the separation operation. The final purity test results are derived from the output data of the regeneration verification unit. During traceability, all records are bound to metal block identification codes, enabling end-to-end tracking. The database structure is hierarchical, with the top layer being the smelting batch node, and lower branches including temperature sub-nodes (raw thermocouple data), impurity sub-nodes (X-ray interface displacement records), and pure quantum nodes (spectral probe scan results). The data compression algorithm uses the Zlib library, maintaining a compression ratio of approximately 70%. To evaluate temperature stability, the traceability module applies the following formula:

[0080]

[0081] Where Δ represents the temperature fluctuation index, N is the total number of time steps in the smelting process, and Q... k This represents the actual temperature value at the k-th time point. This represents the average temperature during the smelting process. This formula is specifically used to quantify temperature consistency during the smelting stage; a low fluctuation index indicates stable smelting control. The database supports forward lookup queries, retrieving particle size distribution data from the original scrap; and extends backward to subsequent quality inspection reports of the metal blocks. The user interface provides multi-dimensional query tools, allowing data to be filtered by time period, equipment number, or anomaly type. Query results are displayed as heatmaps showing the correlation between temperature curves and impurity distribution.

[0082] The anomaly marking engine is activated upon receiving a separation command from the regeneration verification unit. The engine parses the impurity type identifier and processing plan from the command set. Each separation command triggers a quality correction tag, containing the impurity code, occurrence time, and separation count. The tag is attached to the corresponding metal block's storage record. The tag storage format uses a key-value pair structure: the key is the anomaly event ID, and the value field includes the command source, processing result status, and post-processing re-inspection data. The correction tag's physical form is an RFID chip embedded in the metal block's packaging tray. The engine has a built-in priority queue; when three anomaly tags accumulate in the same batch of metal blocks, the storage priority is automatically increased, and the batch is moved to the isolation area. Metal blocks in the isolation area must be manually retested before being released. The engine synchronously updates the anomaly marking fields in the database; field types include continuous anomaly flags (single tag), composite anomaly flags (multiple tag combinations), and processing failure flags.

[0083] The main control unit's instruction scheduling center processes coordinate data packets from the AI ​​sorting unit and separation instruction sets from the regeneration verification unit. The scheduling algorithm prioritizes tasks based on their characteristics. Coordinate data packets, due to their high real-time requirements, are classified as high-priority tasks and immediately enter the execution queue. Separation instruction sets are prioritized according to impurity type, with metallic impurities prioritized over non-metallic impurities. The priority queue employs a weighted round-robin mechanism, with task weights determined by the following factors: task timeliness coefficient (0.5-1.0), resource consumption factor (0.1-0.9), and expected processing time coefficient. Scheduling results are output to the device coordination module, with each task accompanied by a time window limit. Scheduling data is stored in buffer memory, with memory capacity dynamically adjusted to adapt to changes in task load. A task timeout monitoring mechanism operates in real-time, with a timeout threshold set at 500 milliseconds.

[0084] The equipment coordination module synchronously controls the conveyor belt speed, robotic arm movements, and furnace power parameters according to the priorities set by the command scheduling center. The core of the module is the hardware interface layer, which connects to the controllers of each device via the EtherCAT protocol. Conveyor belt speed regulation employs PID closed-loop control, with the speed reference value derived from the timing plan of the scheduling center. The robotic arm movements generate six-axis joint trajectories; trajectory planning is based on inverse kinematics algorithms, and the velocity curve uses an S-shaped acceleration profile to reduce vibration. Furnace power parameter control allocates power output according to priority weights: power output is increased by 10% when a high-priority task starts; power output remains at the baseline value when a low-priority task is running. The module has a built-in status detection mechanism to monitor equipment operation delays or communication interruptions. Delay handling strategies include task rescheduling and device switching; for example, when the robotic arm response times out, the system switches to a backup robotic arm without interrupting the process. All synchronization parameters are recorded to a log file, which is stored in a circular buffer structure.

