328 results about "Manufacturing execution system" patented technology

The present invention relates to a method for constructing a digital twin system for an additive manufacturing process with lifecycle data management. The digital twin system for an additive manufacturing process constructed by the method includes a data acquisition and supervisory control system, a data management and storage system, a manufacturing executing system, a configuration model, a simulation system, and a multi-physics simulation system. The present invention can fully and accurately characterize the molding quality of parts, thereby saving a lot of time required for offline data processing, and achieving real-time acquisition and updating of data.

The invention provides a virtual-real fusion method for digital twinning operation of a cell drug factory in combination with the Internet of Things, which comprises the following steps: constructing geometric models of a culture medium storage tank and a bioreactor through three-dimensional modeling software, mapping and associating historical process parameters of a culture medium supply system and temperature, pH value and dissolved oxygen concentration monitored in real time with the geometric models, and obtaining a virtual-real fusion model of the culture medium storage tank and the bioreactor; constructing a digital twinborn model of the reactor; analyzing historical production data through a machine learning algorithm, extracting a quantitative relationship among key parameters of a cell metabolism rate, a glucose concentration in a culture medium, a pH value and a reactor temperature, establishing a cell growth prediction model, and predicting a growth trend and a metabolism demand of cells in a target time period; a virtual-real mapping mechanism of a culture medium supply system is integrated with an intelligent manufacturing execution system, so that the culture medium supply process and the whole pharmaceutical production process are subjected to collaborative optimization control, and the working procedures of the culture medium production process are automatically dispatched according to a production plan and cell growth requirements.

An AI-driven, cloud-based system (100) for real-time biomedical and pharmaceutical compliance and risk management, including: (a) a compliance knowledge module configured to ingest, interpret and structure regulatory data using natural language processing (NLP) and generate machine-readable compliance rules; (b) a real-time monitoring and event recording module configured to collect and normalise operational data from distributed biomedical and pharmaceutical systems, including laboratory information management systems (LIMS), manufacturing execution systems (MES) and IoT-enabled devices; (c) an intelligent risk assessment and prediction module configured to correlate operational data with compliance rules, calculate dynamic risk scores and predict potential compliance violations using machine learning models; (d) an automated policy and workflow enforcement module configured to initiate remedial actions, assign tasks and log activities based on predefined standard operating procedures (SOPs); (e) an audit readiness and reporting module configured to generate compliance logs, audit trails and standardised regulatory reports in real time; and (f) an adaptive learning and feedback optimization module configured to refine rule sets and predictive models based on feedback, historical data and regulatory updates; g) the modules are integrated into a cloud infrastructure to enable real-time, scalable and predictive compliance and risk management across biomedical and pharmaceutical processes.

The invention relates to the field of manufacturing execution systems, intelligent production scheduling and industrial data integration, in particular to a clothing regulation and control production method and system based on MES, by integrating production state data and clothing order data which are collected in real time, a production graph model is constructed, Bayesian variational inference is adopted to carry out uncertainty modeling to generate graph representation data, and the production state data and the clothing order data are integrated. And constructing a hypergraph model based on graph representation data, generating a scheduling command by using non-Euclidean embedding and agent collaborative decision, issuing the scheduling command to each production unit for execution, collecting execution data and monitoring abnormality, triggering dynamic rescheduling according to abnormality feedback, and realizing continuous closed-loop optimization of the garment production process. The production efficiency and the resource utilization rate are remarkably improved, and the production risk is reduced.

The invention relates to the technical field of computers, and discloses a chip mounter material tracing management method and system based on a knowledge graph. The method comprises the following steps: constructing a dynamic knowledge graph covering the whole process of a patch; injecting multi-source data in real time to drive dynamic updating of the atlas; when the materials are abnormal, reverse tracing and forward influence double-path reasoning are started; isolation, rework and reset instructions are automatically generated and pushed to a manufacturing execution system; and synchronously updating the atlas state to form an auditable record. The system comprises a dynamic knowledge graph construction module, a real-time data injection module, a bidirectional inference engine module, an automatic processing instruction generation module and a graph state synchronization module. According to the scheme, millisecond-level error positioning and closed-loop processing are realized, and the tracing efficiency and the quality guarantee capability are improved.

