699 results about "Smart factory" patented technology

The invention provides a digital factory operation virtual simulation teaching method and system, and relates to the technical field of smart factories, and the method comprises the steps: collecting factory operation data, carrying out the standardization processing and correlation analysis, constructing a multi-dimensional data model, carrying out the feature extraction and semantic mining of the hierarchical data of each dimension, and constructing a multi-level digital twin factory model. Carrying out dynamic modeling, constructing a personalized knowledge graph, generating a learning demand list, matching the learning demand list with a digital twin factory model, and generating a multi-dimensional simulation environment; in the multi-dimensional simulation environment, performing analysis and intention recognition on an operation instruction of the learner, converting the operation instruction into a virtual operation, calculating a normative score of the virtual operation, generating corresponding feedback information based on the normative score and the personalized knowledge graph, updating model parameters based on a response of the learner to the feedback information, and performing simulation on the learner according to the updated model parameters. And completing iteration of the model.

The invention relates to the technical field of factory management. The intelligent factory management method based on digital twinning comprises the following steps: performing dynamic identification and analysis processing on a multi-source heterogeneous communication protocol of factory equipment to obtain a standardized data stream; constructing a three-dimensional digital twinborn model comprising an equipment state and a technological process; generating a virtual control instruction according to an analog control strategy in the three-dimensional digital twin model; sandbox security verification processing is carried out on the virtual control instruction, and a compliance instruction set is screened out; converting the compliance instruction set into a protocol format supported by the target equipment, generating a reverse control instruction and issuing the reverse control instruction to the physical equipment; performing closed-loop feedback optimization processing according to the difference between the execution result of the physical equipment and the prediction result of the three-dimensional digital twinborn model, so as to solve the problem that the digital twinborn model lacks real-time sensor data injection and dynamic behavior constraint embedding, ensure that the model can reflect the real state of the equipment, and improve the real-time performance of the equipment. And the reliability and the execution security of the virtual control instruction are improved.

The invention provides an intelligent factory data processing method and system based on the industrial Internet, and the method comprises the steps: firstly obtaining a real-time industrial data set collected by a multi-mode sensor in a target production region, covering various data, such as equipment vibration signals, carrying out the time sequence synchronization processing of the real-time industrial data set, and generating a synchronization data block; the method comprises the following steps of: extracting dynamic state characteristics of a production line from the data, calling a pre-trained anomaly detection model to carry out multi-dimensional anomaly detection, outputting an anomaly detection result, matching a preset expert knowledge base according to the anomaly detection result, and generating a dynamic control instruction set containing equipment adjustment parameters and the like; finally, production parameter configuration is adjusted based on the dynamic control instruction set and fed back to the industrial control terminal in real time, optimization of current production resource configuration is achieved, and the production efficiency and the resource utilization rate of an intelligent factory are effectively improved.

The invention discloses a multi-task collaborative robot scheduling method and system oriented to an intelligent factory, and the method comprises the steps: receiving the input data of a plurality of tasks to be executed by the intelligent factory, the input data comprising the task type, the dependency relationship between the tasks, the task priority, the resource demand of each task, and the expected completion time of each task; determining an initial task execution sequence of the tasks according to the task types, the dependency relationships and the task priorities; obtaining current resource state data of all available robots in the intelligent factory; according to the resource demand of each task, the expected completion time of each task, the initial task execution sequence and the current resource state data of all available robots, a scheduling algorithm is adopted to generate a multi-task cooperative scheduling rule; and controlling the plurality of target robots to execute the plurality of tasks according to the multi-task cooperative scheduling rule. According to the method, the resource utilization rate of the robot in the intelligent factory can be effectively improved, and the task execution efficiency is optimized.