[0085] The quality tracking module integrates sorting efficiency and smelting purity data for each waste batch. The module extracts the raw dataset from the data traceability module, with sorting efficiency based on the ratio of the output particle count of the AI ​​sorting unit to the total particle count. Smelting purity data originates from the final test report of the regeneration verification unit. The tracking logic establishes a relational mapping table, with the table structure including waste batch number, sorting efficiency, and smelting purity fields. The mapping table uses a hash index to optimize query speed. During module runtime, data normalization is performed, scaling all values ​​to the 0-1 range to eliminate dimensional differences. Normalized data is bound to the storage records of the metal composition library. Key performance indicators are exported through the tracking module. Users can access historical trends of indicators via API, such as a scatter plot showing the correlation between sorting efficiency and smelting purity. The tracking module periodically generates data snapshots, with a default snapshot period of once every 100 kg of waste processed. The snapshot content is compressed and stored on a cloud storage node.

[0086] During system implementation, a distributed architecture is adopted for data flow. The storage area for the metal composition database is deployed on a local server, while the data traceability module and main control unit run on edge computing nodes. The communication protocol is uniformly MQTT message queue, and message encryption adopts the AES-256 standard. The hardware layer adopts a fault-tolerant design; the controller of the device collaboration module is equipped with dual power inputs, and the RFID reader / writer of the anomaly marking engine supports hot-swapping. The system's runtime self-check process includes: verifying database consistency before startup, monitoring the temperature and humidity of the storage area during runtime, and performing data backup upon shutdown. The backup strategy is incremental backup, with a backup frequency of once per hour. The audit log of the user operation terminal records all parameter modifications, and the log entries include the operator ID, change time, and change details. The traceability chain of the metal blocks is reinforced by blockchain technology, and each storage action generates an immutable transaction hash. Interface testing between the hierarchical storage unit and the main control unit is completed before deployment, covering extreme scenarios such as peak smelting temperatures exceeding thresholds or data packet loss.

[0087] In the tiered storage process, temperature monitoring of the metal component bins employs a redundancy strategy. Each bin group is equipped with three thermocouples, and the temperature data fusion algorithm selects the median value as the output. Material flow within the storage area is executed by automated AGVs, with path planning linked to the main control unit. The data traceability module supports batch export of historical records in a standardized CSV file format. The anomaly marking engine's tag management includes an expiration date setting, with a default expiration of 30 days, after which tags are automatically archived to the historical database. The main control unit triggers a cleanup action after completing its scheduled tasks, releasing memory resources. Control parameters for the equipment collaboration module can be adjusted via configuration files, including PID gain values ​​and synchronization delay compensation. The quality tracking module's data association algorithm uses a timestamp alignment mechanism, with time accuracy calibrated to the microsecond level. The entire implementation emphasizes low latency and high reliability. Multimode fiber optic cables are used for data transmission, with a bandwidth configuration of 10Gbps. The space utilization of the tiered storage units is optimized through a dynamic allocation algorithm, which allocates storage cells based on the metal block volume and purity level. The inbound and outbound operations of the metal component warehouse are performed by a robotic arm, and the robotic arm's force feedback protection device prevents damage to the metal blocks.

[0088] The exported data from the quality tracking module is integrated into a visualization and analysis tool, which incorporates a machine learning model to identify hidden patterns. For example, the model analyzes the correlation between sorting efficiency fluctuations and smelting parameters, providing parameter adjustment suggestions. Database maintenance tasks run periodically, including index rebuilding and defragmentation. The system provides expansion interfaces for integration with external environmental monitoring equipment or third-party quality inspection systems. In the implementation environment, the coordinated testing of the hierarchical storage unit and the regeneration unit covers different load scenarios, verifying the time-series compliance of the entire process from receiving solidification parameters to attaching anomaly tags. The instruction response performance of the main control unit is verified through stress testing, ensuring stability under heavy workloads. Ultimately, the system forms a closed data processing loop, supporting continuous iterative improvement.

[0089] Example 4: The real-time data channel between the main control unit and the remote monitoring terminal transmits three types of core data: spectral characteristic data, melting curve parameters, and purity detection results. Spectral characteristic data includes the reflectance spectrum curves of waste particles collected during the sorting process, with each curve recording the reflectance intensity values ​​of 256 wavelengths within the 400-780nm range. The data packet format uses binary encoding, with each data packet appended with a timestamp and device number. Melting curve parameters include the actual temperature values, heating rates, and holding times of each temperature zone in the melting furnace, sampled at 5-second intervals. Purity detection results come from the spectral analysis report of the regeneration verification unit, recording the characteristic wavelength intensity ratios of each impurity element in the platinum melt. The data channel uses compression transmission technology; the raw data is compressed using the Zstandard algorithm before transmission, maintaining a compression ratio within the range of 60%-70%. The transmission protocol is based on WebSocket, maintaining a long connection state to reduce handshake overhead. After the data arrives at the monitoring terminal, the decompression module restores the original data structure, and the verification module verifies the data integrity.

