198 results about "Manufacturing execution system" patented technology

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 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 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 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 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 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 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 discloses a data management method, system and device for a semiconductor manufacturing execution system and a medium, and the method comprises the steps: creating or obtaining a unique identifier for connecting an upper layer component and a lower layer component in a component tree, and enabling each layer component of the component tree to achieve the isolation of component states at a hierarchical boundary through mapping state data corresponding to different action ranges; confirming a mixing mode of context data between the upper-layer component and the lower-layer component; mixing the state data of the upper-layer component and the context data of the lower-layer component based on the mixing mode and the unique identifier to form current action domain data corresponding to the lower-layer component; and the lower-layer component obtains the business data provided for the upper-layer component through the action domain component based on the unique identifier in the current action domain data, so that the business data can be used by the business. According to the method, a dynamic state isolation mechanism is realized, a state management tool can well realize component-level state isolation, and extraction and reuse of complex services are realized; and the data combination form among the components is more flexible.

The invention discloses an online detection system and method for optical indexes of a hollow-core optical fiber, and relates to the technical field of optical performance detection of optical fibers, and the system comprises an optical fiber connection module, an optical performance detection module, a production linkage module and a data interaction module. The method comprises the following steps: welding the inner end of a production optical fiber with a system at one time; the industrial personal computer reads PLC production data and generates a test trigger condition; when the conditions are met, controlling the OTDR to test the optical indexes of the optical fiber; carrying out segment processing on the OTDR curve by using an improved Viterbi algorithm so as to identify fault features; performing automatic environment compensation based on an attenuation reference model established by the LSTM neural network; and finally, uploading the analysis data to a manufacturing execution system. According to the invention, on-line, real-time and accurate detection of the optical indexes of the hollow-core fiber is realized, traditional multi-time welding is replaced by one-time welding, the detection efficiency is greatly improved, and the problems of poor sealing performance, low efficiency and low data reliability in the prior art are solved.

The invention provides an MES (Manufacturing Execution System)-oriented arc-shaped battery manufacturing process data acquisition method and system, and relates to the technical field of manufacturing execution system data acquisition. The method comprises the following steps: firstly, constructing a hierarchical model of a production line, a workshop section, a station, equipment, an acquisition channel and a data field, enabling the hierarchical model to correspond to a manufacturing execution system object, establishing a global unique identifier and time and space marks, forming a unified data item description table, and implementing field acquisition, unified time service and standardized preprocessing; bending features are extracted, mapping entries are generated with process states and quality results, the mapping entries are uploaded to a manufacturing execution system and a data platform, abnormal supplementary transmission and monomer-level tracing are supported, and the data consistency and tracing capability of the arc battery manufacturing process are improved.

The invention belongs to the technical field of automobile flexible manufacturing, and particularly discloses a method and system for analyzing and issuing technological parameters in real time in automobile flexible manufacturing, and the method comprises the following steps: a customer submits required configuration orders of appearance, interior decoration and functions through an Internet of Things customization platform; the Internet of Things customization platform converts the customer order information into a standardized data format and sends the data format to a manufacturing execution system MES; and the MES system presets process basic data required by each vehicle type and configuration rules of the corresponding vehicle type, analyzes production process information of the vehicle required by a client, issues the information to the production equipment through the IOT platform, and executes the steps of collecting the state and production data of the production equipment, judging whether the production equipment works normally or not and judging whether the product quality reaches the standard or not. By adopting the technical scheme, based on a two-way communication mechanism between the MES and the IOT platform, rapid customization, accurate issuing and real-time adjustment of the process data are realized, so that the efficient production requirement of flexible manufacturing of automobiles is met.