The invention provides an intelligent factory production scheduling method and system based on digital twinning, and the method comprises the steps: firstly obtaining a real-time monitoring data set, including equipment operation states, material flow tracks and environment sensing data, of a physical workshop; generating a three-dimensional digital twinborn model containing an equipment dynamic operation topology, a material real-time distribution thermodynamic diagram and an environment state simulation layer, then performing operation state correlation analysis on the three-dimensional digital twinborn model, and generating a feature set of equipment operation efficiency, material flow bottleneck, environment interference and the like; generating a dynamic scheduling parameter set containing equipment load balancing, material priority distribution and environment adaptation compensation parameters, and finally calling a preset scheduling strategy optimization algorithm to perform multi-objective optimization processing on the dynamic scheduling parameter set to generate an equipment scheduling, material distribution and environment regulation and control instruction set. And synchronizing to a physical workshop to execute real-time scheduling operation, thereby realizing intelligent and efficient production scheduling.

The invention provides an intelligent factory monitoring method and system based on multi-sensor fusion, and the method comprises the steps: firstly obtaining real-time monitoring data streams of multiple types of sensors in an intelligent factory, including operation state data collected by an equipment state sensor and scene state data collected by an environment state sensor; performing basic synchronization processing on the real-time monitoring data stream to obtain a standardized monitoring data stream, calling a pre-trained multi-sensor association analysis model to perform cross-source feature fusion processing on the standardized monitoring data stream to generate a fusion feature sequence, and performing abnormal mode detection processing based on the fusion feature sequence to generate an abnormal detection result; and finally, generating a monitoring intervention instruction containing equipment positioning information according to an anomaly detection result, and sending the monitoring intervention instruction to a factory control system to trigger a state adjustment operation, thereby effectively improving the monitoring precision and anomaly processing efficiency of the intelligent factory.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention relates to the technical field of factory energy consumption management, in particular to a smart factory dynamic optimization management system based on digital twinning and big data analysis. Comprising a data acquisition and fusion module, a digital twinning construction module, a data analysis module, a dynamic optimization decision module and an anomaly diagnosis module. Constructing a digital twinborn model of a factory physical entity according to the collected data; constructing an energy consumption prediction model based on deep learning frameworks such as LTSM; when the energy consumption deviation exceeds the limit, abnormal root causes are positioned; and generating an energy consumption scheduling scheme based on a multi-objective optimization algorithm, and issuing an instruction to realize dynamic energy consumption adjustment. Through deep fusion of digital twinning and big data technologies, comprehensive and accurate simulation, multi-target collaborative optimization, rapid abnormality diagnosis and dynamic control of factory energy consumption are realized, the energy utilization efficiency is effectively improved, the cost is reduced, the intelligent level is improved, and the method has remarkable economic benefits and environmental benefits.

The invention provides an intelligent factory fault diagnosis method and system based on an AI prediction model, and the method comprises the steps: obtaining an equipment monitoring data flow of a target production line of an intelligent factory, carrying out the diagnosis feature construction processing of the equipment monitoring data flow, generating a state evolution feature and a component correlation feature, and carrying out the fault diagnosis of the target production line of the intelligent factory; and inputting the state evolution characteristics and the component association characteristics into a pre-trained fault prediction model for fault prediction, and generating diagnosis result data containing fault risk levels. And the potential fault type and the propagation characteristic information are identified according to the diagnosis result data, and finally the maintenance guidance data containing the fault positioning identifier are generated based on the potential fault type and the propagation characteristic information and are transmitted to the factory operation and maintenance system to trigger the fault intervention operation, so that the accuracy and the maintenance efficiency of intelligent factory fault diagnosis are effectively improved.

The invention provides an intelligent factory process optimization method and system based on multi-modal data fusion, and the method comprises the steps: obtaining a multi-modal data set generated in the production process of an intelligent factory, carrying out the multi-modal fusion processing of the multi-modal data set, generating a fusion data set, and carrying out the process feature extraction operation based on the fusion data set. The method comprises the following steps: generating a process optimization feature set, carrying out dynamic optimization strategy matching processing according to the process optimization feature set, generating a process parameter optimization strategy, executing the process parameter optimization strategy, carrying out optimization adjustment on a process execution process of the intelligent factory, and monitoring optimized process execution state data in real time to trigger a strategy iteration updating operation. According to the method, the production quality stability and the process resource utilization efficiency can be synchronously improved.