[0090] The parameter correction interface receives two types of parameters from the monitoring terminal: model training parameters and smelting process parameters. Model training parameters include the kernel size, learning rate, and batch size settings for the convolutional neural network. These parameters are encapsulated in JSON format, with fields including the parameter name, new value, and effective time. Smelting process parameters cover the power gradient, inert gas flow rate, and electromagnetic field strength configuration of the plasma smelting module. After parsing the JSON data, the parameter correction interface first simulates the operation in a sandbox environment for 30 minutes. During the simulation, the interface records the trends of key indicators, such as fluctuations in sorting accuracy and changes in smelting energy consumption. The simulation results are fed back to the monitoring terminal via a graphical interface. Once the operator confirms that everything is correct, a formal update command is triggered. The formal update adopts a canary release strategy, initially updating 10% of the device nodes, and then pushing the update to the entire system after 24 hours without any anomalies.

[0091] The alarm triggering module monitors two key indicators: the number of impurity separation operations and abnormal purity parameters. The module incorporates a sliding time window statistical mechanism with an 8-hour window size. When the cumulative number of impurity separation operations for a batch of waste exceeds 5 within the window, or the final purity test value falls below 99.85%, an equipment maintenance request is generated. The request includes the abnormal equipment number, fault type code, and suggested maintenance measures. The fault type code library contains 12 preset types, such as spectral sensor offset (Code03) and plasma arc instability (Code07). The maintenance request is transmitted to the monitoring terminal via a dedicated alarm channel using the MQTT protocol, with a service quality level set to QoS2. The alarm message includes three priority tags: Emergency (red), Important (yellow), and General (blue). Upon receiving the alarm, the monitoring terminal automatically displays a maintenance work order interface. The work order fields include the equipment location map marker, historical maintenance records, and spare parts inventory status.

[0092] In Example 4, the dynamic optimization mechanism of the deep learning model is specifically manifested as an online learning process. The model maintenance subsystem continuously collects erroneous particle samples, including false positives (misclassifying non-platinum particles as platinum-containing) and false negatives (missing platinum-containing particles). Sample data is stored in a dedicated buffer pool, which employs a first-in, first-out (FIFO) management strategy and has a capacity of 1000 records. A weekly scheduled task initiates the model retraining process: 500 typical erroneous samples are extracted from the buffer pool and mixed with historical correct samples at a 1:3 ratio to form the training set. During training, the loss function weight for erroneous samples is increased to 1.5 times that of regular samples, enhancing the model's ability to identify error-prone features. Cross-validation is performed before deploying a new model, with the validation set containing data from 200 recently sorted particles. Model version management uses semantic numbering rules, such as V2.1.3 representing the third patch update for the first major improvement of the second-generation architecture. The version rollback function allows for a quick switch to the previous stable version when model performance degrades.

[0093] The remote monitoring terminal's user interface is divided into four functional areas: real-time monitoring, parameter configuration, alarm handling, and model management. The real-time monitoring area dynamically displays the conveyor belt's operating status, the furnace temperature distribution, and the robotic arm's movement trajectory. The parameter configuration area provides a visual editor for smelting process parameters, supporting drag-and-drop adjustment of temperature curve shapes. The alarm handling area lists unprocessed maintenance requests, with each record expandable for detailed charts. The model management area presents the topology of a convolutional neural network, with node size reflecting the absolute value of the convolutional kernel weights. The interface refreshes data every second, and key indicators (such as real-time purity values) are displayed in large-font digital dashboards. Operator permissions are divided into three levels: observers can only view data, technicians can adjust process parameters, and administrators have permissions for model training and system restart.

[0094] Event logs generated during system operation are stored categorized by type. Operation logs record parameter modification actions, with fields including operator ID, value before modification, value after modification, and timestamp. Equipment logs collect raw sensor data such as robotic arm joint angles and smelting furnace power fluctuations. Alarm logs save the complete processing history of all maintenance requests, including responders, handling measures, and result confirmation. Log files are compressed and archived daily at midnight, retained locally for 30 days and in the cloud for 180 days. The log retrieval function supports multi-condition combined queries, such as filtering all alarm events of a specific device within a specific time period. Table 1 shows typical smelting curve parameters received by the monitoring terminal.

