Foundry waste sand recycling intelligent manufacturing management system based on artificial intelligence

Abstract

The invention relates to the technical field of casting sand production, and discloses an artificial intelligence-based casting waste sand recycling intelligent manufacturing management system, which comprises a data acquisition module for acquiring production data in a casting waste sand treatment process; the data processing and analyzing module is used for receiving the data transmitted by the data acquisition module and processing and analyzing the data; the intelligent control module is used for adjusting operation parameters of crushing, grinding, sorting and roasting equipment; the production scheduling and management module is used for receiving the control information, making a production plan and performing inventory management; and the quality detection and tracing module is used for establishing a quality tracing system and ensuring the traceability of the data. By constructing a data processing and modeling module based on artificial intelligence, dynamic identification and adjustment optimization of a key process state in a foundry waste sand treatment process are realized, so that the adaptability of the system to complex working conditions is improved.

Description

Technical Field

[0001] This invention relates to the field of foundry sand production technology, specifically to an intelligent manufacturing management system for the recycling of foundry waste sand based on artificial intelligence. Background Technology

[0002] In the foundry industry, foundry sand is one of the key raw materials, and its consumption is extremely large. my country's annual discharge of foundry waste sand exceeds ten million tons; however, the recycling rate of waste sand is less than 10%. Most waste sand is still disposed of by discarding or landfilling. If calculated based on a 3-meter-deep accumulation layer, approximately 25,000 mu (about 1,667 hectares) of land is needed annually for waste sand storage. Furthermore, to meet foundry demands, more than 35 million cubic meters of quartz ore resources need to be mined annually to produce new sand, resulting in a significant waste of natural resources.

[0003] Foundry waste sand contains a certain amount of toxic and harmful substances. Improper handling can cause serious pollution to soil, water, and air, thereby endangering human health. Currently, most enterprises still treat waste sand as solid waste and lack effective recycling methods. This not only exacerbates environmental problems but also results in a significant waste of resources. However, foundry waste sand inherently has high reuse value. With increasingly stringent environmental policies and growing awareness of resource recycling, developing efficient, economical, and environmentally friendly waste sand recycling technologies and management systems is urgently needed.

[0004] Existing waste sand treatment methods have several limitations. First, in terms of data monitoring, the industry generally lacks IoT-based online monitoring systems, making it difficult to promptly grasp changes in waste sand composition and leading to unbalanced facility utilization. Second, the level of equipment intelligence is low; over 80% of treatment equipment lacks intelligent sensing capabilities, making its operating status invisible and uncontrollable, resulting in delayed fault response and impacting production continuity. Third, the resource conversion efficiency of waste sand is low; traditional treatment processes achieve a recovery efficiency of less than 35%, significantly lagging behind advanced countries. Finally, the energy structure is unbalanced; energy consumption distribution during treatment is unreasonable, and the lack of energy flow monitoring means makes energy scheduling and optimization difficult.

[0005] Currently, the recycling of foundry waste sand generally lacks an integrated and intelligent management system. Existing management models rely on manual experience, and the control methods are outdated, making it difficult to achieve real-time monitoring, scientific analysis, and dynamic optimization of the waste sand treatment process. This results in low production efficiency, serious resource waste, and inconsistent product quality. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent manufacturing management system for the recycling of foundry waste sand based on artificial intelligence. This system solves the problem that the management system cannot monitor the status of each stage of waste sand treatment in real time, which leads to low production efficiency, low resource utilization, and unstable product quality.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent manufacturing management system for the recycling of foundry waste sand based on artificial intelligence, comprising:

[0008] The data acquisition module is used to collect production data during the waste sand treatment process and transmit the collected data to the data processing and analysis module in real time.

[0009] The data processing and analysis module is used to receive data transmitted by the data acquisition module, process and analyze the data, and transmit the analysis results to the intelligent control module and the production scheduling and management module.

[0010] The intelligent control module is used to adjust the operating parameters of the crushing, grinding, sorting, and roasting equipment based on the analysis results provided by the data processing and analysis module.

[0011] The production scheduling and management module is used to receive the analysis results provided by the data processing and analysis module and the control information fed back by the intelligent control module, formulate production plans and perform inventory management.

[0012] The quality inspection and traceability module is used to receive information from the data acquisition module and the production scheduling and management module, establish a quality traceability system, and ensure the traceability of data.