This disclosure provides a method for the standardized production, financing and delivery of housing using distributed ledger technology and a decentralized artificially intelligent cyber-physical system (CPS or invention) for compiling data and executing human and deep learning sequences for the purposes of standardizing the planning and execution of real estate development, regulatory compliance, financing, supply chain procurement, industrial manufacturing processes, logistical processes as well as project management tasks, and other various construction, manufacturing and delivery tasks and procedures to standardize development, production, and delivery of standardizes building modules and standardized building components in a standardized smart factory that are used to assemble structures.

The invention belongs to the technical field of lean manufacturing monitoring, and particularly relates to an anomaly detection and processing method for a production process of an intelligent factory, and the method comprises the steps: constructing a dual monitoring node chain and a layered anomaly association evaluation node chain based on a preset production process flow chain, and collecting the operation state and process parameter data of equipment in real time; the hierarchical anomaly association assessment node chain analyzes the equipment health degree through the operation state assessment sub-chain, mines a cross-process anomaly propagation relationship through the whole-process technology anomaly assessment sub-chain, generates a whole-process association anomaly assessment space, fuses real-time data and historical anomaly strategy index information based on a distributed algorithm, and finally performs the whole-process association anomaly assessment on the basis of the real-time data and the historical anomaly strategy index information. Dynamically constructing a difference exception processing strategy library, and realizing processing strategy synchronous feedback of positioning exception; according to the invention, through a dual monitoring architecture and hierarchical association evaluation analysis, the problems of fuzzy abnormal propagation path and response lag are solved, and the real-time performance and processing accuracy of abnormal detection are improved.

Provided is a concrete full lifecycle green quality control method based on Internet of Things and big data, comprising the following steps: step 1, on the basis of an information platform, selecting raw materials required by concrete, so as to achieve the control of the selection of the raw materials of concrete; step 2, on the basis of the information platform, performing engineering concrete mix ratio intelligent design, and obtaining the mix ratio of the raw materials required by concrete corresponding to a project under construction; step 3, on the basis of the information platform, taking into account the obtained concrete mix ratio to mix and prepare concrete by means of an intelligent concrete factory, and automatically testing and controlling the quality of the prepared concrete at the same time; and step 4, on the basis of the information platform, using the mixed and prepared concrete to perform intelligent construction. Automatic, intelligent and green control of concrete throughout the full lifecycle from raw materials, mix ratio design, production, and construction to the service life of the structure are achieved by controlling each stage of the full lifecycle of the concrete.

The invention discloses a smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.

An example system and method that can provide integrity and privacy-preserving communication and integrated operation in smart factory and other IOT applications using smart contracts in blockchain and Merkle Tree-based key management operations. The exemplary Merkle tree-based key management can ensure all communication, control operations, and shared models (e.g., Federated Learning models) are encrypted prior to transmission and sharing with other nodes in the network, with keys being split into parts to each of multiple recipients and later assembled to allow blocking consensus / validation to be performed.

The invention discloses a toxic and harmful substance leakage reinspection method, device and equipment based on an unmanned aerial vehicle, and a storage medium, relates to the technical field of Internet of Things and intelligent factories, and aims to solve the problem that the detection accuracy and efficiency of toxic and harmful substances are low. The method comprises the following steps: dividing a target area into a plurality of grids, and acquiring initial gas concentration data of each grid in real time through a fixed foundation sensing array; the grids with the initial gas concentration data larger than the preset concentration are determined as abnormal grids, and a reinspection flight path is dynamically generated according to the unmanned aerial vehicle parking position, the abnormal grid position and the environment parameters; the unmanned aerial vehicle is controlled to fly to the abnormal grid according to the reinspection flight path, and multi-dimensional data including visible light images, infrared thermal imaging data, gas component spectrums and space coordinate data of the abnormal grid are collected; and fusing the initial gas concentration data and the multi-dimensional data of the abnormal grid, constructing a three-dimensional dynamic concentration field model, and outputting leakage source position information and a diffusion trend.