[0095] Table 1: Showing the typical melting curve parameters received by the monitoring terminal.

[0096] Parameter name Low temperature zone set value medium temperature zone set value High temperature zone setting value Homogenization zone set value Target temperature 650℃ 1350℃ 1720℃ 1750℃ heating rate 50℃ / min 80℃ / min 30℃ / min 5℃ / min Heat preservation time 8min 12min 15min 20min Inert gas flow rate 12L / min 15L / min 18L / min 10L / min Plasma power density 6.5 9.0 12.5 8.0

[0097] The collaborative testing of the main control unit and remote terminals covers various abnormal scenarios. Network interruption testing simulates a 30-minute network outage followed by reconnection, verifying the correctness of the data retransmission mechanism. High-load testing simulates the simultaneous issuance of five sets of parameter modification commands, observing system response latency. Compatibility testing verifies the protocol adaptation capabilities of different terminal versions. Security testing includes brute-force attack protection and man-in-the-middle attack defense. All test results are recorded in a separate verification database, physically isolated from the production environment.

[0098] During implementation, optimization examples of the deep learning model include: addressing the issue of decreased sorting accuracy for a batch of palladium-containing waste, the model restored accuracy to normal levels by enhancing the recognition weight of the palladium characteristic peak (340.5nm). A typical example of adjusting smelting process parameters is: when processing high-sulfur impurity waste, the monitoring terminal issued a new temperature curve, lowering the homogenization zone temperature by 20°C and extending the holding time by 10 minutes, effectively reducing platinum loss due to sulfide vaporization. A practical example of alarm handling shows that when the spectral probe array reports three consecutive calibration failures (Code 09), the system automatically isolates the abnormal probe and prompts for replacement of the fiber optic coupler.

[0099] The data synchronization mechanism employs a differential update strategy. The monitoring terminal periodically sends data version numbers to the main control unit, which compares version differences and then transmits only the changed portions. Version conflict handling rules stipulate that if a terminal modification is not synchronized to the main control unit, the main control unit's data will be used as the baseline; in case of conflicting process parameter modifications, a manual arbitration interface will pop up. The data persistence layer uses multi-replica storage; after successful writing to the primary node, data is asynchronously replicated to two backup nodes.

[0100] System maintenance is scheduled for Sundays from 02:00 to 04:00, during which tasks such as database index optimization and storage defragmentation are performed. When maintenance mode is initiated, the main control unit pauses receiving new tasks, while pending tasks continue to execute until completion. Maintenance status is indicated by status lights: green for normal operation, yellow for maintenance, and red for emergency faults. Physical equipment is equipped with buzzer alarms that issue audible and visual alarms when key indicators exceed limits.

[0101] The remote monitoring terminal's offline mode allows downloading copies of critical data from the past 24 hours. Offline operations include viewing historical data curves and writing maintenance reports; operation records are automatically synchronized upon reconnection to the network. The terminal hardware features both a touchscreen and a physical keyboard for input, accommodating different operating habits. The display brightness automatically adjusts according to ambient light to ensure visibility in bright environments.

[0102] Emphasis is placed on human-machine collaboration. The monitoring terminal pushes expert suggestions, automatically displaying solutions for similar historical cases when an abnormal pattern is detected. The operator confirmation interface features dual verification; critical operations require the input of a dynamic verification code. The training mode provides a virtual waste disposal scenario, allowing novice operators to practice parameter adjustments without disrupting production. The system knowledge base integrates equipment manuals, fault code interpretations, and emergency plans, supporting full-text search and bookmarking.

[0103] Example 5: The operation of the renewable resource optimization module is based on a continuously accumulated dataset of platinum metal production and energy consumption. The system automatically records the total weight of platinum metal for each processing batch. Weight measurement is performed after the molten metal has solidified, using a high-precision electronic scale with an accuracy of 0.01 grams. Energy consumption data is obtained from smart meters monitoring the power consumption of the plasma melting module, robotic arm system, and spectral detection equipment, with a sampling interval of 15 minutes. Auxiliary energy consumption, such as the amount of inert gas used, is recorded via a flow meter, and the cooling water circulation volume is collected by a turbine sensor. All data items are bound to batch numbers, forming structured records stored in the optimization database. The database adopts a time-partitioned storage strategy, with daily data archived independently and retained for five years.