[0013] Furthermore, by modularizing and intelligently integrating the five key links of the waste sand treatment process, a high degree of linkage between data, control, scheduling, and traceability is achieved, enabling the entire process of foundry waste sand recycling to have intelligent sensing, dynamic control, and closed-loop tracking capabilities, thereby improving the stability of system operation and the accuracy of decision-making.

[0014] Preferably, the data acquisition module includes a particle size analyzer, a humidity sensor, a temperature sensor, and a weighing sensor, used to collect particle size, humidity, temperature, and weight parameters.

[0015] Furthermore, by modularizing and intelligently integrating the five key links of the waste sand treatment process, a high degree of linkage between data, control, scheduling, and traceability is achieved, enabling the entire process of foundry waste sand recycling to have intelligent sensing, dynamic control, and closed-loop tracking capabilities, thereby improving the stability of system operation and the accuracy of decision-making.

[0016] Preferably, the data processing and analysis module includes three sub-modules: data cleaning, feature extraction, and analysis modeling.

[0017] Data cleaning removes invalid information through filtering algorithms and data screening, and performs noise reduction, standardization, and normalization.

[0018] Feature extraction extracts key features through orthogonal transformation and principal component analysis.

[0019] The analysis and modeling employed decision trees, support vector machines, and neural networks to build predictive models.

[0020] Furthermore, by configuring a multi-source sensor array, the key dimensions of the physical state changes of waste sand are fully covered, providing a high-resolution, high-frequency dynamic data foundation for subsequent data modeling and control strategies, ensuring that every step of the processing is supported by data.

[0021] Preferably, the intelligent control module can adjust the operating parameters of the crushing and grinding equipment, the vibration frequency of the sorting equipment, the roasting temperature, the fuel consumption, the induced draft volume, and the delivered air volume according to data changes during the production process.

[0022] Furthermore, by constructing a complete data analysis chain from raw data processing to intelligent modeling, this module enables the extraction of high-value information with discriminative power from massive amounts of sensory data, providing a scientific basis and modeling support for intelligent control, and serving as the core foundation for achieving precision manufacturing management.

[0023] Preferably, the production scheduling and management module analyzes historical production data and market demand data through artificial intelligence algorithms. When formulating production plans, it considers the type, quantity, and timing of market demand, the amount and quality of recycled waste sand, the processable proportion of waste sand of different qualities, and also considers equipment capacity, maintenance cycle, repair history, equipment compatibility, equipment maintenance plan, and personnel configuration. It determines the production quantity, production sequence, and amount of waste sand input for various types of reused products, and manages the inventory of foundry waste sand and reused products in real time. It controls the inventory level through inventory data analysis and prediction.

[0024] Furthermore, by deeply integrating the output of the predictive model with the equipment control commands, the system is made adaptive, enabling it to respond and adjust the operating status of each piece of equipment in a timely manner when production conditions fluctuate or waste sand composition changes, thereby ensuring the stability and consistency of the waste sand treatment process.

[0025] Preferably, the quality inspection and traceability module uses blockchain technology to record and save the quality data, processing data and corresponding raw material information of each batch of recycled products. It associates various types of data through unique identifiers, automatically constructs and retrieves traceability paths, and uses blockchain technology to ensure the immutability and traceability of quality traceability files when establishing a quality traceability system.

[0026] Furthermore, the design of this module breaks through the limitations of traditional databases in terms of controllability and security. By using a chain structure to ensure the originality and continuity of data records, it constructs a decentralized and trustworthy quality supervision system, providing technical support for subsequent responsibility definition, quality traceability and regulatory compliance.

[0027] Preferably, the data acquisition module transmits the acquired data to the data processing and analysis module in real time, and records the data source and timestamp according to the installation location of each sensor to support subsequent data tracing and analysis.

[0028] Furthermore, by enhancing the structuring capabilities of sensor data, the system's comprehensive analytical capabilities for multi-dimensional data attributes in terms of time, space, and source have been improved, thus constructing a context-aware data integrity and consistency assurance mechanism for the data management system.

[0029] Preferably, the data processing and analysis module visualizes the analysis and modeling results and generates a model evaluation report to provide a reference for the control and scheduling module.

[0030] Furthermore, this mechanism not only enhances the transparency of algorithm results but also supports human-machine collaborative decision-making, ensuring that the system is interpretable and auditable, and possesses stronger adaptability and technical credibility in industrial settings.