The invention discloses an AGV scheduling system and scheduling method, relates to the technical field of flexible workshop job scheduling, and solves the problems that local optimization damages global circulation efficiency, characterization defects and invalid actions frequently occur and robustness is poor in the prior art. The system comprises a manufacturing execution system, a data acquisition module, a decision-making system and an environment interaction module, the manufacturing execution system decomposes a production plan into discrete manufacturing tasks and stores the discrete manufacturing tasks in a dynamically updated task pool, a workshop environment generates state information in real time, the state information comprises environment dynamics and AGV instant observation data, and the AGV real-time observation data is transmitted to the manufacturing execution system. The decision-making system adopts a neural network model based on multi-agent reinforcement learning, multiple groups of candidate scheduling strategies are generated by processing real-time observation data, the environment interaction module is responsible for verifying feasibility verification and performance evaluation of actions, iterative updating of the system state is achieved by generating observation values, and the decision-making system is in real-time scheduling. The modular design ensures the real-time performance and adaptability of the scheduling decision.

The invention discloses a production process real-time optimization and quality tracing method and system based on multi-source equipment data fusion, and relates to the technical field of intelligent manufacturing and quality control. Real-time standardized fusion of multi-source heterogeneous data is realized through edge calculation, quality risks are accurately evaluated by using a process-level digital twin model and a multi-layer graph neural network, real-time adjustment and optimization of a production process are realized by using a multi-objective optimization algorithm, a complete and credible quality tracing system is constructed in combination with a causal graph model and a block chain technology, and the quality tracing efficiency is improved. The problems of data islands, optimization lag and tracing fracture existing in a traditional manufacturing execution system are fundamentally solved, and transparency, intelligent real-time optimization and full-chain credible quality tracing of the production process are achieved.

The invention relates to an intelligent digital management method for friction plate production, which belongs to the technical field of friction plate processing and comprises the following operation steps of: 1, performing intelligent production scheduling; and 2, processing the core plate, counting and summarizing, and performing formula digitization and mistake proofing according to classification requirements. And a third step of carrying out equipment data acquisition and health management on the equipment based on IIOT and SCADA equipment and data interconnection. And 4, performing appearance defect detection and performance prediction on the friction plate based on intelligent quality control of machine vision and AI. And 5, finally summarizing the number of the types of friction plates required by the order, and meanwhile, displaying the number of semi-finished products in the operation process of each production line and each device in real time. Through fusion of advanced technologies such as industrial Internet of Things, artificial intelligence, big data analysis and a manufacturing execution system, the problems of difficult production scheduling, unstable quality, information isolated island, difficult tracing and the like in traditional friction plate production are solved, so that transparency, automation, intelligence and lean production process are realized.

The invention relates to a wafer-level film stress regulation and control method, module and device. The method comprises the following steps: constructing a stress regulation and control module according to set parameters and a structure, and integrating the constructed stress regulation and control module at a set position of a semiconductor device; wherein the stress regulation and control module comprises a stress regulation and control layer, and the stress regulation and control layer adopts a metal / medium composite laminated structure; in the wafer manufacturing process, a target net stress value needed by a current wafer is obtained through a manufacturing execution system, the optimal thickness proportion needed by a stress regulation and control layer for achieving the target net stress value is calculated through an internal stress prediction model, and the manufacturing execution system generates a technological formula according to the optimal thickness proportion; and the deposition equipment is driven to deposit the stress regulation and control layer under the optimal thickness ratio. By designing the independent stress regulation and control module, it can be ensured that the crystallization quality, the stoichiometric ratio and the electrical / piezoelectric performance of the functional film are not sacrificed, and linear, accurate and predictable stress control over the wafer is achieved.

This disclosure relates to a method, apparatus, electronic device, and storage medium for controlling the transfer of a wound yarn package. [Solution] The method includes, when each of the multiple second controllers is determined to be a target controller, and the communication link from the target controller to the first controller is determined to be in a normal state, receiving target business data transmitted by the target controller, transmitting the target business data to the manufacturing execution system, receiving target transfer instructions transmitted by the manufacturing execution system, and transmitting the target transfer instructions to the target controller. The manufacturing execution system is for acquiring target transfer instructions based on the target business data and transmitting the target transfer instructions to the first controller. The target controller performs transfer control operations on the wound yarn package product passing through the target work station corresponding to the target controller based on the target transfer instructions.