[0104] Platinum recovery rate is calculated based on the ratio of input waste to platinum metal output. Input volume is tallied in the waste pretreatment unit, with the total mass of the raw waste recorded via a conveyor belt weighing system. The system identifies and removes mass interference from non-processed components such as packaging materials. The recovery rate calculation process considers differences in waste sources, establishing independent benchmark values ​​for waste from different sources. For example, the benchmark recovery rate for electronics industry waste is set at 85%, and for chemical catalyst waste at 92%. The percentage deviation of the actual recovery rate from the benchmark value serves as a reference indicator for optimization adjustments. The calculation of the energy consumption ratio covers electricity, gas, and water resources, converting the total energy consumption to standard coal equivalent and dividing it by the platinum metal output. The ratio values ​​are stored by processing batch, and a moving average is calculated to reflect recent trends.

[0105] The generation of the smelting temperature profile adjustment scheme follows a dynamic optimization logic. The system analyzes the correlation between temperature parameters and recovery rate in historical data to identify key temperature zones with significant impact. Typical adjustment scenarios include: appropriately extending the duration of the high-temperature zone when processing high-melting-point impurity waste; and reducing the homogenization zone temperature setpoint when the recovery rate decreases but energy consumption increases. The adjustment scheme is presented in the form of parameter sets, including the target temperature correction for each temperature zone, the percentage adjustment of the heating rate, and the change in holding time. Boundary conditions are automatically checked during scheme generation to prevent parameters from exceeding equipment safety limits. For example, the maximum temperature must not exceed 90% of the smelting furnace's design limit, and the variation in the heating rate must be controlled within ±20%.

[0106] A virtual verification process is performed before implementation. The system loads the spectral characteristic data of the current batch of waste and performs a digital twin simulation based on the temperature parameters to be adjusted. The simulator outputs the predicted range of recovery rate changes and energy consumption fluctuation trends. The deviation rate between the predicted results and historical actual data is used as an evaluation indicator for the feasibility of the solution. When the predicted recovery rate increases by more than 2% or energy consumption decreases by more than 5%, the solution is marked as recommended for implementation. The user interface uses color coding to distinguish the priority of the solution: green indicates high returns and low risk, yellow indicates medium returns, and red indicates potential risks requiring manual review.

[0107] The adjustment plan is implemented using a gradual strategy. Initially, only 30% of the parameter values ​​are modified, with the adjustment scope gradually expanded based on actual feedback. A feedback mechanism compares the expected and actual output data in real time, calculating the execution deviation rate. If the deviation rate exceeds 10%, the plan is terminated, rolling back to the previous stable parameter group. The plan version management system records detailed changes, implementation time, and operator information for each adjustment. The version rollback function retains the five most recent valid versions, supporting quick restoration to any historical state.

[0108] In-depth analysis of energy consumption data identifies the relationship between equipment operating modes and energy efficiency. The system clusters energy consumption curves for different time periods to identify the optimal energy efficiency range. For example, data shows that the platinum recovery per unit of energy consumption peaks when the plasma melting module operates within the 65%-75% rated power range. These patterns are translated into equipment control strategies, such as automatically switching to a high-efficiency operating mode during off-peak hours. A case study of cooling water system optimization shows that when the inlet water temperature is below 25℃, appropriately reducing the pump frequency can still maintain heat dissipation requirements; this adjustment reduces water consumption by 8%.

[0109] The database of matching waste characteristics with process parameters is continuously being expanded. The system establishes a correlation mapping between the peak distribution of waste spectral characteristics and the optimal temperature curve. When the characteristic spectrum of a new batch of waste has a similarity of more than 85% with records in the database, the system automatically recommends historically optimal parameter sets. The matching process uses the nearest neighbor algorithm, and the similarity calculation is based on the characteristic peak position offset and intensity ratio. For newly emerging waste types, the system initiates an exploration mode, trying different temperature combinations within a safe range, recording the recovery rate and energy consumption data of each combination, and gradually establishing a new set of optimized parameters.

[0110] The interaction between the renewable resource optimization module and the main control unit adopts an event-driven mechanism. When a smelting batch begins, the optimization module pushes suggested parameters; when the batch is completed, the main control unit sends back the actual output data. Abnormal events such as equipment failure or parameter exceeding limits immediately interrupt the optimization process, and re-initialize after the problem is resolved. The data synchronization mechanism ensures that even with network latency, the local cache can still maintain basic optimization functions. The module's health status is monitored via heartbeat packets, and the service process is automatically restarted in case of an abnormality.