[0031] Preferably, the intelligent control module achieves local decision-making for adjusting some parameters through edge computing, wherein the local decision-making calculation model of the edge node satisfies the following formula:

[0032] U i (t)=α·S i (t)+β·ΔP i (t)+γ·L i (t);

[0033] Among them, U i (t) represents the comprehensive adjustment decision value of edge node i at time t, S i (t) represents the weighted state index of the sensor-collected data, ΔP i (t) represents the deviation between the current device parameters and the optimal historical parameters, L i (t) represents the local load of the node, and α, β, γ are adjustment coefficients that are dynamically adjusted according to actual control requirements.

[0034] Furthermore, the localized control scheme leverages the near-source computing advantages of edge nodes to reduce the computing load on the central system, improve decision response speed, and lay the foundation for a distributed architecture for multi-node collaborative control. It is one of the key technologies to ensure the continuous operation of the system in complex environments.

[0035] Preferably, the production scheduling and management module supports the classification and management of waste sand of different grades, and allocates it to different recycling product production lines according to the classification results. At the same time, it sets a minimum safety stock line for different types of waste sand in the inventory and generates replenishment or scheduling prompts.

[0036] Furthermore, by constructing a multi-level inventory control model and combining the differences in waste sand properties with the adaptability of production lines, the system can realize the resource allocation logic of warehousing according to quality, classified production, and precise material input, effectively improving the efficiency of raw material utilization and the matching rate of production lines, and realizing the true graded utilization and value maximization of waste sand resources.

[0037] This invention provides an intelligent manufacturing management system for the recycling of foundry waste sand based on artificial intelligence. It has the following beneficial effects:

[0038] 1. This invention, by constructing an artificial intelligence-based data processing and modeling module, achieves dynamic identification and optimization of key process states in the treatment of foundry waste sand, thereby improving the system's adaptability to complex operating conditions. Compared with the existing technology that relies on manual experience to adjust parameters, this solution effectively solves the problems of insufficient control precision and response lag, providing technical assurance for process stability.

[0039] 2. This invention introduces blockchain technology into the quality inspection and traceability module to perform chain-like recording and associated management of batch data, raw material sources, and process parameters, ensuring the integrity and traceability of quality data. Unlike traditional traceability methods that suffer from fragmented information and susceptibility to tampering, this technical solution constructs a product traceability system with reliable data and verifiable sources, enhancing the transparency of the production process and quality control capabilities.

[0040] 3. This invention employs a multi-factor collaborative production scheduling mechanism, combining market demand, equipment capacity, and waste sand properties to achieve dynamic optimization and categorized allocation of the recycling process. Compared to existing technologies that lack global planning and employ extensive resource scheduling methods, this invention solves problems such as low reuse efficiency and insufficient matching between resource allocation and production capacity, thereby improving the systematicness and coordination of overall resource utilization.

[0041] 4. This invention integrates edge computing capabilities into the intelligent control module, endowing field nodes with local decision-making functions and enabling near-source real-time adjustment of key equipment operating parameters. Unlike the traditional approach that relies on unified commands from a central server, this technology alleviates control latency caused by network dependence, significantly improves system response speed and field operation autonomy, and enhances system reliability in complex manufacturing environments. Attached Figure Description

[0042] Figure 1 This is a system framework diagram of the present invention. Detailed Implementation

[0043] The technical solutions in 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.

[0044] Please see the appendix Figure 1 This invention provides an intelligent manufacturing management system for the recycling of foundry waste sand based on artificial intelligence, comprising:

[0045] The data acquisition module is used to collect production data during the waste sand treatment process and transmit the collected data to the data processing and analysis module in real time.

[0046] The data processing and analysis module is used to receive data transmitted by the data acquisition module, process and analyze the data, and transmit the analysis results to the intelligent control module and the production scheduling and management module.

[0047] The intelligent control module is used to adjust the operating parameters of the crushing, grinding, sorting, and roasting equipment based on the analysis results provided by the data processing and analysis module.

[0048] The production scheduling and management module is used to receive the analysis results provided by the data processing and analysis module and the control information fed back by the intelligent control module, formulate production plans and perform inventory management.

[0049] The quality inspection and traceability module is used to receive information from the data acquisition module and the production scheduling and management module, establish a quality traceability system, and ensure the traceability of data.