The invention discloses a multi-warehouse management warehouse-in and warehouse-out method and a digital warehouse management system, and the digital warehouse management system carries out the data interaction with an enterprise resource management system, a manufacturing execution system, a warehouse control system and a robot control system through an API interface. The method comprises the steps of monitoring data sent by a manufacturing execution system and a handheld terminal in real time, if a raw material warehousing order number sent by the handheld terminal is monitored, executing a raw material warehousing process, if a raw material ex-warehouse request of the manufacturing execution system is monitored, executing a raw material ex-warehouse process, and if a semi-finished product warehousing request of the manufacturing execution system is monitored, executing a semi-finished product ex-warehouse process; and if a semi-finished product warehouse-out request of the manufacturing execution system is monitored, executing a semi-finished product warehouse-in process, if a semi-finished product warehouse-out request of the manufacturing execution system is monitored, executing a semi-finished product warehouse-out process, and if carton data of finished product warehouse-in of the manufacturing execution system is monitored, executing a finished product warehouse-in process. According to the invention, a plane library, a three-dimensional library and a line-edge library are all included in a management range, and meanwhile, the dependence on manpower is reduced.

The invention discloses a process parameter switching method and device for multi-variety production, and a server side method comprises the steps: receiving a process parameter switching request sent by a manufacturing execution system used for monitoring a product production state, wherein the process parameter switching request carries production demand description information and a product batch model; according to the production demand description information, corresponding target process parameters are automatically obtained, the target process parameters are determined by adopting a preset comparison function, and the preset comparison function comprises a basic comparison term expression and a physical comparison term expression; pre-checking the target process parameters to obtain a checking result; and under the condition that the verification result indicates that the verification of the target process parameter is passed, binding the target process parameter to a product batch model, and issuing the product batch model to an equipment control layer. Therefore, by adopting the embodiment of the invention, the time required for searching and inputting the process parameters is avoided, so that the equipment can be switched to a new production task more quickly, and the production efficiency and the product quality are remarkably improved.

The invention provides an industrial manufacturing industry-oriented carbon asset full life cycle management method and system, and the method comprises the steps: carrying out the cross-link feature fusion processing through obtaining an original carbon activity data set of an industrial manufacturing whole process, and generating carbon footprint feature data; and calling a pre-trained deep learning model to perform carbon asset state evolution analysis on the carbon footprint feature data, predicting a carbon asset evolution path, generating a carbon asset optimization strategy set based on a prediction result, including equipment energy efficiency regulation and control and a production plan recombination strategy, and pushing the carbon asset optimization strategy set to an industrial manufacturing execution system. And triggering an automatic control instruction reconstruction operation to realize automation and intelligence of carbon asset management, thereby comprehensively and accurately mastering a carbon asset state and an evolution rule, formulating a scientific and effective optimization strategy, reducing carbon emission, improving energy utilization efficiency, and improving green production capacity of an enterprise.

The invention discloses a steelmaking MES manufacturing execution system based on data analysis, and relates to the technical field of monitoring analysis, and the system comprises a data collection module which is used for collecting multivariate heterogeneous data of a steelmaking whole process, and constructing a steelmaking dynamic feature vector; the optimization scheme generation module is used for generating an inter-process parameter coupling relation graph and performing key process parameter optimization generation on the inter-process parameter coupling relation graph to obtain a parameter optimization configuration scheme; the instruction generation module is used for establishing a production execution deviation quantitative index, performing process parameter dynamic compensation calculation according to the production execution deviation quantitative index, and generating a real-time process adjustment instruction set; the simulation verification module is used for carrying out dynamic priority ranking on the parameter optimization configuration scheme and generating an execution strategy optimization scheme; and the manufacturing execution control module is used for executing manufacturing based on the execution strategy optimization scheme and carrying out risk prediction and efficiency evaluation based on an execution manufacturing result. The device has the effect of improving the production efficiency.

The invention provides a manufacturing execution system natural language interaction method and system based on a large language model, and relates to the technical field of intelligent manufacturing and industrial informatization. The method comprises the steps of obtaining a natural language request input by a user through a text interface, performing entity standardization processing on the natural language request through a request rewriting module based on a large language model, and eliminating semantic ambiguity to obtain a standardized request; performing task decomposition on the standardized request by adopting a multi-step dynamic operation planning and execution module to obtain an atomic operation chain; and dynamically executing each step of operation in the atomic operation chain, and generating a user readable response by using a large language model according to an execution result. Natural language interaction replaces a traditional graphical user interface, the system development and maintenance cost and the user learning complexity are remarkably reduced, and the operation efficiency and reliability of a manufacturing execution system are improved.