The invention discloses a smart factory management platform and management method based on digital twinning, and particularly relates to the technical field of digital twinning, and the method comprises the following steps: carrying out the division and preliminary analysis of a digital twinning smart factory, carrying out the feature extraction and anomaly analysis of components with hidden dangers, and carrying out the feature extraction and anomaly analysis of the components with hidden dangers; calculating a data source quality index and a prediction performance quality index, and evaluating the comprehensive operation quality by using a pre-trained machine learning model; based on the evaluation result, dividing the components into low-efficiency and high-efficiency operation components; judging an optimization mode for the low-efficiency component; performing intelligent regulation and control on the efficient component based on the initial sampling frequency; the method can comprehensively evaluate the operation state of the digital twin model assembly, evaluate the adaptation degree of each assembly and the actual factory assembly, intelligently regulate and control the high-efficiency operation assembly, optimize the low-efficiency operation assembly, significantly improve the resource utilization rate, ensure the stability and reliability of the system under various operation conditions, and improve the reliability of the system. And system faults and shutdown are prevented.

The invention relates to the technical field of equipment cooperative control, and discloses a multi-equipment cooperative control method and system under an autonomous cooperation algorithm, and the method comprises the steps: collecting the physical attribute data, real-time operation data, production task data and a smart factory scene graph of operation equipment under a multi-equipment cooperative task in a smart factory application; based on the physical attribute data and the real-time operation data, identifying a space conflict mode and a time conflict mode of the operation equipment to obtain collaborative conflict information; according to the production task data and the collaboration conflict information, carrying out collaboration conflict decoupling on the operation equipment to obtain a feasible collaboration mode; performing obstacle avoidance reinforcement learning on the operation equipment by using the smart factory scene graph to construct an obstacle avoidance global path of the operation equipment in the smart factory; and based on the feasible coordination mode and the obstacle avoidance global path, constructing a target coordination control scheme of the operation equipment so as to perform coordination control on the operation equipment. According to the invention, the reliability of multi-device cooperative control under the autonomous cooperation algorithm can be improved.

The invention relates to the technical field of smart factory management platforms, and discloses a data real-time monitoring method based on a smart factory management platform system, and the method comprises the steps: carrying out the standard format conversion of supplier Excel data, carrier GPS track data flow and warehouse RFID goods location data in a smart factory ERP system, and obtaining standardized business data; calculating comprehensive scores of suppliers based on the standardized business data and generating a dynamic sorting list; receiving purchase order information and creating a logistics scheduling plan, and pre-allocating an optimal goods allocation for the warehousing commodities to obtain a warehousing pre-allocation scheme; monitoring the execution progress of the storage pre-distribution scheme in real time, and synchronously updating service state information; the auditing state of the purchase order information is monitored based on the service state information, and an intelligent collaborative decision-making scheme is generated, real-time synchronous updating of the service states of the supplier management module, the logistics scheduling module and the warehouse management module is ensured, and the supply chain collaborative management efficiency is improved.

The invention discloses an intelligent factory data intelligent analysis and management system, and relates to the technical field of data analysis. Multi-source data are uniformly accessed through an industrial gateway, and a knowledge graph is constructed after cleaning and standardization; an edge node deploys a lightweight AI model to realize real-time analysis such as equipment anomaly detection; intelligent distribution of cloud side tasks is realized based on a decision model; optimizing production scheduling and quality control by using an algorithm; a zero-trust architecture is adopted to guarantee safety, and efficient energy management is realized in combination with reinforcement learning; all the modules work cooperatively, and the intelligent level of a factory is improved. The operation efficiency and quality of the intelligent factory are effectively improved. Efficient data fusion processing is realized, and equipment anomaly detection is more accurate; the order delivery period is shortened, and the product reject ratio is reduced; network security protection is enhanced, and energy waste is reduced; the decision response speed is accelerated, the decision accuracy is improved, cost reduction and efficiency improvement of enterprises are comprehensively assisted, and the competitiveness is enhanced.