[0050] Example 1: System Composition

[0051] Data Acquisition Module — Data Acquisition Types

[0052] Waste sand source information: Recording the name and specific address of the foundry that generates waste sand helps trace its origin and understand the characteristics of waste sand generated by foundries of different regions and sizes. For example, large foundries may have higher waste sand production due to their larger production scale and more complex production processes, resulting in waste sand composition and characteristics that may differ from those of smaller foundries. By analyzing the source information of waste sand from different foundries, targeted waste sand treatment plans can be developed to improve treatment efficiency and quality.

[0053] Physical property data of waste sand include particle size distribution, moisture content, and density. Particle size distribution affects the grinding and sorting effect of waste sand in subsequent processing; moisture content affects the flowability and cohesiveness of waste sand, which has an important impact on crushing and molding processes; density reflects the composition and quality of waste sand.

[0054] Production equipment operating data includes motor speed, temperature, and pressure. Motor speed affects the equipment's processing capacity and efficiency; excessively high temperature may indicate equipment malfunction or overload; pressure data reflects the internal working status of the equipment and is crucial for ensuring its safe and stable operation.

[0055] Data acquisition methods: Data is acquired through various sensors and monitoring devices, such as particle size analyzers, humidity sensors, temperature sensors, pressure sensors, and weighing sensors. These sensors are installed in key parts of the waste sand treatment equipment to collect data in real time and transmit it to the data processing center.

[0056] Data Processing and Analysis Module

[0057] Data cleaning: The collected data is cleaned to remove invalid or redundant information, and denoising, standardization, and normalization are performed. For example, noisy data collected by sensors is denoised using filtering algorithms; data from different units and ranges are standardized and normalized to improve data consistency and comparability, providing a high-quality data foundation for subsequent analysis and modeling.

[0058] Feature Extraction: Combining the characteristics of foundry waste sand recycling with professional knowledge, key features are extracted. For example, features such as average particle size and particle size standard deviation are extracted from the particle size distribution data of waste sand; features such as equipment operating efficiency and energy consumption are extracted from equipment operation data. Methods such as orthogonal transformations are used to reduce data dimensionality, decrease computational load, and improve the effectiveness of features and the generalization ability of the model.

[0059] Data Analysis and Modeling: Artificial intelligence algorithms are used to analyze and model the processed data. For example, machine learning algorithms such as decision trees, support vector machines, and neural networks are employed to establish waste sand quality prediction models, equipment fault diagnosis models, and production process optimization models. By learning from and analyzing historical data, the quality and availability of waste sand are predicted, potential equipment failures are diagnosed, and production process parameter settings, such as grinding time and sorting parameters, are optimized.

[0060] Intelligent control module

[0061] Based on the results of the data processing and analysis module, intelligent control is implemented for the foundry waste sand recycling production process. This module can automatically adjust equipment operating parameters, such as the rotation speed of crushing and grinding equipment, the vibration frequency of sorting equipment, the roasting temperature, fuel consumption, and the induced draft and exhaust air volumes, to achieve precise control of the waste sand treatment process. For example, when the predicted particle size of the waste sand does not meet the requirements, the intelligent control module can automatically adjust the rotation speed and grinding time of the crushing and grinding equipment to ensure that the particle size of the waste sand reaches the expected target. Simultaneously, the module can also issue alarm signals in a timely manner based on the equipment fault diagnosis results and take corresponding measures, such as automatic shutdown, to ensure the safety and stability of production. The intelligent control module achieves local decision-making for some parameter adjustments through edge computing, where the local decision-making calculation model of the edge nodes satisfies the following formula:

[0062] U i (t)=α·S i (t)+β·ΔP i (t)+γ·L i (t);

[0063] Among them, U i (t) represents the comprehensive adjustment decision value of edge node i at time t, S i (t) represents the weighted state index of the sensor-collected data, ΔP i (t) represents the deviation between the current device parameters and the optimal historical parameters, L i (t) represents the local load of the node, and α, β, γ are adjustment coefficients that are dynamically adjusted according to actual control requirements.

[0064] Production scheduling and management module

[0065] Production Planning: A reasonable production plan is formulated based on factors such as market demand, waste sand supply, and equipment capacity. Artificial intelligence algorithms are used to analyze and predict historical production and market demand data to optimize production planning and improve production efficiency and resource utilization. For example, production resources are rationally allocated based on the processing difficulty and market demand for different types of waste sand, prioritizing the processing of waste sand with high demand and low processing difficulty.