The invention relates to the technical field of water pump production, in particular to a semi-automatic water pump assembly line. The production line comprises a stator feeding station, a wave spring mounting and oiling station, a rotor part press-fitting station, a rear cover press-fitting station, a screw locking station, a static ring press-fitting station, an impeller press-fitting station, an electric measurement station, a gas measurement station, a line body, a PLC (Programmable Logic Controller) general controller and an MES (Manufacturing Execution System), the stator feeding work station, the wave spring mounting and oiling work station, the rotor part press-fitting work station, the rear cover press-fitting work station, the screw locking work station, the static ring press-fitting work station, the impeller press-fitting work station, the electric measurement work station and the gas measurement work station are sequentially arranged in the conveying direction of the line body and are in butt joint with the line body, the PLC master controller is connected with the work stations and controls the work stations and the line body, and the MES system is connected with the PLC master controller. The assembly line can achieve semi-automatic production of water pumps, saves labor cost, reduces labor intensity of workers, improves production efficiency and improves the automation degree of enterprises.

The invention provides a production process configuration method and device for a manufacturing execution system in the technical field of industrial manufacturing informatization. The method comprises the steps that S1, customized information is extracted from order data to generate a production task instruction; s2, matching resource requirements based on the production task instruction to generate a resource allocation list; s3, generating a flow configuration file based on the resource allocation list and the production task instruction, and issuing the flow configuration file to a corresponding production execution unit to execute production operation; s4, collecting production data from each production execution unit in real time, recording production logs in real time, and carrying out standardized processing and unified storage on the production logs; and S5, carrying out real-time monitoring on a production task state, a production resource state, a system operation state and a change condition of order data, and dynamically reconstructing a process configuration file. The method has the advantages that the flexibility, the automation level and the response efficiency of production process configuration are greatly improved.

The invention discloses a production line test construction system based on an MES system, and relates to the technical field of intelligent manufacturing and production line testing, and the production line test construction system comprises a test configuration module, a test execution module, a data integration module, a visual interface module and an authority management module. The test configuration module is used for automatically obtaining a test process template from the MES system according to the product model information and completing process customization in a graphical mode; the test execution module identifies a to-be-tested product and calls a process file, and controls the test equipment to execute a test and perform data acquisition and judgment; the data integration module binds the test result with the unique identifier of the product, and uploads the test result to the MES system to realize full-process tracing; the visual interface module is used for displaying a process state and a test result; the authority management module is used for performing hierarchical control and version conflict detection on the operation authority; according to the system, the construction efficiency and execution reliability of the test process are improved, and closed-loop and intelligent management of data in the test process is realized.

The invention discloses an industrial equipment data acquisition system for being integrated with an MES system, and the system comprises a system architecture module which is used for constructing a programming architecture and a system architecture of the industrial equipment data acquisition system; the data storage module is used for storing and preprocessing equipment data and process data of industrial equipment; various types of integrated configurable gateway components used for supporting the equipment to upload data are integrated in the gateway configuration module, and various protocols are correspondingly configured for the various types of integrated configurable gateway components; the data protocol analysis module is used for performing protocol analysis on the process data uploaded by the industrial equipment; and the data interface docking module is used for being integrated with an industrial equipment data acquisition and manufacturing execution system and providing equipment data query, process data query and equipment state query functions. The invention also discloses an application of the industrial equipment data acquisition system integrated with the MES system in the MES system. The MES system is provided with an MES system data integration interface correspondingly connected with the data interface docking module.

The invention provides a precision machining and 3D printing mixed manufacturing execution system, and relates to the technical field of data processing, and the system is used for dividing a three-dimensional model into a configuration area and a finishing area, generating a layer-by-layer closed contour path, obtaining configuration path data, extracting a boundary segment close to the finishing area, and obtaining a configuration path data set; calculating a space projection vector on a finishing target surface to obtain primary finishing path data; calculating a boundary torsion value; superposing interference offset in a historical sample path to a primary finishing path; calculating an interference amplitude index; the fitting path data and the configuration path data are subjected to path connection and unified processing according to the manufacturing sequence, and a mixed execution path instruction is obtained. Precise machining and 3D printing are integrated, and path overlapping, empty cutting or unnecessary machining is avoided.