The invention relates to the technical field of antenna detection, in particular to an antenna detection device based on an MES (Manufacturing Execution System) combined with a vector network analyzer, which comprises the MES, the vector network analyzer (VNA), an automatic test platform and an industrial personal computer, the manufacturing execution system (MES) is configured to generate and issue a structured electronic work order including a product model, test parameter configuration information and a performance qualification limit value; the vector network analyzer (VNA) is an automatic test platform used for executing radio frequency performance parameter measurement on a tested antenna, and comprises a turntable used for fixing and driving the tested antenna to perform azimuth or pitching rotation, and a radio frequency switch matrix used for switching test signal paths among a plurality of tested antenna ports or a plurality of antennas; and the industrial personal computer is in communication connection with the MES, the VNA and the automatic test platform.

The application provides a flexible scheduling group and a scheduling method. The scheduling group comprises a manufacturing execution system, a logistics execution system, an intelligent group control system, a logistics intelligent trolley scheduling system and a process intelligent trolley scheduling system. The intelligent group control system receives a production plan issued by the manufacturing execution system, receives inventory material information of the logistics execution system, receives vehicle position information of the logistics intelligent trolley scheduling system and the process intelligent trolley scheduling system respectively, and formulates scheduling tasks of the process intelligent trolley and the logistics intelligent trolley in real time. Through deep cooperation of the intelligent group control system with the manufacturing execution system, the logistics execution system and other systems, overall management of the whole production process is realized. Through real-time sensing of data of environmental changes and / or data of demand fluctuations by the intelligent group control system, and through on-demand pulling, the coordination and stability of line operation are improved through cooperation with multiple systems.

The invention provides a method and system for collecting working state data of a manufacturing execution system, and the method comprises the steps: obtaining an equipment working data set, a personnel working video set and a workpiece information set, and respectively extracting a corresponding equipment working feature sequence set and a corresponding workpiece feature sequence set, performing illumination compensation, foreground segmentation and time window splitting on the personnel operation video set, and performing multi-dimensional attitude decoupling, action semantic recognition and production rhythm modeling on the personnel operation video set after the time window splitting to obtain a personnel action feature sequence set; performing dynamic time warping, beat alignment and feature binding based on the production task order to obtain a multi-modal production feature sequence set, and performing collaborative feature enhancement and dimension reduction compression to obtain a multi-modal working time sequence feature sequence set; and by taking the production task order as an edge index, constructing a dynamic hypergraph model of the multi-modal working time sequence feature sequence set, and carrying out man-machine collaborative analysis on the dynamic hypergraph model to obtain a working state data sequence.

The invention discloses a question and answer model obtaining method and device based on a manufacturing execution system, and relates to the field of production manufacturing and artificial intelligence large models.The method comprises the steps that different service links are obtained according to multiple service files of the manufacturing execution system; obtaining a reasoning sample set according to the logic relationship of each service action in the service link; the reasoning sample comprises a problem, a reasoning path and a result; and inputting the reasoning sample into the general big language model, so as to obtain the trained question and answer model based on the manufacturing execution system according to the general big language model. According to the technical scheme provided by the embodiment of the invention, the training sample of the question and answer model based on the manufacturing execution system is automatically acquired, so that the labor cost and the time cost consumed in a sample labeling process are avoided, the acquisition efficiency of the question and answer model is improved, the phenomena of wrong labeling and missing labeling existing in manual labeling are avoided, and the accuracy of labeling is improved. And the accuracy of the question-answer model feedback result is improved.

