11836results about "Programme total factory control" patented technology

In example embodiments, an industrial technology stack for an industrial environment includes a set of computational resources and a set of layers executed by the set of computational resources, the set of layers including a governance layer, an enterprise layer, an offering layer, a transaction layer, an operations layer, a network layer, a data layer, and a resource layer. In example embodiments, the industrial technology stack may include one or more artificial intelligence models for implementing one or more components of one or more layers of the set of layers.

The invention relates to the technical field of intelligent control, in particular to a control method and system based on intelligent collaborative production. Comprising the following steps: aiming at a task cut-in influence range, traversing related nodes in a production network graph model, and detecting a device resource occupation conflict with an existing task to obtain a resource conflict detection result; recursively tracking the affected downstream process from the time delay predicted value along the dependence edge of the production network graph model to generate an influence propagation path; according to a weight dynamic quantification result, summarizing weight values of all affected nodes, generating a global view evaluation score, and determining an overall influence degree of new task cut-in; if the global view evaluation score exceeds a preset threshold, adjusting the cut-in node and time of the new task, and re-executing resource conflict detection to obtain an optimized task cut-in scheme; and extracting a process execution sequence and a resource allocation plan from the optimized task cut-in scheme, updating the production network diagram model, and generating a final collaborative production scheduling scheme.

The invention discloses a sewage plant total nitrogen concentration real-time prediction and process regulation and control method based on an optimization integration algorithm, and belongs to the technical field of environment monitoring and treatment. According to the method, sewage plant data are monitored and collected, a sliding window and a time sequence are combined to analyze and clean the data and reconstruct features, total nitrogen concentration strong correlation variables are screened, data quality is standardized and optimized, a plurality of machine learning algorithms are adopted to construct a prediction model, and an optimal model is optimized through cross validation and performance evaluation. The robustness is improved by global parameter adjustment in combination with optimization algorithms such as a particle swarm, process schemes such as aeration intensity and carbon source adding are generated through multi-objective optimization after containerization deployment, and a whole-process intelligent management and control system is constructed by integrating virtual verification, graded early warning and a self-adaptive feedback mechanism. According to the method, the problems of detection lag, insufficient model generalization ability, regulation response delay and the like of a traditional method are solved, and the operation energy consumption and the medicament cost are remarkably reduced while it is guaranteed that the effluent quality stably reaches the standard.

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention discloses an industrial scene-oriented data acquisition system and an industrial scene-oriented data acquisition method. According to the invention, through the multi-level dynamic optimization design, the data acquisition efficiency and the system reliability in a complex environment are significantly improved. The system dynamically allocates thread resources based on the real-time communication state of the equipment, realizes stable throughput under a high-concurrency scene in combination with core binding and polling scheduling strategies, and ensures that millisecond-level response is still maintained when 10000-level equipment is accessed. The memory preloading and protocol template technology eliminates jitter during operation, the hierarchical resource isolation mechanism provides deterministic guarantee for key control instructions, and interruption of the production process due to data delay or resource competition is avoided. The data flow delay is further reduced through multi-protocol efficient analysis and intelligent cache management, seamless compatibility of heterogeneous equipment in a hybrid networking scene is supported, and the strict requirements of continuous production industries such as steel and chemical engineering for real-time performance and stability are met.

The invention provides an intelligent sewage treatment process optimization method and system. The method comprises the following steps: continuously collecting the flux attenuation rate, transmembrane pressure difference and pollutant spectrum data of a ceramic membrane, carrying out time sequence analysis, constructing a pollution accumulation dynamic model, and predicting a critical threshold value; when a threshold value is reached, triggering an ultrasonic cavitation device, adjusting high-frequency vibration to generate a microbubble group, stripping pollutants by utilizing shock waves, and calculating membrane flux recovery data; fusing the spectral data and the recovery data to extract an actual pollution stripping efficiency value, and comparing a theoretical value to obtain a difference proportion; if the actual value is lower than the theoretical value, the cleaning period is shortened, the fluid pressure is increased to strengthen the stripping effect, and meanwhile, dynamic model parameters are reversely corrected based on the pressure adjustment amplitude to form closed-loop regulation and control. According to the invention, intelligent prediction, self-adaptive cleaning and closed-loop optimization of ceramic membrane pollution are realized, the sewage filtering efficiency is improved, and the energy consumption and cleaning cost are reduced.