The invention is suitable for the technical field of production and manufacturing, and provides a production abnormity alarm method and system based on a manufacturing execution system, and the method comprises the steps: obtaining active working state information actively reported by a worker from a production station; acquiring real-time production data from a manufacturing execution system, and analyzing the real-time production data based on a preset passive early warning rule to identify whether a passive early warning event exists; determining and generating alarm information including an alarm type and an alarm level according to the active working state information and the passive early warning event in combination with a preset alarm logic and a grading strategy; triggering the sending of a local sound-light alarm signal corresponding to the determined alarm type and alarm level at a corresponding production station; and pushing the generated alarm information to a preset remote monitoring endpoint and / or notifying a specified manager through a network. According to the invention, the problems of incomplete production abnormal information perception and single alarm mechanism response in the prior art are solved.

The invention discloses a multi-branch industrial ventilation intelligent regulation and control system and method based on a PLC. According to the system, a closed-loop control network is formed by an infrared sensor, a wind speed sensor, a PLC (Programmable Logic Controller), an air door control cabinet, an electric valve, a frequency converter, a frequency conversion fan and an upper computer. The infrared sensor is responsible for monitoring the running state of each production line in real time. The wind speed sensor is used for detecting the wind speed in the wind pipe and verifying wind quantity balance. The PLC processes all sensor data and outputs a control instruction. On one hand, the opening degree of the electric valve is adjusted through the air door control cabinet, and on the other hand, the rotating speed of the fan is adjusted in real time through the frequency converter. The upper computer is used for system parameter monitoring, threshold setting and data interaction with the manufacturing execution system. According to the system, on-demand adjustment of multi-branch ventilation is achieved through intelligent closed-loop control, the problem of response lag of a traditional control mode is effectively solved, the dust removal efficiency and the energy utilization rate are remarkably improved, and safe and efficient operation of the industrial environment is guaranteed.

The invention discloses an automatic personnel allocation method and system suitable for an MES system, a medium and equipment, and the method comprises the following steps: S1, defining a skill label: defining the skill label, and obtaining and storing the skill label of an employee; s2, skill corresponding process: establishing a corresponding relationship between the skill label and the process; s3, production data acquisition: acquiring working data of the employees in a production process in real time; s4, data analysis: generating an employee portrait, and generating an employee skill feature median; s5, optimizing process parameters: adjusting process guidance parameters according to the skill characteristics of the employees in the step S4, and optimizing the production process; and S6, automatically allocating processes: recommending an optimal personnel combination according to the employee portraits and the processes involved in the production plan. The system has the advantages that the skills of the employees and the deficient skills are mastered, so that the employees are accurately allocated to proper posts, and the storage of human resources of enterprises and the reduction of labor cost are facilitated.

The invention discloses a numerical control machine tool cutter management system which comprises a hardware layer, a system layer, a function module and a system interface. The hardware layer is composed of a numerical control machine tool, a tool presetting instrument, a tool recognition device, a tool setting instrument, a bar code scanning gun, an industrial computer, a server and the like and is used for data acquisition, transmission and storage. The system layer comprises tool management software, a database management system, data acquisition and monitoring software and a manufacturing execution system interface, and full life cycle management of tools is achieved. The system is provided with a tool basic data management module, an inventory management module, a service life management module, a circulation management module, an abrasion monitoring and compensation module, a purchase plan management module, a performance parameter management module and other functional modules, and operation and display are carried out through a system interface. The system can improve the tool management efficiency and precision, reduce the tool cost, improve the machining quality and the production efficiency, and is suitable for efficient tool management of numerical control machining enterprises.

The aim of the invention is to define a method that allows the management of a particularly large MES / MOM project to be simplified, which may involve a plurality of manufacturing sites and a plurality of servers that require strict guidance regarding as low as possible to hold patches and update thresholds. According to the invention, this object is achieved by a method for operating a plurality of production plants by means of a manufacturing execution system, said method comprising the following steps: a) providing central computing hardware as a regional hub for running MES software; the MES software can manage a production process running through a plurality of production factories; b) configuring a first manufacturing execution system for a first single plant connected to the regional hub; c) creating a respective manufacturing execution system for each other plant by inheriting all configurations of the first manufacturing execution system to the other manufacturing execution systems; and d) configuring these additional plants with existing solutions in the regional hub over time without the need to reinstall the software of the manufacturing execution system and without the need to reapply some configurations that have been executed in the first plant and / or without the need to stop the system. Therefore, the method provides a better value for a multi-plant solution by reducing TCO. Indeed, the same number of servers required to hosting MES / MOM solutions serving a single manufacturing plant in the prior art can now be used to manage multiple plants (as presented in Figure 1). The set of servers constitutes a single MES / MONM solution distributed environment and is hosted by the same regional hub or at least a single "virtual data center" in the event of selecting a public cloud deployment according to the functionality provided by the cloud provider.