[0066] Equipment scheduling: Rationally schedule production equipment to ensure efficient operation and maintain production continuity.

[0067] Inventory Management: Real-time management of foundry waste sand and recycled product inventory. Through analysis and forecasting of inventory data, inventory levels are rationally controlled to avoid overstocking or stockouts. For example, when the quantity of recycled products in inventory reaches a certain threshold, production plans are adjusted promptly to reduce production; when waste sand inventory is insufficient, timely procurement or recycling is arranged to ensure normal production operations.

[0068] Quality Inspection and Traceability Module

[0069] Quality Inspection: Quality inspection points are set up at each stage of the foundry waste sand recycling process to conduct real-time monitoring of the quality of waste sand and recycled products. Advanced testing technologies and equipment are employed, such as SEM (Scanning Electron Microscopy), TGA (Thermal General Analysis), XRD (X-ray Diffraction), hardness testers, and particle size analyzers, as key analytical tools to test the chemical composition and physical properties of waste sand and recycled products. The test results are compared with quality standards to determine product compliance. If non-compliant products are found, they are promptly processed, such as rework or scrapping.

[0070] Quality Traceability: Establish a comprehensive quality traceability system to ensure full traceability of each batch of waste sand and recycled products. Record information such as the source of waste sand, processing procedures, and test results to create detailed quality traceability files. When quality issues arise, the quality traceability files can be consulted to quickly pinpoint the source and cause of the problem, allowing for appropriate improvements and enhancing product quality and reliability.

[0071] Human-computer interaction module

[0072] This module provides operators with a user-friendly interface for easy monitoring, operation, and management of the system. Operators can view various data and status information of the production process in real time through the interface, such as equipment operating parameters, waste sand quality indicators, and production progress. Simultaneously, operators can also manually control and adjust the system by inputting commands through the interface. Furthermore, the human-machine interface module provides data analysis and report generation functions to offer decision support for management personnel.

[0073] Workflow - Data Acquisition Phase

[0074] The data acquisition module collects various data during the recycling process of foundry waste sand according to preset time intervals or event triggering mechanisms. Sensors and monitoring equipment transmit the collected data to the data processing center in real time to ensure the timeliness and accuracy of the data.

[0075] Data processing and analysis stage

[0076] The data processing and analysis module cleans, extracts features, and performs analytical modeling on the collected data. Data cleaning removes invalid and redundant information to improve data quality; feature extraction extracts key features to reduce data dimensionality; and analytical modeling establishes various prediction and optimization models to provide a basis for intelligent control and production scheduling.

[0077] Intelligent control stage

[0078] Based on the results from the data processing and analysis module, the intelligent control module intelligently controls the production process. It automatically adjusts equipment operating parameters to ensure stable and efficient production. Simultaneously, it monitors equipment operating status in real time, promptly detecting and addressing equipment malfunctions to guarantee production safety.

[0079] Production scheduling and management phase

[0080] The production scheduling and management module formulates production plans, schedules equipment, and manages inventory based on factors such as market demand, waste sand supply, and equipment capacity. By optimizing production plans and equipment scheduling, it improves production efficiency and resource utilization; and through inventory management, it ensures the continuity and stability of production.

[0081] Quality Inspection and Traceability Stage

[0082] The quality inspection and traceability module performs real-time monitoring of the quality of waste sand and recycled products, establishing a quality traceability system. Non-conforming products are handled promptly to ensure product quality meets standards. When quality problems occur, the traceability system quickly identifies the cause and implements corrective measures.

[0083] Human-computer interaction stage

[0084] Operators can monitor the production process in real time through the human-machine interface module, inputting commands for manual control and adjustments. The system provides data analysis and report generation functions to support management decisions. The human-machine interface module also enables remote monitoring and management of the system, allowing managers to stay informed about production status anytime, anywhere.

[0085] Example 2: System Hardware Deployment

[0086] Sensor and monitoring equipment installation

[0087] Various sensors and monitoring devices are installed at key points on the foundry waste sand recycling production line. For example, a particle size analyzer is installed at the discharge port of the grinding equipment to monitor the particle size distribution of the waste sand in real time; a humidity sensor is installed in the waste sand storage silo to monitor the humidity of the waste sand; temperature and pressure sensors are installed on key process equipment components to monitor the operating temperature and pressure of critical links and equipment. It is crucial to ensure the accurate installation of sensors and monitoring devices in critical links and equipment to accurately collect the required data; weighing sensors are used to adjust the particle size distribution of the waste sand to obtain the desired product particle size.