The invention discloses an intelligent scheduling and optimization method for an industrial electrical automation system, and the method comprises the following steps: deploying a distributed sensor network to collect electrical parameters, an equipment vibration spectrum and production work order data in real time, and constructing a unified feature vector based on time-space alignment and confidence weighting; a production system-energy management-external power grid three-layer interaction model is established, and a dynamic carbon emission calculation engine and process deadlock detection module is embedded; an improved NSGA-III algorithm is adopted to solve a multi-target Pareto leading edge, and energy consumption, productivity and carbon emission target priorities are adjusted in real time in combination with a dynamic weight mechanism; distributed optimization is executed through an edge-cloud federated architecture, cross-system instruction synchronization is achieved, and closed-loop dynamic feedback is formed. According to the method, the limitation of traditional single system optimization is broken through, the energy consumption is reduced by 15%-30%, the carbon emission intensity is reduced by 12%-18%, the abnormal response speed is increased to 3 seconds, and intelligent decision making and green transformation in a complex industrial scene are supported.

The invention discloses a multi-source process parameter mapping supervision system and method based on a big data model, and relates to the technical field of process parameter analysis. Initial process parameters in a process production line are collected, the initial process parameters are processed to obtain standardized process parameters, and the process production line is subjected to process stage division; analyzing a stage product deviation degree of the process stage, calculating correlation between the standardized process parameters and the product deviation degree of the process stage, analyzing the stage product deviation degree of the process stage, predicting the product deviation degree of the process stage in production, and predicting a product reject ratio in production based on the product deviation degree of the process stage. According to the method, the product deviation of each stage and the reject ratio of the whole product are continuously predicted, the production state is judged in real time, a dynamic quality control closed loop is constructed, and the pertinence and effectiveness of supervision are improved.

The invention relates to the technical field of workshop scheduling, in particular to a multi-online collaborative scheduling method and system for a flexible intelligent processing production line, and the method comprises the steps: building a numerical control parameter model for processing equipment, decomposing a processing program into basic process instruction units, combining the numerical control parameter model and the real-time operation state of the equipment to analyze the adaptation degree of the basic process instruction unit and the equipment, and generating a preliminary task allocation scheme; constructing a distributed control network among the devices, generating a local task sequence of each device based on the preliminary task allocation scheme and the adaptation degree, and obtaining a final task allocation scheme and a task execution plan set based on a contract network protocol algorithm; establishing a multi-constraint collaborative framework, and generating a collaborative production scheduling scheme under the multi-constraint collaborative framework; and establishing a heterogeneous equipment motion cooperative control model, and performing multi-level adjustment through event-driven feedback control. According to the invention, flexible cooperative scheduling of heterogeneous equipment can be realized.

The invention discloses a distributed intelligent warehouse scheduling system based on artificial intelligence, and belongs to the technical field of warehouse scheduling. Comprising a multi-source environment sensing module, a dynamic inventory management module, a distributed task scheduling module, an intelligent path planning module, a resource dynamic allocation module, an anomaly detection and emergency response module, an energy consumption optimization module, a supply chain collaboration module and a man-machine interaction and visualization module. A warehouse digital twinborn model is constructed, immersive display of a storage state and a scheduling strategy is realized, an AR scene is superposed through a color coding path, a thermodynamic diagram and a particle flow form, a manager can intuitively master inventory distribution, task progress and an abnormal region, eye movement tracking and a gesture recognition technology support an interactive decision, and the workload of the manager is reduced. The AR marking function can mark an abnormal area and synchronize the abnormal area to a decision making system, and through combination of AR and AI, a brand new interaction normal form is provided for intelligence and humanization of warehouse management.