The invention relates to the technical field of data processing, in particular to a smart factory monitoring data dynamic detection method, system and device and a storage medium, and the method is applied to dynamic detection of production monitoring data of production line equipment in a smart factory. Performing anomaly detection, and extracting abnormal equipment in the production line; for each abnormal device, performing quantitative analysis on the abnormal device by using a pre-constructed device abnormality evaluation model to obtain a device abnormality index; acquiring real-time video image information of the abnormal equipment, and extracting operator information of the abnormal equipment based on the real-time video image information; correcting the equipment abnormality index of the abnormal equipment by considering the working experience of the operator information to obtain a corrected equipment abnormality index; according to the invention, comprehensive monitoring of the whole production process can be realized, and problems caused by improper cooperative operation of a plurality of devices can be found.

The invention discloses an AI-based intelligent factory dynamic production scheduling optimization method and system, and belongs to the technical field of factory dynamic production scheduling, and the method comprises the following steps: obtaining and integrating the working states of various types of equipment in a factory, the stock position states of different materials, and the manual data; the production order state, the process route requirement, the plan and the completion time of each process and the delivery date requirement of the customer are determined; generating an initial production scheduling plan through an AI optimization algorithm in combination with supply chain material data, factory storage space, equipment switching cost, production rules, each order process dependency relationship and a production target; according to the method, the initial scheduling plan is generated by acquiring and integrating multiple types of production data, the dynamic events are monitored, multiple schemes are generated through evaluation, the optimal scheme is selected through economic model evaluation, and production refinement, dynamic response and benefit optimization are achieved.

The intelligent factory data acquisition platform comprises a data sensing layer, a multi-protocol transmission layer, a data fusion layer and an application service layer, the data sensing layer is used for collecting equipment state and environment parameter multi-source heterogeneous data through various sensors, preprocessing the collected data and transmitting the preprocessed data to the multi-protocol transmission layer; the multi-protocol transmission layer is used for receiving the preprocessed data transmitted by the data sensing layer, packaging the data through a multi-protocol data transmission unit, then carrying out multi-protocol transmission, and outputting the transmitted data to the data fusion layer; the data fusion layer is used for receiving the multi-source heterogeneous data transmitted by the multi-protocol transmission layer, performing feature extraction and fusion on the heterogeneous data by adopting a self-correcting convolutional recurrent neural network, and outputting fused feature representation to the application service layer; and the application service layer is used for receiving the fusion features output by the data fusion layer, and performing equipment health diagnosis, process parameter optimization and energy consumption statistical analysis intelligent application services based on the fusion features.

The invention relates to the technical field of data analysis, provides an intelligent factory automatic monitoring method and system based on knowledge base enhancement, and realizes more accurate anomaly analysis and more effective process adjustment of an intelligent factory. The method comprises the steps of performing knowledge enhancement fusion processing on an obtained real-time monitoring data set of an intelligent factory through a pre-constructed process knowledge base and a pre-constructed monitoring rule base, and generating a process knowledge graph; performing abnormal mode recognition processing on the process knowledge graph based on a semantic matching strategy, extracting feature description of an abnormal event and a semantic association path with a historical monitoring text, and generating an abnormal mode analysis result containing abnormal root cause inference; according to the abnormal mode analysis result and the dynamic incidence relation in the process knowledge graph, an automatic monitoring report containing root cause priority ranking and optimization operation guidance is generated, and the automatic monitoring report is fed back to the intelligent factory control terminal to trigger process adjustment operation.

The invention relates to the technical field of smart factory decision-making, and discloses a digital twin-driven smart factory AI intelligent decision-making system, which comprises a data acquisition and preprocessing module used for acquiring and preprocessing real-time and historical data of a production line; the twinborn modeling identification module is used for constructing a twinborn body and carrying out parameter identification and uncertainty quantification; the alignment evaluation credible module is used for comparing twin prediction data with real data and generating availability marks and trust scores; the strategy simulation optimization module is used for generating candidate strategies and risk evidences based on the constraints and the key performance indicators; the grayscale online publishing module is used for screening and grayscale publishing a strategy according to the trust score and the performance result; the operation evaluation auditing module is used for collecting operation data and generating an auditing packet; and the drifting detection updating module is used for detecting distribution drifting and recalibrating and updating the twinborn body and the strategy library. According to the invention, the reliability, robustness and continuous optimization of the production decision of the smart factory are realized.