[0088] Data processing center construction

[0089] A dedicated data processing center will be constructed, equipped with high-performance servers and data storage devices. The servers will run data processing and analysis software to process and analyze the collected data in real time. The data storage devices will store large amounts of historical data, providing data support for data analysis and modeling. Simultaneously, the data processing center must have a robust network environment to ensure that data collected by sensors and monitoring equipment can be transmitted to the center in a timely and accurate manner.

[0090] Installation of control equipment and actuators

[0091] Control devices and actuators are installed on the production equipment, such as frequency converters to control motor speed and electric valves to control material flow. These control devices and actuators must be connected to an intelligent control module to receive control commands and achieve precise control of the production equipment.

[0092] System software configuration

[0093] Data acquisition software installation

[0094] Data acquisition software is installed in the data acquisition module. This software is responsible for communicating with sensors and monitoring equipment, collecting data, and transmitting it to the data processing center. The data acquisition software must have data encryption and error correction functions to ensure the security and accuracy of data transmission.

[0095] Data processing and analysis software installation

[0096] Data processing and analysis software is installed in the data processing center. This software includes functional modules such as data cleaning, feature extraction, and analysis modeling. The data cleaning module uses filtering algorithms and data screening methods to clean the collected data; the feature extraction module uses orthogonal transformation and principal component analysis to extract key features; and the analysis modeling module uses machine learning algorithms such as decision trees, support vector machines, and neural networks to build various prediction and optimization models.

[0097] Intelligent control software installation

[0098] Intelligent control software is installed in the intelligent control module. Based on the results from the data processing and analysis module, this software generates control commands and sends them to the control equipment and actuators. The intelligent control software must have real-time response and adaptive adjustment capabilities, enabling it to adjust the control strategy promptly according to changes in the production process.

[0099] Production scheduling and management software installation

[0100] The production scheduling and management module includes production planning, equipment scheduling, and inventory management modules. The production planning module generates reasonable production plans based on market demand, waste sand supply, and equipment capacity. The equipment scheduling module automatically allocates tasks to equipment based on equipment status, task priority, and production progress. The inventory management module manages the inventory of foundry waste sand and recycled products in real time, effectively controlling inventory levels.

[0101] Quality Inspection and Traceability Software Installation

[0102] The quality inspection and traceability module installs quality inspection and traceability software, which is responsible for real-time inspection and recording of the quality of waste sand and recycled products. The quality inspection module compares the inspection results with quality standards to determine whether the product is qualified; the quality traceability module establishes a quality traceability file, recording information such as the source of waste sand, processing procedures, and inspection results, facilitating quality traceability.

[0103] Human-computer interaction software installation

[0104] Install human-machine interface (HMI) software in the HMI module. This software provides a user-friendly interface to facilitate system monitoring, operation, and management by operators. The HMI interface should have functions such as data display, command input, and report generation, capable of displaying various data and status information of the production process in real time, while also allowing operators to input commands for manual control and adjustment of the system.

[0105] System debugging and optimization - System debugging

[0106] After the system hardware and software are installed, system debugging is performed. First, the sensors and monitoring equipment are calibrated to ensure the accuracy and reliability of the collected data. Then, the data processing and analysis software, intelligent control software, production scheduling and management software, quality inspection and traceability software, and human-machine interaction software are debugged to check whether the functions of each module are normal. Finally, system integration testing is performed to simulate the actual production process, check the overall operation of the system, and ensure that the collaboration between the various modules is normal.

[0107] System optimization

[0108] During system operation, production data is continuously collected and optimized. Based on data analysis results, data processing and analysis algorithms are adjusted to improve model accuracy and predictive capabilities; intelligent control strategies are optimized to enhance production process stability and efficiency; production scheduling plans are adjusted to improve resource utilization and production efficiency; and the quality inspection and traceability system is improved to enhance product quality and reliability. Simultaneously, the human-machine interface is optimized based on operator feedback to improve user experience.