The invention discloses a metal cutting process parameter optimization analysis method based on machine learning, and particularly relates to the field of machine learning. Comprising multi-dimensional process parameter feature extraction and preprocessing, cutting state intelligent identification based on integrated learning, dynamic process parameter sensitivity analysis and weight calculation, process parameter intelligent optimization under a multi-target constraint condition, and adaptive parameter adjustment and real-time control strategy. According to the method, the interaction relationship between complex nonlinear features and process parameters in the cutting process is comprehensively captured, and accurate and intelligent recognition of different cutting states such as normal cutting, tool abrasion and abnormal flutter is achieved through a three-layer integrated learning architecture; the technical bottlenecks that an existing system lacks real-time self-adaptive adjustment capacity and is low in process optimization efficiency are overcome, pertinence and effectiveness of parameter adjustment are ensured, and the technical current situation that machining quality fluctuates and repeatability is poor due to traditional fixed parameters is changed.

The invention relates to the technical field of industrial production lines, and discloses an industrial production line multi-device dynamic collaborative scheduling method based on reinforcement learning, comprising the following steps: S1, modeling a three-dimensional state space; s2, hierarchical reinforcement learning architecture; and S3, edge-cloud cooperative execution. According to the industrial production line multi-device dynamic collaborative scheduling method and system based on reinforcement learning, device states, task constraints and resource occupation are integrated into a structured matrix through three-dimensional state space modeling, and a global decision-making layer captures production time sequence dependence by using a bidirectional long-short-term memory network; modeling equipment space association and process constraints through a graph attention network, and generating a global strategy including task allocation, capacity adjustment and resource pre-allocation; and after the edge layer detects the dynamic event, the cloud platform generates a candidate scheme through Monte Carlo tree search, and realizes dynamic event response and multi-target collaborative optimization by combining multiple targets such as global value network evaluation task completion time and equipment load balancing.

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.

The invention provides a workshop multi-agent deep reinforcement learning scheduling method based on real-time production conditions, and relates to the technical field of production scheduling and industrial automation, and the method comprises the steps: collecting production data in real time through a distributed sensor network, and constructing a multi-dimensional time sequence matrix; establishing a three-dimensional decision space containing time, resource and task dimensions, and adopting an improved multi-agent depth deterministic strategy gradient algorithm; dynamically calculating the real-time production urgency degree of the production batch, and analyzing cross-unit cooperation to generate a cooperation efficiency factor; the two are combined to optimize a scheduling scheme through a course learning strategy; and verifying the scheme by using a virtual twin environment and feeding back and updating model parameters. The method breaks through the limitations of insufficient dynamic adaptability, low efficiency of multi-target coordination and the like of traditional scheduling, realizes real-time response to dynamic production conditions such as equipment faults and order adjustment, balances time efficiency, energy consumption economy and abnormal fault tolerance through a multi-agent coordination and closed-loop verification mechanism, and improves resource coordination efficiency under complex production constraints.

An autonomous environmental regulation and behavioral monitoring system is disclosed, configured to adapt temperature, lighting, and acoustic conditions based on real-time emotional and physiological data. The system includes a dual-redundant central processor, hierarchical communication networks, multi-angle visual acquisition units, infrared thermometers, and modular environmental subsystems. It detects posture, gestures, facial expressions, and thermal signals to classify user states and apply individualized airflow, light, and sound modulation without relying on external internet connectivity. The system also monitors connected appliances using voltage-based pressure analysis to forecast device degradation. With integrated gesture recognition, privacy-preserving data handling, and predictive adaptation, the invention enables multi-user personalization, long-term learning, and uninterrupted operation within residential, administrative, or healthcare infrastructures.

The invention provides a production process intelligent monitoring method and system based on an intelligent mine, and the method comprises the steps: collecting a target monitoring data set in the real-time production process of mine equipment, covering equipment vibration time sequence signals, environment temperature and humidity distribution data and an equipment energy consumption fluctuation curve, and carrying out the dynamic standardization processing, the method comprises the following steps: obtaining a standardized monitoring data set matched with an equipment type and a production process stage, calling a pre-trained multi-dimensional feature extraction model to carry out joint feature mapping, generating an equipment operation state, environment association and data abnormal fluctuation features, and carrying out dynamic fusion analysis on the features based on a preset abnormal mode identification model, and finally, according to the risk level, an alarm instruction is triggered, an optimization strategy is fed back to a production control terminal to adjust equipment operation parameters, and intelligent monitoring and optimization of the intelligent mine production process are realized.