The invention provides an intelligent factory management method and system based on the industrial internet, and the method comprises the steps: obtaining original data of a multi-source heterogeneous device, and generating a semantic event structure through preprocessing; the industrial event sequence is mapped into nodes, candidate edges are generated in combination with the physical topology connection relation of the equipment, and a target causal map is constructed; according to the target causal atlas, calculating a node risk score of each event node, and performing balance adjustment on the score by using a graph-level centrality value to generate a normalized node risk score of each event node; mapping the node risk scores to devices, and calculating a risk average value of each device; and converting a scoring matrix of the current task to all candidate devices into a scheduling strategy value, executing a scheduling strategy and collecting task execution feedback, and dynamically adjusting a target causal atlas and a node risk score according to the difference between an actual result and a predicted value to realize dual optimization of the target causal atlas and the scheduling strategy.

The embodiment of the invention provides an intelligent factory semantic decision generation method and system based on a knowledge graph, and the method comprises the steps: obtaining an operation data set of an intelligent factory, carrying out the semantic feature extraction of the operation data set, and generating the target semantic feature of an equipment operation parameter and the context correlation feature of a semantic description text; and based on a pre-constructed knowledge graph structure, performing dynamic semantic matching processing on the target semantic features and the context association features, generating a semantic decision instruction set corresponding to the equipment operation parameters, generating an equipment control strategy set according to instruction priorities and instruction execution conditions in the semantic decision instruction set, and sending the equipment control strategy set to a server. And feeding back the equipment control strategy set to a control system of the intelligent factory to trigger operation optimization operation, and updating a semantic node association relationship in the knowledge graph structure. According to the method, the problem of fragmentation of equipment operation state representation is effectively solved, and the interpretability of feature extraction is improved by utilizing a collaborative verification mechanism of numerical parameters and text description.

The invention provides an intelligent factory production optimization method and system based on AI scheduling, and belongs to the technical field of intelligent factory production management.The method comprises the steps that firstly, process disassembling is conducted on batch production orders, an order task atlas is obtained, a dynamic resource pool is constructed according to the real-time resource state of a factory, the order task atlas is input into a pre-trained scheduling AI model, and a scheduling AI model is established; a dynamic resource pool is combined to generate multiple groups of initial scheduling paths, conflict detection and conflict point identification are performed on the initial scheduling paths, an adaptive adjustment module is called to correct parameters based on conflict point types and influence ranges, a final production optimization scheme is generated and imported into a factory execution system, and intelligentization and high efficiency of production scheduling are realized. And the production efficiency and benefits are improved.

The invention relates to the technical field of information management, and provides a smart factory production line management and control method and device, equipment and a medium, and the method comprises the steps: obtaining work data streams and category information corresponding to a plurality of pieces of production equipment on a production line in a smart factory; obtaining a target state judgment rule according to the work data streams and the category information corresponding to the plurality of production devices; according to a target state judgment rule, performing state judgment on the working data flow corresponding to each production device to obtain a state judgment result corresponding to each production device; carrying out fault trend prediction on the production line according to the state judgment result corresponding to each piece of production equipment to obtain a target fault trend; and according to the target fault trend, generating a production line maintenance plan so as to maintain a plurality of production devices on the production line according to the production line maintenance plan. According to the scheme, the production stability and efficiency of the smart factory production line are improved.

The invention belongs to the technical field of smart factories. The control system of the automatic assembly production line comprises a monitoring module, a parameter calibration module and a database, the parameter calibration module is electrically connected with the monitoring module and the database. According to the method, the corresponding calibration historical data is called according to the component attribute information of the new product, and the proper trial assembly number of the new product required to be used in the calibration stage is determined according to the calibration historical data, so that the trial assembly number of the new product is reduced as much as possible on the premise of completing the calibration of the new working parameter, and the calibration efficiency of the new working parameter is improved. Therefore, the production cost of a manufacturer can be effectively reduced.