[0109] 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. An intelligent manufacturing management system for recycling foundry waste sand based on artificial intelligence, characterized in that, include: The data acquisition module is used to collect production data during the waste sand treatment process and transmit the collected data to the data processing and analysis module in real time. The data processing and analysis module is used to receive data transmitted by the data acquisition module, process and analyze the data, and transmit the analysis results to the intelligent control module and the production scheduling and management module. The intelligent control module is used to adjust the operating parameters of the crushing, grinding, sorting, and roasting equipment based on the analysis results provided by the data processing and analysis module. The production scheduling and management module is used to receive the analysis results provided by the data processing and analysis module and the control information fed back by the intelligent control module, formulate production plans and perform inventory management. The quality inspection and traceability module is used to receive information from the data acquisition module and the production scheduling and management module, establish a quality traceability system, and ensure the traceability of data.

2. The intelligent manufacturing management system for recycling foundry waste sand based on artificial intelligence according to claim 1, characterized in that, The data acquisition module includes a particle size analyzer, a humidity sensor, a temperature sensor, and a weighing sensor, used to collect particle size, humidity, temperature, and weight parameters.

3. The intelligent manufacturing management system for recycling foundry waste sand based on artificial intelligence according to claim 1, characterized in that, The data processing and analysis module includes three sub-modules: data cleaning, feature extraction, and analysis modeling. Data cleaning removes invalid information through filtering algorithms and data screening, and performs noise reduction, standardization, and normalization. Feature extraction extracts key features through orthogonal transformation and principal component analysis. The analysis and modeling employed decision trees, support vector machines, and neural networks to build predictive models.

4. The intelligent manufacturing management system for recycling foundry waste sand based on artificial intelligence according to claim 1, characterized in that, The intelligent control module can adjust the operating parameters of the crushing and grinding equipment, the vibration frequency of the sorting equipment, the roasting temperature, the fuel consumption, the induced draft volume, and the delivered air volume according to data changes during the production process.

5. The intelligent manufacturing management system for recycling foundry waste sand based on artificial intelligence according to claim 1, characterized in that, The production scheduling and management module analyzes historical production data and market demand data through artificial intelligence algorithms. When formulating production plans, it considers the type, quantity, and timing of market demand, the amount and quality of recycled waste sand, the processing ratio of waste sand of different qualities, and also considers equipment capacity, maintenance cycle, repair history, equipment compatibility, equipment maintenance plan, and personnel configuration. It determines the production quantity, production sequence, and amount of waste sand to be input for various types of reused products, and manages the inventory of foundry waste sand and reused products in real time. It controls inventory levels through inventory data analysis and prediction.

6. The intelligent manufacturing management system for recycling foundry waste sand based on artificial intelligence according to claim 1, characterized in that, The quality inspection and traceability module uses blockchain technology to record and save the quality data, processing data and corresponding raw material information of each batch of recycled products. It associates various data with unique identifiers and automatically constructs and retrieves traceability paths. When establishing a quality traceability system, blockchain technology is used to ensure the immutability and traceability of quality traceability files.

7. The intelligent manufacturing management system for recycling foundry waste sand based on artificial intelligence according to claim 1, characterized in that, The data acquisition module transmits the acquired data to the data processing and analysis module in real time, and records the data source and timestamp according to the installation location of each sensor to support subsequent data traceability and analysis.

8. The intelligent manufacturing management system for recycling foundry waste sand based on artificial intelligence according to claim 1, characterized in that, The data processing and analysis module visualizes the analysis and modeling results and generates a model evaluation report, providing a reference for the control and scheduling module.

9. The intelligent manufacturing management system for recycling foundry waste sand based on artificial intelligence according to claim 1, characterized in that, The intelligent control module achieves local decision-making for adjusting some parameters through edge computing, wherein the local decision-making calculation model of the edge node satisfies the following formula: U i (t)=α·S i (t)+β·ΔP i (t)+γ·L i (t); Among them, U i (t) represents the comprehensive adjustment decision value of edge node i at time t, S i (t) represents the weighted state index of the sensor-collected data, ΔP i (t) represents the deviation between the current device parameters and the optimal historical parameters, L i (t) represents the local load of the node, and α, β, γ are adjustment coefficients that are dynamically adjusted according to actual control requirements.

10. The intelligent manufacturing management system for recycling foundry waste sand based on artificial intelligence according to claim 1, characterized in that, The production scheduling and management module supports the classification and management of waste sand of different grades, and allocates it to different recycling product production lines according to the classification results. At the same time, it sets minimum safety stock lines for different types of waste sand in the inventory and generates replenishment or scheduling prompts.

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