This invention discloses an automated tire warehousing system and method for truck cranes, belonging to the field of intelligent manufacturing and logistics automation technology. The system integrates a WMS (Wastewater Management System), MES (Manufacturing Execution System), a central control scheduling system, AGVs (Automated Guided Vehicles), a gantry robot, and a dual-mode vision system. The method is as follows: the MES issues orders, and the central control system breaks down tasks; the WMS schedules AGVs to return empty pallets and deliver tires to the production line; the gantry robot uses 2D vision to scan the tire's QR code for identity verification; if the verification is correct, the tire enters assembly; otherwise, it is returned and reassembled; after assembly, the central control system triggers the warehousing task, the WMS dynamically allocates the optimal storage location, the AGV transports the tire to the temporary storage area, and the gantry robot, guided by 3D vision, accurately grasps and places the tire into the storage location; the WMS updates the inventory. This invention achieves full-process automation and information closure of tires from production line to warehousing, significantly improving efficiency, accuracy, and warehouse utilization, while reducing costs and safety risks.

This invention discloses a steelmaking overhead crane weighing system for low-quality industrial data, belonging to the field of steel production technology. The system includes: a data acquisition and fusion module for collecting real-time crane data and production event data from the manufacturing execution system; a feature extraction and data governance module for identifying hoisting operation segments and performing time-series reconstruction and completion of production event data to generate a unified process time sequence table; and a core matching and calculation engine module, including a multi-condition dynamic matching engine and a multi-mode adaptive weight calculation engine, used respectively for multi-dimensional rule matching of hoisting segments and process nodes, and for calculating the weight of molten steel using robust mean or morphological recognition modes. This invention achieves highly robust and accurate molten steel weight tracking even in environments with missing, delayed, and noisy multi-source data, significantly improving the system's process adaptability and maintainability.

A method and a system for verifying and synchronizing distributed data under an applet belong to the technical field of industrial internet and production, the method is executed by a manufacturing execution system, and the method comprises the following steps: receiving production execution data verified in real time by a mobile terminal; generating a production execution report and synchronizing the production execution report to an enterprise resource planning system to generate a process report; when report data is corrected, collaborative operation is triggered according to the generation state of a process report to maintain data consistency, and the system correspondingly comprises a data acquisition and verification terminal, a manufacturing execution system platform and an enterprise resource planning system platform to realize the collaborative operation. And an intelligent cross-system collaborative correction mechanism is constructed, so that the problems that data verification is lagged, cross-system synchronization is difficult and consistency is difficult to guarantee in traditional production are effectively solved.

This invention provides a self-inspection control method, device, and server for vehicle production and maintenance, relating to the technical field of the automotive industry. The method includes: when the power supply to an ordered vehicle is detected to be on, connecting an information domain controller to the vehicle's manufacturing execution system (MES) based on a key sent by a vehicle networking service platform; sending vehicle information provided by the MES to the information domain controller, enabling the information domain controller to perform a self-inspection process to prevent errors in the vehicle's electronic control system; after the self-inspection of the vehicle's electronic control system passes, switching the information domain controller to production mode and using an electronic inspection program and a set of external cameras to perform a production self-inspection process on the vehicle's electronic control components and external equipment, so that the ordered vehicle can be delivered after the vehicle's production self-inspection passes. This invention can significantly improve the accuracy and efficiency of fault detection during vehicle production and maintenance.

The embodiment of the invention discloses an intelligent mistake proofing method and device for tobacco raw material handover, electronic equipment and a storage medium. The method comprises the following steps: acquiring a tobacco bale image corresponding to a tobacco raw material transmitted to a first area; consistency verification is carried out on the cigarette packet images, and receipt number information corresponding to the cigarette packet images passing the consistency verification is determined; querying a bill of materials associated with the tobacco raw materials from a manufacturing execution system based on the bill number information; tobacco bale information corresponding to the tobacco raw materials transmitted to the second area is obtained; and matching based on the tobacco bale information and the bill of material to carry out handover error-proofing control on the tobacco raw materials. According to the technical scheme, an automatic code scanning and intelligent verification mechanism is adopted, manual intervention is reduced, human errors can be avoided through multiple redundancy verification and real-time monitoring, and the accuracy of intelligent mistake proofing of tobacco raw material handover is improved.