The invention relates to the technical field of process optimization control, and discloses a silk-covered enameled winding process optimization control method and system based on intelligent analysis. The method comprises the steps that multi-area production parameters are collected in real time and preprocessed; constructing parameter and product quality mapping through principal component dimensionality reduction, grey correlation and support vector regression; training an Actor-Critic structure by using a depth deterministic strategy gradient network to obtain an adjustment strategy; a non-dominated sorting genetic algorithm is combined with analytic hierarchy process to carry out multi-objective optimization, and a control scheme for balancing mass, efficiency and energy consumption is realized. According to the application, on the basis of considering the complex coupling relationship among the process parameters, multi-objective dynamic balance optimization of product quality, production efficiency and energy consumption is realized, the process parameters can be adaptively adjusted according to the production state and the task demand, and the stability and the optimization degree of the silk-covered enameled winding production process are improved.

The invention provides a smart home management method and system based on the Internet of Things. The method comprises the steps that indoor and outdoor environment parameters, user physiological data, home equipment operation states and energy consumption data are collected; based on the data, learning behavior preferences of the user in different time and environments, and establishing a personalized behavior prediction model; deploying the model at an edge computing node, combining big data analysis of a cloud server to form a hierarchical intelligent decision-making architecture, and outputting a preliminary decision-making result; the real intention of the user is understood and an intention confidence evaluation mechanism is established; when the consistency of the identification results of the multiple modes is lower than a threshold value, confirmation is actively carried out on the user, and a primary intention is obtained; according to the intention, a control strategy is dynamically generated in combination with the environment state and the equipment capacity; and based on the operation data and the energy consumption data, establishing an equipment health degree evaluation model, and predicting a fault risk and a maintenance demand. Through the scheme of the invention, the intention of the user can be accurately identified, and accurate personalized services are provided, so that the user experience is improved.

The invention relates to the technical field of intelligent manufacturing, and discloses an extreme manufacturing process technological parameter optimization method and system fused with machine learning. The method comprises the following steps: acquiring multi-source data from a manufacturing equipment sensor, and fusing to generate a material state vector; inputting a pre-training model to obtain a material coefficient transition trend; judging whether the trend fluctuation amplitude exceeds a preset threshold value or not, and if yes, marking key nodes and extracting feature parameters; for the key nodes, according to the characteristic parameters and the real-time data of the key nodes, a control algorithm is adopted to calculate the parameter adjustment amount; optimizing the control parameters based on the parameter adjustment amount, generating a control instruction sequence and transmitting the control instruction sequence to an actuator; and obtaining adjusted feedback data, comparing the adjusted feedback data with the transition trend, and if the deviation exceeds an allowable range, updating the pre-training model. Through deep fusion of predictive monitoring and intelligent control, accurate optimization and adaptive control of process parameters are realized, the stability of the extreme manufacturing process and the product quality are improved, and the energy consumption and the defect rate are reduced.

The invention discloses an intelligent household electrical appliance interaction control method and system, and the method comprises the following steps: collecting the operation state parameters, environment perception data and user behavior characteristics of each household electrical appliance in a living space in real time, and generating an environment perception data packet through a multi-modal data fusion model; constructing a user habit preference model library, generating a household appliance control strategy decision tree in combination with historical interaction records, and matching an optimal control strategy set according to a real-time scene type; and when a user active intervention signal or an environment sudden change event is detected, a dynamic strategy reconstruction mechanism is triggered, an instruction updating request carrying a priority identifier is broadcasted to the associated household appliance group, and a redistribution control instruction set is generated. The method has the following advantages and effects: multi-source environment data can be deeply fused, the user habit model is constructed in real time, and the method has a dynamic strategy reconstruction capability so as to solve the problems of response lag, strategy stiffness and energy efficiency imbalance in a complex home environment in the prior art.