The invention relates to the technical field of scheduling, in particular to an artificial intelligence-driven intelligent manufacturing system scheduling method, which comprises the following steps of monitoring order state parameters of a manufacturing execution system in real time, identifying equipment failure shutdown signals or order insertion signals, extracting associated equipment codes and current work order numbers, and establishing core disturbance information pairs. According to the method, the order state parameters of the manufacturing execution system are monitored in real time, the equipment fault shutdown signal and the order insertion signal are dynamically identified, and the equipment code and the current work order number are extracted, so that the core disturbance information pair highly associated with the disturbance source can be constructed, and the pertinence and accuracy of the scheduling process response are ensured. And in combination with the original plan completion time of the affected process, evaluating local delay caused by disturbance, gradually deducing to form the delay amount of the whole plan, describing the diffusion trend of the disturbance in the production progress, and defining a scheduling adjustment time window.

The invention provides a system software design method and system for manufacturability verification of an integrated circuit, and relates to the technical field of integrated circuits, and the method comprises the following steps: constructing a multi-level verification model based on integrated circuit design data, carrying out parallel computing processing on the multi-level verification model, generating a manufacturability prediction result, and identifying potential manufacturing defects. Based on the potential manufacturing defects, an optimization suggestion is automatically generated, and the optimization suggestion comprises a layout modification scheme and a process parameter adjustment scheme; applying the optimization suggestion to the integrated circuit design data to generate optimized design data; iterative verification is carried out on the optimized design data until preset manufacturability indexes are met, and the manufacturability indexes comprise the yield, reliability and performance parameters; and transmitting the integrated circuit manufacturing data packet to a manufacturing execution system for guiding the actual manufacturing process of the integrated circuit.

The invention discloses a combined machining system with five-axis linkage control and four-spindle independent control functions. The combined machining system integrates high-precision combined CNC machining equipment, an AGV (automatic guided vehicle) logistics device and an MES (manufacturing execution system). According to the system, four high-speed main shaft units are arranged in parallel in the X-axis direction, an inclinable workbench is independently installed in front of each main shaft unit, and the system has the functions of inclining by + / -110 degrees and rotating by 360 degrees. Each workbench is provided with a center pressing type circular quick-change clamp adapter, and a 3D identification mark is embedded into each workbench so that the AGV logistics device can automatically identify and replace the center pressing type circular quick-change clamp adapter. According to the system, an MES instructs the AGV to enter a standby state in advance through an M330 forecast signal output by the CNC, then after automatic cleaning and door body opening operation are sequentially executed, the AGV enters equipment to complete clamp replacement, real-time communication among the MES, the CNC and the AGV is achieved on the basis of an OPC-UA protocol in the whole process, and the MES issues a Cycle Start signal only when all working conditions are met, so that the high-speed unmanned continuous machining process is ensured.

The invention relates to the technical field of manufacturing execution systems, in particular to an intelligent scheduling method integrating demand prediction and supply matching, which comprises the following steps of: acquiring to-be-scheduled order information and a workshop real-time resource state in a manufacturing execution system, constructing an order demand feature vector, the method comprises the following steps: acquiring an equipment precision retentivity value, a current load rate value, an energy consumption level value and an operator skill proficiency value, and constructing a resource supply capability vector; according to the method, when an urgent order insertion instruction is received, a clear decision basis is provided, a rigid scheduling locking block which must be kept unchanged and an adjustable liquid adjustable scheduling block which can be adjusted can be distinguished, only the latter is subjected to accurate splitting and backward moving operation, and the target-clear local adjustment mode is adopted, so that the operation efficiency is greatly improved. The huge calculation overhead and the violent fluctuation of the production plan caused by the traditional global rescheduling are avoided, and the agility of the manufacturing system to deal with the market change is enhanced.

The invention relates to the technical field of manufacturing process optimization, and discloses a manufacturing process optimization method based on an MES (Manufacturing Execution System), which comprises the steps of data acquisition, data analysis and resource allocation, and is characterized in that reasonable resource allocation is realized by accurately calculating an equipment utilization rate and a human demand index and combining comprehensive data acquisition and accurate index analysis; equipment scheduling optimization performs task allocation according to the utilization rate, and equipment idling or overfatigue is avoided; personnel allocation optimization ensures that the number of employees is matched with production tasks; the operation state of equipment is determined and the working condition of personnel is mastered, so that problems can be timely processed and personnel management can be performed; the method has flexible optimization measures and can cope with different production conditions, preset parameters can be adjusted, and adaptability is high; in a word, the method realizes comprehensive optimization of the production process, improves the production efficiency, reasonably distributes resources, and strengthens the production management.