[0111] Trend analysis of long-term operational data supports strategic decision-making. The system generates quarterly resource utilization efficiency reports, displaying long-term change curves for recovery rates and energy consumption ratios. The reports highlight process adjustments with significant improvements, tracing back to specific parameter change records. Cross-year data comparisons reveal seasonal influencing factors, such as the increased energy consumption caused by decreased cooling system efficiency in high-temperature summer environments. These analytical results guide equipment upgrade plans, such as enhancing heat dissipation systems or updating insulation materials in melting chambers.

[0112] The core logic of the operator training system's integrated optimization module includes: a training simulator providing a virtual waste processing scenario where trainees can adjust temperature parameters to observe and predict effects; a built-in library of typical cases showcasing the implementation paths of successful optimization solutions; an expert knowledge base containing solutions to common problems, such as pre-reducing the heating rate in the low-temperature zone to avoid smoke when processing waste containing organic impurities; and a training assessment setting that tests the rationality of parameter adjustments to evaluate trainees' understanding of the balance between energy consumption and recovery rate.

[0113] The module's continuous update mechanism incorporates on-site operational experience. Monthly operator feedback on optimization solutions is collected, highlighting their applicability in specific scenarios. Feedback data is used to adjust the weight parameters of the prediction model, such as increasing the importance coefficient of certain waste characteristics. Software updates follow a rolling release model, pushing out algorithm improvements every two months. Version documentation details the technical basis for each optimization for technical personnel reference. Regular hardware sensor calibration is synchronized with software updates to ensure consistent data acquisition accuracy.

[0114] The closed-loop optimization process established during implementation forms a self-improving mechanism. Data from each batch is input into the optimization model, the model outputs adjustment plans to guide production, and production results are fed back to update the model's parameters. This cycle allows the system to adapt to slow changes in waste composition, such as the year-on-year decline in the proportion of platinum alloys in electronic industry waste. Outlier detection algorithms identify data points deviating from the normal range, triggering specialized analysis processes to distinguish between random errors and the beginning of new change patterns.

[0115] Data exchange with external systems expands the optimization dimensions. The energy management system provides real-time electricity price information, and the optimization module automatically reduces the power consumption of non-critical equipment during peak electricity price periods. The environmental monitoring system reports atmospheric humidity data, adjusting the operating parameters of the cooling system under high humidity conditions. This cross-system collaboration extends resource optimization beyond production processes to the overall operating cost level. Data interfaces use standardized protocols to ensure compatibility with equipment from different manufacturers.

[0116] Redundant design of the modules ensures the reliability of critical functions. Core algorithms run simultaneously on edge computing nodes and in the cloud, maintaining basic optimization functions on local nodes even during network outages. Data storage utilizes a RAID10 array to prevent data loss due to single points of failure. Power supply configuration includes dual inputs plus UPS backup, sufficient to support ongoing batch optimization calculations. All hardware components meet industrial environment anti-interference standards, such as an IP54 dustproof rating and a wide operating temperature range of -20℃ to 60℃.

[0117] The visual interface intuitively displays the optimization results. The dynamic dashboard simultaneously displays three key metrics: current batch recovery rate, energy efficiency ratio, and the gap with historical best values. Trend charts compare the parameter adjustment direction and effect changes over the last ten batches. The alarm panel highlights abnormal fluctuations, such as a sudden increase in energy consumption of over 20% in a certain temperature zone. The interface supports multi-dimensional data drill-down, from quarterly summaries to daily details, helping to pinpoint the specific causes of performance fluctuations. The display layout is customizable to meet the focus needs of different personnel.