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention discloses a method for intelligently regulating and controlling production parameters in a fruit concentrated juice production process, which comprises the following steps of: acquiring multi-dimensional process parameters such as temperature, pressure, flow, concentration, equipment state and the like in real time through a multi-channel sensor network, and forming a standardized data sequence after filtering, normalization and drift correction; extracting stage features by using technologies such as a sliding window and Fourier transform, and inputting the stage features into the lightweight classification model to realize production stage identification; in combination with an identification result, dynamically calling a corresponding multi-target optimization sub-model, and realizing nonlinear prediction and optimal solution selection of process parameter setting by adopting an LSTM and a multi-target genetic algorithm; on the basis of real-time feedback, the performance of the model is automatically evaluated, self-adaptive adjustment and optimization of the optimization algorithm are achieved through reinforcement learning and an incremental updating mechanism, multi-target collaborative optimization, self-adaptive adjustment and model switching in the production process can be achieved, and the consistency of production efficiency and product quality is improved.

The invention provides a stamping production line self-organizing production system and method based on a twin intelligent agent, and the twin intelligent agent comprises a data collection layer which is used for collecting the operation state data and production environment parameters of physical equipment; the virtual-real mapping layer is used for constructing a digital twin model of physical equipment and realizing real-time state synchronization and bidirectional control instruction transmission of a physical space and a virtual space; the optimization decision-making layer is used for predicting the performance degradation trend of the equipment based on a deep reinforcement learning algorithm and generating a game parameter adjustment strategy and a preventive maintenance scheme; and the collaborative arbitration layer generates a compromise optimization scheme based on Pareto frontier analysis when the multi-agent strategy conflicts, and the multi-dimensional targets of the production efficiency, the equipment life and the energy consumption are balanced. According to the invention, autonomous task allocation, real-time state monitoring and global resource balance of the stamping production line are realized.

The invention relates to the technical field of intelligent interaction systems, and discloses an intelligent interaction system and method based on a multi-modal large model. The system comprises a multi-modal input layer, a context sensing module, a cross-modal fusion module, a dynamic response generation module and a system monitoring module. The multi-modal input layer preprocesses multi-modal data; the context sensing module constructs a dynamic context vector; the cross-modal fusion module fuses the data to generate joint features; the dynamic response generation module generates a response strategy through hierarchical decision; and the system monitoring module monitors the key indexes and triggers an optimization mechanism. All the modules work cooperatively, multi-modal data deep fusion, precise context sensing, efficient dynamic response and system self-optimization are achieved, the accuracy, adaptability and stability of intelligent interaction are improved, and the method can be widely applied to multiple fields such as intelligent home and intelligent customer service.

The invention provides a discrete MES-oriented intelligent production scheduling system, method and equipment and a medium, and belongs to the technical field of discrete manufacturing industry production scheduling. Data is acquired through a sensor and serves as production scheduling data; establishing a material inventory data association order ID and establishing an index; determining a process sequence constraint, a calculation equipment productivity constraint, a material supply constraint and an order priority constraint; initializing a population based on a genetic algorithm, randomly generating N groups of process sorting schemes, calculating a utilization rate index, and taking a comprehensive score as a fitness value; outputting a better solution set; the optimal solution of the genetic algorithm is used as initial pheromone distribution, high-quality path pheromones are enhanced according to the actual production effect of the completion scheme, and if the preset number of iterations is reached, the operation is stopped, and an optimized production scheduling scheme is output; and checking the production scheduling plan through a graphical interface. Through continuous optimization of the procedure sorting scheme, the equipment utilization rate is effectively improved, the total order completion time is shortened, the production resource configuration is optimized, and the production efficiency is improved.

The invention discloses a process parameter tracing and quality collaborative management system for ceramic production, and relates to the technical field of ceramic production, the management system comprises a step of obtaining a plurality of process data and quality data in a ceramic production process, and each group of process data comprises a raw material ratio, a forming pressure, a firing temperature and a firing time. According to the process parameter tracing and quality collaborative management system for ceramic production, a specific process parameter deviation link can be quickly positioned by reversely tracing to a raw material source from a finished product quality problem and combining batch association identifiers and data records of all links; the quality problem solving efficiency is improved, and batch loss caused by traceability lag is avoided; besides, a process quality association relationship constructed by the module is established, a quality detection result is closely associated with process parameters, and multi-dimensional analysis is performed, so that a quality problem can be fed back to process parameter adjustment in time, and targeted measures can be taken according to an analysis result.