The invention relates to a defect analysis and process optimization method based on artificial intelligence multi-mode fusion, and the method comprises the steps: respectively inputting virtual data of a chip or device design layout, an expected process flow and parameters, and an expected electrical performance curve into an image encoder, a process encoder, and an electrical encoder, extracting a corresponding feature vector; performing cross-modal information interaction on the corresponding feature vectors according to a cross attention fusion mechanism; determining a representation image, a defect category, a root cause category and a process parameter correction suggestion; and based on a manufacturing execution system (MES), an electronic design automation (EDA) tool or an equipment interface, process parameters are corrected, and a closed-loop control result from defect analysis to process optimization is obtained. Therefore, the problems of single data dimension, mutual coupling of various defects, influence of background noise, limited machine identification accuracy, overlarge workload of artificial expert judgment and low efficiency are solved.

The invention provides an early warning method and device for a process machine, electronic equipment and a medium, and is applied to a statistical process control (SPC) system.The method comprises the steps that a piece of production data is received in real time from a manufacturing execution system (MES), and the production data comprises equipment identification and product parameters; calling an out-of-control monitoring strategy bound with a target processing machine indicated by the equipment identifier; performing anomaly detection on the product parameters by using an out-of-control monitoring strategy; if it is identified that the product parameters are abnormal, forming an abnormal record, and storing the abnormal record in a folder corresponding to the target processing machine; identifying whether the abnormal record stored in the folder meets an out-of-control state early warning condition or not; and if yes, outputting an early warning signal for indicating that the target processing machine is in the out-of-control state. According to the invention, the product parameters of the target processing machine are monitored and the early warning signal is output, so that the production process of the target processing machine can be monitored in time.

The invention provides a stereoscopic warehouse and a management method thereof, and relates to the technical field of intelligent warehousing. The stereoscopic warehouse comprises an enterprise resource planning system used for sending order information to a manufacturing execution system; the manufacturing execution system is used for making a production plan according to the order information and sending a warehouse-out request according to the production plan; the warehouse management system is used for generating a warehouse-in instruction according to the warehouse-back request and generating a warehouse-out instruction according to the warehouse-out request; the warehouse control system is used for controlling the stacking machine and the conveying line to operate according to the warehouse-in instruction and the warehouse-out instruction so that materials can be carried to the warehouse location or the warehouse-out opening. And the vehicle scheduling system is used for controlling the operation of the automatic guided vehicle according to the warehouse-in instruction and the warehouse-out instruction, so that the materials are carried to a warehouse-in opening, a line-side warehouse or a production line. According to the invention, high-efficiency automation of material storage and transfer is realized.

The invention discloses an environment monitoring management system and method for a precision workshop, and relates to the technical field of environment monitoring and control, and the method comprises the steps: synchronously collecting multi-dimensional environment parameters through a distributed sensor network, accessing a manufacturing execution system in real time, analyzing the requirements of a process window, and carrying out the real-time monitoring of the environment parameters; and performing fusion and abnormality diagnosis on the data under a unified space-time reference, generating a closed-loop regulation and control instruction, and coordinating and controlling an execution mechanism. The system comprises a multi-mode environment perception unit, a process semantic analysis unit, a space-time fusion and abnormality diagnosis engine, an intelligent regulation and control decision unit and an execution mechanism cooperative control unit. According to the invention, deep understanding of environment disturbance and production process relevance can be realized, compound environment abnormity can be accurately identified, production task requirements can be adapted in real time, environment parameters are ensured to be always in an optimal process tolerance band, the product yield and the process repeatability are obviously improved, and millisecond-level response and cooperative linkage control are realized.

The invention relates to the technical field of multi-dimensional operation instruction arrangement execution engines, in particular to a multi-dimensional operation instruction arrangement execution engine system based on an MES system and an execution method, the system comprises an operation instruction management module, a business rule logic development module, an operation instruction action arrangement module and an execution engine center module; according to the MES system-based multi-dimensional operation instruction arrangement execution engine system and execution method designed by the invention, the accuracy and integrity of instruction analysis can be improved, various changes in the production process can be adapted, and an effective monitoring and feedback mechanism can be provided to deal with emergency situations.