The invention relates to the technical field of production and manufacturing management, and discloses a multi-source demand fusion and dynamic scheduling production plan collaborative management method and system, and the method comprises the steps: automatically obtaining production demands from a plurality of heterogeneous business source systems through a unified interface; performing data mapping and conversion on the heterogeneous demands to generate standardized main production plan demands, and distributing a unique tracing identifier penetrating through a production full link for each demand; calculating and generating a main production plan through a dual-mode scheduling engine supporting forward and reverse scheduling algorithms on the basis of a standardized demand, a product production cycle parameter and a working calendar; and issuing the main production plan to a manufacturing execution system, automatically capturing demand changes, performing versioning processing on affected plan items, and re-triggering scheduling calculation. According to the invention, end-to-end monitoring is realized through the unique tracing identifier, and rapid and closed-loop response is carried out on demand change, so that the accuracy and agility of production plan making are improved.

The invention relates to a defect analysis and process optimization method based on artificial intelligence multi-modal fusion, and the method comprises the steps: respectively inputting a representation image, an electrical curve and process parameters in a reasoning stage into an image encoder, an electrical encoder and a process encoder, so as to extract corresponding feature vectors; performing cross-modal information interaction on the corresponding feature vectors according to a cross attention fusion mechanism, and quantifying the responsibility degree of the process parameters to obtain fusion features; determining a defect category, a defect root cause, a process parameter correction amount 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 discloses a digital twin-driven LCD backlight plate production collaborative management and control method and system, and particularly relates to the technical field of production management. The method is used for solving the problems of resource mismatching, quality risk and efficiency loss caused by neglecting the capability difference of a production line in a specific process and a dynamic coupling relationship between process paths of products in use in an existing static planning method. A digital twin model corresponding to a physical production line is constructed, a process capability vector and a process path vector are integrated, a candidate production line set is identified based on vector data, a parameter resonance risk level is evaluated, a key bottleneck process in a process network topology is positioned, and a dynamic routing scheme is generated to avoid high-risk paths and bottlenecks. And a manufacturing execution system triggers real-time adjustment of a product production path to realize dynamic collaborative optimization of production resources and order demands.

The invention relates to a method for quickly realizing forbidden and limited operation of a cavity during product production, which comprises the following steps that: a manufacturing execution system provides an interface, and a PLC (Programmable Logic Controller) working data address area is added on the manufacturing execution system to match with an automatic production control system; and the manufacturing execution system provides a message interface, generates a chamber forbidding and limiting signal and sends the forbidding and limiting signal to the CVD and DRY equipment through working data, and the equipment automatically realizes chamber forbidding and limiting operation according to the signal after receiving the chamber forbidding and limiting signal. According to the method for quickly realizing the forbidden and limited operation of the chamber during product production, equipment can quickly obtain information and cannot send the existing product to the forbidden and limited chamber through upstream and downstream command control, so that the cost loss is reduced, and the production ex-warehouse efficiency is improved.

The invention discloses an intelligent marinated pig trotter MES (Manufacturing Execution System) with collagen directionally recombined and a use method thereof. The intelligent marinated pig trotter MES comprises an intelligent marinated pig trotter production machine, an edge computing gateway, a conveyor belt and an MES control center, the production process data of unfreezing, singeing, cleaning, cutting, protein recombination, cooling, packaging and sterilization are synchronously displayed in real time through digital twinning, an intelligent adjustment plan is adopted, and the collagen recombination process is increased; empirical formulas of unfreezing duration, singeing duration, cleaning duration, cutting speed, collagen recombination working duration, marinating duration, vacuum cooling duration, vacuum packaging speed and sterilization duration are used, according to the latest production task, PLC parameters in the production process are planned, calculated and determined, and the optimal production target and quality target of the marinated pig trotter are achieved.