The invention relates to the technical field of industrial control, in particular to a robot scheduling method and system based on cooperative control, and the method comprises the steps: obtaining a task proportion and target node information in a task adjustment scheme, judging an optimal task distribution path and an optimal execution sequence according to a task priority order and a node adaptation rule, and carrying out the task scheduling according to the optimal task distribution path and the optimal execution sequence; determining an inspection task distribution blueprint; according to the routing inspection task distribution blueprint, the tasks are distributed in real time by adopting a low-delay scheduling framework, and a dynamic distribution map and real-time distribution update data of the tasks among the nodes are obtained according to node response delay and communication overhead among the nodes; the latest load state of each node is obtained through a dynamic distribution map and real-time distribution update data, and whether a potential calculation pressure overload risk exists or not is judged according to a distribution uniformity index and distribution anomaly detection. The invention aims to solve the problems of large performance difference, unbalanced load and low task scheduling efficiency in a multi-robot collaborative inspection process in a narrow space.

The invention relates to the technical field of factory control, in particular to a 5G factory full-process visual monitoring platform based on digital twinning, which comprises the steps of arranging edge computing nodes in a factory, constructing a space-time coupling control network according to space-time relevance of a production process and a material flow path, and dynamically adjusting a topological structure according to a real-time production state. Equipment operation parameters, environment data and product quality tracing data are collected, and a quality-process parameter association database is established; constructing a multi-physics field digital twinborn model based on the quality-process parameter association database, and establishing an association model from a quality result to a process parameter by the multi-physics field digital twinborn model through a backward reasoning algorithm; the self-learning control unit takes a quality index as a reward function training control strategy based on the correlation model, and adjusts PLC control parameters and formula parameters; the active intervention control unit analyzes the multi-dimensional time sequence data to predict the equipment trend; and superposing the equipment trend on the three-dimensional model in a thermodynamic diagram mode for fusion display.

The invention relates to the field of industrial intelligent control, in particular to a real-time production efficiency optimization system based on an artificial intelligence technology, and the system comprises an equipment fault prediction module which is used for collecting multi-modal time sequence data, and predicting the equipment fault probability based on a deep learning network; the process parameter adjusting and optimizing module is in communication connection with the equipment fault prediction module and is used for receiving the fault prediction probability and dynamically adjusting process parameters based on a reinforcement learning algorithm; the federated learning and incremental training module is in communication connection with the equipment fault prediction module and the process parameter tuning module, and is used for realizing collaborative optimization and privacy protection of a local model and a global model, greatly improving the fault prediction accuracy, reducing the false alarm rate from 20% to 5% or below, prolonging a prediction window from 10 minutes to 30 minutes or above, and improving the prediction efficiency. A sufficient preventive maintenance time window is provided for a production system; and the process parameter optimization effect is remarkably improved, and the comprehensive efficiency improvement space of the equipment is expanded to 8-12% from the traditional 5%.

The invention relates to the technical field of automobile part manufacturing, and discloses an automobile injection molding part production process control system and method, and the system comprises the following modules: a data collection module which is used for collecting technological parameters, molds, raw materials and equipment operation original data, attaching timestamps, and storing the data in a database; the process parameter prediction module is used for reading original data to construct a time sequence data set, inputting the time sequence data set into a TFT model to obtain a pre-training model, and predicting a short-term process parameter fluctuation range in combination with current production working condition parameters; and the quality risk index acquisition module is used for inputting the process parameter data and the mold data into a quality risk index calculation formula to obtain a quality risk index. Through the system, data-driven comprehensive production optimization is realized, the process control accuracy and adaptability are improved, the quality control scientificity and reliability are enhanced, the intelligent level of the production process is improved, the production efficiency is effectively improved, the defective rate is reduced, and the product quality is stabilized. The problem that process control lacks system intelligence is solved.