[0118] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0119] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A platinum scrap AI sorting and recycling management system, characterized by, include: The waste pretreatment unit receives platinum-containing waste and performs physical crushing, outputting a waste particle stream with standardized particle size. The AI ​​sorting unit is connected to the waste pretreatment unit, acquires the spectral feature data of the waste particles, identifies the distribution pattern of platinum elements through a deep learning model, and outputs a set of coordinates of platinum-containing particles. The recycling unit receives the coordinate set of platinum-containing particles sent by the AI ​​sorting unit, controls the robotic arm to grab the target particles for high-temperature melting, separates platinum metal from other impurities, and outputs molten platinum metal. The regeneration verification unit monitors the purity parameters of the platinum molten metal. When the purity does not meet the standard, it triggers an impurity separation command and feeds the separation command back to the recycling regeneration unit to re-execute the smelting operation. The waste pretreatment unit includes: The vibrating screening module performs multi-stage screening of the crushed waste, selects particles that meet the particle size threshold, and outputs them to the spectral acquisition channel. The spectral excitation module emits multi-band excitation light sources during the particle screening and transport process, collects the reflection spectrum curves and generates a three-dimensional feature matrix. The transmission control module dynamically adjusts the transmission band rate according to the acquisition progress of the spectral excitation module to ensure that the particle spacing meets the spectral acquisition requirements. The AI ​​sorting unit includes: The feature extraction engine analyzes the platinum element feature peaks in the three-dimensional feature matrix and calculates the difference between the feature peak intensity and the background noise. The dynamic modeling engine trains a convolutional neural network based on historical waste samples to match the mapping relationship between feature peak difference sequences and platinum element concentration in real time. The coordinate positioning engine marks the spatial coordinates of high-concentration platinum particles based on the matching results and generates a coordinate data packet containing timestamps. The regeneration verification unit includes: A spectral probe array is used to collect atomic emission spectra of molten platinum metal and identify characteristic wavelengths of non-platinum elements. The purity analysis module calculates the ratio of the characteristic wavelength intensity of the target element to the main peak intensity of platinum. The instruction generation module generates a set of separate instructions containing impurity type identifiers when the scaling factor exceeds a preset threshold.

2. The platinum scrap AI sorting and recycling management system of claim 1, wherein, The recycling unit includes: The smelting control core receives the coordinate data packet and calculates the robotic arm's motion trajectory and the smelting furnace's start-up and shutdown sequence. The plasma melting module performs gradient heating on the target particles in an inert gas environment and sets a multi-temperature melting curve based on the melting point of platinum metal. The impurity separation module applies an electromagnetic field in the molten state to separate the platinum liquid from the impurity precipitate and monitors the position of the separation interface in real time.

3. The platinum waste AI sorting and recycling management system according to claim 2, characterized in that, It also includes tiered storage units: The metal composition library receives the solidification parameters of the platinum molten metal output from the recycling unit and divides the storage areas according to the purity level. The data traceability module records the temperature curve, impurity separation efficiency, and final purity test results for each smelting process; The anomaly marking engine adds a quality correction tag to the corresponding storage area when it receives the separation instruction from the regeneration verification unit.

4. The platinum waste AI sorting and recycling management system according to claim 3, characterized in that, The system includes a main control unit: The instruction scheduling center receives the coordinate data packet from the AI ​​sorting unit and the separation instruction set from the regeneration verification unit, and allocates task priorities. The equipment coordination module synchronously controls the conveyor belt speed, robotic arm movements, and smelting furnace power parameters according to task priorities. The quality tracking module associates and stores the sorting efficiency and smelting purity data of each batch of waste materials into the data traceability module.

5. The platinum waste AI sorting and recycling management system according to claim 4, characterized in that, The main control unit is connected to a remote monitoring terminal: The real-time data channel transmits the spectral characteristic data, melting curve parameters, and purity detection results to the monitoring terminal. The parameter correction interface receives model training parameters and smelting process parameters sent by the monitoring terminal. The alarm triggering module sends an equipment maintenance request to the monitoring terminal when the number of impurity separations exceeds the threshold or the purity parameter is abnormal.

6. The platinum waste AI sorting and recycling management system according to claim 5, characterized in that, The deep learning model includes a dynamic optimization mechanism, specifically: Based on the sorting error of each batch of waste, the weights of the convolutional neural network kernels are adjusted to enhance the spectral feature learning intensity for incorrectly identified particle samples.

7. The platinum waste AI sorting and recycling management system according to claim 6, characterized in that, The system also includes a renewable resource optimization module, which specifically operates as follows: Cumulative data on total platinum metal output and energy consumption for each batch; Based on the total platinum metal output and energy consumption data of each batch, the platinum recovery rate and energy consumption ratio per unit of waste were calculated. A melting temperature curve adjustment scheme is generated based on the platinum recovery rate and energy consumption ratio.

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